Lithium-Ion Battery Health Estimation with Incomplete Charging Data and Spiking ResNet

The accurate estimation of state of health (SOH) plays a crucial role in ensuring the safe operation of batteries. Most existing SOH estimation methods are based on complete charging or discharging data. However, in real-world applications, data are often derived from partial charging or discharging processes influenced by varying user habits, which brings great challenges to the accurate SOH estimation of batteries. In this article, a SOH estimation method based on incremental capacity (IC) curve reconstruction and spiking ResNet is proposed to deal with the SOH estimation issue using partial data. The monitoring data are preprocessed using differentiation and Gaussian filtering to obtain the IC curves. A nonparametric degradation model is constructed using functional principal component analysis (FPCA). Subsequently, the functional principal component (FPC) scores are estimated by the maximum likelihood estimation (PAMLE) algorithm to reconstruct the IC curves. Ultimately, the SOH can be estimated through the spiking ResNet. The proposed method is evaluated using the XJTU battery experimental data. The results demonstrate that the proposed method provides more accurate and reliable estimation results than other methods.

I. Introduction

Lithium-ion batteries (LIBs) have been widely applied in fields such as electric vehicles (EVs), portable electronic devices and energy storage systems because of their advantages of long lifespan, low self-discharge, high energy density, and high output voltage [1], [2]. Due to various kinds of physical and chemical mechanisms, the performance of LIBs gradually deteriorates with increasing charge and discharge cycles [3]. If the battery state of health (SOH) cannot be monitored in real-time, it may reduce the performance of devices or even cause catastrophic events, such as explosions and fires [4]. Moreover, accurate SOH estimation is crucial for state of charge (SOC) estimation and fault diagnosis of battery systems [5]. To ensure the safe and efficient operation of LIBs, as well as to schedule timely maintenance plans, it is vitally important to give an accurate estimation of SOH.