Abstract:
A device for predicting remaining capacity of a battery and a method for the same are disclosed. The device is embedded in a battery pack or externally coupled thereto. The device includes a program for proceeding an algorithm of cell capacity calculation, a database stored in a non-volatile memory having a table of open-circuit voltage, a table of current gain and a capacity conversion equation. The program generates a discharging curve according to the cell temperature and load accessed and corrects the database according to the battery voltage and the discharging curve and the coulomb counter.

Description:
FIELD OF THE INVENTION 
       [0001]    The present invention pertains to an algorithm for predicting a depth of energy of a battery and the device for the same, particularly to an algorithm using the temperature of the battery detected and the load current outputting therefrom as parameters to predict a depth of energy of the battery. 
       DESCRIPTION OF THE PRIOR ART 
       [0002]    Battery is knows as a main power for most of probable electric devices. For instance, the mobile phone, notebook, PDA (personal digital assistance), Walkman, etc., all rely on the battery to provide the electrical power for the devices properly work The battery, however, saves only limited electrical capacity. As a probable device is turned on, the charges saved in the battery consumed will sustain. While the residue electrical capacity is not enough to support the probable device work properly, the battery management unit will force a power management program to store the necessary parameters into hard disk or nonvolatile memory and then turned off the power. The latter represents that the electricity stored in the battery is lower than a critical level. For the earth environment and the average cost are concerned, choosing the rechargeable battery for the probable device as the main power is generally taken. 
         [0003]    A lithium battery associated with a good battery management integrate chip may make the lithium battery be recharged for several hundreds or even thousands without make the battery material premature. In addition to the high times of recharging for a good battery, a user may more concern about the accurately remaining run-time estimated by the battery management of a mobile device when the user is using the device. Since the remaining run-time need to be known in mind by the user so that the user can appropriately close the current work before the power management program informs the user that the device is prepared to be turned off for protecting the battery if the user does not plug-in AC (alternatively current) power or a charger immediately. 
         [0004]    Moreover, a good power management program is demanded to accuracy predict the remaining battery capacity and the remaining run-time all the time in accordance with the discharging rate rather than turning off the battery after it has been discharged to a certain low level. A high quality battery management is necessary. 
         [0005]    However, providing such a high quality battery management system is expensive according to the conventional technologies known by the inventors. The battery management system designers have to spend a rather long time to build a database, even worse, the database established by the designers according to a first battery manufacturer may be not apt to a second battery manufacturer it is because the data records in the database are highly relied on the chemical material in the batteries, particularly to the grades of the material vary. Therefore, the IC designers have to repeat developing procedures of the database again for the second battery manufacturer as that of first battery manufacturer. 
         [0006]    For accuracy the predicting residual capacity in accordance with a method of dynamic discharge cutoff voltage, the battery has to be fully charged and then completely discharged for hundreds of times during the database developing processes. Besides, the database is highly dependent on the materials in the battery so that the database used by the battery management IC has to be recreated even the materials of battery are just a subtle difference, as forgoing description. Worse still, the database will be not updated if an end consumer user does not make the battery be fully charged and completely discharged. As a result, the power management program will provide incorrect remaining charge information for the user when the battery is aging. 
         [0007]    Another conventional embodiment is the open circuit method. The encounter difficulties are similar to the forgoing method of dynamic discharge cutoff voltage. It needs a lot of time to develop a database which also material related. 
