基于插电式燃料电池公交车能量管理策略

doi:10.19562/j.chinasae.qcgc.2025.07.008

Abstract

Abstract:

For the problem that Pontryagin's Minimum Principle （PMP） is only applicable to offline calculation and difficult to be applied in real vehicles， an energy management strategy for online identification of working conditions based on density clustering （DBSCAN） is proposed. This strategy combines offline training with online control， and makes full use of the fixed and fragmentary characteristics of bus routes， using the bus stops as nodes to divide the routes into multiple driving segments. During the vehicle's stop， the motor output power of the previous driving segment is identified to calculate the co-state of the next driving segment. When the vehicle starts running， the calculated co-state is applied to the PMP algorithm to complete the real-time power distribution. Finally， by constructing the simulation experiment based on real vehicle data， the proposed strategy is transplanted into the vehicle controller. The results show that compared with the current regular energy management strategy for real vehicle operation， the proposed strategy can reduce the equivalent hydrogen consumption by 17.6% and effectively maintain the state of charge （SOC） of the power battery. Moreover， each calculation step is within 60 ms， which has good real-time performance and can meet the application requirements of energy management strategies in the actual operation of fuel cell buses.

Key words: energy management, condition identification, density clustering, Pontryagin’s maximum principle, hardware in loop

Jing Lian,Peng Yang,Linhui Li,Yafu Zhou,Xuesong Sun. Energy Management Strategy Based on Plug-In Fuel Cell Buses[J].Automotive Engineering, 2025, 47(7): 1305-1316.

Figures/Tables 21

参数	数值	单位
动力电池组容量 $Q b a t$	173	A·h
动力电池标称电压 $U S$	608.58	V
动力电池单体标称电压 $U 0$	3.22	V
动力电池单体电池数 $N b a t$	189
电机峰值功率 $P m o t o r m a x$	200	kW
燃料电池系统峰值功率 $P f c s m a x$	80	kW
燃料电池单体数 $N c e l l$	300
DCDC转化器效率 $η d c d c$	0.95

工况	电压衰减率
启停操作 $δ o n - o f f$	$13.79 ? μ V / c y c l e$
高功率负荷 $δ h i g h$	$8.66 ? μ V / h$
怠速运行 $δ i d l e$	$10.00 ? μ V / h$
变载 $δ l o a d$	$0.044 ? 1 ? μ V / k W$

References 24

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实验编号	工况已知/未知	是否加SOC 惩罚项	计算的协态变量
实验1	已知	是	最优co-state （PMP）
实验2	已知	否	最优co-state （PMP）
实验3	未知	是	最优识别co-state （SSA-DBSCAN）
实验4	未知	是	次优识别co-state （DBSCAN）
实验5	未知	否	最优识别co-state （SSA-DBSCAN）
实验6	未知	否	次优识别co-state （DBSCAN）

驾驶循环策略	DC1	DC2	DC3	DC4	DC5	平均节省
DBCSCN	16.0	16.3	16.2	15.5	13.9	15.6
SSA-DBCSCN	16.8	18.2	17.5	18.6	17.1	17.6
硬件在环	16.8	18.2	17.5	18.6	17.1	17.6
工况已知	18.4	19.7	19.0	20.2	19.2	19.2

驾驶循环	终止SOC/%		等效氢气消耗/g
驾驶循环	MATLAB	硬件在环	MATLAB	硬件在环
DC1	45.00	45.01	1 034.8	1 034.8
DC2	44.95	44.95	1 056.3	1 056.4
DC3	45.07	45.07	1 050.9	1 050.9
DC4	45.02	45.02	988.6	988.5
DC5	45.18	45.18	1 003.7	1 003.7

Energy Management Strategy Based on Plug-In Fuel Cell Buses

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