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基于扩展单粒子模型的锂离子电池参数识别策略

庞辉

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基于扩展单粒子模型的锂离子电池参数识别策略

庞辉

An extended single particle model-based parameter identification scheme for lithium-ion cells

Pang Hui
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  • 为了精确识别电动汽车锂离子动力电池的关键状态参数,基于多孔电极理论和浓度理论,建立了一种考虑液相动力学行为的锂离子电池扩展单粒子模型.相较于传统单粒子模型,该模型增加了对负电极表面固体电解质界面膜参数的描述,并考虑了温度和液相浓度变化对锂离子电池关键参数的耦合影响.基于所建立的扩展单粒子模型,提出一种简化的参数灵敏度分析方法和有效的锂电池参数识别策略,用以确定特定工况下的高灵敏度待识别参数,进而利用遗传算法实现参数的优化求解.最后,通过对比分析本文模型和传统单粒子模型的仿真输出电压和相同工况下电池的实验输出电压验证了提出模型和参数识别方法的有效性和可行性,为电池管理系统的健康状态估计提供了理论基础.
    The accurate modeling and parameter identification of lithium-ion battery are of great significance in real-time control and high-performance operation for advanced battery management system (BMS) in electrified vehicles (EVs). However, it is difficult to obtain the information about the interior state inside battery, because it cannot be directly measured by some electric devices. In order to accurately identify the key state parameters of lithium-ion cell applied to electric ground vehicles, an extended single particle model of lithium-ion cell with electrolyte dynamics behaviors is first built up based on the porous electrode theory and concentration theory in this article. Compared with the conventional single particle cell model, the parameter description of the solid electrolyte interface film is incorporated into this model, and the coupled effects of temperature-dependent and electrolyte-dependent electrochemical parameters on the cell discharge are also taken into consideration. Based on this extended single particle cell model, a simplified parameter sensitivity analysis method and a comprehensive parameter identification scheme for lithium-ion cell are proposed herein, in which a sensitivity analysis of the capacity to a subset of electrochemical parameters that are hypothesized to evolve throughout the battery's life, is conducted to determine the highly sensitive parameters to be identified under some particular operation scenarios, and further to solve the parameter optimization problem using the genetic algorithm. Based on this method, the test data under the working condition of 1 C discharge rate at 23℃ are employed to evaluate the identified parameters of lithium-ion battery cell with a peak value of voltage error less than 3.8%. Afterwards, the effectiveness and feasibility of the proposed parameter identification scheme are validated by the comparative study of the simulated output voltage and the experimental output voltage under the same input current profile. Specifically, the 0.05 C discharge and HPPC (hybrid pulse power characterization) current profile are used to verify the evaluated parameters under the 1 C discharge condition, and the maximum relative errors of voltage with 0.05 C galvanostatic discharge profile at 23 and 45℃ are 3.4% and 2.6% by using our proposed SPMe_SEI model, and 5.7% and 4.0% by using the traditional SPMe model, respectively. Moreover, the maximum relative errors of voltage with HPPC discharge profile at 23 and 45℃ are 1.9% and 1.5% by using our proposed SPMe_SEI model, and 2.1% and 1.8% by using the traditional SPMe model, respectively. It is concluded that the proposed parameter identification scheme for a lithium-ion cell model can provide a solid theory foundation for facilitating the estimation of state-of-health in BMS application.
      通信作者: 庞辉, huipang@163.com
    • 基金项目: 国家自然科学基金(批准号:51675423)资助的课题.
      Corresponding author: Pang Hui, huipang@163.com
    • Funds: Project supported by the National Natural Science Foundation of China (Grant No. 51675423).
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    [2]

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    [3]

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    [5]

    Domenico D, Stefanopoulou A, Fiengo G 2009 J. Dyn. Sys. Meas. Control 132 768

    [6]

    Guo M, Sikha G, White R 2011 J. Electrochem. Soc. 158 A122

    [7]

    Han X, Ouyang M, Lu L, Li J 2015 J. Power Sources 278 814

    [8]

    Guo M, Jin X, White R 2017 J. Electrochem. Soc. 164 E3001

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    Xiang Y, Ma X J, Liu C G, Ke R S, Zhao Z X 2014 Acta Armamentarii 35 1659 (in Chinese) [项宇, 马晓军, 刘春光, 可荣硕, 赵梓旭 2014 兵工学报 35 1659]

    [10]

    Xu X, Wang W, Chen L 2017 Automotive Engineering 39 813 (in Chinese) [徐兴, 王位, 陈龙 2017 汽车工程 39 813]

    [11]

    Marcicki J, Canova M, Conlisk A, Rizzoni G 2013 J. Power Sources 237 310

    [12]

    Dai H, Xu T, Zhu L, Wei X, Sun Z 2016 Appl. Energy 184 119

    [13]

    Feng T, Lin Y, Zhao X, Zhang H, Qiang J 2015 J. Power Sources 281 194

    [14]

