A hybrid algorithm for the state of energy estimation of lithium-ion batteries based on improved adaptive-forgotten-factor recursive least squares and particle swarm optimized unscented particle filtering
Xianfeng Shen, Shunli Wang, Chunmei Yu, Zehao Li, Carlos Fernandez
{"title":"A hybrid algorithm for the state of energy estimation of lithium-ion batteries based on improved adaptive-forgotten-factor recursive least squares and particle swarm optimized unscented particle filtering","authors":"Xianfeng Shen, Shunli Wang, Chunmei Yu, Zehao Li, Carlos Fernandez","doi":"10.1007/s11581-024-05716-w","DOIUrl":null,"url":null,"abstract":"<p>State of energy (SOE) estimation of lithium-ion batteries is the basis of electric vehicle driving range prediction. To improve the estimation accuracy of SOE under complex dynamic working conditions, this paper takes the ternary lithium-ion battery as the research object, chooses the second-order RC-PNGV model to model the polarization reaction inside the battery, and adopts the improved adaptive forgetting factor recursive least squares (MAFFRLS) method to identify the model parameters. For battery SOE estimation, an improved unscented particle filtering algorithm for particle swarm optimization is proposed, which introduces quantum theory into particle swarm optimization to solve the sub-depletion problem of unscented particle filtering and improves the accuracy and adaptability of real-time estimation of SOE in complex environments. Experimental validation is carried out by constructing different working conditions at multiple temperatures, and the results show that the maximum error of parameter identification using recursive least squares based on improved adaptive-forgotten factor is stabilized within 2%. Under the HPPC, BBDST, and DST working conditions, the MAE and RMSE are limited to within 1% when the quantum particle swarm optimized-unscented particle filtering (QPSO-UPF) algorithm is applied to estimate the SOE estimation, which indicates that the proposed algorithm has strong tracking ability and robustness to the SOE of lithium batteries.</p>","PeriodicalId":599,"journal":{"name":"Ionics","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ionics","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1007/s11581-024-05716-w","RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
引用次数: 0
Abstract
State of energy (SOE) estimation of lithium-ion batteries is the basis of electric vehicle driving range prediction. To improve the estimation accuracy of SOE under complex dynamic working conditions, this paper takes the ternary lithium-ion battery as the research object, chooses the second-order RC-PNGV model to model the polarization reaction inside the battery, and adopts the improved adaptive forgetting factor recursive least squares (MAFFRLS) method to identify the model parameters. For battery SOE estimation, an improved unscented particle filtering algorithm for particle swarm optimization is proposed, which introduces quantum theory into particle swarm optimization to solve the sub-depletion problem of unscented particle filtering and improves the accuracy and adaptability of real-time estimation of SOE in complex environments. Experimental validation is carried out by constructing different working conditions at multiple temperatures, and the results show that the maximum error of parameter identification using recursive least squares based on improved adaptive-forgotten factor is stabilized within 2%. Under the HPPC, BBDST, and DST working conditions, the MAE and RMSE are limited to within 1% when the quantum particle swarm optimized-unscented particle filtering (QPSO-UPF) algorithm is applied to estimate the SOE estimation, which indicates that the proposed algorithm has strong tracking ability and robustness to the SOE of lithium batteries.
锂离子电池的能量状态(SOE)估算是电动汽车行驶里程预测的基础。为提高复杂动态工况下 SOE 的估计精度,本文以三元锂离子电池为研究对象,选用二阶 RC-PNGV 模型对电池内部的极化反应进行建模,并采用改进的自适应遗忘因子递归最小二乘法(MAFFRLS)确定模型参数。针对电池 SOE 估计,提出了改进的粒子群优化无香味粒子滤波算法,将量子理论引入粒子群优化,解决了无香味粒子滤波的子耗尽问题,提高了复杂环境下 SOE 实时估计的精度和适应性。通过构建多种温度下的不同工况条件进行了实验验证,结果表明基于改进的自适应遗忘因子的递归最小二乘法的参数识别最大误差稳定在 2% 以内。在 HPPC、BBDST 和 DST 工况下,采用量子粒子群优化-非cented 粒子滤波(QPSO-UPF)算法估计 SOE 估计值时,MAE 和 RMSE 均限制在 1%以内,这表明所提出的算法对锂电池的 SOE 具有较强的跟踪能力和鲁棒性。
期刊介绍:
Ionics is publishing original results in the fields of science and technology of ionic motion. This includes theoretical, experimental and practical work on electrolytes, electrode, ionic/electronic interfaces, ionic transport aspects of corrosion, galvanic cells, e.g. for thermodynamic and kinetic studies, batteries, fuel cells, sensors and electrochromics. Fast solid ionic conductors are presently providing new opportunities in view of several advantages, in addition to conventional liquid electrolytes.