scholarly journals Physical-Based Training Data Collection Approach for Data-Driven Lithium-ion Battery State-of-Charge Prediction

Energy and AI ◽  
2021 ◽  
pp. 100094
Author(s):  
Jie Li ◽  
Will Ziehm ◽  
Jonathan Kimball ◽  
Robert Landers ◽  
Jonghyun Park
Author(s):  
Zhimin Xi ◽  
Rong Jing ◽  
Cheol Lee

This paper investigates recent research on battery diagnostics and prognostics especially for Lithium-ion (Li-ion) batteries. Battery diagnostics focuses on battery models and diagnosis algorithms for battery state of charge (SOC) and state of health (SOH) estimation. Battery prognostics elaborates data-driven prognosis algorithms for predicting the remaining useful life (RUL) of battery SOC and SOH. Readers will learn not only basics but also very recent research developments on battery diagnostics and prognostics.


Sign in / Sign up

Export Citation Format

Share Document