Development and applications of an intelligent big data analysis platform for batteries

doi:10.19799/j.cnki.2095-4239.2024.0635

Abstract

Abstract:

As the demand for electric vehicles and energy storage continues to grow, the production of high-performance batteries is increasing rapidly, leading to more stringent requirements for advanced manufacturing processes. Smart manufacturing is vital in this context, leveraging automation technology, information systems, computational simulation, and artificial intelligence to enhance production efficiency and flexibility, minimize human errors, elucidate material mechanisms, and improve product performance. Battery data analysis platforms are critical for smart manufacturing, helping researchers predict and optimize various battery properties through advanced data analysis techniques. However, current battery big data analysis platforms encounter several challenges, including issues with data integration, limited analysis tools, poor user-friendliness, and insufficient scalability. To address these challenges, we have developed efficient algorithms using machine learning techniques for common tasks in battery data analysis, including feature analysis, battery consistency evaluation, state of health estimation, and remaining useful life prediction. Furthermore, we have created a specialized big data analysis platform for batteries named BatAi Craft. This platform uses comprehensive data analysis and performance prediction to help researchers intuitively understand complex battery datasets and uncover underlying patterns and relationships. By deploying these advanced algorithms, BatAi Craft improves the efficiency of battery analysis and intelligent management, driving the digital and smart advancement of the battery industry.

Key words: lithium battery, big data, artificial intelligence, software platform, features, lifespan, SOH

CLC Number:

TP 18

Junyu JIAO, Quanquan ZHANG, Ningbo CHEN, Jiyu WANG, Qiudi LU, Haohao DING, Peng PENG, Xiaohe SONG, Fan ZHANG, Jiaxin ZHENG. Development and applications of an intelligent big data analysis platform for batteries[J]. Energy Storage Science and Technology, 2024, 13(9): 3198-3213.

Figures/Tables 18

Fig. 1

Fig. 2

Table 1

Fitting formulas"

拟合名称	拟合公式
线性拟合	$y = a x + b$
多项式拟合	$y = p 0 x n + p 1 x n - 1 + ⋯ + p n$
单指数拟合	$y = a e b x + c$
双指数拟合	$y = a e b x + c e d x$
指数+线性	$y = a e b x + c x + d$

Table 1

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Table 2

Table 3

Fig. 8

Fig. 9

Fig. 10

Fig. 11

Table 4

Evaluation of SOH estimation results"

充电模式	均方根误差(RMSE)	平均绝对误差(MAE)	平均百分比误差(MAPE)/%	判定系数( $R 2$ )
一段恒流充电	0.02	0.02	2.43	0.97
多段恒流充电	0.01	0.01	0.55	0.97

