Stationary Battery Balancing Using Predicted Low-Load Windows
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Solution Overview
Problem
Conventional balancing methods for stationary battery storage systems are inefficient and stressful, leading to reduced system performance and lifetime, as they typically require frequent activation and disrupt energy flow, making it difficult to optimize energy usage and minimize public supply network dependence.
Innovation Solution
A data-based model using self-learning clustering methods predicts optimal timing for balancing operations based on energy flow patterns, identifying low-load durations to minimize energy drawn from the public supply network and extend battery and resistor lifetimes by scheduling balancing methods during periods of low energy usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a conventional passive balancing method is performed frequently to align states of charge of battery cells, then the reliability of the battery system is improved, but the lifetime of the battery cells and balancing resistors deteriorates due to high stress and frequent activation cycles
Solution Approach 1:
The system performs preliminary actions by predicting future low-load periods using a data-based model, and schedules balancing operations in advance during these predicted periods. This allows the balancing method to be performed proactively at optimal times rather than reactively, reducing the need for frequent corrections and thereby extending component lifetime while maintaining reliability
Solution Approach 2:
The system implements periodic balancing operations scheduled during predicted low-load periods rather than continuous or frequent balancing. By using a data-based model to identify recurring low-load patterns, the system performs balancing periodically at optimal intervals, reducing the total number of activation cycles while maintaining adequate state of charge alignment across battery cells
2Reliability
If a balancing method is performed to align battery cell states of charge, then the reliability of the battery system is improved, but the productivity of the energy supply system deteriorates due to blocked regular use and disconnection from consumers
Solution Approach 1:
The system performs preliminary scheduling of balancing operations during predicted low-load periods before these periods occur. By using a data-based model to predict future low-load windows, the system can plan and execute balancing in advance during times when energy demand is naturally low, thereby minimizing the impact on productivity and availability while ensuring reliable operation
Solution Approach 2:
The system implements periodic balancing operations during predicted low-load periods rather than continuous balancing. This periodic approach synchronizes balancing activities with natural low-demand intervals, reducing the frequency and duration of disconnections from consumers while maintaining adequate state of charge alignment, thus preserving both reliability and productivity
3Reliability
If balancing operations are performed during high energy flow periods to maintain state of charge alignment, then the reliability of the battery system is improved, but the loss of energy deteriorates due to increased dependence on the public supply network
Solution Approach 1:
The system performs preliminary prediction of low-load periods using a data-based model and schedules balancing operations in advance during these predicted periods. This allows the system to proactively perform balancing when energy flow is naturally low, avoiding the need to draw additional energy from the public supply network during high-demand periods, thereby reducing energy loss and network dependence while maintaining reliability
Solution Approach 2:
The system implements periodic balancing operations during predicted low-load periods rather than during high energy flow periods. By synchronizing balancing with natural low-demand intervals, the system reduces the frequency of high-energy-flow operations and minimizes dependence on the public supply network, thereby reducing energy loss while maintaining adequate state of charge alignment for reliable operation
Data Source
AI summary
A method for operating an energy supply system having a stationary battery storage device includes operating the energy supply system and recording a chronological profile of an energy flow from the battery storage device and providing data points respectively indicative of a low-load duration and a timepoint within a period, wherein the low-load duration is indicative of a duration during which the amount of energy flow into or out of the battery storage device falls below a specified threshold. The method includes creating or further developing a data-based model based on the data points provided, wherein the data-based model is designed to determine at least one most likely timepoint within the period at which a balancing method can be performed without premature termination, and performing the balancing method at the at least one most likely timepoint.