Vehicle Cost Estimation System Using Sensor Data and Tax Modules
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Solution Overview
Problem
Existing systems for estimating the cost of vehicle ownership do not adequately account for all parameters, particularly fiscal ones, and face challenges in updating databases, leading to incomplete and inaccurate cost calculations.
Innovation Solution
A system that uses operational sensors to collect and process data on vehicle parameters such as power, CO2 emissions, energy consumption, and fiscal information, along with tax data and usage information, to calculate comprehensive ownership costs, including acquisition, usage, and tax costs, providing personalized estimates based on individual needs and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If simplified mathematical models are used to calculate ownership costs, then calculation speed is improved, but measurement precision deteriorates
Solution Approach 1:
The system segments the cost estimation into multiple independent modules: acquisition cost calculation, usage cost calculation, and tax cost calculation. Each module processes specific parameters independently, enabling parallel computation that maintains speed while incorporating comprehensive data for precision.
Solution Approach 2:
The system introduces a database as an intermediary layer between sensor data collection and cost calculation. This database stores and manages operational data, tax data, and vehicle specifications, allowing complex queries to be processed efficiently without bottlenecking the calculation speed while ensuring accurate data retrieval for precise estimates.
2Measurement precision
If comprehensive parameters including fiscal data are collected, then measurement precision is improved, but device complexity worsens
Solution Approach 1:
The system implements a universal database structure that handles multiple data types (operational data, tax data, vehicle specifications) through a single integrated platform. This multi-functional approach consolidates what would otherwise require separate systems, reducing overall complexity while maintaining comprehensive data collection for accurate estimates.
Solution Approach 2:
The system automatically retrieves and processes tax data from external sources without manual intervention. The server autonomously manages data collection, storage, and calculation processes, reducing the complexity of manual data management while ensuring comprehensive and up-to-date information is available for precise cost estimation.
3Measurement precision
If historical data from multiple vehicles is processed, then measurement precision is improved, but loss of time worsens
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing operational data from multiple vehicles in the database before actual cost estimation is needed. This includes collecting sensor data, organizing vehicle specifications, and preparing tax data in advance, so that when estimation is required, the system can quickly retrieve pre-organized data without time-consuming processing delays.
Solution Approach 2:
The system replaces manual data collection and processing mechanisms with automated sensor networks and server-based processing. Operational sensors automatically capture vehicle data, and the server automatically processes this information through standardized algorithms, eliminating time-consuming manual operations while maintaining high measurement precision through comprehensive data analysis.
Data Source
Figure 1
AI summary
The invention relates to a system for estimating the total cost of ownership of a vehicle, characterized in that it comprises: a plurality of operational sensors from a plurality of vehicles from at least one potential seller; one or more vehicle cost estimation servers; receiving, via a network from at least one of the plurality of operational vehicle sensors, initial operational vehicle data; receiving, via a network, secondary vehicle tax data; storing the initial operational vehicle data in a database; receiving information on the use of a vehicle to estimate the cost; and calculating a total cost of ownership from a usage cost, an acquisition cost, and a tax cost.