Multi-objective optimization study of energy management strategy and economic analysis for a range-extended electric bus

2017 ◽  
Vol 194 ◽  
pp. 798-807 ◽  
Author(s):  
Junqiu Li ◽  
Xin Jin ◽  
Rui Xiong
Author(s):  
Hanwu Liu ◽  
Yulong Lei ◽  
Yao Fu ◽  
Xingzhong Li

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.


2010 ◽  
Vol 29-32 ◽  
pp. 912-917
Author(s):  
Fei Hu ◽  
Zhi Guo Zhao

Hybrid Electric Vehicle (HEV) provides fairly high fuel economy with lower emissions compared to conventional vehicles. To enhance HEV performance in terms of fuel economy and emissions, subject to the satisfaction of driving performance, multi-objective optimization for parameters of energy management strategy is inevitable. Considering the defect of the method which transfers multi-objective optimization problem into that of single-objective and the shortage of the Pareto-optimum based nondominated sorting genetic algorithm II (NSGA-II), the NSGA-II has been improved and then applied to the optimization in this paper. The simulation results show that each run of the algorithm can produce many Pareto-optimal solutions and the satisfactory solution can be selected by decision-maker according to the requirement. The results also demonstrate the effectiveness of the approach.


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