风光互补式电动汽车充电站储能锂电池的实时状态估计

戈斌; 罗阳; 李家玮; 李丽霞; 王斌; 严亦哲

doi:10.7652/xjtuxb202301006

您当前的位置：

首页 >

文章列表页 >

风光互补式电动汽车充电站储能锂电池的实时状态估计

专题新能源与储能 | 更新时间：2025-07-09

- 风光互补式电动汽车充电站储能锂电池的实时状态估计
- Real-Time State Estimation of Lithium Batteries Used for Energy Storage in Electric Vehicle Charging Stations with Wind-Solar Complementary Power System
- 西安交通大学学报 2023年57卷第1期页码：55-65
- 作者机构：
  
  1. 国网冀北电力有限公司张家口市崇礼区供电分公司,河北,张家口,076350
  2. 西安建筑科技大学机电工程学院,西安,710055
  3. 西安交通大学机械工程学院,西安,710049
- 作者简介：
  
  : 2022-07-24。作者简介: 戈斌(1983—),男,工程师
  王斌(通信作者),男,副研究员,硕士生导师。基金项目: 国家电网有限公司科技资助项目(SGJBZJ00FZJS2250089)
- 基金信息：
  
  : 2022-07-24。作者简介: 戈斌(1983—),男,工程师;王斌(通信作者),男,副研究员,硕士生导师。基金项目: 国家电网有限公司科技资助项目(SGJBZJ00FZJS2250089);国家自然科学基金资助项目(51907160)。
- DOI：10.7652/xjtuxb202301006
  中图分类号： TM912
- 网络首发：2023-01-10，
  
  纸质出版：2023
- 稿件说明：
移动端阅览
戈斌, 罗阳, 李家玮, 等. 风光互补式电动汽车充电站储能锂电池的实时状态估计[J]. 西安交通大学学报, 2023,57(1):55-65.

GE Bin, LUO Yang, LI Jiawei, et al. Real-Time State Estimation of Lithium Batteries Used for Energy Storage in Electric Vehicle Charging Stations with Wind-Solar Complementary Power System[J]. 2023, 57(1): 55-65.
戈斌, 罗阳, 李家玮, 等. 风光互补式电动汽车充电站储能锂电池的实时状态估计[J]. 西安交通大学学报, 2023,57(1):55-65. DOI： 10.7652/xjtuxb202301006.

GE Bin, LUO Yang, LI Jiawei, et al. Real-Time State Estimation of Lithium Batteries Used for Energy Storage in Electric Vehicle Charging Stations with Wind-Solar Complementary Power System[J]. 2023, 57(1): 55-65. DOI： 10.7652/xjtuxb202301006.

摘要

为了解决风光互补式电动汽车充电站储能系统中

传统静态锂电池模型不能实时更新参数导致相应的开路电压和荷电状态(state of charge

SOC)估计误差大等问题

提出一种基于动态一阶RC等效电路模型的锂电池自适应实时状态估计方法。首先

采用滑模控制方法追踪锂电池的实时输出电压

基于动态一阶RC等效电路模型

考虑锂电池内部参数欧姆内阻、极化内阻、极化电容和开路电压的动态变化情况

修正锂电池的端电压状态估计方程; 然后

通过李雅普诺夫函数和稳定性判据推导出状态估计方程参数与实时电压追踪误差、工作电流之间的关系

得出锂电池内部参数的实时更新方法; 进一步

通过实验确定开路电压与锂电池SOC之间的函数关系; 在此基础上

实现锂电池状态的自适应实时估计。仿真结果表明:在风光互补式电动汽车充电站储能系统的连续变化负载工况下

所提自适应实时状态估计方法可以使锂电池估计状态快速收敛至模型参考值

避免了开路电压估计值波动问题; 以安时积分和卡尔曼滤波方法修正的SOC为参考

自适应实时估计SOC的最大误差为0.72%

均方根误差和平均绝对误差分别为0.002 3和0.001 9; 与开路电压-内阻模型估计SOC进行比较

自适应实时估计SOC的精度提高了一个数量级。

Abstract

As for the application of an energy storage system in an electric vehicle charging station with a wind-solar complementary power system

this paper proposes an adaptive real-time state estimation method for the lithium battery based on a dynamic first-order RC equivalent circuit model

so as to solve the problem of big error in estimating the corresponding open-circuit voltage and the state of charge(SOC)due to the incapability of the traditional static lithium battery model to update the parameter states in real-time. First

the sliding mode control method is used for tracking the real-time output voltage of the lithium battery and based on the dynamic first-order RC equivalent circuit model

the terminal voltage state estimation equation of the lithium battery is modified by considering the dynamic variation of such internal parameters as ohmic resistance

polarized resistance

polarized capacitance and open circuit voltage. Then

the relationship among the state estimation equation parameters

the real-time voltage error

and the operating current is derived based on the Lyapunov function and stability criterion

and the real-time update method for the internal parameters of the lithium battery is obtained. Next

