Patent Application
20200309857
The present disclosure relates to batteries, and in particular, to methods, systems, and devices for estimating and predicting battery properties, such as state of charge (SOC).
Batteries have become increasingly important, with a variety of industrial, commercial, and consumer applications. Of particular interest are power applications involving “deep discharge” duty cycles, such as motive power applications. The term “deep discharge” refers to the extent to which a battery is discharged during service before being recharged. By way of counter example, a shallow discharge application is one such as starting an automobile engine wherein the extent of discharge for each use is relatively small compared to the total battery capacity. Moreover, the discharge in such shallow discharge cases is followed soon after by recharging. Over a large number of repeated cycles very little of the battery capacity is used prior to recharging.
Conversely, deep discharge duty cycles are characterized by drawing a substantial majority of the battery capacity before the battery is recharged. Some motive power applications that require deep cycle capability include Class 1 electric rider trucks, Class 2 electric narrow isle trucks and Class 3 electric hand trucks. Desirably, batteries installed in these types of vehicles must deliver a number of discharges during a year that may number in the hundreds. The cycle life of batteries used in these applications typically can range from 500-2000 total cycles so that the battery lasts a number of years before it needs to be replaced.
Interest and research in batteries has resulted in a variety of battery chemistries, with differing benefits and drawbacks. For example, “flooded” lead-acid batteries tend to be more economical, but may require periodic maintenance that includes replenishment of an electrolyte, which can spill; such batteries may also have reduced capacity over time resulting from liberation of acid during charging. Alternative lead-acid batteries may use a gelled electrolyte, which cannot spill and avoid the acid liberation problem, but have their own drawbacks in that the internal resistance may be higher, limiting the ability of such batteries to deliver high currents. Still other types of batteries include lithium-ion or lithium ion polymer batteries, nickel-cadmium, nickel-metal hydride, and others. The benefits and drawbacks of such battery types are known to those in the art and need not be discussed here.
Regardless of the type of battery used in an application, two important properties of a given battery at a given point in time during usage is how much operating time is left before a charge is required, as well as how much charging time is needed for a “full” battery.
Common techniques for providing these measurements suffer from inaccuracy errors. For example, the state of charge of the battery (or of the cells of a multi-cell battery) may be used, which may be defined as an available capacity of a battery (measured in ampere-hours, Ah) as a percentage of a rated capacity of the battery. For example, a state of charge (SOC) of a “full” battery may be 100%, and a SOC of an empty battery may be 0%. In known techniques, the SOC at a given point in time may be simply multiplied by a default usage rate to provide an estimation of discharge time remaining, or by a default charging rate to provide an estimation of charging time remaining.
SOC is difficult to measure directly, and instead it is typically estimated from direct measurement variables. A common technique is simple coulomb counting, which measures battery charge and discharge current and integrates in time. Although measurements of current used in coulomb counting may be precise, simple coulomb counting may be subject to error. Further, it has been recognized by the inventors of this application that known techniques for estimating discharge time remaining and charge time remaining suffer from inaccuracy errors as well, as usage rates and/or charging rates are highly variable and/or non-linear.
Accordingly, the present disclosure and the inventive concepts described herein provide methods, systems, and devices for predicting a future SOC of a battery as a function of a usage pattern, as well as predicting usage-adaptive remaining run time and recharge time. Additionally, the present disclosure and the inventive concepts described herein provide methods, systems, and devices to monitor more accurately a state of charge of a battery using an enhanced coulomb counting technique. The inventive concepts described herein are combinable and provide more accurate monitoring and predicting in a variety of applications, including motive power applications. Furthermore, the inventive concepts herein have separate utility in various applications where prediction and/or estimation of SOC of a battery at a present point in time or a future point of time is desired.
For example, provided herein are methods, systems, and devices that include improvements to determining properties of a battery. For example, a method may include measuring one or more properties of a battery; determining a charging status of the battery based on the measured one or more properties; and updating one or more predictions of properties of the battery based on the determined charging status of the battery, wherein the one or more predictions comprises a prediction of a remaining time to charge the battery and/or a prediction of a remaining time to discharge the battery, resulting in updated one or more predictions of the properties of the battery.
