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The article addresses the usage patterns of plug-in hybrid vehicles (PHEVs) under Polish conditions. The conventional approach to operating such vehicles assumes that they are used with a fully charged battery at the start. However, the economic circumstances of Polish users often do not allow for daily charging of vehicles from the domestic power grid. As a result, these vehicles are used not only in a mode powered solely by the internal combustion engine but also in a mode where the internal combustion engine is primarily utilized to charge the battery. An analysis was conducted on various ways of operating plug-in vehicles, evaluating not only harmful emissions but also fuel consumption (for battery states of charge: SOC = 100%, SOC = 50%, SOC = 0%, and SOC = 0 → 100%—forced charging mode). The study focused on the most characteristic vehicle segment in Poland, SUVs, and employed a methodology for determining exhaust emissions under real-world driving conditions. Results indicate that forced charging of such a vehicle's battery leads to over a 25-fold increase in carbon dioxide emissions (fuel consumption) in urban areas compared to operating the vehicle with a fully charged battery (CO—25× increase, NOx—12× increase, PN—11× increase). Operating a plug-in SUV without charging it from the power grid results in a 13-fold increase in fuel consumption compared to using the vehicle with a fully charged battery (CO—10× increase, NOx—6× increase, PN—4× increase). The emission results were used to evaluate Poland's charging infrastructure in the context of PHEV usage. The current state of the infrastructure and its development plans for 2030 and 2040 were analyzed. It was found that significant reductions in fuel consumption (by approximately 30%) and CO2 emissions are achievable by 2040. Emissions of CO, NOx, and PN are expected to decrease by about 10%, primarily due to the internal combustion engine operating at high load conditions in non-urban or highway scenarios. [ABSTRACT FROM AUTHOR]

India's burgeoning population necessitates innovative transportation solutions, with electric vehicles (EVs) emerging as a locally viable and urgent need. However, a significant obstacle lies in the improper deployment of charging stations, which strains the power distribution system. Recognizing the environmental benefits of Renewable Energy Sources (RES), high-priority buses are now equipped with Plug-in Hybrid Electric Vehicle (PHEV) charging stations, bolstering effective utilization. This study leverages meta-heuristic optimization techniques to propose an efficient allocation strategy for PHEV charging stations powered by RES. The core objective is to establish a cost-effective charging infrastructure while upholding the effective integrity of the network in distribution system. To address these challenges, the study employs meta-heuristic optimization algorithms and optimizes forecasting around sustainable energy systems to achieve desired output. Consequently, the research introduces a novel approach, conceptualizing the distribution of optimal charging stations as a multi-objective problem with added parameters. Furthermore, this study tackles the RES PHEV and charging station site challenge by introducing the innovative Walrus Optimization Algorithm (WaOA), which effectively overcomes issues related to slow convergence and local optima. Experimental findings demonstrate the superiority of the proposed model in terms of minimizing the cost function. This research offers a promising pathway toward sustainable transportation in India's evolving landscape.

Abstract For many years Norway has been in the forefront of promoting electromobility. Today, Norway has the world’s highest per capita fleet of plug-in electric cars. In 2021, 1.6% of the cars in the EU fleet were plug-in electric vehicles, whereas their share was 21% in Norway. Part of the successful market take-up rate is due to wide-ranging tax exemptions. Increasing plug-in electric vehicles numbers causes tax revenue losses, making exemptions unsustainable. Norway has the ambitious goal that from 2025, all newly registered cars shall be zero-emission vehicles. Keeping tax exemptions in place might be crucial for this goal. The objective of this paper is to provide information to solve this dilemma. Tax exemption reduction and abolition paths which offer a compromise between minimal effects on the development of zero-emission vehicles and tax revenues have been identified. An updated and re-calibrated version of the stock-flow-model SERAPIS was used to simulate and assess different scenarios. Results show that a controlled tax phase-in allows Norway to reach its environmental targets of 100% zero emission vehicles by 2025 and a 55% decrease of CO2-emissions in 2030 relative to 2005 while simultaneously increasing public revenues significantly.

The ambient temperature affects the operation of different powertrain systems, including electric, hybrid electric, and internal combustion engines. This study investigated the effect of the ambient temperature on the energy consumption and CO2 emissions of a plug-in hybrid electric vehicle running in different powertrain modes. The vehicle was driven for 4150 km following a selected route 199 times in different powertrain modes and in different ambient temperatures ranging from −24 °C to 32 °C. Instantaneous and cumulative fuel consumptions were measured using a fuel flow meter, and the battery energy usage was determined from the vehicle telematics during each test. The total energy consumption and total CO2 emissions were affected by the ambient temperature in all powertrain modes, including electric, hybrid electric (charge-depleting and charge-sustaining), and conventional internal combustion engine modes. The highest increase was associated with the charge-depleting hybrid electric mode, with 350% and 290% increases in energy consumption and CO2 emissions when the ambient temperature dropped from 29 °C to −24 °C. The conventional internal combustion engine mode was the least affected, with only 7% and 8% increased in energy consumption and CO2 emissions, respectively. [ABSTRACT FROM AUTHOR]

