Systems and methods for estimation and prediction of battery health and performance

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.

What is claimed is: 
     
         1 . A method for analyzing an energy storage device, comprising:
 applying a signal to an energy storage device and measuring a response of the energy storage device to the applied signal as active information;   collecting passive information about the energy storage device;   developing one or more internal parameters for the energy storage device responsive to the active information, the passive information, and one or more learned state models for the energy storage device;   updating the one or more learned state models responsive to the one or more internal parameters, the active information, and the passive information; and   processing the one or more learned state models to determine one or more health conditions for the energy storage device.   
     
     
         2 . The method of  claim 1 , further comprising communicating at least one of the one or more health conditions to a user, a related computing system, or a combination thereof. 
     
     
         3 . The method of  claim 1 , wherein the passive information includes at least one of a temperature, a voltage, and a current of the energy storage device. 
     
     
         4 . The method of  claim 1 , wherein the active information is derived from rapid AC impedance measurements. 
     
     
         5 . The method of  claim 1 , wherein the one or more internal parameters include at least one of a constant phase element exponent, an electrolyte resistance, a charge transfer resistance, and an ohmic resistance. 
     
     
         6 . The method of  claim 1 , wherein the one or more health conditions include one or more present health conditions including at least one of a state-of-charge estimation for the energy storage device and a state-of-health estimation for the energy storage device. 
     
     
         7 . The method of  claim 1 , wherein the one or more health conditions include one or more future health conditions including at least one of a remaining-useful-life prediction for the energy storage device and an end-of-life prediction for the energy storage device. 
     
     
         8 . The method of  claim 1 , further comprising:
 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;   combining the geometric parameters and the optimized parameters to develop one or more new internal parameters for the energy storage device; and   updating the one or more learned state models responsive to the one or more new internal parameters.   
     
     
         9 . The method of  claim 8 , wherein performing the optimization-based parameter identification further comprises deriving one or more of a constant phase element exponent, an electrolyte resistance, an ohmic resistance, and a charge transfer resistance from the active information and performing a nonlinear optimization of each parameter estimated by minimizing a selected objective function. 
     
     
         10 . The method of  claim 1 , further comprising:
 performing two or more analysis processes using the one or more internal parameters to develop two or more health metrics corresponding to each analysis process; and   determining the one or more health conditions for the energy storage device by performing a decision fusion algorithm for combining the two or more health metrics from the two or more analysis processes.   
     
     
         11 . The method of  claim 10 , wherein:
 one analysis process of the two or more analysis processes comprises using the one or more internal parameters in a particle filter (PF) analysis to develop a PF capacity metric, a neural network (NN) analysis to develop an NN capacity metric, and an auto regressive moving average (ARMA) analysis to develop an ARMA capacity metric; and   further comprising performing a capacity decision fusion algorithm for combining the PF capacity metric, the NN capacity metric, and the ARMA capacity metric to develop an overall capacity health metric for inclusion in the decision fusion algorithm as one of the two or more health metrics.   
     
     
         12 . The method of  claim 10 , wherein:
 one analysis process of the two or more analysis processes comprises using the one or more internal parameters in a particle filter (PF) analysis to develop a PF available power metric, a neural network (NN) analysis to develop an NN available power metric, and an auto regressive moving average (ARMA) analysis to develop an ARMA available power metric; and   further comprising performing an available power decision fusion algorithm for combining the PF available power metric, the NN available power metric, and the ARMA available power metric to develop an overall available power health metric for inclusion in the decision fusion algorithm as one of the two or more health metrics.   
     
     
         13 . The method of  claim 10 , wherein:
 one analysis process of the two or more analysis processes comprises using the one or more internal parameters in a particle filter (PF) analysis to develop a PF pulse resistance metric, a neural network (NN) analysis to develop an NN pulse resistance metric, and an auto regressive moving average (ARMA) analysis to develop an ARMA pulse resistance metric; and   further comprising performing a pulse resistance decision fusion algorithm for combining the PF pulse resistance metric, the NN pulse resistance metric, and the ARMA pulse resistance metric to develop an overall pulse resistance health metric for inclusion in the decision fusion algorithm as one of the two or more health metrics.   
     
