Patent Publication Number: US-8116998-B2

Title: Battery health assessment estimator

Description:
BACKGROUND OF INVENTION 
     1. Field of the Invention 
     The present invention relates to a method of assessing the health of one or more traction batteries employed in electric and hybrid electric vehicles and more particularly to a method, system, apparatus, and computer product for estimating the state of health of one or more traction batteries arranged in series and/or parallel. Each battery is assumed to be internally composed of one or more cells, also arranged in series and/or parallel. 
     2. Description of the Related Art 
     Currently, electric and hybrid electric vehicles employing one or more traction batteries are becoming a popular alternative to conventional vehicles employing an internal combustion engine. Not only are electric and hybrid electric vehicles being phased into traditional automobiles but they are also being implemented in trucks, buses as well as streetcars in so-called fleet operations. 
     In fleet operations, various vehicle maintenance protocols are typically implemented to assure consistent operation of electric and hybrid electric vehicles. For, example, a vehicle maintenance protocol for a bus fleet would require that each electric and hybrid electric bus be checked by a technician on either a daily or weekly basis to assure that the one or more traction batteries are in proper working order. 
     The conventional method employed in a typical vehicle maintenance protocol involves one or more of the following methods. One conventional method involves the comparison of battery voltage profiles which requires that a technician remove and access each traction battery to compare the behavior of various traction battery voltages in a pack of traction batteries where data is collected from each traction battery terminal. Outliers from the pack of traction batteries are identified and used to determine if the traction battery is unhealthy. However, outlier analysis has the limitation that it typically requires that several traction batteries be available for comparison. If all the traction batteries in the pack are degrading, outlier analysis would not see any difference between each traction battery. 
     Another conventional method employs electrochemical impedance spectroscopy where technicians test the electrochemical impedance of a pack of traction batteries after removing each traction battery. Each removed traction battery is then placed on a special test stand and analyzed. However, in fleet operation where a bus for example can have ten or more 600-volt traction batteries integrated deep into the fuselage of the vehicle makes removal of one or more traction batteries very maintenance intensive and costly. 
     In another conventional method, a technician monitors the open circuit voltage of a single traction battery. If the open circuit voltage is too low, the battery is deemed unusable. However, monitoring the open circuit voltage is a difficult measurement and is difficult to do on an installed battery. Further, in applications where the battery may be recharged, the open circuit voltage changes with state of charge. As it is difficult to know state of charge, it is difficult to quantify the health of the battery. 
     In yet another conventional method, advanced state estimation techniques are applied to each traction battery using accurate high-frequency data sampling of a single battery. For example, some applications use advanced state estimation techniques to infer battery state of charge or internal impedance. Internal impedance is a common measure for battery health. Typically, battery impedance increases as the battery becomes unhealthy. Internal impedance is not greatly affected by state of charge, hence, it is a good feature for health assessment. Current state estimations techniques are typically applied to a single battery and use highly accurate, high sampling rate data to assess state of charge. Such conventional approaches are used in laboratory applications. In such applications, batteries may only be discharged or use simple charging schemes. For more complex applications, like a stack of traction batteries used in a hybrid electric vehicle with complex charging/discharging cycles, the data is sampled at very slow rates. The voltage and current measurements are typically sampled at much lower accuracy than seen in a laboratory environment. 
     However, the conventional method employing advanced state estimation suffer from the same drawbacks as the other conventional methods described above, such as being maintenance intensive and costly. Moreover, conventional advanced state estimation also have data bandwidth limitations where the ability to collect high-frequency (kilohertz) data for a large number of traction batteries is not currently available since current applications typically collect data at 1 Hz or slower. 
