Prediction of State of Health of Lithium-Ion Battery Using Health Index Informed Attention Model

Abstract

State-of-health (SOH) is a measure of a battery’s capacity in comparison to its rated capacity. Despite numerous data-driven algorithms being developed to estimate battery SOH, they are often ineffective in handling time series data, as they are unable to utilize the most significant portion of a time series while predicting SOH. Furthermore, current data-driven algorithms are often unable to learn a health index, which is a measurement of the battery’s health condition, to capture capacity degradation and regeneration. To address these issues, we first present an optimization model to obtain a health index of a battery, which accurately captures the battery’s degradation trajectory and improves SOH prediction accuracy. Additionally, we introduce an attention-based deep learning algorithm, where an attention matrix, referring to the significance level of a time series, is developed to enable the predictive model to use the most significant portion of a time series for SOH prediction. Our numerical results demonstrate that the presented algorithm provides an effective health index and can precisely predict the SOH of a battery.

Recent Progress in Lithium-Ion Battery Safety Monitoring Based on Fiber Bragg Grating Sensors

“…The BMS requires an accurate estimation of the SOC and SOH to ensure the safety, life, and performance of the batteries [ 32 , 33 , 34 ]. In conventional methods, voltage, current, and surface temperature are employed as input parameters to estimate SOC and SOH [ 35 , 36 , 37 ].…”

A Prediction Framework for State of Health of Lithium-Ion Batteries Based on Improved Support Vector Regression

“…The data-driven method does not need to understand the chemical principle of battery or build a detailed battery model, mainly including Support vector regression machines (SVR), [17][18][19] Gaussian process regression [20][21][22] and machine learning. 23,24 Yang et al 25 proposed a particle swarm optimization-least square SVR approach to estimate SOH, and it had high accuracy and good generalization ability. Wu et al 26 proposed the joint algorithm of Bat Algorithms (BA) and SVR to predict battery SOH, and the BA was used to find the optimal SVR parameters.…”