Search

The effect of energy management strategy (EMS) of the plug-in hybrid electric vehicles (PHEVs) depends heavily on the state-of-charge (SOC) reference. Meanwhile, the optimality of the SOC trajectory is significantly varied with traffic features of the traffic network. However, they are not effectively used in existing SOC deriving methods. Therefore, a two-level based global optimal SOC planning method is proposed based on traffic conditions. At the upper level, traffic conditions are employed to describe the optimal SOC slope of each road segment over the whole path, and a Bidirectional long-short-term-neural network (Bi-LSTM) is proposed to predict it. Based on this, the global optimal guidance for consuming the electric energy can be obtained. Then, at the lower level, the traffic condition factor and the driving condition factor are constructed to plan the SOC trajectory over each road segment adaptively. In this way, the global optimal SOC trajectory can be derived. Finally, the proposed SOC planning method is incorporated with the adaptive equivalent consumption minimization strategy to track the planned global optimal SOC trajectory to achieve the optimal fuel economy. As shown in the test, the fuel economy has been improved by 7.2% compared to the existing widely used EMS. © 2023 IEEE.

The use of multi-stack fuel cells (FCs) is attracting considerable attention in electrified vehicles due to the added degrees of freedom in terms of efficiency and survivability. In a multi-stack FC hybrid electric vehicle, the power sources (FCs and the battery pack) have different energetic characteristics and their operation is influenced by the performance drifts caused by degradation. Hence, efficient power distribution for such a multi-source system is a critical issue. This paper proposes a three-layer online EMS for a recreational vehicle composed of three FCs and a battery pack. In the first layer, two online estimators are responsible for constantly updating the characteristics of each FC and the battery to be used by the power distribution algorithm. In the second layer, a rule-based method is developed to improve the calculation speed of the power distribution algorithm by deciding when it should be activated. The last layer performs the power distribution between FCs and battery using a model-free reinforcement learning (RL) algorithm called Q-learning. The proposed RL-based EMS attempts to meet the requested power while minimizing the costs of hydrogen consumption and degradation of all power sources. To justify the performance of the proposed strategy, a comprehensive benchmark with an offline EMS and two online strategies is performed under two driving cycles. In comparison with the online strategies, the proposed method based on RL reduces the defined trip cost up to 11.5 % and 13.08 % under the Real driving cycle while having a higher cost than the offline strategy by 4.78 %.

Energy management strategies (EMSs) are crucial to the fuel economy of hybrid electric vehicles (HEVs). However, due to the lack of efficient solving approaches, most of existing EMSs mainly focus on the optimal torque split between the internal combustion engine (ICE) and the electric motor but neglect improper ICE on/off switches, and thus usually suffer degraded fuel economy and even unacceptable drivability in practice. To tackle this issue, this paper presents a novel EMS that uses an efficient actor-critic (AC) method to regulate ICE switches with limited computation resources. While common AC methods use complex neural networks (NNs) with arbitrary initialization, the proposed AC uses piecewise cubic polynomials whose parameters are initialized based on optimized solutions of dynamic programming (DP). By this means, the AC can quickly converge with high computation efficiency. The testing results from processor-in-the-loop (PIL) simulations showcase that, compared with a rule-based EMS with tabular value functions, the proposed EMS can greatly improve the equivalent fuel economy by eliminating improper ICE switches after only several iterations of adaptive learning and dramatically save onboard memory space owing to the concise AC structure., QC 20230831

Hybrid electric vehicles (HEVs) rely on energy management strategies (EMSs) to achieve optimal fuel economy. However, both model- and learning-based EMSs have their respective limitations which negatively affect their performances in online applications. This paper presents a computationally efficient adaptive dynamic programming (ADP) approach that can not only rapidly calculate optimal control actions but also iteratively update the approximated value function (AVF) according to the actual fuel and electricity consumption with limited computation resources. Exploiting the AVF, the engine on/off switch and torque split problems are solved by one-step lookahead approximation and Pontryagin's minimum principle (PMP), respectively. To raise the training speed and reduce the memory space, the tabular value function (VF) is approximated by carefully selected piecewise polynomials via the parametric approximation. The advantages of the proposed EMS are threefold and verified by processor-in-the-loop (PIL) Monte Carlo simulations. First, the fuel efficiency of the proposed EMS is higher than that of an adaptive PMP and close to the theoretical optimum. Second, the new method can adapt to the changed driving conditions after a small number of learning iterations and thus has higher fuel efficiency than a non-adaptive dynamic programming (DP) controller. Third, the computation efficiencies of the proposed AVF and a tabular VF are compared. The concise data structure of the AVF enables faster convergence and saves at least 70% of onboard memory space without obviously increasing the average CPU utilization., Not duplicate with DiVA 1753630QC 20230704, XPRES, TECoSA

Hybrid electric vehicles (HEVs) are irreplaceable in attaining sustainable development in contemporary society. Owing to the extra degree of freedom in supplying traction power, HEVs resort to appropriate energy management strategies (EMSs) to present their superiority over conventional internal combustion engine vehicles and pure electric vehicles. Existing EMSs suffer from heavy computation overheads and excessive mode switches. This thesis proposes several novel methods for developing online EMSs for parallel HEVs that achieve both compelling fuel economy and excellent computation efficiency and adaptivity in online applications with uncertain driving conditions. First, the solutions of offline dynamic programming (DP) are exploited to develop online EMSs for close-to-optimal control performances. The optimal speed profile serves as the reference in online control and the optimal value function (VF) is utilized to design control methods. To avoid the “curse of dimensionality”, the tabular VF is approximated by piecewise polynomials to substantially decrease the computation and memory overheads in online usage. Second, to reduce the search space for optimal control actions, two types of special internal combustion engine (ICE) configurations are adopted and analyzed. The first type forces the ICE to strictly operate at the optimal operation line (OOL), whereas the second one allows a narrow band around the OOL. The second one outperforms the first one because it contributes to more robust ICE operations with slightly higher computation complexity. Third, a hierarchical architecture is proposed for online EMSs so that the transient powertrain mode and torque split scheme are optimized by different methods in sequence. To avoid the exponential complexity of finding the optimal trajectory of the powertrain mode, the optimal VF is leveraged for an optimal decision within one sampling period with the aid of simplified assumptions. Model approximations on the ICE and th, Elhybridfordon (HEV) är oersättliga för att uppnå en hållbar utveckling i dagens samhälle. Medelst den extra frihetsgraden för att tillhandahålla dragkraft, använder HEV:erna sig av s.k. energihanteringstrategier (EMS) för att kombinera fördelarna med konventionella förbränningsmotorfordon och rena elfordon. Befintliga EMS lider av tunga beräkningskostnader och överdrivna lägesomkopplare. Denna avhandling flera nya metoder för att utveckla online-EMS för parallella HEV som uppnår både övertygande bränsleekonomi och utmärkt beräkningseffektivitet, samt god anpassningsförmåga i online tillämpningar med osäkra körförhållanden. För det första utnyttjas lösningarna från offline dynamisk programmering (DP) för att utveckla online EMS med nära optimal reglerprestanda. Den optimala hastighetsprofilen tjänar som referens vid online-reglering och den optimala värdefunktionen (VF) används för att utforma reglermetoder. För att undvika de problem som uppstår vid storskaliga beräkningar approximeras VF som en uppsättning polynom, vilket sänker bl.a. minneskostnader vid onlineanvändning. För det andra analyseras två typer av speciella förbränningsmotorkonfigurationer (ICE) vilket minskar sökutrymmet för optimala regleråtgärder. Den första typen tvingar ICE att arbeta strikt vid den optimala driftlinjen (OOL), medan den andra typen tillåter ett smalt band runt den. Den andra typen är bättre än den första eftersom den bidrar till mer robust ICE-drift, men med något högre beräkningskomplexitet. För det tredje föreslås en hierarkisk arkitektur för online-EMS så att det transienta drivlinjeläget och vridmomentfördelningen optimeras med olika metoder i sekvens. För att undvika den exponentiella komplexiteten i att hitta den optimala banan för drivlinjeläget utnyttjas en VF grundad på förenklade antaganden, vilket leder till ett optimalt beslut inom en samplingsperiod. Modellförenklingar av både ICE och elmotorn genomförs för att omvandla det komplexa problemet med vridmomentfördelning

Ocean thermal and power energy systems are promising driving forces for seashore coastal communities to achieve net-zero energy/emission target, whereas energy planning and management on ocean thermal/power and distributed building integrated photovoltaic (BIPV) systems are critical, in terms of serving scale sizing and planning on geographical locations of district building community, and cycling aging of battery storages. However, the current literature provides insufficient studies on this topic. This study aims to address this research gap by transforming towards zero-energy coastal communities from the district level in subtropical regions, including centralised seawater-based chiller systems, distributed BIPVs and coastal oscillating water column technologies, as well as multi-directional Vehicle-to-Building energy interaction paradigms. Advanced energy management strategies were explored to enhance renewable penetration, import cost-saving, and deceleration of battery cycling aging, in response to relative renewable-to-demand difference, off-peak grid information with low price, and real-time battery cycling aging. Furthermore, in accordance with the power generation characteristic of two wave stations (i.e., Kau Yi Chau (KYC) and West Lamma Channel (WLC)) in Hong Kong, energy system planning and structural configurations of the coastal community were proposed and comparatively studied for the multi-criteria performance improvement. Research results showed that, compared to an air-cooled chiller, the water-cooled chiller with a much higher Coefficient of Performance (COP) will reduce the energy consumption of cooling systems, leading to a decrease in total electric demand from 134 to 126.5 kWh/m2·a. The scale for the net-zero energy district community with distributed BIPVs and oscillating water column was identified as 5 high-rise office buildings, 5 high-rise hotel buildings, 150 private cars and 120 public shuttle buses. Furthermore, the geog, Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care. Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Design & Construction Management