         [0008]    Still another conventional embodiment is disclosed by Barsoukov et al, on U.S. Pat. No. 6,832,171 with a title “Circuit and Method for Determining Battery Impedance Increasing with Aging.” In the method a current flowing through the battery is analyzed if a transient condition due to change of current is occurring and determined when the transient condition has ended. A voltage of the battery is measured while a steady current is being supplied by the battery. The present depth of discharge is accessed to determine a corresponding value of open circuit voltage. And then the internal is computed by dividing the difference between the battery voltage and the open-circuit voltage by an average value of the steady current. The remaining run-time is then determined by using a total zero current capacity, integrating the current to determine a net transfer of charge from the battery, determining total run time, determining the duration of the integrating, and determining the remaining run-time by subtracting the duration from total run-time. The method demand a database established by the battery being fully charged and completely discharged for hundreds of time. 
         [0009]    An object of the present invention is to overcome above problems. 
       SUMMARY OF THE INVENTION 
       [0010]    A device for predicting remaining capacity of battery and a method for the same is disclosed. The device built in or external connected to a battery pack comprises a database and a capacity derived algorithm program. The database is stored in a writable-and-erasable non-volatile memory, wherein the database comprises an open-circuit voltage table, a current-gain table and energy-capacity converted equations. The open-circuit voltage table has data of open-circuit voltages of a battery measured at predetermined temperatures T j  and at predetermined depths of (% DOE n ) denoted as OCV (T j , DOE n ). The current-gain table contains data of current-gains, denoted as IGAIN (DOE n ). The energy-capacity converted equations contains a correcting factor so as to solve the problem when the remaining capacity calculated based on the coulomb counter is inconsistent with a remaining capacity obtained based on the terminal voltage and the predicting discharging curve where n is a nature number and j is from 1 to 3. 
         [0011]    The capacity derived algorithm program executed by a microprocessor. The program generates a discharging curve according to the cell temperature and load accessed and corrects the database according to the battery voltage and the discharging curve and the coulomb counter and then reports a remaining capacity. 
         [0012]    In the method, the steps include the steps of: (a) detecting a load current and a surface temperature T B  of the battery; (b) generating a predicting discharging curve which depicts the relationship between voltages and DOE n  based on the database and the data detected in the step (a); (c) fetching a terminal voltage and then determining a DOE % value according to the predicting discharging curve and the terminal voltage of the battery according to the coulomb counter neither in a discharging mode nor a relax mode; (d) correcting the database if the status information is in a discharging mode or in a relax mode and then obtaining the DOE % value according to the updated database. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0013]    The foregoing aspects and many of the attendant advantages of this invention will become more readily appreciated as the same becomes better understood by reference to the following detailed description, when taken in conjunction with the accompanying drawings, wherein: 
           [0014]      FIG. 1  shows a system of predicting remaining capacity of a battery by a self-training algorithm program using the cell voltage, cell temperature and accessed load as inputting parameters. 
           [0015]      FIG. 2  shows apparatus for predicting remaining capacity of battery in accordance with the present invention. 
           [0016]      FIG. 2A  illustrates an OCV discharging curve and a constant load current discharging curve used to calculate the current-gain value at 50% DOE. 
           [0017]      FIG. 2B  depicts a schematic diagram of a constant load current discharging curve shifted from an OCV discharging curve. 
           [0018]      FIG. 3  shows a flow chart of the self-training algorithm program according to the present invention. 
           [0019]      FIG. 4A  shows an interpolation method used to derive a discharge curve while a detected cell temperature is not equal to the temperature in the OCV table. 
           [0020]      FIG. 4B  shows the DOE % value obtained by coulomb counter is not equal to that of derived from the discharging curve and the cell voltage detected 
       
