The electrification of refrigerated transport is challenged by high and variable thermal loads that increase battery demand and compromise vehicle autonomy. Conventional steady-state models and rule-based control strategies support preliminary sizing but fail to capture transient behavior and interactions between thermal regulation, energy consumption, and battery sizing under operating constraints. This work proposes an integrated control-oriented optimization framework in which supervisory control parameters are treated as design variables within a multi-objective optimization. Considering a practically relevant direct motor–compressor coupling, in which the electric motor operates within the same rotational speed range required by the compressor—thereby avoiding mechanical transmissions and associated losses while restricting control flexibility—a fuzzy controller generates the motor speed reference based on battery state-of-charge, compressor efficiency, and compartment air temperature, while a PID layer ensures smooth tracking. The framework jointly optimizes control and system-level characteristics under operational constraints, including compressor cycling limits, minimum on/off times, and battery discharge thresholds. A genetic algorithm explores solutions and reveals effects between energy consumption, battery mass, and thermal regulation near the optimum. Results show that reduced temperature fluctuations yield smoother load profiles and lower compressor cycling, at the expense of higher energy demand and battery capacity. The optimal compromise achieves E c ≈ 10 , 130 kJ, Δ T ≈ 1 . 3 ° C, and a battery mass of 71 kg, enabling continuous operation while limiting compressor cycling to 96 starts over 10 h. Compared to conventional strategies, the proposed approach reduces energy consumption and temperature oscillations, supporting control-aware system design of electrified refrigerated transport. • Fuzzy control improves energy efficiency in refrigerated electric transports. • Integration of drivetrain and refrigeration reduces energy consumption. • Dynamic model captures second-by-second thermal load variations. • System ensures cargo temperature stability under varying conditions. • Approach extends compressor life by limiting excessive cycling.
Ramirez-Quintero et al. (Thu,) studied this question.