Economic optimisation of range-extended electric bus based on AMGA algorithm

The power of a range-extended electric bus comes from its battery and range-extender. How to design the range-extender working point for the vehicle in the process of running is the key factor to achieve energy conservation and emission reduction. To solve this problem, a vehicle model was built by using AVL Cruise simulation software. Through Cruise and Isight co-simulation optimisation, a multi-objective optimisation model for per 100-km fuel consumption and pollutant emission is established. Optimal variables include upper and lower limits of the power unit and working point of the range-extender. Adaptive mutation genetic algorithm (AMGA) was used as optimisation algorithm. Results showed that fuel consumption and pollutant emissions were effectively reduced. The per 100-km fuel consumption decreased by 48.0%, carbon monoxide emission decreased by 49.6%, hydrocarbon emission decreased by 47.28%, and nitrogen oxide emission decreased by 51.1%. The economics of range-extended electric bus have been greatly improved.