Adaptive Real-Time Energy Management Strategy for Plug-In Hybrid Electric Vehicle Based on Simplified-ECMS and a Novel Driving Pattern Recognition Method

Yuping Zeng,Ming Li,Jing Sheng

doi:10.1155/2018/5816861

Abstract

This paper proposes an adaptive real-time energy management strategy for a parallel plug-in hybrid electric vehicle (PHEV). Three efforts have been made. First, a novel driving pattern recognition method based on statistical analysis has been proposed. This method classified driving cycles into three driving patterns: low speed cycle, middle speed cycle, and high speed cycle, and then carried statistical analysis on these three driving patterns to obtain rules; the types of real-time driving cycles can be identified according to these rules. Second, particle swarm optimization (PSO) algorithm is applied to optimize equivalent factor (EF) and then the EF MAPs, indexed vertically by battery’s State of Charge (SOC) and horizontally by driving distance, under the above three driving cycles, are obtained. Finally, an adaptive real-time energy management strategy based on Simplified-ECMS and the novel driving pattern recognition method has been proposed. Simulation on a test driving cycle is performed. The simulation results show that the adaptive energy management strategy can decrease fuel consumption of PHEV by 17.63% under the testing driving cycle, compared to CD-CS-based strategy. The calculation time of the proposed adaptive strategy is obviously shorter than the time of ECMS-based strategy and close to the time of CD-CS-based strategy, which is a real-time control strategy.

Highlights

The energy crisis and environmental pollution problem have become a serious concern in the recent years [1]
The simulation results show that the adaptive energy management strategy can decrease fuel consumption of Plug-in hybrid electric vehicles (PHEVs) by 17.63% under the testing driving cycle, compared to CD-CS-based strategy
To evaluate the control performance of the proposed adaptive energy management strategy, the CD-CS-based strategy and the proposed strategy were simulated in MATLAB/Simulink, and the CD-CS-based strategy was taken as the benchmark

Summary

Introduction

The energy crisis and environmental pollution problem have become a serious concern in the recent years [1]. To solve the above problem and obtain an adaptive realtime energy management strategy for plug-in hybrid electric vehicle, a novel driving pattern recognition method with low computational cost was first proposed This method classified driving cycles into three driving patterns: low speed cycle, middle speed cycle, and high speed cycle, carried statistical analysis on these three driving patterns to obtain rules, and identified the types of real-time driving cycles according to these obtained rules.

The Structure and Parameters of the Powertrain System

Simplified-ECMS-Based EMS

Results and Discussions

Conclusions