A novel method of discharge capacity prediction based on simplified electrochemical model-aging mechanism for lithium-ion batteries

[1]  Haifeng Dai,et al.  Investigation of capacity fade for 18650-type lithium-ion batteries cycled in different state of charge (SoC) ranges , 2021 .

[2]  W. D. Widanage,et al.  Theory of battery ageing in a lithium-ion battery: Capacity fade, nonlinear ageing and lifetime prediction , 2020, Journal of Power Sources.

[3]  Kwok-Leung Tsui,et al.  Lifespan prediction of lithium-ion batteries based on various extracted features and gradient boosting regression tree model , 2020 .

[4]  Yong Guan,et al.  The capacity estimation and cycle life prediction of lithium-ion batteries using a new broad extreme learning machine approach , 2020 .

[5]  Lin Chen,et al.  Remaining useful life prediction for lithium-ion battery by combining an improved particle filter with sliding-window gray model , 2020, Energy Reports.

[6]  Michael Pecht,et al.  Aging modes analysis and physical parameter identification based on a simplified electrochemical model for lithium-ion batteries , 2020 .

[7]  Fengchun Sun,et al.  Online Fault Diagnosis of External Short Circuit for Lithium-Ion Battery Pack , 2020, IEEE Transactions on Industrial Electronics.

[8]  Cheng Cheng,et al.  Remaining useful life prediction of lithium-ion batteries with adaptive unscented kalman filter and optimized support vector regression , 2020, Neurocomputing.

[9]  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 .

[10]  Zhe Li,et al.  A review on the key issues of the lithium ion battery degradation among the whole life cycle , 2019, eTransportation.

[11]  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.

[12]  Fengchun Sun,et al.  A Sensor Fault Diagnosis Method for a Lithium-Ion Battery Pack in Electric Vehicles , 2019, IEEE Transactions on Power Electronics.

[13]  Rui Xiong,et al.  Towards a smarter hybrid energy storage system based on battery and ultracapacitor - A critical review on topology and energy management , 2018, Journal of Cleaner Production.

[14]  M. Eikerling,et al.  Statistical physics-based model of mechanical degradation in lithium ion batteries , 2018, Electrochimica Acta.

[15]  Michael Pecht,et al.  A parameter estimation method for a simplified electrochemical model for Li-ion batteries , 2018, Electrochimica Acta.

[16]  Linlin Li,et al.  An electrochemical model based degradation state identification method of Lithium-ion battery for all-climate electric vehicles application , 2018, Applied Energy.

[17]  Jonghyun Park,et al.  A Single Particle Model with Chemical/Mechanical Degradation Physics for Lithium Ion Battery State of Health (SOH) Estimation , 2018 .

[18]  Huajing Fang,et al.  A new hybrid method for the prediction of the remaining useful life of a lithium-ion battery , 2017 .

[19]  Furong Gao,et al.  Observer based battery SOC estimation: Using multi-gain-switching approach , 2017 .

[20]  Oleg Wasynczuk,et al.  Physically-based reduced-order capacity loss model for graphite anodes in Li-ion battery cells , 2017 .

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

[22]  Simon F. Schuster,et al.  Nonlinear aging of cylindrical lithium-ion cells linked to heterogeneous compression , 2016 .

[23]  Michael Pecht,et al.  A failure modes, mechanisms, and effects analysis (FMMEA) of lithium-ion batteries , 2015 .

[24]  Tanvir R. Tanim,et al.  Aging formula for lithium ion batteries with solid electrolyte interphase layer growth , 2015 .

[25]  Simon F. Schuster,et al.  Nonlinear aging characteristics of lithium-ion cells under different operational conditions , 2015 .

[26]  Balaji Krishnamurthy,et al.  A Mathematical model to study the effect of potential drop across the SEI layer on the capacity fading of a lithium ion battery , 2015 .

[27]  Michael A. Danzer,et al.  Nondestructive detection, characterization, and quantification of lithium plating in commercial lithium-ion batteries , 2014 .

[28]  Mark W. Verbrugge,et al.  Battery Cycle Life Prediction with Coupled Chemical Degradation and Fatigue Mechanics , 2012 .

[29]  M. Verbrugge,et al.  Application of Hasselman’s Crack Propagation Model to Insertion Electrodes , 2010 .

[30]  W. Shyy,et al.  Numerical Simulation of Intercalation-Induced Stress in Li-Ion Battery Electrode Particles , 2007 .

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

[32]  Ralph E. White,et al.  Capacity Fade Mechanisms and Side Reactions in Lithium‐Ion Batteries , 1998 .

[33]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[34]  Lifeng Wu,et al.  A hybrid drive method for capacity prediction of lithium-ion batteries , 2021, IEEE Transactions on Transportation Electrification.

[35]  Ralph E. White,et al.  State of Charge and Loss of Active Material Estimation of a Lithium Ion Cell under Low Earth Orbit Condition Using Kalman Filtering Approaches , 2012 .