Online Diagnostic Method for Health Status of Lithium-ion Battery in Electric Vehicle

doi:10.3901/JME.2019.20.060

Abstract

Abstract: With the rapid development of electric vehicles, the attenuation of high-energy lithium-ion batteries has received increasing attention. Battery health status is a core parameter of durability management, which is essential for extending battery life and improving system reliability. Taking the ternary material lithium ion battery as the research object, based on the open circuit voltage model of positive and negative electrodes, the matching relationship between positive and negative electrodes and the whole battery is described, and the open circuit voltage-state of charge curve is reconstructed on the new battery scale. The evolution characteristics of the positive and negative matching relationship after the battery experiences various aging modes are analyzed, thereby reconstructing the open circuit voltage-state of charge curve of the aging battery on the new battery scale. Based on this, an improved non-destructive quantitative diagnosis method for lithium ion battery aging mode is proposed, which overcomes the limitation that the existing method must take the real open circuit voltage-state of charge curve of the battery as the diagnostic basis, so it is more suitable for real vehicle online application. The extended Kalman filter algorithm is used to identify the curve of the open circuit voltage with the discharge capacity from the battery dynamic current discharge data, and the proposed aging diagnosis method is used to fit the open circuit voltage curve, which can quantitatively analyze the material loss of positive electrode, negative electrode and available lithium ions. On this basis, the estimation method of the maximum available capacity of the battery and the identification method of the true open circuit voltage-state of charge curve are presented. The results show that the capacity estimation error of the method under dynamic conditions is less than 1%, and the root mean square error of the open circuit voltage-state of charge curve is within 6 mV. The method is applied to a battery pack, and can realize the estimation of maximum available capacity and the state of charge consistency of each single battery in the battery pack.

Key words: lithium-ion battery, aging mode, quantitative analysis, health status, online diagnosis

CLC Number:

TG156

JIANG Jiuchun, GAO Yang, ZHANG Caiping, WANG Yubin, ZHANG Weige, LIU Sijia. Online Diagnostic Method for Health Status of Lithium-ion Battery in Electric Vehicle[J]. Journal of Mechanical Engineering, 2019, 55(20): 60-72,84.

References

[1] BERECIBAR M,GANDIAGA I,VILLARREAL I,et al. Critical review of state of health estimation methods of Li-ion batteries for real applications[J]. Renewable & Sustainable Energy Reviews,2016,56:572-587.
[2] WEI Zhongbao,ZHAO Jiyun,JI Dongxu,et al. A multi-timescale estimator for battery state of charge and capacity dual estimation based on an online identified model[J]. Applied Energy,2017,204:1264-1274.
[3] ZHANG Lijun,MU Zhongqiang,SUN Changyan. Remaining useful life prediction for lithium-ion batteries based on exponential model and particle filter[J]. IEEE Access,2018(6):17729-17740.
[4] ZHANG Heng,MIAO Qiang,ZHANG Xin,et al. An improved unscented particle filter approach for lithium-ion battery remaining useful life prediction[J]. Microelectronics Reliability,2018,81:288-298.
[5] WANG J,LIU P,HICKS-GARNER J,et al. Cycle-life model for graphite-LiFePO(4) cells[J]. Journal of Power Sources,2011,196(8):3942-3948.
[6] GUHA A,PATRA A. State of health estimation of lithium-ion batteries using capacity fade and internal resistance growth models[J]. IEEE Transactions on Transportation Electrification,2018,4(1):135-146.
[7] NG K S,MOO C S,CHEN Y P,et al. Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries[J]. Applied Energy,2009,86(9):1506-1511.
[8] KESSELS J T B A,ROSCA B,BERGVELD H J,et al. On-line battery identification for electric driving range prediction[C/CD]//Vehicle Power & Propulsion Conference,2011.
[9] ROSCHER M A,ASSFALG J,BOHLEN O S. Detection of utilizable capacity deterioration in battery systems[J]. IEEE Transactions on Vehicular Technology,2011,60(1):98-103.
[10] BHANGU B S,BENTLEY P,STONE D A,et al. State-of-charge and state-of-health prediction of lead-acid batteries for hybrid electric vehicles using non-linear observers[C]//European Conference on Power Electronics & Applications,2005.
[11] NING Bo,CAO Binggang,WANG Bin,et al. Adaptive sliding mode observers for lithium-ion battery state estimation based on parameters identified online[J]. Energy,2018,153:732-742.
[12] 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.
[13] TAO Laifa,LU Chen,YANG Chao. Battery capacity degradation prediction using similarity recognition based on modified dynamic time warping[J]. Structural Control and Health Monitoring,2018,25(1):e2024.
[14] GOH T,PARK M,SEO M,et al. Capacity estimation algorithm with a second-order differential voltage curve for Li-ion batteries with NMC cathodes[J]. Energy,2017,135:257-268.
[15] CHRISTENSEN J,NEWMAN J. A mathematical model of stress generation and fracture in lithium manganese oxide[J]. Journal of the Electrochemical Society,2006,153(6):A1019-A1030.
[16] CHRISTENSEN J,NEWMAN J. Stress generation and fracture in lithium insertion materials[J]. Journal of Solid State Electrochemistry,2006,10(5):293-319.
[17] RAMADESIGAN V,CHEN K J,BURNS N A,et al. Parameter estimation and capacity fade analysis of lithium-ion batteries using reformulated models[J]. Journal of the Electrochemical Society,2011,158(9):A1048-A1054.
[18] SAFARI M,DELACOURT C. Mathematical modeling of lithium iron phosphate 3lectrode:Galvanostatic charge/discharge and path dependence[J]. Journal of the Electrochemical Society,2011,158(2):A63-A73.
[19] DALVERNY A L,FILHOL J S,DOUBLET M L. Interface electrochemistry in conversion materials for Li-ion batteries[J]. Journal of Materials Chemistry,2011,21(27):10134-10142.
[20] TASAKI K,GOLDBERG A,LIAN J J,et al. Solubility of lithium salts formed on the lithium-ion battery negative electrode surface in organic solvents[J]. Journal of the Electrochemical Society,2009,156(12):A1019-A1027.
[21] TASAKI K,HARRIS S J. Computational study on the solubility of lithium salts formed on lithium ion battery negative electrode in organic solvents[J]. Journal of Physical Chemistry C,2010,114(17):8076-8083.
[22] ANDRE D,APPEL C,SOCZKA-GUTH T,et al. Advanced mathematical methods of SOC and SOH estimation for lithium-ion batteries[J]. Journal of Power Sources,2013,224:20-27.
[23] LI Xiang,DING Qian,SUN Jianqiao. Remaining useful life estimation in prognostics using deep convolution neural networks[J]. Reliability Engineering & System Safety,2018,172:1-11.
[24] PATIL M A,TAGADE P,HARIHARAN K S,et al. A novel multistage support vector machine based approach for Li ion battery remaining useful life estimation[J]. Applied Energy,2015,159:285-297.
[25] CAI Yishan,YANG Lin,DENG Zhongwei,et al. Online identification of lithium-ion battery state-of-health based on fast wavelet transform and cross D-Markov machine[J]. Energy,2018,147:621-635.
[26] DUBARRY M,TRUCHOT C,LIAW B Y. Synthesize battery degradation modes via a diagnostic and prognostic model[J]. Journal of Power Sources,2012,219:204-216.
[27] BIRKL C R,MCTURK E,ROBERTS M R,et al. A parametric open circuit voltage model for lithium ion batteries[J]. Journal of the Electrochemical Society,2015,162(12):A2271-A2280.