Device and method for tracking electrode capacity

Abstract

Apparatus and method for tracking electrode capacity. Specifically, an apparatus and method for tracking battery cells are disclosed, for use in a device having one or more sensors and a controller with a processor and a tangible non-transitory memory. The method includes obtaining corresponding sensor data about an anode and a cathode. Based on the corresponding sensor data, a predicted anode capacity and a predicted cathode capacity are determined. The predicted anode capacity and the predicted cathode capacity each have a corresponding variance value. A Kalman filter module executed by the controller generates an updated set of variables and an updated corresponding variance value based in part on the predicted anode capacity, the predicted cathode capacity, and the measured equilibrium voltage. The updated set of variables includes an updated anode capacity and an updated cathode capacity. Based in part on the updated set of variables and the updated corresponding variance value, the operation of the device is controlled.

Description

Background Art

[0001] The present disclosure relates generally to apparatus and methods for tracking battery cells, and more specifically to apparatus and methods for tracking the capacity of individual electrodes in battery cells. The use of mobile platforms that utilize rechargeable energy sources both as an exclusive and non-exclusive energy source has grown significantly over the past several years. Battery packs can store and release electrochemical energy as needed during a given operating mode. The electrochemical energy can be used for propulsion, heating or cooling the vehicle cabin, powering vehicle accessories, and other purposes. The various cells in a battery pack can be characterized by different powers, states of charge, and capacity rates, which can decay over time and use. Summary of the invention

[0002] Disclosed herein is an apparatus and method for tracking a battery cell in a device having one or more sensors and a controller with a processor and tangible non-transitory memory. The battery cell has an anode and a cathode. The method includes, via the one or more sensors, acquiring corresponding sensor data about the anode and the cathode; and determining a predicted anode capacity and a predicted cathode capacity based on the corresponding sensor data. The predicted anode capacity and the predicted cathode capacity each have a corresponding variance value.

[0003] The controller is configured to selectively execute a Kalman filter module and generate an updated set of variables and updated corresponding variance values, based in part on the predicted anode capacity, the predicted cathode capacity, and the measured equilibrium voltage, via the Kalman filter module. The updated set of variables includes an updated anode capacity and an updated cathode capacity. Operation of the device is controlled based in part on the updated set of variables and the updated corresponding variance values. As the battery degrades over time and use, the method tracks the capacity of a single electrode for applications including, but not limited to, electric vehicles and hybrid vehicles.

[0004] The method includes obtaining a measured balancing voltage based on corresponding sensor data or a battery state estimator module accessible to a controller. Controlling the operation of the battery cell may include sending a message when at least one of the updated variable sets is outside a predetermined range. Controlling the operation of the battery cell may include resting the battery cell for a predetermined time when at least one of the updated variable sets is outside a predetermined range. Controlling the operation of the battery cell may include charging or discharging the battery cell and determining an updated charge capacity when at least one of the updated variable sets is outside a predetermined range. In the case where the battery cell is part of a battery pack, controlling the operation of the battery cell may include reducing the rated power of the battery pack.

[0005] The method may also include, via the controller, determining a cumulative current value based on Coulomb counting and corresponding sensor data. Determining, via the controller, a predicted anode state of charge and a predicted cathode state of charge based at least in part on the predicted anode capacity, the predicted cathode capacity, and the cumulative current value. The updated set of variables may include an updated anode state of charge and an updated cathode state of charge.

[0006] The controller may be configured to determine updated values ​​of multiple parameters of interest and corresponding associated uncertainties based on the updated set of variables and the updated corresponding variance values. For example, the controller may be configured to determine an updated full cell capacity, an updated open circuit voltage, an updated anode capacity decay rate, an updated cathode capacity decay rate, an updated negative positive ratio of electrochemical capacity, and an updated remaining active lithium factor, and their corresponding associated uncertainties.

[0007] The method allows for real-time energy optimization and long-term component health taking into account lithium loss and different aging rates of cathode and anode. By taking into account sensitivity to noise factors and their correlations, the method can be used to determine values ​​for other parameters of interest with reduced variables. In addition, the method estimates its own accuracy, which allows other modules or programs that use the updated set of variables to make better estimates and judgments.

[0008] The above features and advantages and other features and advantages of the present disclosure are readily apparent from the following detailed description of the best modes for carrying out the disclosure when taken in connection with the accompanying drawings.

