融合交通信息的燃料电池汽车能量管理研究进展

doi:10.19562/j.chinasae.qcgc.2024.12.017

Abstract

Abstract:

Energy management determines the power distribution of the power system of fuel cell vehicles （FCVs） and affects the economy and durability of FCVs. As the operating conditions of vehicles are complex and variable， energy management can improve the output performance of FCV power system by integrating traffic information. In this paper， the optimization objectives of FCV energy management are summarized， and the traditional rule-based and optimization-based energy management strategies are analyzed. Then， focusing on the analysis and prediction of traffic information such as vehicle speed and traffic condition， prediction methods such as Markov method and artificial intelligence are reviewed， and the research progress of FCV energy management strategies by integrating traffic information is summarized. Finally， the research direction of development of FCV energy management by integrating traffic information is proposed.

Key words: fuel cell vehicle, energy management, traffic information, predictive method

Yaxiong Wang,Yiying Fan,Kai Ou,Zhongbao Wei,Jiujun Zhang. Research Progress on Traffic Information-Integrated Energy Management for Fuel Cell Vehicles[J].Automotive Engineering, 2024, 46(12): 2314-2328.

Figures/Tables 8

References 84

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算法	实现方式	优缺点	文献
DP	离散化处理问题，将多阶段过程转化成一系列子优化问题，逐段求取最优解	求解结果全局最优，但须提前了解全局工况，计算量大	［59］
PMP	引入协态变量约束系统状态变量SOC的变化，通过最小化每个时刻的哈密顿函数获取最优控制动作	优化性能接近全局最优，且计算量小于DP；但计算精度受初始协态变量影响；须提前了解全局工况	［60］
凸优化	在最小化的要求下，将FCV能量管理问题转换为凸函数形式求解	局部最优解即全局最优解，求解简单，计算效率高；但目标函数必须凸函数，约束条件必须满足凸形式	［61］
ECMS	利用等效因子转化为等效氢消耗模型，构建瞬时等效成本函数，将全局优化问题转化为瞬时优化问题	计算量小，实时性较好，操作简单，可有效控制SOC工作范围；但等效因子的设置决定控制策略的优化效果	［62-63］
ESM	通过搜索燃料电池性能的变化来寻找最大功率和最大效率点	经济性全局最优，更适用于实际汽车；但优化目标单一	［55-56］
MPC	将全局优化问题转化成预测范围内的局部优化问题，在每一个采样时刻搜索有限时域内的最佳控制动作	实时性很强，可实现结果局部最优；但对预测模型的精度要求较高	［64-65］

方法名称	子分类	优点	缺点	文献
马尔可夫	车速预测	1.适用于随机建模 2.在相似的行驶条件下预测精度较高	1.对快速变化的行驶条件预测精度较低 2.对不符合马尔可夫特性的过程预测性能差 3.依赖于历史数据库	［78，80］
	功率预测			［60，79］
	驾驶模式和工况识别			［51，81］
人工智能方法	驾驶模式和工况识别	1.可解决非线性多变量问题 2.学习能力强	1.训练时间长 2.构建预测模型困难，模型复杂程度难以掌握，易引起过拟合和难收敛等问题 3.依赖于历史数据库	［86-87］
	坡度预测			［72］
	车速预测			［85］
远程信息处理技术	交通灯信号及交通拥挤程度	1.可提供更准确的实时驾驶数据 2.具有在高峰时间和交通拥堵情况下的应用潜力	1.计算量大 2.受到交通智能控制系统发展的限制	［88，90］
远程信息处理技术	坡度预测	1.可提供更准确的实时驾驶数据 2.具有在高峰时间和交通拥堵情况下的应用潜力	1.计算量大 2.受到交通智能控制系统发展的限制	［66，89］

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Research Progress on Traffic Information-Integrated Energy Management for Fuel Cell Vehicles

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