1. 国网四川省电力公司自贡供电公司
2. 重庆邮电大学自动化学院
纸质出版:2025
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罗飞, 饶俊星, 王江林, 等. 基于FFRLS-UKF的储能电池荷电状态在线评估[J]. 四川电力技术, 2025,48(1):47-56.
罗飞, 饶俊星, 王江林, et al. 基于FFRLS-UKF的储能电池荷电状态在线评估[J]. 2025, 48(1): 47-56.
罗飞, 饶俊星, 王江林, 等. 基于FFRLS-UKF的储能电池荷电状态在线评估[J]. 四川电力技术, 2025,48(1):47-56. DOI: 10.16527/j.issn.1003-6954.20250107.
罗飞, 饶俊星, 王江林, et al. 基于FFRLS-UKF的储能电池荷电状态在线评估[J]. 2025, 48(1): 47-56. DOI: 10.16527/j.issn.1003-6954.20250107.
对储能锂离子电池荷电状态(state of charge
SOC)的准确估计,能保障储能锂电池系统安全稳定运行。针对储能锂电池等效电路模型参数不准确以及储能复杂环境噪声不确定的问题,基于戴维南等效电路模型,在实际测得的间隔恒流放电数据基础上利用带遗忘因子的递推最小二乘法进行等效电路参数辨识,将辨识后参数计算得到的电压与试验电压进行比较分析,验证所辨识模型的优劣并获取模型最优参数,在此基础上,提出一种基于无迹卡尔曼滤波算法对电池SOC进行准确估计。将所提算法与扩展卡尔曼滤波算法和安培小时积分法进行比较,并分析不同初始SOC对估计结果的影响。结果表明,所提方法在不同初始SOC条件下均可快速收敛并准确估计SOC
在初始SOC与模型参数无误差的条件下,估计精度高达99.2%。
梅简,张杰,刘双宇,裘吕超.电池储能技术发展现状[J].浙江电力,2020(03).
徐谦,孙轶恺,刘亮东,章坚民,张利军,朱国荣.储能电站功能及典型应用场景分析[J].浙江电力,2019(05).
袁小明,程时杰,文劲宇.储能技术在解决大规模风电并网问题中的应用前景分析[J].电力系统自动化,2013(01).
Zhao Chunyang,Andersen Peter Bach,Træholt Chresten,Hashemi Seyedmostafa.Grid-connected battery energy storage system: a review on application and integration[J].Renewable and Sustainable Energy Reviews,2023.
Ul Hassan Masood,Saha Sajeeb,Haque Md. Enamul,Islam Shama,Mahmud Apel,Mendis Nishad.A comprehensive review of battery state of charge estimation techniques[J].Sustainable Energy Technologies and Assessments,2022.
Lian Gaoqi,Ye Min,Wang Qiao,Wei Meng,Ma Yuchuan.A BP neural network-Ant Lion Optimizer and UKF method for SOC estimation of lithium-ion batteries[J].Journal of Physics: Conference Series,2022.
Li Qian,Miao Shuguo,Liu Shengzhong,Ma Boyang,Qi Wenyan,Jin Weijun,Cheng Ze.A Joint State Estimation Framework for Lithium-ion Batteries based on Hybrid Method[J].Journal of Physics: Conference Series,2022.
Wang Zheng,Xiao Yanli,Wan Ye,Liu Ke,Wang Xiyuan.Research on energy management strategy of photovoltaic–battery energy storage system[J].International Journal of Low-Carbon Technologies,2022.
Ding Shuya,Dong Chaoyu,Zhao Tianyang,Koh Liangmong,Bai Xiaoyin,Luo Jun.A Meta-learning Based Multimodal Neural Network for Multistep Ahead Battery Thermal Runaway Forecasting[J].IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,2021.
Huang Zhengjun,Fang Yongshou,Xu Jianjun.SOC Estimation of Li-ION Battery Based on Improved EKF Algorithm[J].International Journal of Automotive Technology,2021.
Chong Bian,Huoliang He,Shunkun Yang.Stacked bidirectional long short-term memory networks for state-of-charge estimation of lithium-ion batteries[J].Energy,2020.
Meng Jinhao,Stroe Daniel Ioan,Ricco Mattia,Luo Guangzhao,Swierczynski Maciej,Teodorescu Remus.A Novel Multiple Correction Approach for Fast Open Circuit Voltage Prediction of Lithium-Ion Battery[J].IEEE Transactions on Energy Conversion,2019.
Meng Jinhao,Ricco Mattia,Luo Guangzhao,Swierczynski Maciej,Stroe Daniel Ioan,Stroe Ana Irina,Teodorescu Remus.An Overview and Comparison of Online Implementable SOC Estimation Methods for Lithium-Ion Battery[J].IEEE Transactions on Industry Applications,2018.
Huang Chao,Wang Zhenhua,Zhao Zihan,Wang Long,Lai Chun Sing,Wang Dong.Robustness Evaluation of Extended and Unscented Kalman Filter for Battery State of Charge Estimation[J].IEEE Access,2018.
Junzhao Zhou, Xiangwen Zhang.Power battery SOC estimation with combination method based on UKF and open circuit voltage[J].Int. J. of Electric and Hybrid Vehicles,2014.
Xiaosong Hu,Fengchun Sun,Yuan Zou.Comparison between two model-based algorithms for Li-ion battery SOC estimation in electric vehicles[J].Simulation Modelling Practice and Theory,2013.
Alvarez Anton, J.C.,Garcia Nieto, P.J.,Blanco Viejo, C.,Vilan Vilan, J.A..Support Vector Machines Used to Estimate the Battery State of Charge[J].IEEE Transactions on Power Electronics,2013.
Mohammad Charkhgard,Mohammad Farrokhi.State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF.[J].IEEE Trans. Industrial Electronics,2010.
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