The energy recovered with regenerative braking system can greatly improve energy efficiency of range-extended electric vehicle (R-EEV). Nevertheless, maximizing braking energy recovery while maintaining braking performance remains a challenging issue, and it is also difficult to reduce the adverse effects of regenerative current on battery capacity loss rate (Q_loss,%) to extend its service life. To solve this problem, a revised regenerative braking control strategy (RRBCS) with the rate and shape of regenerative braking current considerations is proposed. Firstly, the initial regenerative braking control strategy (IRBCS) is researched in this paper. Then, the battery capacity loss model is established by using battery capacity test results. Eventually, RRBCS is obtained based on IRBCS to optimize and modify the allocation logic of braking work-point. The simulation results show that compared with IRBCS, the regenerative braking energy is slightly reduced by 16.6% and Q_loss,% is reduced by 79.2%. It means that the RRBCS can reduce Q_loss,% at the expense of small braking energy recovery loss. As expected, RRBCS has a positive effect on prolonging the battery service life while ensuring braking safety while maximizing recovery energy. This result can be used to develop regenerative braking control system to improve comprehensive performance levels.

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.

For the research on crucial technologies of range-extended electric vehicle, the first problem to be solved is parameter matching and efficiency optimization for range-extended electric vehicle power and transmission system. Parameter matching and optimization of range-extended electric vehicle power and transmission system are multi-objective optimization problem. Evaluation and analysis of multi-objective optimization problem should be mutually independent and balanced. With the aim of guaranteeing vehicle’s comprehensive performance, a parameter matching and optimization method for range-extended electric vehicle power and transmission system is proposed in this paper. First, the house of quality model of range-extended electric vehicle is established to determine weight coefficient of vehicle performance indicator based on market requirements instead of experience. Based on co-simulation control model which is established in Matlab-Simulink and AVL-Cruise, 40 groups of orthogonal tests are performed, and the sensitivity of characteristic parameters is analyzed to explore the coupling law among vehicle performance indicators, so as to clarify the entry point for parameter matching and optimization. The simulation results show that the characteristic parameters not only have a significant influence but also have a coupling effect on the vehicle performance indicators. The analysis of variance shows that there is a limitation in optimal level combination of various factors only by range. Then, particle swarm optimization algorithm is selected to optimize the parameters of range-extended electric vehicle power and transmission system based on sensitivity analysis results obtained above. The study reveals that it is more efficient and reasonable to match the range-extended electric vehicle power and transmission system with a smaller battery capacity and a “medium-sized” auxiliary power unit which can achieve adequate dynamic performance, lower purchase cost, longer driving range and less energy consumption. Finally, a comparative simulation between the range-based analysis and particle swarm optimization-based analysis is conducted, the simulation results indicate that the optimized design parameters solution can significantly improve the technical indicators of the vehicle.

The fuel efficiency of plug-in hybrid electric vehicle is influenced by various factors, including working conditions, driving style, and environmental variables, with the design of their energy management strategy (EMS) serving as the core and critical technology. In order to adapt to traffic environment, it is of great significance to construct driving cycles that align with driving characteristics, providing data support for the optimization of the EMS. This paper carried out research on the EMS optimization for multi-mode hybrid electric vehicle (MMHEV). Firstly, the traffic speed was established interval by using data envelopment analysis (DEA) and the urban comprehensive driving cycles based on the proportion of driving time was constructed. Then, an EMS optimized based on road condition information (RC-EMS) was developed according to the operating curves and interval thresholds of motors and engine. The initial optimal parameters for offline optimization of the EMS were obtained using a bare-bones multi-objective particle swarm optimization algorithm, with the constructed comprehensive driving cycle serving as the input information for the optimization model. After that, a fuzzy adaptive parameter optimization module (ARC-EMS) was designed to update the key parameters of the RC-EMS in real time, so as to realize the optimal dynamic energy allocation adaptively according to the road conditions. Finally, the simulation and experimental results fully showed that the proposed ARC-EMS can balance the performance more effectively. As anticipated, the strategy effectively balances energy savings and battery health, and it can be utilized to develop an EMS that enhances the overall performance of the MMHEV.

With the aim of economy improvement, emission reduction and prolonging the battery service life, an adaptive parameter optimal energy management strategy is proposed for range extended electric vehicle and a method of multi-objective optimization (MOO) is proposed. Firstly, two strategies based on different threshold parameter types, namely velocity-switch-based multi-operation-point control strategy (MCS _v–b) and power-switch-based multi-operation-point control strategy (MCS _p–b) are designed. Then, the oil-electric conversion loss rate, comprehensive exhaust emission, and battery capacity loss rate are selected as the optimization objectives. The barebones multi-objective particle swarm optimization is applied in MCS _v–b and MCS _p–b for solving the MOO problem. The simulation results show a clear conflict that three optimization objectives cannot be optimal under the same solution. And then, the individual with optimal comprehensive objective is taken as the final optimization solution to evaluate the performance of the proposed methodology. As expected, the proposed MCS _p–b has a positive effect on prolonging the battery service life while ensuring high fuel economy and low emission. Experimental test results thoroughly validate the proposed approach and this result can be used to improve comprehensive performance levels.