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A Rapid Batch State of Health Estimation Method for Retired Power Lithium-ion Batteries Using a Causal Dilated Convolution-Based Inception-Residual Hybrid Model

https://doi.org/10.2139/ssrn.5787569
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Lithium‐ion battery SOC / SOH adaptive estimation via simplified single particle model
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Artificial Intelligence-based health diagnostic of Lithium-ion battery leveraging transient stage of constant current and constant voltage charging
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A method to estimate battery SOH indicators based on vehicle operating data only
inferredcites →https://doi.org/10.1016/j.energy.2021.121224openalex-snapshot
Application domain extension of incremental capacity-based battery SoH indicators
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A novel battery health indicator and PSO-LSSVR for LiFePO4 battery SOH estimation during constant current charging
inferredcites →https://doi.org/10.1016/j.est.2023.106680openalex-snapshot
Convolutional autoencoder-based SOH estimation of lithium-ion batteries using electrochemical impedance spectroscopy
inferredcites →https://doi.org/10.1016/j.est.2023.107322openalex-snapshot
Battery SOH estimation and RUL prediction framework based on variable forgetting factor online sequential extreme learning machine and particle filter
inferredcites →https://doi.org/10.1016/j.est.2023.108693openalex-snapshot
A new method for determining SOH of lithium batteries using the real-part ratio of EIS specific frequency impedance
inferredcites →https://doi.org/10.1016/j.jpowsour.2014.11.066openalex-snapshot
Simplified electrochemical multi-particle model for LiFePO4 cathodes in lithium-ion batteries
inferredcites →https://doi.org/10.1016/j.jpowsour.2018.06.036openalex-snapshot
A fast estimation algorithm for lithium-ion battery state of health
inferredcites →https://doi.org/10.1016/j.microrel.2020.113798openalex-snapshot
Lithium-ion battery SoH estimation based on incremental capacity peak tracking at several current levels for online application
inferredcites →https://doi.org/10.1016/j.wasman.2021.02.032openalex-snapshot
On the influence of second use, future battery technologies, and battery lifetime on the maximum recycled content of future electric vehicle batteries in Europe
inferredcites →https://doi.org/10.1109/tec.2023.3282017openalex-snapshot
Lithium-Ion Batteries SOH Estimation With Multimodal Multilinear Feature Fusion
inferredcites →https://doi.org/10.1109/tie.2021.3097668openalex-snapshot
A Fast Impedance Calculation-Based Battery State-of-Health Estimation Method
inferredcites →https://doi.org/10.1109/tte.2016.2571778openalex-snapshot
On the Suitability of Electrochemical-Based Modeling for Lithium-Ion Batteries
inferredcites →https://doi.org/10.1109/tvt.2019.2927120openalex-snapshot
Online State-of-Health Estimation for Li-Ion Battery Using Partial Charging Segment Based on Support Vector Machine
inferredcites →https://doi.org/10.23919/acc53348.2022.9867320openalex-snapshot
A Novel Coupled Electro-thermal-aging Model for Simultaneous SOC, SOH, and Parameter Estimation of Lithium-ion Batteries
inferredcites →https://doi.org/10.3390/batteries9010057openalex-snapshot
A Review on Dynamic Recycling of Electric Vehicle Battery: Disassembly and Echelon Utilization
inferredcites →https://doi.org/10.3390/en10040512openalex-snapshot
An Online SOC and SOH Estimation Model for Lithium-Ion Batteries
inferredcites →https://doi.org/10.3390/en16031240openalex-snapshot
Accurate and Efficient SOH Estimation for Retired Batteries
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