Preview Control of Dog clutch-based Electric Vehicle Transmission via Dynamic Programming: Proof of Concept

Unlike conventional powertrains, electric vehicle (EV) powertrains usually omit transmissions and instead use a large motor and high-power inverters, thanks to their simplicity. Conventional automatic transmissions equipped with friction clutches lead to increased cost, complexity, and loss of efficiency, which offsets the gains from using a transmission. One way to resolve these issues is to use a dog clutch for EV transmission, but it could not be implemented mainly due to torque holes during shift. This paper proposes a novel preview gear-shift strategy coupled with a two-motor architecture that can potentially eliminate the torque holes. The preview gear-shift strategy can minimize the torque holes by utilizing future information such as vehicle velocity and demanded power. In order to prove this EV transmission concept, dynamic programming (DP) is used to find the optimal control strategy assuming that the future information is known a priori. The optimal control problem is formulated so that the torque holes and the number of gear-shift are minimized while maximizing the system efficiency. Simulation results show that the preview gearshift strategy can dramatically reduce torque holes compared to an existing rule-based gear-shift method. Therefore, if future information can be predicted, the torque hole problem of the dog clutch-based transmission can be resolved, which opens up new opportunities in developing EV transmissions using dog clutches.