A neural-driven stochastic degradation model for state-of-health estimation of lithium-ion battery

Abstract

Due to usage for a long time, lithium-ion battery degradation issues may endanger security operations for service equipment. As batteries usually operate under uncertain usage profiles and experience random aging processes, accurately estimating the battery state of health (SOH) can be challenging. Most existing SOH estimation methods are either empirical knowledge-based with complicated parameters or serve as a black box neural network-based without stochastic nature. To address this limitation, a neural-driven stochastic degradation model for battery health estimation is proposed to ensure the security and reliability of the operation. Firstly, the parameters of a stochastic process are estimated by martingale methodology to describe battery degradation characteristics. Then the optimized sequence-to-sequence (Seq2Seq) neural network is utilized to develop a mapping model in order to update drift coefficient online. Concurrently, the martingale estimating function is used to obtain the diffusion coefficient function. Finally, the transition probability density function of SOH estimation is derived. Comparative experiments are carried out on different battery datasets to show the effectiveness of the proposed model against traditional Wiener process-based models and existing methods in SOH estimation. The model proposed provides an effective solution for battery SOH estimation of energy storage equipment.