Powertrain Parameters' Optimization for a Series-Parallel Plug-In Hybrid Electric Bus by Using a Combinatorial Optimization Algorithm

Whether the powertrain parameters are reasonable will directly affect the fuel economy of a plug-in hybrid electric bus (PHEB). In this article, the fuel economy is chosen as the optimization target of a serial-parallel PHEB. A global optimal strategy, which is formulated by dynamic programming (DP) algorithm, is used as an energy management strategy for PHEB. First, PHEB fuel economy is chosen as the optimization objective. Then, a combinatorial optimization algorithm is designed by combining a multi-island genetic algorithm (MIGA) with non-linear programming by quadratic lagrangian (NLPQL). MIGA is used for global optimization, and the NLPQL is used for a local optimization to make up for the poor ability of MIGA in local optimization. Finally, several hardware-in-the-loop (HIL) experiments were carried out, and the results prove that the fuel consumption per 100 km has reduced from 25.7- to 22.9-l diesel, and the electricity consumption per 100 km has reduced from 14.7 to 14.3 kW.h.

Automated summary

This article investigates the impact of powertrain parameters on the fuel economy of a plug-in hybrid electric bus (PHEB) and proposes a global optimal strategy based on the dynamic programming algorithm for energy management. A combinatorial optimization algorithm, combining a multi-island genetic algorithm (MIGA) and non-linear programming by quadratic Lagrangian (NLPQL), is designed for global and local optimization. Hardware-in-the-loop (HIL) experiments validate the effectiveness of the strategy, with the fuel consumption per 100 km reduced from 25.7-l to 22.9-l diesel and the electricity consumption per 100 km reduced from 14.7 to 14.3 kW.h.