Long-range battery state-of-health and end-of-life prediction with neural networks and feature engineering

Abstract

Determining the state of health (SOH) and end of life (EOL) represents a critical challenge in battery management. This study introduces an innovative neural network-based methodology that forecasts both the SOH and EOL, utilizing features engineered from charge-discharge voltage profiles. Specifically, long-short-term memory (LSTM) and gated-recurrent unit (GRU) neural networks are trained against fast-charging datasets with novel loss function that emphasizes SOH regression while penalizing its decay. The devised models yield low average errors in SOH and EOL predictions (5.49% and − 1.27%, respectively, for LSTM), over extended horizons encompassing 80% of the forecast battery lifespan. From a combined evaluation using Pearson's correlation and saliency analysis, it is found that voltages most strongly associated with aging occur after the initial constant current rate step. In short, this study offers a new perspective on the precise prediction of SOH and EOL by integrating feature engineering with neural networks.