Hierarchical energy management strategy for PHEVs based on segmented SOC trajectory prediction

doi:10.3969/j.issn.1674-8484.2025.05.008

Abstract

Abstract:

A hierarchical energy management strategy-adaptive initial equivalent factor strategy (HEMS-AIEFS) was proposed to achieve near-global optimal energy allocation under real driving conditions. HEMS-AIEFS adopted a two-layer structure: The upper layer implemented a node-split state-of-charge (SOC) planning method for batteries, which used a dynamic programming (DP) algorithm to generate the relevant data for training neural network models. These models can predict the SOC node trajectories of different road sections in real time; In the lower layer, the predicted equivalent consumption minimization strategy (P-ECMS) was used to track the predicted SOC trajectories, in which the adaptive initial equivalent factor strategy (AIEFS) was added to set the initial equivalent factor (EF₀). The results show that the proposed AIEFS reduces fuel consumption by 2.36% to 7.69% compared to the conventional method of determining the initial equivalence factor, and that HEMS-AIEFS saves 1.56% to 9.13% of fuel consumption under different operating conditions comparing to the CD-CS strategy and requires 4.9% to 5.6% of the computation time of the DP algorithm. This study provides an effective optimization method for plug-in hybrid elective vehicle (PHEV) energy management optimization and demonstrates the potential application of navigation information in PHEV energy management optimization.

Key words: plug-in hybrid electric vehicle (PHEV), hierarchical energy management strategy-adaptive initial equivalent factor strategy (HEMS-AIEFS), state-of-charge (SOC) trajectory, equivalent factor(EF)

CLC Number:

TH156

DAI Lihong, JIN Nini, MO Zonghua, HU Peng, WAN Wenjun, LIU Haoye, WANG Tianyou. Hierarchical energy management strategy for PHEVs based on segmented SOC trajectory prediction[J]. Journal of Automotive Safety and Energy, 2025, 16(5): 736-746.

Figures/Tables 21

References 32

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组件	参数	值
车辆	整车质量/ kg	2 100.75
	迎风面积/ m²	2.79
	空气阻力系数	0.36
	车轮半径/ m	0.358
	滚动阻力系数	0.007
发动机	最大功率/ kW	205
发动机	最大扭矩/ (N·m)	215
电池	额定电压/ V	350
电池	额定容量/ (kWh)	19.2
电机1	最大功率/ kW	95
	最大转速/ (10³ r·min^-1)	12
	最大扭矩/ (N·m)	150
电机2	最大功率/ kW	105
	最大转速/ (10³ r·min^-1)	12
	最大扭矩/ (N·m)	240
传动系统	电机1惰轮传动比	2.282
	电机2齿轮传动比	2.289
	两挡位传动比	0.731/1.226
	主传动比	3.941

组件	参数	值
车辆	整车质量/ kg	2 100.75
	迎风面积/ m²	2.79
	空气阻力系数	0.36
	车轮半径/ m	0.358
	滚动阻力系数	0.007
发动机	最大功率/ kW	205
发动机	最大扭矩/ (N·m)	215
电池	额定电压/ V	350
电池	额定容量/ (kWh)	19.2
电机1	最大功率/ kW	95
	最大转速/ (10³ r·min^-1)	12
	最大扭矩/ (N·m)	150
电机2	最大功率/ kW	105
	最大转速/ (10³ r·min^-1)	12
	最大扭矩/ (N·m)	240
传动系统	电机1惰轮传动比	2.282
	电机2齿轮传动比	2.289
	两挡位传动比	0.731/1.226
	主传动比	3.941

v_mean/ (km·h^-1)	路段划分范围/ km	二级路段步长/ km	所获得样本数
0~30	0~0.5	0.1	64 000
30~70	0.5~1.167	0.5	9 270
70~200	1.167~3.33	1.0	6 475

v_mean/ (km·h^-1)	路段划分范围/ km	二级路段步长/ km	所获得样本数
0~30	0~0.5	0.1	64 000
30~70	0.5~1.167	0.5	9 270
70~200	1.167~3.33	1.0	6 475

训练集类别	样本数量	占比/ %	说明
训练集	51 200	64.07	来自DP算法生成的原始样本
验证集	6 400	7.99	来自DP算法生成的原始样本
测试集(原始)	6 400	7.99	来自DP算法生成的原始样本
测试集(扩展)	6 975	13.96	来自WLTC×2循环经DP求解的新样本
测试集总计	13 375	21.95	包含原始与扩展测试样本
样本总计	71 000	100