ML-Based System Level Optimization of EV Cooling Circuit

Efficient thermal management is vital for electric vehicles (EVs) to maintain optimal operating temperatures and enhance energy efficiency. Traditional simulation-based design approaches, while accurate, are often computationally expensive and limited in their ability to explore large design spaces. This study introduces a machine learning (ML)-based optimization framework for the design of an EV cooling circuit, targeting a 5°C reduction in the maximum electric motor temperature. A one-dimensional computational fluid dynamics (1D-CFD) model is utilized to generate a Design of Experiments (DOE) matrix, incorporating key parameters such as coolant flow rate and heat exchanger dimensions. A Radial Basis Function (RBF) neural network is trained on the simulation data to serve as a surrogate model, enabling rapid performance prediction. Optimization is performed using the Non-Dominated Sorting Genetic Algorithm II (NSGA2), yielding three distinct design solutions that meet the thermal performance target with varying trade-offs. The proposed ML-based approach achieves a speedup of approximately 30× over conventional methods while maintaining high accuracy, with validation errors below 1% compared to the original CFD model.