The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the inventive concepts and, together with the description, serve to explain principles of the inventive concepts.
The phrase “battery monitoring” as used herein may include measuring values of properties of a battery at a point in time and/or over a period of time. “Battery monitoring” may also include estimating values of battery properties at past and/or present points in time, relative to a time when the estimation is performed. For example, a property may be estimated where the property is difficult, time-consuming, or energy-consuming to measure directly. First and second values measured at first and second points in time, respectively, may be used to estimate a third value at a third point in time occurring in between the first and second points in time. “Battery monitoring” may also include predicting future values of battery properties at a point in time in the future relative to when the prediction is made. Such predicted future values may be based on one or more measured and/or estimated values of properties of the battery, at points in time at and/or before when the prediction is made. Example properties that may be measured, estimated, and/or predicted may include current (e.g., current flowing to the battery, current flowing from the battery), voltage (e.g., open-circuit voltage, voltage applied to load), battery temperature, battery state of charge, time remaining to charge, time remaining to discharge, and so on. Measured, estimated, and/or predicted battery properties may be based on other measured, estimated, and/or predicted properties of the battery. Other data or information available within the battery monitoring system 100 may also be used to measure, estimate, and/or predict battery properties, such as models of complex battery properties, stored history of battery usage data, and so on.
The battery 20 may be of any type compatible with the present disclosure, with examples including lead-acid batteries, lithium-ion batteries, and so on. The battery 20 may have one or more local sensors (not shown in
As illustrated in
The battery monitoring device 25 may be electrically and/or communicatively coupled to the battery 20 and configured to receive measurements from the sensors of battery 20 and/or the sensors of the battery monitoring device 25 and communicate the measurements to one or more recipients. Estimations and/or predictions of battery properties based on the measurements may also be communicated. Examples of recipients may include a user of the vehicle 30 in which the battery 20 is installed. Data may be communicated (e.g., graphically, tabularly, and/or numerically) to the user of the vehicle 30 via an user interface, such as a display device 35 mounted in a dashboard of the vehicle 30 or otherwise visible to the user during operation of the vehicle 30. Other examples of recipient may be computing devices 90 and 95, which may communicate with the battery monitoring device 25 over a network 50, and which may be smartphones, tablets, desktop computers, laptop computers, thin clients, mainframes, servers, and so on. The computing devices 90 and 95 may be running software configured to receive the data and/or other values from the battery 20 and/or the battery monitoring device 25 and perform one or more actions based thereon. As an example of such actions, the computing device 90 may be configured to receive data and/or other values from the battery 20 and/or the battery monitoring device 25, determine a notification (e.g., a notification of a SOC of the battery 20, a notification of a remaining run time of the battery 20) should be sent to the computing device 95, and cause transmission of the notification to the computing device 95, for example via the network 50. In some embodiments, the battery monitoring device 25 may be integrated with the battery 20. In some embodiments, the battery monitoring device 25 may be integrated with the vehicle 30 and/or the charger 40.
In some embodiments, sensed values of properties, estimations of values of properties, and/or predictions of values of properties may be stored in a database at database server 80. The database server 80 may be a part of any of the computing devices of
In some embodiments, functionality described herein as being performed by the battery monitoring device 25 may be performed additionally or alternatively by one or more of the computing devices 90, 95 in the battery monitoring system 100 of
The battery 20, the battery monitoring device 25, the charger 40, and/or the computing devices 90, 95 may include a display device for displaying measurements, estimations, and/or predictions (e.g., graphically, tabularly, and/or numerically). In some embodiments, the battery 20, the battery monitoring device 25, the charger 40, and/or the computing devices 90, 95 may include input devices configured to accept user input, such as an initial state of charge of the battery 20, desired type of output/display, user settings (e.g., temperature values provided in Celsius or Fahrenheit) and so on.
