Patent Abstract:
A season is determined by using a battery sensor for a vehicle, and as a result, a performance of a battery is predicted in advance to improve a monitoring performance of the battery sensor for the vehicle.

Full Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    This application claims priority under 35 U.S.C. §119 to Korean Patent Application No. 10-2014-0048135, filed on Apr. 22, 2014, in the Korean Intellectual Property Office, the disclosure of which is incorporated herein by reference in its entirety. 
       TECHNICAL FIELD 
       [0002]    The present invention relates to a battery sensor for a vehicle, and more particularly, to a method for determining a season using the battery sensor for the vehicle. 
       BACKGROUND 
       [0003]    Recently, in vehicles, various electronic control devices, multimedia devices, and the like have been basically installed. 
         [0004]    The devices operate according to power supply of a vehicle&#39;s battery and thus it is important to manage the vehicle&#39;s battery. 
         [0005]    In order to manage performance of the vehicle&#39;s battery, in the vehicle, a vehicle battery sensor that measures the vehicle battery state is provided. The vehicle battery sensor measures battery performances such as a charge state, the aging degree, and restarting capability of the vehicle battery. 
         [0006]    It is well-known that the vehicle battery performance is closely related with a change in a temperature according to the season. Accordingly, the vehicle battery sensor needs to monitor the vehicle battery performance to which the change in temperature according to the season is reflected. 
         [0007]    To this end, the vehicle battery sensor needs to automatically determine a current season. However, the vehicle battery sensor having the season determining function is not yet developed. 
       SUMMARY 
       [0008]    Therefore, the present invention has been made in an effort to provide a battery sensor for a vehicle that determines a season. 
         [0009]    The present invention has also been made in an effort to provide a method for determining a season using the battery sensor for the vehicle. 
         [0010]    An exemplary embodiment of the present invention provides a battery sensor for a vehicle, including: a prior learning unit classifying daily temperature data into multiple pattern clusters representing seasons to configure a self-organizing map and generating center values of the multiple pattern clusters shown in the self-organizing map as prior-learned seasonal pattern data; a temperature sensing unit measuring outdoor temperature data of the vehicle in real time; and a season classifying unit clustering the outdoor temperature data measured in real time into multiple clusters in accordance with cluster analysis, calculating a center value of the multiple clusters which are clustered, and detecting the pattern cluster having the center value closest to the center value of the multiple clusters by mapping the calculated center value of the multiple clusters to the self-organizing map to classify the season represented by the detected pattern cluster as a current season. 
         [0011]    Another exemplary embodiment of the present invention provides a method for determining a season by using a battery sensor for a vehicle, which measures a vehicle battery state, including: configuring a self-organizing map by classifying daily temperature data into multiple pattern clusters representing seasons; generating a center value the multiple pattern clusters shown in the self-organizing map as prior-learned seasonal pattern data; clustering vehicle outdoor temperatures measured in real time into multiple clusters by using cluster analysis and calculating the center value of the multiple clusters; detecting the pattern cluster having the center value closest to the center value of the multiple clusters by mapping the center value of the multiple clusters to the self-organizing map; and classifying seasonal information represented by the detected pattern cluster as current season information. 
         [0012]    According to the exemplary embodiments of the present invention, the season is determined by using the battery sensor for the vehicle to further improve a monitoring performance of the battery sensor for the vehicle. 
         [0013]    Other features and aspects will be apparent from the following detailed description, the drawings, and the claims. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0014]      FIG. 1  is a block diagram illustrating an entire vehicle system including a battery sensor for a vehicle according to an exemplary embodiment of the present invention. 
           [0015]      FIG. 2  is a block diagram schematically illustrating an internal configuration of the season determining module illustrated in  FIG. 1 . 
           [0016]      FIG. 3  is a configuration diagram illustrating a configuration of an artificial neural network usable in a prior learning unit according to an exemplary embodiment of the present invention. 
           [0017]      FIG. 4  is a diagram for describing a propagation rule of the artificial neural network usable in the prior learning unit according to the exemplary embodiment of the present invention. 
           [0018]      FIG. 5  is a diagram for describing a learning rule of the artificial neural network usable in the prior learning unit according to the exemplary embodiment of the present invention. 
           [0019]      FIG. 6  is a flowchart illustrating a prior learning process performed by the prior learning unit illustrated in  FIG. 2 . 
           [0020]      FIG. 7  is a graph illustrating one example of daily average temperature data used by the prior learning unit illustrated in  FIG. 2 . 
           [0021]      FIG. 8  is a graph for describing histogram data converted according to the exemplary embodiment of the present invention. 
           [0022]      FIG. 9  is a graph illustrating one example of a center position value of respective pattern clusters before starting a prior learning according to the exemplary embodiment of the present invention. 
           [0023]      FIG. 10  is a graph illustrating one example of a center position value of respective pattern clusters moved after completing the prior learning according to the exemplary embodiment of the present invention. 
           [0024]      FIG. 11  is a diagram illustrating prior-learned seasonal pattern data stored in a storage unit according to the exemplary embodiment of the present invention. 
           [0025]      FIG. 12  is a flowchart illustrating a process of classifying seasons in a season classifying unit illustrated in  FIG. 2 . 
           [0026]      FIG. 13  is a graph illustrating outdoor temperature data of a vehicle, which is input in the season classifying unit according to the exemplary embodiment of the present invention. 
           [0027]      FIG. 14  is a diagram illustrating one example of histogram data converted from the outdoor temperature data of the vehicle illustrated in  FIG. 13 . 
           [0028]      FIG. 15  is a diagram illustrating a center position value of the outdoor temperature data of the vehicle, which is calculated through a K-means algorithm according to the exemplary embodiment of the present invention. 
           [0029]      FIG. 16  is a diagram schematically illustrating a season classifying process performed in S 1240  of  FIG. 12 . 
           [0030]      FIG. 17  is a diagram illustrating a cluster in the vehicle, which displays seasonal information received from the season classifying unit illustrated in  FIG. 2 . 
       
