Monitoring self-discharge in operating battery cells

A computer-implemented method, comprising:

• detecting cell voltages for a plurality of battery cells over time, by a computing system;
• running the detected cell voltages through a neural network to detect cell-to-cell charge voltage imbalances between the plurality of battery cells while the plurality of battery cells are at peak charge voltages over a time window, by the computing system;
• and
• controlling resistances across the plurality of battery cells to maintain voltage balance between the plurality of battery cells based on output from the neural network by turning respective resistors across the plurality of battery cells on and off based on a difference between a peak voltage of a respective cell and peak voltages of other battery cells of the plurality of battery cells, by the computing system.

A non-transitory computer-readable medium storing a computer program, wherein the computer program is configured to cause at least one processor to:

• detect cell voltages for a plurality of battery cells over time;
• run the detected cell voltages through a neural network comprising a Long Short-Term Memory (LSTM) network for each battery cell of the plurality of battery cells, the LSTM network configured to learn a relative self-discharge rate of the respective battery cell;
• and
• control resistances across the plurality of battery cells to maintain voltage balance between the plurality of battery cells based on output from the neural network by turning respective resistors across the plurality of battery cells on and off based on a difference between a peak voltage of a respective cell and peak voltages of other battery cells of the plurality of battery cells.

A computing system, comprising:

• memory storing computer program instructions;
• and
• at least one processor configured to execute the stored computer program instructions, wherein the computer program instructions are configured to cause the at least one processor to:
• train a neural network by running detected cell voltages of a plurality of battery cells through the neural network, wherein
• the neural network comprises a Long Short-Term Memory (LSTM) network for each battery cell of the plurality of battery cells,
• the LSTM network is configured to learn a relative self-discharge rate of the respective battery cell, and
• each LSTM network comprises three LSTM neural network units.