Genetic Algorithm-Based Parameter Optimization of Energy Management Strategy and Analysis for Fuel Cell Hybrid Electric Vehicles 2019-01-0358
Fuel cell hybrid electric vehicles (FCHEVs) composed of fuel cells and batteries can improve the dynamic response and durability of vehicle propulsion. In addition, braking energy can be recovered by batteries. The energy management strategy (EMS) for distributing the requested power through different types of energy sources plays an important role in FCHEVs. Reasonable power split not only improves vehicle performance but also enhances fuel economy. In this paper, considering the power tracking control strategy which is widely adopted in Advanced Vehicle Simulator (ADVISOR), a constrained nonlinear programming parameter optimization model was established for minimizing fuel consumption. The principal parameters of power tracking control strategy are set as the optimized variables, with the dynamic performance index of FCHEVs being defined as the constraint condition. Then, the genetic algorithm (GA) is applied in the control strategy design for solving the optimization problem. The GA is combined with the vehicle model in ADVISOR to optimize parameters of control strategy respectively for two standard driving cycles, including the Urban Dynamometer Driving Schedule (UDDS) and the Highway Fuel Economy Test (HWFET). Finally, the performance of control strategies before and after optimization is simulated, then compared, and the optimal control parameters under different driving cycles are analyzed. The simulation results demonstrate that total fuel consumption of FCHEVs can be effectively reduced by using the optimized power tracking control strategy under two standard driving cycles without compromising dynamic performance. Therefore, the GA optimization approach has the potential to reasonably adjust the parameters of energy management strategy. In addition, even with the same control strategy, there should be different optimal control parameters for different driving cycles.
Su Zhou, Zejun Wen, Xuelei Zhi, Jie Jin, Shangwei Zhou