Reliability analysis of battery thermal management system based on DTBN and T-S fault tree

doi:10.3969/j.issn.1674-8484.2023.02.004

Abstract

Abstract:

A reliability analysis algorithm for evaluating the thermal management system of electric vehicle power batteries was proposed. The T-S dynamic fault tree of the battery thermal management system was constructed, which was transformed into a Discrete-Time Bayesian Network (DTBN) model. Meanwhile, the T-S dynamic gate rules were transformed into the conditional probability table of network nodes, and the reliability analysis algorithms were proposed. According to the established reliability model and the failure rates of components, the failure probability value of the power battery thermal management system in the task time was 0.453 by calculated, and the posterior probability, probability importance, and the key importance of each component were calculated. The results show that: Compared with the Monte Carlo simulation method, the error of the calculated value of fault probability were less than 5%. The components with the highest probability importance are the single battery temperature sensor, battery coolant pump, battery coolant pipeline, and other components. This method can overcome the difficulties of traditional fault tree analysis in constructing conditional probability tables for Bayesian networks.

Key words: power battery, thermal management system, reliability analysis, discrete-time Bayesian network (DTBN), T-S dynamic fault tree

CLC Number:

U462.3+5

LIU Chiwei, GUO Meihua. Reliability analysis of battery thermal management system based on DTBN and T-S fault tree[J]. Journal of Automotive Safety and Energy, 2023, 14(2): 173-181.

Figures/Tables 18

References 13

[1]	韩哲鹏, 张笛. 基于动态贝叶斯网络的船舶电力推进系统可靠性评估[J]. 船舶工程, 2021, 43(11): 118-124.
	HAN Zhepeng, ZHANG Di. Reliability evaluation of ship electric propulsion system based on dynamic Bayesian network[J]. Ship Engineering, 2021, 43(11): 118-124. (in Chinese)
[2]	胡启国, 杜春超, 曹历杰, 等. 考虑多种故障状态的机械系统可靠性评估方法研究[J]. 机械强度, 2022, 44(2): 356-361. doi: 10.16579/j.issn.1001.9669.2022.02.014
	HU Qiguo, DU Cunchao, CAO lijie, et al. Research on reliability assessment method of mechanical system considering multiple failure states[J]. J Mech Strength, 2022, 44(2): 356-361. (in Chinese)
[3]	房丙午, 黄志球, 李勇, 等. 基于贝叶斯网络的复杂系统动态故障树定量分析方法[J]. 电子学报, 2016, 44(5): 1234-1238. doi: 10.3969/j.issn.0372-2112.2016.05.032
	FANG Binwu, HUANG Zhiqiu, Li Yong, et al. Quantitative analysis method of dynamic fault tree of complex system using Bayesian network[J]. ACTA Electronica Sinica, 2016, 44(5): 1234-1238. (in Chinese)
[4]	LI X, LI YF, LI H, et al. An algorithm of discrete-time Bayesian network for reliability analysis of multilevel system with warm spare gate[J]. Qual Reliab Eng Int’l, 2020, 37(3): 1-19.
[5]	Kabir S. An overview of fault tree analysis and its application in model based dependability analysis[J]. Expert Syst Appl, 2017, 77: 114-135. doi: 10.1016/j.eswa.2017.01.058 URL
[6]	姚成玉, 陈东宁, 王斌. 基于 T-S 故障树和贝叶斯网络的模糊可靠性评估方法[J]. 机械工程学报, 2014, 50(2): 194-201.
	YAO Chengyu, CHEN Dongning, WANG Bin. Fuzzy reliability assessment method based on T-S fault tree and Bayesian network[J]. J Mecha Eng, 2014, 50(2): 194-201. (in Chinese)
[7]	陈东宁, 侯安农, 姚成玉. 一种新型动态贝叶斯网络分析方法[J]. 中国机械工程, 2020, 31(12): 1394-1406.
	CHEN Dongning, HOU Annong, YAO Chengyu. A novel dynamic Bayesian network analysis method[J]. Chin Mech Eng, 2020, 31(12): 1394-1406. (in Chinese)
[8]	李享, 黄洪钟, 黄鹏, 等. 基于动态贝叶斯网络的电源系统可靠性分析与故障诊断[J]. 电子科技大学学报, 2021, 50(4): 603-607.
	LI Xiang, HUANG Hongzhong, HUANG Peng, et al. Reliability Analysis and fault diagnosis for ower system via dynamic Bayesian network[J]. J Univ Elec Sci Tech of China, 2021, 50(4): 603-607. (in Chinese)
[9]	焦志秀, 于龙. 基于动态贝叶斯网络的接触网系统可靠性研究[J]. 铁道科学与工程学报, 2021, 18(11): 3040-3046.
	JIAO Zhixiu, YU Long. Research on reliability of catenary system based on dynamic Bayesian network[J]. J Railway Sci Eng, 2021, 18(11): 3040-3046. (in Chinese)
[10]	柳炽伟. 插电式混合动力城市客车的预防性维修决策研究[D]. 广州: 华南理工大学, 2017.
	LIU Chiwei. Research on preventative maintenance decision making of plug-in hybrid electric city bus[D]. Guangzhou: South China University of Technology, 2017. (in Chinese)
[11]	Boudali H, Dugan J B. A discrete-time Bayesian network reliability modeling and analysis framework[J]. Reliab Eng Syst Safety, 2005, 87(3): 337-349. doi: 10.1016/j.ress.2004.06.004 URL
[12]	陈东宁, 姚成玉, 党振. 基于T-S模糊故障树和贝叶斯网络的多态液压系统可靠性分析[J]. 中国机械工程, 2013, 24(7): 899-905.
	CHEN Dongning, YAO Chengyu, DANG Zhen. Riliability analysis of multi-state hydraulic system base on T-S fuzzy fault tree and Bayesian network[J]. Chin Mech Eng, 2013, 24(7): 899-905. (in Chinese)
[13]	石德恩. 捷豹I-PACE纯电动汽车热管理系统介绍[J]. 汽车维修与保养, 2019(9): 64-67.
	SHI De’en. Introduction to jaguar I-PACE pure electric vehicle thermal management system[J]. Motor-China, 2019(9): 64-67. (in Chinese)

