DIGITAL TWIN PERFORMANCE ASSESSMENT ON AN ELECTRIC VEHICLE AND COMPARATIVE COST ANALYSIS WITH GASOLINE VEHICLE

Abstract

The paper evaluates the performance of a digital twin on an electric vehicle and compares its operation costs with a gasoline vehicle of equivalent energy usage. This study uses a digital twin technique in the automotive industry to assess performance and compare the cost of electric vehicles to gasoline cars. The twin system measures output data from a converted gasoline Toyota starlet car, comparing its energy usage with gasoline cars. The technology includes internet of thing (IoT), artificial intelligence (AI), machine learning (ML), predictive analysis, simulation tools, virtual sensors and 5G. The outcome reveals that as time increases, speed increases, and battery level depreciates, leading to decreased fuel levels in conventional vehicles, with higher battery consumption voltage in low-speed tests. Low-speed driving consumes more energy than high-speed driving, causing momentum to increase and initial decrease. An electric vehicle was 50.9% less expensive to operate than a gasoline powered vehicle for the same distance, and over a five-year period, maintenance costs were predicted to be 50.6% lower. The operation cost of internal combustion engine (ICE) vehicles doubled that of novel electric vehicles (EVs), and with increasing petroleum prices, the cost increased over 400%. The use of digital twin technology in studied electric-powered vehicle is economical, environmentally friendly and maximizes resources use in favour of global circular economy vision.