    
    
     DETAILED DESCRIPTION OF EMBODIMENTS 
       [0031]    Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. 
         [0032]      FIG. 1  is a block diagram illustrating an entire vehicle system including a battery sensor for a vehicle according to an exemplary embodiment of the present invention. 
         [0033]    Referring to  FIG. 1 , the entire vehicle system includes a vehicle battery sensing module  100  sensing an internal temperature and a charging state of a vehicle battery  500  and sensing an outdoor temperature of the vehicle to determine a season, an engine ECU  200  receiving internal temperature information and charging state information of the vehicle battery  500  from the vehicle battery sensing module  100  through local interconnect network (LIN) communication and controlling an engine according to the received internal temperature information and charging state information, an engine  300  controlled by the engine ECU  200 , a main ECU  220  receiving seasonal data (alternatively, seasonal information) determined by the vehicle battery sensing module  100  through the LIN communication, and an electric load  400  constituted by various in-vehicle electric devices receiving power from the vehicle battery  500 . 
         [0034]    Since the remaining components  200 ,  300 , and  400  other than the vehicle battery sensing module  100  among the components provided in the entire vehicle system are widely known components, a description thereof will be omitted. 
         [0035]    The main ECU  220  transfers the seasonal data received from the vehicle battery sensing module  100  to various in-vehicle electric devices. 
         [0036]    The main ECU  220  may control a vehicle cluster so as for a driver to visually verify the seasonal data by transferring the seasonal data to a cluster device of the vehicle. 
         [0037]    The driver may determine whether the vehicle is idle upon initial starting or a tire replacement time depending on the season from the seasonal data displayed on the vehicle cluster. 
         [0038]    Hereinafter, the vehicle battery sensing module  100  will be described in detail. 
         [0039]    The vehicle battery sensing module  100  determines a current season by collecting the outdoor temperature of the vehicle. 
         [0040]    The vehicle battery sensing module  100  is electrically connected to each of a (+) terminal of the battery  500  and a (−) terminal of the battery  500  through a shunt resistor  110 . 
         [0041]    The vehicle battery sensing module  100  includes a calculation module  120  sensing an internal temperature and a charging state of the vehicle battery  500  and a season determining module  130  determining the season. 
         [0042]    The calculation module  120  includes a voltage sensing unit  121  measuring a voltage of the battery  500 , a temperature sensing unit  123  measuring the internal temperature and the vehicle outdoor temperature of the sensor module  100  in real time, a current sensing unit  125  measuring a current that flows on the vehicle battery  500  according to a difference in voltage between both terminals of the shunt resistor  110 , a battery internal temperature analyzing unit (battery temp model (BTM))  127  analyzing the internal temperature of the vehicle battery  500  based on the internal temperature, a charging state analyzing unit (state of charge (SOC)  128  analyzing the charging state of the battery  500  based on the measured battery voltage and battery current, and an aging state analyzing unit (state of health (SOH)) analyzing an aging state of the battery  500  based on the internal temperature, the battery voltage, and the battery current. 
         [0043]    Information on the battery internal temperature, charging state, and aging state analyzed by the respective components  127 ,  128 , and  129  of the calculation module  120  is transferred to the engine ECU  200  through the LIN communication. The engine ECU  200  controls the engine based on each received information. 
         [0044]    The season determining module  130  acquires seasonal data representing the current season by using prior learned seasonal pattern data. A detailed description thereof will be described below in detail with reference to  FIG. 2  given below. 
         [0045]      FIG. 2  is a block diagram schematically illustrating an internal configuration of the season determining module illustrated in  FIG. 1 . 
         [0046]    Referring to  FIG. 2 , the season determining module  130  includes a prior learning unit  132  learning the seasonal pattern data, a storage unit  134  storing the seasonal pattern data prior-learned by the prior learning unit  132 , and a season classifying unit  136  classifying the seasons by using the prior-learned seasonal pattern data stored in the storage unit  134 . 
