Optimizing combined cooling and power systems in refrigerated trucks: a deep deterministic policy gradient approach

Abstract

The CO₂-based combined cooling and power (CCP) system is regarded as a highly promising alternative for waste heat recovery in refrigerated trucks, owing to its environmental advantages and multienergy output. The CCP system implemented in refrigerated trucks is more intricate than conventional waste heat recovery systems. It not only produces energy to satisfy demand via waste heat recovery but also incorporates refrigeration capabilities, substituting the standalone refrigeration unit to sustain low temperatures in refrigerated trucks. This coupling of power and refrigeration subcycles significantly increases the complexity of system control and the requirements for stability. Current research primarily focuses on the steady-state performance of CCP systems, neglecting the impact of load variations on the system's dynamic response in real operating conditions, thereby limiting a comprehensive assessment of operational performance under complex scenarios. This study proposes a hybrid control strategy based on deep deterministic policy gradient deep reinforcement learning and conducts dynamic simulations to comprehensively evaluate the energy efficiency performance of the CCP system. The results show that under the China Heavy-Duty Commercial Vehicle Test Cycle conditions, this strategy reduces fuel consumption by 6.63 % per 100 km while ensuring that the CCP system remains within safety constraints throughout the entire operation. These findings provide important insights for the application of CCP systems in the cold chain transportation sector.