Abstract
In this paper, an optimal torque distribution strategy (OTDS) for four-motorized-wheel electric vehicle (4MWEV) is proposed aiming at improving the tractive efficiency and braking energy recovery performance. Based on the motor efficiency map, the objective functions of tractive efficiency and braking energy recycling are established first. Under driving conditions, genetic algorithm (GA) is proposed to obtain the optimal torque distribution ratio of front and rear axles considering the constraints of motor characteristics and desired torque. During braking conditions, an exhaustive search method (ESM) is proposed to improve the energy recovery performance considering the constraints of motor characteristics, battery, braking theory and ECE Regulations. For the realization of efficient and accurate computation, a control scheme of off-line optimization and on-line allocation based on the optimal torque distribution ratio map is presented, which stores the optimal ratios in all available motor operating regions into two-dimensional lookup tables. Finally, co-simulation experiments based on MATLAB/Simulink and Carsim are conducted to verify the effectiveness of the proposed OTDS under NEDC and UDDS driving cycles. Comparing to the typical torque distribution strategy (TTDS), the simulation results demonstrate that the proposed OTDS can enhance the energy-saving performance by 7.01% under NEDC and 6.69% under UDDS.
Highlights
Increasing concern about energy shortage and environment issues, as well as recent advancements in electric motor and motor controller technologies, have allowed electric vehicles to become the most promising alternative for the research and design of fuel-efficient vehicles [1], [2]
One electric vehicle configuration with great potential is known as the Four-motorized-wheel EV (4MWEV), which employs four in-wheel motors that are attached to each wheel and controlled independently [3], [4]. 4MWEV has many advantages, such as better driving performance, quick and precise torque response, and light weight design, and offers excellent prospects for the enhancement of vehicle economy [5]
Yu et al [20] developed an optimal fuzzy neural network braking control strategy to determine the allocation of front and rear regenerative braking torque and friction braking torque for the independent four-wheel motor driven electric vehicle
Summary
Increasing concern about energy shortage and environment issues, as well as recent advancements in electric motor and motor controller technologies, have allowed electric vehicles to become the most promising alternative for the research and design of fuel-efficient vehicles [1], [2]. Lin et al [16] developed a multi-objective optimal torque distribution strategy for four in-wheel-motor drive electric vehicles considering the system efficiency, lateral stability, and safety, which proposed energy efficiency control allocation (EECA) and hybrid model predictive control (hMPC) to improve the performance of energy consumption and vehicle stability. Yu et al [20] developed an optimal fuzzy neural network braking control strategy to determine the allocation of front and rear regenerative braking torque and friction braking torque for the independent four-wheel motor driven electric vehicle. Considering the integrated torque control of both driving and braking conditions, Yuan et al [21] designed a scheme which emphasizes the energy consumption issues when total desired torque is low, while an on-line optimization algorithm based on an efficiency map is applied to determine the operating mode of motors.
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