Hybrid electric vehicle modeling accuracy verification and global optimal control algorithm research

Abstract

Vehicle modeling and simulation is a very important part in the design and development of electric and hybrid vehicles. To precisely simulate the powertrain running status, many commercial software products are applied into the development of control strategy. However, these models cost too much time in the simulation analysis, which would be difficult to apply in the control strategy optimization. Therefore, vehicle dynamic system model is built by the software of matlab/Simulink and this vehicle model is compared with the commercial vehicle simulation model. Simulation results show that this model has relatively high simulation accuracy, which lays the foundation for further vehicle performance optimization. In this study, dynamic programming global optimization algorithm is applied on the hybrid electric vehicle to search the optimal solution in the determinate speed cycle. Obviously, this method can obtain the minimum fuel economy while keep the balance of battery state of charge (SOC), but it is difficult to apply in the real-time control system. Therefore, the optimal gear-shifting rules, vehicle operation mode control rules and engine torque control rules under the different of battery SOC are extracted into the rule-based control algorithm. Simulation results show that rule-based optimized control based on the DP control rules can reduce about 13.20% of fuel consumption when compared to original logic rule control algorithm under the China's urban driving cycle. It could obtain better fuel economy under the Beijing driving cycle and easily be implemented in a vehicle prototype.