Journal of Environmental Science International (한국환경과학회지)

The Korean Environmental Sciences Society (한국환경과학회)
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Development and Empirical Validation of an Electric Vehicle Battery Consumption Analysis Model

전기차 배터리 소모량 분석모형 개발 및 실증

  • In-Seon Suh (ECOBRAIN Co.,Ltd.) ;
  • Young-Mi Lee (ECOBRAIN Co.,Ltd.) ;
  • Sang-Yul Oh (ECOBRAIN Co.,Ltd.) ;
  • Myeong-Chang Gwak (ECOBRAIN Co.,Ltd.) ;
  • Hyeon-Ji Lee (ECOBRAIN Co.,Ltd.)
  • 서인선 ;
  • 이영미 ;
  • 오상율 ;
  • 곽명창 ;
  • 이현지
  • Received : 2024.06.17
  • Accepted : 2024.07.05
  • Published : 2024.07.31
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Abstract

In popular tourist destinations such as Jeju and Gangwon, electric rental cars are increasingly adopted. However, sudden battery drain due to weather conditions can pose safety issues. To address this, we developed a battery consumption analysis model that considers resistive energy factors such as acceleration, rolling resistance, and aerodynamic drag. Focusing on the effects of ambient temperature and wind speed, the model's performance was evaluated during an empirical validation period from November to December 2023. Comparing predicted and actual state of charge (SoC) across different routes identified ambient temperature, wind speed, and driving time as major sources of error. The mean absolute error (MAE) increased with lower temperatures due to reduced battery efficiency. Higher wind speeds on routes 1 and 6 resulted in larger errors, indicating the model's limitation in considering only tailwinds for aerodynamic drag calculations. Additionally, longer driving times led to higher actual SoC than predicted, suggesting the need to account for varying driver habits influenced by road conditions. Our model, providing more accurate SoC predictions to prevent battery depletion incidents, shows high potential for application in navigation apps for electric vehicle users in tourist areas. Future research should endeavor to the model by including wind direction, HVAC system usage, and braking frequency to improve prediction accuracy further.

Keywords

Acknowledgement

이 연구는 2022년도 중소벤처기업부의 기술개발사업(S3310841) 지원을 받아 수행되었습니다.

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