BATTERY STATE OF CHARGE ESTIMATION
The present disclosure provides techniques and solutions for obtaining state of charge estimates for one or more battery cells. A set of values is obtained for a set of one or more battery cells. The set of values includes a least one voltage measurement, at least one present current measurement, and at least one temperature measurement. The set of input values is submitted to a state of charge estimation model, as well as at least one prior current value for the set of one or more battery cells. A state of charge estimate is received for the set of one or more battery cells. In various implementations, the state of charge estimation model can be implemented as a machine learning model or as a lookup table. An estimate from the charge estimation model may be combined with one or more other state of charge estimates.
1 . A computing system comprising:
at least one memory;
one or more hardware processing units coupled to the at least one memory; and
one or more computer readable storage media storing computer-executable instructions that, when executed, cause the computing system to perform operations comprising:
receiving a first set of values from one or more sets of hardware sensors associated with a first set of one or more battery cells, the first set of values comprising at least voltage measurement value, at least one present current measurement value, and at least one temperature measurement value;
submitting a first set of input values to a first state of charge estimation model, the first set of input values from the first set of values and at least one prior current value for the first set of one or more battery cells; and
receiving a first state of charge estimate for the first set of input values from the first state of charge estimation model.
2 . The computing system of claim 1 , the operations further comprising:
subsequent to receiving the first state of charge estimate for the first set of input values, receiving a command to increase an amount of energy supplied to at least one battery cell of the first set of one or more battery cells; and
suppling energy to the at least one battery cell.
3 . The computing system of claim 1 , the operations further comprising:
subsequent to receiving the first state of charge estimate for the first set of input values, receiving a command to withdraw energy from at least one battery cell of the first set of one or more battery cells; and
withdrawing energy from the at least one battery cell.
4 . The computing system of claim 1 , the operations further comprising:
subsequent to receiving the first state of charge estimate for the set of input values, receiving a command to disconnect at least one battery cell of the first set of one or more battery cells from a circuit; and
disconnecting the at least one battery cell from the circuit.
5 . The computing system of claim 1 , wherein the first state of charge estimation model comprises a lookup table.
6 . The computing system of claim 1 , wherein the first state of charge estimation model comprises a machine learning model.
7 . The computing system 1 , the operations further comprising:
training the first state of charge estimation model, the training the first state of charge estimation model comprising:
receiving a plurality of training data sets, a given training data set of the plurality of training data sets comprising, for a second set of one or more battery cells, wherein the second set of one or more battery cells is the same as the first set of one or more battery cells or where one or more battery cells of the first set of one or more battery cells are not included in the second set of one or more battery cells, a second set of input values, the second set of input values comprising at least voltage measurement value, at least one current measurement value, at least one prior current measurement value, at least one temperature measurement value, and at least one state of charge estimate;
submitting at least a portion of the training data sets to a machine learning algorithm to provide a trained machine learning model.
8 . The computing system of claim 7 , the operations further comprising:
filtering the plurality of training data sets by comparing an error value associated with a given state of charge estimate with a first threshold and not submitting training data sets of the plurality of training data sets to the machine learning model that do not satisfy the first threshold.
9 . The computing system of claim 8 , wherein the filtering further comprises comparing the current measurement to one or more second thresholds and not submitting training data sets of the training data sets to the machine learning model that do not satisfy at least one threshold of the one or more second thresholds.
10 . The computing system of claim 7 , wherein the at least one state of charge estimate is produced by measuring coulombs provided to and withdrawn from battery cells of the second set of one or more battery cells.
11 . The computing system of claim 1 , the operations further comprising:
receiving a plurality of training data sets, a given training data set of the plurality of training data sets comprising, for a second set of one or more battery cells, wherein the second set of battery cells is the same as the first set of one or more battery cells or where one or more battery cells of the first set of one or more battery cells are not included in the second set of one or more battery cells, a second set of input values, the second set of input values comprising at least voltage measurement value, at least one current measurement value, at least one prior current measurement value, at least one temperature measurement value, and at least one state of charge estimate; and
generating a lookup table using at least a portion of the plurality of training data sets.
12 . The computing system of claim 11 , the operations further comprising:
filtering the plurality of training data sets by comparing an error value associated with a given state of charge estimate with a threshold and not using training data sets of the plurality of training sets that do not satisfy the threshold to generate the lookup table.
13 . The computing system of claim 12 , wherein the filtering further comprises comparing the current measurement value to one or more thresholds and not using training data sets of the plurality of training data sets that do not satisfy at least one threshold of the one or more thresholds to generate the lookup table.
14 . The computing system of claim 1 , the operations further comprising:
combining the first state of charge estimate with at least a second state of charge estimate.
15 . The computing system of claim 14 , wherein the combining the first state of charge estimate with the at least a second state of charge estimate is based at least in part on respective uncertainties associated with the first state of charge estimate and the at least a second state of charge estimate.
16 . The computing system of claim 15 , wherein the combining employs a Kalman filter.
17 . The computing system of claim 13 , wherein the at least a second state of charge estimate is based on a coulomb counting technique.
18 . The computing system of claim 17 , wherein the coulomb counting technique measure coulombs provided to, and coulombs withdrawn from, the first set of one or more battery cells.
19 . A method, implemented in a computing system comprising at least one hardware processor and at least one memory coupled to the at least one hardware processor, the method comprising:
receiving a first set of values from one or more sets of hardware sensors associated with a first set of one or more battery cells, the first set of values comprising at least voltage measurement value, at least one present current measurement value, and at least one temperature measurement value;
submitting a first set of input values to a first state of charge estimation model, the first set of input values from the first set of values and at least one prior current value for the first set of one or more battery cells; and
receiving a first state of charge estimate for the first set of input values from the first state of charge estimation model.
20 . One or more computer-readable storage media comprising:
computer-executable instructions that, when executed by a computing system comprising at least one hardware processor and at least one memory coupled to the at least one hardware processor, cause the computing system to receive a first set of values from one or more sets of hardware sensors associated with a first set of one or more battery cells, the first set of values comprising at least voltage measurement value, at least one present current measurement value, and at least one temperature measurement value;
computer-executable instructions that, when executed by the computing system, cause the computing system to submit a first set of input values to a first state of charge estimation model, the first set of input values from the first set of values and at least one prior current value for the first set of one or more battery cells; and
computer-executable instructions that, when executed by the computing system, cause the computing system to receive a first state of charge estimate for the first set of input values from the first state of charge estimation model.