Trip-Based Optimal Power Management of Plug-in Hybrid Electric Vehicles

• Published in: IEEE transactions on vehicular technology Vol. 57; no. 6; pp. 3393 - 3401
• Main Authors: Qiuming Gong, Qiuming Gong, Yaoyu Li, Yaoyu Li, Zhong-Ren Peng, Zhong-Ren Peng
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-11-2008; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied sciences; Automobile industry; Batteries; Connection and protection apparatus; Depletion; Direct energy conversion and energy accumulation; Driving; Dynamic programming; Dynamic programming (DP); Electric power generation; Electric vehicles; Electrical engineering. Electrical power engineering; Electrical power engineering; Electrochemical conversion: primary and secondary batteries, fuel cells; Energy efficiency; Energy management; Exact sciences and technology; Fuel consumption; Fuel economy; Ground, air and sea transportation, marine construction; Hybrid electric vehicles; Hybrid vehicles; Intelligent transportation systems; intelligent transportation systems (ITSs); Management; Management information systems; Optimization; plug-in hybrid electric vehicles (PHEVs); power management; Power system management; Power system modeling; Road transportation and traffic; Studies; Systems, networks and services of telecommunications; Telecommunications; Telecommunications and information theory; Teletraffic; Traffic control; Traffic flow; Trucks; Vehicles; Plug-in Hybrid Electric Vehicles; Intelligent Transportation system; Power Management; Dynamic Programming; Hybrid electric powered vehicles; Information system; Transportation system; Radiolocalisation; Fuel consumption; Electric vehicle; Electric utility; Modeling; Dynamic programming (DP); Traffic flow; Fuel economy; power management; Dynamic programming; Mathematical model; Intelligent system; plug-in hybrid electric vehicles (PHEVs); intelligent transportation systems (ITSs); Depletion mode; GPS system; Hybrid vehicle; Gas insulated switchgear; Teletraffic; Secondary cell; System on a chip; Algorithm; Power consumption; Satellite navigation; Integrated circuit; A priori estimation
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2008.921622

Hybrid electric vehicles (HEVs) have demonstrated the capability to improve fuel economy and emissions. The plug-in HEV (PHEV), utilizing more battery power, has become a more attractive upgrade of the HEV. The charge-depletion mode is more appropriate for the power management of PHEVs, i.e., the state of charge (SOC) is expected to drop to a low threshold when the vehicle reaches the trip destination. Trip information has so far been considered as future information for vehicle operation and is thus not available a priori . This situation can be changed by the recent advancement in intelligent transportation systems (ITSs) based on the use of on-board global positioning systems (GPSs), geographical information systems (GISs), and advanced traffic flow modeling techniques. In this paper, a new approach to optimal power management of PHEVs in the charge-depletion mode is proposed with driving cycle modeling based on the historic traffic information. A dynamic programming (DP) algorithm is applied to reinforce the charge-depletion control such that the SOC drops to a specific terminal value at the end of the driving cycle. The vehicle model was based on a hybrid electric sport utility vehicle (SUV). Only fuel consumption is considered for the current stage of the study. A simulation study was conducted for several standard driving cycles and two trip models using the proposed method, and the results showed significant improvement in fuel economy compared with a rule-based control and a depletion sustenance control for most cases. Furthermore, the results showed much better consistency in fuel economy compared with rule-based and depletion sustenance control.