    
    
     DESCRIPTION OF THE PREFERRED EMBODIMENT 
       [0021]    As aforementioned conventional techniques, no matter what the methods of predicting cell capacity according to prior art including the dynamic discharging voltage cutoff method or open-circuit voltage method is taken, the whole processes of them demand the battery being fully charging and discharging repeatedly for hundreds of times and still if the end user does not often do the fully charging and discharging process, then the database will not be updated, results in predicting incorrectly predicting the residual capacity. The problem will be more serious while the material of the battery has aged. 
         [0022]    The present invention provides an algorithm to predict remaining capacity of a battery by using the cell temperature (surface temperature), accessed load current, and cell voltage as input parameter, as is shown in  FIG. 1 . A device  260  for predicting a remaining capacity may embed in a battery pack or externally connected to the battery pack, as shown in  FIG. 2 . The device includes an algorithmic program  255 , a database  250 , and a microprocessor  240  so as to carry out a self-training procedure. The microprocessor  240  may includes in the battery pack. The input terminals of the apparatus  260  are provided to retrieve the battery voltage, the surface temperature of the battery and the accessed load to perform a self-training procedure, please see  FIG. 3 . Upon the self-training procedure accomplished, the database is updated and the residual charge capacity of the battery is predicted accordingly. Thereafter, the new basic data in the database are then provided for the next self-training procedure after a predetermined time according the buffer  201 , as shown in  FIG. 1 . Each cycle of the self-training procedure takes only about 1 second or several seconds. 
         [0023]    As shown in  FIG. 2 , the battery pack comprises multi-cells  215 , a battery protective circuit  210 , an electrical measuring unit  220   a,  and a non-electrical measuring unit  220   b,  an analog-to-digital (ADC) converter  225 , a coulomb counter  230 , and a battery communicative protocol controller  235 . 
         [0024]    The electrical measuring unit  220   a  is to detect the terminal voltage of the multi-cells  215 , and the current output. The non-electrical measuring unit  220   b  is to detect the surface of multi-cells  215 . The forgoing temperature, terminal voltage and the current all will be converted to digital data by an ADC converter  225  for microprocessor. Aside from that, the current is also counted by the coulomb counter  230  and the resulted outputting data by the device  260  will provide to battery communicative protocol controller  235 . 
         [0025]    In accordance with the present invention, to carry out the present invention, a database  250  has to prepared or provided in advance. The database includes (1) an open-circuit voltage table (OCV Table), (2) a current-gain table, and (3) capacity-energy converted equations. The open circuit voltage hereinafter is to indicate that the natural discharging of the battery is simulated by using a small discharging rate rather than absolutely natural discharging the battery through the open circuit. 
         [0026]    The steps of OCV table established include: fully charging a battery and then discharging the battery with a small but constant discharging current such as 1/20 C or below at a predetermined constant ambient temperature wherein C is the specified capacity of the battery. The voltage and the surface temperature of the battery will be measured when a predetermined depth of energy (DOE %) is reached. The processes of fully charging and discharging to the predetermined DOE % are performed repeatedly so as to get the DOE %, OCV relationships at the predetermined ambient temperature. 
         [0027]    For instance, the ambient temperature is set to 5° C. and the battery is fully charged and then it is discharging by a rate of about 1/20 C to 10% DOE and then the surface temperature and the voltage are measured. The surface temperature may be higher than the ambient such as 6° C. Thus a first data is OCV1 (10% DOE, T 1 ) where T 1 =6° C. 
         [0028]    The other data of the OCV table with different ambient temperatures such as 25° C., and 45° C. may be obtained using the steps as above so as to get the data OCV2 (10% DOE, T 2 ) and OCV3 (10% DOE, T 3 ). Generally, the surface temperatures of the battery measured are different from the ambient temperature set. The data may be adjusted by using the interpolation or extrapolation method to the assigned temperatures so as to reduce the data number. 
         [0029]    The data of OCV table with different DOE % values can be obtained as forgoing steps. Moreover, the discharge curve is steeper at the neighbor of fully charge point (0% DOE) and EDV point (end of discharge voltage), therefore the DOE % values for such region are preferred denser than others. Table 1 is an example of the initial OCV table with an unit (mV), as follows: 
         [0000]    
       
         
               
               
               
             
               
               
               
               
               
             
               
               
               
               
               
             
           
               
                   
                 TABLE 1 
               
             
             
               
                   
                   
               
               
                   
                   
                 T 
               
             
          
           
               
                   
                 DOE % 
                 5° C. 
                 25° C. 
                 45° C. 
               
