Multi-objective Energy Management Strategy of HEV Based on Improved Dynamic Programming Method

doi:10.12141/j.issn.1000-565X.210771

Abstract

Abstract:

Hybrid electric vehicle Energy Management Strategy (EMS) optimization is a multi-objective and multi-stage decision-making problem that needs to comprehensively optimize several performance indicators of hybrid electric vehicles. The traditional multi-objective optimization algorithm faces challenges such as low efficiency and difficult to guarantee convergence when dealing with these problems. Combined with the idea of non-dominated sorting algorithm, this paper extended the traditional Dynamic Programming (DP) to the field of multi-objective optimization, and proposed Non-dominated Sorting Dynamic Programming (NSDP). When using this algorithm, the driving condition was divided into several stages firstly. In each stage, the cumulative target value vector generated by the hybrid electric vehicle in different control strategies was obtained, and the current non dominated solution set and the corresponding control strategy were obtained through the non dominated sorting algorithm. Then, the non dominated solution set of each stage was used for reverse iteration in turn, until the leading edge of the non dominated solution set and the corresponding energy management control strategy of the whole driving cycle were obtained. In the simulation experiment, Weighting Dynamic Programming (WDP) and Non-dominated Sorting Dynamic Programming were applied to solve the optimization problem of multi-objective energy management strategy for power split hybrid electric vehicles and series parallel hybrid electric vehicles under constant acceleration conditions. The results show that NSDP not only can effectively complete the solution and ensure convergence, but also has significant advantages in homogeneity of solution set and solving efficiency. Furthermore, NSDP was used to solve the energy management optimization problem of series parallel hybrid electric vehicles running in Worldwide Harmonized Light Duty Vehicle Test Cycle (WLTC). The non dominated solution set can be used to analyze the working characteristics of vehicles and provides a reliable reference for the formulation of actual energy management strategy.

Key words: multi-objective optimization, energy management strategy, improved dynamic programming, non-dominated sorting

CLC Number:

U461.7

ZHAO Kegang, HE Kunyang, LI Jie, et al. Multi-objective Energy Management Strategy of HEV Based on Improved Dynamic Programming Method[J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(9): 138-148.

Figures/Tables 11

Fig.1

Table 1

Power system parameters （Option 1）"

总成	参数	参数值
发动机	转速范围/（r·min^-1）	800~6 000
	最高功率/kW	110
	最高转矩/（N·m）	225
电机1	最高转速/（r·min^-1）	14 000
	最高转矩/（N·m）	120
	最高功率/kW	60
电机2	最高转速/（r·min^-1）	14 000
	最高转矩/（N·m）	250
	最高功率/kW	60
动力电池	电池容量/kW·h	12.96
动力电池	额定电压/V	350.4
传动系统	齿圈到轮端速比	3.303
	电机2到轮端速比	8.211
	$α$ ^1）	2.6
	传动效率	0.965

Table 1

Fig.2

Table 2

Fig.3

Table 3

Index values in the solution results of experiment 1 and experiment 2"

解集个数	间距指标（SP）		解集距离指标 $D (?, S 2)$		解集覆盖率指标 $C (g, S 2)$
解集个数	实验1	实验2	实验1	实验2	实验1	实验2
40	9.480 6	12.855 9	0.168 4	0.781 8	0.325 0	0.700 0
80	3.277 8	5.965 5	0.013 0	0.258 6	0.075 0	0.425 0
120	1.694 5	5.356 7	0.007 2	0.157 8	0.050 0	0.250 0
160	1.312 1	4.995 3	0.002 3	0.105 4	0.025 0	0.200 0
200	1.169 9	4.774 4

Table 3

Fig.4

Table 4

Index value in the solution results of experiment 3 and experiment 4"

实验	计算时间/s	有效解的个数	超体积指标HV^1）	间距指标SP	D（ $?$ ， NSDP）	D（ $?$ ， WDP）	C（ $?$ ， NSDP）	C（ $?$ ， WDP）
NSDP（实验3）	621	120	624.6	1.713 0	—	7.593 4	—	0.050 0
WDP（实验3）	66 046	14	634.8	40.038 4	4.168 4×10^-14	—	0.066 7	—
NSDP（实验4）	2 519	120	3 603.6	5.226 6	—	5.544 5	—	0
WDP（实验4）	297 354	46	3 574.8	5.341 7	1.628 3	—	0	—

Table 4

Fig.5

Table 5

Fig.6

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序号		燃油消耗总量/g	有效安时流通总量/（A·s）	纯电模式次数	纯发动机次数	并联驱动次数	串联驱动次数	APU直驱占比^1）/%
转折前	1	895.24	21 323.21	389	70	141	507	11.05
	2	907.92	13 144.60	344	159	107	497	35.41
	3	932.61	7 496.83	264	263	75	505	79.01
转折后	4	951.35	6 167.15	260	272	51	524	97.33
	5	963.22	6 089.18	291	260	25	531	97.61
	6	968.49	6 068.41	300	254	20	533	98.69

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