Exploration the route of information integration for vehicle design: A knowledge-enhanced energy management strategy

Abstract

With the development of the technologies for Vehicle-to-Everything (V2X), driving information sharing leads to a potential solution for intelligent vehicles to enhance their controlling effect, particularly for energy management of the vehicles equipped with the complex powertrain. Transplanting the optimal and adaptive knowledge of the vehicle driving data into a target vehicle can enhance both the adaptability and economic optimization of energy management. In this paper, a V2X-based knowledge-enhanced energy management strategy (KE-EMS) is proposed to transplant offline optimal-oriented prognostic knowledge and adaptive-oriented heuristic knowledge into online driving in practice. Firstly, in the offline processing, based on micro trip segments collected from volunteered sharing vehicles by V2X, optimal-oriented prognostic knowledge and adaptive-oriented heuristic knowledge are generated by dynamic programming (DP) and improved particle swarm optimization algorithm (iPSO) respectively. In the online implementation, the KE-EMS invokes the generated knowledge accordingly by a driving segment matching method (DSM) estimating the similarity between the current driving condition and micro driving segments. Finally, an improved equivalent consumption minimization strategy (ECMS) embedded into KE-EMS further adjusts the electric charging tendency to accomplish the energy-saving potential release. A simulation evaluation and hardware-in-the-loop (HIL) test manifest that the proposed KE-EMS can improve economic performance and guarantee real-time performance for the studied plug-in hybrid electric vehicles (PHEVs).