Predictive Maintenance of Shared Electric Vehicle Battery Using Telematics Data

Abstract

The future of transportation lies in connected mobility. With the aid of telematics devices and analytics of car data, it is significantly disrupting the automobile industry and changing the way businesses are conducted. The bridge for linked mobility is telematics, which is an IOT device fitted in the car that collects CAN and GPS data. Over 60% of the IoT market is accounted for by telematics. With the introduction of connected vehicles and machinery, the market is expanding very quickly; the potential might be compared to the early 2010s mobile phone market. Up to 2027, the market will expand at a CAGR (Compound Annual Growth Rate) of almost 25%. The study investigates the use of telematics data to forecast battery heat and State of Charge (SOC) in shared mobility cars. The goal is to offer fleet owners data-driven decision tools that can assist them improve maintenance schedules and lower maintenance expenses. The condition-monitoring data obtained from sensors in automotive equipment via Telematics Device was analyzed using a solution identification and machine learning model building approach. The best-performing machine learning algorithm accurately predicted potential faults in automotive equipment, with an accuracy of over 97%. These findings demonstrate that battery heat can be accurately predicted from telematics data, and that adding battery heat as a predictor variable can enhance SOC estimates. The research's conclusions have significant practical ramifications for the automotive sector. The study adds to the body of knowledge on predictive maintenance in the automotive industry by presenting a thorough approach to solution discovery and machine learning model creation. The study demonstrates the potential for big data and AI to enhance maintenance procedures and lower costs while also emphasizing the practical consequences of these tactics for the automotive sector. © The Institution of Engineering & Technology 2023.