A rapid online calculation method for state of health of lithium-ion battery based on coulomb counting method and differential voltage analysis

Abstract Accurate estimation of state of health (SOH) is crucial for battery management system in ensuring the reliability and safety for system operation. For SOH estimation, the model-based methods require sophisticated battery models while the data-driven methods need huge battery data and computation burden. To avoid these drawbacks, a model-free SOH calculation method by fusion of coulomb counting method and differential voltage analysis (DVA) is proposed, realizing rapid online SOH calculation under constant current discharging stage. Firstly, the conventional coulomb counting method is converted to calculate SOH, which needs two state of charge (SOC) and corresponding measurement time to further proceed. Subsequently, DV curves are obtained based on the battery measurable parameters without smoothing or function fitting, then the x-axis of DV curves is replaced by SOC axis to get SOC based DV curves. Using discrete analysis, two SOC feature points are identified from SOC based DV curves, whose corresponding measurement time and mean SOC are used to compute SOH directly. In addition, the SOH calculation accuracy of the proposed method is verified by experimental data. The validation results indicate that this method can provide online accurate SOH calculation under constant current discharging stage with low computation.

[1]  Pan Chaofeng,et al.  State of health estimation of battery modules via differential voltage analysis with local data symmetry method , 2017 .

[2]  Qiang Ling,et al.  Battery Health Prognosis Using Brownian Motion Modeling and Particle Filtering , 2018, IEEE Transactions on Industrial Electronics.

[3]  Amaury Lendasse,et al.  OP-ELM: Optimally Pruned Extreme Learning Machine , 2010, IEEE Transactions on Neural Networks.

[4]  Christian Fleischer,et al.  Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles , 2014 .

[5]  Jae Sik Chung,et al.  A Multiscale Framework with Extended Kalman Filter for Lithium-Ion Battery SOC and Capacity Estimation , 2010 .

[6]  Zonghai Chen,et al.  Multi-timescale power and energy assessment of lithium-ion battery and supercapacitor hybrid system using extended Kalman filter , 2018, Journal of Power Sources.

[7]  D. Sauer,et al.  Analysis of cyclic aging performance of commercial Li4Ti5O12-based batteries at room temperature , 2019, Energy.

[8]  Dylan Dah-Chuan Lu,et al.  Incremental capacity analysis and differential voltage analysis based state of charge and capacity estimation for lithium-ion batteries , 2018 .

[9]  Guangzhong Dong,et al.  Remaining Useful Life Prediction and State of Health Diagnosis for Lithium-Ion Batteries Using Particle Filter and Support Vector Regression , 2018, IEEE Transactions on Industrial Electronics.

[10]  Yi-Jun He,et al.  State of health estimation of lithium‐ion batteries: A multiscale Gaussian process regression modeling approach , 2015 .

[11]  Xu Guo,et al.  Multi-objective decision analysis for data-driven based estimation of battery states: A case study of remaining useful life estimation , 2020 .

[12]  M. Dubarry,et al.  Identifying battery aging mechanisms in large format Li ion cells , 2011 .

[13]  Michel Kinnaert,et al.  State of health estimation for lithium ion batteries based on an equivalent-hydraulic model: An iron phosphate application , 2019, Journal of Energy Storage.

[14]  Ali Ghorbani Kashkooli,et al.  Representative volume element model of lithium-ion battery electrodes based on X-ray nano-tomography , 2017, Journal of Applied Electrochemistry.

[15]  Michael A. Osborne,et al.  Gaussian process regression for forecasting battery state of health , 2017, 1703.05687.

[16]  Ji Wu,et al.  A Novel Estimation Method for the State of Health of Lithium-Ion Battery Using Prior Knowledge-Based Neural Network and Markov Chain , 2019, IEEE Transactions on Industrial Electronics.

[17]  Michael Osterman,et al.  Prognostics of lithium-ion batteries based on DempsterShafer theory and the Bayesian Monte Carlo me , 2011 .

[18]  Jiuchun Jiang,et al.  State of health estimation of second-life LiFePO4 batteries for energy storage applications , 2018, Journal of Cleaner Production.

[19]  Gregory L. Plett,et al.  Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation , 2004 .

