This paper presents the hardware implementation of a hybrid control framework that combines fuzzy logic and fuzzy Q-learning for intelligent energy management in battery electric vehicles (BEVs). The proposed architecture integrates two fuzzy controllers: one dedicated to traction motor power regulation and another designed for HVAC energy optimization. Initially modeled and validated in MATLAB, the controllers were subsequently ported to C and CUDA to enable real-time deployment on embedded platforms, specifically Jetson Nano and Raspberry Pi. To improve adaptability, a fuzzy Q-learning layer was embedded into the motor controller, allowing dynamic adjustment in challenging scenarios such as zero-speed transitions, abrupt accelerations, and varying load conditions. Experimental validation was carried out on three distinct real-world driving datasets, covering diverse operational profiles. Each system version (MATLAB, C on PC, CUDA on Jetson, and ARM implementation on Raspberry Pi) was benchmarked in terms of accuracy and computational efficiency. The results demonstrate that the hybrid fuzzy–Q learning controller consistently achieved high precision (MAE < 0.3) across all datasets, while maintaining low execution times compatible with real-time automotive requirements. These findings confirm both the effectiveness and the portability of the proposed system across heterogeneous hardware architectures, highlighting its suitability for embedded energy management applications in BEVs.