摘要: 針對(duì)電動(dòng)汽車鋰離子電池健康狀態(tài)在線估算問題，提出了一種基于偽二維模型參數(shù)的估算方法. 該方法通過拆解同類估算目標(biāo)電池，以掃描電鏡測(cè)量電池結(jié)構(gòu)參數(shù)，利用遺傳算法辨識(shí)其他未知電化學(xué)模型參數(shù)，建立一種新的基于化學(xué)計(jì)量比的電池正極容量計(jì)算法則，估算電池健康狀態(tài). 同時(shí)考慮老化對(duì)電池正極化學(xué)計(jì)量比的影響，進(jìn)一步提高健康狀態(tài)估算精度. 采用電池老化數(shù)據(jù)集驗(yàn)證該方法的有效性，結(jié)果表明所提出的估算方法能在短時(shí)動(dòng)態(tài)工況下實(shí)現(xiàn)電池健康狀態(tài)的準(zhǔn)確在線估算.

Abstract: To estimate online health state of Li-Ion batteries in electric vehicles accurately, a method was proposed based on the parameters of a pseudo-two-dimensional model. Firstly, disassembling congeneric objective batteries and measuring their structural parameters using scanning electron microscopy, the method was arranged to get some unknown parameters based on genetic algorithm for an electrochemical model. Then, a new stoichiometry ratio-based battery positive capacity calculation was established to estimate the health state of battery. Considering the influence of aging on the stoichiometry ratio in the positive electrode, the estimation accuracy of health state was further improved. Finally, a battery aging dataset was used to verify the validity of the method. The results show that the proposed estimation method can achieve an accurate online estimation of battery health state in short dynamic loading.

圖  1   老化實(shí)驗(yàn)流程

Figure  1.   The flowchart of aging experiment

圖  2   電極材料及局部放大

Figure  2.   Electrode material and partial zoom

圖  3   電池結(jié)構(gòu)厚度測(cè)量

Figure  3.   Thickness measurement

圖  4   P2D模型評(píng)估

Figure  4.   Evaluation of P2D model

圖  5   不同優(yōu)化方法的辨識(shí)精度

Figure  5.   Identification accuracy of different optimization methods

圖  6   老化過程中的正極化學(xué)計(jì)量比的變化

Figure  6.   The stoichiometric proportion of aging process

表  1   SOH估算誤差

Table  1   Errors of SOH estimation

容量測(cè)試序號(hào)電池真實(shí)容量/Ah修正前誤差/%修正后誤差/%SAM1SAM2SAM1SAM2SAM1SAM2 12.6282.7100.000.000.000.0052.5972.6721.161.461.111.46102.5822.6560.141.59?0.170.61152.4642.600 5.052.892.520.39202.2442.5063.642.841.66?0.17251.9292.4064.402.880.720.54301.9812.941.91MAE2.402.091.030.73RMSE3.152.331.350.97

表  2   SOH估算誤差對(duì)比

Table  2   Comparison of errors of SOH estimation

方法索引及方法平均絕對(duì)誤差/%平均絕對(duì)誤差
的均值/% 模型法文獻(xiàn)[4], 電化學(xué)模型1.61, 2.522.07文獻(xiàn)[5], 等效電路模型11數(shù)據(jù)
驅(qū)動(dòng)法文獻(xiàn)[6], 優(yōu)化擬合模型約1.02, 0.98, 0.59, 0.97,
1.03, 0.82 0.97 ,
0.67 ,1.060.90文獻(xiàn)[8], 神經(jīng)網(wǎng)絡(luò)3.52, 3.41, 3.25, 2.793.24文獻(xiàn)[9], 極限學(xué)習(xí)機(jī)1.121.12文獻(xiàn)[10], 高斯過程回歸3.70, 1.00, 0.521.74文中方法1.03, 0.730.88 [1] 陳德海, 華銘, 鄒爭(zhēng)明, 等. 優(yōu)化分級(jí)T-S模糊控制動(dòng)態(tài)估計(jì)純電動(dòng)汽車電池健康狀態(tài)[J]. 北京理工大學(xué)學(xué)報(bào), 2019, 39(6):609 ? 614.

