Predictive Transmission Shift Schedule for Improving Fuel Economy and Drivability Using Electronic Horizon

This paper proposes an approach that uses the road preview data to optimize a shift schedule for a vehicle equipped with an automatic transmission. The road preview is inferred here from the so-called electronic horizon of a digital map that includes road attributes such as road grade, curvature, segment speed limit, functional class, etc. The optimized shift schedule selects the gear ratio whose optimization is conducted through applying a hybrid model predictive control method to the powertrain system, which is modelled as the multiple plants associated with multiple gears together with engine models. The goal of this optimization of shift schedule includes improving real world fuel economy and drivability. The real-world fuel economy gains using the proposed approach are achieved through optimizing gear ratio w.r.t. the road grade variations of the road ahead. The drivability improvement focuses on enhancing a driver's curve negotiation performance through eliminating back-to-back shifting that can cause shift business. The proposed strategy is verified in a simulation environment, which integrates the vehicle model architecture for powertrain systems (VMAPS), the vehicle dynamics simulation package, and the road preview model. The simulation results showed the effectiveness of the proposed predictive shift strategy in achieving the desired goal.