[20]  Bhaskar Saha,et al.  Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework , 2009, IEEE Transactions on Instrumentation and Measurement.

[21]  Matthieu Dubarry,et al.  Identify capacity fading mechanism in a commercial LiFePO4 cell , 2009 .

[22]  Nicholas G. Garafolo,et al.  A Review of Inactive Materials and Components of Flexible Lithium‐Ion Batteries , 2017 .

[23]  Kaike Wang,et al.  Synchronous estimation of state of health and remaining useful lifetime for lithium-ion battery using the incremental capacity and artificial neural networks , 2019 .

[24]  Chaoyang Wang,et al.  Modeling of lithium plating induced aging of lithium-ion batteries: Transition from linear to nonlinear aging , 2017 .

[25]  Markus Lienkamp,et al.  Revisiting the dual extended Kalman filter for battery state-of-charge and state-of-health estimation: A use-case life cycle analysis , 2018, Journal of Energy Storage.

[26]  Mohd Yamani Idna Idris,et al.  Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries , 2019, Renewable and Sustainable Energy Reviews.

[27]  Andrea Marongiu,et al.  Differential voltage analysis as a tool for analyzing inhomogeneous aging: A case study for LiFePO 4 |Graphite cylindrical cells , 2017 .

[28]  Guangzhao Luo,et al.  Lithium-ion battery state of health estimation with short-term current pulse test and support vector machine , 2018, Microelectron. Reliab..

[29]  Laisuo Su,et al.  Identifying main factors of capacity fading in lithium ion cells using orthogonal design of experiments , 2016 .

[30]  Ali Ghorbani Kashkooli,et al.  Morphological and Electrochemical Characterization of Nanostructured Li4Ti5O12Electrodes Using Multiple Imaging Mode Synchrotron X-ray Computed Tomography , 2017 .

[31]  Lei Zhang,et al.  Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks , 2019, Journal of Energy Storage.

[32]  Michael Lang,et al.  The influence of cycling temperature and cycling rate on the phase specific degradation of a positive electrode in lithium ion batteries: A post mortem analysis , 2016 .

[33]  S. Zhang,et al.  Modeling of Back-Propagation Neural Network Based State-of-Charge Estimation for Lithium-Ion Batteries with Consideration of Capacity Attenuation , 2019, Advances in Electrical and Computer Engineering.

[34]  Zhenpo Wang,et al.  Prognostic health condition for lithium battery using the partial incremental capacity and Gaussian process regression , 2019, Journal of Power Sources.

[35]  Haritza Camblong,et al.  A critical review on self-adaptive Li-ion battery ageing models , 2018, Journal of Power Sources.

[36]  Xuezhe Wei,et al.  Investigation of lithium-ion battery degradation mechanisms by combining differential voltage analysis and alternating current impedance , 2020 .

[37]  D. Dragičević,et al.  Quantification of aging mechanisms and inhomogeneity in cycled lithium-ion cells by differential voltage analysis , 2019, Journal of Energy Storage.

[38]  Xiaofeng Wang,et al.  A Battery Management System With a Lebesgue-Sampling-Based Extended Kalman Filter , 2019, IEEE Transactions on Industrial Electronics.

[39]  Lei Zhang,et al.  State-of-health estimation for Li-ion batteries by combing the incremental capacity analysis method with grey relational analysis , 2019, Journal of Power Sources.

[40]  Joeri Van Mierlo,et al.  A quick on-line state of health estimation method for Li-ion battery with incremental capacity curves processed by Gaussian filter , 2018 .

[41]  Gregory J. Offer,et al.  How Observable Is Lithium Plating? Differential Voltage Analysis to Identify and Quantify Lithium Plating Following Fast Charging of Cold Lithium-Ion Batteries , 2019, Journal of The Electrochemical Society.

[42]  Yonggang Liu,et al.  State of Health Estimation for Lithium-ion Batteries Based on Fusion of Autoregressive Moving Average Model and Elman Neural Network , 2019, IEEE Access.

[43]  Amit Patra,et al.  State of Health Estimation of Lithium-Ion Batteries Using Capacity Fade and Internal Resistance Growth Models , 2018, IEEE Transactions on Transportation Electrification.