Intelligent gear decision method for vehicle automatic transmission system based on data mining

Abstract

The gear decision logic of automatic transmission directly affects the vehicle's dynamic, fuel economic, and comfort performance. This study employs data mining techniques to address the issues of low adaptability and low recognition rate in the intelligent gear decision of vehicle automatic transmission systems. The research further proposes the utilization of Kalman filter, Hidden Markov Models, and Long Short-Term Memory networks for condition feature recognition and time series classification. Subsequently, dynamic programming algorithms are employed to optimize intelligent gear decisions. Combining driver intent and driving environment, an intelligent gear decision method is formulated. The results indicate that, during a 430 s driving segment, the intelligent gear decision method consumes only 464 mL of fuel, closely resembling the economic strategy's 457 mL, with a gear shift frequency of 53, significantly better than the 79 shifts in the economic strategy. Moreover, the error rate for slope condition recognition is only 0.062 %. In a 200 s coupled condition, the intelligent gear decision results in fuel consumption of 207 mL, approximating the actual vehicle's 219 mL, while power-shifting consumes 316 mL, and economic shifting only 202mL. This study not only improves the accuracy of gear decisions but also effectively enhances vehicle operational efficiency, providing valuable insights for future automatic transmission systems with significant practical value.