A review of stochastic battery models and health management

Batteries are promising sources of green and sustainable energy that have been widely used in various applications. Battery modelling as the basis of battery management system is vital for both technology development and applications of batteries. Compared with other battery models, stochastic battery models feature high accuracy and low time consumption. Moreover, charging profile, battery behavior, and discharging profile can all be considered to optimize battery performance and usage, which is a key issue in battery usage in real life. Given the significance of stochastic modelling and the progress of battery health management, this paper reviews various aspects of related studies and developments from different fields, while identifying their corresponding merits and weaknesses. Remaining challenges are discussed, and several suggestions are offered as possible inspirations for further research.

[1]  M. Doyle,et al.  Simulation and Optimization of the Dual Lithium Ion Insertion Cell , 1994 .

[2]  James D. Hamilton.  Analysis of time series subject to changes in regime , 1990 .

[3]  Leonardo Badia,et al.  Impact of battery degradation on optimal management policies of harvesting-based wireless sensor devices , 2013, 2013 Proceedings IEEE INFOCOM.

[4]  Louis L. Bucciarelli,et al.  Estimating loss-of-power probabilities of stand-alone photovoltaic solar energy systems , 1984 .

[5]  Hao Mu,et al.  A novel multi-model probability battery state of charge estimation approach for electric vehicles using H-infinity algorithm , 2016 .

[6]  Michael S. Okundamiya,et al.  Energy Storage Models for Optimizing Renewable Power Applications , 2010 .

[7]  Leonardo Badia,et al.  Energy Management Policies for Harvesting-Based Wireless Sensor Devices with Battery Degradation , 2013, IEEE Transactions on Communications.

[8]  Ralph E. White,et al.  Mathematical modeling of the capacity fade of Li-ion cells , 2003 .

[9]  Mark Wild,et al.  Lithium sulfur batteries, a mechanistic review , 2015 .

[10]  Nicolo Michelusi,et al.  Coping with spectrum and energy scarcity in Wireless Networks: a Stochastic Optimization approach to Cognitive Radio and Energy Harvesting , 2013 .

[11]  J. Anta.  Random walk numerical simulation for solar cell applications , 2009 .

[12]  M. Doyle,et al.  Relaxation Phenomena in Lithium‐Ion‐Insertion Cells , 1994 .

[13]  Il-Doo Kim,et al.  Mass-scalable synthesis of 3D porous germanium–carbon composite particles as an ultra-high rate anode for lithium ion batteries , 2015 .

[14]  M. Safari,et al.  Mathematical Modeling of Lithium Iron Phosphate Electrode: Galvanostatic Charge/Discharge and Path Dependence , 2011 .

[15]  Ramachandran Rajesh,et al.  Capacity of a Gaussian MAC with energy harvesting transmit nodes , 2012, 2012 Information Theory and Applications Workshop.

[16]  Dimitrios D. Vergados,et al.  Energy-Efficient Routing Protocols in Wireless Sensor Networks: A Survey , 2013, IEEE Communications Surveys & Tutorials.

[17]  Kaibin Huang,et al.  Opportunistic Wireless Energy Harvesting in Cognitive Radio Networks , 2013, IEEE Transactions on Wireless Communications.

[18]  Jianqiu Li,et al.  A review on the key issues for lithium-ion battery management in electric vehicles , 2013 .

[19]  C. Robert,et al.  Bayesian estimation of hidden Markov chains: a stochastic implementation , 1993 .

[20]  M. Safari,et al.  Simulation-Based Analysis of Aging Phenomena in a Commercial Graphite/LiFePO4 Cell , 2011 .

[21]  M. Safari,et al.  Life Prediction Methods for Lithium-Ion Batteries Derived from a Fatigue Approach II. Capacity-Loss Prediction of Batteries Subjected to Complex Current Profiles , 2010 .

[22]  Ralph E. White,et al.  Development of First Principles Capacity Fade Model for Li-Ion Cells , 2004 .