Energy management strategy (EMS) is a key technology for plug‐in hybrid electric vehicles (PHEVs). The energy management of certain series–parallel PHEVs involves the control of continuous variables, such as engine torque, and discrete variables, such as clutch engagement/disengagement. Herein, a control‐oriented model is established for a series–parallel plug‐in hybrid system with clutch engagement control from the perspective of mixed‐integer programming. Subsequently, an EMS based on continuous‐discrete reinforcement learning (CDRL), which enables simultaneous output of continuous and discrete variables, is designed. During training, state‐of‐charge (SOC) randomization is introduced to ensure that the hybrid system exhibits optimal energy‐saving performance in both high and low SOC. Finally, the effectiveness of the proposed CDRL strategy is verified by comparing EMS based on charge‐depleting charge‐sustaining (CD‐CS) with rule‐based clutch engagement control and dynamic programming (DP). In the simulation results, it is shown that, under a high SOC, the CDRL strategy proposed in this article can improve energy efficiency by 8.3% compared to CD‐CS, and the energy consumption is just 6.6% higher than the global optimum based on DP, while under a low SOC, the numbers are 4.1% and 3.9%, respectively. [ABSTRACT FROM AUTHOR]

For many years Norway has been in the forefront of promoting electromobility. Today, Norway has the world's highest per capita fleet of plug-in electric cars. In 2021, 1.6% of the cars in the EU fleet were plug-in electric vehicles, whereas their share was 21% in Norway. Part of the successful market take-up rate is due to wide-ranging tax exemptions. Increasing plug-in electric vehicles numbers causes tax revenue losses, making exemptions unsustainable. Norway has the ambitious goal that from 2025, all newly registered cars shall be zero-emission vehicles. Keeping tax exemptions in place might be crucial for this goal. The objective of this paper is to provide information to solve this dilemma. Tax exemption reduction and abolition paths which offer a compromise between minimal effects on the development of zero-emission vehicles and tax revenues have been identified. An updated and re-calibrated version of the stock-flow-model SERAPIS was used to simulate and assess different scenarios. Results show that a controlled tax phase-in allows Norway to reach its environmental targets of 100% zero emission vehicles by 2025 and a 55% decrease of CO2-emissions in 2030 relative to 2005 while simultaneously increasing public revenues significantly. [ABSTRACT FROM AUTHOR]

This paper investigates a variant of the traveling salesman problem (TSP) with speed optimization for a plug-in hybrid electric vehicle (PHEV), simultaneously optimizing the average speed and operation mode for each road segment in the route. Two mixed-integer nonlinear programming models are proposed for the problem: one with continuous speed decision variables and one with discretized variables. Because the models are nonlinear, we propose reformulation schemes and introduce valid inequalities to strengthen them. We also describe a branch-and-cut algorithm to solve these reformulations. Extensive numerical experiments are performed to demonstrate the algorithm's performance in terms of computing time and energy consumption costs. Specifically, the proposed solution method can efficiently solve instances with a realistic number of customers and outperforms the benchmark approaches from the literature. Integrating speed optimization into the TSP of a PHEV can lead to significant energy savings compared with the fixed-speed TSP. In addition, the proposed model is extended to investigate the impact of the presence of charging stations, which makes the problem harder to solve but has the potential to further reduce energy consumption costs. Funding: F. Wu gratefully acknowledges the support of the National Natural Science Foundation of China [Grant 72271161]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0247. [ABSTRACT FROM AUTHOR]

This paper investigates the frequency control strategy of the Virtual Power Plant (VPP), with the structure of an independent control area that includes diverse Distributed Energy Resources. The proposed VPP system comprises generating units such as Solar PV, Archimedes Wave Energy Conversion, Biodiesel driven Generator, and Plug-in Hybrid Electric Vehicle, which are interconnected to conventional thermal power plant. Centralized control strategy is adopted for frequency control studies, by considering the communication time delays. Fractional order Proportional Integral Derivative (FOPID), Proportional Integral Derivative (PID), and Proportional Integral (PI) controllers are employed to perform the control action on generating units to perform the frequency control. Tuning of the controllers is done using optimization algorithms such as Particle Swarm Optimization, Firefly Algorithm, Grasshopper Optimization Algorithm, Ant Lion Optimizer, Sine Cosine Algorithm, and Salp Swarm Algorithm (SSA). By comparative analysis, SSA algorithm is proved to outperform the other algorithms in terms of minimizing the objective function of Integral of Squared Error of the deviations in system frequencies and tie-line power. The system is also investigated under various scenarios of generation and load disturbances to study the comparative performance of the FOPID controller over PID and PI controllers. Results prove the superiority of SSA-tuned FOPID controller over PID and PI controllers in terms of providing better system responses. Sensitivity test has been conducted on the system without resetting nominal controller gain values and the proposed SSA-tuned FOPID controller is proved to be robust against the uncertainties in the load and parameters of our system. [ABSTRACT FROM AUTHOR]

