Prediction of residual service life of lithium-ion battery using WOA-XGBoost

doi:10.19799/j.cnki.2095-4239.2022.0126

Abstract

Abstract:

Using early data to accurately predict the remaining service life (RUL) of a battery can accelerate the improvement and optimization of the battery. However, the battery degradation process is nonlinear, and the capacity attenuation can be neglected in the early stage, which makes the RUL prediction challenging. To solve this problem, this paper uses the early cycle data of batteries, and constructs a hybrid prediction model of the WOA algorithm and the XGBoost algorithm to predict RUL. In this study, the experimental data of batteries are preprocessed, and the changes in discharge voltage-capacity degradation curve and capacity increment curve are observed. Then, the potential characteristics with high a correlation as well as actual capacity state are selected, and the time series data are used as the input of the XGBoost prediction model. Then, the parameters of the model are optimized by the WOA algorithm. Finally, 84 battery data provided by Toyota Research Institute using multi-step charging and constant current discharging are used to verify the model. The results show that the proposed model can predict the whole battery life only using the data of the first 100 cycles, and the test error is 4%.

Key words: life prediction, early data, voltage characteristics, limit gradient lifting, whale optimization

CLC Number:

TM 912

Yongsheng SHI, Jin LI, Jiarui REN, Kai ZHANG. Prediction of residual service life of lithium-ion battery using WOA-XGBoost[J]. Energy Storage Science and Technology, 2022, 11(10): 3354-3363.

