Hybrid Vehicle Charge Mode Control via Predicted Torque
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional hybrid vehicles face inefficiencies in controlling the lock-up charge mode, leading to unnecessary engine on/off cycles and abnormal braking sensations, which affect drivability and fuel efficiency, due to inadequate prediction of driver torque and braking intentions.
Innovation Solution
A method and system for a hybrid vehicle that includes a driving information detection unit, a driver acceleration/deceleration prediction unit, and a hybrid controller to determine current and predicted torques, allowing for precise control of the lock-up charge mode based on threshold values related to coasting and driving mode changes, ensuring efficient operation and maintaining or releasing the mode accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the lock-up charge mode is maintained based on conventional torque control without prediction, then the engine can be kept running to avoid frequent on/off cycles, but this causes unnecessary engine operation and reduces fuel efficiency
Solution Approach 1:
The system performs preliminary prediction of driver torque and braking intentions using machine learning models before making engine on/off decisions. By predicting future driver behavior, the system can maintain the lock-up charge mode only when truly necessary, avoiding unnecessary engine operation while preventing frequent on/off cycles, thus resolving the contradiction between operational stability and fuel efficiency
2Object-affected harmful factors
If the lock-up charge mode is released when braking torque exceeds a fixed threshold, then the abnormal braking sensation can be avoided, but this causes unnecessary mode switching and reduces drivability
Solution Approach 1:
The system predicts future braking intentions using machine learning models before executing mode switching. By anticipating whether the driver will continue braking or return to acceleration, the system avoids unnecessary lock-up charge mode releases that would cause drivability issues, while still preventing abnormal braking sensations through intelligent threshold management
Solution Approach 2:
The system continuously monitors actual driver behavior and compares it with predicted behavior, using this feedback to refine the lock-up charge mode control strategy. This adaptive feedback mechanism allows the system to optimize the balance between preventing abnormal braking sensations and maintaining smooth drivability based on learned driver patterns
Data Source
AI summary
A hybrid vehicle and a method of controlling a charge mode therefor are provided. The control method includes determining a first torque, which is a currently requested torque and determining a second torque, which is a predicted requested torque that is predicted to be generated in the near future from the present time, or predicted acceleration. Additionally, the method includes releasing a lock-up charge mode when the first torque is less than a first threshold value relevant to a reference for determining coasting driving and the second torque or the predicted acceleration is less than a second threshold value relevant to a driving mode change reference.