               
                   
                   
               
             
          
           
               
                   
                 5 
                 4129 
                 4151 
                 4164 
               
               
                   
                 10 
                 4086 
                 4108 
                 4119 
               
               
                   
                 15 
                 4048 
                 4069 
                 4077 
               
               
                   
                 20 
                 4010 
                 4032 
                 4039 
               
               
                   
                 30 
                 3948 
                 3966 
                 3969 
               
               
                   
                 40 
                 3895 
                 3912 
                 3915 
               
               
                   
                 50 
                 3825 
                 3851 
                 3855 
               
               
                   
                 60 
                 3792 
                 3802 
                 3803 
               
               
                   
                 70 
                 3774 
                 3778 
                 3780 
               
               
                   
                 80 
                 3755 
                 3757 
                 3761 
               
               
                   
                 85 
                 3729 
                 3737 
                 3738 
               
               
                   
                 90 
                 3688 
                 3710 
                 3710 
               
               
                   
                 95 
                 3676 
                 3677 
                 3685 
               
               
                   
                   
               
             
          
         
       
     
         [0030]    The current-gain table (IGAIN table) is obtained by the following steps: firstly, the battery is fully charged and then discharged with a higher but constant discharging rate such as 0.2 C or 0.5 C. The expression is: 
         [0000]        V ( DOE,T,I ) =OCV ( DOE,T ) +I    
         [0031]    The equation represents that the IGIN is equivalent to a resistance and the terminal voltage of the battery is level shifted up or down while the battery is discharged using a higher discharging rate. 
         [0032]    An exemplary of the IGAIN obtained is shown in  FIG. 2  A. The curve  202  is a OCV discharging curve and the point P corresponding to OCV (30° C., 50% DOE)=3741 mV and the discharging curve  204  is obtained by using a discharging current 1000 mA. The point P′ corresponding to (30° C., 50% DOE) is of 3529 mV so that the IGAIN (30° C., 50% DOE) is: 
         [0000]        IGAN= 3741−3529/1000=0.212
 
         [0033]    IGAIN table is acquired by discharging the battery from a known DOE % value point to a target % DOE value by a constant discharging current. Upon reaching the target, a voltage is measured. For example, a battery is fully charged, at which 0% DOE, and then discharged by a rate such as 0.2 C to 5% DOE and a voltage is measured. Then the battery is discharged from 5% DOE to 10% DOE by the same discharging current, then another voltage is measured. The processes repeat to discharge the capacity downward to every target DOE %. The IGAIN data for a discharging rate of about 0.2 C is denoted as IGAIN 0.2 . 
         [0034]    Similarly, another set of IGAIN data may be obtained by a different discharging rate such as 0.3 C or o.5 C and they as denoted as IGAIN 0.3  and IGAIN 0.5    
         [0035]    However, to simplify the database, only one IGAIN value is taken for each target % DOE of the IGAIN table though different discharging rates may generate different IGAIN values at the same % DOE value. According to an embodiment of the present invention, for each assigned % DOE but different discharging rates, just only one IGAN value is selected and recorded. An IGAN value is selected when they have a common feature but an average or a middle value of IGAN values may be recorded when the common feature cannot be determined. 
         [0036]    An exemplary of the IGAIN table is shown in table 2, as follows: 
         [0000]    
       
         
               
             
               
               
               
               
               
               
               
               
               
             
           
               
                   
               
               
                 IGAIN table 
               
               
                 Depth of Energy(DOE %) vs. IGAN 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 DOE % 
                 5 
                 10 
                 15 
                 20 
                 30 
                 40 
                 50 
                 60 
               
               
                 IGAN 
                 0.063 
                 0.057 
                 0.058 
                 0.058 
                 0.060 
                 0.063 
                 0.056 
                 0.055 
               
               
                 DOE % 
                 70 
                 80 
                 85 
                 90 
                 95 
               
               
                 IGAN 
                 0.062 
                 0.063 
                 0.060 
                 0.051 
                 0.061 
               
               
                   
               
             
          
         
       
     
         [0037]    As to (3), the capacity converted equation is expressed as energy 
         [0000]    
       
         
           
             
               
                 
                   
                     
                       E 
                       
                         ma 
                          
                         
                             
                         
                          
                         x 
                       
                     
                     = 
                     
                       
                         Δ 
                          
                         
                             
                         
                          
                         Cap 
                       
                       
                         Δ 
                          
                         
                           ( 
                           
                             
                               DOE 
                               X 
                             
                             - 
                             
                               DOE 
                               
                                 X 
                                 - 
                                 1 
                               
                             
                           
                           ) 
                         
                       
                     
                   
                   , 
                 
               
               
                 
                   ( 
                   2 
                   ) 
                 
               
             
           
         
       
     
         [0038]    Where E max  is the maximum energy the battery contained therein.
       ΔC ap  is the capacity difference between two % DOE values.       
 
