AUTHORS:
Prashant Electrical Engineering Department, National Institute of Technology Warangal, Warangal, India
Rupesh Kumar Nirala Electrical Engineering Department, National Institute of Technology Warangal, Warangal, India
Sailaja Kumari M Electrical Engineering Department, National Institute of Technology Warangal, Warangal, India

ABSTRACT:
Electric vehicle adoption is being promoted as a major counteract to mitigate vehicular pollution. This calls for improvement of technologies to enhance the reliability and safety of electric vehicles (EV). The battery pack of an EV being its major component needs to be monitored properly in every charge-discharge cycle as it is subjected to different drive profiles in terms of current flowing through them. Battery Management System (BMS) considers the dynamic parameters of the pack and performs various functionalities which are helpful for the safekeeping of vehicles as well as users. State of Charge (SoC) estimation is an important function that helps BMS to monitor deterioration in charge available in cell. This paper implements the state of art techniques on an accurate Enhanced Self-Correcting model of Li-ion cell which considers dynamics and hysteresis profile observed in cell subjected to drive cycles. This paper describes three approaches i.e. EKF, SPKF and Bar delta algorithms for SoC estimation. Case studies and comparison of all the techniques are presented.

Keywords: Proportional Integral Derivative (PID), Swarm Intelligence (SI), Renewable Energy (RE), Statistical Analysis

Conference Venue: Mövenpick Hotel & Apartments Bur Dubai, Dubai-UAE
Conference Date: 29-30 April 2023

ISBN Number: 978-625-00-1526-1
DOI Number: https://doi.org/10.53375/icmame.2023.226


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