State-of-Health Forecasting for Battery Cells using Bayesian Inference and LSTM-based Change Point Detection
Author:
Affiliation:
1. University of Michigan – Dearborn,Industrial and Manufacturing Systems Engineering Department,Dearborn,MI,USA
2. Prince Mohammad Bin Fahd University,Electrical Engineering Department,Al Khobar,Saudi Arabia
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10361838/10361943/10362886.pdf?arnumber=10362886
Reference28 articles.
1. A comprehensive review of lithium-ion batteries used in hybrid and electric vehicles at cold temperatures
2. Cause and Mitigation of Lithium-Ion Battery Failure—A Review
3. Data-driven prediction of battery cycle life before capacity degradation
4. Battery monitoring and prognostics optimization techniques: Challenges and opportunities
5. Fusion estimation strategy based on dual adaptive Kalman filtering algorithm for the state of charge and state of health of hybrid electric vehicle Li‐ion batteries
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