Adaptive Drive Control for Eco-Mode Engagement
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Solution Overview
Problem
Existing adaptive drive control systems do not effectively engage a fuel-economic mode based on real-time environmental and driver-specific conditions, such as traffic and location, which limits their ability to optimize fuel efficiency and driver convenience.
Innovation Solution
A processor-based system that automatically engages a fuel-economic driving mode (eco-mode) by evaluating environmental context data, including traffic and vehicle-location information, and driver aggressiveness levels, to determine if the eco-mode should be activated, and adjusts its engagement based on predefined thresholds and driver behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If adaptive drive control systems use manual or basic automatic mode selection, then driver convenience is improved, but fuel efficiency optimization is limited due to lack of real-time environmental adaptation
Solution Approach 1:
The system pre-defines multiple drive modes (sport, normal, comfort, eco-mode) with optimized parameters for different conditions. These modes are prepared in advance and can be quickly switched based on real-time environmental assessment, avoiding complex real-time optimization calculations while maintaining fuel efficiency benefits
Solution Approach 2:
The system dynamically selects between pre-defined drive modes based on real-time evaluation of environmental context data (traffic conditions, location, driver behavior). This dynamic mode selection allows the system to adapt to changing conditions without requiring complex real-time parameter adjustments, balancing fuel efficiency optimization with manageable system complexity
2Use of energy by moving object
If the system automatically engages eco-mode based on environmental data, then fuel efficiency is improved, but driver preference and driving experience may be compromised
Solution Approach 1:
The system continuously monitors driver behavior (acceleration patterns, steering input, brake usage) and environmental conditions, then adjusts eco-mode engagement decisions based on this feedback. If driver behavior indicates aggressive driving or sport mode preference, the system suppresses automatic eco-mode engagement, maintaining driver convenience while still optimizing fuel efficiency when conditions are appropriate
Solution Approach 2:
The system dynamically adjusts the threshold for eco-mode engagement based on real-time driver behavior analysis. When driver aggressiveness is detected, the threshold for eco-mode engagement is raised or disabled, allowing the driver to maintain control over the driving experience while still benefiting from automatic eco-mode selection during calm, fuel-efficient driving conditions
3Use of energy by moving object
If the system evaluates multiple environmental factors for eco-mode engagement, then fuel efficiency optimization is improved, but processing requirements and system complexity increase
Solution Approach 1:
The system segments the drive mode selection process into distinct, manageable components: environmental context data collection (traffic, location), driver behavior analysis (aggressiveness detection), and mode selection logic. Each component processes specific data types independently, reducing overall processing complexity while enabling comprehensive fuel efficiency optimization through multi-factor evaluation
Data Source
AI summary
A system includes a processor configured to receive environmental context data upon which automatic engagement of a fuel economic driving mode (eco-mode) is conditioned. The processor is also configured to evaluate the context data to determine if the eco-mode should be automatically engaged based on a data correspondence to an engagement factor and engage the eco-mode upon correspondence of the data to an engagement factor.