Vehicle Driving Efficiency Control Using Cross-Vehicle Parameter Comparison
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Solution Overview
Problem
Existing technologies have not adequately addressed the various factors affecting driving efficiency, including vehicle parameters and driver skills, leading to suboptimal energy consumption and operational inefficiencies.
Innovation Solution
A method and system that compare driving parameters between a host vehicle and one or more other vehicles to identify efficient driving practices, using sensors and processing units to provide input to the driver or autonomous systems for optimizing energy use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If driving parameters are monitored and compared between multiple vehicles, then driving efficiency is improved through informed adjustments, but system complexity increases due to additional sensors and data processing requirements
Solution Approach 1:
The system uses a multi-functional approach where the same sensor network and processing unit serve both traditional vehicle monitoring functions and the new efficiency optimization function. The sensors monitor parameters for both individual vehicle control and fleet-wide comparison, eliminating the need for separate dedicated hardware for each function.
Solution Approach 2:
A communication network acts as an intermediary between vehicles, allowing parameter data to be shared and compared without requiring direct complex connections between all vehicles. The processing unit receives data from multiple sources, compares parameters, and provides feedback through the user interface, mediating the information flow to reduce system complexity.
2Use of energy by moving object
If real-time parameter monitoring and comparison is implemented across multiple vehicles, then energy consumption is reduced through optimized driving practices, but information processing requirements increase
Solution Approach 1:
The system extracts only the most relevant parameters for energy efficiency comparison from the full set of monitored vehicle data. By selecting and processing only key parameters such as speed, acceleration patterns, and route information rather than all available sensor data, the information processing load is reduced while still achieving energy optimization goals.
Solution Approach 2:
The system implements partial monitoring and comparison focused specifically on parameters that most significantly impact energy consumption, rather than analyzing all vehicle parameters in full detail. This selective approach processes sufficient information to achieve energy savings without the overhead of complete comprehensive analysis.
3Loss of energy
If driver behavior is optimized through parameter feedback, then fuel consumption decreases, but driver autonomy and control are reduced
Solution Approach 1:
The system provides feedback to the driver through the user interface displaying compared parameters and efficiency information, enabling the driver to make informed decisions about driving behavior. The driver retains full control and makes voluntary adjustments based on the information provided, maintaining autonomy while achieving fuel savings through conscious behavioral changes.
Solution Approach 2:
The driver uses the provided parameter information and comparisons to self-optimize their driving behavior without external control or automation. The system empowers the driver to independently adjust their driving style based on feedback, maintaining full operational control while achieving energy efficiency improvements through self-directed behavioral changes.
Data Source
AI summary
A vehicle, system and method for improving driving efficiency for a first host vehicle with at least one propulsion unit and at least one energy storage unit are provided. The method comprises; determining a first set of parameters which affects driving efficiency before a driving session with the first host vehicle is initiated and providing input indicative thereof to a user interface or an autonomous or semi-autonomous driving system of the first host vehicle, monitoring a second set of parameters which affects driving efficiency during the driving session of the first host vehicle, comparing parameters of the first and second set of parameters with corresponding parameters received from at least one second host vehicle, and in response to identified differences between parameters of the first and second host vehicle, and providing input indicative thereof to the user interface or the autonomous or semi-autonomous driving system of the first host vehicle.