A three-position valve and flow variator cut pump displacement at rest while still allowing hydraulic motor piston retraction.
Reinforcement learning replaces numerous shift patterns by selecting gear stages from driving, road, and driver data to reduce busy shifting and fuel use.
Ambient temperature, pressure, and gradient are used to choose launch ratio, cutting shift map variants across engines and vehicles.
Pre-shifting based on vehicle deceleration and road gradient reduces post-bump acceleration delay and gearshift calculation load.
An integrated shift sensor and link arm detects lever input even with a short shift rod, cutting package size and parts count.
Simulated stepped torque and acoustic cues restore perceptible acceleration changes, reducing driver fatigue in smooth-drive vehicles.
A sliding sleeve keeps gear teeth meshed during EV shifting, preventing one-way clutch power interruption and stabilizing torque transfer.
Multiple gear actions are simulated against a road speed profile to cut fuel use, limit transmission wear, and keep vehicle speed within bounds.
Dynamic HST relief pressure setting keeps circuit pressure in range to stabilize torque and reduce jerking during load fluctuations.
A speed ratio change threshold replaces fixed distance switching, letting manual mode return to automatic at the desired ratio with fewer driver inputs.
Brake and pedal-off signal logic holds LFU shifts during deceleration to prevent busy upshifts, cut energy loss, and improve drivability.
Torque-based range switching in a two-motor hydraulic travel drive cuts transmission losses and improves fuel-efficient power delivery.
Energy-based state transition costs replace nonlinear force models to compute optimal vehicle speed and gear profiles with lower embedded resource use.