The network 50 may include a local network, a wireless, coaxial, fiber, or hybrid fiber/coaxial distribution system, a Wi-Fi or Bluetooth network, or any other desired network. The network 50 may be made up of one or more subnetworks, each of which may include interconnected communication links of various types, such as coaxial cables, optical fibers, wireless links, and the like. The network 50 and/or the subnetworks thereof may include, for example, networks of Internet devices, telephone networks, cellular telephone networks, fiber optic networks, local wireless networks (e.g., WiMAX, Bluetooth), satellite networks, and any other desired network, and each device of
Returning to the battery monitoring device 25, the battery monitoring device 25 may be configured to perform one or more methods to provide an estimation and/or a prediction of a SOC of the battery 20, a remaining run time of the battery 20, and/or a recharge time of the battery 20. For example, the remaining run time of the battery 20 may be a function of the remaining capacity of the battery 20 and the rate of usage of the charge of the battery 20. The rate of usage may be variable in many applications. For example, in motorized vehicles, such as electric rider trucks, electric narrow isle trucks, or electric hand trucks, the rate of usage of a battery 20 may be dependent on one or more of a mass of the motorized vehicle and/or a load carried by the motorized vehicle, an operating speed of the motorized vehicle, characteristics of a motor of the motorized vehicle, an ambient temperature in a location where the motorized vehicle is operated, and so on. Additionally, the capacity of the battery can be a function of the usage pattern. For example, Peukert's Law provides that
where H is the rated discharge time of the battery 20 (provided by the manufacturer), C is the rated capacity (in Ah), I is the actual usage, k is a constant dependent on the type of battery 20, typically between 1.0 and 1.5, and t is the time in hours that the battery 20 will last at the increased current I. Increased usage of the battery above the rated capacity will result in a lower time t, and decreased usage of the battery below the related capacity will result in a greater time t. The variability and interrelation of usage rate and rated capacity may make prediction of the remaining run time difficult.
The time needed to recharge the battery 20 may also be a difficult value to predict, as the charge acceptance of the battery 20 may exhibit nonlinear behavior. The recharge time may be a function of several parameters, including rate of charge, battery voltage, temperature, and/or other parameters.
The prediction accuracy of both a remaining run time prediction and a recharge time prediction depend on accurate knowledge of the SOC of the battery 20 at a time when such predictions are made. It is recognized by the present inventors that charge inefficiencies are not considered in the known coulomb counting techniques. Charge inefficiencies, that is, inefficiencies in charge acceptance by the battery 20, may be based on rate of charge and/or temperature of the battery 20.
As illustrated in
In operation 220, one or more properties of the battery may be measured at a first point in time (t1), using the sensors of the battery 20 and/or of the battery monitoring device 25 as discussed above. Examples of measured battery properties may include voltage, current, and temperature. In operation 225, the charge status of the battery 20 may be determined, for example, based on a flow of current to or from the battery 20. Herein, a flow of current to the battery (e.g., a charging current) may be referred to as a positive current, and a flow of current from the battery (e.g., a discharging current) may be referred to as a negative current.
In operation 225, if a measured current is greater than a first current threshold |IMIN|, then the battery 20 may be charging, and operation 230 may be performed, where one or more predictions, such as a remaining time to charge, are updated. For example, the initial prediction of the remaining time to charge the battery 20, or a previous prediction of the remaining time to charge the battery 20, may be updated in operation 230. Additionally and/or alternatively in operation 230, a prediction of the remaining time to discharge may be updated, as the current flowing to the battery 20 may result in increased charge in the battery 20, increasing the remaining capacity. Accordingly, the initial prediction of the remaining time to discharge the battery 20, or a previous prediction of the remaining time to discharge the battery 20, may be updated in operation 230. Further details of operation 230 are provided with reference to
In operation 225, if the measured current is less than a second current threshold −|IMIN|, then the battery 20 may be discharging, and operation 240 may be performed, where one or more predictions, such as a remaining time to discharge, are updated. For example, the initial prediction of the remaining time to discharge the battery 20, or a previous prediction of the remaining time to discharge the battery 20, may be updated in operation 240. Additionally and/or alternatively in operation 240, a prediction of the remaining time to charge may be updated, as the current flowing from the battery 20 may result in decreased charge in the battery 20, decreasing the remaining capacity. Accordingly, the initial prediction of the remaining time to charge the battery 20, or a previous prediction of the remaining time to charge the battery 20, may be updated in operation 240. Further details of operation 240 are provided with reference to
In operation 225, if the measured current is greater than the second current threshold −|IMIN| and less than the first current threshold |IMIN| (e.g., the measured current is proximate to zero), then the battery 20 may be idle and then operation 250 may be performed. Operation 250 may be a periodic calibration operation that includes sub-operations similar to those discussed with respect to the initialization operation 210. Further details of operation 250 are provided with reference to
After performance of one of operations 230, 240, or 250, optionally operation 260 may be performed, in which one or more actions are taken, for example based on the updated predictions and/or estimations of values determined in the performed operation 230, 240, or 250. Such actions may include, for example, transmitting a notification to a user or a device (e.g., the display device 35 of the vehicle 30, the computing devices 90, 95, the database 80) indicating the updated predictions and/or estimations of values determined in the performed operation 230, 240, or 250. As another example, a reservation may be made at the battery charger 40 to charge the battery 20 at a point in time based on the updated predicted discharge time of the battery 20.
