Intelligent energy management agent for a parallel hybrid vehicle-part I-System Architecture and

Intelligent Energy Management Agent for a Parallel Hybrid Vehicle—Part I:System Architecture and Design of the Driving Situation Identification Process

Reza Langari,Senior Member,IEEE,and Jong-Seob Won

Abstract—This two part paper proposes an intelligent energy management agent(IEMA)for parallel hybrid vehicles.IEMA incorporates a driving situation identification component whose role is to assess the driving environment,the driving style of the driver and the operating mode of the vehicle using long and short term statistical features of the drive cycle.This information is subsequently used by the torque distribution and charge suste-nance components of IEMA to determine the power split strategy, which is shown to lead to enhanced fuel economy and reduced emissions.In Part I,the overall architecture of IEMA is presented and the driving situation identification process is described.It is specifically shown that a learning vector quantization(LVQ) network can effectively determine the driving condition using a limited duration of driving data.The overall performance of the system under a range of drive cycles is discussed in the second part of this paper.

Index Terms—Charge sustenance,drive cycle,energy manage-ment,fuzzy rule base,hybrid vehicle,torque distribution.

I.I NTRODUCTION

H YBRID electric vehicles(HEVs)have great potential

as new alternative means of transportation.The specific benefits of HEVs,compared to conventional vehicles,include improved fuel economy and reduced emissions[1]–[3].On the other hand,design and implementation of HEVs present a number of challenging problems.In particular,management of energy and distribution of torque(power)are two of the key issues in the development of hybrid electric vehicles[4]–[28]. These issues can be summarily stated as follows.

•How to meet the driver’s torque demand while achieving satisfactory fuel consumption and emis-

sions.

•How to maintain the battery state of charge(SOC)at a satisfactory level to enable effective delivery of torque

to the vehicle over a wide range of driving situations. In order to address these issues,an extensive set of studies has been conducted over the past two decades[4]–[28].In partic-ular,at least three logic-based control strategies for distributing

Manuscript received June20,2003;revised February23,2004and May26, 2004.The review of this paper was coordinated by Dr.B.Fahimi.

ngari is with the Department of Mechanical Engineering,Texas A&M University,College Station,TX77843USA(e-mail:rlangari@).

J.-S.Won was with the Department of Mechanical Engineering,Texas A&M University,College Station,TX77843USA.He is now with Hyundai Motor Company and Kia Motors Corporation,Korea(e-mail: wjsdw@).

Digital Object Identifier10.1109/TVT.2005.844685power demand have been suggested in[4]–[6].Likewise,sev-eral fuzzy logic-based energy management strategies have been proposed in[7]–[15].These approaches are adopted mainly due to their effectiveness in dealing with problems appearing in the complexity of hybrid drivetrain via both heuristics(and human expertise)and mathematical models.However,these approaches generally do not address the driving situation that may affect the operation of the vehicle.

The application of optimal control theory to power distribu-tion for hybrid vehicles appears promising as well,as noted in[16]–[18].In addition,a number of studies,dating back to 1980s,have focused on the application of dynamic program-ming to HEVs[19]–[21].These,and the aforementioned op-timal control strategies are,however,generally based on afixed drive cycle,and as such do not deal with the variability in the driving situation.In view of this issue,a number of alternative approaches have been proposed in[22]–[27].In particular[28] formulated a drive cycle dependent optimization approach that selects the optimal power split ratio between the motor and the engine according to the characteristic features of the drive cycle. In general,however,few,if any,of the aforementioned studies, have given appropriate consideration to the driving situation and/or the driving style of the driver.The proposed intelligent energy management agent(IEMA)on the other hand,explic-itly incorporates a driving situation identifier whose role is to identify the roadway type,the driving style of the driver as well as the current driving mode and trend.This information is sub-sequently integrated in a fuzzy logic-based torque distribution and state of charge compensation strategy to provide enhanced operation over the spectrum of driving situations to which the vehicle may be subjected.The simulation study reveals that the proposed“driving situation awareness”-based energy manage-ment strategy provides a platform of new energy management system and gives improved performance of a parallel hybrid ve-hicle.

The aim of this paper is to describe the underlying framework for IEMA and to further describe each of its components and their contribution to the overall performance of the system.To this end,the paper is divided into two parts.In Part I,the basic framework of IEMA is presented and its various components are discussed at length.In addition,Part I presents a detailed description of the driving situation identification component of IEMA.In Part II,we discuss the role of this component in the fuzzy torque distribution and charge sustenance strategies.In addition,we will discuss the overall results of the study and

0018-9545/$20.00©2005IEEE