     Moreover, none of the above described conventional methods or any other known method in the prior art can detect whether or not a traction battery or a pack of traction batteries is merely suffering what is termed as a “Blue Monday” condition. A Blue Monday condition is a situation where a traction battery sits idle for an extended period of time and shows a degraded status upon conventional assessment techniques. Accordingly, a technician may likely unnecessarily waste time and resources to subject a traction battery to recharging or removal when in fact the battery is merely in a Blue Monday condition. 
     Having set forth the limitations of the prior art, it is clear that what is required is a method, system, apparatus or computer product which can provide a state estimation technique which can be applied to a single battery without requiring the removal of the battery from the vehicle, can work with low-frequency data sampling and can detect a Blue Monday condition. 
     SUMMARY OF THE INVENTION 
     The present invention provides a method of estimating a state of health (SOH) of one or more batteries. 
     In particular, estimating internal resistances of the one or more batteries, generating a time history of the internal resistances over a predetermined amount of time, generating a cumulative internal resistance histogram from the time history, calculating a final estimate of the internal resistance of the batteries which represents the calculated SOH of the one or more batteries and comparing the calculated SOH to a predetermined critical threshold, resistance, wherein if the calculated SOH is less than the predetermined critical resistance threshold, the one or more batteries are in no worst than a Blue Monday condition, and if the calculated SOH is greater than the predetermined critical resistance threshold, then one or more of the batteries has failed. 
     In the present invention the sampled internal resistance is estimated by employing a state estimator, which can be a Kalman filter. 
     In the step of generating a cumulative histogram a further step is included where a step of setting a smallest partition is provided. In particular, in the cumulative internal resistance histogram, the smallest partition is set to zero count. 
     In another embodiment of the present invention a further subsequent step of determining if the one or more batteries has one or more intermittent or continuously shorted cells is provided, comprising, generating an open circuit voltage (OCV) histogram of a nominal open circuit voltage from each of the one or more batteries, selecting a peak OCV value from the generated OCV histogram, and comparing the selected peak OCV value to a predetermined nominal range. If the selected peak OCV value is outside the predetermined nominal range, the one or more batteries has a shorted cell. 
     In another embodiment of the present invention there is provided the further subsequent steps of determining the state of charge of the one or more batteries, estimating an open circuit voltage (OCV) of the one or more batteries as a function of a predetermined state of charge and comparing the estimated OCV to another predetermined nominal value. If the estimated OCV is below the other predetermined nominal value, one or more batteries are degraded. 
     The present invention also provides a system of estimating a state of health (SOH) of one or more batteries, comprising, means for estimating internal resistances of the one or more batteries, means for generating a time history of each of the internal resistances over a predetermined amount of time, means for generating a cumulative internal resistance histogram from the time history, means for calculating a final estimate of the internal resistance of the one or more batteries which represents the calculated SOH of the one or more batteries, and means for comparing the calculated SOH to a predetermined critical resistance threshold. If the calculated state of health is less than the predetermined critical resistance threshold, the one or more batteries are in no worse than a Blue Monday condition, and if the calculated SOH is greater than the predetermined critical resistance threshold then the battery has failed. 
     An additional object of the present invention is to provide a program storage device readable by a machine, tangibly embodying a program of instructions executable by the machine to perform the above-described estimation of the state of health (SOH) of one or more batteries. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The objects, features and advantages of the present invention will become apparent to one skilled in the art, in view of the following detailed description taken in combination with the attached drawings, in which: 
         FIG. 1  is a battery state of health estimator according to one embodiment of the present invention. 
