Abstract

A model for predicting lower limit performance including variation terms was constructed to help achieve performance after durability testing of products equipped with lithium-ion batteries. To predict degradation, the model formula includes two types of stochastic variables: random effects multiplied by the battery durability degradation trend term, and a diffusion process in which variation expands proportionally with time. Parameter optimization was conducted by the maximum likelihood method aided by the Markov Chain Monte Carlo method. As a result, the lower limit performance after degradation, which had conventionally been predicted based on 12 months of measured durability data, was successfully computed based on only four months of durability data. It was also demonstrated that prediction of lower limit performance, which previously required the two pieces of information of the predicted typical performance values after durability testing and performance variation during durability, could be achieved with just one piece of information by using the proposed model.

References

1. (1) New Energy and Industrial Technology Development Organization: Battery Road Map 2013, (2013) (in Japanese), https://www.jstage.jst.go.jp/article/ieejias/134/3/134_NL3_5/_pdf, (accessed 2024/12/24)
2. (2) Fuji Keizai Group Co., Ltd.: Energi, ogata nijidenchi, zairyo no shorai tenbo 2021 – Dendo jidosha, shasaidenchi bunya hen -, p. 5-12, (2021) (in Japanese)
3. (3) METI: Ministry of Economy, Trade and Industry, https://www.meti.go.jp/policy/mono_info_service/joho/conference/battery_strategy/0001/03r2.pdf, (accessed 2024/12/24) (in Japanese)
4. (4) Dunn, B., Kamath, H., Tarascon, J.-M.: Electrical Energy storage for the Grid: A Battery of Choices, Science, Vol. 334, Issue 6058, p. 928-935, (2011), doi: 10.1126/science.1212741
5. (5) Schmuch, R., Wagner, R., Hörpel, G., Placke, T., Winter, M.: Performance and cost of materials for lithium-based rechargeable automotive batteries, Nature Energy, Vol. 3, p. 267-278, (2018), doi: 10.1038/s41560-018-0107-2
6. (6) Final Regulation Order, Section 1962.8, Title 13, California Code of Regulations, (2022), https://ww2.arb.ca.gov/sites/default/files/barcu/regact/2022/accii/acciifro1962.8.pdf, (accessed 2024/12/24)
7. (7) Waag, W., Fleischer, C., Sauer, D. U.: Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles, Journal of Power Sources, Vol. 258, p. 321–339, (2014), doi: 10.1016/j.jpowsour.2014.02.064
8. (8) LcGrasso, J., Ressler, G., Inguva, S.: MEETING CUSTOMER EXPECTATIONS: Life and Safety of Batteries in Hybrid and Electric Vehicles, SAE Technical Paper TP-AAB-AABC_US_2011_20, (2011)
9. (9) Tominaga, Y.: Degradation Model for Battery Performance Based on Performance Mechanism, The Electrochemical Society of Japan, Vol. 89, No. 2, p. 125-132, (2021), doi: 10.5796/denkikagaku.21-FE0014 (in Japanese)
10. (10) Omichi, K., Kaga, T., Nishiimura, T., Aida, N., Tominaga, Y.: Modeling of Efficient Lifetime Prediction for Lithium-ion Battery Employing Orthogonal Array, Honda R&D Technical Review, Vol. 35, p. 32-38
11. (11) Xu, X., Tang, S., Yu, C., Xie, J., Han, X., Ouyang, M.: Remaining Useful Life Prediction of Lithium-ion Batteries Based on Wiener Process Under Time-Varying Temperature Condition, Reliability Engineering & System Safety, Vol. 214, (2021), doi: 10.1016/j.ress.2021.107675
12. (12) Russell, M. B., King, E. M., Parrish, C. A., Wang, P.: Stochastic modeling for tracking and prediction of gradual and transient battery performance degradation, Journal of Manufacturing Systems, Vol. 59, p. 663-674, (2021), doi: 10.1016/j.jmsy.2021.04.008
13. (13) Wu, C. F. J., Hamada, M. S.: Experiments: Planning, Analysis, and Optimization, John Wiley & Sons, Inc., (2021), doi: 10.1002/9781119470007
14. (14) Castillo, E., Hadi, A. S.: Modeling Lifetime Data with Application to Fatigue Models, Journal of the American Statistical Association, Vol. 90, Issue 431, p. 1041-1054, (1995), doi: 10.2307/2291341
15. (15) Jacquier, E., Johannes, M., Polson, N.: MCMC maximum likelihood for latent state models, Journal of Econometrics, Vol. 137, Issue 2, p. 615-640, (2007), doi: 10.1016/j.jeconom.2005.11.017

Author (organization or company)

• Kaoru OMICHI（Innovative Research Excellence, Power Unit & Energy）
• Kotone SUZUKI（Innovative Research Excellence, Power Unit & Energy）
• Hideki NAGATSUKA（Chuo University）
• Michihiro YASUDA（TechnoPro, Inc. TechnoPro Design Company）

Publication Information

Article of Honda R&D Technical Review Vol.38 (48 - 55)

PDF

Citation Information