Unified Energy Management System for Vehicle Efficiency Optimization
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Solution Overview
Problem
Modern vehicles lack a unified system to effectively utilize disparate vehicle state information, such as sensor data and GPS data, to optimize performance, and do not share this information with neighboring vehicles to collectively improve efficiency.
Innovation Solution
A vehicle system comprising a wireless communications device, an on-board data processing unit, and sensors, which uses an energy management system to store and analyze historic vehicle state information, infrastructure data, and neighbor vehicle data to create predictive models for optimizing performance, including energy efficiency, and shares this information with neighboring vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If vehicles store and process disparate vehicle state information using multiple electronic control units and sensors, then the quantity of vehicle data increases, but the system complexity increases and the information cannot be effectively unified or optimized
Solution Approach 1:
The patent merges data from multiple electronic control units (ECUs) and sensors into a unified energy management system. The energy management system receives and processes vehicle state information from various sources including engine control units, transmission control units, and environmental sensors, consolidating disparate data streams into a single optimization framework that reduces system complexity while maintaining data quantity.
Solution Approach 2:
The energy management system serves as a universal platform that handles multiple functions: collecting data from various ECUs, processing sensor information, generating predictive models, optimizing vehicle performance, and communicating with neighboring vehicles. This multi-functional system replaces the need for separate specialized systems for each function.
2Use of energy by moving object
If vehicles use historical vehicle state information and GPS data to optimize performance, then energy efficiency improves, but the system requires sophisticated predictive modeling capabilities that increase computational complexity
Solution Approach 1:
The system performs preliminary actions by storing historical vehicle state information and generating predictive models in advance. The energy management system uses historical data to create predictive models that forecast future vehicle states, allowing the system to optimize energy efficiency proactively rather than reactively, reducing the need for complex real-time computations.
Solution Approach 2:
The system implements feedback mechanisms where predictive models continuously compare expected vehicle performance with actual performance, using the discrepancy to refine optimization strategies. The energy management system receives feedback from vehicle sensors and adjusts predictive models accordingly, improving energy efficiency through iterative learning rather than requiring increasingly complex computational models.
3Productivity
If vehicles share state information with neighboring vehicles to collectively optimize performance, then overall fleet efficiency improves, but the quantity of data transmission and communication requirements increase
Solution Approach 1:
The system extracts only the essential and relevant vehicle state information for sharing with neighboring vehicles, rather than transmitting all available data. The energy management system identifies and transmits key parameters such as location, speed, and performance metrics that are most useful for collective optimization, reducing data transmission volume while maintaining fleet efficiency benefits.
4Reliability
If a unified energy management system processes all vehicle state information, then optimization effectiveness improves, but the device complexity and processing requirements increase
Solution Approach 1:
The unified energy management system processes vehicle state information in segmented stages: data collection from ECUs and sensors, historical data storage, predictive model generation, optimization calculation, and control signal output. This segmentation allows the system to achieve effective optimization while managing processing complexity through structured, modular operations.
Data Source
AI summary
Historic vehicle state information specifying a plurality of performance values associated with the plurality of subsystems of the vehicle at a plurality of time points, including a current time point is stored at the vehicle. Historic neighbor vehicle state information specifying a plurality of performance values associated with a plurality of subsystems of a vehicle at a plurality of time points, including a current time point is received from a neighbor vehicle proximate to the vehicle. A forward-looking model is generated based on vehicle state information. An performance value associated with a subsystem of the plurality of subsystems of the vehicle is determined based on the historic vehicle state information, the historic neighbor vehicle state information, and the forward-looking model. A recommendation to a driver of the vehicle is provided based on the optimized performance value.