State of health estimation of lithium-ion batteries based on the regional triangle

[1]  W. Liu,et al.  Characterization of aging mechanisms and state of health for second-life 21700 ternary lithium-ion battery , 2022, Journal of Energy Storage.

[2]  Hailin Feng,et al.  State of health estimation of large-cycle lithium-ion batteries based on error compensation of autoregressive model , 2022, Journal of Energy Storage.

[3]  W. Liu,et al.  State-of-health estimation of retired lithium-ion battery module aged at 1C-rate , 2022, Journal of Energy Storage.

[4]  X. Mei,et al.  Constant Current Charging Time Based Fast State-of-Health Estimation for Lithium-Ion Batteries , 2022, SSRN Electronic Journal.

[5]  Xiaosong Hu,et al.  Battery health evaluation using a short random segment of constant current charging , 2022, iScience.

[6]  A. Hentunen,et al.  State of health estimation of cycle aged large format lithium-ion cells based on partial charging , 2022, Journal of Energy Storage.

[7]  Yajie Liu,et al.  State-of-health estimation for lithium-ion batteries by combining model-based incremental capacity analysis with support vector regression , 2022, Energy.

[8]  Shao-Chu Huang,et al.  State of health estimation of lithium-ion batteries based on the regional frequency , 2022, Journal of Power Sources.

[9]  Ping Wang,et al.  A remaining capacity estimation approach of lithium-ion batteries based on partial charging curve and health feature fusion , 2021, Journal of Energy Storage.

[10]  Qichao Zhang,et al.  State-of-health estimation of batteries in an energy storage system based on the actual operating parameters , 2021 .

[11]  Qichao Zhang,et al.  Aging performance characterization and state-of-health assessment of retired lithium-ion battery modules , 2021 .

[12]  Xiaosong Hu,et al.  General Discharge Voltage Information Enabled Health Evaluation for Lithium-Ion Batteries , 2021, IEEE/ASME Transactions on Mechatronics.

[13]  Zhiyong Zhang,et al.  State-of-charge estimation of lithium-ion battery pack by using an adaptive extended Kalman filter for electric vehicles , 2021 .

[14]  P. Venet,et al.  A method to estimate battery SOH indicators based on vehicle operating data only , 2021 .

[15]  Daniel-Ioan Stroe,et al.  Incremental Capacity Analysis Applied on Electric Vehicles for Battery State-of-Health Estimation , 2021, IEEE Transactions on Industry Applications.

[16]  Jun Wang,et al.  Estimation and prediction of state of health of electric vehicle batteries using discrete incremental capacity analysis based on real driving data , 2021, Energy.

[17]  Heath Hofmann,et al.  Robust State of Health estimation of lithium-ion batteries using convolutional neural network and random forest , 2020, Journal of Energy Storage.

[18]  Bijaya Ketan Panigrahi,et al.  Deep learning networks for capacity estimation for monitoring SOH of Li‐ion batteries for electric vehicles , 2020, International Journal of Energy Research.

[19]  Kun Li,et al.  A review of the state of health for lithium-ion batteries: Research status and suggestions , 2020 .

[20]  Xuezhe Wei,et al.  Incremental capacity analysis based adaptive capacity estimation for lithium-ion battery considering charging condition , 2020 .

[21]  Yan Li,et al.  Performance assessment of retired EV battery modules for echelon use , 2020 .

[22]  Renjing Gao,et al.  A model‐based and data‐driven joint method for state‐of‐health estimation of lithium‐ion battery in electric vehicles , 2019, International Journal of Energy Research.

[23]  Xuning Feng,et al.  Online State-of-Health Estimation for Li-Ion Battery Using Partial Charging Segment Based on Support Vector Machine , 2019, IEEE Transactions on Vehicular Technology.

[24]  Feng Liu,et al.  A Neural-Network-Based Method for RUL Prediction and SOH Monitoring of Lithium-Ion Battery , 2019, IEEE Access.

[25]  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.

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

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

[28]  Zhenpo Wang,et al.  State-of-Health Estimation for Lithium-Ion Batteries Based on the Multi-Island Genetic Algorithm and the Gaussian Process Regression , 2017, IEEE Access.

[29]  Weige Zhang,et al.  Recognition of battery aging variations for LiFePO 4 batteries in 2nd use applications combining incremental capacity analysis and statistical approaches , 2017 .

[30]  Zhendong Zhang,et al.  Recording frequency optimization for massive battery data storage in battery management systems , 2016 .

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

[32]  Xiaosong Hu,et al.  Data-Driven Battery State of Health Estimation Based on Random Partial Charging Data , 2022, IEEE Transactions on Power Electronics.

[33]  Zhenpo Wang,et al.  State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression , 2020 .

[34]  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 .