     
         14 . The method of  claim 10 , wherein:
 performing the two or more analysis processes comprises:
 performing a capacity analysis to develop an overall capacity health metric; 
 performing an available power analysis to develop an overall available power health metric; and 
 performing a pulse resistance analysis to develop an overall pulse resistance health metric; and 
   performing the decision fusion algorithm comprises using a weighted average to combine the overall capacity health metric, the overall available power health metric, and the overall pulse resistance health metric to determine the one or more health conditions.   
     
     
         15 . A method for analyzing an energy storage device, comprising:
 applying a signal to an energy storage device and measuring a response of the energy storage device to the applied signal as active information;   collecting passive information about the energy storage device;   developing one or more internal parameters for the energy storage device responsive to the active information, the passive information, and one or more state models for the energy storage device; and   performing one or more what-if scenarios using one or more aging models to predict one or more future internal parameters of the energy storage device to characterize any changes to the one or more internal parameters as time progresses.   
     
     
         16 . The method of  claim 15 , wherein the one or more what-if scenarios use assumed future operational conditions derived from previously observed operational conditions. 
     
     
         17 . The method of  claim 15 , wherein using the one or more aging models further comprises:
 performing a relevance vector machine algorithm using Bayesian inference to obtain possible solutions for a specific parameter of the one or more internal parameters when the energy storage device is new and an aging parameter indicating how the specific parameter will change over time; and   performing a regression analysis to predict a future value for the specific parameter.   
     
     
         18 . The method of  claim 17 , wherein the specific parameter comprises an electrolyte resistance, a charge transfer resistance, constant phase element exponent, or an ohmic resistance. 
     
     
         19 . A method for analyzing an energy storage device, further comprising:
 applying a signal to an energy storage device and measuring a response of the energy storage device to the applied signal as active information;   collecting passive information about the energy storage device;   developing one or more internal parameters for the energy storage device responsive to the active information, the passive information, and one or more state models for the energy storage device; and   identifying a life extension strategy comprising:
 defining an operational profile; 
 evaluating one or more aging models using the operational profile to predict one or more future internal parameters; and 
 evaluating the one or more future internal parameters to determine a predicted future health condition; and 
   communicating the operational profile for the life extension strategy to a user, a related computing system, or a combination thereof.   
     
     
         20 . The method of  claim 19 , wherein:
 identifying the life extension strategy further comprises:
 modifying the operational profile; and 
 repeating the evaluating and the modifying until a desired value for the predicted future health condition is met; and 
   communicating the operational profile comprises communicating the modified operational profile when the desired value for the predicted future health condition is met.   
     
     
         21 . A monitoring system for an energy storage device, comprising:
 one or more active data acquisition units configured to apply a signal to an energy storage device and measure a response of the energy storage device to the applied signal as active information;   one or more passive data acquisition units configured to collect passive information about the energy storage device; and   processing circuitry configured to:
 develop one or more internal parameters for the energy storage device responsive to the active information, the passive information, and one or more learned state models for the energy storage device; 
 update the one or more learned state models responsive to the one or more internal parameters, the active information, and the passive information; and 
 process the one or more learned state models to determine one or more health conditions for the energy storage device. 
   
     
     
         22 . The system of  claim 21 , wherein the processing circuitry is further configured to communicate at least one of the one or more health conditions to a user, a related computing system, or a combination thereof. 
     
     
         23 . The system of  claim 21 , wherein the passive information includes at least one of a temperature of the energy storage device, voltage of the energy storage device, and current of the energy storage device. 
     
     
         24 . The system of  claim 21 , wherein the one or more health conditions include one or more present health conditions including at least one of a state-of-charge estimation for the energy storage device and a state-of-health estimation for the energy storage device. 
     
     
         25 . The system of  claim 21 , wherein the one or more health conditions include one or more future health conditions including at least one of a remaining-useful-life prediction for the energy storage device and an end-of-life prediction for the energy storage device. 
     
     
         26 . The system of  claim 21 , wherein the processing circuitry is further configured to:
 perform geometric-based parameter identification responsive to the active information relative to an electrical equivalent circuit model to develop geometric parameters;   perform optimization-based parameter identification responsive to the active information relative to the electrical equivalent circuit model to develop optimized parameters;   combine the geometric parameters and the optimized parameters to develop one or more new internal parameters for the energy storage device; and   update the one or more learned state models responsive to the one or more new internal parameters.   
     
