OPITZ A, BADAMI P, SHEN L, et al. Can Li-IonBatteries Be the Panacea for Automotive Applications?[J]. Renewable and Sustainable Energy Reviews, 2017, 68: 685-692. doi: 10.1016/j.rser.2016.10.019
HAN X B, LU L G, ZHENG Y J, et al. A Review on the Key Issues of the Lithium Ion Battery Degradation among the Whole Life Cycle[J]. ETransportation, 2019, 1: 100005. doi: 10.1016/j.etran.2019.100005
HASIB S A, ISLAM S, CHAKRABORTTY R K, et al. A Comprehensive Review of Available Battery Datasets, RUL Prediction Approaches, and Advanced Battery Management[J]. IEEE Access, 2021, 9: 86166-86193. doi: 10.1109/ACCESS.2021.3089032
TULSYAN A, TSAI Y, GOPALUNI R B, et al. State-of-Charge Estimation in Lithium-Ion Batteries: a Particle Filter Approach[J]. Journal of Power Sources, 2016, 331: 208-223. doi: 10.1016/j.jpowsour.2016.08.113
RAHIMIAN S K, RAYMAN S, WHITE R E. State of Charge and Loss of Active Material Estimation of a Lithium Ion Cell under Low Earth Orbit Condition Using Kalman Filtering Approaches[J]. Journal ofthe Electrochemical Society, 2012, 159(6): A860-A872. doi: 10.1149/2.098206jes
FAN G D, LI X Y, ZHANG R G. Global Sensitivity Analysis on Temperature-Dependent Parameters of a Reduced-Order Electrochemical Model and Robust State-of-Charge Estimation at Different Temperatures[J]. Energy, 2021, 223: 120024. doi: 10.1016/j.energy.2021.120024
JIANG B, DAI H F, WEI X Z, et al. Multi-Kernel Relevance Vector Machine with Parameter Optimization for Cycling Aging Prediction of Lithium-Ion Batteries[J]. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2023, 11(1): 175-186. doi: 10.1109/JESTPE.2021.3133697
YANG F F, WANG D, XU F, et al. Lifespan Prediction of Lithium-Ion Batteries Based on Various Extracted Features and Gradient Boosting Regression Tree Model[J]. Journal of Power Sources, 2020, 476: 228654. doi: 10.1016/j.jpowsour.2020.228654
TIAN J P, XIONG R, SHEN W X, et al. Flexible Battery State of Health and State of Charge Estimation Using Partial Charging Data and Deep Learning[J]. Energy Storage Materials, 2022, 51: 372-381. doi: 10.1016/j.ensm.2022.06.053
REN L, DONG J B, WANG X K, et al. A Data-Driven Auto-CNN-LSTM Prediction Model for Lithium-Ion Battery Remaining Useful Life[J]. IEEE Transactions on Industrial Informatics, 2021, 17(5): 3478-3487. doi: 10.1109/TII.2020.3008223
ZHU J G, WANG Y X, HUANG Y, et al. Data-Driven Capacity Estimation of Commercial Lithium-Ion Batteries from Voltage Relaxation[J]. Nature Communications, 2022, 13(1): 2261. doi: 10.1038/s41467-022-29837-w
WANG Y X, ZHU J G, CAO L, et al. A Generalizable Method for Capacity Estimation and RUL Prediction in Lithium-Ion Batteries[J]. Industrial & Engineering Chemistry Research, 2024, 63(1): 345-357.
LIN C P, XU J, HOU J Y, et al. A Fast Data-Driven Battery Capacity Estimation Method under Non-Constant Current Charging and Variable Temperature[J]. Energy Storage Materials, 2023, 63: 102967. doi: 10.1016/j.ensm.2023.102967
WANG Y X, ZHU J G, CAO L, et al. Online Capacity Estimation of Lithium-Ion Batteries by Partial Incremental Capacity Curve[C] //2022 IEEE Vehicle Power and Propulsion Conference (VPPC). NewYork: IEEE Press, 2022: 1-6.