The plug-in hybrid vehicles (PHEV) technology can effectively address the issues of poor dynamics and higher energy consumption commonly found in traditional mining dump trucks. Meanwhile, plug-in hybrid electric trucks can achieve excellent fuel economy through efficient energy management strategies (EMS). Therefore, a series hybrid system is constructed based on a 100-ton mining dump truck in this paper. And inspired by the dynamic programming (DP) algorithm, a predictive equivalent consumption minimization strategy (P-ECMS) based on the DP optimization result is proposed. Based on the optimal control manifold and the SOC reference trajectory obtained by the DP algorithm, the P-ECMS strategy performs real-time stage parameter optimization to obtain the optimal equivalent factor (EF). Finally, applying the equivalent consumption minimization strategy (ECMS) realizes real-time control. The simulation results show that the equivalent fuel consumption of the PECMS strategy under the experimentally collected mining cycle conditions is 150.8 L/100 km, which is 10.9% less than that of the common CDCS strategy (169.3 L/100 km), and achieves 99.47% of the fuel saving effect of the DP strategy (150 L/100 km). [ABSTRACT FROM AUTHOR]

This paper proposes obtaining maximum and minimum daily cumulative energy curves and introduces novel hourly and daily energy flexibility indices. Also, it develops a generic methodology that quantifies and formulates energy flexibility as the possible power increase (P_in) or decrease (P_dec) within operational limits. The proposed method can be applied to derive maximum and minimum energy flexibility curves for different devices and aggregate them to extract hourly or daily energy flexibility indices based on the calculation area between daily cumulative energy curves in an hour and 24 hours. The proposed energy flexibility estimation is evaluated by doing offline digital time-domain simulations on a 100-bus home-residential active distribution network (ADN), including flexible equipment/devices (e.g., washing machines, dishwashers, domestic heat water, battery, photovoltaic (PV) panels, and plug-in hybrid electric vehicle (PHEV) charging stations) in MATLAB/Simulink software environment. Then, a price-sensitive model of every flexible equipment is introduced, and ultimately, the effect of electricity price changes on energy flexibility is evaluated. The simulations and comparisons of the energy flexibility potential of different pricing scenarios effectively prove the proposed strategy’s effectiveness, accuracy, and authenticity.

The plug-in hybrid electric vehicles (PHEVs) are playing an increasingly important role in urban public transportation systems for their unique potential for energy saving and emission reduction. However, as an enabling technology for the cost-efficient operation of PHEVs, the current energy management strategies (EMSs) rarely consider the transient characteristics of the engine, especially the limit of engine transient performance and the extra fuel consumption due to engine state changes. To improve the energy-saving effect of EMSs, an optimization study on energy management considering engine transient characteristics for PHEV is carried out in this paper. Firstly, a high-precision transient fuel consumption model is established based on artificial neural network (ANN) to accurately evaluate the real fuel consumption of engine under steady and unsteady states. Secondly, an adaptive equivalent consumption minimization strategy (A-ECMS) is constructed for PHEV, and the engine transient performance boundary is defined in the strategy to avoid unreasonable engine power surge decision. Thirdly, the transient fuel consumption model is incorporated into the equivalent fuel consumption cost function of A-ECMS to fully consider the impact of engine transient fuel consumption on the real-time power allocation of PHEV. The results show that the improved strategy weakens the state fluctuation of the engine, and makes the engine run more smoothly, resulting in a 99.16% reduction in the extra fuel consumption due to engine state changes. Finally, the fuel economy of the PHEV under the combined driving cycle based on the C-WTVC improved by 3.37%.

Among various Hybrid Electric Vehicles (HEVs), Plug-in Hybrid Electric Vehicles (PHEVs) charged from the grid are seen as the most advanced ones, as they can drive dozens of kilometers using only the electric engine and thus producing less tailpipe greenhouse gas emissions than vehicles with internal combustion engines or other HEVs. The proportion of PHEVs among all vehicles is still relatively low but increasing rapidly in many countries. However, the real-world emissions from these novel hybrid technologies are not straightforward to estimate. This study investigates multiple properties of the particle emissions of a PHEV, with gasoline direct injection, GDI, compared to two conventional gasoline vehicles, one with port fuel injection, PFI and one with GDI. Distance-based emission factors (EFs) for each vehicle in various driving modes, including battery-hold and battery-charge driving modes for the PHEV, were analyzed. The results showed that the PHEV produced smaller particles in size, resulting that particle mass (PM) and black carbon (BC) were lower by factor of ten in comparison to EFs from the vehicles with PFI and GDI engines. The PHEV consistently emitted lower distance-based EFs than the PFI and GDI vehicles in all driving modes, though EF for particle number (PN) in battery-charge mode was close to the EFs from the other two vehicles. The study also found that the vehicle cold start effect was present in the case of the shorter driving route but not as significant in the longer one. Overall, the study demonstrated that PHEVs could produce lower particle and BC emissions compared to traditional gasoline-powered vehicles. The vehicle cold start and systematic combustion engine restart effects still can have significant impacts on particle emissions, especially in shorter trips.

