Optimization of the powertrain and energy management control parameters of a hybrid hydraulic vehicle based on improved multi-objective particle swarm optimization

The concurrent optimization of powertrain component parameters and energy management strategy for a hybrid hydraulic vehicle (HHV) is the key to implementing improved fuel economy while satisfying driving performance criteria. In this article, which considers coupled parameters and conflicting objectives in the optimization, an improved multi-objective particle swarm optimization (IMOPSO) is proposed from the perspective of inertia weight, and global and local optimal information to overcome the problem of multi-objective particle swarm optimization (MOPSO) falling into local optimization prematurely. The IMOPSO is applied to the component parameter optimization to find the Pareto optimal solution set that provides a wide range of options for HHV powertrain design successfully. In order to improve the management control effect of the equivalent consumption minimization strategy (ECMS), the equivalence factors (EFs) are optimized offline by the IMOPSO to obtain the EF map between different torque demands and the state of charge of the accumulator, and further, to establish the online ECMS with the EFs optimized by the IMOPSO (I-ECMS). The simulation results verify the advantage of the IMOPSO-based component parameter optimization and the proposed I-ECMS.