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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

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

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 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.

[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

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 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

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.

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

In a hydrogen-based DC microgrid (MG), the integration of hydrogen subsystem increases the system complexity and flexibility. Effective power split, bus voltage stability and reliable operation become significant control issues of hydrogen-based DC MG. This paper proposes a simple and effective decentralized energy management strategy based on a mode-triggered droop scheme for an islanded PV/hydrogen/battery DC MG. In a decentralized manner, the proposed decentralized energy management strategy is implemented through two control steps: mode divisions and droop control. For practical implementation purposes of the proposed energy management strategy, the mode divisions scheme autonomously divides this DC MG into eight operating modes based on the system local information; the adaptive droop control method controls the distributed generation units based on their droop relationships under different operating modes. In addition, an islanded DC MG hardware-in-the-loop (HIL) RT-LAB simulation platform and a lab-scaled experimental platform are built to validate the effectiveness of the proposed decentralized strategy. The simulation and experiment results show that the proposed energy management strategy has an efficient power distribution ability without communication link under various operating modes.

In the present study, a Model Predictive Control (MPC) - based, Energy Management Strategy (EMS) is proposed, aiming at the minimisation of the fuel consumption of vessels with hybrid propulsion and power supply. This approach is capable of utilising online information regarding realistic future predictions of the propulsive power demand, addressing the current gap in the literature regarding the implementation of MPC EMSs onboard ships, and pragmatic future mission information utilisation. To the author's best knowledge, MPC for fuel consumption minimisation has not yet been implemented at this timescale in maritime propulsion plants with hybrid power supply. Furthermore, online mission information from the Integrated Bridge Systems (IBSs) and the governor's human input cannot be utilised by EMSs onboard ships, due to the lack of a mathematical pathway to enable such an information feedback flow. Increasingly powerful processing units in ship controllers and a wider application of batteries in marine powertrains motivated such a computationally heavy and, simultaneously, highly fuel-efficient control approach. The proposed controller framework has been tested and verified in a case study using a Mean Value First Principle (MVFP) propulsion plant model of a hybrid naval vessel with batteries in Simulink®, provided by DAMEN® SNS. By using provided mission data, realistic mission profiles were generated, in which the proposed controller is validated against perfectly and piecewise, non-perfectly informed Dynamic Programming (DP) solutions in an information barrier scheme, yielding close to optimal performance, and a fuel consumption reduction of up to 3.5% when compared to any non-long-term feedback enabled controller., Marine Technology

This paper deals with the design of an energy management strategy (EMS) for an industrial hybrid self-guided vehicle (SGV), considering the size of a fuel cell (FC) stack and degradation of a battery pack. In this context, first, a realistic energy model of the SGV was proposed and validated, based on experiments. This model provided a basis for individual components analysis, estimating energy requirements, component sizing, and testing various EMSs, prior to practical implementation. Second, the performance of the developed FC/battery SGV powertrain was validated under three EMS modes. Each mode was studied by considering four different FC sizes and three battery degradation levels. The final results showed that a small FC as a range extender is recommended, to reduce system cost. It is also important to maintain the FC in its high efficiency zones with a minimum ON/OFF cycle, leading to efficiency and lifetime enhancement of FC system. Battery SOC have to be kept at a high level during SGV operation, to support the FC during SGV acceleration. In order to improve the SGV’s overall autonomy, it is also important to minimize the stop and go and rotational SGV motion with appropriate acceleration and deceleration rate.