    Zhang X, Lu J, Yuan S, Yang J, Zhou X 2017 J. Power Sources 345 21

    [15]

    Chaoui H, Mejdoubi A, Gualos H 2017 IEEE Trans. Veh. Technol. 66 2000

    [16]

    Santhanagopalan S, Guo Q, White R 2007 J. Electrochem. Soc. 154 A198

    [17]

    Forman J, Moura S, Stein J, Fathy H 2011 American Control Conference (ACC 2011) San Francisco, California, USA, June 29-July 1, 2011 p362

    [18]

    Forman J, Moura S, Stein J, Fathy H 2012 J. Power Sources 210 263

    [19]

    Zhang L, Yu C, Hinds G, Wang L, Luo W, Zheng J, Hua M 2014 J. Electrochem. Soc. 161 A762

    [20]

    Zhang L, Wang L, Hinds G, Yu C, Zheng J, Li J 2014 J. Power Sources 270 367

    [21]

    Li J, Zou L, Tian F, Yang H, Dong X, Zou Z 2016 J. Electrochem. Soc. 163 A1646

    [22]

    Rahman M, Anwar S, Izadian A 2016 J. Power Sources 307 86

    [23]

    Shen W, Li H 2017 Energies 10 432

    [24]

    Doyle M, Newman J 1995 Electrochim. Acta 40 2191

    [25]

    Pang H 2017 Acta Phys. Sin. 66 238801 (in Chinese) [庞辉 2017 物理学报 66 238801]

    [26]

    Diwakar V 2009 Towards efficient models for lithium ion batteries Ph. D. Dissertation (St. Louis: Washington University)

    [27]

    Moura S, Argomedo F, Klein R, Mirtabatabaei A, Krstic M 2017 IEEE Trans. Contr. Syst. Technol. 2 453

    [28]

    Valoen L, Reimers J 2005 J. Electrochem. Soc. 152 A882

    [29]

    Jiang Y H, Ai L, Jia M, Cheng Y, Du S L, Li S G 2017 Acta Phys. Sin. 66 118202 (in Chinese) [蒋跃辉, 艾亮, 贾明, 程昀, 杜双龙, 李书国 2017 物理学报 66 118202]

    [30]

    Tanim T, Rahn C, Wang C 2015 J. Dyn. Sys. Meas. Control 137 011005

    [31]

    Tanim T, Rahn C, Wang C 2015 Energy 80 731

    [32]

    Smith K, Wang C 2006 J. Power Sources 161 628

    [33]

    Di D, Stefanopoulou A, Fiengo G 2009 J. Dyn. Sys. Meas. Control 132 768

    [34]

    Fan G, Pan K, Canova M, Marcicki J, Yang X 2016 J. Electrochem. Soc. 163 A666

    [35]

    Bartlett A, Marcicki J, Onori S, Rizzoni G, Yang X, Miller T 2016 IEEE Trans. Contr. Syst. Technol. 24 384

    [36]

    Marcicki J, Canova M, Conlisk A, Rizzoni G 2013 J. Power Sources 237 310

    [37]

    Marcicki J, Todeschini F, Onori S, Canova M 2012 American Control Conference (ACC 2012) Montreal, Canada, June 27-29, 2012 p572

  • [1]

    Huang L,Li J Y 2015 Acta Phys. Sin. 64 108202 (in Chinese) [黄亮, 李建元 2015 物理学报 64 108202]

    [2]

    Cheng Y, Li J, Jia M, Tang Y W, Du S L, Ai L H, Yin B H, Ai L 2015 Acta Phys. Sin. 64 210202 (in Chinese) [程昀, 李劼, 贾明, 汤依伟, 杜双龙, 艾立华, 殷宝华, 艾亮 2015 物理学报 64 210202]

    [3]

    Boovaragavan V, Harinipriya S, Subramanian V 2008 J. Power Sources 183 361

    [4]

    Fleischer C, Waag W, Bai Z, Sauer D 2013 J. Power Sources 243 728

    [5]

    Domenico D, Stefanopoulou A, Fiengo G 2009 J. Dyn. Sys. Meas. Control 132 768

    [6]

    Guo M, Sikha G, White R 2011 J. Electrochem. Soc. 158 A122

    [7]

    Han X, Ouyang M, Lu L, Li J 2015 J. Power Sources 278 814

    [8]

    Guo M, Jin X, White R 2017 J. Electrochem. Soc. 164 E3001

    [9]

    Xiang Y, Ma X J, Liu C G, Ke R S, Zhao Z X 2014 Acta Armamentarii 35 1659 (in Chinese) [项宇, 马晓军, 刘春光, 可荣硕, 赵梓旭 2014 兵工学报 35 1659]

    [10]

    Xu X, Wang W, Chen L 2017 Automotive Engineering 39 813 (in Chinese) [徐兴, 王位, 陈龙 2017 汽车工程 39 813]