Global average temperatures are setting new records compared to the previous decade, primarily due to the accumulation of greenhouse gases such as CO2. The growth potential in emerging economies worsens the current global situation. These CO2 emissions are needed to be curtained and efforts are underway. The emissions target set by the International Maritime Organization seems far-fetched given the forecast for the growth of the shipping sector. Although electrification has proven to reduce emissions, the energy efficiency metrics (EEDI, EEXI) are incapable of providing the quantitative benefits that can be realized by switching to a bipolar DC (BiDC) grid when compared with the AC grid. This thesis aims to evaluate the feasibility of BiDC grids over the complete operating profile of the ship by comparing ten arrangements, five for AC and five for DC, with the Key Performance Indicators (KPIs). The arrangements are two diesel generators (AC1 & DC1), two diesel generators and battery (AC2 & DC2), two diesel generators, battery, and shore power (AC3 & DC3), one diesel generator and battery (AC4 & DC4), one diesel generator, battery, and shore power (AC5 & DC5). The comparison is made for key performance indicators like CO2 emissions, fuel consumption, electrical power requirement, propulsion power cable losses, capital costs, operating costs, propulsion system weight, and load carrying capacity of the ship. In this study, a ferry is chosen to compare the two grid architectures. To establish the required background knowledge for the reader of this thesis, the literature on the current emission regulations, the different propulsion systems, and the differences in AC and DC grids are presented. The ship’s propulsion and auxiliary power demand are not freely available and are in the possession of the ship operator. Therefore, first, a preliminary methodology was developed for estimating the ship’s mechanical pow, Electrical Engineering

Hybrid electric vehicles (HEVs) are irreplaceable in attaining sustainable development in contemporary society. Owing to the extra degree of freedom in supplying traction power, HEVs resort to appropriate energy management strategies (EMSs) to present their superiority over conventional internal combustion engine vehicles and pure electric vehicles. Existing EMSs suffer from heavy computation overheads and excessive mode switches. This thesis proposes several novel methods for developing online EMSs for parallel HEVs that achieve both compelling fuel economy and excellent computation efficiency and adaptivity in online applications with uncertain driving conditions. First, the solutions of offline dynamic programming (DP) are exploited to develop online EMSs for close-to-optimal control performances. The optimal speed profile serves as the reference in online control and the optimal value function (VF) is utilized to design control methods. To avoid the “curse of dimensionality”, the tabular VF is approximated by piecewise polynomials to substantially decrease the computation and memory overheads in online usage. Second, to reduce the search space for optimal control actions, two types of special internal combustion engine (ICE) configurations are adopted and analyzed. The first type forces the ICE to strictly operate at the optimal operation line (OOL), whereas the second one allows a narrow band around the OOL. The second one outperforms the first one because it contributes to more robust ICE operations with slightly higher computation complexity. Third, a hierarchical architecture is proposed for online EMSs so that the transient powertrain mode and torque split scheme are optimized by different methods in sequence. To avoid the exponential complexity of finding the optimal trajectory of the powertrain mode, the optimal VF is leveraged for an optimal decision within one sampling period with the aid of simplified assumptions. Model approximations on the ICE and th, Elhybridfordon (HEV) är oersättliga för att uppnå en hållbar utveckling i dagens samhälle. Medelst den extra frihetsgraden för att tillhandahålla dragkraft, använder HEV:erna sig av s.k. energihanteringstrategier (EMS) för att kombinera fördelarna med konventionella förbränningsmotorfordon och rena elfordon. Befintliga EMS lider av tunga beräkningskostnader och överdrivna lägesomkopplare. Denna avhandling flera nya metoder för att utveckla online-EMS för parallella HEV som uppnår både övertygande bränsleekonomi och utmärkt beräkningseffektivitet, samt god anpassningsförmåga i online tillämpningar med osäkra körförhållanden. För det första utnyttjas lösningarna från offline dynamisk programmering (DP) för att utveckla online EMS med nära optimal reglerprestanda. Den optimala hastighetsprofilen tjänar som referens vid online-reglering och den optimala värdefunktionen (VF) används för att utforma reglermetoder. För att undvika de problem som uppstår vid storskaliga beräkningar approximeras VF som en uppsättning polynom, vilket sänker bl.a. minneskostnader vid onlineanvändning. För det andra analyseras två typer av speciella förbränningsmotorkonfigurationer (ICE) vilket minskar sökutrymmet för optimala regleråtgärder. Den första typen tvingar ICE att arbeta strikt vid den optimala driftlinjen (OOL), medan den andra typen tillåter ett smalt band runt den. Den andra typen är bättre än den första eftersom den bidrar till mer robust ICE-drift, men med något högre beräkningskomplexitet. För det tredje föreslås en hierarkisk arkitektur för online-EMS så att det transienta drivlinjeläget och vridmomentfördelningen optimeras med olika metoder i sekvens. För att undvika den exponentiella komplexiteten i att hitta den optimala banan för drivlinjeläget utnyttjas en VF grundad på förenklade antaganden, vilket leder till ett optimalt beslut inom en samplingsperiod. Modellförenklingar av både ICE och elmotorn genomförs för att omvandla det komplexa problemet med vridmomentfördelning

Hybrid electric vehicles (HEVs) are irreplaceable in attaining sustainable development in contemporary society. Owing to the extra degree of freedom in supplying traction power, HEVs resort to appropriate energy management strategies (EMSs) to present their superiority over conventional internal combustion engine vehicles and pure electric vehicles. Existing EMSs suffer from heavy computation overheads and excessive mode switches. This thesis proposes several novel methods for developing online EMSs for parallel HEVs that achieve both compelling fuel economy and excellent computation efficiency and adaptivity in online applications with uncertain driving conditions. First, the solutions of offline dynamic programming (DP) are exploited to develop online EMSs for close-to-optimal control performances. The optimal speed profile serves as the reference in online control and the optimal value function (VF) is utilized to design control methods. To avoid the “curse of dimensionality”, the tabular VF is approximated by piecewise polynomials to substantially decrease the computation and memory overheads in online usage. Second, to reduce the search space for optimal control actions, two types of special internal combustion engine (ICE) configurations are adopted and analyzed. The first type forces the ICE to strictly operate at the optimal operation line (OOL), whereas the second one allows a narrow band around the OOL. The second one outperforms the first one because it contributes to more robust ICE operations with slightly higher computation complexity. Third, a hierarchical architecture is proposed for online EMSs so that the transient powertrain mode and torque split scheme are optimized by different methods in sequence. To avoid the exponential complexity of finding the optimal trajectory of the powertrain mode, the optimal VF is leveraged for an optimal decision within one sampling period with the aid of simplified assumptions. Model approximations on the ICE and th, Elhybridfordon (HEV) är oersättliga för att uppnå en hållbar utveckling i dagens samhälle. Medelst den extra frihetsgraden för att tillhandahålla dragkraft, använder HEV:erna sig av s.k. energihanteringstrategier (EMS) för att kombinera fördelarna med konventionella förbränningsmotorfordon och rena elfordon. Befintliga EMS lider av tunga beräkningskostnader och överdrivna lägesomkopplare. Denna avhandling flera nya metoder för att utveckla online-EMS för parallella HEV som uppnår både övertygande bränsleekonomi och utmärkt beräkningseffektivitet, samt god anpassningsförmåga i online tillämpningar med osäkra körförhållanden. För det första utnyttjas lösningarna från offline dynamisk programmering (DP) för att utveckla online EMS med nära optimal reglerprestanda. Den optimala hastighetsprofilen tjänar som referens vid online-reglering och den optimala värdefunktionen (VF) används för att utforma reglermetoder. För att undvika de problem som uppstår vid storskaliga beräkningar approximeras VF som en uppsättning polynom, vilket sänker bl.a. minneskostnader vid onlineanvändning. För det andra analyseras två typer av speciella förbränningsmotorkonfigurationer (ICE) vilket minskar sökutrymmet för optimala regleråtgärder. Den första typen tvingar ICE att arbeta strikt vid den optimala driftlinjen (OOL), medan den andra typen tillåter ett smalt band runt den. Den andra typen är bättre än den första eftersom den bidrar till mer robust ICE-drift, men med något högre beräkningskomplexitet. För det tredje föreslås en hierarkisk arkitektur för online-EMS så att det transienta drivlinjeläget och vridmomentfördelningen optimeras med olika metoder i sekvens. För att undvika den exponentiella komplexiteten i att hitta den optimala banan för drivlinjeläget utnyttjas en VF grundad på förenklade antaganden, vilket leder till ett optimalt beslut inom en samplingsperiod. Modellförenklingar av både ICE och elmotorn genomförs för att omvandla det komplexa problemet med vridmomentfördelning

Ocean thermal and power energy systems are promising driving forces for seashore coastal communities to achieve net-zero energy/emission target, whereas energy planning and management on ocean thermal/power and distributed building integrated photovoltaic (BIPV) systems are critical, in terms of serving scale sizing and planning on geographical locations of district building community, and cycling aging of battery storages. However, the current literature provides insufficient studies on this topic. This study aims to address this research gap by transforming towards zero-energy coastal communities from the district level in subtropical regions, including centralised seawater-based chiller systems, distributed BIPVs and coastal oscillating water column technologies, as well as multi-directional Vehicle-to-Building energy interaction paradigms. Advanced energy management strategies were explored to enhance renewable penetration, import cost-saving, and deceleration of battery cycling aging, in response to relative renewable-to-demand difference, off-peak grid information with low price, and real-time battery cycling aging. Furthermore, in accordance with the power generation characteristic of two wave stations (i.e., Kau Yi Chau (KYC) and West Lamma Channel (WLC)) in Hong Kong, energy system planning and structural configurations of the coastal community were proposed and comparatively studied for the multi-criteria performance improvement. Research results showed that, compared to an air-cooled chiller, the water-cooled chiller with a much higher Coefficient of Performance (COP) will reduce the energy consumption of cooling systems, leading to a decrease in total electric demand from 134 to 126.5 kWh/m2·a. The scale for the net-zero energy district community with distributed BIPVs and oscillating water column was identified as 5 high-rise office buildings, 5 high-rise hotel buildings, 150 private cars and 120 public shuttle buses. Furthermore, the geographical locat