[0009] The present invention also provides the following technical solutions:

[0010] 1. A method for tracking a battery cell in a device having one or more sensors and a controller with a processor and tangible non-transitory memory, the battery cell having an anode and a cathode, the method comprising:

[0011] acquiring, via the one or more sensors, corresponding sensor data about the anode and the cathode;

[0012] determining, via the controller, a predicted anode capacity and a predicted cathode capacity based on corresponding sensor data, the predicted anode capacity and the predicted cathode capacity each having a corresponding variance value;

[0013] selectively executing, via the controller, a Kalman filter module;

[0014] generating, via the Kalman filter module, an updated set of variables and updated corresponding variance values ​​based in part on the predicted anode capacity, the predicted cathode capacity, and a measured equilibrium voltage, the updated set of variables including an updated anode capacity and an updated cathode capacity; and

[0015] Operation of the battery cell is controlled, via the controller, based in part on the updated set of variables and the updated corresponding variance values.

[0016] 2. The method according to claim 1, further comprising:

[0017] determining, via the controller, a cumulative current value based on the coulomb count and the corresponding sensor data;

[0018] determining, via the controller, a predicted anode state of charge and a predicted cathode state of charge based at least in part on the predicted anode capacity, the predicted cathode capacity, and the accumulated current value; and

[0019] The updated variable set includes an updated anode state of charge and an updated cathode state of charge.

[0020] 3. The method according to claim 1, wherein:

[0021] The controller is configured to obtain the measured balancing voltage based on at least one of a battery state estimator module and corresponding sensor data; and

[0022] Controlling operation of the battery unit includes sending a message when at least one of the updated sets of variables is outside of a predetermined range.

[0023] 4. The method according to claim 1, wherein controlling the operation of the battery cell comprises:

[0024] When at least one of the updated variable sets is outside a predetermined range, the battery unit is rested for a predetermined time.

[0025] 5. The method according to claim 1, wherein controlling the operation of the battery cell comprises:

[0026] When at least one of the updated variable sets is outside a predetermined range, the battery cell is charged or discharged and an updated charge capacity is determined.

[0027] 6. The method according to scheme 1 further comprises:

[0028] An updated full battery capacity and associated uncertainty for the battery cell is determined, via the controller, based in part on the updated set of variables and the updated corresponding variance values.

[0029] 7. The method according to scheme 1 further comprises:

[0030] An updated open circuit voltage and associated uncertainty of the battery cell is determined, via the controller, based in part on the updated set of variables and the updated corresponding variance values.

[0031] 8. The method according to scheme 1 further comprises:

[0032] An updated anode capacity fade rate and an updated cathode capacity fade rate and respective associated uncertainties are determined, via the controller, based in part on the updated set of variables and the updated respective variance values.

[0033] 9. The method according to scheme 1 further comprises:

[0034] An updated proportionality of electrochemical capacity and an associated uncertainty are determined, via the controller, based in part on the updated set of variables and the updated corresponding variance values.

[0035] 10. The method according to scheme 1 further comprises:

[0036] An updated remaining active lithium factor and associated uncertainty is determined, via the controller, based in part on the updated set of variables and the updated corresponding variance values.

[0037] 11. A device comprising:

[0038] a battery cell with an anode and a cathode;

[0039] one or more sensors configured to acquire respective sensor data regarding the anode and the cathode;

[0040] a controller in communication with the one or more sensors, the controller configured to selectively execute a Kalman filter module;

[0041] The controller includes a processor and a tangible non-transitory memory, and instructions are recorded in the tangible non-transitory memory. The controller executes the instructions so that the controller:

[0042] acquiring, via the one or more sensors, corresponding sensor data about the anode and the cathode;

[0043] determining, via the controller, a predicted anode capacity and a predicted cathode capacity based on corresponding sensor data, the predicted anode capacity and the predicted cathode capacity each having a corresponding variance value;

[0044] generating, via the Kalman filter module, an updated set of variables and updated corresponding variance values ​​based in part on the predicted anode capacity, the predicted cathode capacity, and a measured equilibrium voltage, the updated set of variables including an updated anode capacity and an updated cathode capacity; and

[0045] Operation of the battery cell is controlled, via the controller, based in part on the updated set of variables and the updated corresponding variance values.

[0046] 12. The apparatus of claim 11, wherein the battery cell is part of a battery pack, and wherein:

[0047] Controlling the operation of the battery cells includes reducing the power rating of the battery pack.

[0048] 13. The apparatus of claim 11, wherein:

[0049] The controller is configured to obtain the measured balancing voltage based on at least one of a battery state estimator module and corresponding sensor data; and

[0050] Controlling operation of the battery unit includes sending a message when at least one of the updated sets of variables is outside of a predetermined range.

[0051] 14. The apparatus of claim 11, wherein controlling the operation of the battery cell comprises:

[0052] When at least one of the updated variable sets is outside a predetermined range, the battery unit is rested for a predetermined time.