the functional relationship between the open-circuit voltage and SOC of the lithium battery is established based on the experimental results. On this basis

the adaptive real-time state estimation of the lithium battery is realized. Simulation results show that the adaptive real-time state estimation method can make the estimated states of the lithium battery converge to the model reference values quickly under the continuous load conditions of the energy storage system in an electric vehicle charging station with a wind-solar complementary power system. At the same time

the adaptive real-time state estimation method can effectively avoid the fluctuation of the estimated open-circuit voltage. By using the corrected SOC based on ampere-hour integration and Kalman filter methods as a reference

the maximum error of the adaptive real-time SOC estimation is 0.72%

and the root mean square error and the average absolute error are 0.0023 and 0.0019

respectively. Compared with the estimated SOC based on the open circuit voltage-internal resistance model

the estimated SOC accuracy based on the adaptive real-time estimation is improved by an order of magnitude.

关键词

Keywords

references

刘永奇, 陈龙翔, 韩小琪. 能源转型下我国新能源替代的关键问题分析 [J]. 中国电机工程学报, 2022, 42(2): 515-523, 中插6.

LIU Yongqi, CHEN Longxiang, HAN Xiaoqi. The key problem analysis on the alternative new energy under the energy transition [J]. Proceedings of the CSEE, 2022, 42(2): 515-523, 中插6.

刘柏良, 黄学良, 李军, 等. 含分布式电源及电动汽车充电站的配电网多目标规划研究 [J]. 电网技术, 2015, 39(2): 450-456.

LIU Bailiang, HUANG Xueliang, LI Jun, et al. Multi-objective planning of distribution network containing distributed generation and electric vehicle charging stations [J]. Power System Technology, 2015, 39(2): 450-456.

LAWDER M T, SUTHAR B, NORTHROP P W C, et al. Battery energy storage system(BESS)and battery management system(BMS)for Grid-Scale applications [J]. Proceedings of the IEEE, 2014, 102(6): 1014-1030.

MÜLLER M, VIERNSTEIN L, TRUONG C N, et al. Evaluation of grid-level adaptability for stationary battery energy storage system applications in Europe [J]. Journal of Energy Storage, 2017, 9: 1-11.

刘敦楠, 王玲湘, 汪伟业, 等. 基于深度强化学习的大规模电动汽车充换电负荷优化调度 [J]. 电力系统自动化, 2022, 46(4): 36-46.

LIU Dunnan, WANG Lingxiang, WANG Weiye, et al. Optimal scheduling of electric vehicle load for large-scale battery charging and swapping based on deep reinforcement learning [J]. Automation of Electric Power Systems, 2022, 46(4): 36-46.

SHRIVASTAVA P, SOON T K, IDRIS M Y I B, et al. Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries [J]. Renewable and Sustainable Energy Reviews, 2019, 113: 109233.

WANG Yujie, TIAN Jiaqiang, SUN Zhendong, et al. A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems [J]. Renewable and Sustainable Energy Reviews, 2020, 131: 110015.

BIAN Xiaolei, WEI Zhongbao, HE Jiangtao, et al. A two-step parameter optimization method for low-order model-based state-of-charge estimation [J]. IEEE Transactions on Transportation Electrification, 2021, 7(2): 399-409.

HE Hongwen, XIONG Rui, FAN Jinxin. Evaluation of lithium-ion battery equivalent circuit models for state of charge estimation by an experimental approach [J]. Energies, 2011, 4(4): 582-598.

朱瑞, 段彬, 温法政, 等. 基于分布式最小二乘法的锂离子电池建模及参数辨识 [J]. 机械工程学报, 2019, 55(20): 85-93.

ZHU Rui, DUAN Bin, WEN Fazheng, et al. Lithium-ion battery modeling and parameter identification based on decentralized least squares method [J]. Journal of Mechanical Engineering, 2019, 55(20): 85-93.

KLEE BARILLAS J, LI Jiahao, GÜNTHER C, et al. A comparative study and validation of state estimation algorithms for Li-ion batteries in battery management systems [J]. Applied Energy, 2015, 155: 455-462.

SONG Ziyou, LI Jianqiu, HAN Xuebing, et al. Multi-objective optimization of a semi-active battery/supercapacitor energy storage system for electric vehicles [J]. Applied Energy, 2014, 135: 212-224.

ZOU Changfu, KLINTBERG A, WEI Zhongbao, et al. Power capability prediction for lithium-ion batteries using economic nonlinear model predictive control [J]. Journal of Power Sources, 2018, 396: 580-589.

LIN Qian, WANG Jun, XIONG Rui, et al. Towards a smarter battery management system: a critical review on optimal charging methods of lithium ion batteries [J]. Energy, 2019, 183: 220-234.

宁博, 徐俊, 曹秉刚, 等. 采用等效电路的参数自适应电池模型及电池荷电状态估计方法 [J]. 西安交通大学学报, 2015, 49(10): 67-71, 78.