         [0047]    The prior learning unit  132  receives previous-year daily temperature data and learns the daily temperature data by using a self-organizing map (SOM) to generate the prior-learned seasonal pattern data. 
         [0048]    The prior-learned seasonal pattern data is stored in the storage unit  134 . 
         [0049]    The season classifying unit  136  classifies the seasons by using the vehicle outdoor temperature data and prior-learned seasonal pattern data measured, in real time, by the temperature sensing unit  123 . 
         [0050]    The classified seasonal data is transferred to the main ECU  220  and the main ECU  220  processes the received seasonal data and transfers the processed seasonal data to the corresponding in-vehicle electronic device requesting the seasonal data. 
         [0051]    Hereinafter, a prior learning process of the seasonal pattern data performed by the prior learning unit  132  will be described in detail. 
         [0052]    The prior learning unit  132  learns the seasonal pattern data by using the self-organizing map (SOM). 
         [0053]    The self-organizing map (SOM) is one of self-learning methods using an artificial neural network. 
         [0054]    Self-organizing represents not providing an accurate output pattern for a pattern of input information, but clustering the pattern of the input information and learning any specific output pattern from a clustered result. 
         [0055]    The artificial neural network will be introduced in brief in order to help understand the learning process of the seasonal pattern data. 
         [0056]    Artificial Neural Network 
         [0057]    The artificial neural network models a method of a biological neural system recognizing an object or event and mathematically uses and processes the modeled method. That is, in the case of the artificial neural network, the artificial neural network completing learning of an input pattern may induce a correct output pattern even with respect to an unlearned input pattern. 
         [0058]      FIG. 3  is a configuration diagram illustrating a configuration of an artificial neural network usable in a prior learning unit according to an exemplary embodiment of the present invention. 
         [0059]    As illustrated in  FIG. 3 , the artificial neural network includes an input layer, a hidden layer, and an output layer. 
         [0060]    The input layer means a data input for learning and the output layer means an output of a learning result value. In addition, the hidden layer means propagation, learning, and activation of information. 
         [0061]    Propagation Rule of Artificial Neural Network 
         [0062]    The propagation rule of the artificial neural network means a rule by which a new state may be acquired from a current state in a system by combining input patterns of the system. 
         [0063]      FIG. 4  is a diagram for describing a propagation rule of the artificial neural network usable in the prior learning unit according to the exemplary embodiment of the present invention. 
         [0064]    As illustrated in  FIG. 4 , according to the propagation rule of the artificial neural network, an input block  41  of the system receives an input pattern X and transfers the received input pattern X to a sigma calculation block  43 . The sigma calculation block  43  calculates a sum (NET=ΣX) of the received input patterns X and transfers the calculation result to an activation function block  45 . The activation function block  45  combines the sum (NET, threshold weight) of the received input patterns by using an activation function (f( )) and transfers the combination result (f(NET)) to an output block  47 . The output block  47  outputs the received combination result (f(NET)) according to the rule (Y=f(NET)) to acquire the new state from the current state. 
         [0065]    Activation Rule of Artificial Neural Network 
         [0066]    The activation rule of the artificial neural network means a threshold rule in which the input weight of data input in the artificial neural network influences an output. The activation rule may be expressed as follows. If (NET&gt;T) Y=1, ELSE Y=0, wherein, NET represents the threshold weight, T represents a threshold, and Y represents the activation function. 
         [0067]    Learning Rule of Artificial Neural Network 
         [0068]    The learning rule of the artificial neural network represents a process of adopting a connection strength between neurons to be suitable for a specific application purpose. 
         [0069]      FIG. 5  is a diagram for describing the learning rule of the artificial neural network performed by the prior learning unit according to the exemplary embodiment of the present invention. 