Hybrid electric vehicles (HEVs) are irreplaceable in attaining sustainable development in contemporary society. Owing to the extra degree of freedom in supplying traction power, HEVs resort to appropriate energy management strategies (EMSs) to present their superiority over conventional internal combustion engine vehicles and pure electric vehicles. Existing EMSs suffer from heavy computation overheads and excessive mode switches. This thesis proposes several novel methods for developing online EMSs for parallel HEVs that achieve both compelling fuel economy and excellent computation efficiency and adaptivity in online applications with uncertain driving conditions. First, the solutions of offline dynamic programming (DP) are exploited to develop online EMSs for close-to-optimal control performances. The optimal speed profile serves as the reference in online control and the optimal value function (VF) is utilized to design control methods. To avoid the “curse of dimensionality”, the tabular VF is approximated by piecewise polynomials to substantially decrease the computation and memory overheads in online usage. Second, to reduce the search space for optimal control actions, two types of special internal combustion engine (ICE) configurations are adopted and analyzed. The first type forces the ICE to strictly operate at the optimal operation line (OOL), whereas the second one allows a narrow band around the OOL. The second one outperforms the first one because it contributes to more robust ICE operations with slightly higher computation complexity. Third, a hierarchical architecture is proposed for online EMSs so that the transient powertrain mode and torque split scheme are optimized by different methods in sequence. To avoid the exponential complexity of finding the optimal trajectory of the powertrain mode, the optimal VF is leveraged for an optimal decision within one sampling period with the aid of simplified assumptions. Model approximations on the ICE and th, Elhybridfordon (HEV) är oersättliga för att uppnå en hållbar utveckling i dagens samhälle. Medelst den extra frihetsgraden för att tillhandahålla dragkraft, använder HEV:erna sig av s.k. energihanteringstrategier (EMS) för att kombinera fördelarna med konventionella förbränningsmotorfordon och rena elfordon. Befintliga EMS lider av tunga beräkningskostnader och överdrivna lägesomkopplare. Denna avhandling flera nya metoder för att utveckla online-EMS för parallella HEV som uppnår både övertygande bränsleekonomi och utmärkt beräkningseffektivitet, samt god anpassningsförmåga i online tillämpningar med osäkra körförhållanden. För det första utnyttjas lösningarna från offline dynamisk programmering (DP) för att utveckla online EMS med nära optimal reglerprestanda. Den optimala hastighetsprofilen tjänar som referens vid online-reglering och den optimala värdefunktionen (VF) används för att utforma reglermetoder. För att undvika de problem som uppstår vid storskaliga beräkningar approximeras VF som en uppsättning polynom, vilket sänker bl.a. minneskostnader vid onlineanvändning. För det andra analyseras två typer av speciella förbränningsmotorkonfigurationer (ICE) vilket minskar sökutrymmet för optimala regleråtgärder. Den första typen tvingar ICE att arbeta strikt vid den optimala driftlinjen (OOL), medan den andra typen tillåter ett smalt band runt den. Den andra typen är bättre än den första eftersom den bidrar till mer robust ICE-drift, men med något högre beräkningskomplexitet. För det tredje föreslås en hierarkisk arkitektur för online-EMS så att det transienta drivlinjeläget och vridmomentfördelningen optimeras med olika metoder i sekvens. För att undvika den exponentiella komplexiteten i att hitta den optimala banan för drivlinjeläget utnyttjas en VF grundad på förenklade antaganden, vilket leder till ett optimalt beslut inom en samplingsperiod. Modellförenklingar av både ICE och elmotorn genomförs för att omvandla det komplexa problemet med vridmomentfördelning

The use of multi-stack fuel cells (FCs) is attracting considerable attention in electrified vehicles due to the added degrees of freedom in terms of efficiency and survivability. In a multi-stack FC hybrid electric vehicle, the power sources (FCs and the battery pack) have different energetic characteristics and their operation is influenced by the performance drifts caused by degradation. Hence, efficient power distribution for such a multi-source system is a critical issue. This paper proposes a three-layer online EMS for a recreational vehicle composed of three FCs and a battery pack. In the first layer, two online estimators are responsible for constantly updating the characteristics of each FC and the battery to be used by the power distribution algorithm. In the second layer, a rule-based method is developed to improve the calculation speed of the power distribution algorithm by deciding when it should be activated. The last layer performs the power distribution between FCs and battery using a model-free reinforcement learning (RL) algorithm called Q-learning. The proposed RL-based EMS attempts to meet the requested power while minimizing the costs of hydrogen consumption and degradation of all power sources. To justify the performance of the proposed strategy, a comprehensive benchmark with an offline EMS and two online strategies is performed under two driving cycles. In comparison with the online strategies, the proposed method based on RL reduces the defined trip cost up to 11.5 % and 13.08 % under the Real driving cycle while having a higher cost than the offline strategy by 4.78 %.

This paper studies the eco-driving problem of finding the fuel-optimal velocity and power-split control for parallel hybrid electric vehicles that must drive over a finite distance in limited time. Specifically, we combine Pontryagin's minimum principle and singular control theory to derive the minimum-fuel control policy for the engine and the electric motor, and leverage it to rewrite the original optimal control problem into a Hamiltonian boundary value problem that can be efficiently solved with standard numerical methods. We showcase our findings with numerical simulations, revealing the fuel-optimal strategies favoring a faster, electrically-assisted, initial acceleration so that the required travel distance can be covered at the lowest possible cruising speed.

An energy management strategy (EMS) is responsible for distributing the power between the electrochemical power sources of a fuel cell hybrid electric vehicle (FCHEV) with a view to minimizing the hydrogen consumption and maximizing the lifetime of the system. However, the energetic characteristics of the electrochemical devices (fuel cell, battery, and supercapacitor) are time-varying due to the influence of ageing, and different ambient and operating conditions. Any drift in the characteristics of the power sources can lead to the mismanagement of an EMS. According to the literature, ignorance of health adaptation can increase the hydrogen consumption from almost 6.5%–24% depending on the EMS. Therefore, it is necessary to develop a strategy which is aware of the actual state of the components while conducting the power split. Health monitoring techniques are potential candidates to deal with the uncertainties arising from the mentioned factors. In this respect, this paper first puts forward a concise review of the general modeling techniques which are essential for developing precise health monitoring techniques and in turn EMSs. Subsequently, the utilized methods for prognosis, diagnosis, and health state tracking of each of the mentioned power sources in a FCHEV are introduced. Then, a new taxonomy for the classification of the EMSs based on their health-awareness is proposed based on which three categories of prognostic-based, diagnostic-based, and systemic EMSs are formed. Each category is thoroughly explained, and a state-of-the-art review of these health-aware EMSs is presented. Finally, future perspectives of this new line of research and development are discussed before drawing a conclusion.

The temporal changes of power and efficiency in a fuel cell (FC) stack can cause malperformance in the energy management strategy (EMS) of a FC hybrid electric vehicle. Therefore, the online estimation of these physical attributes is becoming an integral part of any EMS. This paper aims to utilize a two-step method to extract the maximum power and efficiency points of a FC system online. In this respect, an online parameter estimation technique, composed of smooth variable structure filter (SVSF) and Kalman filter (KF), is utilized in the first step to estimate the parameters of a FC semi-empirical voltage model. KF generates statistically optimal estimates for a linear, well-designed system model in the existence of Gaussian noise. However, these assumptions do not always hold in real applications and can lead to unstable estimation. A practical solution to deal with these instabilities is to enforce boundaries on the state estimates through SVSF which is based on sliding mode estimation concept. Hence, unlike the other similar studies, this paper synthesizes the robustness of SVSF with the precision of KF to enhance the characteristics estimation process of a FC stack. In the second step, the updated voltage model is utilized to extract the efficiency and power curves of the real FC system. To corroborate the potential of the proposed approach, a thorough comparison with KF, as an attested estimation method, is performed. The experimental tests on a 500-W FC stack indicate the superior performance of the SVSF-KF compared to that of KF.

The temporal changes of power and efficiency in a fuel cell (FC) stack can cause malperformance in the energy management strategy (EMS) of a FC hybrid electric vehicle. Therefore, the online estimation of these physical attributes is becoming an integral part of any EMS. This paper aims to utilize a two-step method to extract the maximum power and efficiency points of a FC system online. In this respect, an online parameter estimation technique, composed of smooth variable structure filter (SVSF) and Kalman filter (KF), is utilized in the first step to estimate the parameters of a FC semi-empirical voltage model. KF generates statistically optimal estimates for a linear, well-designed system model in the existence of Gaussian noise. However, these assumptions do not always hold in real applications and can lead to unstable estimation. A practical solution to deal with these instabilities is to enforce boundaries on the state estimates through SVSF which is based on sliding mode estimation concept. Hence, unlike the other similar studies, this paper synthesizes the robustness of SVSF with the precision of KF to enhance the characteristics estimation process of a FC stack. In the second step, the updated voltage model is utilized to extract the efficiency and power curves of the real FC system. To corroborate the potential of the proposed approach, a thorough comparison with KF, as an attested estimation method, is performed. The experimental tests on a 500-W FC stack indicate the superior performance of the SVSF-KF compared to that of KF.