     
         27 . The system of  claim 21 , wherein the processing circuitry is further configured to perform one or more what-if scenarios using one or more aging models to predict one or more future internal parameters of the energy storage device to characterize any changes to the one or more internal parameters as time progresses. 
     
     
         28 . The system of  claim 27 , wherein the one or more what-if scenarios use assumed future operational conditions derived from previously observed operational conditions. 
     
     
         29 . The system of  claim 27 , wherein the processing circuitry is further configured to use the one or more aging models to:
 perform a relevance vector machine algorithm using Bayesian inference to obtain possible solutions for a specific parameter of the one or more internal parameters when the energy storage device is new and an aging parameter indicating how the specific parameter will change over time; and   perform a regression analysis to predict a future value for the specific parameter.   
     
     
         30 . The system of  claim 21 , wherein the processing circuitry is further configured to:
 perform a life extension strategy comprising:
 defining an operational profile; 
 evaluating one or more aging models using the operational profile to predict one or more future internal parameters; and 
 evaluating the one or more future internal parameters to determine a predicted future health condition; and 
   communicating the operational profile to the user, the related computing system, or a combination thereof.   
     
     
         31 . A computer-implemented method for analyzing energy storage device information, comprising:
 a feature extraction module for:
 receiving input data including active information collected from active measurements of a response of the energy storage device to a stimulus signal applied to the energy storage device; 
 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 
 combining the geometric parameters and the optimized parameters to develop new internal parameters; 
   a state estimation module for updating an internal state model of the energy storage device responsive to the new internal parameters;   a health estimation module for processing the internal state model to determine one or more present health conditions for the energy storage device; and   a communication module for communicating at least one of the one or more present health conditions to a user, a related computing system, or a combination thereof.   
     
     
         32 . The method of  claim 31 , wherein the one or more present health conditions include a state-of-charge estimation for the energy storage device, a state-of-health estimation for the energy storage device, or a combination thereof. 
     
     
         33 . The method of  claim 31 , further comprising:
 a health prediction module for processing the internal state model with a remaining-useful-life analysis responsive to the one of the one or more present health conditions to develop a remaining-useful-life prediction; and   wherein the communication module is further for communicating the remaining-useful-life prediction to the user, the related computing system, or a combination thereof.   
     
     
         34 . The method of  claim 33 , wherein:
 the health prediction module is further for processing the internal state model with an end-of-life analysis responsive to the one or more present health conditions and the remaining-useful-life prediction to develop an end-of-life prediction; and   wherein the communication module is further for communicating the end-of-life prediction to a user, a related computing system, or a combination thereof.   
     
     
         35 . The method of  claim 31 , wherein:
 the input data includes passive information about the energy storage device, the passive information selected from the group consisting of a temperature of the energy storage device, voltage of the energy storage device, current of the energy storage device, and combinations thereof; and   the state estimation module is further for modifying the internal state model responsive to the passive information.   
     
     
         36 . The method of  claim 31 , further comprising one or more what-if models using one or more aging models to predict one or more future internal parameters by using assumed future operational conditions derived from previously observed operation conditions to characterize any changes to the one or more present internal parameters as time progresses. 
     
     
         37 . The method of  claim 36 , wherein using the one or more aging models further comprises:
 performing a relevance vector machine algorithm using Bayesian inference to obtains possible solutions for a specific internal parameter of the one or more present internal parameters when the energy storage device is new and an aging parameter indicating how the specific internal parameter will change over time; and   performing a regression analysis to predict a future value for the specific internal parameter.   
     
     
         38 . The method of  claim 36 , further comprising:
 identifying a life extension strategy by:
 defining an operational profile; 
 evaluating the one or more aging models using the operational profile to predict the future internal parameters; 
 evaluating the future internal parameters to determine a predicted future health condition; 
 modifying the operational profile; and 
 repeating the evaluating and the modifying until a desired value for the predicted future health condition is met; and 
   wherein the communication module is further for communicating the resulting operational profile to the user, the related computing system, or the combination thereof.