Neural-network state-of-charge and state of health estimation
AssigneesThe Trustees of Columbia University in the City of New York, McMaster University
InventorsEphram Chemali, Matthias Preindl
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Abstract
An approach to control or monitoring of battery operation makes use of an artificial neural network (ANN), which receives one or more battery attributes for a Lithium ion (Li-ion) battery, and determines, based on the received one or more battery attributes, a state-of-charge (SOC) and/or a state-of-health (SOH) estimate for the Li-ion battery. The ANN includes at least one of a recurrent neural network (RNN) and a convolutional neural network (CNN), and the series of values of the battery attributes includes at one of battery voltage values, battery current values, and battery temperature values.
Claims20
A method for monitoring a battery state, the method comprising:
- receiving by a processor-based artificial neural network (ANN), encoding characteristic behavior of a battery at multiple different ambient temperatures, a time series of values, determined based on measurements performed by one or more sensors monitoring the battery, for one or more battery attributes for the battery, the one or more battery attributes comprising at least a current ambient temperature;
- and
- determining by the processor-based ANN encoding the characteristic behavior of the battery at the multiple different ambient temperatures, based on the time series of values comprising the current ambient temperature, the battery state;
- wherein the processor-based ANN includes at least one of a recurrent neural network (RNN) or a convolutional neural network (CNN), the battery state includes at least one of a state of charge (SOC) or a state of health (SOH), and the time series of values of the battery attributes includes at least one of battery voltage values, battery current values, or battery temperature values.
A battery monitoring system comprising:
- one or more sensors monitoring battery attributes of a battery;
- and
- a processor-based artificial neural network (ANN), encoding characteristic behavior of the battery at multiple different ambient temperatures, the processor-based ANN configured to:
- receive a time series of values, determined based on measurements performed by the one or more sensors, for one or more battery attributes for the battery, the one or more battery attributes comprising at least a current ambient temperature, and
- determine, by the processor-based ANN encoding the characteristic behavior of the battery at the multiple different ambient temperatures, based on the time series of values, the battery state;
- wherein the processor-based ANN includes at least one of a recurrent neural network (RNN) or a convolutional neural network (CNN), the battery state includes at least one of a state of charge (SOC) or a state of health (SOH), and the time series of values of the battery attributes includes at least one of battery voltage values, battery current values, or battery temperature values.
A non-transitory computer readable media programmed h instructions, executable on one or more processors, to:
- receive by a processor-based artificial neural network (ANN), encoding characteristic behavior of a battery at multiple different ambient temperatures, a time series of values, determined based on measurements performed by one or more sensors monitoring the battery, for one or more battery attributes for a battery, the one or more battery attributes comprising at least a current ambient temperature;
- and
- determine by the processor-based ANN encoding the characteristic behavior of the battery at the multiple different ambient temperatures, based on the time series of values comprising the current ambient temperature, the battery state;
- wherein the processor-based ANN includes at least one of a recurrent neural network (RNN) or a convolutional neural network (CNN), the battery state includes at least one of a state of charge (SOC) or a state of health (SOH), and the time series of values of the battery attributes includes at least one of battery voltage values, battery current values, or battery temperature values.
Dependencies are inferred from the claim text.