An energy management strategy (EMS) is responsible for distributing the power between the electrochemical power sources of a fuel cell hybrid electric vehicle (FCHEV) with a view to minimizing the hydrogen consumption and maximizing the lifetime of the system. However, the energetic characteristics of the electrochemical devices (fuel cell, battery, and supercapacitor) are time-varying due to the influence of ageing, and different ambient and operating conditions. Any drift in the characteristics of the power sources can lead to the mismanagement of an EMS. According to the literature, ignorance of health adaptation can increase the hydrogen consumption from almost 6.5%–24% depending on the EMS. Therefore, it is necessary to develop a strategy which is aware of the actual state of the components while conducting the power split. Health monitoring techniques are potential candidates to deal with the uncertainties arising from the mentioned factors. In this respect, this paper first puts forward a concise review of the general modeling techniques which are essential for developing precise health monitoring techniques and in turn EMSs. Subsequently, the utilized methods for prognosis, diagnosis, and health state tracking of each of the mentioned power sources in a FCHEV are introduced. Then, a new taxonomy for the classification of the EMSs based on their health-awareness is proposed based on which three categories of prognostic-based, diagnostic-based, and systemic EMSs are formed. Each category is thoroughly explained, and a state-of-the-art review of these health-aware EMSs is presented. Finally, future perspectives of this new line of research and development are discussed before drawing a conclusion.

The fuel economy of a hybrid electric vehicle(HEV) is determined by its energy management strategy (EMS), while the conventional EMS usually suffers from enormous computation loads when solving a nonlinear optimization problem. To resolve this issue, this paper presents a computationally efficient EMS with close-to-optimal performance using very limited computation resources. Relying on the optimal solutions by offline dynamic programming (DP), a constrained model predictive control (MPC) can quickly determine the engine on/off status and then the torque split problem is solved by a value-based Pontryagin’s minimum principle (PMP). Two measures are taken to further reduce the online computation cost: by surface fitting, the tabular value function is replaced by piecewise linear polynomials and thus the memory occupation is greatly reduced; and by model approximation, the nonlinear torque split problem becomes a quadratic programming one that can be more rapidly solved. The testing results from processor-in-the-loop (PIL) simulation indicate that the proposed EMS can generate a fuel efficiency close to the one by DP, but saves 70% onboard memory space and 30% CPU utilization compared with the benchmark EMS without taking the two measures., Part of proceedings ISBN 978-1-6654-9043-6QC 20230227

Deep reinforcement learning (DRL)-based energy management strategy (EMS) is attractive for fuel cell vehicle (FCV). Nevertheless, the fuel economy and lifespan durability of proton exchange membrane fuel cell (PEMFC) stack and lithium-ion battery (LIB) may not be synchronously optimized since transient degradation variations of PEMFC stack and LIB are not generally regarded for DRL-based EMSs. Furthermore, the inappropriate action space and the overestimated value function of DRL can lead to suboptimal EMS for on-line control. To this end, the objective of this research endeavors to formulate a twin delayed deep deterministic policy gradient (TD3)based EMS integrating durability information of PEMFC stack and LIB, which can interact with the vehicle operating states to continuously control the hybrid powertrain and limit the overestimation of DRL value function for ensuring maximum multi-objective reward at each moment. Unlike traditional DRL-based EMSs, the multi-objective reward function for this study is enlarged to incorporate the hydrogen consumption, state of charge (SOC)-sustaining penalty and transient lifespan degradation information of PEMFC stack and LIB in offline training and on-line control. The results demonstrate that the proposed EMS can drastically lessen the training time and computational burden. Meanwhile, in contrast with deep Q-network (DQN)-based and deep deterministic policy gradient (DDPG)-based EMSs in the various real-world urban and standard driving cycles, the proposed EMS can achieve hydrogen abatement at least 9.76% and 1.07%, and slow down total powertrain degradation at least 9.11% and 2.62%, respectively., QC 20221223

This paper highlights the importance of considering required control rules from the real-world implementation inoffline optimal control optimisations used to generate online energy management strategies (EMS). The controlrules are constraints on the optimal control problem. If not considered, the control optimisation results do notrepresent the reality and the EMS will have poor performance. In this paper, a neural network predicts the optimalcontrol decisions whenever the rules are not taking place. It is a rule- and neural network-based energymanagement strategy. A limitation to the use of neural networks as part of the EMS is that they do not ensurestable behaviour outside the region they were trained for. In the real application – in this case, a hybrid wheelloader – they will be deployed alongside control rules to ensure safety and reasonable operation. Hence theimportance of implementing the rules in the optimal control problem. Results show that better performance of theEMS is achieved if the rules from the application are considered in the optimal control problem.

This paper studies the eco-driving problem of finding the fuel-optimal velocity and power-split control for parallel hybrid electric vehicles that must drive over a finite distance in limited time. Specifically, we combine Pontryagin's minimum principle and singular control theory to derive the minimum-fuel control policy for the engine and the electric motor, and leverage it to rewrite the original optimal control problem into a Hamiltonian boundary value problem that can be efficiently solved with standard numerical methods. We showcase our findings with numerical simulations, revealing the fuel-optimal strategies favoring a faster, electrically-assisted, initial acceleration so that the required travel distance can be covered at the lowest possible cruising speed.

An energy management strategy (EMS) is responsible for distributing the power between the electrochemical power sources of a fuel cell hybrid electric vehicle (FCHEV) with a view to minimizing the hydrogen consumption and maximizing the lifetime of the system. However, the energetic characteristics of the electrochemical devices (fuel cell, battery, and supercapacitor) are time-varying due to the influence of ageing, and different ambient and operating conditions. Any drift in the characteristics of the power sources can lead to the mismanagement of an EMS. According to the literature, ignorance of health adaptation can increase the hydrogen consumption from almost 6.5%–24% depending on the EMS. Therefore, it is necessary to develop a strategy which is aware of the actual state of the components while conducting the power split. Health monitoring techniques are potential candidates to deal with the uncertainties arising from the mentioned factors. In this respect, this paper first puts forward a concise review of the general modeling techniques which are essential for developing precise health monitoring techniques and in turn EMSs. Subsequently, the utilized methods for prognosis, diagnosis, and health state tracking of each of the mentioned power sources in a FCHEV are introduced. Then, a new taxonomy for the classification of the EMSs based on their health-awareness is proposed based on which three categories of prognostic-based, diagnostic-based, and systemic EMSs are formed. Each category is thoroughly explained, and a state-of-the-art review of these health-aware EMSs is presented. Finally, future perspectives of this new line of research and development are discussed before drawing a conclusion.

The temporal changes of power and efficiency in a fuel cell (FC) stack can cause malperformance in the energy management strategy (EMS) of a FC hybrid electric vehicle. Therefore, the online estimation of these physical attributes is becoming an integral part of any EMS. This paper aims to utilize a two-step method to extract the maximum power and efficiency points of a FC system online. In this respect, an online parameter estimation technique, composed of smooth variable structure filter (SVSF) and Kalman filter (KF), is utilized in the first step to estimate the parameters of a FC semi-empirical voltage model. KF generates statistically optimal estimates for a linear, well-designed system model in the existence of Gaussian noise. However, these assumptions do not always hold in real applications and can lead to unstable estimation. A practical solution to deal with these instabilities is to enforce boundaries on the state estimates through SVSF which is based on sliding mode estimation concept. Hence, unlike the other similar studies, this paper synthesizes the robustness of SVSF with the precision of KF to enhance the characteristics estimation process of a FC stack. In the second step, the updated voltage model is utilized to extract the efficiency and power curves of the real FC system. To corroborate the potential of the proposed approach, a thorough comparison with KF, as an attested estimation method, is performed. The experimental tests on a 500-W FC stack indicate the superior performance of the SVSF-KF compared to that of KF.

