OPTIMIZING BATTERY THERMAL MANAGEMENT SYSTEMS FOR ELECTRIC VEHICLES USING FIREFLY OPTIMIZATION AND SUPPORT VECTOR MACHINES

Abstract

Battery Electric Vehicles (BEVs) have a high dependency on effective Battery Thermal Management Systems 
(BTMS) to guarantee better performance, safety and long-life cycle of lithium-ion battery. Such issues associated 
with temperatures variations demand efficient thermal control systems. The research paper is carried out under 
the scope of exploring the applicability of machine learning (especially, Support Vector Machines (SVM) and 
Firefly Optimization Algorithm (FOA)) in optimising BTMS behaviour in real-time. This combination of 
techniques enables efficiency and flexibility in manipulating cooling systems in the changing conditions of 
operation, greatly benefiting efficiency and minimizing the requirements of the costly testing facilities. Findings 
demonstrate that the proposed model manages to predict and optimize cooling power and Coefficient of 
Performance (COP) with a relatively low cost and scalable solution of thermal management of BEVs.