Accurate prediction of the remaining useful life(RUL)of lithium-ion batteries(LIBs)is pivotal for enhancing their operational efficiency and safety in diverse applications.Beyond operational advantages,precise RUL pre...Accurate prediction of the remaining useful life(RUL)of lithium-ion batteries(LIBs)is pivotal for enhancing their operational efficiency and safety in diverse applications.Beyond operational advantages,precise RUL predictions can also expedite advancements in cell design and fast-charging methodologies,thereby reducing cycle testing durations.Despite artificial neural networks(ANNs)showing promise in this domain,determining the best-fit architecture across varied datasets and optimization approaches remains challenging.This study introduces a machine learning framework for systematically evaluating multiple ANN architectures.Using only 30%of a training dataset derived from 124 LIBs subjected to various charging regimes,an extensive evaluation is conducted across 7 ANN architectures.Each architecture is optimized in terms of hyperparameters using this framework,a process that spans 145 days on an NVIDIA GeForce RTX 4090 GPU.By optimizing each model to its best configuration,a fair and standardized basis for comparing their RUL predictions is established.The research also examines the impact of different cycling windows on predictive accuracy.Using a stratified partitioning technique underscores the significance of consistent dataset representation across subsets.Significantly,using only the features derived from individual charge–discharge cycles,our top-performing model,based on data from just 40 cycles,achieves a mean absolute percentage error of 10.7%.展开更多
基金supported by First Automotive Work Group Research Institute and Jilin Province,China,under the key scientific and technological program grant number 20210301027GX.
文摘Accurate prediction of the remaining useful life(RUL)of lithium-ion batteries(LIBs)is pivotal for enhancing their operational efficiency and safety in diverse applications.Beyond operational advantages,precise RUL predictions can also expedite advancements in cell design and fast-charging methodologies,thereby reducing cycle testing durations.Despite artificial neural networks(ANNs)showing promise in this domain,determining the best-fit architecture across varied datasets and optimization approaches remains challenging.This study introduces a machine learning framework for systematically evaluating multiple ANN architectures.Using only 30%of a training dataset derived from 124 LIBs subjected to various charging regimes,an extensive evaluation is conducted across 7 ANN architectures.Each architecture is optimized in terms of hyperparameters using this framework,a process that spans 145 days on an NVIDIA GeForce RTX 4090 GPU.By optimizing each model to its best configuration,a fair and standardized basis for comparing their RUL predictions is established.The research also examines the impact of different cycling windows on predictive accuracy.Using a stratified partitioning technique underscores the significance of consistent dataset representation across subsets.Significantly,using only the features derived from individual charge–discharge cycles,our top-performing model,based on data from just 40 cycles,achieves a mean absolute percentage error of 10.7%.
