电源学报

燃料电池客车模型预测控制能量管理策略

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徐鑫中国电源学会学生会员 ¹ , 武小花中国电源学会会员 ¹^,² , 杨佳珞 ¹ , 舒俊豪 ¹

电源学报 | 电池与储能 2025,23(6): 309-319

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电源学报 |电池与储能 2025 , 23 (6) : 309 -319

燃料电池客车模型预测控制能量管理策略

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徐鑫中国电源学会学生会员¹, 武小花中国电源学会会员¹^,², 杨佳珞¹, 舒俊豪¹

作者信息

^1.西华大学汽车测控与安全四川省重点实验室，成都　610039

^2.四川省新能源汽车智能控制与仿真测试技术工程研究中心，成都　610039

通讯作者:

武小花（1984—　），女，中国电源学会会员，通信作者，博士，教授。研究方向：新能源汽车控制技术。E-mail：xiaohuawu13@163.com。

作者简介:

徐鑫（1995—　），男，中国电源学会学生会员，硕士研究生。研究方向：燃料电池汽车能量管理策略。E-mail：m18328396506@163.com。

杨佳珞（1998—　），男，硕士研究生。研究方向：燃料电池汽车全生命周期建模。E-mail：799810316@qq.com。

舒俊豪（1998—　），男，硕士研究生。研究方向：动力电池建模和寿命预测。E-mail：1356655853@qq.com。

Model Predictive Control-based Energy Management Strategy for Fuel Cell Buses

Xin XU Student Member, CPSS¹, Xiaohua WU Member, CPSS¹^,², Jialuo YANG¹, Junhao SHU¹

Affiliations

^1.Vehicle Measurement Control and Safety Key Laboratory of Sichuan Province, Xihua University, Chengdu 610039, China

^2.Sichuan New Energy Vehicle Intelligent Control and Simulation Testing Technology Engineering Research Center, Chengdu 610039, China

出版时间: 2025-10-30 doi: 10.13234/j.issn.2095-2805.2025.6.309

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摘要

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为提高燃料电池客车燃料经济性，针对基于规则能量管理策略依赖工程经验和参数校准的问题，提出1种模型预测控制的能量管理策略。为减少计算量，策略采用分层控制方式实现。在顶层，采用动态规划能量管理策略确定锂电池荷电状态最佳参考轨迹；在底层，采用径向基函数神经网络预测未来车辆速度，以预测时域内燃料电池功率高效输出与荷电状态跟随最佳参考轨迹为目标，并通过二次规划算法求解出功率分配最优决策。详细研究了速度预测模型和预测时域范围的确定、荷电状态参考轨迹对所提策略的影响及不同策略下荷电状态变化和燃料经济性。结果表明，在初始荷电状态为0.7时，所提模型预测控制策略的燃料经济性能够达到动态规划策略燃料经济性的92.99%以上，相比基于规则的策略，燃料经济性至少提高11.78%。随着初始荷电状态的增加，所提策略表现出更好的燃料经济性。

关键词

燃料电池客车 / 能量管理策略 / 速度预测 / 动态规划 / 模型预测控制

Abstract

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To improve the fuel economy of fuel cell buses, an energy management strategy based on model predictive control was proposed to solve the problem that the rule-based energy management strategy depends on engineering experience and parameter calibration. This strategy was implemented in a layered control manner to reduce the amount of computation. At the top level, a dynamic programming energy management strategy was used to determine the best reference trajectory for the lithium battery's state-of-charge (SOC). At the bottom layer, radial basis function neural network was used to predict the future vehicle speed, with the goal of predicting the efficient output of fuel cell power and SOC to follow the best reference trajectory in the time domain. In addition, the optimal decision making on power distribution was solved by the quadratic programming algorithm. The determination of the speed prediction model and prediction time-domain, the influence of SOC reference trajectories on the proposed strategy, and the changes in SOC and fuel economy under different strategies were studied in detail. Results show that when the initial SOC is 0.7, the fuel economy under the proposed model predictive control-based strategy can reach more than 92.99% of the fuel economy under the dynamic programming strategy, which is at least 11.78% higher than that under the rule-based strategy. Furthermore, the proposed strategy exhibits better fuel economy as the initial SOC increases.

Key words

Fuel cell bus / energy management strategy / speed prediction / dynamic programming / model predictive control

引用本文

徐鑫中国电源学会学生会员, 武小花中国电源学会会员, 杨佳珞, 舒俊豪. 燃料电池客车模型预测控制能量管理策略. 电源学报, 2025 , 23 (6) : 309 -319 . DOI: 10.13234/j.issn.2095-2805.2025.6.309

Xin XU, Xiaohua WU, Jialuo YANG, Junhao SHU. Model Predictive Control-based Energy Management Strategy for Fuel Cell Buses[J]. Journal of Power Supply, 2025 , 23 (6) : 309 -319 . DOI: 10.13234/j.issn.2095-2805.2025.6.309

基金

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四川省科技计划资助项目(2020YFQ0037; 2021YFG0071)
成都市科技局资助项目(2021-RK00-00024-ZF)
四川省西华大学研究生创新基金资助项目(YCJJ2021092)

参考文献

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2025年第23卷第6期

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文章信息

doi: 10.13234/j.issn.2095-2805.2025.6.309

接收时间：2022-10-27
首发时间：2026-10-26
出版时间：2025-10-30

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出版历史

收稿日期：2022-10-27
修回日期：2023-02-10
录用日期：2023-03-16

基金

Sichuan Science and Technology Plan(2020YFQ0037; 2021YFG0071)

四川省科技计划资助项目(2020YFQ0037; 2021YFG0071)

Chengdu Science and Technology Bureau(2021-RK00-00024-ZF)

成都市科技局资助项目(2021-RK00-00024-ZF)

Graduate Innovation Fund of Xihua University, Sichuan Province(YCJJ2021092)

四川省西华大学研究生创新基金资助项目(YCJJ2021092)

作者信息

^1.西华大学汽车测控与安全四川省重点实验室，成都　610039

^2.四川省新能源汽车智能控制与仿真测试技术工程研究中心，成都　610039

通讯作者:

武小花（1984—　），女，中国电源学会会员，通信作者，博士，教授。研究方向：新能源汽车控制技术。E-mail：xiaohuawu13@163.com。

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https://castjournals.cast.org.cn/joweb/dyxb/CN/10.13234/j.issn.2095-2805.2025.6.309

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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