Real-Time Optimal Energy Management of Dual-Motor Battery Electric Vehicles Using MPC on an Automotive-grade Microcontroller

Dual-motor electric vehicles offer superior driving performance and energy efficiency. However, existing energy management strategies often rely on offline optimization or are difficult to implement in real-time embedded controllers. To address these limitations, this study proposes a real-time energy management strategy based on Nonlinear Model Predictive Control (NMPC) and Linear Model Predictive Control (LMPC). The energy management problem is formulated as either an Nonlinear Programming (NLP) or a Quadratic Programming (QP) depending on the formulation approach, and is solved using NMPC and LMPC, respectively. The LMPC controller is implemented on a 32-bit MCU (Teensy 4.1) equipped with an ARM Cortex-M7 processor, and its real-time performance is validated through Processor-in-the-Loop Simulation (PiLS). Simulation results show that NMPC achieves approximately 99% of the performance of Dynamic Programming (DP), which provides the global optimum, while LMPC achieves approximately 98%. Furthermore, LMPC satisfies real-time control constraints, demonstrating its practical feasibility for in-vehicle deployment.