In order to simultaneously improve the fuel economy and overall performance of plug-in hybrid electric vehicles (PHEVs), this study selected the P1 + P3 configuration as its research object. Through a configuration analysis of hybrid vehicles, it confirmed the feasibility of P1 + P3 configuration-PHEV operating modes. Based on this, a rule-based control strategy was developed, and simulation models for the entire vehicle and control strategy were constructed in both Cruise and MATLAB/Simulink software. The study conducted simulation analysis by combining three sets of Worldwide Harmonized Light vehicles Test Cycle (WLTC) driving cycles to assess the fuel-saving potential of the dual-motor P1 + P3 configuration. The simulation results showed that the vehicle model was reasonably constructed and the proposed control strategy had good control effects on the entire vehicle. Compared to conventional gasoline vehicles, the P1 + P3 configuration PHEV achieved a 67.4% fuel economy improvement, demonstrating a significant enhancement in fuel efficiency with the introduction of electric motors. [ABSTRACT FROM AUTHOR]

Now a days, electric power infrastructure is an essential section of the world due to the increase in power demand and industrialization. The smart grid is also one of the profoundly evolved innovations, which influences the synchronization between demand and renewable energy reactions. The smart grids contain many operations with power calculations such as smart functions, assurance, and control techniques to provide steadiness and proficiency to the system performance. However, the quality of power such as voltage deviation minimization, sag/swell, power loss minimization, and Total Harmonic Distortion (THD) appear to be the major issue. Therefore, in this paper, a novel Generalized Approximate Reasoning Intelligent Control along with Multi-objective African Buffalo Optimization is proposed to control the imperatives of the smart grid. In the grid, current controllers are upgraded by the proposed Optimal Pseudospectral Bang Bang Control technique, and voltage controllers are improved by the proposed Bessel Filter Sallen Key Topology. The simulation of the proposed method is actualized with MATLAB/Simulink. Consequently, the projected results are compared with the traditional control techniques and the outcomes show that the projected replica improved system efficiency concerning power quality problems in terms of reduced 14 MW of power loss and 2.18% of THD. [ABSTRACT FROM AUTHOR]

Power distribution and battery thermal management are important technologies for improving the energy efficiency of plug-in hybrid electric vehicles (PHEVs). In response to the global optimization of integrated energy thermal management strategy (IETMS) for PHEVs, a dynamic programming algorithm based on adaptive grid optimization (AGO–DP) is proposed in this paper to improve optimization performance by reducing the optimization range of SOC and battery temperature, and adaptively adjusting the grid distribution of state variables according to the actual feasible region. The simulation results indicate that through AGO–DP optimization, the reduction ratio of the state feasible region is more than 30% under different driving conditions. Meanwhile, the algorithm can obtain better global optimal driving costs more rapidly and accurately than traditional dynamic programming algorithms (DP). The computation time is reduced by 33.29–84.67%, and the accuracy of the global optimal solution is improved by 0.94–16.85% compared to DP. The optimal control of the engine and air conditioning system is also more efficient and reasonable. Furthermore, AGO–DP is applied to explore IETMS energy-saving potential for PHEVs. It is found that the IETMS energy-saving potential range is 3.68–23.74% under various driving conditions, which increases the energy-saving potential by 0.55–3.26% compared to just doing the energy management. [ABSTRACT FROM AUTHOR]

Uncertainties and variability of renewable generation pose scheduling and operational challenges at the distribution level. Further, the introduction of electric vehicle charging load modifies the existing load demand pattern and introduces additional uncertainties. Therefore, an efficient energy management approach, considering input uncertainties, and involving active and reactive power dispatch is imperative for a safe, profitable, and environment-friendly operation. A multi-objective energy management scheme in a probabilistic framework is proposed in this work. The energy management scheme aims to maximize the expected daily profit, improve voltage stability, and reduce emissions from power generation activities. Probabilistic models of input uncertainties (renewable generation, load demand, demand for electric vehicles, grid energy price) are formulated by adopting the "Hong's point estimate method" and incorporated into the problem. Renewable units are interfaced using voltage source converters. Voltage source converters can simultaneously regulate active and reactive power injections to the network, which is explored in this work. The energy management scheme involves optimally coordinating activities like active and reactive power procurement from renewable units, energy management of batteries, optimal control of a smart transformer, and implementation of a demand response program. The multi-objective problem is modelled in the fuzzy domain and solved using a multi-stage approach involving linear programming, dynamic programming, and particle swarm optimization. A thirty-three-bus distribution network is used in simulation studies for validation. Studies confirm that the proposed method increases the profit by ∼ 67.25 % , reduces emission by ∼ 17.08 % , and improves the voltage stability by ∼ 41.66 % . [ABSTRACT FROM AUTHOR]

The evaluation method of energy consumption and E-range of heavy-duty plug-in hybrid electric vehicle is mainly composed of test cycle, test procedure and calculation method of test result. At present, there are some problems in the national standard being implemented at present. Combined with the research results in the process of national standard revision, this paper comprehensively optimizes the evaluation method and carries out experimental verification. In terms of test cycle, the reasons for the application of China automotive test cycle are analyzed. In terms of test procedure, the deadline conditions of each test phase are analyzed, which makes the test more operable. In terms of calculation methods, based on UF, the method of each phase and comprehensive energy consumption, as well as energy consumption conversion, are analyzed. The test results show that, after the optimization of the evaluation method, the electricity consumption of the phase I increases by 78.99%, the fuel consumption of the phase III increases by 19.58%, and the E-range decreases by 39.06%. At the same time, the comprehensive energy consumption of the vehicle is obtained. After considering the conversion of electricity consumption, the fuel consumption increases significantly, among which, the simple conversion method increases the lowest, while the carbon dioxide emission conversion method increases the highest with an increase of nearly 51%.

