A Nonlinear Model Predictive Control framework for TEC-based BTMS in Light Electric Vehicles

This paper presents a Nonlinear Model Predictive Control (NLMPC) strategy for the energy-efficient thermal management of Light Electric Vehicle (LEV) batteries using Thermoelectric Coolers (TECs). While LEVs feature compact battery packs with small thermal masses, TECs provide a solid-state, space-efficient cooling solution ideal for this platform. However, TECs act as a parasitic load, drawing energy from the battery and increasing internal heat generation. While linear Model Predictive Control (MPC) strategies have been considered, their reliance on a model linearized around a specific operating point renders them inadequate for the strong, non-linear dynamics of a TEC-BTMS. A full NLMPC strategy, despite its theoretical superiority, has not yet been applied to this problem. Furthermore, existing controllers ignore the parasitic load of TECs, which draws energy from the battery, thereby increasing the heat generation. To address this, the NLMPC’s internal model is developed to explicitly couple the TEC’s parasitic load with the battery’s heat generation. Simulations show that the NLMPC controller provides a 47.3% to 74.2% reduction in energy usage compared to a relay controller across all tested conditions. Compared to the Proportional-Integral-Derivative (PID) controller tuned at an ambient temperature of 25 °C, the NLMPC provides similar energy consumption while at higher ambient temperatures it saves up to 51.8% energy. The novel parasitic load coupling of TECs within the NLMPC accounts for up to 25% energy saving from baseline NLMPC. The results demonstrate that NLMPC significantly enhances both thermal management and energy efficiency, addressing key challenges in LEV battery systems and extending vehicle range.