| 45 | 0 | 26 |
| 下载次数 | 被引频次 | 阅读次数 |
为解决锂电池复杂动态工况下模型参数时变、端电压噪声干扰及磷酸铁锂电池OCV-SOC平台区弱可观测问题，该文提出一种定向遗忘RLS与物理互锁MI-AEKF相结合的参数-SOC协同估计方法。该方法以280 Ah磷酸铁锂电池为对象，采样周期为1 s，建立二阶RC等效电路模型，采用15点滑动中值滤波处理电压、电流数据；在参数层引入激励丰富度判据和定向遗忘机制，在线辨识欧姆内阻与极化参数；在状态层构建多新息自适应扩展卡尔曼滤波，并通过内阻-观测噪声互锁和OCV斜率-SOC增益互锁约束SOC修正。UDDS和DST工况验证结果表明，该方法能够实现端电压动态拟合和SOC稳定跟踪；在UDDS工况下，SOC估计RMSE为0.107%，MAE为0.09%，低于FFRLS-AEKF的0.3285%和0.2794%。结果表明，该方法可提高动态工况下锂电池参数辨识与SOC估计精度。
Abstract：To address time-varying model parameters， terminal voltage noise， and weak observability in the OCV-SOC plateau region of lithium iron phosphate batteries under complex dynamic operating conditions， this paper proposes a parameter-SOC collaborative estimation method combining directional-forgetting RLS and physically interlocked MI-AEKF. Taking a 280 Ah lithium iron phosphate battery as the research object with a sampling interval of 1 s， a second-order RC equivalent circuit model is established， and a 15-point sliding median filter is used to preprocess voltage and current data. At the parameter layer， an excitation richness criterion and directional-forgetting mechanism are introduced to identify ohmic resistance and polarization parameters online. At the state layer， a multi-innovation adaptive extended Kalman filter is constructed， and SOC correction is constrained through resistance-observation noise interlocking and OCV slope-SOC gain interlocking. Verification under UDDS and DST conditions shows that the proposed method achieves dynamic terminal voltage fitting and stable SOC tracking. Under the UDDS condition， the SOC estimation RMSE and MAE are 0.107% and 0.09%， respectively， lower than 0.3285% and 0.2794% obtained by FFRLS-AEKF. The results indicate that the proposed method can improve the accuracy of lithium battery parameter identification and SOC estimation under dynamic operating conditions.
[1] DEMIRCI O，TASKIN S，SCHALTZ E，et al．Review of battery state estimation methods for electric vehicles - Part I：SOC estimation[J]．Journal of Energy Storage，2024，87：111435．
[2] FATHONI G，WIDAYAT S A，ALI TOPAN P，et al．Comparison of State-of-Charge (SOC) estimation performance based on three popular methods：Coulomb counting，open circuit voltage，and Kalman filter[C]//2017 2nd International Conference on Automation，Cognitive Science，Optics，Micro Electro-Mechanical System，and Information Technology (ICACOMIT)．October 23-24，2017，Jakarta，Indonesia．IEEE，2017：70-74．
[3] RIVERA-BARRERA J，MUÑOZ-GALEANO N，SARMIENTO-MALDONADO H．SoC estimation for lithium-ion batteries：Review and future challenges[J]．Electronics，2017，6(4)：102．
[4] NG M F，ZHAO J，YAN Q Y，et al．Predicting the state of charge and health of batteries using data-driven machine learning[J]．Nature Machine Intelligence，2020，2(3)：161-170．
[5] İPEK E，YILMAZ M．A novel method for SOC estimation of Li-ion batteries using a hybrid machine learning technique[J]．Turkish Journal of Electrical Engineering & Computer Sciences，2021，29(1)：18-31．
[6] WANG H Y，WEI W，CUI L F，et al．State of charge prediction for lithium-ion batteries based on multi-process scale encoding and adaptive graph convolution[J]．Journal of Energy Storage，2025，113：115482．
[7] CHEN L，WANG Z Z，LÜ Z Q，et al．A novel state-of-charge estimation method of lithium-ion batteries combining the grey model and genetic algorithms[J]．IEEE Transactions on Power Electronics，2018，33(10)：8797-8807．
