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Mathematical Problems in Engineering
Volume 2015, Article ID 614989, 12 pages
Research Article

Adaptive Gearshift Strategy Based on Generalized Load Recognition for Automatic Transmission Vehicles

1State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China
2Geely Group R&D Center, Hangzhou 311200, China
3China FAW Group Corporation R&D Center, Changchun 130013, China

Received 28 January 2015; Revised 17 May 2015; Accepted 31 May 2015

Academic Editor: Dan Simon

Copyright © 2015 Yulong Lei et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Recognizing various driving conditions in real time and adjusting control strategy accordingly in automatic transmission vehicles are important to improve their adaptability to the external environment. This study defines a generalized load concept which can comprehensively reflect driving condition information. The principle of a gearshift strategy based on generalized load is deduced theoretically, adopting linear interpolation between the shift lines on flat and on the largest gradient road based on recognition results. For the convenience of application, normalization processing is used to transform generalized load results into a normalized form. Compared with the dynamic three-parameter shift schedule, the complex tridimensional curved surface is not needed any more, so it would reduce demands of memory space. And it has a more concise expression and better real-time performance. For the target vehicle, when driving uphill with gradient 11%, the vehicle load is about 280~320 Nm; when driving downhill, the value is around −340~−320 Nm. Road tests show that generalized vehicle load keeps near 0 in zero-load condition after calibration, and an 11% grade can be estimated with less than 1.8% error. This method is convenient and easy to implement in control software and can identify the driving condition information effectively.