The method 200 may then return to operation 220 and perform another measurement of one or more of the properties of battery 20, as discussed above, for a second point in time (t2). As an example, a measurement of one or more of the properties of battery 20 may occur once every second, multiple times a second, or periodically every n seconds, where n>=2. In some embodiments, operations 225, 230, 240, 250, and/or 260 may also be performed once every second, multiple times a second, or periodically every n seconds, where n≥2. In some embodiments, operations 225, 230, 240, 250, and/or 260 may be performed at a different rate than the measurement of the one or more properties of the battery 20, and at different rates from each other. For example, operation 230, 240, or 250 may be performed less frequently than operation 220 or 225, and operation 260 may be performed less frequently than operation 230, 240, or 250.
Reference is now made to
In operation 310, the voltage of the battery 20 (which was measured, for example, in operation 220 of
If, however, the voltage is not greater than VMAX (e.g., NO branch from operation 310), then a charge efficiency may be calculated in operation 320. The charge efficiency may be calculated based on the initial SOC or a previously estimated SOC, the measured current, and the measured temperature of the battery 20 (which were measured, for example, in operation 220 of
In operation 340, the battery monitoring device 25 may determine whether the battery 20 is in a constant current (CC) or a constant voltage (CV) charging stage. In charging profiles where multiple charging stages are used, a CC stage may be used until a pre-set voltage level is reached. The battery 20 and/or the charger 40 may then switch to the CV stage and decrease the current as the charge approaches completion. To determine whether the battery 20 is in the CC or CV charging stage, a magnitude of the measured voltage of the battery 20 at a present point in time is used, as is average measured voltage over the past x seconds. In some embodiments, x may be between 5 seconds and 60 seconds, as examples. If a difference between the magnitude of the measured voltage and the average voltage is greater than a predetermined threshold, then the battery 20 is in the CC stage, and the method 300 may proceed to operation 350. If the difference between the magnitude of the measured voltage and the average voltage is less than or equal to the predetermined threshold, then the battery 20 is in the CV stage, and the method 300 may proceed to operation 360.
In operation 350, the battery monitoring device 25 may predict the time remaining to charge in the CC stage as well as the predicted time to charge in the CV stage when that stage is reached. In some embodiments, the SOC of the battery 20, as well as the measured current of the battery are used as inputs to a CC pre-trained multi-variable model, which predicts how much energy will be accepted by the battery 20 before the voltage of the battery rises to the maximum charge voltage, and consequently the battery 20 switches to the CV stage. The CC pre-trained multi-variable model may be stored in memory within the battery monitoring device 25 and/or elsewhere within the battery monitoring system 100. In some embodiments, the CC pre-trained multi-variable model may be further dependent on a type of the battery 20. Different CC pre-trained multi-variable models may be available to the battery monitoring device 25, and a CC pre-trained multi-variable model may be selected from the different CC pre-trained multi-variable models based on a type of the battery 20, a user preference, or the like. The output of the CC pre-trained multi-variable model (e.g., the CC selected pre-trained multi-variable model) may be used to estimate the duration of the CC stage, resulting in a value TCC. A predicted SOC at the end of the CC stage (SOCCC may also be determined).
