Adaptive Derating Algorithm for EV Application Based-Li-Ion Battery for Safe and Healthy Operation

Abstract

<div class="section abstract"><div class="htmlview paragraph">Battery packs used in Electric Vehicles (EVs) pose significant safety risks and can incur additional costs and downtime when facing extreme conditions such as thermal and undervoltage hard cut-off. This article emphasizes the importance of implementing thermal and voltage based derating techniques to ensure the safe operation of battery packs. Thermal derating controls the maximum allowed battery current to prevent thermal runaway along with maintaining the health of the cells. While voltage derating prevents cut-off at low SOC regions by managing the cell voltage operating range through real time calculation of DCIR based voltage drop. By adopting these methods, battery packs can operate more safely and reliably in various environment conditions, which is essential in many applications.</div><div class="htmlview paragraph">The article introduces the adaptive derating index, which utilizes State of Health (SoH) and real-time measurements of battery parameters such as temperature, voltage drop, and current to adjust the derating levels accurately. This approach provides more efficient and precise control over battery operation, particularly in dynamic conditions. Derating through battery current is seamlessly done to make sure rider does not feel sudden drop/jerk in power delivery. This algorithm is developed in Matlab/Simulink environment which has properly correlated model wrt real system. The same is then tested and validated in real vehicle. By incorporating the adaptive derating index, battery packs can operate at their optimal levels while maintaining their safety and reliability.</div></div>