In-Vehicle Sensor Data Aggregation for Efficiency Rating Calculation
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Solution Overview
Problem
The automotive industry faces challenges in comprehensively and quantitatively evaluating the performance and emissions impacts of various energy-conserving vehicle hardware strategies and components, which are essential for balancing personal mobility with sustainability and reducing environmental and emissions impacts.
Innovation Solution
A system that collects and processes energy consumption data, emissions data, and mileage data from in-vehicle sensors, calculates an efficiency rating, and transmits summarized data to a centralized system for analysis, providing real-time feedback to operators and aggregating data for overall performance and emissions evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive sensor data collection and processing is implemented to evaluate vehicle performance and emissions, then measurement precision and data reliability are improved, but device complexity and processing requirements increase
Solution Approach 1:
The system segments the complex evaluation task by collecting data from separate specialized sensors (energy consumption sensor, emissions sensor, mileage sensor) and processing them through distinct functional modules. This segmentation allows each component to focus on a specific measurement task, improving precision while managing overall system complexity through modular architecture.
Solution Approach 2:
The processor acts as an intermediary that receives raw data from multiple sensors, processes and integrates this information, and generates simplified performance indicators. This intermediary function manages the complexity by centralizing the processing logic and providing a unified interface between diverse sensor inputs and evaluation outputs.
2Productivity
If real-time data processing and efficiency rating calculation are performed in-vehicle, then productivity and feedback speed are improved, but use of energy increases
Solution Approach 1:
The system performs partial processing in-vehicle by calculating basic efficiency ratings from sensor data, then transmits only the processed results and summarized data to remote systems for further analysis. This partial action approach provides real-time feedback productivity while minimizing the energy burden by avoiding exhaustive real-time processing of all data aspects within the vehicle.
Solution Approach 2:
The system implements feedback by calculating efficiency ratings based on collected sensor data and providing this information back to users through the vehicle interface. This feedback mechanism improves productivity by enabling real-time performance monitoring while the energy consumption is managed through efficient algorithms that provide actionable insights without requiring continuous high-power processing.
3Loss of information
If aggregated data from multiple vehicles is collected and analyzed, then loss of information is reduced and overall performance evaluation is improved, but quantity of data to be transmitted and stored increases
Solution Approach 1:
The system extracts and transmits only the essential processed information (efficiency ratings, summarized performance indicators) from the raw sensor data, leaving the detailed raw data to be stored locally in the vehicle. This extraction approach reduces the quantity of data transmitted and stored centrally while maintaining completeness of information by preserving the ability to access full data locally when needed.
Solution Approach 2:
The system merges data from multiple vehicles at remote computing systems to generate aggregated performance evaluations and industry-wide benchmarks. This merging process reduces information loss by combining multiple data sources to create comprehensive insights, while the data volume management is handled through selective aggregation of processed results rather than transmission of all raw data from each vehicle.
Data Source
AI summary
Data collection and analysis processes include collecting energy consumption data from an in-vehicle energy source sensor, collecting emissions data from an in-vehicle emissions sensor, the emissions data reflecting emissions produced by a vehicle, and collecting mileage data from a mileage sensor in the vehicle. The data collection and analysis processes also include processing the energy consumption data and the emissions data as a function of the mileage data, calculating an efficiency rating from the processing, and transmitting results of the processing to a data collection system.