Next Article in Journal
Joint Evaluation of the Wave and Offshore Wind Energy Resources in the Developing Countries
Previous Article in Journal
Photovoltaic Device Performance Evaluation Using an Open-Hardware System and Standard Calibrated Laboratory Instruments
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Energies 2017, 10(11), 1871; doi:10.3390/en10111871

Intelligent Energy Management Control for Extended Range Electric Vehicles Based on Dynamic Programming and Neural Network

1
Beijing Key Laboratory of Powertrain for New Energy Vehicle, Beijing Jiaotong University, Beijing 100044, China
2
Beijing Electric Vehicle Co. LTD., Beijing 102606, China
*
Author to whom correspondence should be addressed.
Received: 10 October 2017 / Revised: 28 October 2017 / Accepted: 7 November 2017 / Published: 15 November 2017
View Full-Text   |   Download PDF [2119 KB, uploaded 15 November 2017]   |  

Abstract

The extended range electric vehicle (EREV) can store much clean energy from the electric grid when it arrives at the charging station with lower battery energy. Consuming minimum gasoline during the trip is a common goal for most energy management controllers. To achieve these objectives, an intelligent energy management controller for EREV based on dynamic programming and neural networks (IEMC_NN) is proposed. The power demand split ratio between the extender and battery are optimized by DP, and the control objectives are presented as a cost function. The online controller is trained by neural networks. Three trained controllers, constructing the controller library in IEMC_NN, are obtained from training three typical lengths of the driving cycle. To determine an appropriate NN controller for different driving distance purposes, the selection module in IEMC_NN is developed based on the remaining battery energy and the driving distance to the charging station. Three simulation conditions are adopted to validate the performance of IEMC_NN. They are target driving distance information, known and unknown, changing the destination during the trip. Simulation results using these simulation conditions show that the IEMC_NN had better fuel economy than the charging deplete/charging sustain (CD/CS) algorithm. More significantly, with known driving distance information, the battery SOC controlled by IEMC_NN can just reach the lower bound as the EREV arrives at the charging station, which was also feasible when the driver changed the destination during the trip. View Full-Text
Keywords: energy management strategy; extended range electric vehicle; dynamic programming; neural network; state of charge energy management strategy; extended range electric vehicle; dynamic programming; neural network; state of charge
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Xi, L.; Zhang, X.; Sun, C.; Wang, Z.; Hou, X.; Zhang, J. Intelligent Energy Management Control for Extended Range Electric Vehicles Based on Dynamic Programming and Neural Network. Energies 2017, 10, 1871.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top