Abstract
In this paper, a multi-objective energy management strategy with an adaptive equivalent factor is proposed to improve the fuel economy, system durability, and charge-sustenance performance of fuel cell hybrid electric vehicles. Firstly, the total hydrogen consumption and degradation cost of power sources can be calculated by flexible empirical models. Then, the multi-objective optimization problem can be transformed into an objective function, which can be solved by quadratic programming to improve the real-time performance. Furthermore, an adaptive Unscented Kalman filter is designed to estimate the aging state of the fuel cell system. The equivalent factor in the objective function can be adaptively updated by the estimated aging state, which can balance the conflict between the fuel economy and the system durability while keeping the state-of-charge in an ideal range. Finally, simulation results show that when the fuel cell system is obviously damaged during the operation, the proposed energy management strategy still can minimize the total cost and maintain the charge-sustenance performance under different driving cycles compared with other methods.
Rule learning based energy management strategy of fuel cell hybrid vehicles considering multi-objective optimization
