Range anxiety and forced downtime due to system failure are two critical issues that desist widespread adoption of electric vehicles by fleet management and logistic companies. Our research aims to solve such issues by using,
We have an in-house simulation platform for complete xEV simulation, to study EV system performance. The dynamic simulator consists of multi-domain physics-based engine model, electric power train, thermal management systems, electronic actuators, engine control unit, vehicle control unit, etc. This platform enables the analysis and optimization of xEV range and fuel consumption.
To extend the life of Lithium-ion batteries in electric vehicles and energy storage systems, the degradation of its performance over time needs to be estimated. Hence, we have developed an estimation method for Lithium-ion battery packs that finds the distribution of State of Health (SOH) by actively sensing the voltage transient response at certain time points in its operation cycle. Our approach works even if the battery management system (BMS) shares the voltage data of only limited cells[1]. This method judges the distribution of SOH among the cells of the battery pack, thereby helping the user to predict the end of battery life.
[1] Miftahullatif, E. B., Yamauchi, S., Subramanian, J., Ikeda, Y., & Kohnoet al. "Novel state-of-health prediction method for lithium-ion batteries in battery storage system by using voltage variation at rest period after discharge." 2019 IEEE 4th International Future Energy Electronics Conference (IFEEC). IEEE, 2019.