[23]  Tao Zhang,et al.  Challenges of non-aqueous Li–O2 batteries: electrolytes, catalysts, and anodes , 2013 .

[24]  Vojtech Svoboda,et al.  Capacity and power fading mechanism identification from a commercial cell evaluation , 2007 .

[25]  N. H. Saad,et al.  Enhancing the design of battery charging controllers for photovoltaic systems , 2016 .

[26]  Joost-Pieter Katoen,et al.  Computing Optimal Schedules of Battery Usage in Embedded Systems , 2010, IEEE Transactions on Industrial Informatics.

[27]  Henrik Madsen,et al.  Optimal charging of an electric vehicle using a Markov decision process , 2013, 1310.6926.

[28]  Leonardo Badia,et al.  Optimal Transmission Policies for Energy Harvesting Devices With Limited State-of-Charge Knowledge , 2014, IEEE Transactions on Communications.

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

[30]  Yuying Yan,et al.  A critical review of thermal management models and solutions of lithium-ion batteries for the development of pure electric vehicles , 2016 .

[31]  Ajay Kapoor,et al.  Review of mechanical design and strategic placement technique of a robust battery pack for electric vehicles , 2016 .

[32]  Mohamed K. Watfa,et al.  A Battery-Aware High-Throughput MAC Layer Protocol in Sensor Networks , 2010, Int. J. Distributed Sens. Networks.

[33]  Chao Yang,et al.  Driving-behavior-aware stochastic model predictive control for plug-in hybrid electric buses , 2016 .

[34]  Delphine Riu,et al.  A review on lithium-ion battery ageing mechanisms and estimations for automotive applications , 2013 .

[35]  Min Gyu Kim,et al.  Green energy storage materials: Nanostructured TiO2 and Sn-based anodes for lithium-ion batteries , 2009 .

[36]  Toshio Ando,et al.  Single Molecular Imaging of a micro-Brownian Motion and a Bond Scission of a Supramolecular Chiral π-Conjugated Polymer as a Molecular Bearing Driven by Thermal Fluctuations , 2007 .

[37]  Doron Aurbach,et al.  On the capacity fading of LiCoO2 intercalation electrodes:: the effect of cycling, storage, temperature, and surface film forming additives , 2002 .

[38]  Liu Zhou,et al.  Development of novel lithium borate additives for designed surface modification of high voltage LiNi0.5Mn1.5O4 cathodes , 2016 .

[39]  Xianke Lin,et al.  A Comprehensive Capacity Fade Model and Analysis for Li-Ion Batteries , 2013 .

[40]  Chen Lu,et al.  Residual lifetime prediction for lithium-ion battery based on functional principal component analysis and Bayesian approach , 2015 .

[41]  Ren Asmussen,et al.  Fitting Phase-type Distributions via the EM Algorithm , 1996 .

[42]  Ridha Bouallegue,et al.  Energy Consumption Model in ad hoc Mobile Network , 2012, ArXiv.

[43]  Jun Zhang,et al.  The mean field theory in EM procedures for blind Markov random field image restoration , 1993, IEEE Trans. Image Process..

[44]  Eberhard Meissner,et al.  The challenge to the automotive battery industry : the battery has to become an increasingly integrated component within the vehicle electric power system , 2005 .

[45]  F. Baronti,et al.  Battery Management System: An Overview of Its Application in the Smart Grid and Electric Vehicles , 2013, IEEE Industrial Electronics Magazine.

[46]  Zechang Sun,et al.  ANFIS (adaptive neuro-fuzzy inference system) based online SOC (State of Charge) correction considering cell divergence for the EV (electric vehicle) traction batteries , 2015 .

[47]  V. Makis,et al.  Recursive filters for a partially observable system subject to random failure , 2003, Advances in Applied Probability.

[48]  R. Spotnitz.  Simulation of capacity fade in lithium-ion batteries , 2003 .

[49]  Chen Lu,et al.  Li-ion battery capacity estimation: A geometrical approach , 2014 .

[50]  Michael Pecht,et al.  Battery Management Systems in Electric and Hybrid Vehicles , 2011 .

