Joint Prediction of Li-Ion Battery Cycle Life and Knee Point Based on Early Charging Performance DOI Open Access

Xinru Cui,

Jinlong Zhang, Di Zhang

et al.

Symmetry, Journal Year: 2025, Volume and Issue: 17(3), P. 351 - 351

Published: Feb. 26, 2025

With the rapid development of lithium-ion batteries, predicting battery life is critical to safe operation devices such as electric ships, vehicles, and energy storage systems. Given complexity internal aging mechanism their process exhibits prominent nonlinear characteristics. Knee point, a distinctive sign this process, plays crucial role in battery’s lifetime. In paper, cycle knee point are firstly predicted using time dimension space features early external characteristics battery, respectively. Then, capture batteries more comprehensively, we innovatively propose joint prediction method point. incorporated into model fully account for batteries. The experimental validation results show that TECAN model, which combines series information, performs well, with root mean square error (RMSE) 106 cycles absolute percentage (MAPE) only 12%.

Language: Английский

Physics-informed Neural Network Supported Wiener Process for Degradation Modeling and Reliability Prediction DOI
Zewen He, Shaoping Wang, Jian Shi

et al.

Reliability Engineering & System Safety, Journal Year: 2025, Volume and Issue: unknown, P. 110906 - 110906

Published: Feb. 1, 2025

Language: Английский

Citations

1
Joint Prediction of Li-Ion Battery Cycle Life and Knee Point Based on Early Charging Performance DOI Open Access

Xinru Cui,

Jinlong Zhang, Di Zhang

et al.

Symmetry, Journal Year: 2025, Volume and Issue: 17(3), P. 351 - 351

Published: Feb. 26, 2025

With the rapid development of lithium-ion batteries, predicting battery life is critical to safe operation devices such as electric ships, vehicles, and energy storage systems. Given complexity internal aging mechanism their process exhibits prominent nonlinear characteristics. Knee point, a distinctive sign this process, plays crucial role in battery’s lifetime. In paper, cycle knee point are firstly predicted using time dimension space features early external characteristics battery, respectively. Then, capture batteries more comprehensively, we innovatively propose joint prediction method point. incorporated into model fully account for batteries. The experimental validation results show that TECAN model, which combines series information, performs well, with root mean square error (RMSE) 106 cycles absolute percentage (MAPE) only 12%.

Language: Английский

Citations

0