Figures/Tables 17

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Table 1

Fig. 11

Fig. 12

Table 2

Fig. 13

Fig. 14

Table 3

References 25

1	GE M F, LIU Y B, JIANG X X, et al. A review on state of health estimations and remaining useful life prognostics of lithium-ion batteries[J]. Measurement, 2021, 174: 1045-1057.
2	梁新成, 张勉, 黄国钧. 基于BMS的锂离子电池建模方法综述[J]. 储能科学与技术, 2020, 9(6): 1933-1939.
	LIANG X C, ZHANG M, HUANG G J. Review on lithium-ion battery modeling methods based on BMS[J]. Energy Storage Science and Technology, 2020, 9(6): 1933-1939.
3	DUFFNER F, WENTKER M, GREENWOOD M, et al. Battery cost modeling: A review and directions for future research[J]. Renewable and Sustainable Energy Reviews, 2020, 127: doi: 10.1016/j.rser.2020.109872.
4	肖瑶. 锂离子电池SOC估计和循环寿命预测方法研究[D]. 成都: 电子科技大学, 2020.
	XIAO Y. Research on soc estimation and cycle life prediction methods for lithium ion battery[D]. Chengdu: University of Electronic Science and Technology of China, 2020.
5	XIONG R, ZHANG Y Z, WANG J, et al. Lithium-ion battery health prognosis based on a real battery management system used in electric vehicles[J]. IEEE Transactions on Vehicular Technology, 2019, 68(5): 4110-4121.
6	WANG S Q, GUO D X, HAN X B, et al. Impact of battery degradation models on energy management of a grid-connected DC microgrid[J]. Energy, 2020, 207: doi: 10.1016/j.‍energy.2020. 118228.
7	陈翌, 白云飞, 何瑛. 数据驱动的锂电池健康状态估算方法比较[J]. 储能科学与技术, 2019, 8(6): 1204-1210.
	CHEN Y, BAI Y F, HE Y. Comparison of data-driven lithium battery state of health estimation methods[J]. Energy Storage Science and Technology, 2019, 8(6): 1204-1210.
8	朱奕楠, 吕桃林, 赵芝芸, 等. 基于并行卡尔曼滤波器的锂离子电池荷电状态估计[J]. 储能科学与技术, 2021, 10(6): 2352-2362.
	ZHU Y N, LÜ T L, ZHAO Z Y, et al. State of charge estimation of lithium ion battery based on parallel Kalman filter[J]. Energy Storage Science and Technology, 2021, 10(6): 2352-2362.
9	任璞, 王顺利, 何明芳, 等. 基于内阻增加和容量衰减双重标定的锂电池健康状态评估[J]. 储能科学与技术, 2021, 10(2): 738-743.
	REN P, WANG S L, HE M F, et al. State of health estimation of Li-ion battery based on dual calibration of internal resistance increasing and capacity fading[J]. Energy Storage Science and Technology, 2021, 10(2): 738-743.
10	TIAN J Q, XU R L, WANG Y J, et al. Capacity attenuation mechanism modeling and health assessment of lithium-ion batteries[J]. Energy, 2021, 221: doi: 10.1016/j.energy.2020.119682.
11	ŠERUGA D, GOSAR A, SWEENEY C A, et al. Continuous modelling of cyclic ageing for lithium-ion batteries[J]. Energy, 2021, 215: doi: 10.1016/j.energy.2020.119079.
12	易灵芝, 张宗光, 范朝冬, 等. 基于EEMD-GSGRU的锂电池寿命预测[J]. 储能科学与技术, 2020, 9(5): 1566-1573.
	YI L Z, ZHANG Z G, FAN C D, et al. Life prediction of lithium battery based on EEMD-GSGRU[J]. Energy Storage Science and Technology, 2020, 9(5): 1566-1573.
13	陈峥, 李磊磊, 舒星, 等. 基于特征处理与径向基神经网络的锂电池剩余容量估算方法[J]. 储能科学与技术, 2021, 10(1): 261-270.
	CHEN Z, LI L L, SHU X, et al. Efficient remaining capacity estimation method for LIB based on feature processing and the RBF neural network[J]. Energy Storage Science and Technology, 2021, 10(1): 261-270.
14	王英楷,张红,王星辉.基于1DCNN-LSTM的锂离子电池SOH预测[J].储能科学与技术, 2022, 11(1): 240-245.
	WANG Y K, ZHANG H, WANG X H. Hybrid 1DCNN-LSTM model for predicting lithium ion battery state of health[J]. Energy Storage Science and Technology, 2022, 11(1): 240-245.
15	易顺民, 谢林柏, 彭力. 基于VF-DW-DFN的锂离子电池剩余寿命预测[J]. 储能科学与技术, 2022, 11(7): 2305-2315.
	YI S M, XIE L B, PENG L. Remaining useful life prediction of lithium-ion batteries based on VF-DW-DFN[J]. Energy Storage Science and Technology, 2022, 11(7): 2305-2315.
16	李练兵, 李思佳, 李洁, 等. 基于差分电压和Elman神经网络的锂离子电池RUL预测方法[J]. 储能科学与技术, 2021, 10(6): 2373-2384.
	LI L B, LI S J, LI J, et al. RUL prediction of lithium-ion battery based on differential voltage and Elman neural network[J]. Energy Storage Science and Technology, 2021, 10(6): 2373-2384.
17	SEVERSON K A, ATTIA P M, JIN N, et al. Data-driven prediction of battery cycle life before capacity degradation[J]. Nature Energy, 2019, 4(5): 383-391.
18	LIU C L, LI Q, WANG K. State-of-charge estimation and remaining useful life prediction of supercapacitors[J]. Renewable and Sustainable Energy Reviews, 2021, 150: doi: 10.1016/j.rser.2021.111408.
19	FERMÍN-CUETO P, MCTURK E, ALLERHAND M, et al. Identification and machine learning prediction of knee-point and knee-onset in capacity degradation curves of lithium-ion cells[J]. Energy and AI, 2020, 1: doi: 10.1016/j.egyai.2020.100006.
20	HU X S, CHE Y H, LIN X K, et al. Battery health prediction using fusion-based feature selection and machine learning[J]. IEEE Transactions on Transportation Electrification, 2021, 7(2): 382-398.
21	CHEN Z, XUE Q, WU Y T, et al. Capacity prediction and validation of lithium-ion batteries based on long short-term memory recurrent neural network[J]. IEEE Access, 2020, 8: 172783-172798.
22	YIN A J, TAN Z B, TAN J. Life prediction of battery using a neural Gaussian process with early discharge characteristics[J]. Sensors (Basel, Switzerland), 2021, 21(4): 1087.
23	WANG J W, DENG Z W, PENG K L, et al. Early prognostics of lithium-ion battery pack health[J]. Sustainability, 2022, 14(4): 2313.
24	CHEN T Q, GUESTRIN C. XGBoost: A scalable tree boosting system[C]//KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016: 785-794.
25	MIRJALILI S, LEWIS A. The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95: 51-67.

[1]	Wei ZENG, Junjie XIONG, Jianlin LI, Suliang MA, Yiwen WU. Optimal configuration of energy storage power station in multi-energy system based on weight adaptive whale optimization algorithm [J]. Energy Storage Science and Technology, 2022, 11(7): 2241-2249.
[2]	Shunmin YI, Linbo XIE, Li PENG. Remaining useful life prediction of lithium-ion batteries based on VF-DW-DFN [J]. Energy Storage Science and Technology, 2022, 11(7): 2305-2315.
[3]	Lingzhi YI, Zongguang ZHANG, Chaodong FAN, Xianguang LUO, Wang LI, Wenhan LIU. Life prediction of lithium battery based on EEMD-GSGRU [J]. Energy Storage Science and Technology, 2020, 9(5): 1566-1573.