Abstract
Currently, the researches on the regenerative braking system (RBS) of the range-extended electric vehicle (R-EEV) are inadequate, especially on the comparison and analysis of the multi-objective optimization (MOO) problem. Actually, the results of the MOO problem should be mutually independent and balanced. With the aim of guaranteeing comprehensive regenerative braking performance (CRBP), a revised regenerative braking control strategy (RRBCS) is introduced, and a method of the MOO algorithm for RRBCS is proposed to balance the braking performance (BP), regenerative braking loss efficiency (RBLE), and battery capacity loss rate (BCLR). Firstly, the models of the main components related to the RBS of the R-EEV for the calculation of optimization objectives are built in MATLAB/Simulink and AVL/Cruise. The BP, RBLE, and BCLR are selected as the optimization objectives. The non-dominated sorting genetic algorithm (NSGA-II) is applied in RRBCS to solve the MOO problem, and a group of the non-inferior Pareto solution sets are obtained. The simulation results show a clear conflict that three optimization objectives cannot be optimal at the same time. Then, we evaluate the performance of the proposed method by taking the individual with the optimal CRBP as the final optimal solution. The comparation among BP, RBLE, BCLR, and CRBP before and after optimization are analyzed and discussed. The results illustrate that characteristic parameters of RRBCS is crucial to optimization objectives. After parameters optimization, regenerative braking torque works early to increase braking energy recovery on low tire-road adhesion condition, and to reduce the battery capacity loss rate at the expense of small braking energy recovery on the medium tire-road adhesion condition. In addition, the results of the sensitivity analysis show that after parameter optimization, RRBCS is proved to perform better road adaptability regarding the distribution of solutions. These results thoroughly validate the proposed approach for multi-objective optimization of RRBCS and have a strong directive to optimize the control strategy parameters of RBS.
Highlights
The range-extended electric vehicle (R-EEV) can greatly improve its driving range through the auxiliary power unit, so as to solve the range anxiety from consumers
The results of the sensitivity analysis show that after parameter optimization, regenerative braking control strategy (RRBCS) is proved to perform better road adaptability regarding the distribution of solutions
A multi-objective optimization system with various conflicting objectives and a parameters optimization method of RRBCS were proposed to design an optimal regenerative braking control strategy (RBCS) for R-EEV
Summary
The range-extended electric vehicle (R-EEV) can greatly improve its driving range through the auxiliary power unit, so as to solve the range anxiety from consumers. As one of the main working modes, the energy recovered with regenerative braking system (RBS) provides an effective approach so as to greatly improve energy efficiency of R-EEV. Research shows that about 20%–50% or more of the driving energy is lost during the braking process [1,2,3,4]. The characteristic parameters of regenerative braking control strategy (RBCS) are crucial to optimization objectives. Thereby, the parameters optimization study on RBCS and the comprehensive regenerative braking performance (CRBP) analysis have gotten a lot of attention and deserve to be studied.
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