[ES] La necesidad de mejorar el consumo de combustible y las emisiones de los sistemas propulsivos de automoción en condiciones reales de conducción es la base de esta tesis. Para ello, se exploran dos ejes: En primer lugar, el control de los sistemas de propulsión. El estado del arte de control en los sistemas propulsivos de automoción se basa en gran medida en el uso de técnicas de optimización que buscan las leyes de control que minimizan una función de coste en un conjunto de condiciones de operación denidas a priori. Estas leyes se almacenan en las ECUs de producción en forma de mapas de calibración de los diferentes actuadores del motor. Las incertidumbres asociadas al conjunto limitado de condiciones en el proceso de calibración dan lugar a un funcionamiento subóptimo del sistema de propulsión en condiciones de conducción real. Por lo tanto, en este trabajo se proponen métodos de control adaptativo que optimicen la gestión de la planta propulsiva a las condiciones esperadas de funcionamiento para un usuario y un caso determinado en lugar de a un conjunto genérico de condiciones. El segundo eje se reere a optimizar, en lugar de los parámetros de control del sistema propulsivo, la demanda de potencia de este, introduciendo al propio conductor en el bucle de control, sugiriéndole las acciones a tomar. En particular, este segundo eje se reere al control de la velocidad del vehículo (conocido popularmente como Eco-Driving en la literatura) en condiciones reales de conducción. Se proponen sistemas de aviso en tiempo real al conductor acerca de la velocidad óptima para minimizar el consumo del vehículo. Los métodos de control desarrollados para cada aplicación se describen en detalle en la tesis y se muestran ensayos experimentales de validación en los casos de estudio diseñados. Ambos ejes representan un problema de control óptimo, denido por un sistema dinámico, unas restricciones a cumplir y un coste a minimizar, en este sentido las herramientas desarrolladas en, [CA] La necessitat de millorar el consum de combustible i les emissions dels sistemes propulsius d'automoció en condicions reals de conducció és la base d'aquesta tesi. Per a això, s'exploren dos eixos: En primer lloc, el control dels sistemes de propulsió. L'estat de l'art de control en els sistemes propulsius d'automoció es basa en gran manera en l'ús de tècniques d'optimització que busquen les lleis de control que minimitzen una funció de cost en un conjunt de condicions d'operació denides a priori. Aquestes lleis s'emmagatzemen en les Ecus de producció en forma de mapes de calibratge dels diferents actuadors del motor. Les incerteses associades al conjunt limitat de condicions en el procés de calibratge donen lloc a un funcionament subòptim del sistema de propulsió en condicions de conducció real. Per tant, en aquest treball es proposen mètodes de control adaptatiu que optimitzen la gestió de la planta propulsiva a les condicions esperades de funcionament per a un usuari i un cas determinat en lloc d'un conjunt genèric de condicions. El segon eix es refereix a optimitzar, en lloc dels paràmetres de control del sistema propulsiu, la demanda de potència d'aquest, introduint al propi conductor en el bucle de control, suggerint-li les accions a prendre. En particular, aquest segon eix es refereix al control de la velocitat del vehicle (conegut popularment com Eco-*Driving en la literatura) en condicions reals de conducció. Es proposen sistemes d'avís en temps real al conductor sobre la velocitat òptima per a minimitzar el consum del vehicle. Els mètodes de control desenvolupats per a cada aplicació es descriuen detalladament en la tesi i es mostren assajos experimentals de validació en els casos d'estudi dissenyats. Tots dos eixos representen un problema de control òptim, denit per un sistema dinàmic, unes restriccions a complir i un cost a minimitzar, en aquest sentit les eines desenvolupades en la tesi són comunes als dos eixos: Un model de vehicle, una eina de pr, [EN] The need of improving the real-world fuel consumption and emission of automotive applications is the basis of this thesis. To this end, two verticals are explored: First is the online control of the powertrain systems. In state-of-the-art Optimal Control techniques (such as Dyanmic Programming, Pontryagins Minimum Principle, etc...) are extensively used to formulate the optimal control laws. These laws are stored in the production ECUs in the form of feedforward calibration maps. The unaccounted uncertainities related to the real-world during the powertrain calibration result in suboptimal operations of the powertrain in actual driving. Therefore, adaptive control methods are proposed in this work which, optimise the energy management of the conventional and the HEV powertrain control on real driving mission. The second vertical is regarding the vehicle speed control (popularly known as Eco-Driving in the literature) methods in real driving condition. In particular, speed advisory systems are proposed for real time application on a vehicle. The control methods developed for each application are described in details with their verication and validation on the designed case studies. Apart from the developed control methods, there are three tools that were developed and used at various stages of this thesis: A vehicle model, A driving cycle prediction tool and optimal control methods (dynamic programming, PMP and ECMS). Depending on the application, the developed methods were implemented on the Hardware-In-Loop Internal Combustion Engine testing setup or on a real vehicle. The results show signicant improvements in the performance of the powertrain in terms of fuel economy and emissions in comparison to the state-of-the-art methods.

[ES] La necesidad de mejorar el consumo de combustible y las emisiones de los sistemas propulsivos de automoción en condiciones reales de conducción es la base de esta tesis. Para ello, se exploran dos ejes: En primer lugar, el control de los sistemas de propulsión. El estado del arte de control en los sistemas propulsivos de automoción se basa en gran medida en el uso de técnicas de optimización que buscan las leyes de control que minimizan una función de coste en un conjunto de condiciones de operación denidas a priori. Estas leyes se almacenan en las ECUs de producción en forma de mapas de calibración de los diferentes actuadores del motor. Las incertidumbres asociadas al conjunto limitado de condiciones en el proceso de calibración dan lugar a un funcionamiento subóptimo del sistema de propulsión en condiciones de conducción real. Por lo tanto, en este trabajo se proponen métodos de control adaptativo que optimicen la gestión de la planta propulsiva a las condiciones esperadas de funcionamiento para un usuario y un caso determinado en lugar de a un conjunto genérico de condiciones. El segundo eje se reere a optimizar, en lugar de los parámetros de control del sistema propulsivo, la demanda de potencia de este, introduciendo al propio conductor en el bucle de control, sugiriéndole las acciones a tomar. En particular, este segundo eje se reere al control de la velocidad del vehículo (conocido popularmente como Eco-Driving en la literatura) en condiciones reales de conducción. Se proponen sistemas de aviso en tiempo real al conductor acerca de la velocidad óptima para minimizar el consumo del vehículo. Los métodos de control desarrollados para cada aplicación se describen en detalle en la tesis y se muestran ensayos experimentales de validación en los casos de estudio diseñados. Ambos ejes representan un problema de control óptimo, denido por un sistema dinámico, unas restricciones a cumplir y un coste a minimizar, en este sentido las herramientas desarrolladas en, [CA] La necessitat de millorar el consum de combustible i les emissions dels sistemes propulsius d'automoció en condicions reals de conducció és la base d'aquesta tesi. Per a això, s'exploren dos eixos: En primer lloc, el control dels sistemes de propulsió. L'estat de l'art de control en els sistemes propulsius d'automoció es basa en gran manera en l'ús de tècniques d'optimització que busquen les lleis de control que minimitzen una funció de cost en un conjunt de condicions d'operació denides a priori. Aquestes lleis s'emmagatzemen en les Ecus de producció en forma de mapes de calibratge dels diferents actuadors del motor. Les incerteses associades al conjunt limitat de condicions en el procés de calibratge donen lloc a un funcionament subòptim del sistema de propulsió en condicions de conducció real. Per tant, en aquest treball es proposen mètodes de control adaptatiu que optimitzen la gestió de la planta propulsiva a les condicions esperades de funcionament per a un usuari i un cas determinat en lloc d'un conjunt genèric de condicions. El segon eix es refereix a optimitzar, en lloc dels paràmetres de control del sistema propulsiu, la demanda de potència d'aquest, introduint al propi conductor en el bucle de control, suggerint-li les accions a prendre. En particular, aquest segon eix es refereix al control de la velocitat del vehicle (conegut popularment com Eco-*Driving en la literatura) en condicions reals de conducció. Es proposen sistemes d'avís en temps real al conductor sobre la velocitat òptima per a minimitzar el consum del vehicle. Els mètodes de control desenvolupats per a cada aplicació es descriuen detalladament en la tesi i es mostren assajos experimentals de validació en els casos d'estudi dissenyats. Tots dos eixos representen un problema de control òptim, denit per un sistema dinàmic, unes restriccions a complir i un cost a minimitzar, en aquest sentit les eines desenvolupades en la tesi són comunes als dos eixos: Un model de vehicle, una eina de pr, [EN] The need of improving the real-world fuel consumption and emission of automotive applications is the basis of this thesis. To this end, two verticals are explored: First is the online control of the powertrain systems. In state-of-the-art Optimal Control techniques (such as Dyanmic Programming, Pontryagins Minimum Principle, etc...) are extensively used to formulate the optimal control laws. These laws are stored in the production ECUs in the form of feedforward calibration maps. The unaccounted uncertainities related to the real-world during the powertrain calibration result in suboptimal operations of the powertrain in actual driving. Therefore, adaptive control methods are proposed in this work which, optimise the energy management of the conventional and the HEV powertrain control on real driving mission. The second vertical is regarding the vehicle speed control (popularly known as Eco-Driving in the literature) methods in real driving condition. In particular, speed advisory systems are proposed for real time application on a vehicle. The control methods developed for each application are described in details with their verication and validation on the designed case studies. Apart from the developed control methods, there are three tools that were developed and used at various stages of this thesis: A vehicle model, A driving cycle prediction tool and optimal control methods (dynamic programming, PMP and ECMS). Depending on the application, the developed methods were implemented on the Hardware-In-Loop Internal Combustion Engine testing setup or on a real vehicle. The results show signicant improvements in the performance of the powertrain in terms of fuel economy and emissions in comparison to the state-of-the-art methods.

[EN] This paper proposes an Energy Management Strategy (EMS) for a plug-in parallel Hybrid Electric Vehicle (pHEV) with the goal of minimising the fuel consumption while fulfilling the constraint on the terminal battery State-ofCharge (SoC). The proposed strategy assumes that the route was previously covered several times by the vehicle, in order to extract information about the feasible operating conditions in the driving cycle. Note that this situation is usual in commuting and daily trips. In this sense, the history of vehicle speeds and positions are used to build space-dependent transition probability matrices that are latter used for driving cycle estimation by means of Markov-Chain approach. Once the driving cycle is estimated, the torque-split problem in parallel hybrid powertrain is addressed using the Equivalent Consumption Minimisation Strategy (ECMS), where the associated boundary value problem of finding the weighting factor between battery and fuel cost that drives the SoC to the desired level at the end of the estimated cycle is solved and applied to the system. Finally, in order to make up for cycle estimation error, the ECMS is solved recurrently. For the sake of clarity, the proposed strategy is initially developed and analysed in a modelling environment. Then tests in an engine-in-the-loop basis are done for validation. In order to show the potential of proposed strategy, results are presented using a trade-off between the fuel consumption and the terminal-SoC for four different methods: the optimal power-split that requires a priori knowledge of the driving cycle for benchmarking, online ECMS with a fixed cycle estimation (average speed profile obtained from previous trips on the route), the proposed method, i.e. online ECMS with a dynamic cycle estimation and finally a rule-based charge depleting and charge sustaining strategy. The results demonstrate that the online ECMS outperforms the rest of online applicable methods.