CHEN Dehai, HUA Ming, ZOU Zhengming, et al. Dynamic prediction of pure electric vehicle battery state of health by optimized and graded t-s fuzzy control[J]. Transactions of Beijing Institute of Technology, 2019, 39(6):609 ? 614. (in Chinese)

[2] 龐曉瓊, 王竹晴, 曾建潮, 等. 基于PCA-NARX的鋰離子電池剩余使用壽命預(yù)測(cè)[J]. 北京理工大學(xué)學(xué)報(bào), 2019, 39(4):406 ? 412.

PANG Xiaoqiong, WANG Zhuqing, ZENG Jianchao, et al. Prediction for the remaining useful life of lithium-ion battery based on PCA-NARX[J]. Transactions of Beijing Institute of Technology, 2019, 39(4):406 ? 412. (in Chinese)

[3]

LI J, ADEWUYI K, LOTFI N, et al. A single particle model with chemical/mechanical degradation physics for lithium ion battery State of Health (SOH) estimation[J]. Applied Energy, 2018, 212:1178 ? 1190. doi: 10.1016/j.apenergy.2018.01.011

[4]

XIONG Rui, LI Linlin, LI Zhirun, et al. An electrochemical model based degradation state identification method of Lithium-ion battery for all-climate electric vehicles application[J]. Applied Energy, 2018, 219:264 ? 275. doi: 10.1016/j.apenergy.2018.03.053

[5] 陳猛, 烏江, 焦朝勇, 等. 鋰離子電池健康狀態(tài)多因子在線估計(jì)方法[J]. 西安交通大學(xué)學(xué)報(bào), 2020, 54(1):169 ? 175.

CHEN Meng, WU Jiang, JIAO Chaoyong, et al. Multi-factor online estimation method for health status of lithium-ion battery[J]. Journal of Xi'an Jiaotong University, 2020, 54(1):169 ? 175. (in Chinese)

[6] 南金瑞, 孫路. 基于粒子群算法估計(jì)實(shí)際工況下鋰電池SOH[J]. 北京理工大學(xué)學(xué)報(bào), 2021, 41(1):59 ? 64.

NAN Jinrui, SUN Lu. Estimation of lithium battery soh under actual operating conditions based on particle swarm optimization[J]. Transactions of Beijing Institute of Technology, 2021, 41(1):59 ? 64. (in Chinese)

[7]

YANG Duo, ZHANG Xu, PAN Rui, et al. A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve[J]. Journal of Power Sources, 2018, 384:387 ? 395. doi: 10.1016/j.jpowsour.2018.03.015

[8]

ZHANG Shuzhi, ZHAI Baoyu, GUO Xu, et al. Synchronous estimation of state of health and remaining useful lifetime for lithium-ion battery using the incremental capacity and artificial neural networks[J]. Journal of Energy Storage, 2019, 26:100951. doi: 10.1016/j.est.2019.100951

[9]

CHEN Lin, WANG Huimin, LIU Bohao, et al. Battery state-of-health estimation based on a metabolic extreme learning machine combining degradation state model and error compensation[J]. Energy, 2021, 215:119078. doi: 10.1016/j.energy.2020.119078

[10]

ROMAN Darius, SAXENA Saurabh, ROBU Valentin, et al. Machine learning pipeline for battery state-of-health estimation[J]. Nature Machine Intelligence, 2021, 3(5):447 ? 456. doi: 10.1038/s42256-021-00312-3

[11]

KHODADADI Sadabadi Kaveh, JIN Xin, RIZZONI Giorgio. Prediction of remaining useful life for a composite electrode lithium ion battery cell using an electrochemical model to estimate the state of health[J]. Journal of Power Sources, 2021, 481:228861. doi: 10.1016/j.jpowsour.2020.228861

[12]

Lü Zhiqiang, GAO Renjing. A model‐based and data‐driven joint method for state‐of‐health estimation of lithium‐ion battery in electric vehicles[J]. International Journal of Energy Research, 2019, 43:7956 ? 7969.

[13]

XIONG Rui. Battery management algorithm for electric vehicles[M]. Singapore: Springer, 2020.

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