Battery lifetime prediction and performance assessment of different modeling approaches

Summary Lithium-ion battery technologies have conquered the current energy storage market as the most preferred choice thanks to their development in a longer lifetime. However, choosing the most suitable battery aging modeling methodology based on investigated lifetime characterization is still a challenge. In this work, a comprehensive aging dataset of nickel-manganese-cobalt oxide (NMC) cell is used to develop and/or train different capacity fade models to compare output responses. The assessment is conducted for semi-empirical modeling (SeM) approach against a machine learning model and an artificial neural network model. Among all, the nonlinear autoregressive network (NARXnet) can predict the capacity degradation most precisely minimizing the computational effort as well. This research work signifies the importance of lifetime methodological choice and model performance in understanding the complex and nonlinear Li-ion battery aging behavior.

[1]  Haritza Camblong,et al.  A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation , 2018 .

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

[3]  Zonghai Chen,et al.  An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks , 2016 .

[4]  Kristen A. Severson,et al.  Data-driven prediction of battery cycle life before capacity degradation , 2019, Nature Energy.

[5]  Md Sazzad Hosen,et al.  Electro-aging model development of nickel-manganese-cobalt lithium-ion technology validated with light and heavy-duty real-life profiles , 2020 .

[6]  Joshua B. Tenenbaum,et al.  Structure Discovery in Nonparametric Regression through Compositional Kernel Search , 2013, ICML.

[7]  Yang Li,et al.  Technological Developments in Batteries: A Survey of Principal Roles, Types, and Management Needs , 2017, IEEE Power and Energy Magazine.

[8]  D. Sauer,et al.  Investigation of the influence of different bracing of automotive pouch cells on cyclic liefetime and impedance spectra , 2019, Journal of Energy Storage.

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

[10]  Dirk Uwe Sauer,et al.  Influence of operational condition on lithium plating for commercial lithium-ion batteries – Electrochemical experiments and post-mortem-analysis , 2017 .

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

[12]  J. Marcoc,et al.  Data-driven nonparametric Li-ion battery ageing model aiming at learning from real operation data-Part B_ Cycling operation , 2020 .

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

[14]  Yu Peng,et al.  Prognostics for state of health estimation of lithium-ion batteries based on combination Gaussian process functional regression , 2013, Microelectron. Reliab..

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

[16]  M. Broussely,et al.  Aging mechanism in Li ion cells and calendar life predictions , 2001 .

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

[18]  Michael Buchholz,et al.  Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods , 2013 .

[19]  Dong Wang,et al.  Piecewise model based intelligent prognostics for state of health prediction of rechargeable batteries with capacity regeneration phenomena , 2019 .

[20]  Kwok-Leung Tsui,et al.  An ensemble model for predicting the remaining useful performance of lithium-ion batteries , 2013, Microelectron. Reliab..

[21]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[22]  B. Nykvist,et al.  Rapidly falling costs of battery packs for electric vehicles , 2015 .

[23]  D. Sauer,et al.  Calendar and cycle life study of Li(NiMnCo)O2-based 18650 lithium-ion batteries , 2014 .

[24]  Xiaosong Hu,et al.  Battery Lifetime Prognostics , 2020 .

[25]  Ala A. Hussein,et al.  Capacity Fade Estimation in Electric Vehicle Li-Ion Batteries Using Artificial Neural Networks , 2015, IEEE Transactions on Industry Applications.

[26]  Euan McTurk,et al.  Identification and machine learning prediction of knee-point and knee-onset in capacity degradation curves of lithium-ion cells , 2020 .

[27]  Joris de Hoog,et al.  Combined cycling and calendar capacity fade modeling of a Nickel-Manganese-Cobalt Oxide Cell with real-life profile validation , 2017 .

[28]  Simona Onori,et al.  A control-oriented cycle-life model for hybrid electric vehicle lithium- ion batteries , 2016 .

[29]  Dirk Uwe Sauer,et al.  Development of a lifetime prediction model for lithium-ion batteries based on extended accelerated aging test data , 2012 .

[30]  Haritza Camblong,et al.  Data-driven nonparametric Li-ion battery ageing model aiming at learning from real operation data – Part A: Storage operation , 2020 .

[31]  Fu-Kwun Wang,et al.  A hybrid model based on support vector regression and differential evolution for remaining useful lifetime prediction of lithium-ion batteries , 2018, Journal of Power Sources.

[32]  M. R. Palacín,et al.  Why do batteries fail? , 2016, Science.

[33]  Michael A. Osborne,et al.  Battery health prediction under generalized conditions using a Gaussian process transition model , 2018, Journal of Energy Storage.

[34]  Joeri Van Mierlo,et al.  Random forest regression for online capacity estimation of lithium-ion batteries , 2018, Applied Energy.

[35]  David Flynn,et al.  A Physics-Based Electrochemical Model for Lithium-Ion Battery State-of-Charge Estimation Solved by an Optimised Projection-Based Method and Moving-Window Filtering , 2018, Energies.

[36]  Joeri Van Mierlo,et al.  A combined thermo-electric resistance degradation model for nickel manganese cobalt oxide based lithium-ion cells , 2018 .

[37]  Jean-Michel Vinassa,et al.  Behavior and state-of-health monitoring of Li-ion batteries using impedance spectroscopy and recurrent neural networks , 2012 .

[38]  M Rosa Palacín,et al.  Understanding ageing in Li-ion batteries: a chemical issue. , 2018, Chemical Society reviews.

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

[40]  K. Goebel,et al.  An integrated approach to battery health monitoring using bayesian regression and state estimation , 2007, 2007 IEEE Autotestcon.

[41]  Shan Zhu,et al.  Predicting battery life with early cyclic data by machine learning , 2019, Energy Storage.

[42]  Xiaolong Zhang,et al.  A Semi-Empirical Capacity Degradation Model of EV Li-Ion Batteries Based on Eyring Equation , 2013, 2013 IEEE Vehicle Power and Propulsion Conference (VPPC).

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

[44]  P. Bruce,et al.  Degradation diagnostics for lithium ion cells , 2017 .

[45]  Dirk Uwe Sauer,et al.  Cycle and calendar life study of a graphite|LiNi1/3Mn1/3Co1/3O2 Li-ion high energy system. Part A: Full cell characterization , 2013 .

[46]  Joeri Van Mierlo,et al.  Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review , 2019, Renewable and Sustainable Energy Reviews.

[47]  B. Liaw,et al.  Path dependence of lithium ion cells aging under storage conditions , 2016 .

[48]  Lucia Gauchia,et al.  Deterministic models of Li-ion battery aging: It is a matter of scale , 2018, Journal of Energy Storage.

[49]  Bhaskar Saha,et al.  An Adaptive Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-ion Batteries , 2010 .

[50]  Hicham Chaoui,et al.  State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks , 2017, IEEE Transactions on Vehicular Technology.

[51]  Yi Li,et al.  Modified Gaussian Process Regression Models for Cyclic Capacity Prediction of Lithium-Ion Batteries , 2019, IEEE Transactions on Transportation Electrification.

[52]  Frede Blaabjerg,et al.  State of Charge Estimation for Lithium-Ion Battery Using Recurrent NARX Neural Network Model Based Lighting Search Algorithm , 2018, IEEE Access.

[53]  Yi Li,et al.  Gaussian Process Regression With Automatic Relevance Determination Kernel for Calendar Aging Prediction of Lithium-Ion Batteries , 2020, IEEE Transactions on Industrial Informatics.

[54]  B. Scrosati,et al.  Lithium batteries: Status, prospects and future , 2010 .