SOH estimation for lithium-ion batteries: An improved GPR optimization method based on the developed feature extraction

Accurate estimation of the State of Health (SOH) for lithium-ion batteries is necessary for the stable operation of the battery system. To accurately estimate the SOH for lithium-ion batteries, we propose an SOH estimation method based on the features of the variation coefficient of partial charging curves, feature processing, and Gaussian Process Regression (GPR). Firstly, the features of the variation coefficient are extracted from the partial charging voltage and current curves as health indicators. The extracted features are efficient and practical, and can effectively reflect the aging phenomenon of batteries. Subsequently, to suppress existing noises, Box-Cox transform (BCT) and discrete wavelet packet transform (DWPT) are employed for the extracted feature signals, thus improving the correlation between the features and the SOH, and ensuring the reliability of the overall framework. Moreover, aiming at the parameters selection problem of the GPR model, an improved particle swarm optimization algorithm with mutation factor and self-adaptive weight adjustment according to population diversity is introduced. Finally, the proposed SOH estimation framework is verified on the NASA battery data set. The experimental results show that the estimation error of the proposed model can be kept within 1.5 % based on different training sample sizes. The results show that the proposed model has high estimation accuracy, generalization, and adaptability.