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With the help of several in-wheel motors, four-wheel independent-drive electric vehicle (4WIDEV) has tremendous potential to improve vehicle performance. Except for the theme of stability enhancement during the past two decades, energy saving topic becomes more attractive recently. However, it is difficult to achieve stability and economy performance simultaneously. Given the commonly used control method of rigid yaw rate tracking may limit the energy saving potential of 4WIDEV in cornering. Therefore, be different from previous studies which often focus on a sole target, in this paper, a driving energy management strategy for 4WIDEV based on multi-objective online optimization of four-wheel torque distribution is proposed in this paper, which includes weighted yaw rate tracking error into its parameterized control objectives besides electric drive system efficiency, tire slip losses and wheel torque ripple. Meanwhile, for better coordination of stability and economy in different situation, fuzzy logic control is adopted to dynamically adjust the weight of the control objective indexes of the proposed multi-objective online optimization function. Then, a particle swarm optimization algorithm is adopted to solve the optimization function. Finally, the proposed strategy for energy-efficient driving is verified based on the built Simulink and CarSim co-simulation model. The results show that the proposed four-wheel torque distribution strategy based on the multi-objective online optimization is more effective than average distribution strategy and offline optimization strategy. [Display omitted] • Propose a driving energy management strategy based on multi-objective online optimization. • Integrate yaw rate error, in-wheel motor efficiency, and tire slip into optimization function. • Design a weight coefficient fuzzy controller to better coordinate the stability and economy. [ABSTRACT FROM AUTHOR]

The proposed supervisory control system (SCS) uses a control map to maximize the powertrain efficiency of a hybrid electric vehicle (HEV) in real-time. The paper presents the methodology and structure of the control, including a novel, comprehensive and unified expression for the overall powertrain efficiency that considers the engine-generator set and the battery in depth as well as the power electronics. A control map is then produced with instructions for the optimal power share between the engine branch and battery branch of the vehicle such that the powertrain efficiency is maximized. This map is computed off-line and can thereafter be operated in real-time at very low computational cost. A charge sustaining factor is also developed and introduced to ensure the SCS operates the vehicle within desired SOC bounds. This SCS is then tested and benchmarked against two conventional control strategies in a high-fidelity vehicle model, representing a series HEV. Extensive simulation results are presented for repeated cycles of a diverse range of standard driving cycles, showing significant improvements in fuel economy (up to 20%) and less aggressive use of the battery. [ABSTRACT FROM AUTHOR]

Abstract: This paper investigates a hydrogen-based genset maximum efficiency tracking problem in the context of electric vehicle range extension. This genset is cheaper than fuel cells and has the desirable property of being greenhouse gas emission free in addition to being less pollutant than the conventional gasoline based gensets. Using Taylor's series, a parametric efficiency model is built iteratively. This model is used by a nonlinear optimization method which searches for the optimal operating conditions for a maximum achievable efficiency. The root-mean-square-error between experimental data and the model is less than 5 × 10−4. The hardware-in-the-loop simulation demonstrated that the proposed tracking approach is effective. In addition, it can improve the hydrogen-based genset efficiency up to 7.15% compared to the commonly used industrial method based on a constant speed drive approach. [Copyright &y& Elsevier]

Abstract: In this paper, we investigate an adaptive energy management and power splitting system for a fuel cell hybrid electric vehicle. The battery pack is the main power source whereas the fuel cell is considered as a range extender that cannot sustain alone the vehicle traction power. In addition, the fuel cell contributes to reduce the battery pack degradation by limiting its depth-of-discharge (DoD). This energy management system is based on a two layer architecture in which the upper layer computes the anticipated end-of-trip DoD using online mass estimation. The lower layer is designed to split the driver power demand by minimizing a cost function which includes the hydrogen/electricity cost ratio. Therefore, the best trade-off between reducing battery pack degradation and using cost effective energy is provided. Furthermore, the system allows the fuel cell to operate at its maximum efficiency. Comparative study results indicate that using online mass estimation improves the overall fuel consumption efficiency whilst contributing at the same time to DoD reduction. [Copyright &y& Elsevier]

Abstract: In this paper, the energy management problem for a Fuel Cell Plug-in Hybrid Electric Vehicle is investigated using a real-time power splitting method that preserves the battery pack life. The reduction of high-power demand during the vehicle stop-and-go operations and the limitation of the batteries'' minimum energy to a prescribed level are the two aspects considered in this study. Using a longitudinal motion model of an experimental small utility truck, a multi-criteria optimization problem, including the hydrogen/electricity cost ratio and the anticipation of the high-power demand, is formulated and solved. The analysis of the effects of the fuel cell and the battery efficiency degradations demonstrated that there exists a minimum cost ratio that prevents the Energy Management System to adequately limit the batteries'' depletion with the contribution of the Fuel Cell power. The overall control system does not require a prior knowledge of the trip information. Therefore, the real-time implementation of this system is realistic. Compared with a rule-based Energy Management System, the proposed optimal and real-time approach provides up to 5% of hydrogen saving whilst maintaining the Battery Pack energy close to the minimum prescribed energy. The high-power demand reduction and the depletion limitation enable the Energy Management System to preserve the batteries'' life. [Copyright &y& Elsevier]

Abstract: This paper investigates the Proton Exchange Membrane Fuel Cell (PEMFC) Cold Startup problem within the specific context of the Plugin Hybrid Electric Vehicles (PHEV). A global strategy which aims at providing an efficient method to minimize the energy consumption during the startup of a PEMFC is proposed. The overall control system is based on a supervisory architecture in which the Energy Management System (EMS) plays the role of the power flow supervisor. The EMS estimates in advance, the time to start the fuel cell (FC) based upon the battery energy usage during the trip. Given this estimation and the amount of additional energy required, the fuel cell temperature management strategy computes the most appropriate time to start heating the stack in order to reduce heat loss through the natural convection. As the cell temperature rises, the PEMFC is started and the reaction heat is used as a self-heating power source to further increase the stack temperature. A time optimal self-heating approach based on the Pontryagin minimum principle is proposed and tested. The experimental results have shown that the proposed approach is efficient and can be implemented in real-time on FC-PHEVs. [Copyright &y& Elsevier]