Energy-Efficiency Coach With Adaptive Scoring Normalization
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Solution Overview
Problem
Existing systems fail to provide equitable scoring of operator behaviors impacting energy efficiency across different vehicle types, operators, and varying environmental conditions, leading to inconsistent rewards and feedback effectiveness.
Innovation Solution
An intelligent energy-efficiency coach that uses tunable variables and reinforcement learning to normalize performance scores by adjusting weighting constants based on vehicle and environmental factors, providing adaptive feedback and rewards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a standardized scoring method is used to evaluate operator behaviors across different vehicles and conditions, then the scoring process is simple and consistent, but the scoring becomes inequitable due to factors outside operator control (vehicle configuration, environment, geography)
Solution Approach 1:
The system applies different weighting constants to efficiency metrics based on local conditions such as vehicle type, operator characteristics, and environmental factors. This allows the scoring to be tailored to specific contexts while maintaining an overall standardized framework, thereby achieving both simplicity and equity in scoring.
2Ease of operation
If weighting constants for efficiency metrics are fixed, then the calculation process is straightforward, but the system cannot adapt to different vehicle types, operators, and operating conditions
Solution Approach 1:
The weighting constants are made dynamic rather than fixed. The system automatically adjusts weighting constants based on detected vehicle characteristics, operator behavior patterns, and environmental conditions. This dynamic adjustment maintains calculation simplicity through automated processes while significantly improving adaptability to diverse operating scenarios.
3Productivity
If feedback is provided without normalization for external factors, then the feedback process is direct and simple, but the feedback validity and operator receptivity are reduced
Solution Approach 1:
The system introduces normalization calculations as an intermediary step between raw efficiency metric collection and feedback delivery. This intermediary process adjusts for external factors like vehicle configuration and environmental conditions, thereby enhancing feedback validity and operator receptivity while maintaining efficient processing through automated calculations.
Data Source
AI summary
Systems and methods of providing intelligent energy-efficiency performance coaching. Energy efficiency performance is measured by a performance score calculated in association with various efficiency metrics. The performance score may be provided as feedback to the operator and may further be used by an operator reward system and/or gamification system. An intelligent energy-efficiency coach responds to variability in operator behaviors by using tunable variables to bias weighting constants assigned to the efficiency metrics to normalize calculations of performance scores. The intelligent energy-efficiency coach may use reinforcement machine learning techniques to calculate tunable variables and adjust performance scores on an ad hoc basis. A mutual learning process may be performed, where the operator may learn energy saving behaviors from feedback provided by the intelligent energy-efficiency coach to improve energy efficiency and the intelligent energy-efficiency coach adjusts tunable variables to help the operator improve their energy efficiency performance.