Table 4

Fig. 12

Fig. 13

Table 5

References 42

1	XU X D, HAN X B, LU L G, et al. Challenges and opportunities toward long-life lithium-ion batteries[J]. Journal of Power Sources, 2024, 603: 234445. DOI: 10.1016/j.jpowsour. 2024. 234445.
2	FRITH J T, LACEY M J, ULISSI U. A non-academic perspective on the future of lithium-based batteries[J]. Nature Communications, 2023, 14(1): 420. DOI: 10.1038/s41467-023-35933-2.
3	SULZER V, MOHTAT P, AITIO A, et al. The challenge and opportunity of battery lifetime prediction from field data[J]. Joule, 2021, 5(8): 1934-1955. DOI: 10.1016/j.joule.2021.06.005.
4	FINEGAN D P, ZHU J E, FENG X N, et al. The application of data-driven methods and physics-based learning for improving battery safety[J]. Joule, 2021, 5(2): 316-329. DOI: 10.1016/j.joule.2020.11.018.
5	JIAO J Y, LAI G M, QIN S H, et al. Tuning of surface morphology in Li layered oxide cathode materials[J]. Acta Materialia, 2022, 238: 118229. DOI: 10.1016/j.actamat.2022.118229.
6	JIAO J Y, LAI G M, ZHAO L, et al. Self-healing mechanism of lithium in lithium metal[J]. Advanced Science, 2022, 9(12): e2105574. DOI: 10.1002/advs.202105574.
7	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: 383-391. DOI: 10.1038/s41560-019-0356-8.
8	LU Y F, LI K, HAN X B, et al. A method of cell-to-cell variation evaluation for battery packs in electric vehicles with charging cloud data[J]. eTransportation, 2020, 6: 100077. DOI: 10.1016/j.etran.2020.100077.
9	LU J H, XIONG R, TIAN J P, et al. Deep learning to estimate lithium-ion battery state of health without additional degradation experiments[J]. Nature Communications, 2023, 14(1): 2760. DOI: 10.1038/s41467-023-38458-w.
10	ZHU J G, WANG Y X, HUANG Y, et al. Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation[J]. Nature Communications, 2022, 13(1): 2261. DOI: 10.1038/s41467-022-29837-w.
11	ZHAO J Y, LING H P, WANG J B, et al. Data-driven prediction of battery failure for electric vehicles[J]. iScience, 2022, 25(4): 104172. DOI: 10.1016/j.isci.2022.104172.
12	ZHAO J Y, FENG X N, TRAN M K, et al. Battery safety: Fault diagnosis from laboratory to real world[J]. Journal of Power Sources, 2024, 598: 234111. DOI: 10.1016/j.jpowsour. 2024. 234111.
13	LI Y, LIU K L, FOLEY A M, et al. Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review[J]. Renewable and Sustainable Energy Reviews, 2019, 113: 109254. DOI: 10.1016/j.rser.2019.109254.
14	Battery quality analytics for cell manufacturers \| Voltaiq[EB/OL]. [2024-07-19]. https://www.voltaiq.com/
15	TWAICE battery analytics software[EB/OL]. [2024-07-19]. https://www.twaice.com/
16	BatteryArchive.org[EB/OL]. [2024-07-19]. https://batteryarchive.org/
17	SAHA B, GOEBEL K. NASA Ames Prognostics Data Repository. NASA Ames Research Center. Battery Data Set[EB/OL]. (2007).[2024-06-01]. https://phm-datasets.s3.amazonaws.com/NASA/5.+Battery+Data+Set.zip
18	YE M, WEI M, WANG Q, et al. State of health estimation for lithium-ion batteries based on incremental capacity analysis under slight overcharge voltage[J]. Frontiers in Energy Research, 2022, 10: 1001505. DOI: 10.3389/fenrg.2022.1001505.
19	SUN H L, YANG D F, DU J X, et al. Prediction of Li-ion battery state of health based on data-driven algorithm[J]. Energy Reports, 2022, 8: 442-449. DOI: 10.1016/j.egyr.2022.11.134.
20	XIONG W, XU G, LI Y M, et al. Early prediction of lithium-ion battery cycle life based on voltage-capacity discharge curves[J]. Journal of Energy Storage, 2023, 62: 106790. DOI: 10.1016/j.est.2023.106790.
21	AITIO A, HOWEY D A. Predicting battery end of life from solar off-grid system field data using machine learning[J]. Joule, 2021, 5(12): 3204-3220. DOI: 10.1016/j.joule.2021.11.006.
22	YU C, ZHU J G, WEI X Z, et al. Research on temperature inconsistency of large-format lithium-ion batteries based on the electrothermal model[J]. World Electric Vehicle Journal, 2023, 14(10): 271. DOI: 10.3390/wevj14100271.
23	TIAN Y, SHE Y, WU J F, et al. Thermal runaway propagation characteristics of lithium-ion batteries with a non-uniform state of charge distribution[J]. Journal of Solid State Electrochemistry, 2023, 27(8): 2185-2197. DOI: 10.1007/s10008-023-05496-9.
24	FENG F, HU X S, HU L, et al. Propagation mechanisms and diagnosis of parameter inconsistency within Li-Ion battery packs[J]. Renewable and Sustainable Energy Reviews, 2019, 112: 102-113. DOI: 10.1016/j.rser.2019.05.042.
25	OUYANG M G, ZHANG M X, FENG X N, et al. Internal short circuit detection for battery pack using equivalent parameter and consistency method[J]. Journal of Power Sources, 2015, 294: 272-283. DOI: 10.1016/j.jpowsour.2015.06.087.
26	XIA B, SHANG Y L, NGUYEN T, et al. A correlation based fault detection method for short circuits in battery packs[J]. Journal of Power Sources, 2017, 337: 1-10. DOI: 10.1016/j.jpowsour. 2016.11.007.
27	XUE Q, LI G, ZHANG Y J, et al. Fault diagnosis and abnormality detection of lithium-ion battery packs based on statistical distribution[J]. Journal of Power Sources, 2021, 482: 228964. DOI: 10.1016/j.jpowsour.2020.228964.
28	WANG Z L, ZHAO X Y, FU L, et al. A review on rapid state of health estimation of lithium-ion batteries in electric vehicles[J]. Sustainable Energy Technologies and Assessments, 2023, 60: 103457. DOI: 10.1016/j.seta.2023.103457.
29	SALDAÑA G, MARTÍN J I S, ZAMORA I, et al. Empirical electrical and degradation model for electric vehicle batteries[J]. IEEE Access, 2020, 8: 155576-155589. DOI: 10.1109/ACCESS. 2020.3019477.
30	LIU S L, DONG X, YU X D, et al. A method for state of charge and state of health estimation of lithium-ion battery based on adaptive unscented Kalman filter[J]. Energy Reports, 2022, 8: 426-436. DOI: 10.1016/j.egyr.2022.09.093.
31	LI J, ADEWUYI K, LOTFI N, et al. A single particle model with chemical/mechanical degradation physics for lithium ion battery State of Health (SOH) estimation[J]. Applied Energy, 2018, 212: 1178-1190. DOI: 10.1016/j.apenergy.2018.01.011.
32	CHEN Z, SUN M M, SHU X, et al. Online state of health estimation for lithium-ion batteries based on support vector machine[J]. Applied Sciences, 2018, 8(6): 925. DOI: 10.3390/app8060925.
33	XING Y J, MA E W M, TSUI K L, et al. An ensemble model for predicting the remaining useful performance of lithium-ion batteries[J]. Microelectronics Reliability, 2013, 53(6): 811-820. DOI: 10.1016/j.microrel.2012.12.003.
34	BAGHDADI I, BRIAT O, GYAN P, et al. State of health assessment for lithium batteries based on voltage-time relaxation measure[J]. Electrochimica Acta, 2016, 194: 461-472. DOI: 10.1016/j.electacta.2016.02.109.
35	YAO J W, POWELL K, GAO T. A two-stage deep learning framework for early-stage lifetime prediction for lithium-ion batteries with consideration of features from multiple cycles[J]. Frontiers in Energy Research, 2022, 10: 1059126. DOI: 10.3389/fenrg.2022.1059126.
36	ZHAO J, ZHU Y, ZHANG B, et al. Review of state estimation and remaining useful life prediction methods for lithium-ion batteries[J]. Sustainability, 2023, 15(6): 5014.
37	WU T Z, ZHAO T, XU S Y. Prediction of remaining useful life of the lithium-ion battery based on improved particle filtering[J]. Frontiers in Energy Research, 2022, 10: 863285. DOI: 10.3389/fenrg.2022.863285.
38	WANG X X, YE P L, LIU S R, et al. Research progress of battery life prediction methods based on physical model[J]. Energies, 2023, 16(9): 3858. DOI: 10.3390/en16093858.
39	PARK K, CHOI Y, CHOI W J, et al. LSTM-based battery remaining useful life prediction with multi-channel charging profiles[J]. IEEE Access, 2020, 8: 20786-20798. DOI: 10.1109/ACCESS.2020.2968939.
40	ZHANG H, ALTAF F, WIK T. Battery capacity knee-onset identification and early prediction using degradation curvature[J]. Journal of Power Sources, 2024, 608: 234619. DOI: 10.1016/j.jpowsour.2024.234619.
41	IBRAHEEM R, WU Y, LYONS T, et al. Early prediction of lithium-ion cell degradation trajectories using signatures of voltage curves up to 4-minute sub-sampling rates[J]. Applied Energy, 2023, 352: 121974. DOI: 10.1016/j.apenergy.2023.121974.
42	BACHMANN R, MIZRAHI D, ATANOV A, et al. MultiMAE: multi-modal multi-task masked autoencoders[M]//Lecture Notes in Computer Science. Cham: Springer Nature Switzerland, 2022: 348-367. DOI: 10.1007/978-3-031-19836-6_20.

