Optimizing Thermal Energy Management in BEVs via Distributed Optimization

Licentiatavhandling, 2024

The increasing global focus on energy conservation and environmental sustainability has highlighted the critical role of reducing greenhouse gas (GHG) emissions, particularly from the transportation sector. Battery Electric Vehicles (BEVs) have emerged as a key solution, driven by stringent regulatory targets and rising consumer demand for sustainable mobility. However, achieving widespread adoption of BEVs requires addressing challenges such as "range anxiety," which stems from the limited driving range due to high energy consumption, particularly for thermal management.

This thesis explores optimizing Thermal Energy Management (TEM) systems in BEVs to enhance energy efficiency and extend vehicle range. A novel control-oriented, system-level model is developed for a state-of-the-art Flexible Thermal Energy Management (FTEM) system, integrating HVAC and heat pump functionalities. The research focuses on applying distributed optimization techniques, leveraging Model Predictive Control (MPC) and the Alternating Direction Method of Multipliers (ADMM), to achieve real-time energy savings. The proposed methods target significant reductions in energy consumption, particularly under varying environmental conditions, making BEVs more competitive in the mass market.

This work contributes to the broader transition to zero-emission transportation by demonstrating advanced TEM strategies that improve both vehicle performance and sustainability.