State-of-health monitoring of lithium-ion battery modules and packs via incremental capacity peak tracking

Incremental capacity analysis (ICA) is a widely used technique for lithium-ion battery state-of-health (SOH) evaluation. The effectiveness and robustness of ICA for single cell diagnostics have been reported in many published work. In this study, we extend the ICA based SOH monitoring approach from single cells to battery modules, which consist of battery cells with various aging conditions. In order to achieve on-board implementation, an IC peak tracking approach based on the ICA principles is proposed. Analytical, numerical and experimental results are presented to demonstrate the utility of the IC peak tracking framework on multi-cell battery SOH monitoring and the effects of cell non-uniformity on the proposed method. Results show that the methods developed for single cell capacity estimation can also be used for a module or pack that has parallel-connected cells.

[1]  Zhe Li,et al.  A dynamic capacity degradation model and its applications considering varying load for a large format Li-ion battery , 2016 .

[2]  John Newman,et al.  I. A simplified model for determining capacity usage and battery size for hybrid and plug-in hybrid electric vehicles , 2008 .

[3]  Nigel P. Brandon,et al.  Differential thermal voltammetry for tracking of degradation in lithium-ion batteries , 2014 .

[4]  Kwok L. Tsui,et al.  A naive Bayes model for robust remaining useful life prediction of lithium-ion battery , 2014 .

[5]  M. Dubarry,et al.  Incremental Capacity Analysis and Close-to-Equilibrium OCV Measurements to Quantify Capacity Fade in Commercial Rechargeable Lithium Batteries , 2006 .

[6]  Huei Peng,et al.  On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression , 2013 .

[7]  Dahn,et al.  Phase diagram of LixC6. , 1991, Physical review. B, Condensed matter.

[8]  Jens Groot,et al.  State-of-Health Estimation of Li-ion Batteries: Ageing Models , 2014 .

[9]  Huei Peng,et al.  A unified open-circuit-voltage model of lithium-ion batteries for state-of-charge estimation and state-of-health monitoring , 2014 .

[10]  Yann Guezennec,et al.  Lithium Ion Dynamic Battery Pack Model and Simulation for Automotive Applications , 2009 .

[11]  Scott J. Moura,et al.  Techniques for Battery Health Conscious Power Management via Electrochemical Modeling and Optimal Control. , 2011 .

[12]  Nigel P. Brandon,et al.  Module design and fault diagnosis in electric vehicle batteries , 2012 .

[13]  Jianqiu Li,et al.  Cell state-of-charge inconsistency estimation for LiFePO4 battery pack in hybrid electric vehicles using mean-difference model , 2013 .

[14]  Jonghoon Kim,et al.  State-of-Charge Estimation and State-of-Health Prediction of a Li-Ion Degraded Battery Based on an EKF Combined With a Per-Unit System , 2011, IEEE Transactions on Vehicular Technology.

[15]  Zhe Li,et al.  A comparative study of commercial lithium ion battery cycle life in electrical vehicle: Aging mechanism identification , 2014 .

[16]  B. Scrosati,et al.  Advances in lithium-ion batteries , 2002 .

[17]  A. H. Thompson Electrochemical Potential Spectroscopy: A New Electrochemical Measurement , 1979 .

[18]  Shriram Santhanagopalan,et al.  Quantifying Cell-to-Cell Variations in Lithium Ion Batteries , 2012 .

[19]  M. Verbrugge,et al.  Aging Mechanisms of LiFePO4 Batteries Deduced by Electrochemical and Structural Analyses , 2010 .

[20]  Jing Sun,et al.  Model Parametrization and Adaptation Based on the Invariance of Support Vectors With Applications to Battery State-of-Health Monitoring , 2015, IEEE Transactions on Vehicular Technology.

[21]  Peng Wu,et al.  Thermal runaway propagation model for designing a safer battery pack with 25Ah LiNixCoyMnzO2 large format lithium ion battery , 2015 .