Plug-in hybrid electric vehicles (PHEVs) have gradually become an important member of new energy vehicles because of the advantages of both electric and hybrid electric vehicles. A fast and effective energy management strategy can significantly improve the fuel-saving performance of vehicles. By observing the dynamic programming (DP) simulation results, it was found that the vehicle is in the charge-depleting mode, the state of charge (SOC) drops to the minimum at the end of the journey, and the SOC decreases linearly with the mileage. As such, this study proposed an improved rule-based (IRB) strategy enlightened by the DP strategy, which is different from previous rule-based (RB) strategies. Introducing the reference SOC curve and SOC adaptive adjustment, the IRB strategy ensures that the SOC decreases linearly with the driving distance, and the SOC drops to the minimum at the end of the journal, similar to the result of the DP strategy. The fuel economy of PHEV in the RB and DP energy management strategies can be considered as their worst-case and best-case scenarios, respectively. The simulation results show that the fuel consumption of the IRB strategy under the China Light-duty Vehicle Test Cycle is 3.16 L/100 km, which is 7.87% less than that of the RB strategy (3.43 L/100 km), and has reached 44.41% of the fuel-saving effect of the DP strategy (2.84 L/100 km). [ABSTRACT FROM AUTHOR]

In order to explore the influence of the thermal management system (TMS) on vehicle energy management and tap the energy saving potential of TMS, this study establishes a vehicle energy management strategy control model oriented to reduce energy consumption of the TMS based on MATLAB/Simulink for a plug-in hybrid electric vehicle with planetary hybrid configuration. Firstly, a simulation model of vehicle dynamic machine - electric - thermal coupling working process is introduced, to evaluate the impact of TMS in the high and low temperature environment on energy consumption of the vehicle running. Then, based on the equivalent fuel consumption minimum strategy (ECMS) and considering the influence of TMS on energy consumption, an adaptive equivalent consumption minimum strategy model considering thermal characteristics (TAECMS) is established, which propose an improved adaptive equivalent factor adjustment method considering the thermal characteristics of the system is proposed. By establishing the Hamiltonian function to achieve the goal of minimum equivalent fuel consumption, considering the temperature penalty, the power of the engine and the power of the battery is reasonably allocated. Finally, the TAECMS control strategy achieves fuel saving of 6.2 % and 8.4 % respectively in high and low temperature environments through simulation verification and comparison. [ABSTRACT FROM AUTHOR]

Battery life management is critical for plug-in hybrid electric vehicles (PHEVs) to prevent dangerous situations such as overcharging and over-discharging, which could cause thermal runaway. PHEVs have more complex operating conditions than EVs due to their dual energy sources. Therefore, the SOH estimation for PHEV vehicles needs to consider the specific operating characteristics of the PHEV and make calibrations accordingly. Firstly, we estimated the initial SOH by combining data-driven and empirical models. The data-driven method used was the incremental state of charge (SOC)-capacity method, and the empirical model was the Arrhenius model. This method can obtain the battery degradation trend and predict the SOH well in realistic applications. Then, according to the multiple characteristics of PHEV, we conducted a correlation analysis and selected the UF as the calibration factor because the UF has the highest correlation with SOH. Finally, we calibrated the parameters of the Arrhenius model using the UF in a fuzzy logic way, so that the calibrated fitting degradation trends could be closer to the true SOH. The proposed calibration method was verified by a PHEV dataset that included 11 vehicles. The experiment results show that the root mean square error (RMSE) of the SOH fitting after UF calibration can be decreased by 0.2–14% and that the coefficient of determination ( R 2 ) for the calibrated fitting trends can be improved by 0.5–32%. This provides more reliable guidance for the safe management and operation of PHEV batteries. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Henan University of Science & Technology, Natural Science is the property of Editorial Office of Journal of Henan University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to simultaneously improve the fuel economy and overall performance of plug-in hybrid electric vehicles (PHEVs), this study selected the P1 + P3 configuration as its research object. Through a configuration analysis of hybrid vehicles, it confirmed the feasibility of P1 + P3 configuration-PHEV operating modes. Based on this, a rule-based control strategy was developed, and simulation models for the entire vehicle and control strategy were constructed in both Cruise and MATLAB/Simulink software. The study conducted simulation analysis by combining three sets of Worldwide Harmonized Light vehicles Test Cycle (WLTC) driving cycles to assess the fuel-saving potential of the dual-motor P1 + P3 configuration. The simulation results showed that the vehicle model was reasonably constructed and the proposed control strategy had good control effects on the entire vehicle. Compared to conventional gasoline vehicles, the P1 + P3 configuration PHEV achieved a 67.4% fuel economy improvement, demonstrating a significant enhancement in fuel efficiency with the introduction of electric motors.

