基于随机模型预测控制的燃料电池混合动力汽车多目标能量管理策略

Multi-objective energy management strategy for fuel cell hybrid electric vehicle based on stochastic model predictive control.

作者信息

Ma Yan, Li Cheng, Wang Siyu

机构信息

State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, China; Department of Control Science and Engineering, Jilin University, Renmin Street 5988, Changchun 130012, China.

Department of Control Science and Engineering, Jilin University, Renmin Street 5988, Changchun 130012, China.

出版信息

ISA Trans. 2022 Dec;131:178-196. doi: 10.1016/j.isatra.2022.04.045. Epub 2022 Apr 29.

DOI:10.1016/j.isatra.2022.04.045

PMID:35581024

Abstract

The fuel cell hybrid electric vehicle (FCHEV) has earned great interest among the automotive industry in recent years. However, the power allocation strategy between proton exchange membrane fuel cell (PEMFC) and lithium-ion battery remains a technological challenge. To conquer this problem, a multi-objective predictive energy management strategy (EMS) based on model predictive control (MPC) is proposed in this paper, combined with velocity forecast and driving pattern recognition. The comparative study is conducted to reveal the interaction between each optimization objectives. Simulation results illustrate that the proposed EMS could maintain SOC around reference, reduce fuel consumption by 6.67%, and avoid PEMFC degradation which caused by frequent start-off and rapid load change.

摘要

近年来，燃料电池混合动力电动汽车（FCHEV）在汽车行业中引起了极大的关注。然而，质子交换膜燃料电池（PEMFC）与锂离子电池之间的功率分配策略仍然是一项技术挑战。为了解决这个问题，本文提出了一种基于模型预测控制（MPC）的多目标预测能量管理策略（EMS），并结合了速度预测和驾驶模式识别。通过对比研究揭示了各优化目标之间的相互作用。仿真结果表明，所提出的EMS能够将电池荷电状态（SOC）维持在参考值附近，降低燃料消耗6.67%，并避免因频繁启动和快速负载变化导致的PEMFC退化。