Abstract
This paper aims at proposing an efficient energy management strategy of the series-parallel hybrid electric bus (SPHEB) by using improved genetic algorithm. Firstly, the energy management strategy based on the logical threshold value is developed. The simulation model considering the vehicle dynamic performance is established by the combination of Matlab and Cruise software. Then, an improved genetic algorithm based on adaptive crossover probability and mutation probability is proposed to solve local convergence and premature convergence. Eventually, Chinese typical city bus driving cycle and the composite driving cycle are considered to show the effectiveness of the proposed energy management strategy in terms of the fuel economy. The results indicate that the fuel consumption are improved by 5.85% and 5.01% respectively, and the parameters obtained by optimizing for the composite driving cycle are more adaptable to the driving conditions and have better economic performance in all driving scenarios.
Highlights
26.8 26.6 26.4 conditions may not have better economic performance under any working condition. It shows that the optimized value based on the comprehensive scenario has better economy than the optimized value based on the single cycle under many scenarios, which indicates that the optimization method based on the comprehensive scenario is better than the optimization method based on the single cycle in terms of the adaptability of working condition
Aiming at the deficiency of traditional genetic algorithm, the crossover probability and mutation probability of adaptive change with evolutionary generation and population fitness were introduced, and the improved genetic algorithm was tested by using Ackley function
The results show that compared with the traditional genetic algorithm, the improved genetic algorithm has better convergence speed and optimization quality
Summary
Due to the better performance of fuel economy and emission compared with the traditional vehicles, hybrid electric vehicles (HEV) become the most promising vehicle models. Gradient-based search methods require that the objective function be continuous, differentiable, and satisfy the Lipschitz condition [6]. This method has slower convergence speed, lower the probability of the global value. Non-gradient based algorithm could calculate the global optimal solution without the gradient information of the objective function [7,8,9]. Genetic algorithm can solve the HEV parameter optimization problems, it still has limitation in the process of evolution [12,13]. The vehicle fuel economy is taken as the optimization objective, and the improved genetic algorithm is used to optimize the relevant parameters of the energy management strategy for different operating conditions. The results show that the fuel economy is markedly improved after optimization
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