Electrification in the transportation industry is becoming more important to face global warming and replace fossil fuels in the future. Among the available energy sources Li-ion battery and proton exchange membrane fuel cell (PEMFC) are the most promising energy sources. Therefore, employing them in fuel cell hybrid electric vehicles (FCHEVs) to combine their advantages is one of the favorable solutions. However, they still face a major challenge residing in their aging that cause the drop of system performance. On one hand, the degradation is the result of the interaction between several aging mechanisms that react differently with various operating conditions. On the other hand, a hybrid system requires an essential energy management strategy (EMS) for fuel economy and optimal power share. At the end, this EMS has an important impact on the lifetime of sources in term of reducing or favorizing the degradation. Therefore, it is important to consider the degradation in the objectives of the designed EMS. Since the degradation is usually neglected when designing an EMS, this paper tends to review the possible methods for designing a health-conscious EMS. Hence, this paper presents a summary of the main fuel cell (FC) and Li-ion battery aging mechanisms as well as the useful degradation models for state of health estimation. In addition, the existing works that consider the degradation of on-board energy sources in their approaches for increasing their durability are classified and analysed. Remaining challenges are detailed along with a discussion and outlooks about current and future trends of health-conscious EMS. Author

This paper presents a simulation-based analysis of hybrid and plug-in hybrid propulsion system concepts for diesel-electric multiple unit regional railway vehicles. These alternative concepts primarily aim to remove emissions in terminal stops with longer stabling periods, with additional benefits reflected in the reduction of overall fuel consumption, produced emissions, and monetary costs. The alternative systems behavior is modeled using a backward-looking quasi-static simulation approach, with the implemented energy management strategy based on a finite state machine control. A comparative assessment of alternative propulsion systems is carried out in a case study of a selected regional railway line operated by Arriva, the largest regional railway undertaking in the Netherlands. The conversion of a standard diesel-electric multiple unit vehicle, currently operating on the network, demonstrated a potential GHG reduction of 9.43–56.92% and an energy cost reduction of 9.69–55.46%, depending on the type of service (express or stopping), energy storage technology selection (lithium-ion battery or double-layer capacitor), electricity production (green or grey electricity), and charging facilities configuration (charging in terminal stations with or without additional charging possibility during short intermediate stops) used. As part of a bigger project aiming to identify optimal transitional solutions towards emissions-free trains, the outcomes of this study will help in the future fleet planning., Transport and Planning

Employing semi-empirical models to estimate some characteristics of a fuel cell (FC) stack, such as power and polarization curves, is demanded for efficient design of a power allocation strategy in a FC hybrid electric vehicle. However, the multivariate nature of a FC system has made the design of an accurate model challenging. Since each semi-empirical model has its own pros and cons, this paper puts forward a data fusion approach for online characteristics estimation of a FC stack utilizing four well-known models, namely Mann, Squadrito, Amphlett, and Srinivasan. Despite the other similar techniques, the suggested one utilizes the strengths of each mentioned FC model while avoiding their drawbacks. Kalman filter is employed to identify the parameters of the models online to embrace the uncertainties caused by the alteration of operating conditions and degradation level. Considering the parameters, the output voltage given by each model as well as their covariance are computed. Then, a covariance intersection algorithm is proposed to fuse the estimated output voltages. The fusion of the models’ outputs leads to the estimation of fused characteristics curves. To underline the effectiveness of the proposed method, it is applied to four different experimental datasets extracted from three 500-W Horizon FCs. The obtained results demonstrate the superior performance of the suggested estimator in the sense of mean square error. On average, the mean square error of the data fusion method is 39.64% and 36.59% lower than other studied methods while estimating the polarization curve and power curve, respectively.

Hybridization of diesel multiple unit railway vehicles is an effective approach to reduce fuel consumption and related emissions in regional non-electrified networks. This paper is part of a bigger project realized in collaboration with Arriva, the largest regional railway undertaking in the Netherlands, to identify optimal solutions in improving trains’ energy and environmental performance. A significant problem in vehicle hybridization is determining the optimal size for the energy storage system, while incorporating an energy management strategy as well as technical and operational requirements. With the primary requirement imposed by the railway undertaking to achieve emission-free and noise-free operation within railway stations, we formalize this as a bi-level multi-objective optimization problem, including vehicle performance, the trade-off between fuel savings and hybridization cost, influence of the energy management strategy, and other constraints. By deriving a Li-ion battery parameters at the cell level, a nested coordination framework is employed, where a brute force search finds the optimal battery size using dynamic programming for full controller optimization for each feasible solution. In this way, the global minimum for fuel consumption for each battery configuration is achieved. The results from a Dutch case study demonstrated fuel savings and CO2 emission reduction of more than 34% compared to a standard vehicle. Additionally, benefits in terms of local pollutants (NOx and PM) emissions are observed. Using an alternative sub-optimal rule-based control demonstrated a significant impact of the energy management on the results, reflected in higher fuel consumption and increased battery size together with corresponding costs., Transport and Planning, Railway Engineering

To improve the fuel efficiency of a parallel hybrid electric racing car, this paper presents a computationally efficient energy management strategy (EMS) that regulates the internal combustion engine (ICE) to either operate with high efficiency or be switched off. Dynamic programming (DP) is employed to compute the optimal trajectories of vehicle speed and supercapacitor voltage. These trajectories are used as references for real-time online control. The online control contains two parts: a binary model predictive control (B-MPC) that determines the ICE on/off status and a Pontryagin’s minimum principle (PMP) that allocates the total torque demand to fuel and electric paths. The usual challenge to MPC is the large computational complexity and to PMP is the search of optimal costate The proposed EMS evades these two challenges. The binary property of MPC significantly reduces the computational complexity and the optimal costate of PMP is obtained from the value function computed by DP. The testing results by processor-in-the-loop simulation indicate the proposedEMS can realize a close to optimal performance and can be applied on a low-cost microprocessor., QC 20220125

To improve the fuel efficiency of a parallel hybrid electric racing car, this paper presents a computationally efficient energy management strategy (EMS) that regulates the internal combustion engine (ICE) to either operate with high efficiency or be switched off. Dynamic programming (DP) is employed to compute the optimal trajectories of vehicle speed and supercapacitor voltage. These trajectories are used as references for real-time online control. The online control contains two parts: a binary model predictive control (B-MPC) that determines the ICE on/off status and a Pontryagin’s minimum principle (PMP) that allocates the total torque demand to fuel and electric paths. The usual challenge to MPC is the large computational complexity and to PMP is the search of optimal costate The proposed EMS evades these two challenges. The binary property of MPC significantly reduces the computational complexity and the optimal costate of PMP is obtained from the value function computed by DP. The testing results by processor-in-the-loop simulation indicate the proposedEMS can realize a close to optimal performance and can be applied on a low-cost microprocessor., QC 20220125

Electrification of vehicles is an indispensable step in improving fuel economy and reducing fossil fuel emissions. In particular, hybrid electric vehicle market has gained popularity as one such reliable solution. With the global rise in environmental concerns, the need for advancement of the relevant technologies has become more noticeable than before. In this pursuit, it is well-known that design of effective energy management strategies (EMS) that govern power distribution among the onboard energy sources is key in reducing fuel consumption and its adverse environmental impacts. This thesis is concerned with EMS design for series hybrid electric vehicles from two standpoints. Powertrain component durability is often neglected in EMS development. In particular, batteries are prone to degradation through usage, a phenomenon widely known as cycle aging, and contribute largely to vehicle cost. In the first part of the thesis, therefore, battery lifetime optimization is integrated into the design of fuel-efficient energy management strategies. An empirical capacity degradation model is adopted from the literature and is modified in order to predict battery lifetime. The multi-objective problem is to compromise between fuel consumption reduction and battery wear minimization. The problem is formulated within two control theory frameworks, namely Pontryagin's minimum principle and model predictive control. Simulation results suggest that there is an enormous potential in prolonging battery lifetime by sacrificing negligible to no excessive amount of fuel consumption. Performance of the developed methodology in the Pontryagin's minimum principle framework exhibits an inverse correlation with the root-mean-square of power request of drive cycles. The results can be used to develop real-time rule-based methods. The application considered in this part is a hybrid electric wheel loader. While prolonging battery lifetime is economically beneficial for any hybrid electric v

Employing semi-empirical models to estimate some characteristics of a fuel cell (FC) stack, such as power and polarization curves, is demanded for efficient design of a power allocation strategy in a FC hybrid electric vehicle. However, the multivariate nature of a FC system has made the design of an accurate model challenging. Since each semi-empirical model has its own pros and cons, this paper puts forward a data fusion approach for online characteristics estimation of a FC stack utilizing four well-known models, namely Mann, Squadrito, Amphlett, and Srinivasan. Despite the other similar techniques, the suggested one utilizes the strengths of each mentioned FC model while avoiding their drawbacks. Kalman filter is employed to identify the parameters of the models online to embrace the uncertainties caused by the alteration of operating conditions and degradation level. Considering the parameters, the output voltage given by each model as well as their covariance are computed. Then, a covariance intersection algorithm is proposed to fuse the estimated output voltages. The fusion of the models’ outputs leads to the estimation of fused characteristics curves. To underline the effectiveness of the proposed method, it is applied to four different experimental datasets extracted from three 500-W Horizon FCs. The obtained results demonstrate the superior performance of the suggested estimator in the sense of mean square error. On average, the mean square error of the data fusion method is 39.64% and 36.59% lower than other studied methods while estimating the polarization curve and power curve, respectively.

