A Review of Intelligent Hybrid Vehicle Control Unit and a Proposed ESP32-Based ECU Framework for an Educational Hybrid Electric Vehicle Trainer

An electronic control unit (ECU) in a hybrid electric vehicle (HEV) receives information from sensors, evaluates the vehicle operating condition, and selects suitable control states through an energy-management strategy (EMS). The choice of control method affects decision quality, response, computational demand, and implementation cost. This paper reviews recent HEV energy-management research published from 2022 to 2026, with emphasis on rule-based control, reinforcement learning, deep reinforcement learning, model predictive control, and low-cost hardware validation. The Ten relevant journal studies are compared according to their control approach, input information, validation method, reported contribution, and limitations. The review indicates that learning- and prediction-based methods can improve optimisation under varying driving conditions, but often depend on detailed models, training data, or host-computer resources. Rule-based control remains attractive where transparency, predictable execution, and low implementation complexity are important. Based on these findings, a input– decision–output using an ESP32 microcontroller is presented for an educational HEV trainer. The ECU accepts driver-demand, vehicle-state, battery-state, and safety-related inputs; applies sensor validation and priority-based decision rules; and produces operating-mode and warning outputs for display. It defines a technically grounded architecture and a future validation plan. The main contribution is to connect recent HEV EMS research with a simple, transparent, and potentially low-cost embedded ECU suitable for educational demonstration.