Hierarchical Priority Control Algorithm for Grid-Integrated Vehicle Energy Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for controlling power flow in electric vehicles and energy storage devices connected to the electric power grid do not consider the status and needs of the grid, leading to inefficient energy management and utilization.
Innovation Solution
The implementation of a vehicle monitoring and control system that includes an energy storage unit, a power conversion unit, and a controller to manage energy transfer between the vehicle and the grid based on anticipated use, utilizing a Hierarchical Priority and Control Algorithm (HPCA) to optimize energy flow and grid interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the charger charges the vehicle at maximum rate immediately upon plugging in, then the charging speed is improved, but the energy management efficiency deteriorates because the status and needs of the electric power grid are not considered
Solution Approach 1:
The charging system dynamically adjusts the charging rate based on real-time grid conditions and vehicle needs. The control algorithm modifies charging parameters (current, voltage, power) dynamically rather than maintaining a fixed maximum rate, allowing the system to respond to changing grid status and optimize energy management while maintaining high charging speeds when appropriate.
Solution Approach 2:
The system implements feedback control by continuously monitoring grid status, vehicle battery state, and charging progress. The control algorithm uses this feedback information to adjust charging rates in real-time, ensuring that charging operations align with both vehicle requirements and grid conditions, thereby improving overall energy management efficiency.
2Device complexity
If the charger uses simple current limit and battery resistance control, then the device complexity is reduced, but the adaptability deteriorates because it cannot respond to grid conditions or optimize energy flow
Solution Approach 1:
The control algorithm autonomously manages charging operations by independently assessing grid conditions, vehicle battery state, and optimal charging strategies. The system self-regulates charging parameters without requiring complex external control infrastructure, achieving enhanced adaptability while maintaining relatively simple device architecture through intelligent autonomous decision-making.
Solution Approach 2:
The system achieves adaptability by dynamically changing operational parameters (charging current, voltage, power level) based on grid conditions and vehicle needs. Rather than requiring complex hardware modifications, the system adapts through software-based parameter adjustment, maintaining device simplicity while enhancing grid interaction capability.
3Ease of manufacture
If no components for charge rate control are added, then the manufacturing cost is reduced, but the energy utilization efficiency deteriorates because the charging process does not consider grid status
Solution Approach 1:
The system replaces physical charge rate control components with a software-based control algorithm. Instead of using additional hardware components to regulate charging, the invention uses intelligent software to optimize charge rates based on grid status and vehicle needs, maintaining manufacturing simplicity while significantly improving energy utilization efficiency through algorithmic control.
4Ease of operation
If the charger operates without considering grid needs, then the ease of operation is improved, but the system reliability deteriorates because it cannot provide grid services or optimize power flow
Solution Approach 1:
The control algorithm autonomously handles complex grid interaction tasks without requiring user intervention. The system automatically assesses grid conditions, adjusts charging parameters, and manages power flow optimization, maintaining ease of operation for the user while achieving high system reliability through intelligent autonomous grid integration.
Solution Approach 2:
The system performs preliminary assessment of grid conditions and vehicle needs before initiating charging operations. By evaluating grid status and battery requirements in advance, the control algorithm can optimize charging parameters from the start, ensuring reliable grid integration and efficient energy utilization without complicating the user operation process.
Data Source
AI summary
A method and apparatus for managing system energy flow. The apparatus includes an energy storage unit to store energy to be used by a system and a power conversion unit configured to be coupled between the energy storage unit and a utility grid. The apparatus also includes a controller to selectively control the power conversion unit to transfer energy between the utility grid and the energy storage unit based at least in part on an anticipated use of the system.