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. This study conducts a comparative analysis between validated white-box models and the generated black-box models, focusing on their precision and computational speed, to highlight the trade-offs and advantages inherent to each modeling approach. The findings from this study suggest that employing neural network-based black-box models can enhance the development of advanced control strategies, such as model predictive control (MPC) and reinforcement learning-based control in BEVs. As a forward-looking perspective, the research outlines a specific approach for the integration of these models into control strategy development. Furthermore, it discusses the potential for methodological enhancements and the application of the system identification process to additional thermal system components, with the overall goal of enhancing energy management in BEVs.
Session:
Electric Powertrains I
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| 14:30 - 15:00