An On-Line State of Health Estimation of Lithium-Ion Battery Using Unscented Particle Filter

Abstract

As one of the key functions in lithium-ion battery management system, the state-of-health (SOH) estimation is of great significance to ensure the safe and reliable operation and reduce the maintenance cost of the battery energy storage system. Unscented particle filter (UPF) algorithm is becoming a promising method for battery state estimation since it combines the latest measurement information to give the proposal distribution which is closer to the true posterior distribution. At the same time, UPF algorithm is able to represent the uncertainty involved in the estimation results, which makes great significance for battery SOH estimation. On the other hand, it is difficult to measure the battery actual capacity in practice despite the capacity is a direct indicator of battery SOH. In this paper, an on-line health indicator (HI) is extracted from the measurable parameters while battery is working. The mapping model between the extracted HI and battery SOH is established and applied as the observation in the state-space model. An on-line estimator based on UPF algorithm is developed for battery SOH assessment. The maximum estimation error based on battery cycling test data is less than 5%. This indicates that the proposed method has a good adaptability for lithium-ion battery degradation with non-linear and non-Gaussian characteristics. Additionally, the experiments on different types of lithium-ion battery show the good robustness and applicability of this approach.