Continuing in operation 350, the time of the CC stage (e.g., TCC) and the predicted SOC at the end of the CC stage (e.g., SOCCC) may be used as inputs to predict the duration of the CV stage, using a CV pre-trained multi-variable model may be stored in memory within the battery monitoring device 25 and/or elsewhere within the battery monitoring system 100. In some embodiments, the CV pre-trained multi-variable model may be further dependent on a type of the battery 20. Different CV pre-trained multi-variable models may be available to the battery monitoring device 25, and a CV pre-trained multi-variable model may be selected from the different CV pre-trained multi-variable models based on a type of the battery 20, a user preference, or the like. The output of the CV pre-trained multi-variable model (e.g., the selected CV pre-trained multi-variable model) may be used to estimate the duration of the CV stage, resulting in a value TCV. The predicted time remaining in charge (e.g., to fully charge) may be based on the predicted duration of the CC stage (TCC) and the predicted duration of the CV stage (TCV), less the time the battery 20 has already spent in charging, which may be stored in memory in the battery monitoring device 25.
An initial prediction of the remaining time to charge the battery 20, or a previous prediction of the remaining time to charge the battery 20, may be updated in operation 370 based on the result of operation 350, that is, using the predicted time remaining in charge (e.g., to fully charge) based on the predicted duration of the CC stage (TCC) and the predicted duration of the CV stage (TCV), less the time the battery 20 has already spent in charging. Additionally and/or alternatively in operation 370, a prediction of the remaining time to discharge may be updated, as the current flowing to the battery 20 may result in increased charge in the battery 20, increasing the remaining capacity. For example, the new estimated capacity of the battery 20 may be used to calculate a new time to discharge the battery 20. Accordingly, the initial prediction of the remaining time to discharge the battery 20, or a previous prediction of the remaining time to discharge the battery 20, may be updated in operation 370.
In some embodiments, an updated prediction of the time remaining to fully charge may only be calculated if the rate of charge, which is based on the measured current, changes by an amount greater than a threshold. For example, if the change in current is greater than 5%, an updated prediction of the time remaining to fully charge may be calculated, and if the change in current is not greater than 5%, the updated prediction of the time remaining to fully charge may not be calculated. This may preserve resources, as once the predicted duration of the CC stage (TCC) and the predicted duration of the CV stage (TCV) are calculated, the values may not significantly change absent a change in the measured current. Thus, some performances of operation 350 and 370 may refrain from new predictions of the durations of the CC stage (TCC) and of the CV stage (TCV) and instead decrement the previously predicted time remaining by the increase in time that the battery 20 has already spent in charging.
If the battery monitoring device 25 determines that the battery 20 is in the CV stage at operation 340, then at operation 360, a predicted time remaining in the CV stage is determined. The time of the CC stage (e.g., TCC) and the SOC at the end of the CC stage (e.g., SOCCC) may be used as inputs to predict the duration of the CV stage, using a CV pre-trained multi-variable model may be stored in memory within the battery monitoring device 25 and/or elsewhere within the battery monitoring system 100, which may be selected from a plurality of pre-trained multi-variable models as discussed above.
In some embodiments, during operation 360 a refinement calculation is made to the duration of the CV stage predicted by the CV pre-trained multi-variable model based on a rate of decay in the current over time. As discussed above, the voltage is held relatively constant in the CV stage, and the current may decrease as charge nears completion. To predict the rate of decay, the following equation may be used:
In Equation (2), I is the most recent current measured, I0 is the current at the end of the CC stage, and Δt is a duration in time between the measurement time of I0 and I, with ln being the natural log function. A refinement to the duration of the CV stage (T′) is calculated as follows:
In Equation (3), β is a constant that may be determined based on a type of the battery 20 and/or based on charge test data. In Equation (4), TCV1 is the duration of the CV stage predicted by the CV pre-trained multi-variable model.
