Enhanced Load Balancing and Aggregation Strategy for Uneven Battery Charging Rates

[ogurevich]

Context:

We’ve observed a notable issue in scenarios where batteries connected to the same system charge at different rates. The attached image highlights a specific instance where one battery (Battery-2) reaches a charge rate (74.1A) that exceeds the predefined maximum charge current (43A), while the aggregate charge current remains below the combined potential maximum (92A). This discrepancy suggests that the other batteries in the system are under-utilized in terms of their charging capacity.

Problem:

The current aggregation algorithm seems to not dynamically adjust the distribution of charge currents based on the real-time charge acceptance of each battery. This leads to scenarios where one battery could potentially be overcharged or charged at a rate higher than its set maximum, while others might not utilize their full charging capability, resulting in inefficient overall system performance.

Suggested Enhancement:

1.  Dynamic Adjustment of Charge Rates: Implement an algorithm within the BatteryAggregator that continuously monitors the charge rates and capacities of individual batteries and adjusts the distribution of current to maximize the overall efficiency and safety. The algorithm should reduce the charge current of batteries nearing their maximum charge rate and redistribute the excess to other batteries which are charging below their maximum capacity.
2.  Utilization of Unused Margins: For batteries that rapidly reach their max charge current, the system should consider the ‘unused’ charge capacity (margins) of these batteries and dynamically reallocate it to other batteries in the system which are underperforming. This ensures a balanced charge state across all batteries and maximizes the usage of available current.
3.  Alternative Solutions: Exploration of other potential solutions or enhancements that could optimize charge balancing and efficiency, such as machine learning algorithms predicting battery behavior based on historical data to pre-adjust charge rates dynamically.

Impact:

Addressing this issue will not only enhance the operational efficiency of the battery system but also prolong the lifespan of the batteries by preventing scenarios of overcharging and under-utilization. This improvement is crucial for maintaining the health and efficacy of power storage systems, especially in complex setups where multiple batteries are involved.

[ogurevich]

one more example

thank you

[ogurevich]

ups, i had installed some old version, will check it again.