Systems and methods for estimation and prediction of battery health and performance
First Claim
1. A computer-implemented method for analyzing energy storage device information, comprising:
- a feature extraction module configured for;
receiving input data including passive information collected from passive measurements of a battery and active information collected from active measurements of a response of the battery to a stimulus signal applied to the battery;
performing geometric-based parameter identification responsive to the input data relative to an electrical equivalent circuit model to develop geometric parameters;
performing optimization-based parameter identification responsive to the input data relative to the electrical equivalent circuit model to develop optimized parameters; and
performing a decision fusion algorithm for combining the geometric parameters and the optimized parameters to develop new internal battery parameters including at least a constant phase element exponent, electrolyte resistance, and charge transfer resistance;
a state estimation module for updating an internal state model of the battery responsive to the new internal battery parameters;
a health estimation module for processing the internal state model to determine a present battery health including one or both of a state-of-health (SOH) estimation and a state-of-charge (SOC) estimation for the battery; and
a communication module for communicating one or more of the SOH estimation and the SOC estimation to a user, a related computing system, or a combination thereof.
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Accused Products
Abstract
Systems and computer-implemented methods are used for analyzing battery information. The battery information may be acquired from both passive data acquisition and active data acquisition. Active data may be used for feature extraction and parameter identification responsive to the input data relative to an electrical equivalent circuit model to develop geometric-based parameters and optimization-based parameters. These parameters can be combined with a decision fusion algorithm to develop internal battery parameters. Analysis processes including particle filter analysis, neural network analysis, and auto regressive moving average analysis can be used to analyze the internal battery parameters and develop battery health metrics. Additional decision fusion algorithms can be used to combine the internal battery parameters and the battery health metrics to develop state-of-health estimations, state-of-charge estimations, remaining-useful-life predictions, and end-of-life predictions for the battery.
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Citations
20 Claims
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1. A computer-implemented method for analyzing energy storage device information, comprising:
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a feature extraction module configured for; receiving input data including passive information collected from passive measurements of a battery and active information collected from active measurements of a response of the battery to a stimulus signal applied to the battery; performing geometric-based parameter identification responsive to the input data relative to an electrical equivalent circuit model to develop geometric parameters; performing optimization-based parameter identification responsive to the input data relative to the electrical equivalent circuit model to develop optimized parameters; and performing a decision fusion algorithm for combining the geometric parameters and the optimized parameters to develop new internal battery parameters including at least a constant phase element exponent, electrolyte resistance, and charge transfer resistance; a state estimation module for updating an internal state model of the battery responsive to the new internal battery parameters; a health estimation module for processing the internal state model to determine a present battery health including one or both of a state-of-health (SOH) estimation and a state-of-charge (SOC) estimation for the battery; and a communication module for communicating one or more of the SOH estimation and the SOC estimation to a user, a related computing system, or a combination thereof. - View Dependent Claims (2, 3, 4, 5)
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6. A computer-implemented method for analyzing battery information, comprising:
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receiving input data including passive information collected from passive measurements of a battery and active information collected from active measurements of a response of the battery to a stimulus signal applied to the battery; performing a feature extraction process using the input data to develop geometric parameters responsive to an analysis of the input data relative to an electrical equivalent circuit model, develop optimized parameters responsive to another analysis of the input data relative to the electrical equivalent circuit model, and combining the geometric parameters and the optimized parameters to develop internal battery parameters including at least a constant phase element exponent, electrolyte resistance, and charge transfer resistance; performing two or more analysis processes using the internal battery parameters to develop two or more health metrics corresponding to each analysis process, wherein the health metrics from the analysis processes are selected from capacity, available power, or pulse resistance; determining a state-of-health (SOH) estimation for the battery by performing a decision fusion algorithm for combining the two or more health metrics from the two or more analysis processes; and communicating the SOH estimation to a user, a related computing system, or a combination thereof. - View Dependent Claims (7, 8, 9, 10, 11, 12)
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13. A computer-implemented method for analyzing battery information, comprising:
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receiving input data including passive information collected from passive measurements of a battery and active information collected from active measurements of a response of the battery to a stimulus signal applied to the battery; performing a feature extraction process using the input data to develop internal battery parameters including at least a constant phase element exponent, electrolyte resistance, and charge transfer resistance; performing two or more analysis processes using the internal battery parameters to develop two or more state-of-charge (SOC) estimates corresponding to each analysis process, wherein; a first analysis processes comprises a particle filter analysis for estimating battery internal states responsive to the internal battery parameters followed by a first neural network analysis for developing a first SOC estimate from the battery internal states; and additional analysis processes are selected from a second neural network (NN) analysis and an auto regressive moving average (ARMA) analysis; determining an SOC estimation for the battery by performing a decision fusion algorithm for combining the two or more SOC estimates from the two or more analysis processes; and communicating the SOC estimation to a user, a related computing system, or a combination thereof. - View Dependent Claims (14)
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15. A computer-implemented method for analyzing battery information, comprising:
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receiving input data including passive information collected from passive measurements of a battery and active information collected from active measurements of a response of the battery to a stimulus signal applied to the battery; performing a feature extraction process using the input data to develop geometric parameters responsive to an analysis of the input data relative to an electrical equivalent circuit model, develop optimized parameters responsive to another analysis of the input data relative to the electrical equivalent circuit model, and combining the geometric parameters and the optimized parameters to develop internal battery parameters including at least a constant phase element exponent, electrolyte resistance, and charge transfer resistance; performing two or more analysis processes using the internal battery parameters to develop two or more Remaining-useful-life (RUL) estimates corresponding to each analysis process and two or more End-of-life (EOL) estimates corresponding to each analysis process, wherein the analysis processes are selected from a particle filter (PF) analysis, a neural network (NN) analysis, and an auto regressive moving average (ARMA) analysis; determining an RUL prediction for the battery by performing a decision fusion algorithm to combine the two or more RUL estimates from the two or more analysis processes, determining an EOL prediction for the battery by performing the decision fusion algorithm to combine the two or more EOL estimates from the two or more analysis processes; and communicating at least one of the RUL prediction and the EOL prediction to a user, a related computing system, or a combination thereof. - View Dependent Claims (16, 17)
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18. A battery condition monitoring system, comprising:
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one or more active data acquisition units configured for applying a signal to a battery and measuring a response of the battery to the applied signal as active information; and processing circuitry configured for; performing geometric-based parameter identification responsive to the active information relative to an electrical equivalent circuit model to develop geometric parameters; performing optimization-based parameter identification responsive to the active information relative to the electrical equivalent circuit model to develop optimized parameters; performing a decision fusion algorithm for combining the geometric parameters and the optimized parameters to develop new internal battery parameters including at least a constant phase element exponent, electrolyte resistance, and charge transfer resistance; updating an internal state model of the battery responsive to the new internal battery parameters; processing the internal state model to determine a battery health prediction including one or both of a remaining-useful-life (RUL) prediction and an end-of-life (EOL) prediction for the battery; and communicating one or more of the RUL prediction, the EOL prediction, to a user, a related computing system, or a combination thereof. - View Dependent Claims (19, 20)
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Specification