Device detecting abnormality of secondary battery, abnormality detection method, and program

Abstract

A secondary battery control system that conducts abnormality detection while predicting other parameters (internal resistance, SOC, and the like) with high accuracy is provided. A difference between an observation value (voltage) at a certain point in time and a voltage that is estimated using a prior-state variable is sensed. A threshold voltage is set in advance, and from the voltage difference that is sensed, a sudden abnormality, specifically a micro-short circuit or the like is detected. Furthermore, it is preferable that detection be performed by using a neural network to learn data on voltage difference in a time series and determine abnormality or normality.

1 . An abnormality detection device of a secondary battery comprising:
 a first sensing circuit configured to sense a voltage value of the secondary battery to be a first observation value;   a second sensing means sensing circuit configured to sense a current value of the secondary battery to be a second observation value;   a calculation unit calculating an estimated voltage value range using a regression model, and   a determination unit finding a difference between the voltage value of the first observation value and the estimated voltage value obtained from a previous time and determining the secondary battery has an abnormality when the difference exceeds a certain threshold value range.   
     
     
         2 . The abnormality detection device of a secondary battery according to  claim 1 , wherein the regression model is a Kalman filter on the basis of a state equation. 
     
     
         3 . The abnormality detection device of a secondary battery according to  claim 1 , wherein the determination unit comprises one or a plurality of comparators. 
     
     
         4 . The abnormality detection device of a secondary battery according to  claim 1 , further comprising a neural network structure portion inputting the difference between the voltage value of the first observation value and the estimated voltage value obtained from a previous time. 
     
     
         5 . The abnormality detection device of a secondary battery according to  claim 1 , wherein the secondary battery is a lithium-ion secondary battery. 
     
     
         6 . The abnormality detection device of a secondary battery according to of  claim 1 , wherein the secondary battery is an all-solid-state battery. 
     
     
         7 . An abnormality detection method determining whether a secondary battery has an abnormality, comprising:
 a prior-estimation prediction step outputting an estimated voltage value using a regression model, and   a filtering step calculating a post-state estimation value and a post error covariance matrix.   
     
     
         8 . The abnormality detection method according to  claim 7 , wherein the regression model is a Kalman filter on the basis of a state equation. 
     
     
         9 . A non-transitory computer readable medium storing a program, the program being for making a computer which comprises:
 a calculation unit calculating an estimated voltage value using a regression model, and   a determination unit finding a difference between a voltage value of the observation value and the estimated voltage value obtained from a previous time determining a secondary battery has an abnormality when the difference exceeds a certain threshold value range.   
     
     
         10 . The non-transitory computer readable medium storing the program according to  claim 9 , wherein the regression model is a Kalman filter on the basis of a state equation. 
     
     
         11 . A state estimation method of a secondary battery estimating a charging state of the secondary battery,
 wherein data on an observation value is obtained from the secondary battery,   wherein a prior-state estimation value is calculated using a regression model,   wherein a forecast error voltage Vd which is a difference between the observation value and the prior-state estimation value is calculated,   wherein whether data is noise is determined on the basis of whether or not data of the forecast error voltage Vd exceeds a threshold value set in advance,   wherein instead of data that is determined as noise, a mean value of k data before abnormality sensing is input to the regression model after which correction is performed, and   wherein abnormality detection is continued even after noise sensing.   
     
     
         12 . The state estimation method of a secondary battery according to  claim 11 , wherein the regression model is a Kalman filter on the basis of a state equation. 
     
     
         13 . The state estimation method of a secondary battery according to  claim 11 , wherein noise is generated when a micro-short circuit of the secondary battery occurs. 
     
     
         14 . A charging state estimation device of a secondary battery estimating a charging state of the secondary battery, comprising:
 a first sensing circuit configured to sense a voltage value of the secondary battery that is to be a first observation value,   a calculation unit calculating an estimated voltage value using a regression model, and   a determination unit finding a difference between the voltage value of the first observation value and the estimated voltage value obtained from a previous time and determining the secondary battery has an abnormality when the difference exceeds a certain threshold value range,   wherein the determination unit comprises one or a plurality of comparators, a multiplexer, and a delay circuit.   
     
     
         15 . The charging state estimation device of a secondary battery according to  claim 14 , wherein the regression model is a Kalman filter on the basis of a state equation. 
     
     
         16 . The charging state estimation device of a secondary battery according to  claim 14 , wherein the determination unit comprises one or a plurality of comparators. 
     
     
         17 . The charging state estimation device of a secondary battery according to  claim 14 , further comprising a second sensing circuit configured to sense a current value of the secondary battery to be a second observation value.