NING Bo, XU Jun, CAO Binggang, et al. A battery model with adaptive parameters based on equivalent circuit for state of charge estimation [J]. Journal of Xi'an Jiaotong University, 2015, 49(10): 67-71, 78.

刘海东, 周萍, 周正, 等. 锂离子电池开路电压快速估计研究 [J]. 机械工程学报, 2022, 58(8): 227-235, 243.

LIU Haidong, ZHOU Ping, ZHOU Zheng, et al. Fast estimation of open circuit voltage for lithium-ion batteries [J]. Journal of Mechanical Engineering, 2022, 58(8): 227-235, 243.

MENG Jinhao, STROE D I, RICCO M, et al. A simplified model-based state-of-charge estimation approach for lithium-ion battery with dynamic linear model [J]. IEEE Transactions on Industrial Electronics, 2019, 66(10): 7717-7727.

GENG Zeyang, WANG Siyang, LACEY M J, et al. Bridging physics-based and equivalent circuit models for lithium-ion batteries [J]. Electrochimica Acta, 2021, 372: 137829.

LAI Xin, ZHENG Yuejiu, SUN Tao. A comparative study of different equivalent circuit models for estimating state-of-charge of lithium-ion batteries [J]. Electrochimica Acta, 2018, 259: 566-577.

HOSSAIN M, HAQUE M E, ARIF M T. Kalman filtering techniques for the online model parameters and state of charge estimation of the Li-ion batteries: a comparative analysis [J]. Journal of Energy Storage, 2022, 51: 104174.

PRADHAN S K, CHAKRABORTY B. Battery management strategies: an essential review for battery state of health monitoring techniques [J]. Journal of Energy Storage, 2022, 51: 104427.

RAHIMI-EICHI H, BARONTI F, CHOW M Y. Online adaptive parameter identification and state-of-charge coestimation for lithium-polymer battery cells [J]. IEEE Transactions on Industrial Electronics, 2014, 61(4): 2053-2061.

XUAN Dongji, SHI Zhuangfei, CHEN Jinzhou, et al. Real-time estimation of state-of-charge in lithium-ion batteries using improved central difference transform method [J]. Journal of Cleaner Production, 2020, 252: 119787.

WANG Bin, WANG Chaohui, WANG Zhiyu, et al. Adaptive energy estimation for supercapacitor based on a real-time voltage state observer in electric vehicle applications [J]. IEEE Transactions on Power Electronics, 2021, 36(7): 7337-7341.

陈英杰, 杨耕, 祖海鹏, 等. 基于恒流实验的锂离子电池开路电压与内阻估计方法 [J]. 电工技术学报, 2018, 33(17): 3976-3988.

CHEN Yingjie, YANG Geng, ZU Haipeng, et al. An open circuit voltage and internal resistance estimation method of lithium-ion batteries with constant current tests [J]. Transactions of China Electrotechnical Society, 2018, 33(17): 3976-3988.

赵云飞, 徐俊, 王霄, 等. 双自适应衰减卡尔曼滤波锂电池荷电状态估计 [J]. 西安交通大学学报, 2018, 52(12): 99-105.

ZHAO Yunfei, XU Jun, WANG Xiao, et al. An estimation method for state of charge of lithium-ion batteries using dual adaptive fading extended Kalman filter [J]. Journal of Xi'an Jiaotong University, 2018, 52(12): 99-105.

韩雪冰, 欧阳明高, 卢兰光, 等. 电动车磷酸铁锂电池衰减后开路电压特性分析 [J]. 电源技术, 2015, 39(9): 1876-1878.HAN Xuebing, OUYANG Minggao, LU Languang, et al. Characteristics analysis of open circuit voltage of aged LiFePO4 battery for electric vehicle [J]. Chinese Journal of Power Sources, 2015, 39(9): 1876-1878.

谭文, 方淼, 段峰, 等. 基于三次样条插值函数的3D激光图像去噪算法研究 [J]. 中南大学学报(自然科学版), 2020, 51(9): 2496-2503.

TAN Wen, FANG Miao, DUAN Feng, et al. Research on 3D laser image denoising algorithm based on three splines interpolation function [J]. Journal of Central South University(Science and Technology), 2020, 51(9): 2496-2503.

KOUIBIA A, PASADAS M. An approximation problem of noisy data by cubic and bicubic splines [J]. Applied Mathematical Modelling, 2012, 36(9): 4135-4145.

LUCU M, MARTINEZ-LASERNA E, GANDIAGA I, et al. A critical review on self-adaptive Li-ion battery ageing models [J]. Journal of Power Sources, 2018, 401: 85-101.

HAN Xuebing, LU Languang, ZHENG Yuejiu, et al. A review on the key issues of the lithium ion battery degradation among the whole life cycle [J]. eTransportation, 2019, 1: 100005.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

矿用场景下采用深度学习-安时积分云边协同的锂电池荷电状态估计

锂电池双向均衡及模型预测控制优化

锂离子电池全生命周期的动静态荷电状态估算方法

储能锂电池系统综合管理研究进展

椭球状态定界的数值稳定算法