Koopman-Based Methods for EV Climate Dynamics: Comparing eDMD Approaches
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909566874157056 |
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| author | Meda, Luca Stockar, Stephanie |
| author_facet | Meda, Luca Stockar, Stephanie |
| contents | In this paper, data-driven algorithms based on Koopman Operator Theory are applied to identify and predict the nonlinear 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 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 libraries, radial basis functions (RBF), and neural network-based dictionary learning) is conducted. Accurate prediction of power consumption over entire 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_03872 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Koopman-Based Methods for EV Climate Dynamics: Comparing eDMD Approaches Meda, Luca Stockar, Stephanie Systems and Control In this paper, data-driven algorithms based on Koopman Operator Theory are applied to identify and predict the nonlinear 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 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 libraries, radial basis functions (RBF), and neural network-based dictionary learning) is conducted. Accurate prediction of power consumption over entire 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. |
| title | Koopman-Based Methods for EV Climate Dynamics: Comparing eDMD Approaches |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2504.03872 |