Intelligent Hybrid Vehicle Power Control - Part II: Online Intelligent Energy Management

This is the second paper in a series of two that describe our research in intelligent energy management in a hybrid electric vehicle (HEV). Energy management in Hybrid Electric Vehicles (HEV) has been actively studied recently because of its potential to significantly improve fuel economy and emission control. Because of the dual-power-source nature and the complex configuration and operation modes in a HEV, energy management is more complicated and important than in a conventional vehicle. Most of the existing vehicle energy optimization approaches do not incorporate knowledge about driving patterns into their vehicle energy management strategies. Our approach is to use machine learning technology combined with roadway type and traffic congestion level specific optimization to achieve quasi-optimal energy management in hybrid vehicles. In this series of two papers, we present a machine learning framework that combines Dynamic Programming with neural networks to learn about roadway type and traffic congestion level specific energy optimization, and an integrated online intelligent energy controller to achieve quasi-optimal power management in hybrid vehicles. In the first paper we presented a machine learning framework, ML_EMO_HEV, developed for learning the knowledge about energy optimization in an HEV. The framework consists of machine learning algorithms for predicting driving environments and for generating optimal power split of the HEV system for a given driving environment. Experiments are conducted to evaluate these algorithms using a simulated Ford Escape Hybrid vehicle model provided in PSAT (Powertrain Systems Analysis Toolkit). In this second paper, we present three online intelligent energy controllers, IEC_HEV_SISE, IEC_HEV_MISE, and IEC_HEV_MIME. All three online intelligent energy controllers were trained within the machine learning framework, ML_EMO_HEV were trained to generate the best combination of engine power and battery power in real-time such that the total fuel consumption over whole driving cycle is minimized while still meeting the driver’s demand and the system constraints including engine, motor, battery, and generator operation limits. The three online controllers were integrated into the Ford Escape Hybrid vehicle model for online performance evaluation. Based on their performances on 10 test drive cycles provided by the PSAT library, we can conclude that the roadway type and traffic congestion level specific machine learning of optimal energy management is effective for in-vehicle energy ______________________________________ "Distribution Statement A Approved For Public Release Distribution Unlimited" Intelligent Hybrid Vehicle Power ControlPart II: Online Intelligent Energy Management Yi L. Murphey, Senior Member, IEEE, Jungme Park, Leonidas Kiliaris , Ming Kuang, Member, IEEE, Abul Masrur, Fellow, IEEE, Anthony Phillips ,Qing Wang, Member IEEE 1 Department of Electrical and Computer Engineering, University of Michigan-Dearborn (Phone: 313-593-5028, Fax: 313-583-6336, Email: [email protected]) 2 Ford Motor Company, Dearborn, MI, USA. 3 The US Army RDECOM-TARDEC, Warren, MI 49307. Report Documentation Page Form Approved OMB No. 0704-0188 Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. 1. REPORT DATE 30 JUN 2012 2. REPORT TYPE Journal Article 3. DATES COVERED 30-06-2012 to 30-06-2012 4. TITLE AND SUBTITLE Intelligent Hybrid Vehicle Power ControlPart II: Online Intelligent Energy Management 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Abul Masrur; Yi. Murphey; Jungme Park; Ming Kuang 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) University of Michigan Dearborn,Department of Electrical and Computer Engineering,Dearborn,MI,48128 8. PERFORMING ORGANIZATION REPORT NUMBER ; #23019 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) U.S. Army TARDEC, 6501 E.11 Mile Rd, Warren, MI, 48397-5000 10. SPONSOR/MONITOR’S ACRONYM(S) TARDEC 11. SPONSOR/MONITOR’S REPORT NUMBER(S) #23019 12. DISTRIBUTION/AVAILABILITY STATEMENT Approved for public release; distribution unlimited 13. SUPPLEMENTARY NOTES Paper submitted to IEEE Transactions on Vehicular Technology 14. ABSTRACT This is the second paper in a series of two that describe our research in intelligent energy management in a hybrid electric vehicle (HEV). Energy management in Hybrid Electric Vehicles (HEV) has been actively studied recently because of its potential to significantly improve fuel economy and emission control. Because of the dual-power-source nature and the complex configuration and operation modes in a HEV, energy management is more complicated and important than in a conventional vehicle. Most of the existing vehicle energy optimization approaches do not incorporate knowledge about driving patterns into their vehicle energy management strategies. Our approach is to use machine learning technology combined with roadway type and traffic congestion level specific optimization to achieve quasi-optimal energy management in hybrid vehicles. In this series of two papers, we present a machine learning framework that combines Dynamic Programming with neural networks to learn about roadway type and traffic congestion level specific energy optimization, and an integrated online intelligent energy controller to achieve quasi-optimal power management in hybrid vehicles. In the first paper we presented a machine learning framework, ML_EMO_HEV, developed for learning the knowledge about energy optimization in an HEV. The framework consists of machine learning algorithms for predicting driving environments and for generating optimal power split of the HEV system for a given driving environment. Experiments are conducted to evaluate these algorithms using a simulated Ford Escape Hybrid vehicle model provided in PSAT (Powertrain Systems Analysis Toolkit). In this second paper, we present three online intelligent energy controllers, IEC_HEV_SISE, IEC_HEV_MISE, and IEC_HEV_MIME. All three online intelligent energy controllers were trained within the machine learning framework, ML_EMO_HEV were trained to generate the best combination of engine power and battery power in real-time such that the total fuel consumption over whole driving cycle is minimized while still meeting the driver?s demand and the system constraints including engine, motor, battery, and generator operation limits. The three online controllers were integrated into the Ford Escape Hybrid vehicle model for online performance evaluation. Based on their performances on 10 test drive cycles provided by the PSAT library, we can conclude that the roadway type and traffic congestion level specific machine learning of optimal energy management is effective for in-vehicle energy. The best controller, IEC_HEV_MISE, trained with the optimal power split generated by the DP optimization algorithm with multiple initial SOC points and single ending point can provide fuel saving range from 5% through 19%. 15. SUBJECT TERMS Fuel economy, machine learning, energy optimization, HEV power management 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT Same as Report (SAR) 18. NUMBER OF PAGES 22 19a. NAME OF RESPONSIBLE PERSON a. REPORT unclassified b. ABSTRACT unclassified c. THIS PAGE unclassified Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18