Hybrid Vehicle Gear Shift Control via Predicted Driving Load
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Solution Overview
Problem
Hybrid electric vehicles face inefficiencies due to mismatched gear shift strategies between electric vehicle (EV) and hybrid electric vehicle (HEV) modes, leading to non-driving fuel consumption and reduced fuel efficiency, especially when the actual driving load does not align with the applied gear shift map.
Innovation Solution
A method for predicting the required power of the forward driving path to determine a representative driving mode, allowing for the application of either an EV or HEV gear shift map based on the predicted mode, ensuring the gear shift strategy aligns with the actual driving source.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If a gear shift map optimized for EV mode is used during CD mode, then fuel efficiency is improved under normal driving conditions, but fuel efficiency deteriorates when high power is required (e.g., uphill driving)
Solution Approach 1:
The patent applies dynamics by making the gear shift strategy adaptive rather than static. The control device dynamically selects between EV-optimized and HEV-optimized gear shift maps based on real-time predictions of required power and actual driving conditions. This allows the system to transition from a fixed EV-mode strategy to a dynamic strategy that adapts to varying power requirements, resolving the contradiction between fuel efficiency under normal conditions and adaptability under high-power conditions.
Solution Approach 2:
The patent changes the parameter of gear shift strategy selection based on predicted required power and actual driving mode. By introducing a prediction mechanism that estimates future power requirements and compares them with available motor power, the system adjusts the gear shift map selection parameter dynamically. This resolves the contradiction by enabling the system to switch between EV-optimized and HEV-optimized strategies based on changing conditions.
2Use of energy by moving object
If a gear shift map optimized for HEV mode is used during CS mode, then engine driving efficiency is improved, but non-driving fuel consumption increases during mode switching
Solution Approach 1:
The patent applies preliminary action by predicting the required power for upcoming driving segments before actual mode switching occurs. The control device uses prediction information to determine in advance whether EV mode or HEV mode should be maintained, allowing the system to prepare the appropriate gear shift strategy before switching. This prevents premature or incorrect mode switching that would cause non-driving fuel consumption, while still enabling engine-driven efficiency when appropriate.
Solution Approach 2:
The patent implements feedback by continuously comparing predicted required power with actual driving conditions and battery state of charge. The control device uses this feedback loop to determine the most appropriate driving mode and gear shift strategy, adjusting decisions based on real-time system state. This feedback mechanism prevents unnecessary mode switching and reduces non-driving fuel consumption while maintaining engine efficiency when HEV mode is truly beneficial.
3Extent of automation
If mode switching is based solely on battery state of charge, then charge depletion and charge sustaining modes are clearly defined, but gear shift strategy mismatch occurs when actual driving load differs from predicted conditions
Solution Approach 1:
The patent applies preliminary action by introducing prediction of required power before making mode determination decisions. Instead of relying solely on current battery state of charge, the system predicts future power requirements and uses this information to determine whether EV mode or HEV mode should be maintained. This preliminary assessment improves the accuracy of driving mode determination by considering actual driving load conditions in advance.
Solution Approach 2:
The patent introduces prediction information as an intermediary between battery state of charge and gear shift strategy selection. Rather than directly mapping SoC to mode, the prediction mechanism serves as an intermediary that refines mode determination by incorporating expected driving conditions. This intermediary layer improves measurement precision of driving mode determination while maintaining automated management.
Data Source
AI summary
Disclosed are a hybrid vehicle and a method of calculating driving load therefor for determining a more effective gear shift reference in consideration of a driving mode. The method of controlling gear shift of a hybrid vehicle includes predicting required power of a forward driving path, determining a representative driving mode based on mode switch power as a reference of switch between a first driving mode using only an electric motor and a second driving mode using at least an engine and the predicted required power, and applying any one of a first gear shift map corresponding to the first driving mode based on the determined representative driving mode or a second gear shift map corresponding to the second driving mode.