Abstract
The energy saving of Plug-in hybrid electric bus (PHEB) can be maximized by co-design method (integrated design of topology, component sizing and energy management). In many cases, the components have been manufactured, so it is a shortcut to realize the co-design with existing components. Motivated by this, a Taguchi robust design (TRD) method is proposed for the robust co-design of the PHEB. The main innovation is that the noises of driving cycles and stochastic vehicle mass are considered in the TRD, and the TRD is formulated as a smaller-the-better (STB) problem. Moreover, the signal-to-noise ratio (SNR) is taken as the analysis index, where the fuel economy together with its robustness can be simultaneously enhanced. In specific, the dynamic programming (DP) is firstly taking as the fuel consumption calculation module of the TRD. Then, a series of historical driving cycles and the stochastic vehicle mass are designed as noise factors; the components are designed as control factors. Finally, a receding horizon control (RHC) strategy is deployed to realize adaptive energy management control using the robust component match and the same DP. The TRD results demonstrate that the proposed co-design method is applicable and reasonable; the simulation results show that the RHC strategy can realize adaptive control, and averagely improve the fuel economy by 10.85% compared to a rule-based control strategy.
Highlights
Plug-in hybrid electric vehicle (PHEV) is a promising technology to mitigate the current fossil energy over-consuming and environment pollution problems [1], [2]
An online correction predictive energy management (OCPEM) strategy was proposed by combining dynamic programming (DP) and reinforcement learning (RL) algorithms [11]
The co-design is an indispensible approach to tap the potential of energy saving [12]
Summary
Plug-in hybrid electric vehicle (PHEV) is a promising technology to mitigate the current fossil energy over-consuming and environment pollution problems [1], [2]. A fast Q-learning-based energy management strategy was proposed for hybrid electric tracked vehicles to improve the fuel economy [10]. X. Zhou et al proposed a multi-objective optimization method by deploying dynamic programming (DP)-based energy management, based on a fixed driving cycle [17]. In terms of the second level, the investigations mainly focus on the component sizing optimization It can be further classified into indirect and direct methods. X. Hu et al proposed a method to simultaneously design the battery sizing, charging, and on-road energy management, based on convex programming [23]. Apart from the fuzzy logic control strategy in [24] and [25], the optimization-based energy managements were deployed and difficult to be directly used in real-world.
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