[51]  Azah Mohamed,et al.  A review of the stage-of-the-art charging technologies, placement methodologies, and impacts of electric vehicles , 2016 .

[52]  Mohammadhosein Safari,et al.  Life-Prediction Methods for Lithium-Ion Batteries Derived from a Fatigue Approach I. Introduction: Capacity-Loss Prediction Based on Damage Accumulation , 2010 .

[53]  Ann Marie Sastry,et al.  Particle Interaction and Aggregation in Cathode Material of Li-Ion Batteries: A Numerical Study , 2011 .

[54]  Viliam Makis,et al.  Maximum Likelihood Estimation for a Hidden Semi-Markov Model with Multivariate Observations , 2012, Qual. Reliab. Eng. Int..

[55]  Ronald J. Jaszczak,et al.  Fully Bayesian estimation of Gibbs hyperparameters for emission computed tomography data , 1997, IEEE Transactions on Medical Imaging.

[56]  Saswati Sarkar,et al.  A framework for optimal battery management for wireless nodes , 2003, IEEE J. Sel. Areas Commun..

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

[58]  Deniz Gündüz,et al.  A general framework for the optimization of energy harvesting communication systems with battery imperfections , 2011, Journal of Communications and Networks.

[59]  A. Kwasinski,et al.  Development of a Markov-Chain-Based Energy Storage Model for Power Supply Availability Assessment of Photovoltaic Generation Plants , 2013, IEEE Transactions on Sustainable Energy.

[60]  Bo-Suk Yang,et al.  Intelligent prognostics for battery health monitoring based on sample entropy , 2011, Expert Syst. Appl..

[61]  M. Doyle,et al.  Modeling of Galvanostatic Charge and Discharge of the Lithium/Polymer/Insertion Cell , 1993 .

[62]  T. Rydén.  On recursive estimation for hidden Markov models , 1997 .

[63]  D. Titterington,et al.  Parameter estimation for hidden Markov chains , 2002 .

[64]  Yang Yang,et al.  Harnessing battery recovery effect in wireless sensor networks: Experiments and analysis , 2010, IEEE Journal on Selected Areas in Communications.

[65]  Ralph E. White,et al.  Calendar life study of Li-ion pouch cells: Part 2: Simulation , 2008 .

[66]  Rajab Khalilpour,et al.  Planning and operation scheduling of PV-battery systems: A novel methodology , 2016 .

[67]  Kaibin Huang,et al.  Energy Harvesting Wireless Communications: A Review of Recent Advances , 2015, IEEE Journal on Selected Areas in Communications.

[68]  Xinyi Wang,et al.  Computational model of 18650 lithium-ion battery with coupled strain rate and SOC dependencies , 2016 .

[69]  Kevin G. Gallagher,et al.  The significance of Li-ion batteries in electric vehicle life-cycle energy and emissions and recycling's role in its reduction , 2015 .

[70]  Chris Yuan,et al.  Multiphysics modeling of lithium ion battery capacity fading process with solid-electrolyte interphase growth by elementary reaction kinetics , 2014 .

[71]  Rodney M. LaFollette,et al.  Design Fundamentals of High Power Density, Pulsed Discharge, Lead‐Acid Batteries II . Modeling , 1990 .

[72]  Stefano Longo,et al.  A review on electric vehicle battery modelling: From Lithium-ion toward Lithium–Sulphur , 2016 .

[73]  Rodney M. LaFollette,et al.  Design fundamentals of high power density, pulsed discharge, lead acid batteries. I, Experimental , 1990 .

[74]  Richard D. Braatz,et al.  Modeling and Simulation of Lithium-Ion Batteries from a Systems Engineering Perspective , 2010 .

[75]  C. Moo,et al.  Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries , 2009 .

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

[77]  Andreas Jossen,et al.  Operating conditions of batteries in off-grid renewable energy systems , 2007 .

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

[79]  Dirk Uwe Sauer,et al.  A holistic aging model for Li(NiMnCo)O2 based 18650 lithium-ion batteries , 2014 .

