Nonlinear autoregressive models for high accuracy early prediction of Li-ion battery end-of-life

Predictions of the state-of-health (SOH) of Li-ion batteries is an important goal in the monitoring and management of electric vehicles. In recent years, a number of pure machine-learning methods have been proposed for such predictions. In this paper, we instead consider autoregression methods and embedding strategies, which are specifically tailored to time-series problems. For the first time, we comprehensively compare both linear and nonlinear approaches, including six deep learning architectures, autoregressive integrated moving average (ARIMA) models and seasonal ARIMA models. In particular, for the first time we introduce Gaussian process nonlinear autoregression (GPNAR) for SOH prediction and show that it is superior in terms of accuracy and computational costs to the other autoregressive approaches. On the basis of two different datasets, we also demonstrate that accurate early predictions of the end-of-life (based on 50% of the data) is achievable with GPNAR without the use of features, which keeps data acquisition and processing to a minimum. Finally, we show that GPNAR is capable of capturing seasonal trends such as regeneration without additional time-consuming data analyses. Comparisons to other state-of-the-art methods in the recent literature confirm the superior performance of GPNAR.

Automated summary

This paper proposes a method for predicting the state-of-health (SOH) of Li-ion batteries based on autoregression models and embedding strategies specifically tailored to time-series problems. By comparing linear and nonlinear approaches, including six deep learning architectures and ARIMA models, the study demonstrates the superiority of Gaussian process nonlinear autoregression (GPNAR) in terms of accuracy and computational costs. Additionally, GPNAR is capable of accurately predicting the end-of-life of batteries without the use of features and capturing seasonal trends.