The result of Equation (4) and of operation 360 is used in operation 370 to update an initial prediction of the remaining time to charge the battery 20, or a previous prediction of the remaining time to charge the battery 20. That is, a prediction of the remaining time to charge the battery 20 is updated using the predicted duration of the CV stage (TCV), less the time the battery 20 has already spent in charging in the CV stage. Additionally and/or alternatively in operation 370, a prediction of the remaining time to discharge may be updated, as the current flowing to the battery 20 may result in increased charge in the battery 20, increasing the remaining capacity. For example, the new estimated capacity of the battery 20 may be used to calculate a new time to discharge the battery 20. Accordingly, the initial prediction of the remaining time to discharge the battery 20, or a previous prediction of the remaining time to discharge the battery 20, may be updated in operation 370.
An example performance of the method 300 of
Reference is now made to
In operation 410, the voltage of the battery 20 (which was measured, for example, in operation 220 of
If, however, the voltage is not less than VMIN (e.g., NO branch from operation 410), then in operation 420, the SOC of the battery 20 may be updated. For example, the initial SOC of battery 20, or a previous SOC of the battery 20, may be updated in operation 420 by first calculating a relative change in capacity (Ah) based on the measured current and Δt, the difference in time between the present measurement of the current and the previous measurement of the current. This relative change in capacity (which may be negative, as the current is flowing from the battery 20 in discharge) is then summed with the present estimated capacity of the battery 20, resulting in a new estimated capacity of the battery 20. An updated SOC is determined based on the new estimated capacity of the battery 20 and the nominal capacity of the battery (provided by the manufacturer or determined empirically).
In operation 430, recent and global usage patterns may be determined. For example, the measured current may be appended to a vector of recent current measurements (e.g., current measurements over the last x hours). In some embodiments, the recent current measurement may be filtered (for example using a moving-average or other filter) prior to appending the measurement of the current to the vector of recent current measurements. A global usage pattern may be determined from the vector of recent current measurements by taking an average (e.g., arithmetic mean) of the vector of recent current measurements, and storing this taken average in a vector of recent vector averages. The most recent average is representative of the average usage rate in the past x hours and represents the global pattern of the data at the present point in time. Local changes in the usage pattern may also be determined in operation 430, for example by fitting a linear regression curve to the past y values from vector of recent vector averages. In some embodiments, y≤x/2.
A predicted future usage rate is determined from the linear regression curve in operation 440. For example, a usage rate at a future point in time may be determined from a weighted average of an extrapolated point on the linear regression curve at a time (t+y) and the average of the vector of recent current measurements. In other words, the predicted future usage rate may be taken from a weighted average of a long-term global usage pattern and a recent short-term local usage pattern.
In operation 450, a correction factor may be applied to the predicted future usage rate determined in operation 440. For example, over a period of time (e.g., z hours), an actual usage of energy may be measured by the battery monitoring device 25. This actual usage of energy may be compared to the number of predicted used amp-hours over the same period of time. For example, a prediction may be made a time T0 for amp-hour usage over a period of time from T0 to T1 (e.g., a period of z hours), and at T1 the predicted amp-hour usage over the period of time from T0 to T1 may be compared with the actual usage over the period of time from T0 to T1. A calculated difference between the predicted usage over the period of time from T0 to T1 and the actual usage of the period of time from T0 to T1 may be used to adjust the newly predicted usage rate. This may be performed using Equation (5), below:
In Equation (5), Iprd1 is the newly predicted usage rate from operation 440, e is the calculated difference between the predicted usage over the period of time from T0 to T1 and the actual usage of the period of time from T0 to T1, z is the length of the period of time from T0 to T1, and alpha (α) is an adjustable self-learning rate with a value between zero and one (e.g., 0≤α≤1). In some embodiments, the correction factor may only be periodically determined and/or periodically applied, for example to preserve computational resources and/or to limit vacillating behavior in the predicted future rate, e.g., from over and under correcting.