[8] HOSSAIN LIPU M S，HANNAN M A，HUSSAIN A，et al．Optimal BP neural network algorithm for state of charge estimation of lithium-ion battery using PSO with PCA feature selection[J]．Journal of Renewable and Sustainable Energy，2017，9(6)：064102．
[9] TIAN J P，XIONG R，SHEN W X，et al．State-of-charge estimation of LiFePO_(4) batteries in electric vehicles：A deep-learning enabled approach[J]．Applied Energy，2021，291：116812．
[10] HOW D N T，HANNAN M A，LIPU M S H，et al．State-of-charge estimation of Li-ion battery in electric vehicles：A deep neural network approach[J]．IEEE Transactions on Industry Applications，2020，56(5)：5565-5574．
[11] LI I H，WANG W Y，SU S F，et al．A merged fuzzy neural network and its applications in battery state-of-charge estimation[J]．IEEE Transactions on Energy Conversion，2007，22(3)：697-708．
[12] HU J N，HU J J，LIN H B，et al．State-of-charge estimation for battery management system using optimized support vector machine for regression[J]．Journal of Power Sources，2014，269：682-693．
[13] PLETT G L．Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 3．State and parameter estimation[J]．Journal of Power Sources，2004，134(2)：277-292．
[14] HE W，WILLIARD N，CHEN C C，et al．State of charge estimation for electric vehicle batteries using unscented Kalman filtering[J]．Microelectronics Reliability，2013，53(6)：840-847．
[15] YANG A，WANG Y，YANG F F，et al．A comprehensive investigation of lithium-ion battery degradation performance at different discharge rates[J]．Journal of Power Sources，2019，443：227108．
[16] TABORELLI C，ONORI S．State of charge estimation using extended Kalman filters for battery management system[C]//2014 IEEE International Electric Vehicle Conference (IEVC)．December 17-19，2014，Florence，Italy．IEEE，2014：1-8．
[17] LIU G A，XU C，JIANG K，et al．State of charge and model parameters estimation of liquid metal batteries based on adaptive unscented Kalman filter[J]．Energy Procedia，2019，158：4477-4482．
[18] PENG S M，ZHU X L，XING Y J，et al．An adaptive state of charge estimation approach for lithium-ion series-connected battery system[J]．Journal of Power Sources，2018，392：48-59．
[19] WANG Y J，ZHANG C B，CHEN Z H．A method for state-of-charge estimation of LiFePO_(4) batteries at dynamic currents and temperatures using particle filter[J]．Journal of Power Sources，2015，279：306-311．
[20] WANG Y J，CHEN Z H．A framework for state-of-charge and remaining discharge time prediction using unscented particle filter[J]．Applied Energy，2020，260：114324．
[21] HU X S，SUN F C，ZOU Y．Estimation of state of charge of a lithium-ion battery pack for electric vehicles using an adaptive luenberger observer[J]．Energies，2010，3(9)：1586-1603．
[22] KIM D，KOO K，JEONG J，et al．Second-order discrete-time sliding mode observer for state of charge determination based on a dynamic resistance Li-ion battery model[J]．Energies，2013，6(10)：5538-5551．
[23] XU J，MI C C，CAO B G，et al．The state of charge estimation of lithium-ion batteries based on a proportional-integral observer[J]．IEEE Transactions on Vehicular Technology，2014，63(4)：1614-1621．
基本信息:
中图分类号:TM912;TN713
引用信息:
[1]蔡明宪,刘思华,甄久国,等.基于遗忘RLS与MI-AEKF的锂离子电池SOC估计方法[J].电池工业().
基金信息:
山东省科技厅，重点研发计划（重大科技创新工程）驻鲁部属高效“十四五”服务山东重点建设项目，（2024ZLGX04，高压大功率电池智能测试与模拟装备研发及产业化）
2026-09-28
2026-09-28
2026-09-28