• Complex
  • Title
  • Keyword
  • Abstract
  • Scholars
  • Journal
  • ISSN
  • Conference
成果搜索

author:

Lin, Xinyou (Lin, Xinyou.) [1] | Wu, Jiayun (Wu, Jiayun.) [2] | Wei, Yimin (Wei, Yimin.) [3]

Indexed by:

EI

Abstract:

The fuel economy of a plug-in hybrid electric vehicle is largely dependent on the battery energy usage during various driving cycles. In this research, within the model predictive control (MPC) principle, an Ensemble Learning Velocity Prediction (ELVP)-based energy management strategy (EMS) considering the driving pattern Adaptive Reference State of Charge (AR-SOC) is proposed. Firstly, the existing methods including Markov chain (MC), back propagation (BP) and radial basis function (RBF) neural network (NN)-based velocity prediction models are described. Then, these models are embedded into MPC-based EMS respectively, and the validation results show that the NN performs better than the MC by comparing the prediction precision, computational cost, and resultant vehicular fuel economy. By incorporating these prior knowledges, a novel ensemble learning velocity prediction method is established by blending BP-NN and RBF-NN. Subsequently, based on the expected trip distance and the velocity prediction results, an adaptive reference SOC (AR-SOC) trajectory planning method is developed to direct the distribution of battery energy for different driving patterns. Combining with the ELVP and the AR-SOC, the MPC-based EMS derives the optimal torque-distribution decisions. Finally, the validation results indicate that the proposed strategy achieves superior fuel economy under various driving cycle compared with the benchmark strategies. © 2021 Elsevier Ltd

Keyword:

Backpropagation Battery management systems Blending Charging (batteries) Dynamic programming Forecasting Fuel economy Fuels Markov processes Model predictive control Pattern recognition Plug-in hybrid vehicles Predictive control systems Radial basis function networks Secondary batteries Velocity

Community:

  • [ 1 ] [Lin, Xinyou]College of Mechanical Engineering & Automation, Fuzhou University, Fuzhou; Fujian Province; 350108, China
  • [ 2 ] [Wu, Jiayun]College of Mechanical Engineering & Automation, Fuzhou University, Fuzhou; Fujian Province; 350108, China
  • [ 3 ] [Wei, Yimin]21C Innovation Laboratory, Contemporary Amperex Technology Ltd, Ningde; 352100, China

Reprint 's Address:

Email:

Show more details

Related Keywords:

Source :

Energy

ISSN: 0360-5442

Year: 2021

Volume: 234

8 . 8 5 7

JCR@2021

9 . 0 0 0

JCR@2023

ESI HC Threshold:105

JCR Journal Grade:1

CAS Journal Grade:1

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 45

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

Affiliated Colleges:

Online/Total:26/10806989
Address:FZU Library(No.2 Xuyuan Road, Fuzhou, Fujian, PRC Post Code:350116) Contact Us:0591-22865326
Copyright:FZU Library Technical Support:Beijing Aegean Software Co., Ltd. 闽ICP备05005463号-1