规则	X₁	X₂	X₃	P(Y_q = W_h\|X₁, X₂, X₃)
规则	X₁	X₂	X₃	h =1	h =2	h =3
1	1	1	1	P(Y_1,1)	P(Y_1,2)	P(Y_1,3)
2	1	1	2	P(Y_2,1)	P(Y_2,2)	P(Y_2,3)
…	…	…	…	…	…	…
l	k₁	k₂	k₃	P(Y_l,1)	P(Y_l,₂)	P(Y_l,3)
…	…	…	…	…	…	…
27	3	3	3	P(Y_27,1)	P(Y_27,2)	P(Y_27,3)

规则	X₁	X₂	X₃	P(Y_q = W_h\|X₁, X₂, X₃)
规则	X₁	X₂	X₃	h =1	h =2	h =3
1	1	1	1	P(Y_1,1)	P(Y_1,2)	P(Y_1,3)
2	1	1	2	P(Y_2,1)	P(Y_2,2)	P(Y_2,3)
…	…	…	…	…	…	…
l	k₁	k₂	k₃	P(Y_l,1)	P(Y_l,₂)	P(Y_l,3)
…	…	…	…	…	…	…
27	3	3	3	P(Y_27,1)	P(Y_27,2)	P(Y_27,3)

规则	X₁	X₂	P(Y_q = 1\|X₁, X₂)
规则	X₁	X₂	1	2	3
1	1	2	0	1	0
2	1	3	0	0	1
3	2	3	0	0	1
4	3	3	0	0	1

规则	X₁	X₂	P(Y_q = 1\|X₁, X₂)
规则	X₁	X₂	1	2	3
1	1	2	0	1	0
2	1	3	0	0	1
3	2	3	0	0	1
4	3	3	0	0	1

规则	X₁	X₂	P(Y_q = 1\|X₁, X₂)
规则	X₁	X₂	1	2	3
1	1	1	1	0	0
2	1	2	0	1	0
…	…	…	…	…	…
9	3	3	0	0	1