Control Strategy and Parameter Optimization of Hybrid Vehicle Drive System - Li, Penghui; Chen, Guoqiang; Kang, Jianli; Guo, Desui; Kang, Jincan (Recent Patents on Mechanical Engineering, 2025)
BackgroundPure electric vehicles still have the problem with range anxiety, but hybrid vehicles can solve this problem well. Parameter optimization and adaptation of control strategies are the keys to improving the economy of hybrid vehicles.ObjectiveThis study aimed to improve the economy of hybrid vehicles to get more mileage in the same working conditions. On the basis of a large number of invention patents, this study establishes and optimizes the parameters and control strategy of hybrid vehicles in order to obtain better driving parameters and a more appropriate control strategy for hybrid vehicle drive system.MethodsThe key parameters of each component of the drive system are defined under dynamic objectives of hybrid vehicles. The control strategy adopts a logic gate-based approach to determine driving mode and braking of hybrid vehicles by limiting the speed, SOC, and power demand. Finally, the particle swarm optimization algorithm is used to optimize the key parameters to obtain the economic optimal solution without losing the vehicle power.ResultsIn the China light-duty vehicle test cycle-passenger (CLTC-P) cycle condition, the optimized parameters can improve the fuel economy of fuel economy by 16.06%, and in the worldwide harmonized light vehicles test cycle (WLTC) cycle condition, the optimized parameters can improve the fuel economy of hybrid vehicles by 12.17%.ConclusionBy establishing and optimizing driving system parameters and control strategy of the hybrid vehicles, this study improves the economy and achieves the expected effect without losing the vehicle power. However, in further research, driving conditions and mileage under different working conditions should be further studied, and on this basis, the optimization of control strategies should be continued.
Keywords: particle swarm optimization