    [11]

    Marcicki J, Canova M, Conlisk A, Rizzoni G 2013 J. Power Sources 237 310

    [12]

    Dai H, Xu T, Zhu L, Wei X, Sun Z 2016 Appl. Energy 184 119

    [13]

    Feng T, Lin Y, Zhao X, Zhang H, Qiang J 2015 J. Power Sources 281 194

    [14]

    Zhang X, Lu J, Yuan S, Yang J, Zhou X 2017 J. Power Sources 345 21

    [15]

    Chaoui H, Mejdoubi A, Gualos H 2017 IEEE Trans. Veh. Technol. 66 2000

    [16]

    Santhanagopalan S, Guo Q, White R 2007 J. Electrochem. Soc. 154 A198

    [17]

    Forman J, Moura S, Stein J, Fathy H 2011 American Control Conference (ACC 2011) San Francisco, California, USA, June 29-July 1, 2011 p362

    [18]

    Forman J, Moura S, Stein J, Fathy H 2012 J. Power Sources 210 263

    [19]

    Zhang L, Yu C, Hinds G, Wang L, Luo W, Zheng J, Hua M 2014 J. Electrochem. Soc. 161 A762

    [20]

    Zhang L, Wang L, Hinds G, Yu C, Zheng J, Li J 2014 J. Power Sources 270 367

    [21]

    Li J, Zou L, Tian F, Yang H, Dong X, Zou Z 2016 J. Electrochem. Soc. 163 A1646

    [22]

    Rahman M, Anwar S, Izadian A 2016 J. Power Sources 307 86

    [23]

    Shen W, Li H 2017 Energies 10 432

    [24]

    Doyle M, Newman J 1995 Electrochim. Acta 40 2191

    [25]

    Pang H 2017 Acta Phys. Sin. 66 238801 (in Chinese) [庞辉 2017 物理学报 66 238801]

    [26]

    Diwakar V 2009 Towards efficient models for lithium ion batteries Ph. D. Dissertation (St. Louis: Washington University)

    [27]

    Moura S, Argomedo F, Klein R, Mirtabatabaei A, Krstic M 2017 IEEE Trans. Contr. Syst. Technol. 2 453

    [28]

    Valoen L, Reimers J 2005 J. Electrochem. Soc. 152 A882

    [29]

    Jiang Y H, Ai L, Jia M, Cheng Y, Du S L, Li S G 2017 Acta Phys. Sin. 66 118202 (in Chinese) [蒋跃辉, 艾亮, 贾明, 程昀, 杜双龙, 李书国 2017 物理学报 66 118202]

    [30]

    Tanim T, Rahn C, Wang C 2015 J. Dyn. Sys. Meas. Control 137 011005

    [31]

    Tanim T, Rahn C, Wang C 2015 Energy 80 731

    [32]

    Smith K, Wang C 2006 J. Power Sources 161 628

    [33]

    Di D, Stefanopoulou A, Fiengo G 2009 J. Dyn. Sys. Meas. Control 132 768

    [34]

    Fan G, Pan K, Canova M, Marcicki J, Yang X 2016 J. Electrochem. Soc. 163 A666

    [35]

    Bartlett A, Marcicki J, Onori S, Rizzoni G, Yang X, Miller T 2016 IEEE Trans. Contr. Syst. Technol. 24 384

    [36]

    Marcicki J, Canova M, Conlisk A, Rizzoni G 2013 J. Power Sources 237 310

    [37]

    Marcicki J, Todeschini F, Onori S, Canova M 2012 American Control Conference (ACC 2012) Montreal, Canada, June 27-29, 2012 p572

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出版历程
  • 收稿日期:  2017-10-06
  • 修回日期:  2017-12-08
  • 刊出日期:  2018-03-05

基于扩展单粒子模型的锂离子电池参数识别策略

  • 1. 西安理工大学机械与精密仪器工程学院, 西安 710048
  • 通信作者: 庞辉, huipang@163.com
    基金项目: 国家自然科学基金(批准号:51675423)资助的课题.

摘要: 为了精确识别电动汽车锂离子动力电池的关键状态参数,基于多孔电极理论和浓度理论,建立了一种考虑液相动力学行为的锂离子电池扩展单粒子模型.相较于传统单粒子模型,该模型增加了对负电极表面固体电解质界面膜参数的描述,并考虑了温度和液相浓度变化对锂离子电池关键参数的耦合影响.基于所建立的扩展单粒子模型,提出一种简化的参数灵敏度分析方法和有效的锂电池参数识别策略,用以确定特定工况下的高灵敏度待识别参数,进而利用遗传算法实现参数的优化求解.最后,通过对比分析本文模型和传统单粒子模型的仿真输出电压和相同工况下电池的实验输出电压验证了提出模型和参数识别方法的有效性和可行性,为电池管理系统的健康状态估计提供了理论基础.

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