Electrification in the transportation industry is becoming more important to face global warming and replace fossil fuels in the future. Among the available energy sources Li-ion battery and proton exchange membrane fuel cell (PEMFC) are the most promising energy sources. Therefore, employing them in fuel cell hybrid electric vehicles (FCHEVs) to combine their advantages is one of the favorable solutions. However, they still face a major challenge residing in their aging that cause the drop of system performance. On one hand, the degradation is the result of the interaction between several aging mechanisms that react differently with various operating conditions. On the other hand, a hybrid system requires an essential energy management strategy (EMS) for fuel economy and optimal power share. At the end, this EMS has an important impact on the lifetime of sources in term of reducing or favorizing the degradation. Therefore, it is important to consider the degradation in the objectives of the designed EMS. Since the degradation is usually neglected when designing an EMS, this paper tends to review the possible methods for designing a health-conscious EMS. Hence, this paper presents a summary of the main fuel cell (FC) and Li-ion battery aging mechanisms as well as the useful degradation models for state of health estimation. In addition, the existing works that consider the degradation of on-board energy sources in their approaches for increasing their durability are classified and analysed. Remaining challenges are detailed along with a discussion and outlooks about current and future trends of health-conscious EMS. Author

This paper improves the fuel efficiency of a student-made parallel hybrid electric racing car whose internal combustion engine (ICE) either operates with peak efficiency or is turned off. The control to the ICE thus becomes a binary problem. Owing to the very limited computation resource onboard, the energy management strategy (EMS) for this car must have small time and space complexities. A computationally efficient controller that combines the advantages of dynamic programming (DP) and Pontryagins minimum principle (PMP) is developed to run on a low-cost microprocessor. DP is employed offline to calculate the optimal speed trajectory, which is used as the reference for the online PMP to determine the real-time ICE on/off status and the electric motor (EM) torques. The normal PMP derives the optimal costate trajectory through solving partial differential equations. The proposed quasi-PMP (Q-PMP) method finds the costate from the value function obtained by DP. The fuel efficiency and computational complexity of the proposed controller are compared against several state of art methods through both model-in-the-loop (MIL) and processor-in-the-loop (PIL) simulations. The new method reaches similar fuel efficiency as the explicit DP, but requires less than 1% onboard flash memory. The performance of the Q-PMP controller is compared between binary-controlled and continuously controlled engines. It achieves roughly 12% higher fuel efficiency for the binary engine with only approximately 1/3 CPU utilization., QC 20210505

Electrification of vehicles is an indispensable step in improving fuel economy and reducing fossil fuel emissions. In particular, hybrid electric vehicle market has gained popularity as one such reliable solution. With the global rise in environmental concerns, the need for advancement of the relevant technologies has become more noticeable than before. In this pursuit, it is well-known that design of effective energy management strategies (EMS) that govern power distribution among the onboard energy sources is key in reducing fuel consumption and its adverse environmental impacts. This thesis is concerned with EMS design for series hybrid electric vehicles from two standpoints. Powertrain component durability is often neglected in EMS development. In particular, batteries are prone to degradation through usage, a phenomenon widely known as cycle aging, and contribute largely to vehicle cost. In the first part of the thesis, therefore, battery lifetime optimization is integrated into the design of fuel-efficient energy management strategies. An empirical capacity degradation model is adopted from the literature and is modified in order to predict battery lifetime. The multi-objective problem is to compromise between fuel consumption reduction and battery wear minimization. The problem is formulated within two control theory frameworks, namely Pontryagin's minimum principle and model predictive control. Simulation results suggest that there is an enormous potential in prolonging battery lifetime by sacrificing negligible to no excessive amount of fuel consumption. Performance of the developed methodology in the Pontryagin's minimum principle framework exhibits an inverse correlation with the root-mean-square of power request of drive cycles. The results can be used to develop real-time rule-based methods. The application considered in this part is a hybrid electric wheel loader. While prolonging battery lifetime is economically beneficial for any hybrid electric v

This paper presents a mathematical model for estimating the optimal sizing and assessing a standalone hybrid power system's performance entirely based on variable renewable energy sources and coupled with a hybrid energy storage system. This study evaluates how different levels of the main components' capital cost and the loss of power supply probability would affect the cost of energy and the power system's optimal sizing. The case study selected for this study was Ometepe Island in Nicaragua, where the crater lake of an extinct volcano was considered a feasible upper reservoir of a pumped storage hydropower plant, reducing the investments associated with this component. The mathematical formulation considers energy storage losses and gains, and the Pareto efficient solutions of the multi-objective optimization model simultaneously increase reliability, reduce the cost of energy, and minimize curtailment energy. By employing time-series with an hourly resolution, the model allows assessing the impact of the interannual variability of renewable energy sources on the system's performance. As for the case study, the cost of energy obtained from the model results ranges between (sic)0.047/kWh and (sic)0.095/kWh, based on international reference values, and these values match the information available in the literature and other databases.

Researchers have often overlooked the emissions problem during the suitable selection of renewable energy (RE) systems. Therefore, there is a need for an integrated framework that simultaneously considers the economic, technical and environmental criteria for the selection of the appropriate configuration. Firstly, a multi-objective optimization is performed using the epsilon- constraint method with a simulated annealing algorithm. Then a scenario-based method with a hybrid multi-criterion decision-making approach is used to rank all the available configurations. Five operating strategies are developed to make different configurations, i.e. battery only, pumped hydro storage (PHS), battery-diesel generator (DG), PHS-DG, and hybrid pumped-battery storage. A total of seven scenarios are made based on the weightage given to each main criterion. The study reveals that solar-wind-PHS-DG was the top-ranking alternative under four scenarios, solar-wind-PHS ranked first in two scenarios, and solar-wind-DG-battery got preference under the no-preference scenario; this shows that preferential selection (assigning a weighting to each criterion) significantly affects results. Emissions of all considered RE-based alternatives range from 0.072 to 0.148 kg (CO2 equivalent) per kWh of the served load. Furthermore, sensitivity analysis reveals that technical criteria conflict more with economic criteria than with environmental criteria. The impact of land requirements (an environmental sub-criterion) is visible in this study, indicating the high requirement of land for RE systems. The most appropriate configuration type is selected depending solely on the priorities defined by investors and policy-makers.

This paper presents a simulation-based analysis of hybrid and plug-in hybrid propulsion system concepts for diesel-electric multiple unit regional railway vehicles. These alternative concepts primarily aim to remove emissions in terminal stops with longer stabling periods, with additional benefits reflected in the reduction of overall fuel consumption, produced emissions, and monetary costs. The alternative systems behavior is modeled using a backward-looking quasi-static simulation approach, with the implemented energy management strategy based on a finite state machine control. A comparative assessment of alternative propulsion systems is carried out in a case study of a selected regional railway line operated by Arriva, the largest regional railway undertaking in the Netherlands. The conversion of a standard diesel-electric multiple unit vehicle, currently operating on the network, demonstrated a potential GHG reduction of 9.43–56.92% and an energy cost reduction of 9.69–55.46%, depending on the type of service (express or stopping), energy storage technology selection (lithium-ion battery or double-layer capacitor), electricity production (green or grey electricity), and charging facilities configuration (charging in terminal stations with or without additional charging possibility during short intermediate stops) used. As part of a bigger project aiming to identify optimal transitional solutions towards emissions-free trains, the outcomes of this study will help in the future fleet planning., Transport and Planning

Hybridization of diesel multiple unit railway vehicles is an effective approach to reduce fuel consumption and related emissions in regional non-electrified networks. This paper is part of a bigger project realized in collaboration with Arriva, the largest regional railway undertaking in the Netherlands, to identify optimal solutions in improving trains’ energy and environmental performance. A significant problem in vehicle hybridization is determining the optimal size for the energy storage system, while incorporating an energy management strategy as well as technical and operational requirements. With the primary requirement imposed by the railway undertaking to achieve emission-free and noise-free operation within railway stations, we formalize this as a bi-level multi-objective optimization problem, including vehicle performance, the trade-off between fuel savings and hybridization cost, influence of the energy management strategy, and other constraints. By deriving a Li-ion battery parameters at the cell level, a nested coordination framework is employed, where a brute force search finds the optimal battery size using dynamic programming for full controller optimization for each feasible solution. In this way, the global minimum for fuel consumption for each battery configuration is achieved. The results from a Dutch case study demonstrated fuel savings and CO2 emission reduction of more than 34% compared to a standard vehicle. Additionally, benefits in terms of local pollutants (NOx and PM) emissions are observed. Using an alternative sub-optimal rule-based control demonstrated a significant impact of the energy management on the results, reflected in higher fuel consumption and increased battery size together with corresponding costs., Transport and Planning, Railway Engineering

To improve the fuel efficiency of a parallel hybrid electric racing car, this paper presents a computationally efficient energy management strategy (EMS) that regulates the internal combustion engine (ICE) to either operate with high efficiency or be switched off. Dynamic programming (DP) is employed to compute the optimal trajectories of vehicle speed and supercapacitor voltage. These trajectories are used as references for real-time online control. The online control contains two parts: a binary model predictive control (B-MPC) that determines the ICE on/off status and a Pontryagin’s minimum principle (PMP) that allocates the total torque demand to fuel and electric paths. The usual challenge to MPC is the large computational complexity and to PMP is the search of optimal costate The proposed EMS evades these two challenges. The binary property of MPC significantly reduces the computational complexity and the optimal costate of PMP is obtained from the value function computed by DP. The testing results by processor-in-the-loop simulation indicate the proposedEMS can realize a close to optimal performance and can be applied on a low-cost microprocessor., QC 20220125

