Abstract
The increase in electric vehicles has surpassed expectations leading to the eventual replacement of traditional IC (internal combustion) engine vehicles. However, to achieve this, it is crucial to research and develop more efficient and reliable electric batteries to create a sustainable transportation system. The performance of the battery directly impacts the power and range of the vehicle making battery management research imperative. Accurate estimation of battery state of charge (SoC) and temperature is vital for the overall performance, drivability and safety of the vehicle. This paper proposes a comprehensive approach to create an AI-based model to estimate the battery SoC and temperature that matches the performance of conventional vehicles. Various regression models are used as prediction models and the results are presented. These insights offer valuable understandings of battery thermal behavior, aiding in the design of an effective battery management system.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
