The traction battery cycle life prediction method using performance degradation data was proposed. The example battery was a commercialized lithium-ion cell with LiMn2O4/Graphite cell system. The capacity faded with c...The traction battery cycle life prediction method using performance degradation data was proposed. The example battery was a commercialized lithium-ion cell with LiMn2O4/Graphite cell system. The capacity faded with cycle number follows a traction function path. Two cycle life predicting models were established. The possible cycle life was extrapolated, which follows normal distribution well. The distribution parameters were estimated and the battery reliability was evaluated. The models＇ precision was validated and the effect of the cycle number on the predicting precision was analysed. The cycle life models and reliability evaluation method resolved the difficulty of battery life appraisal, such as long period and high cost.展开更多
With the wide application of the LFP lithium-ion batteries,more attention is paid to the battery life and future aging behaviors as the safety and performance of the battery are guaranteed by accurate battery aging mo...With the wide application of the LFP lithium-ion batteries,more attention is paid to the battery life and future aging behaviors as the safety and performance of the battery are guaranteed by accurate battery aging monitoring.In recent years,long-term aging trajectory prediction of the lithium-ion battery is always a challenge due to its complex nonlinear aging behaviors especially the aging behaviors in the two aging stages are quite different when the battery experiences the two-stage aging process under fast-charging conditions.Thus,it is harder to achieve accurate long-term aging trajectory prediction of the LFP lithium-ion batteries on the condition of the two-stage aging process.To address it,a novel transfer learning strategy combined with the cycle life prediction technology is presented in this paper.Specifically,a new cycle life prediction method is proposed based on feature extraction and deep learning technology and achieves accurate cycle life prediction.The transfer learning is started by developing a base aging model offline to learn the information of the two-stage aging process.Then,taking the predicted cycle life as its prior information,the Bayesian model migration technology is employed to predict the aging trajectory accurately,and the uncertainty of the aging trajectory is quantified.Two batches of the battery datasets are used for performance evaluation and comparison with two benchmarks.It is novel to combine the cycle life prediction and transfer learning technique to achieve accurate two-stage aging trajectory prediction with only a few data available(first 30%).展开更多
文摘The traction battery cycle life prediction method using performance degradation data was proposed. The example battery was a commercialized lithium-ion cell with LiMn2O4/Graphite cell system. The capacity faded with cycle number follows a traction function path. Two cycle life predicting models were established. The possible cycle life was extrapolated, which follows normal distribution well. The distribution parameters were estimated and the battery reliability was evaluated. The models＇ precision was validated and the effect of the cycle number on the predicting precision was analysed. The cycle life models and reliability evaluation method resolved the difficulty of battery life appraisal, such as long period and high cost.
基金the National Natural Science Foundation of China(No.52172400).
文摘With the wide application of the LFP lithium-ion batteries,more attention is paid to the battery life and future aging behaviors as the safety and performance of the battery are guaranteed by accurate battery aging monitoring.In recent years,long-term aging trajectory prediction of the lithium-ion battery is always a challenge due to its complex nonlinear aging behaviors especially the aging behaviors in the two aging stages are quite different when the battery experiences the two-stage aging process under fast-charging conditions.Thus,it is harder to achieve accurate long-term aging trajectory prediction of the LFP lithium-ion batteries on the condition of the two-stage aging process.To address it,a novel transfer learning strategy combined with the cycle life prediction technology is presented in this paper.Specifically,a new cycle life prediction method is proposed based on feature extraction and deep learning technology and achieves accurate cycle life prediction.The transfer learning is started by developing a base aging model offline to learn the information of the two-stage aging process.Then,taking the predicted cycle life as its prior information,the Bayesian model migration technology is employed to predict the aging trajectory accurately,and the uncertainty of the aging trajectory is quantified.Two batches of the battery datasets are used for performance evaluation and comparison with two benchmarks.It is novel to combine the cycle life prediction and transfer learning technique to achieve accurate two-stage aging trajectory prediction with only a few data available(first 30%).