The implementation of an energy management strategy plays a key role in improving the fuel economy of plug-in hybrid electric vehicles (PHEVs). In this article, a bi-level energy management strategy with a novel speed prediction method leveraged by reinforcement learning is proposed to construct the optimization scheme for the inner energy allocation of PHEVs. First, the powertrain transmission model of the PHEV in a power-split type is analyzed in detail to obtain the energy routing and its crucial characteristics. Second, a Q-learning (QL) algorithm is applied to establish the speed predictor. Third, the double QL algorithm is introduced to train an effective controller offline that realizes the optimal power distribution. Finally, given a reference battery's state of charge (SOC), a model predictive control framework solved by the reinforcement learning agent with a novel speed predictor is proposed to build the bi-level energy management strategy. The simulation results show that the proposed method performs with a satisfying fuel economy in different driving scenarios while tracking the corresponding SOC references. Moreover, the calculation performance also implies the potential online capability of the proposed method.

Copyright of Journal of Fuzhou University is the property of Journal of Fuzhou University, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper proposes a dynamic characteristic improvement of an integrated on-board charger (OBC) using a model predictive control (MPC) method. The integrated OBC performs both battery charging and starter generator (SG) driving for engine starting in plug-in hybrid electric vehicles (PHEVs). If it performs battery charging, battery-side voltage and battery-side current are control objects which are usually controlled by using a proportional-integral (PI) controller. However, it has the disadvantage of undesirable dynamic characteristics, and gain tuning of the PI controller is necessary to properly control the voltage and current. Therefore, this paper proposes the MPC method for the dynamic characteristic improvement of integrated OBC. It can achieve not only dynamic characteristic improvement, but also robustness from the abrupt change of load impedance. By using the proposed MPC method for integrated OBC, the settling time to control the output voltage is decreased by 50% in the transient state compared to that by using the PI controller. The effectiveness of the proposed MPC method is verified by simulation and experimental results. [ABSTRACT FROM AUTHOR]

The paper proposes a power flow control strategy for a P2 parallel plug-in hybrid electric vehicle (PHEV) which takes into account torque and power losses related to engine-on and gear shift transients. An extended backward-looking (EXT-BWD) model is proposed to account for the transient losses, while the control strategy combines a rule-based controller with an equivalent consumption minimization strategy. To describe the transient losses, the EXT-BWD model includes additional state variables related to engine on/off flag and gear ratio in the previous time step. To establish a performance benchmark for control strategy verification, a dynamic programming-based control variable optimization framework is established based on the EXT-BWD model. The proposed control strategy is demonstrated to improve the fuel efficiency and drivability compared to the original control strategy while retaining comparable computational efficiency. [ABSTRACT FROM AUTHOR]

In photovoltaic (PV) panels comprising multiple substrings, a mismatch in substring characteristics is known to reduce energy yield significantly. Substring characteristics in solar roofs of plug-in hybrid vehicles (PHVs) are always mismatched to some extent due to the curved surface of the panel. This paper proposes a PWM differential power processing (DPP) converter based on a panel-to-substring power redistribution architecture and its dual maximum power point tracking (MPPT) control technique for PHEVs’ solar roofs. Depending on the degree of characteristic mismatch, the proposed DPP converter adjusts its output current to maximize the energy yield of the panel. With the proposed dual MPPT control, a duty cycle $d_{DPP}$ of the DPP converter is manipulated to optimize the panel characteristic while an external boost converter manipulates its duty cycle $D_{boost}$ to track an MPP of the panel. A prototype for 180-W solar roofs comprising seven substrings was built, and laboratory testing was performed using solar array simulators to emulate characteristic mismatch conditions. Thanks to the dual MPPT control technique, the proposed DPP converter operated with the optimal $d_{DPP}$ while the external boost converter tracked the MPP by manipulating $D_{boost}$ , realizing the enhanced energy yield.

Representative driving cycles and specific vehicle types play an important role in analyzing energy consumption to provide valuable references for manufacturers and government inspections. In this study, a parallel plug-in hybrid electric vehicle with different driven configurations is employed to analyze energy consumption under a typical driving cycle. Specifically, a typical driving cycle of Tianjin is constructed by integrating the clustering and Markov chain algorithms. Considering the various layouts of energy systems in plug-in hybrid electric vehicles, three different driven configurations (P2, P3, P4) of the parallel plug-in hybrid electric vehicle are established. Finally, the energy consumptions of the different configurations are analyzed under the proposed typical driving cycle and the compared China Automotive Test Cycle. Results of two driving cycles demonstrate that the fuel consumption of the P3 configuration is lower than that of the P2 and P4 configurations, respectively, while the purely electric driving range of the P3 configuration is longer than the other two configurations, respectively. These results provide a basis for the formulation of China’s new light vehicle emission standards and reduce exhaust emission by making the vehicles more economically suitable for the road conditions in Tianjin.