         [0070]    As illustrated in  FIG. 5 , the learning rule of the artificial neural network includes a process  51  of initializing a connection strength, a process  53  of calculating an output with an input pattern, a process  55  of updating the connection strength, and a process  57  of completing learning. 
         [0071]    The self-organizing map (SOM) used to prior-learn the seasonal pattern data in the prior learning unit  132  of  FIG. 2  is generated by using an artificial neural network algorithm constituted by the propagation rule of the artificial neural network, the activation rule, and the learning rule of the artificial neural network described above. 
         [0072]    Hereinafter, a process of learning the seasonal pattern data using the SOM performed by the prior learning unit will be described with reference to  FIG. 6 . 
         [0073]      FIG. 6  is a flowchart illustrating a prior learning process performed by the prior learning unit illustrated in  FIG. 2 . 
         [0074]    Referring to  FIG. 6 , first, in step S 610 , a process of receiving learning data is performed. The learning data is assumed as 2013-year daily temperature data as illustrated in  FIG. 7 . 
         [0075]    In S 620 , in order to reduce a prior learning processing time, a process of changing the received 2013-year daily temperature data into histogram data is performed. 
         [0076]    The process of changing the daily temperature data to the histogram data includes a process of setting multiple temperature intervals and a process of changing daily temperature data corresponding to each set temperature interval to the histogram data having a bar graph shape as illustrated in  FIG. 8 . The daily temperature data is changed to the histogram data to reduce the number of data used in the prior learning. 
         [0077]    In S 630 , a process of setting the self-organizing map (SOM) to be used to learn the changed histogram data is performed. That is, a process of setting the neuron used in the hidden layer illustrated in  FIG. 3  is performed. 
         [0078]    A change in temperature data depending on a seasonal change has a consecutive characteristic. By considering the consecutive characteristic, an example of setting five neurons constituted by winter, winter/autumn, autumn/spring, spring/summer, and summer is described in the exemplary embodiment. 
         [0079]    In the exemplary embodiment, an example of setting pattern clusters determining the season as five neurons constituted by winter, winter/autumn, autumn/spring, spring/summer, and summer is described by considering a consecutive change characteristic of temperature data depending on a seasonal change. 
         [0080]    The self-organizing map (SOM) in which five neurons constituted by winter, winter/autumn, autumn/spring, spring/summer, and summer are set is illustrated in  FIG. 9 . 
         [0081]    In  FIG. 9 , as a graph showing the position of a center (weight or connection strength) of a pattern cluster before learning, Weight  1  of an x axis represents temperature and Weight  2  of a y axis represents a count of histogram data counted for each temperature. 
         [0082]    In  FIG. 9 , points P 1  having a small size and 5 points P 2  having a relatively large size are illustrated. 
         [0083]    The points P 1  are the histogram data and 5 points P 2  are pattern clusters constituted by winter, winter/autumn, autumn/spring, spring/summer, and summer before learning. 
         [0084]    In S 640 , a learning process of the histogram data is performed by using the SOM set in S 630 . Repeated execution of the number of learning times improves accuracy of a learning result. In the exemplary embodiment, a learning process may be performed approximately 1000 times. 
         [0085]    When the learning processes are completed, in S 650   a  process of verifying a learned seasonal pattern after completing the learning is performed. 
         [0086]    The verification process is a process of verifying a center position value (weight position or connection strength) of each moved pattern cluster at the time when the learning is completed. In  FIG. 10 , the center position value of each pattern cluster moved after the learning process is performed at 1000 times is illustrated. 
         [0087]    In S 660 , a process of storing the verified center position values of the respective pattern clusters in the storage unit  134  illustrated in  FIG. 2  is performed. For example, when the verified center position value of each pattern cluster is as illustrated in  FIG. 11 , the prior-learned seasonal pattern data (alternatively, prior-learned seasonal pattern coordinate data) may be expressed by Table 1 given below. 
         [0000]    
       