         FIG. 2  is an illustration of a flow diagram showing the method of assessing the state of health of one or more batteries according to one embodiment of the present invention. 
         FIG. 3(   a ) is an illustration of a representative prior art charge well model of a battery. 
         FIG. 3(   b ) is a prior art circuit schematic illustration of a representative voltage source model for a battery. 
         FIG. 4(   a ) is a graphical illustration of a sample of a measured battery current, according to one aspect of the present invention. 
         FIG. 4(   b ) is a graphical illustration of a sample of the filter response of a measured and predicted battery voltage without using the state equations system, according to one aspect of the present invention. 
         FIG. 5  is a graphical illustration of a sample of the filter response of the predicted battery voltage and open circuit voltage versus estimated state of charge (SOC) according to one aspect of the present invention. 
         FIG. 6(   a ) is a graphical illustration of a sample of the filter response of internal resistance plotted against the measured current of a traction battery according to one aspect of the present invention. 
         FIG. 6(   b ) is a graphical illustration of a of the filter response of a histogram of internal resistance of a traction battery, according to one aspect of the present invention. 
         FIG. 7  is a graphical illustration of a sample of the filter response of a histogram of open circuit voltage of a traction battery with a shorted cell according to one aspect of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. It should be noted that the similar components are designated by similar reference numerals although they are illustrated in different drawings. For the purposes of clarity and simplicity, a detailed description of known functions and configurations incorporated herein will be omitted as it may obscure the subject matter of the present invention. 
       FIG. 1  is an illustration of a battery state of health (SOH) estimator according to one embodiment of the present invention. As shown in  FIG. 1 , a battery state of health (SOH) estimator  100  includes a voltage and current sensor module  110 , which obtains voltage and current measurements from a direct connection to the terminals of one or more traction batteries (not shown) by way of connection leads  112  to  112 ( 1 - n ). State estimator module  120 , as more thoroughly explained below, provides estimates of the system states of one or more traction batteries. Blue Monday detector module  130  determines whether a particular battery has failed or is in no worse than a Blue Monday condition by comparing a predetermined resistance threshold to a calculated SOH. Shorted cell detector module  140  determines if a peak open circuit voltage (OCV) value from a histogram of the open circuit voltage is outside a predetermined nominal range, and if the peak OCV is outside the normal range then the battery has a shorted celled. The state of charge estimator  150  determines that if an estimated open circuit voltage is below a predetermined nominal value, then the battery is degraded. 
     Indicators  132 ,  142 , and  152  are LEDs or other devices capable of indicating a status by illuminating as known in the art. Indicator  132  is coupled to the Blue Monday detector module and when illuminated indicates that the battery has failed. Indicator  142  coupled to the shorted cell detector module illuminates to indicate that a cell in a traction battery is shorted. Indicator  152  coupled to the state of charge estimator illuminates to indicate a traction battery is degraded. If none of the indicators  142 , or  152  are illuminated, the traction batteries being monitored are in normal operating condition, and in the case of indicator  132 , that the traction batteries are in no worse than a Blue Monday condition. 
     Referring now to  FIG. 2 , the operation of the method of assessing the state of health of one or more batteries will now be described according to one aspect of the invention. In  FIG. 2  a flow diagram showing the method of assessing the state of health of one or more batteries  200  according to one embodiment of the present invention is shown. In  FIG. 2 , a battery bank  210  is shown. The present invention first generates estimates of a sampling of the internal resistances  220  of the one or more batteries in the battery bank  210  employing a state estimator, discussed below. A time history  230  of the sampled of internal resistances are generated over a predetermined amount of time and stored in a memory such as a random access memory RAM. A cumulative histogram  240  as known to those skilled in the art is generated from the stored time history which is a mapping that counts the cumulative number of observations over time in uniform subintervals called “bins.” That is, as known in the art, the cumulative histogram M i  of a histogram m i  is defined as: 
     