[0053] 15. The apparatus of claim 11, wherein controlling the operation of the battery cell comprises:

[0054] When at least one of the updated variable sets is outside a predetermined range, the battery cell is charged or discharged and an updated charge capacity is determined.

[0055] 16. The device according to claim 11, wherein the controller is configured as:

[0056] An updated full battery capacity and associated uncertainty for the battery cell is determined, via the controller, based in part on the updated set of variables and the updated corresponding variance values.

[0057] An updated open circuit voltage and associated uncertainty of the battery cell is determined, via the controller, based in part on the updated set of variables and the updated corresponding variance values.

[0058] 17. The device according to claim 11, wherein the controller is configured as:

[0059] determining, via the controller, a cumulative current value based on the coulomb count and corresponding sensor data;

[0060] determining, via the controller, a predicted anode state of charge and a predicted cathode state of charge based at least in part on the predicted anode capacity, the predicted cathode capacity, and the accumulated current value; and

[0061] The updated variable set includes an updated anode state of charge and an updated cathode state of charge.

[0062] 18. The device according to claim 11, wherein the controller is configured as:

[0063] An updated anode capacity fade rate and an updated cathode capacity fade rate and respective associated uncertainties are determined, via the controller, based in part on the updated set of variables and the updated respective variance values.

[0064] 19. The device according to claim 11, wherein the controller is configured as:

[0065] determining, via the controller, an updated negative proportionality and associated uncertainty of an electrochemical capacity based in part on the updated set of variables and the updated corresponding variance values; and

[0066] An updated remaining active lithium factor and associated uncertainty is determined, via the controller, based in part on the updated set of variables and the updated corresponding variance values.

[0067] 20. The apparatus of claim 11, wherein the one or more sensors include:

[0068] a voltage sensor configured to generate a voltage signal representative of a voltage between the anode and the cathode during equilibrium conditions;

[0069] a current sensor configured to generate a current signal representative of a current associated with the battery cell; and

[0070] A temperature sensor is configured to generate respective temperature signals associated with the anode and the cathode. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 is a schematic diagram of a device having a rechargeable energy storage unit, a controller and / or a mobile application; and

[0072] Figure 2 yes Figure 1A schematic flow chart of a method executable by a controller and / or mobile application for tracking one or more components of a rechargeable energy storage unit. DETAILED DESCRIPTION

[0073] Referring to the drawings, wherein like reference numerals refer to like components. Figure 1 A system 10 for tracking or monitoring one or more components of a rechargeable energy storage unit 12 is schematically shown, which rechargeable energy storage unit 12 may be part of a device 14. The device 14 may be a mobile platform, such as, but not limited to, a passenger car, a sport utility vehicle, a light truck, a heavy vehicle, an all-terrain vehicle, a multi-purpose recreational vehicle, a bus, a van, a bicycle, a mobile robot, agricultural machinery (e.g., a tractor), sports-related equipment (e.g., a golf cart), a boat, an airplane, and a train. The device 14 may be a non-mobile platform, such as a desktop computer. It should be understood that the device 14 may take many different forms and have additional components.

[0074] Reference Figure 1 , the rechargeable energy storage unit 12 includes at least one battery pack, such as battery pack 16, which has a plurality of modules 18. Figure 1 , the plurality of modules 18 include at least one battery cell 20 (hereinafter "at least one") connected for current flow between a first terminal 22 and a second terminal 24. Each battery cell 20 includes an anode 26 and a cathode 28, respectively. The battery cell 20 may include single cells with different chemical properties, including but not limited to lithium-ion, lithium iron, nickel metal hydride, and lead-acid batteries. The number of single cells per module and the number of modules per battery pack may be selected according to the current application.

[0075] refer to Figure 1 The system 10 includes a controller C having at least one processor P and at least one memory M (or a non-transitory tangible computer-readable storage medium) on which is recorded a program for executing the following reference Figure 2 The memory M may store an executable instruction set, and the processor P may execute the instruction set stored in the memory M. The controller C may be an integrated part of other controllers of the device 14, or a separate module operatively connected to other controllers of the device 14.

[0076] As described below, the method 100 utilizes time-based measurements (e.g., voltage, current, and temperature) of the battery cell 20 with associated uncertainty quantification, and predicts changes in individual electrode capacity and anode state of charge and cathode state of charge based on cumulative current values. The system 10 is configured to calculate a balance voltage based on the anode state of charge and cathode state of charge, and compare the calculated balance voltage to the measured balance voltage.

