Hybrid Energy Storage Control for Converter-Aware Power Sharing
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Solution Overview
Problem
Existing energy management algorithms for hybrid energy storage devices, such as Li-ion batteries and supercapacitors, fail to effectively account for real-time operations and often neglect the dimensioning of converters, leading to suboptimal performance and lifespan in both stationary and mobile systems.
Innovation Solution
A predictive energy management process involving a first dimensioning step with MPC1 for determining the necessary components and a second stage using a non-linear MPC2 algorithm for dynamic power sharing between Li-ion batteries and supercapacitors, with optional real-system testing, to optimize performance and lifespan.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing energy management algorithms are used for hybrid energy storage devices, then the system can operate with simple control logic, but the performance and lifespan of the energy storage devices are suboptimal
Solution Approach 1:
The patent applies preliminary action by performing dimensioning calculations and predictive modeling before actual system operation. The method calculates optimal component sizes and predicts future states of the energy storage system, allowing the system to be pre-configured for optimal performance and lifespan under various operating conditions before real-time control begins.
Solution Approach 2:
The patent implements feedback mechanisms through continuous monitoring of system states (charge levels, power flows, temperature) and adjusting control strategies accordingly. The energy management algorithm uses real-time feedback to optimize power distribution between battery and supercapacitor, thereby extending lifespan while adapting to changing operating conditions.
2Power
If converter dimensioning is neglected in existing sizing methods, then the sizing process is simpler and faster, but the overall system performance is suboptimal
Solution Approach 1:
The patent merges the dimensioning of multiple components (battery, supercapacitor, and converter) into a unified sizing process. Rather than treating converter dimensioning as a separate or neglected step, the method integrates converter sizing with energy storage component sizing, ensuring all components are optimally matched for the hybrid system's specific power and energy requirements.
Solution Approach 2:
The patent uses parameter changes by optimizing multiple design parameters simultaneously (power ratings, capacity values, converter specifications) based on system requirements. The method adjusts these parameters iteratively to find the optimal configuration that balances performance, cost, and complexity for the entire hybrid energy storage system including the converter.
3Productivity
If dynamic programming is used for energy management optimization, then optimal power sharing can be achieved, but the real-time nature and embedded system requirements cannot be met
Solution Approach 1:
The patent applies partial action by using dynamic programming only for offline optimization and dimensioning, where computational intensity is acceptable. For real-time operation, the patent transitions to simpler control strategies (rule-based or model predictive control) that provide sufficient performance without the computational burden of full dynamic programming, thus meeting real-time requirements while still achieving near-optimal results.
Data Source
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AI summary
The present invention relates to a method for managing the energy of a hybrid energy storage device in a stationary or mobile system, to allow for its optimal sizing, the hybrid energy storage device comprising at least one main energy source such as a battery, at least one auxiliary energy source such as a supercapacitor, and at least one converter,