Vehicle State Prediction for Fuel-Efficient Engine Control
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Solution Overview
Problem
Conventional vehicle control devices limit opportunities to improve fuel consumption and fail to accurately reflect universal driving characteristics of drivers in varying traffic environments, leading to suboptimal engine control.
Innovation Solution
A vehicle control device incorporating a time-series database and a statistical database that predicts future vehicle states based on past data, allowing for accurate reflection of driving characteristics and traffic environment impacts, thereby optimizing engine control for improved fuel efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If conventional engine control methods are used, then the control system is simple, but fuel consumption cannot be improved in diverse traffic environments
Solution Approach 1:
The system performs preliminary classification of traffic environments and pre-calculates appropriate engine control parameters before actual driving situations occur. By categorizing traffic environments in advance and preparing control strategies, the system can quickly adapt to different conditions without complex real-time calculations, thus improving fuel consumption while maintaining manageable system complexity.
Solution Approach 2:
The invention changes control parameters based on classified traffic environment types. By identifying distinct traffic environment categories and applying optimized control parameters for each category, the system achieves improved fuel consumption efficiency without requiring overly complex adaptive control mechanisms, as the parameter sets are predetermined for each environment type.
2Measurement precision
If driving characteristics are not accurately reflected, then control responsiveness is maintained, but prediction accuracy of driving forces deteriorates
Solution Approach 1:
The system segments driving characteristics into distinct categories based on traffic environment types. By dividing the continuous spectrum of driving behaviors into discrete categories, the system achieves accurate prediction of driving forces without requiring overly complex data processing, as each segment can be handled with specialized, optimized algorithms.
Solution Approach 2:
The system incorporates feedback mechanisms that compare predicted driving forces with actual driving characteristics. This feedback loop continuously refines the classification and prediction accuracy, allowing the system to maintain high measurement precision while managing data processing complexity through iterative optimization rather than purely complex initial processing.
3Use of energy by moving object
If universal driving characteristics are not reflected, then control is simple, but fuel consumption efficiency in varying traffic environments deteriorates
Solution Approach 1:
The system creates a universal classification framework for traffic environments that can be applied across different driving scenarios. By establishing a multi-functional classification system that identifies common traffic environment patterns, the achievement improved fuel consumption efficiency while maintaining adaptability to varying traffic conditions through a unified approach rather than separate specialized systems.
Data Source
AI summary
An object of the present invention is to provide a vehicle control device that controls an engine so as to improve fuel consumption in consideration of driving characteristics of a driver and an automatic driving system, the vehicle control device being capable of appropriately reflecting the driving characteristics of the driver and the automatic driving system caused by a difference in traffic environment in control, and a data adjustment method of a database. A time-series database 3 that holds a vehicle state including at least acceleration of a host vehicle in time series, a statistical database 4 that divides the vehicle state into a plurality of classes and holds the number of appearances of the vehicle state belonging to any of the divided classes, and a vehicle state prediction unit 2 that predicts a future vehicle state based on information regarding a vehicle state held in the time-series database 3 and the statistical database 4 and a vehicle state newly acquired during traveling of the host vehicle are provided.