Adaptive Equivalent Consumption Minimization Strategy for Hybrid Heavy-Duty Truck Based on Driving Condition Recognition and Parameter Optimization

Pei Zhang,Hongming Xu,Huawu Wang,Xianpan Wu,Changqing Du

doi:10.3390/en13205407

Abstract

The accurate determination and dynamic adjustment of key control parameters are challenges for equivalent consumption minimization strategy (ECMS) to be implemented in real-time control of hybrid electric vehicles. An adaptive real-time ECMS is proposed for hybrid heavy-duty truck in this paper. Three efforts have been made in this study. First, six kinds of typical driving cycle for hybrid heavy-duty truck are obtained by hierarchical clustering algorithm, and a driving condition recognition (DCR) algorithm based on a neural network is put forward. Second, particle swarm optimization (PSO) is applied to optimize three key parameters of ECMS under a specified driving cycle, including equivalent factor, scale factor of penalty function, and vehicle speed threshold for engine start-up. Finally, combining all the above two efforts, a novel adaptive ECMS based on DCR and key parameter optimization of ECMS by PSO is presented and validated through numerical simulation. The simulation results manifest that proposed adaptive ECMS can further improve the fuel economy of a hybrid heavy-duty truck while keeping the battery charge-sustainability, compared with ECMS and PSO-ECMS under a composite driving cycle.

Highlights

In the context of current heavy-duty truck market demand and strict fuel consumption and emission limits, energy saving and emissions reduction are of great significance to the development of heavy-duty trucks
The primary purpose of this paper is to propose a novel A-equivalent consumption minimization strategy (ECMS) by combining driving condition recognition (DCR) and optimization of three key of factors for improving control performance of combining
The flow chart of particle swarm optimization (PSO)-ECMS is shown in Figure 16, which can be divided into offline part and Firstly, the PSO is used to optimize equivalent factor (EF), scale factor of penalty function, and vehicle speed onlinepart

Summary

Introduction

Heavy-duty trucks play an important role in the modernization of the national infrastructure. In the context of current heavy-duty truck market demand and strict fuel consumption and emission limits, energy saving and emissions reduction are of great significance to the development of heavy-duty trucks. The increasingly strict fuel consumption standard of the vehicle makes it difficult to effectively resolve the contradiction between economic demands and power demands of traditional heavy-duty trucks [1,2]. The development of an efficient hybrid power system is an effective way to meet the above challenges, and has become a common demand for the development of the world’s heavy-duty truck industry. Establishing an appropriate energy management strategy (EMS) is the core difficulty of the design and development of hybrid heavy-duty trucks, and it is the key to Energies 2020, 13, 5407; doi:10.3390/en13205407 www.mdpi.com/journal/energies

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Conclusion