[22]  J. Barker,et al.  Differential capacity as a spectroscopic probe for the investigation of alkali metal insertion reactions , 1996 .

[23]  Xuning Feng,et al.  Using probability density function to evaluate the state of health of lithium-ion batteries , 2013 .

[24]  J. Shim,et al.  The development of low cost LiFePO4-based high power lithium-ion batteries , 2003 .

[25]  Matthieu Dubarry,et al.  Origins and accommodation of cell variations in Li‐ion battery pack modeling , 2010 .

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

[27]  Michael Buchholz,et al.  State-of-health monitoring of lithium-ion batteries in electric vehicles by on-board internal resistance estimation , 2011 .

[28]  G. Pistoia,et al.  Electric and hybrid vehicles : power sources, models, sustainability, infrastructure and the market , 2010 .

[29]  N. Omar,et al.  Lithium iron phosphate based battery: Assessment of the aging parameters and development of cycle life model , 2014 .

[30]  Andrea Marongiu,et al.  Critical review of on-board capacity estimation techniques for lithium-ion batteries in electric and hybrid electric vehicles , 2015 .

[31]  A. J. Smith,et al.  Delta Differential Capacity Analysis , 2012 .

[32]  J. Bernard,et al.  A Simplified Electrochemical and Thermal Aging Model of LiFePO4-Graphite Li-ion Batteries: Power and Capacity Fade Simulations , 2013 .

[33]  Michael A. Roscher,et al.  Detection of Utilizable Capacity Deterioration in Battery Systems , 2011, IEEE Transactions on Vehicular Technology.

[34]  M. Wohlfahrt‐Mehrens,et al.  Ageing mechanisms in lithium-ion batteries , 2005 .

[35]  Rachid Yazami,et al.  A reversible graphite-lithium negative electrode for electrochemical generators , 1983 .

[36]  Ping Shen,et al.  Determination of the battery pack capacity considering the estimation error using a Capacity–Quantity diagram , 2016 .

[37]  Matthieu Dubarry,et al.  Synthesize battery degradation modes via a diagnostic and prognostic model , 2012 .

[38]  John Newman,et al.  Cyclable Lithium and Capacity Loss in Li-Ion Cells , 2005 .

[39]  Minggao Ouyang,et al.  Characterization of large format lithium ion battery exposed to extremely high temperature , 2014 .

[40]  Aaron Smith A HIGH PRECISION STUDY OF LI-ION BATTERIES , 2012 .

[41]  Xuning Feng,et al.  Online internal short circuit detection for a large format lithium ion battery , 2016 .

[42]  Chunting Chris Mi,et al.  Study of the Characteristics of Battery Packs in Electric Vehicles With Parallel-Connected Lithium-Ion Battery Cells , 2015 .

[43]  Henk Jan Bergveld,et al.  Battery Management Systems: Accurate State-of-Charge Indication for Battery-Powered Applications , 2008 .

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

[45]  Matthew B. Pinson,et al.  Internal resistance matching for parallel-connected lithium-ion cells and impacts on battery pack cycle life , 2014 .

[46]  Matthieu Dubarry,et al.  From single cell model to battery pack simulation for Li-ion batteries , 2009 .

[47]  Ken Darcovich,et al.  Modelling the impact of variations in electrode manufacturing on lithium-ion battery modules , 2012 .

[48]  Mohammadhosein Safari,et al.  Modeling of a Commercial Graphite/LiFePO4 Cell , 2011 .

[49]  Joongpyo Shim,et al.  Cycling performance of low-cost lithium ion batteries with natural graphite and LiFePO4 , 2003 .

[50]  Nigel P. Brandon,et al.  Comparative analysis of battery electric, hydrogen fuel cell and hybrid vehicles in a future sustainable road transport system , 2010 .

[51]  Michael Pecht,et al.  A generic model-free approach for lithium-ion battery health management , 2014 .

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

[53]  Xiaosong Hu,et al.  A comparative study of equivalent circuit models for Li-ion batteries , 2012 .