Koopman-Based Methods for EV Climate Dynamics: Comparing eDMD Approaches

In this paper, data-driven algorithms based on Koopman Operator Theory are applied to identify and predict the dynamics of a vapor compression system and cabin temperature in a light-duty electric vehicle. By leveraging a high-fidelity nonlinear HVAC model, the system's behavior is captured in a lifted higher-dimensional state space, enabling a linear representation. A comparative analysis of three Koopman-based system identification approaches-polynomial, radial basis functions (RBF), and neural network dictionary learning-is conducted, evaluating, for each of these libraries, the impact of the dimension of the lifted space on the accuracy of the model. Accurate prediction of power consumption over driving cycles is demonstrated by incorporating power as a measurable output within the Koopman framework. The performance of each method is rigorously evaluated through simulations under various driving cycles and ambient conditions, highlighting their potential for real-time prediction and control in energy-efficient vehicle climate management. This study offers a scalable, data-driven methodology that can be extended to other complex nonlinear systems.