Electric Vehicle (EV) Preventive Diagnostic System: Solution for Thermal Management of Battery packs using AIOT

• Published in: 2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC) pp. 0041 - 0046
• Main Authors: Kumar, Lohith, Choudhury, Debajit, Paduri, Anwesh Reddy, Kumar, Senthil, Sahoo, Devashis, Murthy, Jalaja, Darapaneni, Narayana
• Format: Conference Proceeding
• Language: English
• Published: IEEE 08.03.2023
• Subjects: AIOT; Artificial Neural Network (ANN); Batteries; Big data analytics; Electric vehicles; Li-ion batteries; Temperature; Temperature dependence; Temperature measurement; Temperature sensors; Thermal management; vehicle preventive maintenance
• DOI: 10.1109/CCWC57344.2023.10099185

Penetration of E-Mobility recent years in moving towards exponential growth having target towards carbon neutral agreement across the globe. However, electric vehicles come with challenges specific reliability, durability, operability and maintenance. The foremost important is thermal management as directly affects the performance, and operability of the e-mobility. Li-ion batteries used in EV are packed with very higher energy density in the e-mobility triggering to lower thermal stability, leading to safety critical problems, e.g., Thermal Incidents (TR). The operability, service life and cost of battery packs are the driving factors. The battery functional parameters i.e. charge & discharge cycle, peak power during various load profiles depending on the drive terrain are optimized when they are operated at optimized temperature. The variation in temperature has negative impact on battery delivery parameters, overall service life & efficiency of the system. Battery thermal management became critical for safe drivability. Using the abuse parameters of batteries monitored by Battery Thermal Management System (BTMS), the target is to come up with an AIOT based preventive diagnostic system enabling safe drivability and efficient maintainability thereby addressing complete supply chain by recommend/notification predictions to Run-to-Failure (RtF) maintenance, planned Preventive Maintenance (PvM) and Predictive Maintenance (PdM) to end users, dealers, and OEM to manage under development and after sales services effectively. The final target to achieve fault prediction/notification system with AIOT based solution.