A State-of-Health Estimation Method for Lithium Batteries under Multi-Dimensional Features

Abstract

In recent years, the number of new energy vehicles has increased rapidly. The online state-of-health (SOH) prediction of lithium-ion batteries, which are core components of new energy vehicles, is crucial for maintaining vehicle safety. However, existing data-driven methods encounter challenges such as the difficult application of health feature extraction methods in practice, single feature dimensions, and complex algorithm models. This study extracted the peak height of the incremental capacity (IC) curve, constant-current charging time, and time when the battery surface temperature reaches its maximum value as health features from multiple dimensions. Furthermore, by randomly generating prey, the Pelican Optimization Algorithm (POA) fundamentally overcomes the shortcomings of traditional swarm intelligence optimization algorithms which are prone to falling into local optimal solutions. POA was introduced to optimize the Deep Extreme Learning Machine (DELM), which maximally simplified the algorithm model while ensuring accuracy. The experimental results demonstrate that this method does not require extensive historical data support. Whether applied to batteries under the same or different working conditions, all four battery groups exhibit excellent prediction results, with Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) values below 1.2%.

Keywords

• multi-dimensional health features
• POA-DELM
• SOH
• incremental capacity analysis