Fuel Consumption Analysis via Driver-Vehicle Segmentation
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Solution Overview
Problem
Current technologies fail to accurately analyze and differentiate between driver-related and vehicle-related factors contributing to fuel consumption in vehicles, making it difficult to identify and reduce fuel consumption effectively, leading to increased costs and environmental impact.
Innovation Solution
A method and system that divides energy consumption into various categories, using a calculation device and display to visualize and analyze the fuel consumption, allowing for identification of abnormal increases and providing recommendations for improved driving style or vehicle maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If fuel consumption is monitored without causal analysis, then fuel cost is reduced through awareness, but the ability to identify specific causes of high consumption is insufficient
Solution Approach 1:
The system segments total fuel consumption into distinct components: driver-related factors (acceleration, braking, cruising speed) and vehicle-related factors (rolling friction, air resistance, gradient). This segmentation enables identification of specific causes rather than treating fuel consumption as a single aggregate metric.
Solution Approach 2:
The system introduces an intermediary analysis layer that processes raw fuel consumption data and attributes it to specific causal factors. This intermediary layer includes algorithms that calculate the contribution of each factor (e.g., braking losses, rolling friction) to total consumption, providing actionable insights between simple monitoring and complex engineering analysis.
2Loss of energy
If driver behavior is targeted for improvement through training and incentives, then driver-related fuel consumption is reduced, but vehicle-related issues are misdiagnosed and not addressed
Solution Approach 1:
The system separates driver-related consumption components (acceleration, braking, cruising speed) from vehicle-related components (rolling friction, air resistance, gradient effects). This precise segmentation enables accurate attribution, ensuring that drivers are only evaluated on factors within their control while vehicle issues are identified separately for mechanical intervention.
Solution Approach 2:
The system changes the parameter of attribution accuracy by introducing multiple measurement parameters for different consumption causes. Instead of a single fuel consumption metric, the system measures and attributes consumption based on acceleration patterns, braking events, speed profiles, and vehicle condition parameters, enabling precise differentiation between driver and vehicle responsibilities.
3Loss of information
If detailed energy loss analysis is performed to identify all factors, then comprehensive understanding is achieved, but the analysis becomes complex and unintelligible
Solution Approach 1:
The system divides comprehensive energy loss analysis into manageable segments: driver-related losses (acceleration, braking, cruising) and vehicle-related losses (rolling friction, air resistance, gradient). Each segment is calculated and presented separately, making the overall complex analysis intelligible through structured breakdown into cause-and-effect categories.
Solution Approach 2:
The system applies local quality by providing different levels of analysis detail for different user needs. The interface can display summarized views for drivers (focusing on controllable factors) and detailed technical breakdowns for fleet managers or mechanics (showing all factors including vehicle condition). This localized presentation quality maintains comprehensiveness while avoiding overwhelming complexity for each user type.
4Loss of energy
If aggressive driving style is not detected and corrected, then fuel consumption increases, but repair and maintenance costs also increase due to increased wear and tear
Solution Approach 1:
The system provides feedback by monitoring driving behavior parameters (acceleration, braking, speed) and linking them to both fuel consumption and wear indicators. This feedback loop enables drivers to understand how aggressive driving affects not only fuel economy but also vehicle reliability, encouraging behavior changes that benefit both parameters simultaneously.
Solution Approach 2:
The system takes preliminary anti-action by detecting aggressive driving patterns before they cause significant wear or fuel waste. By providing real-time or near-real-time feedback on driving behavior, the system enables drivers to correct aggressive patterns proactively, preventing both fuel consumption increases and reliability degradation before they occur.
Data Source
AI summary
Method (400), calculation device (131) and display (130) for causal analysis of the fuel/energy consumption in a vehicle (100), which is driven by a driver (101): Division (401) of the vehicle's fuel/energy consumption over a number of fuel/energy consumers, calculation (402) of the subdivided (401) fuel/energy consumers' fuel/energy consumption in a calculation device (131), and visualization (403) of the subdivided (401) fuel/energy consumers' calculated (402) fuel/energy consumption, on a display (130), which is controlled by the calculation device (131).