An Optimization Approach for Energy Management of Hybrid Vehicles Based on Dynamic Programming and Cuckoo Search

This paper proposes optimization models integrating dynamic programming and cuckoo search algorithms for energy management and saving in hybrid electric vehicles (HEV). An energy management strategy based on dynamic programming is designed to allocate power across engine, motor and battery. A cuckoo search-based model tunes the electronic throttle controller to improve fuel economy. Experiments validated the efficacy of the models. The dynamic programming strategy reduced fuel consumption by 4.31 L/100 km and stabilized battery state of charge compared to rule-based methods. The cuckoo search algorithm tuned controller decreased system response time to 0.0132 s and tracking error to zero. The models provide an effective optimization framework for HEV energy management and fuel saving. However, considerations like exhaust emissions were not included. Further research should evaluate the models on real HEV test data and incorporate additional objectives like emissions. In conclusion, this paper proposed and validated optimization models integrating dynamic programming and cuckoo search algorithms to address hybrid electric vehicle energy management and fuel economy.