An initial prediction of the remaining time in discharge of the battery 20, or a previous prediction of the remaining time in discharge of the battery 20, may be updated in operation 460 based on the results of operations 420 and 450, that is, using the new estimated capacity of the battery 20 and the predicted future usage rate that has been periodically corrected. Additionally and/or alternatively in operation 460, a prediction of the remaining time to charge may be updated, as the current flowing to the battery 20 may result in decreased charge in the battery 20, decreasing the remaining capacity. For example, the new estimated capacity of the battery 20 may be used to calculate a new time to charge the battery 20. Accordingly, the initial prediction of the remaining time to charge the battery 20, or a previous prediction of the remaining time to charge the battery 20, may be updated in operation 460.
An example performance of the method 400 of
However, if the current is zero (e.g., YES branch from operation 510), then a timer or other counter value may be incremented in operation 520 until a period of time has elapsed. As discussed above, the length of the period of time may be dependent on the type or chemical properties of the battery 20, and may be (as an example) between 1-4 hours to permit relaxation of the battery 20. In some embodiments, this time may improve accuracy in determination of the SOC of the battery 20, as charge may distribute (e.g., evenly distribute) through the internal chemistry of the battery 20. If the period of time has not elapsed (e.g., NO branch from operation 520), then conditions may not be appropriate for calibration and the method of
If the period of time has elapsed (e.g., YES branch from operation 520) then a calibrated SOC of the battery 20 may be determined based on a measurement of an open circuit voltage (OCV) after a period of time where the battery 20 is idle. From the open circuit voltage, the initial SOC may be determined, for example using a curve or relationship between SOC and OCV. The calibrated SOC determined in this manner may then be stored in a memory device. Additional battery properties may be determined or estimated from the calibrated SOC. Such properties may include, in some embodiments, a remaining capacity of the battery (in units of Ah). The remaining capacity of the battery 20 may be determined from a product of the initial SOC with a nominal capacity of the battery 20, which may be retrieved from a memory device.
In operation 540, the timer incremented in operation 520 may be reset. Optionally, in operation 550, predicted or estimated values of properties of the battery 20 may be reset in favor of estimated or predicted values based on the calibrated SOC. For example, a prediction of the remaining time to charge may be determined from the calibrated SOC and a default time to completely charge the battery 20, as provided from a manufacturer of the battery 20 and/or based on empirical data collected for the battery 20 or the type of the battery 20. A calibrated prediction of the remaining time to discharge the battery 20 may be determined from the initial SOC and a nominal usage rate (in units of current) for an application (e.g., Class 1 electric rider trucks, Class 2 electric narrow isle trucks and Class 3 electric hand trucks). As discussed above, application may be provided as input to the battery monitoring device 25, or a default application (and hence a default nominal usage rate) may be used in the calibration method 500.
In some embodiments, the calibration method 500 of
A computing device 600 may include one or more processors 601, which may execute instructions of a computer program to perform any of the features described herein. The instructions may be stored in any type of computer-readable medium or memory, to configure the operation of the processor 601. For example, instructions may be stored in a read-only memory (ROM) 602, random access memory (RAM) 603, removable media 604, such as a Universal Serial Bus (USB) drive, compact disk (CD) or digital versatile disk (DVD), floppy disk drive, or any other desired electronic storage medium. Instructions may also be stored in an attached (or internal) hard drive 605. The computing device 600 may be configured to provide output to one or more output devices (not shown) such as printers, monitors, display devices, and so on, and receive inputs, including user inputs, via input devices (not shown), such as a remote control, keyboard, mouse, touch screen, microphone, or the like. The computing device 200 may also include input/output interfaces 607 which may include circuits and/or devices configured to enable the computing device 600 to communicate with external input and/or output devices (e.g., the battery 20, network devices of the network 50) on a unidirectional or bidirectional basis. The components illustrated in
The various inventive concepts provide several distinctive advantages. First, the inventive concepts provided herein provide a comprehensive algorithm for estimating the present state of charge of a battery and of predicting a future state of charge of the battery in both charge and discharge. The inventors have recognized that prior systems did not provide such comprehensiveness. For example, some previous systems provided only neural networks for state of charge estimation without prediction, or proposed algorithms that predict capacity or runtime in discharge only and not charge.