[80]  Aylin Yener,et al.  Optimum Transmission Policies for Battery Limited Energy Harvesting Nodes , 2010, IEEE Transactions on Wireless Communications.

[81]  Ramesh R. Rao,et al.  Improving battery performance by using traffic shaping techniques , 2001, IEEE J. Sel. Areas Commun..

[82]  Humberto Verdejo,et al.  Stochastic modeling to represent wind power generation and demand in electric power system based on real data , 2016 .

[83]  Heinz Wenzl,et al.  Life prediction of batteries for selecting the technically most suitable and cost effective battery , 2005 .

[84]  Dusit Niyato,et al.  Sleep and Wakeup Strategies in Solar-Powered Wireless Sensor/Mesh Networks: Performance Analysis and Optimization , 2007, IEEE Transactions on Mobile Computing.

[85]  T. Rydén.  Consistent and Asymptotically Normal Parameter Estimates for Hidden Markov Models , 1994 .

[86]  Sebastian Risse,et al.  Capacity fading in lithium/sulfur batteries: A linear four-state model , 2014 .

[87]  Fayssal M. Safie,et al.  Probabilistic modeling of solar power systems , 1989, Proceedings., Annual Reliability and Maintainability Symposium.

[88]  D. Linden.  Handbook Of Batteries , 2001 .

[89]  Hamid Khayyam,et al.  Stochastic optimization models for energy management in carbonization process of carbon fiber production , 2015 .

[90]  Doron Aurbach,et al.  Challenges in the development of advanced Li-ion batteries: a review , 2011 .

[91]  Jay Lee,et al.  Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle (EV) safety and mobility , 2014 .

[92]  R. K. Krishna,et al.  POWER SAVING STRATEGIES IN WIRELESS SENSOR NETWORKS , 2011 .

[93]  Jean Alzieu,et al.  Improvement of intelligent battery controller : state-of-charge indicator and associated functions , 1997 .

[94]  W. Qian,et al.  Estimation of parameters in hidden Markov models , 1991, Philosophical Transactions of the Royal Society of London. Series A: Physical and Engineering Sciences.

[95]  Ramesh R. Rao,et al.  Energy efficient battery management , 2001, IEEE J. Sel. Areas Commun..

[96]  Jay Lee,et al.  A review on prognostics and health monitoring of Li-ion battery , 2011 .

[97]  Marijn R. Jongerden,et al.  Model-based energy analysis of battery powered systems , 2010 .

[98]  Anubhav Jain,et al.  Voltage, stability and diffusion barrier differences between sodium-ion and lithium-ion intercalation materials , 2011 .

[99]  Jun Chen,et al.  Porous LiMn2O4 nanorods with durable high-rate capability for rechargeable Li-ion batteries , 2011 .

[100]  Yi-Hsien Chiang,et al.  Online estimation of internal resistance and open-circuit voltage of lithium-ion batteries in electr , 2011 .

[101]  Sarma B. K. Vrudhula,et al.  Battery Modeling for Energy-Aware System Design , 2003, Computer.

[102]  Eberhard Meissner,et al.  Battery Monitoring and Electrical Energy Management , 2003 .

[103]  Viliam Makis,et al.  Model parameter estimation and residual life prediction for a partially observable failing system , 2015 .

[104]  John McPhee,et al.  A survey of mathematics-based equivalent-circuit and electrochemical battery models for hybrid and electric vehicle simulation , 2014 .

[105]  Laifa Tao,et al.  Similarity recognition of online data curves based on dynamic spatial time warping for the estimation of lithium-ion battery capacity , 2015 .

[106]  I. Villarreal,et al.  Critical review of state of health estimation methods of Li-ion batteries for real applications , 2016 .

[107]  Weerakorn Ongsakul,et al.  An efficient two stage stochastic optimal energy and reserve management in a microgrid , 2015 .

[108]  Lixia Yuan,et al.  Development and challenges of LiFePO4 cathode material for lithium-ion batteries , 2011 .

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