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A systematic review of lumped-parameter equivalent circuit models for real-time estimation of lithium-ion battery states

机译:实时评估锂离子电池状态的集总参数等效电路模型的系统综述

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摘要

This paper presents a systematic review for the most commonly used lumped-parameter equivalent circuit model structures in lithium-ion battery energy storage applications. These models include the Combined model, Rint model, two hysteresis models, Randles' model, a modified Randles' model and two resistor-capacitor (RC) network models with and without hysteresis included. Two variations of the lithium-ion cell chemistry, namely the lithium-ion iron phosphate (LiFePO4) and lithium nickel manganese-cobalt oxide (LiNMC) are used for testing purposes. The model parameters and states are recursively estimated using a nonlinear system identification technique based on the dual Extended Kalman Filter (dual-EKF) algorithm. The dynamic performance of the model structures are verified using the results obtained from a self-designed pulsed-current test and an electric vehicle (EV) drive cycle based on the New European Drive Cycle (NEDC) profile over a range of operating temperatures. Analysis on the ten model structures are conducted with respect to state-of-charge (SOC) and state-of-power (SOP) estimation with erroneous initial conditions. Comparatively, both RC model structures provide the best dynamic performance, with an outstanding SOC estimation accuracy. For those cell chemistries with large inherent hysteresis levels (e.g. LiFePO4), the RC model with only one time constant is combined with a dynamic hysteresis model to further enhance the performance of the SOC estimator. (C) 2016 Elsevier B.V. All rights reserved.
机译:本文对锂离子电池储能应用中最常用的集总参数等效电路模型结构进行了系统综述。这些模型包括组合模型,Rint模型,两个滞后模型,Randles模型,修改后的Randles模型以及两个带有和不带有滞后的电阻器(RC)网络模型。锂离子电池化学的两种变化,即锂离子磷酸铁锂(LiFePO4)和锂镍锰钴氧化物(LiNMC)用于测试目的。使用基于双重扩展卡尔曼滤波器(dual-EKF)算法的非线性系统识别技术,递归估计模型参数和状态。使用自行设计的脉冲电流测试和基于新欧洲行驶周期(NEDC)曲线的电动车辆(EV)行驶周期在一定温度范围内获得的结果,验证了模型结构的动态性能。针对具有错误初始条件的充电状态(SOC)和电源状态(SOP)估计,对十个模型结构进行了分析。相比之下,这两种RC模型结构都提供了最佳的动态性能,并具有出色的SOC估计精度。对于固有滞后水平较高的电池化学性质(例如LiFePO4),将只有一个时间常数的RC模型与动态滞后模型结合起来,可以进一步提高SOC估计器的性能。 (C)2016 Elsevier B.V.保留所有权利。

著录项

  • 来源
    《Journal of power sources》 |2016年第1期|183-196|共14页
  • 作者单位

    Univ Sheffield, Dept Elect & Elect Engn, Mappin St, Sheffield S1 3JD, S Yorkshire, England;

    Univ Sheffield, Dept Elect & Elect Engn, Mappin St, Sheffield S1 3JD, S Yorkshire, England;

    Univ Sheffield, Dept Elect & Elect Engn, Mappin St, Sheffield S1 3JD, S Yorkshire, England;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Battery modelling; Persistent excitation; Real-time estimation; State-of-charge; State-of-power;

    机译:电池建模;持续激励;实时估计;充电状态;功率状态;
  • 入库时间 2022-08-18 00:22:18

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