ResearchArticle Adaptive Gearshift Strategy Based on Generalized Load Recognition for Automatic Transmission Vehicles Yulong Lei,1Ke Liu,1Yuanxia Zhang,1Yao Fu,1Hongbo Liu,2Ge Lin,3and Hui Tang1 1StateKeyLaboratoryofAutomotiveSimulationandControl,JilinUniversity,Changchun130025,China 2GeelyGroupR&DCenter,Hangzhou311200,China 3ChinaFAWGroupCorporationR&DCenter,Changchun130013,China CorrespondenceshouldbeaddressedtoKeLiu;liuke12@mails.jlu.edu.cn Received28January2015;Revised17May2015;Accepted31May2015 AcademicEditor:DanSimon Copyright © 2015 YulongLeietal. This is an open access article distributed under the Creative Commons Attribution License, whichpermitsunrestricteduse,distribution,andreproductio ninanymedium,providedtheoriginalworkisproperlycited. Recognizingvariousdrivingconditionsinrealtimeandadjustingcontrolstrategyaccordinglyinautomatictransmissionvehicles areimportanttoimprovetheiradaptability totheexternalenvironment.Thisstudydefinesageneralized loadconceptwhichcan comprehensively reflect driving condition information. Theprinciple of a gearshift strategy based on generalized load isdeduced theoretically, adopting linear interpolation between the shift l i n e so nfl a ta n do nt h el a r g e s tg r a d i e n tr o a db a s e do nr e c o g n i t i o n results.Fortheconvenienceofapplication,normalizationprocessingisusedtotransformgeneralizedloadresultsintoanormalizedform. Compared with the dynamic three-parameter shift schedule, the complex tridimensional curved surface is not needed any more,soitwouldreducedemandsofmemoryspace.Andithasamoreconciseexpressionandbetterreal-timeperformance.For thetargetvehicle,whendrivinguphillwithgradient11%,thevehicleloadisabout280 ∼320Nm;whendrivingdownhill,thevalue isaround −340∼−320Nm.Roadtestsshowthatgeneralizedvehicleloadkeepsnear0inzero-loadconditionaftercalibration,and an11%gradecanbeestimatedwithlessthan1.8%error.Thismethodisconvenientandeasytoimplementincontrolsoftwareand canidentifythedrivingconditioninformationeffectively. 1. Introduction Theeffectsofvariousdrivingconditions,suchasgrade,vehi- cle loading, and road resistance, on the powertrain controlstrategy should be considered carefully. For instance, thegrade resistance increases during uphill driving, so a largetransmissionratioshouldbeselectedtoavoidfrequentshift-ing.Alargeratioshouldalsobeusedduringdownhilldrivingto take full advantage of the engine braking effect and avoidgear shift-up. Similarly, aerodynamic resistance and rollingresistance increase during vehicle loading, so a large ratio shouldstillbeusedtoimprovevehicledynamicperformance. Therefore, automatic transmission vehicles should recognizetheaforementioneddrivingconditionsinrealtimeandadjust the corresponding shift algorithm to improve the vehicle’s dynamic, passing, and comfort performance. Driving envi-ronmentrecognitionisaprerequisiteinachievingintelligent control.Many driving environment recognition algorithms are available. For instance, Yuhai et al. [1] and Jin et al. [2,3] developed certain methods to calculate the grade usingan equation deduced by the principle of vehicle systemdynamics.Ohnishietal.[4]utilizedanadditionalsensor,andJo et al. [5] used GPS to identify the ramp and load, whichwill increase costs in practical application. Parameter iden-tification is widely used to identify the driving environment[6–8], which not only depends on some vehicle parametersbut also requires additional vehicle sensors. In addition,the process of real-time parameter estimation requires theelectronic control unit (ECU) to have a higher computingspeed.Anothercommonlyusedmethodisbasedonthefuzzylogic inference model [9–13], where the fuzzy rule can beflexiblyadjustedaccordingtotheactualapplicationsituation.However,recognitionresultsaregenerallythejudgmentandclassification of the current vehicle condition instead of the preciseslopeorvehicleload. Hindawi Publishing Corporation Mathematical Problems in Engineering Volume 2015, Article ID 614989, 12 pages http://dx.do