I samband med den globala uppvärmningen ökar efterfrågan för rena och förnybara bränslen alltmer i dagens samhälle. Eftersom flygindustrin idag är ansvarig för samma mängd växthusgaser som all motortrafik i Sverige, skulle ett byte till en avgasfri energikälla för flygfarkoster vara ett stort framsteg. Därför har projektet genom modellering framtagit ett hybridsystem av ett batteri och en bränslecell och undersökt hur kombinationen av olika storlekar på dem presterar i en driftcykel. Då batterier har hög specifik effekt men är tunga, kompletteras de med fördel av bränsleceller, som är lättviktiga och bidrar med uthållig strömförsörjning. På så sätt blir hybriden optimal för flygfarkoster. Kandidatarbetet är en del av projektet Green Raven, ett tvärvetenskapligt samarbete mellan instutitionerna Tillämpad Elektrokemi, Mekatronik och Teknisk Mekanik på Kungliga Tekniska Högskolan. Driftcykelmodelleringen gjordes i Simulink, och flera antaganden gjordes beträffande effektprofilen, samt bränslecellens mätvärden och effekt. Tre olika energihushållningsscheman skapades, vilka bestämde bränslecellseffekten beroende på vätgasnivån och batteriets laddningstillstånd. Skillnaden på systemen var vilka intervall av laddningstillstånd hos batteriet som genererade olika effekt hos bränslecellen. Det bästa alternativet visade sig vara 0/100-systemet, eftersom det var det enda som inte orsakede någon degradering av bränslecellens kapacitet., In today’s society, with several environmental challenges such as global warming, the demand for cleanand renewable fuels is ever increasing. Since the aviation industry in Sweden is responsible for the sameamount of greenhouse gas emissions as the motor traffic, a change to a non-polluting energy source forflying vehicles would be considerable progress. Therefore, this project has designed a hybrid system of abattery and a fuel cell and investigated how different combinations of battery and fuel cell sizes perform ina drive cycle, through computer modelling. As batteries possess a high specific power but are heavy, thefuel cells with high specific energy complement them with a sustained and lightweight power supply,which makes the hybrid perfect for aviation. The bachelor thesis is a part of Project Green Raven, aninterdisciplinary collaboration with the institutions of Applied Electrochemistry, Mechatronics andEngineering Mechanics at KTH Royal Institute of Techology. The drive cycle simulations were done inSimulink, and several assumptions regarding the power profile, fuel cell measurements and power weremade. Three different energy management strategies were set up, determining the fuel cell powerdepending on hydrogen availability and state of charge of the battery. The strategies were called 35/65,20/80 and 0/100, and the difference between them was at which state of charge intervals the fuel cellchanged its power output. The best strategy proved to be 0/100, since it was the only option which causedno degradation of the fuel cell whatsoever.

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 ultracapacitor. 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 20230328

For many maritime applications Li-Ion batteries are foreseen as energy storage units that can improve the performance of the on-board power system in terms of continuity of service, fuel consumption, emissions and running hours of main engines. However, one of main limitations of battery application in on-board power systems is the aging of batteries. Applications of instantaneous power input/output such as propulsion dynamic assistance and heavy seas operation of ships, have an adverse effect on battery lifetime meaning that degradation of energy capacity over time is accelerated. Additionally, limited by their maximum current rating, batteries cannot deliver effectively high C-rates and therefore are not able to fully absorb the engine fluctuations. A typical solution for this problem is to over-size the battery system. By paralleling more batteries, the max. C-rate is lowered, and battery lifetime can be extended. On the other hand, an over-sized battery system will result in additional capital cost and weight. Therefore, it is evident that in conventional approaches there is an undesirable trade-off between battery aging and battery size. As an alternative practical approach into this problem, this thesis proposes an on-board hybrid energy storage system (HESS) that comprises of a battery and a supercapacitor component. By placing the supercapacitor in parallel to the battery and by using it for high peak currents, it is possible to reduce the stress on the battery and thus extend battery lifetime while improving the availability and the reliability of the power system. In addition, by taking advantage of the high specific power of the supercapacitor and the high specific energy of the battery it is possible to optimize sizing of the energy storage system for high power applications. In this thesis, a parametric approach of combined sizing and energy management for hybrid energy storage system is developed and integrated into a typical DC shipboard power system, Marine Technology | Marine Engineering

With the fast development of DC Microgrid (MG) technology, its operating economy and reliability are getting more and more concern. The traditional distributed control method is aimed at power balance and system stability, and is difficult to meet the requirement of energy management system for multi-source hybrid DC MG. This paper provides a two-level energy management strategy for PV-fuel cell-battery-based DC MG, which is divided into device control level and system control level. At the device control level, the distributed control methods based on MPPT-droop dual-mode control and droop control are proposed to enhance system reliability; at the system control level, the equivalent consumption minimization strategy (ECMS) is used to distribute system net power between battery pack and fuel cell system. A lab-scale DC microgrid platform is developed to verify the proposed energy management strategy in this paper. Moreover, the analysis and compare of the results show that the proposed two-level energy management strategy can achieve lower equivalent hydrogen consumption than classical PI control and state machine control method.

