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Poster Session I : Next Generation Computing Applications I

Polynomial Regression Modeling for Efficient Prediction of Battery Rate Capability

첫 페이지 보기
  • 발행기관
    한국차세대컴퓨팅학회 바로가기
  • 간행물
    한국차세대컴퓨팅학회 학술대회 바로가기
  • 통권
    The 10th International Conference on Next Generation Computing 2024 (2024.11)바로가기
  • 페이지
    pp.78-81
  • 저자
    Sang Il Yoon, Min Je Kim, Noman Khan, Hikmat Yar, Seoa Kim, Jungwook Choi, Chan Mi Jeon, Huisu Jeung, Kyungjung Kwon, Sung Wook Baik
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A468803

원문정보

초록

영어
The battery market is experiencing rapid growth due to advancements in technology and increased recycling efforts. Verifying the suitability of developed batteries through rate capability experiments, which measure capacity based on charging and discharging speeds, is essential but resource-intensive and time-consuming. This research proposes a method to predict battery rate capability using a polynomial regression model based on similar data groups, aiming to shorten these experiments. The research was conducted in two main stages, namely the construction of the dataset and the development of the predictive model. Data was collected from experimental graphs in existing literature and new experiments on Coin Cell batteries. Through preprocessing steps including deduplication, interpolation, and extrapolation, a comprehensive dataset was created. A combined Quadratic and Linear Piecewise Interpolation method was developed to handle missing data efficiently. In the model development stage, polynomial regression models were created for groups of similar battery data, allowing accurate predictions for partial rate capability experiments. Experimental results demonstrated high accuracy, significantly reducing the need for extensive testing. The proposed method offers substantial time and resource savings, enhancing the efficiency of the battery development process.

목차

Abstract
I. INTRODUCTION
II. PROPOSED METHODOLOGY
A. Data Collection
B. Data Preprocess
C. Prediction
III. RESULTS
A. Data Grouping
B. Evaluation
IV. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

키워드

Battery Rate Capability Data Interpolation Data Extrapolation Polynomial Regression Model

저자

  • Sang Il Yoon [ Sejong University Seoul, Republic of Korea ]
  • Min Je Kim [ Sejong University Seoul, Republic of Korea ]
  • Noman Khan [ Sejong University Seoul, Republic of Korea ]
  • Hikmat Yar [ Sejong University Seoul, Republic of Korea ]
  • Seoa Kim [ Sejong University Seoul, Republic of Korea ]
  • Jungwook Choi [ Sejong University Seoul, Republic of Korea ]
  • Chan Mi Jeon [ Sejong University Seoul, Republic of Korea ]
  • Huisu Jeung [ Sejong University Seoul, Republic of Korea ]
  • Kyungjung Kwon [ Sejong University Seoul, Republic of Korea ]
  • Sung Wook Baik [ Sejong University Seoul, Republic of Korea ] Corresponding Author

참고문헌

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    한국차세대컴퓨팅학회 [Korean Institute of Next Generation Computing]
  • 설립연도
    2005
  • 분야
    공학>컴퓨터학
  • 소개
    본 학회는 차세대 PC 및 그 관련분야의 학술활동을 통하여 차세대 PC의 학문 및 기술발전을 도모하고 산업발전 및 국제협력 증진을 목적으로 한다.

간행물

  • 간행물명
    한국차세대컴퓨팅학회 학술대회
  • 간기
    반년간
  • 수록기간
    2021~2025
  • 십진분류
    KDC 566 DDC 004

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