Genetic Algorithm-Based Parameter Optimization of Energy Management Strategy and Its Analysis for Fuel Cell Hybrid Electric Vehicles
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 is 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.