D20.10 - Co-Estimation of SOC and SOT in Lithium-Ion Batteries using an RLS-Based Heat Generation Model

Thursday, March 20, 2025

11:30 AM – 1:30 PM ET

Location: Level One, Hall A3

Technical Dialogue Presenter(s)

JK

Jonghoon Kim

Chungnam National University

Presentation Paper

Co-Author(s)

Seongkyu Lee

Chungnam National University
MK

Minhyeok Kim

Chungnam National University
MS

Minwoo Song

Chungnam National University
EK

Eunjin Kang

Chungnam National University
JL

Jaea Lee

Chungnam National University
SL

Seunghyun Lee

Chungnam National University
WN

Woonki Na

California State University Fresno

Estimating the environmental temperature of a battery is very important. High temperatures can reduce the stability of the battery and accelerate the aging process, while low temperatures can decrease conductivity and diffusion rates, leading to reduced capacity and output. Therefore, maintaining a stable temperature during battery operation is crucial for ensuring optimal performance and longevity. This paper performs a pulse power characterization (HPPC) test to assess the initial characteristics of the battery, especially heat generation across various temperature ranges. Additionally, the recursive least squares (RLS) method is used to inversely estimate the parameters of the battery model from voltage and current data, allowing for accurate prediction of heat generation. Furthermore, this study integrates state-of-charge (SOC) estimation into the analysis to better understand battery behavior under varying operational conditions. The designed battery heat generation model is validated by comparing it with state-of-temperature (SOT) estimates and experimental data. The maximum temperature error observed was 7.8%, indicating that the model design is effective.