This paper proposes a robust design approach based on the Design for Six Sigma (DFSS), to promote the robustness of our previous model-free-adaptive-control-based (MFAC-based) energy management strategy (EMS) for the plug-in hybrid electric vehicles (PHEVs) in real-time application. First, the multi-island genetic algorithm (MIGA) is employed for a deterministic design of the MFAC-based EMS, and the Monte Carlo simulation (MCS) is utilized to evaluate the sigma level of the strategy with the deterministic design results. Second, a DFSS framework is formulated to reinforce the robustness of the MFAC-based EMS, in which the velocity and the vehicle mass are considered external disturbances whilst the terminal state of charge (SOC) of the battery and the fuel consumption (FC) are conducted as responses. In addition, real-time SOC constraints are incorporated into Pontryagin's minimum principle (PMP) to confine the fluctuation of battery SOC in MFAC-based EMS to make it closer to the solution of the dynamic programming (DP). Finally, the effectiveness of the robust design results is assessed by contrasting with other strategies for various combined driving cycles (including velocity, vehicle mass, and road slope). The comparisons demonstrate the remarkable promotion of the robust design in terms of the energy-saving potential and the performance against external disturbance. The average improvement of the FCs can reach up to a considerable 19.66% and 9.79% in contrast to the charge-depleting and charge-sustaining (CD-CS) strategy as well as the deterministic design of MFAC-based EMS. In particular, the energy-saving performance is comparable to DP, where there is only a gap of −1.68%. [ABSTRACT FROM AUTHOR]

This article aims to calculate, analyse and compare the optimal powertrain sizing solutions for a long-haul plug-in series hybrid coach running on diesel and hydrotreated vegetable oil (HVO) using a co-design optimisation approach for: (1) lowering lifetime carbon footprint; (2) minimising the total cost of ownership (TCO); (3) finding the right sizing compromise between environmental impact and economic feasibility for the two fuel cases. The current vehicle use case derived from the EU H2020 LONGRUN project features electrical auxiliary loads and a 100 km zero urban emission range requiring a considerable battery size, which makes its low carbon footprint and cost-effective sizing a crucial challenge. Changing the objective between environmental impact and overall cost minimisation or switching the energy source from diesel to renewable HVO could also significantly affect the optimal powertrain dimensions. The approach uses particle swarm optimisation in the outer sizing loop while energy management is implemented using an adaptive equivalent consumption minimisation strategy (A-ECMS). Usage of HVO fuel over diesel offered an approximately 62% reduction in lifetime carbon footprint for around a 12.5% increase in overall costs across all sizing solutions. For such an unconventional powertrain topology, the fuel economy-focused solution neither achieved the lowest carbon footprint nor overall costs. In comparison, C O 2 − cost balanced sizing resulted in reductions close to the single objective-focused solutions (5.7% against 5.9% for the C O 2 solution, 7.7% against 7.9% for the TCO solution on HVO) with lowered compromise on other side targets ( C O 2 reduction of 5.7% against 4.9% found in the TCO-focused solution, TCO lowering of 7.7% against 4.4% found in the C O 2 -focused solution). [ABSTRACT FROM AUTHOR]

To further improve the energy‐saving potential and robustness of the energy management strategy (EMS) for plug‐in hybrid electric vehicles (PHEVs) in a real‐time application, this paper proposes a modified model‐free‐adaptive‐control‐based (MFAC‐based) EMS to overcome the disadvantages in our previous MFAC‐based EMS. First, the influence of external disturbance on MFAC‐based EMS is discussed, and the results show that both the vehicle velocity and load change have a significant impact on its performance. Second, a modified MFAC‐based real‐time EMS is designed based on history driving data obtained from a repeated route in which a state‐of‐charge (SOC)‐constraint‐based reference SOC planning method is firstly proposed to simultaneously consider the vehicle velocity and changing load. Then, global SOC constraints are incorporated in Pontryagin's minimum principle (PMP) to enhance the adaptive capability of the proposed method. Finally, the optimal solution of PMP (i.e., optimal constant) is deployed as a benchmark, and the performance of the modified MFAC‐based EMS (namely MFAC‐II and MFAC‐III) is in contrast to the previous one (MFAC for short) under various real‐world driving cycles. The results demonstrate that the MFAC‐III has a remarkable improvement in both economic performance and robustness. Particularly, the energy‐saving effectiveness is close to the global optimum one in some driving conditions. [ABSTRACT FROM AUTHOR]

