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Modeling thermal systems in Battery Electric Vehicles (BEVs) is crucial for enhancing energy efficiency through predictive control strategies, thereby extending vehicle range. A major obstacle in this modeling is the often limited availability of detailed system information. This research introduces a methodology using neural networks for system identification, a powerful technique capable of approximating the physical behavior of thermal systems with minimal data requirements. By employing black-box models, this approach supports the creation of optimization-based operational strategies, such as Model Predictive Control (MPC) and Reinforcement Learning-based Control (RL). The system identification process is executed using MATLAB Simulink, with virtual training data produced by validated Simulink models to establish the method's feasibility. The neural networks utilized for system identification are implemented in MATLAB code.

Climate change and global warming are one of the major challenges faced by the world today. A significant number of Indian cities rank among the most polluted globally, with vehicular emissions being the primary contributor. To address this issue, the Government of India is actively advocating for the adoption of zero-emission vehicles such as electric vehicles through policies and initiatives like FAME II [1], PMP and the National Mission for Transformative Mobility and Storage. The acceptance of electric vehicles is growing in the Indian market seeing more than 200% increase in sales in the year 2022 compared to 2021 with a large share of 2-wheelers, 3-wheelers and compact cars getting electrified. Further adoption of electrification on a much larger scale currently faces the major challenge of high overall vehicle cost compared to conventional vehicles, with the major contribution coming from the HV battery which is the costliest system on the electric vehicles.