Electric vehicle thermal system concept development for multiple variants using digital prototype and AI

The automotive industry is experiencing a surge in system complexity driven by the ever-growing number of interacting components, subsystems, and control systems. This complexity is further amplified by the expanding range of component options available to original equipment manufacturers (OEMs). OEMs work in parallel on more than one vehicle model, with multiple vehicle variants for each vehicle model. With the increasing number of vehicle variants needed to cater to diverse regional needs, development complexity escalates. To address this challenge, modern techniques like Model-Based Systems Engineering (MBSE), digitalization, and Artificial Intelligence (AI) are becoming essential tools. These advancements can streamline concept development, optimize thermal and HVAC system design across variants, and accelerate the time-to-market for next-generation EVs. The development of battery electric vehicles (BEVs) needs a strong focus on thermal management systems (TMSs) and heating, ventilation, and air conditioning (HVAC) systems. These systems play a critical role in maintaining optimal battery temperature, maximizing range and efficiency, and ensuring passenger comfort. This article proposes a digital prototype (DP) and AI-based methodology to specify BEV thermal system and HVAC system components in the concept phase. This methodology uses system and variant thinking in combination with digital prototype (DP) and AI to verify BEV thermal system architecture component specifications for future variants without extensive simulation. A BEV cabin cooling requirement of 22 °C to be achieved within 1800s at a high ambient temperature (45 °C) is required, and its verification is used to prove this methodology.<p></p>