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

Abstract

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.