Multi-observation Fusion Method for Predicting the Remaining Useful Life of Lithium-ion Batteries

A major challenge in ensuring the reliability of battery systems is the uncertainty surrounding their service life. An accurate prediction of the remaining useful life (RUL) is essential for effective maintenance and operation. Traditional extended Kalman filter (EKF) algorithms, which rely heavily on historical data, often have limited long-term prediction accuracy. To overcome this problem, a multi-observation fusion approach is proposed to enhance the performance of the EKF for battery life prediction. To improve the practical applicability, the conventional aging capacity test values with operational capacity data obtained under real-world conditions are replaced. This modification refined the trajectory of the battery aging state space, thereby reducing the dependency on the quantity and quality of historical data while simultaneously boosting the long-term prediction accuracy and stability. Furthermore, a semi-empirical aging model is introduced to extract prior knowledge from offline data. This provided valuable insights into the life degradation trends and guided the filtering process. The resulting framework forms the basis for a novel RUL prediction method that utilizes a multi-observation fusion EKF. Validation experiments show that the proposed method enhanced the prediction accuracy by over 60% compared with traditional EKF and particle filter algorithms throughout the lifecycle of lithium-ion batteries. Additionally, the technique exhibited robust stability (with no divergence observed over the full battery life) and demonstrated notable improvements in early stage RUL prediction.