In recent years many companies have investigated the use of hybrid technology due to the potential of increasing the driveline’s efficiency and thus reducing fuel consumption. Previous studies show that hydraulic hybrid technology can be favourable to use in construction machinery such as wheel loaders, which often operate in repetitive drive cycles and have high transient power demands. Parallel as well as Series hybrid configurations are both found suitable for wheel loader applications as the hybrid configurations can decrease the dependency on the torque converter. This project has investigated a novel hydraulic hybrid concept which utilizes the wheel loaders auxiliary pump as a supplement to enable both Series and Parallel hybrid operation. Impact of accumulator sizes has also been investigated, for which smaller accumulator sizes resembles a hydrostatic transmission. The hybrid concept has been evaluated by developing a wheel loader simulation model and a control system based on a rule-based energy management strategy. Simulation results indicate improved energy efficiency of up to 18.80 % for the Combined hybrid. Moreover, the accumulator sizes prove to have less impact on the energy efficiency. A hybrid system with decreased accumulator sizes shows improved energy efficiency of up to 16.40 %.

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works, The energy management strategy plays a major role in hybrid platforms powered by fuel cells (FCs) and batteries. This paper presents an assessment of energy management focused on fuel economy and battery degradation. Particularly, a proposed heuristic strategy and the widely known equivalent consumption minimization strategy are compared with the optimal solution obtained offline via dynamic programming. The case study is based on a real FC hybrid vehicle. Accordingly, the powertrain model of the vehicle used for the simulations is validated experimentally, and the profile of the power demand is measured from the real application. The results show that the proposed strategy offers the same performance as the equivalent consumption minimization strategy when the battery degradation is prioritized, and in comparison with the optimal off-line solution, it can be seen that there is still margin for improvement in terms of battery degradation., Peer Reviewed, Postprint (published version)

The development of energy storage systems paves the way towards a high integration of renewable energy sources in the electricity generation sector. Considering above, this paper introduces a new energy management strategy to efficiently coordinate a hybrid energy storage system based on pumped hydro storage (long term bulk storage) with batteries (short term, more flexible). For the purpose of this analysis, hourly time series of irradiation, wind speed, temperature and real measured load (characteristic for farmstead) covering one year were gathered for the selected site in Algeria. The optimal size of the system is determined based on a multi-objective optimization using a grey wolf optimizer implemented in MATLAB software. The results indicate that the hybrid storage system enables achieving higher reliability at lower cost in comparison to a system with single storage technology. The use of hybrid storage also reduces the curtailment of renewable generation. Further findings reveal that the cost of an optimal energy supply system with 97.5% reliability is 0.162 €/kWh, 0.207 €/kWh and 1.462 €/kWh for hybrid storage, battery and pumped storage, respectively. However, sensitivity analysis shows that the optimal hybrid storage configuration is less resilient when changes in irradiation/temperature/load are considered. This indicates that special actions (upscale of installed power) must be undertaken to avoid lower performance of hybrid storage systems. In summary, the hybrid storage system seems to be better sized for consideration in optimized solar/wind conditions, but by avoiding oversizing they are less resilient to future potential changes in renewable energy availability.

