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

Patent metadata

Field	Value
Publication number	US-2018143257-A1
Application number	US-201615357322-A
Country	US
Kind code	A1
Filing date	Nov 21, 2016
Priority date	Nov 21, 2016
Publication date	May 24, 2018
Grant date	—

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.

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.

2 . The computer-implemented method of claim 1 , wherein performing the geometric-based parameter identification further comprises deriving the constant phase element exponent, the electrolyte resistance, and the charge transfer resistance from a Nyquist plot representation of electrochemical impedance spectroscopy data collected from the active measurements.

3 . The computer-implemented method of claim 1 , wherein performing the optimization-based parameter identification further comprises deriving the constant phase element exponent, the electrolyte resistance, and the charge transfer resistance from electrochemical impedance spectroscopy data collected from the active measurements and performing a nonlinear optimization of each parameter estimated by minimizing a selected objective function.

4 . The computer-implemented method of claim 1 , wherein performing the decision fusion algorithm further comprises combining the geometric parameters and the optimized parameters with a weighted average.

5 . The computer-implemented method of claim 1 , further comprising:
a health prediction module for 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 wherein the communication module is further configured for communicating one or more of the RUL prediction and the EOL prediction, to a user, a related computing system, or a combination thereof.

6 . A computer-implemented method for analyzing battery information, comprising:
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 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.

7 . The computer-implemented method of claim 6 , wherein:
one analysis process of the two or more analysis processes comprises using the internal battery 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.

8 . The computer-implemented method of claim 6 , wherein:
one analysis process of the two or more analysis processes comprises using the internal battery 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.

9 . The computer-implemented method of claim 6 , wherein:
one analysis process of the two or more analysis processes comprises using the internal battery 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.

10 . The computer-implemented method of claim 6 , 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 SOH estimation.

11 . The computer-implemented method of claim 6 , further comprising:
performing a remaining-useful-life (RUL) analysis responsive to the SOH estimation to develop an RUL prediction; and communicating the RUL prediction to a user, a related computing system, or a combination thereof.

12 . The computer-implemented method of claim 11 , further comprising:
performing an end-of-life (EOL) analysis responsive to the SOH estimation and the RUL prediction to develop an EOL prediction; and communicating the EOL prediction to a user, a related computing system, or a combination thereof.

13 . A computer-implemented method for analyzing battery information, comprising:
receiving input data including passive information collected from passive measurements of a ba

Assignees

• Battelle Energy Alliance Llc

Inventors

• Garcia Humberto E
• Christophersen Jon P

Classifications

• G06N3/09

  Supervised learning · CPC title

• G06N3/0499

  Feedforward networks · CPC title

• G01R31/3679

  Physics · mapped topic

• G01R31/3651 Primary

  Physics · mapped topic

• G01R31/3606

  Physics · mapped topic