Optimal Gearshift Strategy using Predictive Algorithm for Fuel Economy Improvement 2014-01-1743
Availability of road navigation data and route pattern details to the vehicle controller allows the use of predictive algorithms to obtain optimal performance from the vehicle. Conventionally, in the automated transmissions, gear position values are decided from predefined maps depending on the load demand and vehicle velocity at that instant. Due to the instantaneous decisions taken to get the gear position, minor changes in terrain sometimes might cause multiple unwanted gear shifts.
The paper presents the concept of predictive optimal gear shifting strategy, utilizing the route information from the vehicle navigation system and vehicle state. Route terrain information is processed to analyze the vehicle behavior at future route gradient segments. Several categories of vehicle behavior are identified and at each decision point, the driving state is classified into one of these categories.
Each category of vehicle driving state has an associated predefined shift behavior calculated for optimal fuel economy and vehicle dynamics for that particular state. While implementing the driving state specific gear shift strategies, the boundary conditions of safe engine speed, gear shift hunting & clutch life are ensured. This strategy not only improves fuel economy without compromising on vehicle drivability, but also improves the transmission durability and clutch life (in Automated Manual Transmission) by optimizing number of gear shifts.
In Hybrid Electric Vehicles with Parallel configuration, reduction in number of up-shift instances during recuperation improves the energy recapturing potential thereby augmenting the fuel economy improvement.
Simulations are conducted using Heavy Duty Truck model and highway routes to assess the benefit of this technology.
A patent application based on this idea is in the process of being filed in Germany.