Multi-Objective Predictive Energy Management Strategy for Heavy-Duty Fuel Cell Trucks Based on Dynamic Weighting Factors

As the different driving modes have a great influence on the performance of fuel cell hybrid electric heavy trucks, it's vital to study a driving mode-consensus energy management strategy. In order to improve the economy and durability of the system and further enhance the real-time performance of energy management strategy (EMS) in different driving conditions, in this paper, a multi-dimension fuzzy control energy management strategy based on a model predictive control framework (MPC) was raised. In the offline phase, the proposed strategy selects the appropriate parameters for each mode by an evaluation function which give full consideration to fuel cell hydrogen consumption, battery consumption, and degradation of fuel cells and batteries. Meanwhile, in the online phase, the proposed strategy dynamically matches the weighting coefficients of objective functions and optimal Markov transfer probability matrix (TPM) by multi-dimensional fuzzy optimization of parameters to better adapting to changing driving patterns overtime. The simulation results demonstrate that compared to traditional MPC-EMS proposed strategy can maintains a more stable battery SoC and reduce the total consumption function by 8.75%, hydrogen consumption by 22.65%, batteries degradation functions by 6.56% and the fuel cells degradation functions by 47.22% under driving cycle1. Moreover, the robustness of the proposed method is verified by testing it under two different driving cycles. The proposed method under CWTVC still shows better performance. Therefore, the proposed strategy can effectively improve the economy and durability of fuel cell hybrid electric heavy trucks system.