         
               
               
               
             
               
               
               
               
             
           
               
                   
                 TABLE 1 
               
               
                   
                   
               
               
                   
                 Weight 1 
                 Weight 2 
               
               
                   
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 Winter 
                 −10.1500 
                 0.8500 
               
               
                   
                 Winter/autumn 
                 1.7500 
                 4.9167 
               
               
                   
                 Autumn/spring 
                 13.2857 
                 3.6190 
               
               
                   
                 Spring/summer 
                 23.7778 
                 7.1667 
               
               
                   
                 Summer 
                 31.5769 
                 0.7692 
               
               
                   
                   
               
             
          
         
       
     
       [Seasonal Pattern Data or Seasonal Pattern Data Coordinate] 
       [0088]    As such, when the seasonal pattern data prior-learned by the prior learning unit  132  is acquired through the processes of  FIG. 6 , a process of classifying the seasons corresponding to the vehicle outdoor temperature measured by the vehicle battery sensor is performed in the season classifying unit  136  of  FIG. 2  based on the acquired prior-learned seasonal pattern data. 
         [0089]    Hereinafter, the process of classifying the seasons by using the seasonal pattern data prior-learned up to now will be described in detail with reference to  FIG. 12 . 
         [0090]      FIG. 12  is a flowchart illustrating a process of classifying seasons in the season classifying unit  136  illustrated in  FIG. 2 . Unless particularly mentioned, as an execution agent of each step given below, the season classifying unit  136  illustrated in  FIG. 2  is assumed. 
         [0091]    Referring to  FIG. 12 , in step S 1210 , the temperature sensing unit  123  of the vehicle battery sensor  100  measures the vehicle outdoor temperature in real time. For example, as illustrated in  FIG. 13 , the temperature sensing unit  123  in the vehicle battery sensor measures the vehicle outdoor temperature on Mar. 6, 2014 in real time at a time interval of 3 hours. For accurate measurement, the vehicle outdoor temperature is preferably measured while the vehicle stops. When the vehicle outdoor temperature is measured while the vehicle is driven, since the temperature sensing unit  123  may be influenced by an engine temperature, the vehicle outdoor temperature may not be accurately measured. 
         [0092]    In S 1220 , a profile of the vehicle outdoor temperature measured in real time by the temperature sensing unit  123  is input in the season classifying unit  136  of the season determining module  130 . The season classifying unit  136  performs a process of changing the profile of the vehicle outdoor temperature measured in real time to the histogram data having the bar graph shape as illustrated in  FIG. 14 . 
         [0093]    In S 1230 , a process of verifying a temperature pattern of the vehicle outdoor temperature measured by using cluster analysis for the changed histogram data is performed. The verification process is a process of clustering similar histogram data into multiple clusters and calculating a center value of the multiple clusters. 
         [0094]    In S 650  of  FIG. 6 , the center position value (weight position or connection strength) of each pattern cluster is verified by the self-organizing map (SOM) algorithm in S 650  of  FIG. 6 , but herein, since the temperature pattern of the measured vehicle outdoor temperature needs to be verified in real time, the center value of the clusters needs to be verified by using an algorithm having a smaller processing quantity than the SOM algorithm. For example, a center value of the clusters of the vehicle outdoor temperature clustered may be calculated by using a K-means clustering algorithm. 
