Hybrid Power Source Control for Real-Time Load Distribution
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Solution Overview
Problem
Existing power management systems for hybrid power sources, such as fuel cell and battery systems, face challenges in determining optimal energy source distribution for real-time load requirements, especially considering efficiency losses and environmental conditions.
Innovation Solution
A method and system for optimizing power management in hybrid systems, which involves generating a plant model based on a machine recipe, creating algorithms in an algorithm library for various scenarios using selected key performance indicators (KPIs), and integrating a refined algorithm into machine operation to optimize power distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a complex power management system with multiple energy sources (fuel cell, battery, ICE) is used to optimize fuel economy, then energy source distribution can be optimized, but the system complexity and computational requirements increase significantly
Solution Approach 1:
The power management system is segmented into multiple independent energy sources (fuel cell, battery, internal combustion engine), each with its own control algorithm. This allows the complex optimization problem to be divided into manageable subsystems that can be controlled independently while working together to achieve overall fuel economy optimization.
Solution Approach 2:
The system dynamically changes operating parameters (power distribution ratios, engine load points, battery charge/discharge rates) based on real-time conditions to optimize fuel economy. By continuously adjusting these parameters across different energy sources, the system achieves optimal performance without requiring a completely complex integrated control architecture.
2Measurement precision
If real-time power distribution optimization is implemented considering efficiency losses and environmental conditions, then energy distribution accuracy improves, but computational complexity increases
Solution Approach 1:
The system pre-establishes efficiency maps and operating characteristics for each energy source before real-time operation. These pre-calculated data structures allow the control algorithm to quickly determine optimal power distribution without performing complex real-time calculations, thus achieving high measurement precision with reduced computational complexity.
Solution Approach 2:
The system uses simplified mathematical models and lookup tables that replicate the complex physical behavior of each energy source. These copied representations allow accurate prediction of efficiency losses and optimal power distribution without requiring computationally intensive real-time simulations of the actual physical systems.
3Adaptability or versatility
If multiple controllers with different computational capabilities are used to manage hybrid power sources, then system adaptability improves, but integration complexity increases
Solution Approach 1:
The control algorithm is designed to be universal and can be implemented on controllers with varying computational capabilities. The same basic algorithm structure works across different hardware platforms, allowing the system to adapt to available resources without requiring complex integration schemes for different controller types.
Solution Approach 2:
The system implements a tiered control strategy where essential power distribution functions are performed on all controllers, while advanced optimization features are only activated on controllers with sufficient computational resources. This partial implementation approach allows adaptability across different hardware capabilities while avoiding the complexity of fully integrating all features on all platforms.
Data Source
AI summary
A method and system for optimizing power management for a hybrid system of a machine are provided. The method includes generating a plant model based on a machine recipe of the machine; generating, in an algorithm library, algorithms for a plurality of scenarios simulated based on selected key performance indicators (KPIs) associated with the machine recipe, optimization connections, and machine requirements of the machine; selecting an algorithm from the algorithm library based on computational capabilities of a controller associated with the machine and machine requirements for the algorithm; simplifying the algorithm based on removing one or more KPIs of the selected KPIs; refining the algorithm based on weighing one or more remaining KPIs of the selected KPIs; and integrating the algorithm into machine operation to be performed by a control module of the machine.