Engine RPM and Battery SOC Activation Optimization in Hybrid Vehicle Energy Management System Utilizing BPNN - Genetic Algorithm and BPNN – Particle Swarm Optimization

Rhema Adi Magiza Wicaksana (ITS)
Bambang Sudarmanta (ITS)
Mohammad Khoirul Effendi (ITS)

JMES The International Journal of Mechanical Engineering and Sciences
Vol 6, No 2 (2022)

The energy used in the hybrid vehicle needs to be regulated to gain further mileage and lower fuel consumption. It is achieved by selecting the correct levels of hybrid energy management system (EMS) parameters (i.e., vehicle speed, engine RPM, and activation State of Charge (SOC) of battery). This study focused on the modeling and optimization of Sepuluh Nopember Institute of Technology (ITS)’s series plug-in hybrid electric vehicle (PHEV) car mileage and fuel consumption by comparing the backpropagation neural network (BPNN) method – genetic algorithm (GA) and BPNN – particle swarm optimization (PSO). The BPNN was used to model the character of ITS’s series PHEV EMS and predict mileage and fuel consumption. The BPNN’s model obtained the best EMS parameters, most extended mileage, and minimum fuel consumption. The result of the validation experiment showed that both the integration of BPNN - GA and BPNN - PSO were able to predict and optimize the multi-objective characteristic with the same results.