Gear Selection Control for Fuel-Optimal Hybrid Drivetrains
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Solution Overview
Problem
Conventional methods for selecting gears in automatic transmissions are labor-intensive, inefficient in terms of fuel consumption, especially in hybrid vehicles, and fail to account for variable torque characteristics of electrified drive trains and different energy costs, such as those encountered with boost functions or predictive route data.
Innovation Solution
A method for selecting a gear in a motor vehicle's transmission that calculates drive train parameters based on the accelerator pedal position, allowing for the selection of a target gear that optimizes fuel efficiency and simplifies the calibration process by using physical modeling, eliminating the need for complex shift maps and high data input efforts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional shift maps with upshift and downshift characteristics are used for transmission control, then a desired sensory shifting behavior can be explicitly specified in every situation, but the calibration process becomes very labor and memory intensive and the result is not optimal in terms of fuel consumption
Solution Approach 1:
The patent changes the fundamental parameters of gear selection from static shift map thresholds to dynamic optimization based on instantaneous fuel consumption calculations. The system calculates fuel consumption for each available gear based on current operating conditions (engine speed, torque, vehicle speed, accelerator position) and selects the gear that minimizes fuel consumption, eliminating the need for complex calibration of shift characteristics.
Solution Approach 2:
The patent replaces the mechanical/calibration-based shift map system with a computational optimization approach. Instead of using pre-calibrated lookup tables with fixed upshift/downshift characteristics, the system uses real-time calculations of fuel consumption across different gears to determine optimal gear selection, substituting physical calibration effort with algorithmic optimization.
2Device complexity
If shift maps are applied statically for various driving situations such as ecological driving, sporty driving, electric driving, then a simplified control approach is used, but the variable characteristics of torque sources in electrified drive trains and different energy costs cannot be taken into account
Solution Approach 1:
The patent introduces dynamic adaptability by calculating fuel consumption based on real-time operating conditions rather than using static shift maps. The system continuously evaluates engine speed, torque, vehicle speed, and accelerator position to determine instantaneous fuel consumption for each gear, allowing the gear selection to adapt dynamically to changing driving conditions and torque source characteristics.
Solution Approach 2:
The system changes from fixed parameter thresholds in static shift maps to dynamic parameter evaluation that considers instantaneous operating conditions. By calculating fuel consumption based on current engine state, vehicle speed, and accelerator position, the system adapts to variable torque source characteristics and different energy costs without requiring multiple static control maps.
3Device complexity
If conventional gear selection methods are used in hybrid vehicles with variable load point shifts, then the control is simplified, but fuel consumption optimization is compromised due to inability to account for different energy costs of the battery and variable torque characteristics
Solution Approach 1:
The patent performs preliminary calculations of fuel consumption for each available gear based on current operating conditions before making the gear selection decision. By pre-calculating the fuel consumption impact of each gear option considering engine speed, torque, vehicle speed, and accelerator position, the system ensures optimal fuel efficiency is achieved without complex real-time optimization during the gear selection moment itself.
Solution Approach 2:
The system uses feedback from multiple sensors (engine speed, torque, vehicle speed, accelerator position) to continuously evaluate the fuel consumption characteristics of different gears. This feedback mechanism allows the system to adapt to variable torque source characteristics and energy costs in hybrid vehicles, selecting the gear that minimizes fuel consumption based on actual operating conditions rather than pre-defined shift points.
Data Source
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AI summary
The present invention relates to a method for selecting a gear of a transmission (14) in a drive train (1) of a motor vehicle. The method includes obtaining (30, 40, 50) a desired wheel torque (MRW) or a desired mileage, calculating (31, 41, 51) a powertrain parameter for at least two gears of the transmission, and selecting (33, 44, 54) one Target gear based on the calculated drive train parameters, taking into account the desired wheel torque (MRW) or the desired driving performance.