Return to search

An intelligent energy allocation method for hybrid energy storage systems for electrified vehicles

Electrified vehicles (EVs) with a large electric energy storage system (ESS), including Plug-in Hybrid Electric Vehicles (PHEVs) and Pure Electric Vehicles (PEVs), provide a promising solution to utilize clean grid energy that can be generated from renewable sources and to address the increasing environmental concerns. Effectively extending the operation life of the large and costly ESS, thus lowering the lifecycle cost of EVs presents a major technical challenge at present. A hybrid energy storage system (HESS) that combines batteries and ultracapacitors (UCs) presents unique energy storage capability over traditional ESS made of pure batteries or UCs. With optimal energy management system (EMS) techniques, the HESS can considerably reduce the frequent charges and discharges on the batteries, extending their life, and fully utilizing their high energy density advantage. In this work, an intelligent energy allocation (IEA) algorithm that is based on Q-learning has been introduced. The new IEA method dynamically generate sub-optimal energy allocation strategy for the HESS based on each recognized trip of the EV. In each repeated trip, the self-learning IEA algorithm generates the optimal control schemes to distribute required current between the batteries and UCs according to the learned Q values. A RBF neural networks is trained and updated to approximate the Q values during the trip. This new method provides continuously improved energy sharing solutions better suited to each trip made by the EV, outperforming the present passive HESS and fixed-cutoff-frequency method.

To efficiently recognize the repeated trips, an extended Support Vector Machine (e-SVM) method has been developed to extract significant features for classification. Comparing with the standard 2-norm SVM and linear 1-norm SVM, the new e-SVM provides a better balance between quality of classification and feature numbers, and measures feature observability. The e-SVM method is thus able to replace features with bad observability with other more observable features. Moreover, a novel pattern classification algorithm, Inertial Matching Pursuit Classification (IMPC), has been introduced for recognizing vehicle driving patterns within a shorter period of time, allowing timely update of energy management strategies, leading to improved Driver Performance Record (DPR) system resolution and accuracy. Simulation results proved that the new IMPC method is able to correctly recognize driving patterns with incomplete and inaccurate vehicle signal sample data.

The combination of intelligent energy allocation (IEA) with improved e-SVM feature extraction and IMPC pattern classification techniques allowed the best characteristics of batteries and UCs in the integrated HESS to be fully utilized, while overcoming their inherent drawbacks, leading to optimal EMS for EVs with improved energy efficiency, performance, battery life, and lifecycle cost. / Graduate

  1. https://dspace.library.uvic.ca//handle/1828/9416
  2. Xing Zhang, Zuomin Dong and Curran Crawford, “Hybrid Energy Storage System for Hybrid and Electric Vehicles: Review and a New Control Strategy”, Proceedings of the ASME 2011 International Mechanical Engineering Congress & Exposition, November 11-17, 2011, Denver, Colorado, USA
  3. Xing Zhang, Zuomin Dong and Curran Crawford, “A Novel Method for Driving Condition Recognition Based on Compressed Sensing”, Proceedings of the ASME 2013 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, August 4-7, 2013, Portland, Oregon, USA
  4. Xing Zhang, Guang Wu, Zuomin Dong, Curran Crawford, Embedded feature-selection support vector machine for driving pattern recognition, Journal of the Franklin Institute, Available online 9 May 2014, ISSN 0016-0032
  5. Xing Zhang, Guang Wu, Zuomin Dong, Curran Crawford, “Driving Condition Recognition Based on Compressed Sensing”, IEEE Transactions on Intelligent Transportation Systems. (Submitted)
  6. Xing Zhang, Yun Xiong, Zechang Sun, “Object-Oriented Modeling for Fault Detection of In-wheel Motor used in Fuel Cell Vehicle”, Power Electronics and Drive Systems, 2009. PEDS 2009. International Conference on, vol., no., pp.204,209, 2-5 Nov. 2009
  7. Wu, G., Xing Zhang, and Dong, Z., "Impacts of Two‐Speed Gearbox on Electric Vehicle's Fuel Economy and Performance", SAE World Congress & Exhibition, Paper #: 2013‐01‐0349, Detroit, US
  8. Wu, G., Xing Zhang, and Dong, Z., "Optimal Control for Ensured Driveability of Parallel HEVs/PHEVs During Mode Transition", SAE World Congress & Exhibition, Paper #: 2014‐01‐1895, Detroit, US
  9. Wu, G., Xing Zhang, and Dong, Z., "Powertrain Architectures of Electrified Vehicles: Review, Classification and Comparison", Journal of The Franklin Institute
Identiferoai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/9416
Date31 May 2018
CreatorsZhang, Xing
ContributorsDong, Zuomin, Crawford, Curran
Source SetsUniversity of Victoria
LanguageEnglish, English
Detected LanguageEnglish
TypeThesis
Formatapplication/pdf
RightsAvailable to the World Wide Web

Page generated in 0.0029 seconds