Vehicle Energy Arbitration via Predictive Power Allocation
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Solution Overview
Problem
Current energy management systems for vehicles fail to optimize power distribution from multiple sources in real-time, leading to inefficiencies and potential damage to components like battery modules due to inadequate balancing and blending of energy consumption.
Innovation Solution
An intelligent energy management system with a controller that uses sensors and a processor to determine an arbitration vector based on current and future power demands, minimizing energy loss by allocating power from various sources such as battery modules and supercapacitors, while considering factors like electrical loss, charge depletion, and current limiting to optimize power distribution and charging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If power distribution is not optimized in real-time, then energy efficiency deteriorates and component damage risk increases, but implementing optimization requires complex control systems and multiple sensors
Solution Approach 1:
The system performs preliminary actions by predicting future power demands and pre-positioning power sources in an optimal state before demands occur. The prediction module forecasts upcoming power requirements, and the control module proactively adjusts power distribution strategies ahead of time, rather than merely reacting to current conditions. This allows the system to maintain energy efficiency without requiring overly complex real-time control mechanisms.
Solution Approach 2:
The system implements feedback mechanisms where the control module continuously monitors actual power consumption and compares it against predicted demands. This feedback loop enables the system to learn from discrepancies between predicted and actual usage patterns, refining future predictions and adjusting power distribution strategies accordingly. The feedback mechanism allows simpler control logic to achieve sophisticated optimization over time.
2Loss of energy
If power distribution is optimized for immediate energy efficiency, then short-term energy loss decreases, but long-term component health may deteriorate due to inadequate balancing
Solution Approach 1:
The system performs preliminary actions by predicting future power demands and pre-positioning power sources in an optimal state before demands occur. The prediction module forecasts upcoming power requirements, and the control module proactively adjusts power distribution strategies ahead of time, rather than merely reacting to current conditions. This allows the system to maintain energy efficiency without requiring overly complex real-time control mechanisms.
Solution Approach 2:
The system implements feedback mechanisms where the control module continuously monitors actual power consumption and compares it against predicted demands. This feedback loop enables the system to learn from discrepancies between predicted and actual usage patterns, refining future predictions and adjusting power distribution strategies accordingly. The feedback mechanism allows simpler control logic to achieve sophisticated optimization over time.
3Power
If multiple power sources are used without intelligent arbitration, then power availability increases, but energy loss increases due to inadequate blending
Solution Approach 1:
The system implements self-service through autonomous arbitration where the control module automatically selects and coordinates multiple power sources based on real-time conditions and predictions. The system serves itself by independently determining optimal power combinations without external intervention, dynamically arbitrating between battery modules, supercapacitors, and other sources to minimize energy loss while maintaining power availability.
Solution Approach 2:
The system dynamically changes operational parameters of power sources based on predicted demands and current states. The control module adjusts charging/discharging rates, voltage levels, and current distributions for different power sources according to forecasted requirements. This parameter optimization enables intelligent blending of multiple sources, reducing energy losses from inefficient operation while preserving overall power availability.
Data Source
AI summary
An energy management system for a vehicle is disclosed. The vehicle includes one or more power sources configured to provide power to one or more recipients. The system includes a controller configured to determine an arbitration vector based at least partially on a state vector and an initial transformation function. The arbitration vector is determined as one or more points for which the initial transformation function attains a maximum value. The controller is configured to determine a current reward based on the arbitration vector and the state vector, the current reward being configured to minimize energy loss in the power sources. The controller is configured to determine an updated transformation function based at least partially on the initial transformation function and a total reward. The controller is configured to arbitrate a power distribution based in part on the updated arbitration vector.