         [0040]    Fully charged (FCC) equation: 
         [0000]        FCC=E   max   ×DOE   E ×ω  (3)
 
         [0041]    where ω is a correcting factor and DOE E  is a depth of energy corresponding to the end of discharging voltage. 
         [0042]    Remaining capacity equations are expressed as: (equations (4)&amp;(5)) 
         [0000]        RM   @Initial   =E   max ×( DOE   E   −DOE   ε )×ω  (4)
 
         [0043]    Where DOE ε is the depth of energy corresponding to the current voltage of the battery. 
         [0000]        RSOC   @Initial   =RM   @Initial   /FCC    (5)
 
         [0044]    The self-training procedure is shown in  FIG. 3 , a flow chart thereof. It starts from the step  305 , which claims the steps of procedure start therefrom. 
         [0045]    Next, in the step  310 , the current load and the surface temperature of the battery is measured by the electrical measured unit  220   a  and non-electrical measured unit  220   b,  respectively. The data measured hereinafter all will be converted by ADC  225  for microprocessor  240  to access. 
         [0046]    Turning to the step  320 , the battery management program  260  will generate an OCV discharging curve according to the temperature measured and the bases data in the OCV Table 1 of the database. If the temperature is equal to T 1 , T 2  or T 3  in the Table 1, then the discharging curve  401 ,  402  or  403  will be generated according to the data, depicted in the OCV Table 1. Otherwise, when the temperature measured is T y  within the specification of the battery, but T y ≠T 1 , T 2  or T 3  then the curve  405  is generated by a method of interpolation or extrapolation for each data point in the Table 1, as shown in  FIG. 4A . For example, the V(DOE 1 , T y ) is obtained according to V(DOE 1 , T 1 ), V(DOE 1 , T 2 ) and V(DOE 1 , T 3 ) using an interpolation method. Other data are gotten by a similar process. 
         [0047]    Still referring to  FIG. 4A , the OCV discharging curves  401 ,  402 , and  403  are adjusted according to the IGAIN Table 2, load current detected and the formula (1) to obtain the constant predicting discharge curves  401 ′,  402 ′, and  403 ′. Accordingly, if the detected temperature is Ty then, the predicted discharging curve is generated by the interpolation or the extrapolation based on the data on curves  401 ′,  402 ′, and  403 .′ 
         [0048]    Thereafter, a step  330  is followed. A voltage of the battery is measured by the electrical detected modules  220   a.  The present % DOE is obtained according to the predicted discharging curve at step  320  and the detected temperature as is shown in  FIG. 4B . 
         [0049]    Turning to the step  340 , the step is to determining whether the status of the battery is complied with available discharging conditions. The conditions include the discharging current over a predetermined criteria and the temperature within a range which battery can be operated normally as well as the delayed time. The predetermined criteria of the discharging current is at least over 0.1 C and the temperature detected is within 0° C. to 60° C. Preferably, the temperature is demanded to be within 5° C. to 50° C. Moreover, the accuracy point of the capacity for discharging must be known before performing a discharging process. The delayed time for a battery to perform the self-training algorithm from a known discharging point should not over one day to prevent the known discharging point become inaccuracy since the battery will self-discharging. 
         [0050]    When the status of the battery does not satisfy the conditions of available discharging, the step is jumped to the step  350  to report the remaining capacity according to the % DOE value obtained in the step  330 . 
         [0051]    On the contrary, when the condition is true, the step goes to step  360 , which is to accumulate the charges released from the known discharging point of the battery using the coulomb counter to determine which modes that the battery runs accordingly, wherein the modes include a discharging mode, a relax mode and the others through a comparison using the value of charges accumulated at currently and at the last time by the coulomb counter. The time interval may be 1 s or 10 s. 
         [0052]    mined according to accumulated charges counted by the coulomb will be compared with the % DOE value by correspondence the terminal voltage in the step  330  with the predicted discharging curve in the step  320 . 
         [0053]    An example is shown in  FIG. 4B , the value read from the coulomb counter is 2500 mAh from a known discharging point 10% DOE and E max  at that time is known to be of 3571 mAh. Therefore, the % DOE value will be 80% DOE according to equation (2). The voltage corresponding to the 80% DOE of the predicted discharging curve  405  is V″. If the voltage V″=V′, no correction is demanded where V′ is detected at step  330 . 
         [0054]    However, if V″≠V′ such as V″&gt;V′, as shown in  FIG. 4B , then the % DOE corresponding the voltage V′ to the predicted discharging curve  405 ′ is of 82%. According to the energy converted equation (3), the E max  is determined to be 3472 mAh. 
         [0055]    After that, the step turns to step  350 , to calculate the capacity of the battery. As shown in  FIG. 4B , FCC is determined to be: 
         [0000]        FCC=E   max ×95%=3298 mAh;
 
