Estimating the State of Health for Lithium-ion Batteries:A Particle Swarm Optimization-Assisted Deep Domain Adaptation Approach

The state of health(SOH)is a critical factor in evaluating the performance of the lithium-ion batteries(LIBs).Due to various end-user behaviors,the LIBs exhibit different degradation modes,which makes it challenging to estimate the SOHs in a personalized way.In this article,we present a novel particle swarm optimization-assisted deep domain adaptation(PSO-DDA)method to estimate the SOH of LIBs in a personalized manner,where a new domain adaptation strategy is put forward to reduce cross-domain distribution discrepancy.The standard PSO algorithm is exploited to automatically adjust the chosen hyperparameters of developed DDA-based method.The proposed PSODDA method is validated by extensive experiments on two LIB datasets with different battery chemistry materials,ambient temperatures and charge-discharge configurations.Experimental results indicate that the proposed PSO-DDA method surpasses the convolutional neural network-based method and the standard DDA-based method.The Py Torch implementation of the proposed PSO-DDA method is available at https://github.com/mxt0607/PSO-DDA.

State-of-Health Map of Lithium-ion Batteries Established by ICE Map Approach

State-of-health(SOH) is one of the main factors for lithium-ion batteries that indicate their life information. Thus it is essential to estimate SOH accurately during the operation of lithium-ion batteries. In this paper, an SOH map is proposed to illustrate the SOH of lithium-ion batteries by an internal combustion engine(ICE) map approach. Both direct current internal resistance(DCR) and open circuit voltage(OCV) are key parameters of lithium-ion batteries, which are obtained through metering and computing. Due to serious affection by environmental temperature, temperature translation is proposed to translate DCR/OCV of different temperature into a nominal value at 25 ℃. Compared with ICE map, SOH map is illustrated by the nominal DCR and OCV, which can be looked up to get a nominal SOH. In the SOH map, a pair of the DCR and the OCV can only map out a unique SOH, which is beneficial for application in engineering practice in most cases.

State-of-health estimation of lithium-ion batteries based on electrochemical impedance spectroscopy: a review

Lithium-ion batteries(LIBs)are crucial for the large-scale utilization of clean energy.However,because of the com-plexity and real-time nature of internal reactions,the mechanism of capacity decline in LIBs is still unclear.This has become a bottleneck restricting their promotion and application.Electrochemical impedance spectroscopy(EIS)contains rich electrochemical connotations and significant application prospects,and has attracted widespread atten-tion and research on efficient energy storage systems.Compared to traditional voltage and current data,the state-of-health(SOH)estimation model based on EIS has higher accuracy.This paper categorizes EIS measurement methods based on different principles,introduces the relationship between LIBs aging mechanism and SOH,and compares the advantages of different SOH estimation methods.After a detailed analysis of the latest technologies,a review is given.The insights of this review can deepen the understanding of the relationship between EIS and the aging effect mechanism of LIBs,and promote the development of new energy storage devices and evaluation methods.

SOH Estimation of Lithium Batteries Based on ICA andWOA-RBF Algorithm

Accurately estimating the State of Health(SOH)of batteries is of great significance for the stable operation and safety of lithiumbatteries.This article proposes amethod based on the combination of Capacity Incremental Curve Analysis(ICA)andWhale Optimization Algorithm-Radial Basis Function(WOA-RBF)neural network algorithm to address the issues of low accuracy and slow convergence speed in estimating State of Health of batteries.Firstly,preprocess the battery data to obtain the real battery SOH curve and Capacity-Voltage(Q-V)curve,convert the Q-V curve into an IC curve and denoise it,analyze the parameters in the IC curve that may serve as health features;Then,extract the constant current charging time of the battery and the horizontal and vertical coordinates of the two IC peaks as health features,and perform correlation analysis using Pearson correlation coefficient method;Finally,theWOA-RBF algorithmwas used to estimate the battery SOH,and the training results of LSTM,RBF,and PSO-RBF algorithms were compared.The conclusion was drawn that theWOA-RBF algorithm has high accuracy,fast convergence speed,and the best linearity in estimating SOH.The absolute error of its SOHestimation can be controlled within 1%,and the relative error can be controlled within 2%.