Electrification of vehicles is an indispensable step in improving fuel economy and reducing fossil fuel emissions. In particular, hybrid electric vehicle market has gained popularity as one such reliable solution. With the global rise in environmental concerns, the need for advancement of the relevant technologies has become more noticeable than before. In this pursuit, it is well-known that design of effective energy management strategies (EMS) that govern power distribution among the onboard energy sources is key in reducing fuel consumption and its adverse environmental impacts. This thesis is concerned with EMS design for series hybrid electric vehicles from two standpoints. Powertrain component durability is often neglected in EMS development. In particular, batteries are prone to degradation through usage, a phenomenon widely known as cycle aging, and contribute largely to vehicle cost. In the first part of the thesis, therefore, battery lifetime optimization is integrated into the design of fuel-efficient energy management strategies. An empirical capacity degradation model is adopted from the literature and is modified in order to predict battery lifetime. The multi-objective problem is to compromise between fuel consumption reduction and battery wear minimization. The problem is formulated within two control theory frameworks, namely Pontryagin's minimum principle and model predictive control. Simulation results suggest that there is an enormous potential in prolonging battery lifetime by sacrificing negligible to no excessive amount of fuel consumption. Performance of the developed methodology in the Pontryagin's minimum principle framework exhibits an inverse correlation with the root-mean-square of power request of drive cycles. The results can be used to develop real-time rule-based methods. The application considered in this part is a hybrid electric wheel loader. While prolonging battery lifetime is economically beneficial for any hybrid electric v

Electrification of vehicles is an indispensable step in improving fuel economy and reducing fossil fuel emissions. In particular, hybrid electric vehicle market has gained popularity as one such reliable solution. With the global rise in environmental concerns, the need for advancement of the relevant technologies has become more noticeable than before. In this pursuit, it is well-known that design of effective energy management strategies (EMS) that govern power distribution among the onboard energy sources is key in reducing fuel consumption and its adverse environmental impacts. This thesis is concerned with EMS design for series hybrid electric vehicles from two standpoints. Powertrain component durability is often neglected in EMS development. In particular, batteries are prone to degradation through usage, a phenomenon widely known as cycle aging, and contribute largely to vehicle cost. In the first part of the thesis, therefore, battery lifetime optimization is integrated into the design of fuel-efficient energy management strategies. An empirical capacity degradation model is adopted from the literature and is modified in order to predict battery lifetime. The multi-objective problem is to compromise between fuel consumption reduction and battery wear minimization. The problem is formulated within two control theory frameworks, namely Pontryagin's minimum principle and model predictive control. Simulation results suggest that there is an enormous potential in prolonging battery lifetime by sacrificing negligible to no excessive amount of fuel consumption. Performance of the developed methodology in the Pontryagin's minimum principle framework exhibits an inverse correlation with the root-mean-square of power request of drive cycles. The results can be used to develop real-time rule-based methods. The application considered in this part is a hybrid electric wheel loader. While prolonging battery lifetime is economically beneficial for any hybrid electric v

To improve the fuel efficiency of a parallel hybrid electric racing car, this paper presents a computationally efficient energy management strategy (EMS) that regulates the internal combustion engine (ICE) to either operate with high efficiency or be switched off. Dynamic programming (DP) is employed to compute the optimal trajectories of vehicle speed and supercapacitor voltage. These trajectories are used as references for real-time online control. The online control contains two parts: a binary model predictive control (B-MPC) that determines the ICE on/off status and a Pontryagin’s minimum principle (PMP) that allocates the total torque demand to fuel and electric paths. The usual challenge to MPC is the large computational complexity and to PMP is the search of optimal costate The proposed EMS evades these two challenges. The binary property of MPC significantly reduces the computational complexity and the optimal costate of PMP is obtained from the value function computed by DP. The testing results by processor-in-the-loop simulation indicate the proposedEMS can realize a close to optimal performance and can be applied on a low-cost microprocessor., QC 20220125

To improve the fuel efficiency of a parallel hybrid electric racing car, this paper presents a computationally efficient energy management strategy (EMS) that regulates the internal combustion engine (ICE) to either operate with high efficiency or be switched off. Dynamic programming (DP) is employed to compute the optimal trajectories of vehicle speed and supercapacitor voltage. These trajectories are used as references for real-time online control. The online control contains two parts: a binary model predictive control (B-MPC) that determines the ICE on/off status and a Pontryagin’s minimum principle (PMP) that allocates the total torque demand to fuel and electric paths. The usual challenge to MPC is the large computational complexity and to PMP is the search of optimal costate The proposed EMS evades these two challenges. The binary property of MPC significantly reduces the computational complexity and the optimal costate of PMP is obtained from the value function computed by DP. The testing results by processor-in-the-loop simulation indicate the proposedEMS can realize a close to optimal performance and can be applied on a low-cost microprocessor., QC 20220125

Electrification of vehicles is an indispensable step in improving fuel economy and reducing fossil fuel emissions. In particular, hybrid electric vehicle market has gained popularity as one such reliable solution. With the global rise in environmental concerns, the need for advancement of the relevant technologies has become more noticeable than before. In this pursuit, it is well-known that design of effective energy management strategies (EMS) that govern power distribution among the onboard energy sources is key in reducing fuel consumption and its adverse environmental impacts. This thesis is concerned with EMS design for series hybrid electric vehicles from two standpoints. Powertrain component durability is often neglected in EMS development. In particular, batteries are prone to degradation through usage, a phenomenon widely known as cycle aging, and contribute largely to vehicle cost. In the first part of the thesis, therefore, battery lifetime optimization is integrated into the design of fuel-efficient energy management strategies. An empirical capacity degradation model is adopted from the literature and is modified in order to predict battery lifetime. The multi-objective problem is to compromise between fuel consumption reduction and battery wear minimization. The problem is formulated within two control theory frameworks, namely Pontryagin's minimum principle and model predictive control. Simulation results suggest that there is an enormous potential in prolonging battery lifetime by sacrificing negligible to no excessive amount of fuel consumption. Performance of the developed methodology in the Pontryagin's minimum principle framework exhibits an inverse correlation with the root-mean-square of power request of drive cycles. The results can be used to develop real-time rule-based methods. The application considered in this part is a hybrid electric wheel loader. While prolonging battery lifetime is economically beneficial for any hybrid electric v

Electrification in the transportation industry is becoming more important to face global warming and replace fossil fuels in the future. Among the available energy sources Li-ion battery and proton exchange membrane fuel cell (PEMFC) are the most promising energy sources. Therefore, employing them in fuel cell hybrid electric vehicles (FCHEVs) to combine their advantages is one of the favorable solutions. However, they still face a major challenge residing in their aging that cause the drop of system performance. On one hand, the degradation is the result of the interaction between several aging mechanisms that react differently with various operating conditions. On the other hand, a hybrid system requires an essential energy management strategy (EMS) for fuel economy and optimal power share. At the end, this EMS has an important impact on the lifetime of sources in term of reducing or favorizing the degradation. Therefore, it is important to consider the degradation in the objectives of the designed EMS. Since the degradation is usually neglected when designing an EMS, this paper tends to review the possible methods for designing a health-conscious EMS. Hence, this paper presents a summary of the main fuel cell (FC) and Li-ion battery aging mechanisms as well as the useful degradation models for state of health estimation. In addition, the existing works that consider the degradation of on-board energy sources in their approaches for increasing their durability are classified and analysed. Remaining challenges are detailed along with a discussion and outlooks about current and future trends of health-conscious EMS. Author

Employing semi-empirical models to estimate some characteristics of a fuel cell (FC) stack, such as power and polarization curves, is demanded for efficient design of a power allocation strategy in a FC hybrid electric vehicle. However, the multivariate nature of a FC system has made the design of an accurate model challenging. Since each semi-empirical model has its own pros and cons, this paper puts forward a data fusion approach for online characteristics estimation of a FC stack utilizing four well-known models, namely Mann, Squadrito, Amphlett, and Srinivasan. Despite the other similar techniques, the suggested one utilizes the strengths of each mentioned FC model while avoiding their drawbacks. Kalman filter is employed to identify the parameters of the models online to embrace the uncertainties caused by the alteration of operating conditions and degradation level. Considering the parameters, the output voltage given by each model as well as their covariance are computed. Then, a covariance intersection algorithm is proposed to fuse the estimated output voltages. The fusion of the models’ outputs leads to the estimation of fused characteristics curves. To underline the effectiveness of the proposed method, it is applied to four different experimental datasets extracted from three 500-W Horizon FCs. The obtained results demonstrate the superior performance of the suggested estimator in the sense of mean square error. On average, the mean square error of the data fusion method is 39.64% and 36.59% lower than other studied methods while estimating the polarization curve and power curve, respectively.

Nowadays, hybrid vehicles have attracted major attention of researchers due to their capabilities to reduce consumption of fossil fuel, decrease air pollution and improve energy and environment sustainability. One of the most important challenges in production of hybrid vehicles is the optimal design of the energy management system in order to reduce energy consumption and costs. In this paper, an adaptive fuzzy logic based energy management scheme is proposed for a combined power generation system containing FC (FC), battery and ultra-capacitor. In the mentioned system, the FC is the main source while both the battery and ultra-capacitor are used as auxiliary sources of energy. To evaluate performance of the proposed energy management scheme, ADVISOR toolbox in Matlab software is utilized for modelling different parts of the hybrid vehicle. Furthermore, to adjust scaling factors of the proposed system in order to reduce both the speed and fuel consumption errors, the Social Spider Algorithm (SSA) is suggested. The simulation results in Matlab verify that the proposed method provides a better performance than the well-known Power Tracking Control (PTC) system because this scheme updates its parameters online. Moreover, it reduces fuel consumption and tracks the reference speed better in comparison with PTC., QC 20210720