Electric forklifts, dominantly powered by lead acid batteries, are widely used for material handling in factories, warehouses, and docks. The long charging time and short working time characteristics of the lead acid battery module results in the necessity of several battery modules to support one forklift. Compared with the cost and time consuming lead acid battery charging system, a fuel cell/battery hybrid power module could be more convenient for a forklift with fast hydrogen refueling and long working time. In this paper, based on the characteristics of a fuel cell and a battery, a prototype hybrid forklift with a fuel cell/battery hybrid power system is constructed, and its hardware and software are designed in detail. According to the power demand of driver cycles and the state of charge (SOC) of battery, an energy management strategy based on load current following for the hybrid forklift is proposed to improve system energy efficiency and dynamic response performance. The proposed energy management strategy will fulfill the power requirements under typical driving cycles, achieve reasonable power distribution between the fuel cell and battery and, thus, prolong its continuous working time. The proposed energy management strategy is implemented in the hybrid forklift prototype and its effectiveness is tested under different operating conditions. The results show that the forklift with the proposed hybrid powered strategy has good performance with different loads, both lifting and moving, in a smooth and steady way, and the output of the fuel cell meets the requirements of its output characteristics, its SOC of battery remaining at a reasonable level.

In recent years, rules and regulations regarding fuel consumption of vehicles and the amount of emissions produced by them are becoming stricter. This has led the automotive industry to develop more advanced solutions to propel vehicles to meet the legal requirements. The Hybrid Electric Vehicle is one of the solutions that is becoming more popular in the automotive industry. It consists of an electrical driveline combined with a conventional powertrain, propelled by either a diesel or petrol engine. Two power sources create the possibility to choose when and how to use the power sources to propel the vehicle. The strategy that decides how this is done is referred to as an energy management strategy. Today most energy management strategies only try to reduce fuel consumption using models that describe the steady state behaviour of the engine. In other words, no reduction of emissions is achieved and all transient behaviour is considered negligible. In this thesis, an energy management strategy incorporating engine dynamics to reduce fuel consumption and nitrogen oxide emissions have been designed. First, the models that describe how fuel consumption and nitrogen oxide emissions behave during transient engine operation are developed. Then, an energy management strategy is developed consisting of a model predictive controller that combines the equivalent consumption minimization strategy and convex optimization. Results indicate that by considering engine dynamics in the energy management strategy, both fuel consumption and nitrogen oxide emissions can be reduced. Furthermore, it is also shown that the major reduction in fuel consumption and nitrogen oxide emissions is achieved for short prediction horizons.

More restrictive emission legislations, rising fuel prices and the realisation that oil is a limited resource have lead to the emergence of the hybrid electric vehicles.To fully utilise the potential of the hybrid electric vehicles, energy management strategies are needed. The main objective of the strategy is to ensure that the limited electric energy is utilised in an efficient manner.This thesis develops and evaluates an optimisation based energy management strategy for plug-in hybrid electric vehicles. The optimisation methods used are based on a dynamic programming and ECMS approach. The strategy is validated against Vsim, Volvo Cars' performance and fuel consumption analysis tool as well as against strategies where parts of the optimisation is replaced by logic. The results show that the developed strategy consumes less fuel both compared to the corresponding Vsim strategy and the logic strategies.

Offering high efficiency and producing zero emissions Fuel Cells (FCs) represent an excellent alternative to internal combustion engines for powering vehicles to alleviate the growing pollution in urban environments. Due to inherent limitations of FCs which lead to slow transient response, FC-based vehicles incorporate an energy storage system to cover the fast power variations. This paper considers a FC/supercapacitor platform that configures a hard constrained powertrain providing an adverse scenario for the energy management strategy (EMS) in terms of fuel economy and drivability. Focusing on palliating this problem, this paper presents a novel EMS based on the estimation of short-term future energy demand and aiming at maintaining the state of energy of the supercapacitor between two limits, which are computed online. Such limits are designed to prevent active constraint situations of both FC and supercapacitor, avoiding the use of friction brakes and situations of non-power compliance in a short future horizon. Simulation and experimentation in a case study corresponding to a hybrid electric bus show improvements on hydrogen consumption and power compliance compared to the widely reported Equivalent Consumption Minimization Strategy. Also, the comparison with the optimal strategy via Dynamic Programming shows a room for improvement to the real-time strategies., Peer Reviewed, Postprint (author's final draft)

A fuzzy logic controller strategy for battery energy management in a grid connected electro-thermal residential microgrid is presented. The fuzzy control policy manages the power of the microgrid storage elements in order to minimize a set of quality indices involving, among others, the power profile exchanged with the mains. Numerical simulations using real measured data generation and consumption are provided to both validate the control design and to highlight the benefits of including thermal elements in the overall energy management strategy of the system. © 2014 IEEE., Peer Reviewed, Postprint (published version)

In this research, the significant influence of engine and cabin thermal management on the fuel efficiency and emissions of plug-in hybrid electric vehicles is investigated. A practical solution to implement an optimal energy management strategy of plug-in hybrid electric vehicles which considers the temperature noise factor is introduced.

