ABSTRACT

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.

摘要

混合动力液压车辆 (HHV) 的动力总成组件参数和能量管理策略的同时优化是在满足驾驶性能标准的同时提高燃油经济性的关键。本文在优化中考虑耦合参数和冲突目标，从惯性权重、全局和局部最优信息的角度提出改进的多目标粒子群优化（IMOPSO），以克服多目标粒子群优化问题。群优化（MOPSO）过早地陷入局部优化。IMOPSO 应用于组件参数优化，以找到帕累托最优解集，成功为 HHV 动力系统设计提供了广泛的选择。为了提高等效消耗最小化策略（ECMS）的管理控制效果，IMOPSO离线优化等效因子（EFs）以获得不同扭矩需求与蓄电池荷电状态之间的EF映射，并进一步, 使用 IMOPSO (I-ECMS) 优化的 EF 建立在线 ECMS。仿真结果验证了基于 IMOPSO 的组件参数优化和所提出的 I-ECMS 的优势。