       
         
           
             
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     The amount of time required to generate a statistically reliable time history can take as long as ten to fifteen minutes, as known to those skilled in the art. After a statistically reliable time history is obtained, and a cumulative histogram is generated, a final estimate of the internal resistance of the one or more batteries, which represents the calculated state of health (SOH)  250  of the one or more batteries. The calculated SOH is compared to a predetermined critical resistance threshold  260 , wherein if the calculated SOH is less than the predetermined critical resistance threshold, one or more of the batteries are in no worse than a Blue Monday condition, and if the calculated SOH is greater than the critical resistance threshold, then one or more of the batteries has failed and needs to be replaced. The present invention&#39;s method recognizes as known in the art the fact that as batteries get unhealthy, the internal resistance will increase. However, in a Blue Monday condition, an increase in the battery&#39;s internal resistance does not mean that the battery has failed and needs to be replaced. 
     As mentioned above, the step of generating samples of the internal resistances  220  of the one or more batteries in the battery bank  210  can be carried out by way of a state estimator. As known in the art, a state estimator such as a non-linear Kalman filter can be employed to optimally combine measurements (current and voltage) within a set of state equations to provide estimates of samples of the internal resistances of the batteries. A Kalman filter is a recursive filter that estimates the state of a dynamic system from a series of incomplete and noisy measurements by providing estimations of past, present, and even future states, and it can do so even when the precise nature of the modeled system is unknown. 
     After running the state estimator and generating a time history of the estimated sampling of internal resistances, a cumulative histogram R 0   240  of each battery is generated and an open circuit voltage E histogram  270  from the open circuit voltage of each battery is also generated. At the same time an approximate open circuit voltage is calculated as a function of a state of charge  280 . The generation of the above referenced histograms is well known in the art and in literature such as  Intuitive Probability and Random Processes using Matlab , by Steve Kay, page 14, published by Springer in 2006. 
     In the present invention, the first value of the internal resistance histogram bin (related to a resistance value of 0) is set to zero to aid in selecting a peak resistance value as will be explained later when referring to  FIG. 6(   b ). The reason for this step of setting the 0 bin to zero as it is well known in the art in that it is physically impossible to consider any peaks at zero resistance. The value of the predetermined critical resistance threshold would be determined from the tolerances listed in datasheets available from battery manufacturers, which would list what the maximum value of the internal resistance of a battery could be before it could be determined that the battery has failed. 
     A shorted battery cell condition is determined by picking a peak value  272  of the open circuit voltage E histogram  270 . A shorted cell is determined by evaluating whether the selected peak value  272  of the open circuit voltage histogram  270  is outside a predetermined nominal range  274 . The predetermined nominal range is determined from the tolerances listed in datasheets available from battery manufacturers. If the selected peak value  272  is outside the predetermined nominal range, a flag is triggered or in case of the apparatus of  FIG. 1 , LED  142  would illuminate to indicate that at least one cell of the battery is, in fact shorted. 
     To determine if one or more traction battery&#39;s current capacity is degraded, as known in the art, an estimated open circuit voltage is calculated as a function of a state of charge  280  and if the estimated open circuit voltage is determined to be below a predetermined nominal value  282 , a flag is triggered or in case of the apparatus of  FIG. 1 , LED  152  would illuminate. Again, this predetermined nominal value is determined from the tolerances listed in data&#39;s sheets available from battery manufactures. Obviously, if LED  152  is not illuminated one or more traction battery&#39;s current capacity is not degraded. 
     To more thoroughly understand the state estimator of module  120  of  FIG. 1  and the state estimator  220  of  FIG. 2  and the present invention&#39;s method of assessing a battery&#39;s health, the following background explanation is provided.  FIGS. 3(   a ) and  3 ( b ) are provided to illustrate generic models of a battery or a so-called Sheppard&#39;s model as known in the art.  FIG. 3(   a ) is an illustration of a representative charge well model of a battery. In  FIG. 3(   a ), a battery charge well  300 A reflects the state of charge, which is directly related to the current i flowing in and out of the battery. Also shown in  FIG. 3(   a ) is the available (current) charge qa in the battery and the maximum charge capacity q 0 . 
       FIG. 3(   b ) is a circuit schematic illustration of representative voltage source model for a battery. As shown in  FIG. 3(   b ), a battery can also be modeled as a complex voltage source which has relationships between the internal impedance, open circuit voltage, state of charge (SOC) and battery terminal voltage. The state equations for the system of  FIGS. 3(   a ) and  3 ( b ) at time step j are given by the following formulations: 
     Charge Well Relationship:
 
 qa   j   =qa   j−1   +i   j   Δt   (Equation No. 1)
 