Las pilas de combustible son muy ventajosas debido a su alta eficiencia en la conversión de energía y nula contaminación. En esta tesis se realiza un extenso estudio sobre el control y diseño de sistemas de generación eléctrica basados en pilas de combustible. El núcleo principal de la misma son los sistemas híbridos con pilas de combustible y supercapacitores como elementos almacenadores de energía, orientado a aplicaciones automotrices. La determinación del Grado de Hibridización (i.e. la determinación del tamaño de la pila de combustible y del número de supercapacitores) se realiza mediante una metodología propuesta con el objetivo de satisfacer requisitos de conductibilidad y consumiendo la menor cantidad de hidrógeno posible. El proceso de diseño comienza con la determinación de la estructura eléctrica de generación del vehículo y utiliza un modelo detallado realizado en ADVISOR, una herramienta para modelado y estudio de sistemas híbridos. Se analiza el flujo de energía a través de los componentes del vehículo cuando el vehículo sigue diferentes ciclos de conducción estándares, mostrando las pérdidas en cada componente que degradan la eficiencia del sistema y limitan la recuperación de energía de frenado. Con respecto a la recuperación de energía, se ha definido y analizado un parámetro que cuantifica la cantidad de energía que realmente es reaprovechada: el ratio frenado/hidrógeno. Para controlar el flujo de energía entre la pila de combustible, los almacenadores de energía y la carga eléctrica, se proponen tres Estrategias de Gestión de Energía (EMS) para Vehículos Híbridos con Pila de Combustible (FCHVs) basadas en el mapa de eficiencia de la pila y se validan mediante un montaje experimental desarrollado para emular el sistema híbrido. Los resultados de consumo de hidrógeno son comparados con dos referencias: el consumo correspondiente al caso del vehículo sin hibridización y el caso óptimo con el menor consumo para el vehículo propuesto., The use of fuel cell systems based on hydrogen is advantageous because of their high efficiency in the energy conversion and null emissions. In this thesis, an extensive study about the control and design of electrical generation systems based on fuel cells is performed. The main focus is in hybrid systems composed of fuel cells and supercapacitors as energy storage elements, oriented to automotive applications. The determination of the hybridization degree (i.e. the determination of the fuel cell size and the number of supercapacitors) is performed through a proposed methodology with the objective to fulfil the conductibility requirements and to consume the lowest amount of hydrogen. The process of design starts with the determination of the electrical structure and utilizes a detail model developed using ADVISOR, a MATLAB toolbox for modelling and studying hybrid vehicles. The energy flow between the vehicle components is analyzed when the vehicle is tested with different Standard Driving Cycles, showing how the losses in each component degrade the efficiency of the system and limit the energy recovery from braking. With regard to the energy recovery, a parameter to quantify the amount of energy that is actually reused is defined and analyzed: the braking/hydrogen ratio. To control the energy flow between the fuel cell, the energy storage system, and the electrical load in Fuel Cell Hybrid Vehicles (FCHVs), three Energy Management Strategies (EMSs) based on the fuel cell efficiency map are presented and validated through an experimental setup, which is developed to emulate the FCHV. The resulting hydrogen consumptions are compared with two references: the consumption of the pure fuel cell case, a vehicle without hybridization, and the optimal case with the minimum consumption. The optimal consumption for a given vehicle is determined through a methodology proposed that, unlike other previous methodologies, avoids the discretization of the sta, Postprint (published version)

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 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 then 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 AVF data structure enables faster convergence and saves at least 70% of onboard memory space without obviously increasing the average CPU utilization., QC 20230503