         [0056]    The remaining capacity RM is determined to be: 
         [0000]        RM=E   max (95%−82%)=451 mAh;
 
         [0057]    On the other hand, since the starting discharging point is known to be10% DOE, where E MAX  has a value of 3571 mAh and then, accordingly, the capacity of the battery FCC is of 
         [0000]        FCC=E   max ×95%=3392 mAh;
 
         [0058]    The remaining capacity RM is of 
         [0000]        RM=E   max (95%−10%)=3035 mAh; and
 
         [0059]    since the accumulated charges released counted by the coulomb counter from the known points 10% DOE to a second points 80% DOE, are of 2500 mAh and thus accordingly, the RM would be of: 
         [0060]    RM=3035−2500=535 mAh; therefore, according to the equation (4) the correcting factor ω=1.186. 
         [0061]    As the mode of the battery is in a relax mode, as shown in step  365 , the open-circuit voltage (OCV) table I in the database will be requested to be updated. The conditions of the relax mode include that the discharging current is lower than a second criteria and sustain for 30 min and/or above. The second criteria are set to be one twentieth of the full battery capacity. 
         [0062]    The correction of the OCV table I is to correct the OCV (DOE, T) according to the newly detected temperature at the surface of the battery by the step  310  and the % DOE in accordance with the. step  330  to update the values of all OCV (DOE, T). Thereafter, the step goes to step  350  to calculate the capacity of the battery according to the updated OCV table 1, and the information collected at step  310 . 
         [0063]    As the mode of the battery is neither in relax mode nor in discharging mode then the step directly goes to the step  350  to calculate the capacity of the battery at the present time. 
         [0064]    The benefits of the present invention: 
         [0065]    (1) The database is easily to develop; 
         [0066]    (2) The processed time spent for every self-training cycle is about 5 s to 10 s, even more, the time spent required may be 1 or 2 s after the database is developed. 
         [0067]    (3) The remaining capacity of the battery can be determined at every self-training cycle if the DOE % value or energy is known at the starting discharging point. The database will be updated if the battery runs in the discharging mode or relax mode. 
         [0068]    (4) In comparison with that of the prior art, the time spent for a database development according to the present is much less. According to the present invention, the database build demands the battery fully charged and discharged for several hundred of times. 
         [0069]    As is understood by a person skilled in the art, the foregoing preferred embodiments of the present invention are illustrated of the present invention rather than limiting of the present invention. It is intended to cover various modifications and similar arrangements included within the spirit and scope of the appended claims, the scope of which should be accorded the broadest interpretation so as to encompass all such modifications and similar structures.