Battery life management is critical for plug-in hybrid electric vehicles (PHEVs) to prevent dangerous situations such as overcharging and over-discharging, which could cause thermal runaway. PHEVs have more complex operating conditions than EVs due to their dual energy sources. Therefore, the SOH estimation for PHEV vehicles needs to consider the specific operating characteristics of the PHEV and make calibrations accordingly. Firstly, we estimated the initial SOH by combining data-driven and empirical models. The data-driven method used was the incremental state of charge (SOC)-capacity method, and the empirical model was the Arrhenius model. This method can obtain the battery degradation trend and predict the SOH well in realistic applications. Then, according to the multiple characteristics of PHEV, we conducted a correlation analysis and selected the UF as the calibration factor because the UF has the highest correlation with SOH. Finally, we calibrated the parameters of the Arrhenius model using the UF in a fuzzy logic way, so that the calibrated fitting degradation trends could be closer to the true SOH. The proposed calibration method was verified by a PHEV dataset that included 11 vehicles. The experiment results show that the root mean square error (RMSE) of the SOH fitting after UF calibration can be decreased by 0.2–14% and that the coefficient of determination (R2) for the calibrated fitting trends can be improved by 0.5–32%. This provides more reliable guidance for the safe management and operation of PHEV batteries.

The equivalent consumption minimization strategy (ECMS) with pre-calibrated constant equivalence factor (EF) can ensure near global optimal solution for certain driving cycle and enable good real-time capability, but it is difficult to adapt to a wide range of driving conditions. To this end, aiming at the optimal energy management problem of a plug-in hybrid electric vehicle (PHEV), this paper proposes a deep reinforcement learning (DRL) based adaptive ECMS by combing the double deep Q-network (DDQN) and the driving cycle information. The DDQN is applied to correct the EF of the ECMS in a feed-forward manner with the battery state-of-charge (SOC) and the periodic predicted driving cycle information as inputs, and the ECMS is utilized to calculate the engine torque and gear ratio of the powertrain. The driving cycle information is represented by the average velocity, which is predicted by the historical velocity sequence based on the back-propagation (BP) neural network, and the difference of the average velocity between two continuous time windows. The hardware-in-the-loop (HIL) platform is constructed to test the performance of the proposed strategy. It is shown that the future average velocity can be well predicted by the historic velocity sequence. Both simulation and HIL test results demonstrate that the proposed adaptive ECMS based on DDQN exhibits superior performance in improving the vehicle fuel economy. • A deep reinforcement learning based adaptive ECMS for the PHEV is proposed. • The driving cycle information is used by the DDQN to update the equivalence factor. • The ECMS is applied to optain the engine torque and powertrain gear ratios. • The average velocity is predicted by BP network with historical velocity sequence. • The controller performance is validated by simulation and hardware-in-the-loop tests. [ABSTRACT FROM AUTHOR]

Aiming at the problems of conventional plug-in hybrid electric vehicle (PHEV), a novel PHEV configuration called DH-PHEV is proposed based on double-rotor motor (DRM) and hybrid energy storage system (HESS). For improving the comprehensive efficiency and reducing the charging/discharging rate of battery, the comprehensive energy management strategy (CEMS) is studied. Firstly, a rule-based torque allocation strategy is proposed based on the DH-PHEV configuration, the DRM operating characteristics, and the HESS operating states. The torque allocation of DRM is optimized based on the sequential quadratic programming. Secondly, the power allocation between battery and supercapacitor is optimized based on the dynamic programming (DP), with the goal of reducing the charging/discharging rate of battery and minimizing the energy consumption. The CEMS for DH-PHEV is proposed based on the DP optimization results and DRM torque allocation. Finally, the feasibility and adaptability of DH-PHEV configuration and CEMS are verified. The results show that the adaptability of CEMS under different driving cycles is excellent, and the operating modes are reasonable. The comprehensive efficiency and the charging/discharging rate of battery are optimized effectively. Under 6 WLTCs and 16 actual vehicle driving cycles, the pure electric driving range for DH-PHEV is improved by 1.07 % and 4.36 %, respectively. • A novel PHEV configuration is proposed and its operating modes are analyzed. • For improving system efficiency, torque allocation is optimized based on SQP. • The power allocation strategy with DP joint rules is studied. • The comprehensive energy management strategy for this configuration is proposed. • The real-world operation data is collected and is used to verify the configuration. [ABSTRACT FROM AUTHOR]

For plug-in hybrid electric vehicle (PHEV), using a hybrid energy storage system (HESS) instead of a single battery system can prolong the battery life and reduce the vehicle cost. To develop a PHEV with HESS, it is a key link to obtain the optimal size of the power supply and energy system that can meet the load requirements of a driving cycle. Since little effort has been dedicated to simultaneously performing the component sizing of PHEV and HESS, this paper proposes an approach based on the particle swarm optimization (PSO) algorithm to simultaneously determine the sizes of the engine, motor, battery and supercapacitor (SC) in a PHEV with HESS. The drivetrain cost is minimized in a different all-electric range (AER)—and depends on the battery type—while ensuring the driving performance requirements. In addition, the effects of the power system and drive cycle on the component sizes were analyzed and compared. The simulation results show that the cost of the PHEV drivetrain with the Ni-MH battery/SC HESS is reduced by up to 12.21% when compared to the drivetrain with the Li-ion battery/SC HESS. The drivetrain cost is reduced by 8.79% when compared to analysis-based optimization. The type of power supply system and drive cycle can significantly affect the optimization results. [ABSTRACT FROM AUTHOR]