Second, the present inventive concepts provide prediction of a future usage pattern of a battery in discharge based on extraction of both a global or long term usage pattern as well as local or short term changes in the usage pattern occurring in the near past. The inventors have recognized that previous systems usually only use the average usage pattern in the past or the present rate of discharge as an indication of the future usage pattern; or alternatively information from the battery voltage is used to predict the remaining run time of the battery system.
Third, the inventive concepts herein improve the prediction accuracy of the remaining discharge time by adding a self-learning feature, as discussed above. For example, the algorithm is “penalized” when past prediction error occurs, which may enforce faster adaptation to a new usage pattern. Advantageously, in some embodiments the self-learning feature may use only a relatively small amount of memory storage to store data representing only a few seconds to minutes of data to provide the correction factor, and may be computationally efficient.
Fourth, predicting time remaining to charge in both constant current and constant voltage phases may include usage of models of non-linear behavior in both the constant current and constant voltage stages, as well as usage of an analytical model to predict temporal changes in current in the constant voltage stage of charging. It is submitted that the topic of predicting the time remaining to full charge in battery systems has received little attention by the field, with progress limited to systems that use lookup tables based on the battery current at a point in time. The inventive concepts, in contrast, provide improved accuracy over such systems.
The inventive concepts provided by the present disclosure have been be described above with reference to the accompanying drawings and examples, in which examples of embodiments of the inventive concepts are shown. The inventive concepts provided herein may be embodied in many different forms than those explicitly disclosed herein, and the present disclosure should not be construed as limited to the embodiments set forth herein. Rather, the examples of embodiments disclosed herein are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the inventive concepts to those skilled in the art. Like numbers refer to like elements throughout.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the specification and relevant art and should not be interpreted in an idealized or overly formal sense unless expressly so defined herein. Well-known functions or constructions may not be described in detail for brevity and/or clarity.
Some of the inventive concepts are described herein with reference to block diagrams and/or flowchart illustrations of methods, apparatus (systems) and/or computer program products, according to embodiments of the inventive concepts. It is understood that one or more blocks of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, and/or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer and/or other programmable data processing apparatus, create means for implementing the functions/acts specified in the block diagrams and/or flowchart block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function/act specified in the block diagrams and/or flowchart block or blocks.
The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions/acts specified in the block diagrams and/or flowchart block or blocks.
Accordingly, the inventive concepts may be embodied in hardware and/or in software (including firmware, resident software, micro-code, etc.). Furthermore, embodiments of the present inventive concepts may take the form of a computer program product on a computer-usable or computer-readable non-transient storage medium having computer-usable or computer-readable program code embodied in the medium for use by or in connection with an instruction execution system.
The computer-usable or computer-readable medium may be, for example but not limited to, an electronic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory such as an SD card), an optical fiber, and a portable compact disc read-only memory (CD-ROM).
The terms first, second, etc. may be used herein to describe various elements, but these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of the present inventive concepts. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should be further understood that the terms “comprises,” “comprising,” “includes,” and/or “including” when used herein, specify the presence of stated features, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, operations, elements, components, and/or groups thereof.
When an element is referred to as being “on” another element, it can be directly on the other element or intervening elements may also be present. In contrast, when an element is referred to as being “directly on” another element, there are no intervening elements present. When an element is referred to as being “connected” or “coupled” to another element, it can be directly connected or coupled to the other element or intervening elements may be present. In contrast, when an element is referred to as being “directly connected” or “directly coupled” to another element, there are no intervening elements present. Other words used to describe the relationship between elements should be interpreted in a like fashion (i.e., “between” versus “directly between”, “adjacent” versus “directly adjacent”, etc.).
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present disclosure.
Aspects and elements of all of the embodiments disclosed above can be combined in any way and/or combination with aspects or elements of other embodiments to provide a plurality of additional embodiments. Although a few exemplary embodiments of the inventive concepts have been described, those skilled in the art will readily appreciate that many modifications are possible in the exemplary embodiments without materially departing from the novel teachings and advantages of the inventive concepts provided herein. Accordingly, all such modifications are intended to be included within the scope of the present application as defined in the claims.
