Apparatus and method for testing state of charge in battery

Abstract

Disclosed is an apparatus and method for estimating a state of charge (SOC) in a battery, in which the battery SOC is estimated using a fusion type soft computing algorithm, thereby accurately estimating the battery SOC in a high C-rate environment. The apparatus includes a detector unit for detecting current, voltage and temperature of a battery cell; and soft computing unit for outputting a battery SOC estimation value of processing the current, the voltage and the temperature detected by the detector unit using a radial function based on a neural network algorithm. Especially, the soft computing unit combines the neural network algorithm with any one of a fuzzy algorithm, a genetic algorithm (GA), a cellular automata (CA) algorithm, an immune system algorithm, and a rough-set algorithm, and thereby adaptively updates the parameters of the neural network algorithm.

1 . An apparatus for estimating a state of charge (SOC) in a battery, the apparatus comprising: 
 a detector unit for detecting current, voltage and temperature of a battery cell; and    a soft computing unit for outputting a battery SOC estimation value of processing the current, the voltage and the temperature detected by the detector unit using a radial function based on a neural network algorithm.    
     
     
         2 . The apparatus according to  claim 1 , wherein the soft computing unit: 
 combines the neural network algorithm with any one of a fuzzy algorithm, a genetic algorithm (GA), a cellular automata (CA) algorithm, an immune system algorithm, and a rough-set algorithm, all of which adaptively update parameters; and    adaptively updates the parameters of the neural network algorithm.    
     
     
         3 . The apparatus according to  claim 1 , wherein the neural network algorithm is updated based on a learning algorithm in which, when a difference between the estimation value output from the soft computing unit and a predetermined target value is outside of a critical range, learning is made so as to follow the predetermined target value.  
     
     
         4 . The apparatus according to  claim 3 , wherein the target value is a reference value obtained through a corresponding test on a specific condition.  
     
     
         5 . The apparatus according to  claim 3 , wherein the reference value is a value of complementing an amp-hour (Ah) counting value and an open circuit voltage (OCV) value, which are input from the charger-discharger, to rated capacity of the battery.  
     
     
         6 . The apparatus according to  claim 3 , wherein the learning algorithm is any one of a back propagation learning algorithm, a Kalman filter, a genetic algorithm, and a fuzzy learning algorithm.  
     
     
         7 . The apparatus according to  claim 2 , wherein the neural network algorithm, which is combined with any one of a fuzzy algorithm, a genetic algorithm (GA), a cellular automata (CA) algorithm, an immune system algorithm, and a rough-set algorithm, is updated based on a learning algorithm in which, when a difference between the estimation value output from the soft computing unit and a predetermined target value is outside of a critical range, learning is made so as to follow the predetermined target value.  
     
     
         8 . The apparatus according to  claim 7 , wherein the target value is a reference value obtained through a corresponding test on a specific condition.  
     
     
         9 . The apparatus according to  claim 8  using fusion type soft computing, wherein the reference value is a value of complementing an amp-hour (Ah) counting value and an open circuit voltage (OCV) value, which are input from the charger-discharger, to rated capacity of the battery.  
     
     
         10 . The apparatus according to  claim 7  using fusion type soft computing, wherein the learning algorithm is any one of a back propagation learning algorithm, a Kalman filter, the genetic algorithm, and a fuzzy learning algorithm.  
     
     
         11 . A method for estimating a state of charge (SOC) in a battery, the method comprising the steps of: 
 detecting current, voltage and temperature of a battery cell; and    outputting a battery SOC estimation value of processing the current, voltage and temperature detected by the detector unit using a radial function based on a neural network algorithm.    
     
     
         12 . The method according to  claim 11 , wherein the neural network algorithm: 
 is combined with any one of a fuzzy algorithm, a genetic algorithm (GA), a cellular automata (CA) algorithm, an immune system algorithm, and a rough-set algorithm, all of which adaptively update parameters; and    adaptively update the parameters of the neural network algorithm.    
     
     
         13 . The method according to  claim 11 , wherein the neural network algorithm is updated based on a learning algorithm in which, when a difference between the estimation value and a predetermined target value is outside of a critical range, learning is made so as to follow the predetermined target value.  
     
     
         14 . The method according to  claim 13 , wherein the target value is a reference value obtained through a corresponding test on a specific condition.  
     
     
         15 . The method according to  claim 13 , wherein the reference value is a value of complementing an amp-hour (Ah) counting value and an open circuit voltage (OCV) value, which are input from the charger-discharger, to rated capacity of the battery.  
     
     
         16 . The method according to  claim 13 , wherein the learning algorithm is any one of a back propagation learning algorithm, a Kalman filter, a genetic algorithm, and a fuzzy learning algorithm.  
     
     
         17 . The method according to  claim 12 , wherein the neural network algorithm, which is combined with any one of a fuzzy algorithm, a genetic algorithm (GA), a cellular automata (CA) algorithm, an immune system algorithm, and a rough-set algorithm, is updated based on a learning algorithm in which, when a difference between the estimation value output from the soft computing unit and a predetermined target value is outside of a critical range, learning is made so as to follow the predetermined target value.  
     
     
         18 . The method according to  claim 17 , wherein the target value is a reference value obtained through a corresponding test on a specific condition.  
     
     
         19 . The method according to  claim 18  using fusion type soft computing, wherein the reference value is a value of complementing an amp-hour (Ah) counting value and an open circuit voltage (OCV) value, which are input from the charger-discharger, to rated capacity of the battery.  
     
     
         20 . The method according to  claim 17  using fusion type soft computing, wherein the learning algorithm is any one of a back propagation learning algorithm, a Kalman filter, the genetic algorithm, and a fuzzy learning algorithm.