特征	名称	皮尔逊相关性系数
Q_sum	片段累计充电量	0.98
V_IDR	片段电压十分位距	0.93
vq_s21	片段容量电压曲线下面积	0.87
E_entropy	片段能量曲线熵	0.82
V_var	片段电压方差	0.76
E_slope_total	片段始末能量变化斜率	0.75
T_slope_equal_time	片段内等时间温度变化斜率	0.64
Q_incremental_equal_time	片段内等时间电量增量	0.58
Q_entropy	片段电量曲线熵	0.48
T_IQR	片段温度四分位距	0.41

特征	名称	皮尔逊相关性系数
Log_Var_Q	等压点容量差方差	0.93
Log_IQR_dQdV	等压点增量容量差四分位距	0.76
Log_IQR_Q	等压点容量差四分位距	0.76
Final_peak_loc_dQdV	结束增量容量曲线峰位置	0.72
Max_lower_area_voltage	电压曲线下面积最大值	0.68
Log_Min_dQdV	等压点增量容量差最小值	0.67
Log_Var_time	等压点放电时间差方差	0.54
intercept_root	均方根衰减模型截距	0.53
intercept_linear	线性衰减模型截距	0.52
Upper_area_voltage	电压曲线上面积差值	0.46
Discharge_cc_time_final	结束循环恒流放电时间	0.34
Initial_SoH	初始SOH	0.33

任务	均方根误差(RMSE)循环	平均绝对误差(MAE)循环	平均百分比误差(MAPE)/%
循环寿命	74.42	48.33	5.39
衰减轨迹膝点法	46.52	37.46	6.14
衰减轨迹SOH法	51.52	39.14	6.96

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