{"title":"利用回归树和改进的自适应无气味粒子滤波增强了锂离子电池充电状态和能量状态的联合估计","authors":"Junhao Xu, Li Zhang, Lijun Xu, Qing Huang, Jianming Zhu, Wenyi Yuan","doi":"10.1016/j.est.2025.118889","DOIUrl":null,"url":null,"abstract":"<div><div>Precise estimation of battery state of charge (SOC) and state of energy (SOE) is critical for enhancing the performance of electric vehicle battery management systems. This work presents a high-precision joint estimation method for SOC and SOE based on a regression tree (RT) model and an improved adaptive unscented particle filtering algorithm. Firstly, a first-order resistance-capacitance (RC) equivalent circuit model is employed, with model parameters identified online across a wide temperature range using variable forgetting factor recursive least squares method, addressing the insufficient performance of conventional offline parameter identification. Secondly, an RT-based open-circuit voltage (OCV)-SOC/SOE mapping approach is proposed, significantly reducing errors compared to traditional polynomial fitting. Finally, to overcome the particle degeneracy limitation of standard particle filters, an improved adaptive unscented particle filtering algorithm is introduced, substantially improving estimation accuracy and stability. Experimental validation under dynamic stress test and federal urban driving schedule profiles at 0 °C, 25 °C, and 45 °C demonstrates that the proposed method achieves root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) values of SOC estimation results below 0.96 %, 0.77 %, 1.61 % respectively, while those metrics corresponding to SOE estimation are less than 0.96 %, 0.84 % and 1.59 % respectively. Those results showcase the developed join estimation framework's high precision and enough robustness ability across wide-temperature range usage.</div></div>","PeriodicalId":15942,"journal":{"name":"Journal of energy storage","volume":"139 ","pages":"Article 118889"},"PeriodicalIF":8.9000,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Enhanced joint estimation of lithium-ion battery state of charge and state of energy using regression tree and improved adaptive unscented particle filtering\",\"authors\":\"Junhao Xu, Li Zhang, Lijun Xu, Qing Huang, Jianming Zhu, Wenyi Yuan\",\"doi\":\"10.1016/j.est.2025.118889\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Precise estimation of battery state of charge (SOC) and state of energy (SOE) is critical for enhancing the performance of electric vehicle battery management systems. This work presents a high-precision joint estimation method for SOC and SOE based on a regression tree (RT) model and an improved adaptive unscented particle filtering algorithm. Firstly, a first-order resistance-capacitance (RC) equivalent circuit model is employed, with model parameters identified online across a wide temperature range using variable forgetting factor recursive least squares method, addressing the insufficient performance of conventional offline parameter identification. Secondly, an RT-based open-circuit voltage (OCV)-SOC/SOE mapping approach is proposed, significantly reducing errors compared to traditional polynomial fitting. Finally, to overcome the particle degeneracy limitation of standard particle filters, an improved adaptive unscented particle filtering algorithm is introduced, substantially improving estimation accuracy and stability. Experimental validation under dynamic stress test and federal urban driving schedule profiles at 0 °C, 25 °C, and 45 °C demonstrates that the proposed method achieves root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) values of SOC estimation results below 0.96 %, 0.77 %, 1.61 % respectively, while those metrics corresponding to SOE estimation are less than 0.96 %, 0.84 % and 1.59 % respectively. Those results showcase the developed join estimation framework's high precision and enough robustness ability across wide-temperature range usage.</div></div>\",\"PeriodicalId\":15942,\"journal\":{\"name\":\"Journal of energy storage\",\"volume\":\"139 \",\"pages\":\"Article 118889\"},\"PeriodicalIF\":8.9000,\"publicationDate\":\"2025-10-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of energy storage\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2352152X25036023\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of energy storage","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352152X25036023","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
Enhanced joint estimation of lithium-ion battery state of charge and state of energy using regression tree and improved adaptive unscented particle filtering
Precise estimation of battery state of charge (SOC) and state of energy (SOE) is critical for enhancing the performance of electric vehicle battery management systems. This work presents a high-precision joint estimation method for SOC and SOE based on a regression tree (RT) model and an improved adaptive unscented particle filtering algorithm. Firstly, a first-order resistance-capacitance (RC) equivalent circuit model is employed, with model parameters identified online across a wide temperature range using variable forgetting factor recursive least squares method, addressing the insufficient performance of conventional offline parameter identification. Secondly, an RT-based open-circuit voltage (OCV)-SOC/SOE mapping approach is proposed, significantly reducing errors compared to traditional polynomial fitting. Finally, to overcome the particle degeneracy limitation of standard particle filters, an improved adaptive unscented particle filtering algorithm is introduced, substantially improving estimation accuracy and stability. Experimental validation under dynamic stress test and federal urban driving schedule profiles at 0 °C, 25 °C, and 45 °C demonstrates that the proposed method achieves root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) values of SOC estimation results below 0.96 %, 0.77 %, 1.61 % respectively, while those metrics corresponding to SOE estimation are less than 0.96 %, 0.84 % and 1.59 % respectively. Those results showcase the developed join estimation framework's high precision and enough robustness ability across wide-temperature range usage.
期刊介绍:
Journal of energy storage focusses on all aspects of energy storage, in particular systems integration, electric grid integration, modelling and analysis, novel energy storage technologies, sizing and management strategies, business models for operation of storage systems and energy storage developments worldwide.