Voltage Source Relationship:
 
 v   bj =( E 0 j−1   +K   j−1 signum( i   j ) Q norm j−1 )+ i   j   R 0 j−1   +i   j−n   R   j−1   , n= 1 or 2  (Equation No. 2)
 
 E 0 j   =E 0 j−1  
 
 R 0 j   =R 0 j−1  
 
 R   j   =R   j−1  
 
 K   j   =K   j−1  
 
     where:
         qa—available (current) charge in battery   v b —terminal voltage   E 0 —open circuit voltage at 0% SOC   Qnorm—normalized charge term   i—current   R 0 —equivalent battery resistance   R—dynamic resistance term
 
The open circuit voltage (E) is defined as:
 
 E=E 0 +K signum( i ) Q norm  (Equation No. 3)
 
where:
 
q n /q 0  Positive i  (22)
 
 Q norm=( q   0   −q   a )/ q   0  Negative i  (24)
       

     q 0 —total charge capacity of battery 
     Accordingly, the above formulation renders six (6) states (v b , qa, E 0 , R 0 , R, and K) and one system input (current—i). It should be noted that only one of the states is measured, the battery terminal voltage across positive terminal  22  and negative terminal  24 , vb. As known in the art, the above equations numbers 1-3 can be used within a state estimator such as a non-linear Kalman filter to optimally combine the measurements (current and voltage) within the state equations to provide estimates of the internal resistance of the batteries. 
     Referring now to  FIGS. 4(   a ),  4 ( b ),  5 ,  6 ( a ) and  6 ( b ), an example of the present invention&#39;s method of assessing the state of battery health of one or more batteries is described by way of showing the filter response of low sample-rate data from a hybrid electric vehicle application employing multiple traction batteries.  FIGS. 4(   a ) and  4 ( b ) are graphical illustrations of the filter response of a measured battery current and measured and predicted voltage according to one aspect of the present invention. In other words, in both  FIG. 4(   a ) and  FIG. 4  ( b ), the filter is merely showing the propagation of each current and voltage filter in time (seconds). Accordingly, as seen in  FIGS. 4(   a ) and  4 ( b ), the current and voltages demonstrate that the raw output measured from any traction battery produced poorly sampled data where the peak values are not straight lines. In other words, applying the standard Ohm&#39;s law calculation to obtain the internal resistance value is not easily obtainable. Accordingly, it is necessary to apply the system state equations No. 1-3 above to model each traction battery to obtain an estimated internal resistance value. 
     In  FIG. 5  a graphical illustration of a sample of the filter response of the predicted battery voltage and open circuit voltage versus estimated state of charge (SOC) according to one aspect of the present invention is shown. 
       FIG. 6(   a ) is a graphical illustration of a sample of the filter response of an internal resistance plotted against battery current of a traction battery according to one aspect of the present invention. As shown in  FIG. 6(   a ), the internal resistance demonstrates more variability/scatter than would normally be useful to assess the battery health. This variation is attributed to high-frequency dynamics, which cannot be adequately accounted for by low speed sampling, process noise associated with modeling errors, state equations, and any measurements errors (biases or delays) in the voltage and current data. To remedy this situation as discussed, above a Kalman filter approach is employed. Hence, since the Kalman filter is stochastic in nature, the state estimates should be looked at stochastically. 
       FIG. 7  is a graphical illustration of a sample of the filter response of an internal resistance estimate versus voltage of a traction battery showing a shorted cell according to one aspect of the present invention.  FIG. 7  illustrates the shorted cell detector module  140  of  FIG. 1 . When a shorted cell is present in a battery the battery actually is operating in two modes with respect to internal resistance. In normal operation a battery will operate in the 12 to 13 volt range. On the other hand, when a battery contains a shorted cell the battery&#39;s voltage will show for example a voltage in the 10 to 11 voltage range as shown in  FIG. 7 . Accordingly, the present invention determines if the peak value from OCV histogram is outside the nominal range. 