         [0095]      FIG. 15  is a diagram illustrating the center value of the clusters of the vehicle outdoor temperature calculated by using the K-means clustering algorithm. 
         [0096]      FIG. 15  illustrates an example in which a coordinate of the center value of the vehicle outdoor temperature is 0.5000, 0.5000. 
         [0097]    In S 1240 , a process is performed, which classifies the current season by comparing the center value of the vehicle outdoor temperature calculated in S 1230  with the seasonal pattern prior-learned in  FIG. 6 . In the K-means clustering algorithm, by comparing distances between the input data (center value coordinate of the vehicle outdoor temperature) and the center values (alternatively, center value coordinates) of the prior-learned pattern clusters, a closest pattern cluster is allocated as the input data. After the allocation, a center value coordinate of a new pattern cluster is calculated by using the input data and the center value coordinate of each prior-learned pattern cluster and the calculated center value coordinate is updated. 
         [0098]      FIG. 16  is a diagram schematically illustrating the season classifying process performed in S 1240  of  FIG. 12 . 
         [0099]      FIG. 16A  illustrates the prior-learned seasonal pattern described in  FIG. 6  and  FIG. 16B  illustrates the center value of the vehicle outdoor temperature calculated by applying the K-means clustering algorithm to the vehicle outdoor temperature data measured through the temperature sensing unit in the vehicle battery sensor. In addition,  FIG. 16C  illustrates a result of classifying the current season by using the K-means clustering algorithm with respect to the center value of the prior-learned seasonal pattern data of  FIG. 16A  and the cluster (the center value of the clustered vehicle outdoor temperature) of  FIG. 16B . 
         [0100]    As illustrated in  FIG. 16C , the center value of the vehicle outdoor temperature measured in real time and the center value coordinate of the prior-learned seasonal pattern data are mapped to one coordinate axis illustrated in  FIG. 16C . Thereafter, the distances between the center value coordinate of the vehicle outdoor temperature and the center values (alternatively, center value coordinates) of each prior-learned pattern cluster are compared and a pattern cluster closest to the center value coordinate (point A) of the vehicle outdoor temperature is selected.  FIG. 16C  illustrates an example in which ‘winter/autumn’ is selected as the pattern cluster closest to the center value coordinate (point A) of the vehicle outdoor temperature. When the pattern cluster is selected, the center value coordinate of the new pattern cluster, ‘winter/autumn’ is calculated by using the center value coordinate of each prior-learned pattern cluster to update the prior-learned pattern cluster corresponding to ‘winter/autumn’ as the center value coordinate of the new pattern cluster ‘winter/autumn’ 
         [0101]    As such, when the center value of the vehicle outdoor temperature measured in real time by the vehicle battery sensor is used as the input of the prior-learned seasonal pattern, it can be verified that the center value is allocated to the pattern cluster corresponding to ‘winter/autumn’ as described above. Therefore, the vehicle battery sensor finally determines ‘winter/autumn’ as the current season. The determined season data is transferred to the main ECU  220  of  FIG. 1  through a communication channel in the vehicle. The main ECU displays the season data received from the vehicle battery sensor on the vehicle cluster to help a user to prepare for a driving state suitable for the season as illustrated in  FIG. 17 . For example, the user may determine whether the vehicle is idle upon initial starting or a tire replacement time depending on the season from the seasonal information displayed on the cluster. 
         [0102]    The present invention may not limitatively adopt the configurations and methods of the exemplary embodiments as described, but all or some of the respective exemplary embodiments may be selectively combined and configured so that the exemplary embodiments may be variously modified.

Technology Classification (CPC): 8