     It should be noted that other statistical properties of the internal resistance histogram (i.e., the mean and mode) can also provide detail as to the accuracy of the final estimate of the internal resistance (e.g., confidence bounds). The peaks from the internal resistance histogram can be generated after every operation (e.g., daily) using the Kalman filter state estimation technique. It can be trended daily and used in a simple logical expression to assess battery health, if (Ri−S)&gt;Rcritical_threshold (i.e. critical resistance threshold), then battery is faulty or on the other hand, if Rcritical_threshold&gt;(Ri−S) the battery is in no worse than a Blue Monday condition, where Ri is the peak sampled resistance value extracted from the cumulative histogram, and S is a standard deviation. The standard deviation S includes noise and an adjustment factor extracted from the cumulative histogram such that when it is subtracted from the peak sampled resistance value Ri results in providing the final estimate of the battery&#39;s internal resistance, otherwise referred to as the SOH of the battery. The standard deviation S can be determined using techniques well known to those skilled in the art such as and in literature such as, for example,  Intuitive Probability and Random Processes using Matlab , by Steve Kay, page 356, published by Springer in 2006. 
     This technique can be applied to batteries, which were known to be faulty and were shown to trend in a fashion similar to other approaches (specifically, behaves similarly to outlier detection techniques). One obvious advantage of this approach is that it allows the estimation of battery health for a single battery using low sample-rate data. The technique also provides statistical measures as to the adequacy of the internal resistance estimate. Note that the open circuit voltage of a battery also varies as the health of the battery degrades. Typically the open circuit voltage will vary with the state of charge within the battery. As the battery degrades, the open circuit voltage determined from the SOC may operate below its nominal value computed from the state of charge. This open circuit voltage can be compared to a predetermined nominal value. If it falls below the nominal value, then the battery may be deemed unhealthy. 
     Modern batteries used for high-power applications (hybrid vehicles, etc.) are typically composed of a number of internal cells connected in series or parallel. Overall degradation of the cells is typically noted as in the increase of battery impedance or the reduction of open circuit voltage. Besides age/usage-related cell degradation, the cells may exhibit shorts or opens. An open cell is easily detected (e.g. current measurements are zero and the full battery voltage appears across the cell), but a shorted cell is less obvious. Inspection of battery voltage may not easily indicate a shorted cell as the voltage variation is related to open circuit voltage, impedance and current. However, if the open circuit voltage is examined, a short will be evident, particularly if the histogram of open circuit voltage is generated as shown in  FIG. 7 . In  FIG. 7 , the open circuit voltage would have a peak within some nominal range. A shorted cell appears as a peak at a value markedly out of range from the nominal value. 
     As will be readily apparent to those skilled in the art, the present invention or aspects of the invention can be realized in hardware, or as some combination of hardware and software. Any kind of computer/server system(s)—or other apparatus adapted for carrying out the methods described herein—is suitable. A typical combination of hardware and software could be a general-purpose computer system with a computer program that, when loaded and executed, carries out methods described herein. Alternatively, a special purpose computer, containing specialized hardware for carrying out one or more of the functional tasks of the invention, could be utilized. 
     The present invention or aspects of the invention can also be embodied in a computer program product, which comprises all the respective features enabling the implementation of the methods described herein, and which—when loaded in a computer system—is able to carry out these methods. Computer program, software program, program, or software, in the present context mean any expression, in any language, code or notation, of a set of instructions intended to cause a system having an information processing capability to perform a particular function either directly or after either or both of the following: (a) conversion to another language, code or notation; and/or (b) reproduction in a different material form.