[0077] The method 100 may utilize a Kalman filter module 150 available to those skilled in the art and selectively executable by the controller C (see Figure 1 ) to track the respective capacity and the respective state of charge (SOC) of each electrode (i.e., anode 26 and cathode 28). Over the life of the battery cell 20, the respective cathode and anode capacities may decay at different rates, thereby reshaping the open circuit voltage (OCV) curve of the battery cell 20. By tracking the individual electrode capacities, the system 10 provides a more detailed view of the health state of the battery cell 20. Once the individual electrode capacities and the individual electrode state of charge have been determined, other parameters of interest can be calculated. Reference Figure 1 , the controller C may be configured to access and / or execute a battery state estimator module 160 available to those skilled in the art. The Kalman filter module 150 and the battery state estimator module 160 may be embedded in the controller C or located remotely (e.g., in the cloud 52) and otherwise accessible to the controller C.

[0078] The controller C is specifically configured to execute the blocks of the method 100 and may receive input from one or more sensors S, such as a first sensor 30, a second sensor 32, and a third sensor 34, configured to acquire corresponding sensor data about the battery cell 20. The first sensor 30 may be a voltage sensor configured to generate a voltage signal representing a voltage between the anode 26 and the cathode 28 during a balanced condition. The second sensor 32 may be a current sensor configured to generate a current signal representing a current associated with the battery cell 20. The third sensor 34 may be a temperature sensor configured to generate a corresponding temperature signal associated with the anode 26 and the cathode 28.

[0079] refer to Figure 1 , the module management unit 36 ​​may be embedded in each of the plurality of modules 18 and configured to collect and / or measure parameters such as single cell voltage, current and temperature. The sensor S may also include group sensors 40, 42, 44 configured to measure parameters related to the battery pack 16. The sensor S and the module management unit 36 ​​may communicate with the controller C via a communication bus 46, which may be in the form of a serial controller area network (CAN-BUS).

[0080] refer to Figure 1 , the controller C may be configured to communicate with a remote server 50 and / or a cloud unit 52 via a wireless network 54, which may be a short-range network or a long-range network. The remote server 50 may be a private or public information source maintained by an organization such as, for example, a research institution or a company. The cloud unit 52 may include one or more servers hosted on the Internet to store, manage, and process data.

[0081] The controller C can be configured via the mobile application 56 ( Figure 1 ) receives and transmits wireless communications to a remote server 50. Figure 1 , the mobile application 56 may include a communication interface that enables vehicle-to-vehicle (V2V), vehicle-to-everything (V2X), and other types of communications, such as V2I, V2N, V2V, V2P, V2D, and V2G. The mobile application 56 may communicate with the controller C so that it can access data in the controller C. For example, the mobile application 56 may be embedded in a smartphone belonging to a user of the device 14 and inserted or otherwise linked to the device 14. The mobile application 56 may be physically (e.g., wired) connected to the controller C. Alternatively, the mobile application 56 may be embedded in the controller C. Circuits and components of the mobile application 56 ("app") available to those skilled in the art may be employed.

[0082] The wireless network 54 may be a wireless local area network (LAN) that links multiple devices using a wireless distribution method, a wireless metropolitan area network (MAN) that connects several wireless LANs, or a wireless wide area network (WAN) that covers a large area such as neighboring villages and towns. The wireless network 54 may be WIFI or Bluetooth. TM Connectivity, defined as a short-range radio technology (or wireless technology) designed to simplify communication between Internet devices and between devices and the Internet. Bluetooth TM Is an open wireless technology standard for transmitting data between fixed and mobile electronic devices over short distances and creating personal networks operating in the 2.4 GHz frequency band. Other types of connections may be used.

[0083] Reference now Figure 2 , which shows a flow chart of method 100. Method 100 may be stored in Figure 1 The method 100 is performed on and by at least one of the controller C and the mobile application 56. The method 100 need not be applied in the specific order described herein and may be performed dynamically. In addition, it should be understood that some steps may be omitted. As used herein, the terms "dynamic" and "dynamically" describe steps or processes that are performed in real time and are characterized in that the state of the parameter is determined by monitoring or otherwise and the state of the parameter is regularly or periodically updated during the execution of the routine or between iterations of the routine execution. Although the method 100 is described below for the controller C, it should be understood that the method 100 may be performed by the mobile application 56.

[0084] according to Figure 2At block 102 , the controller C is configured to acquire corresponding sensor data about the anode 26 and the cathode 28 via one or more sensors S. Although the method 100 is described herein for one battery cell 20 , it should be understood that the method 100 may be performed for each of the battery cells in the battery pack 16 .

[0085] according to Figure 2 In block 104, the controller C is configured to determine at time n-1 The predicted anode capacity at (or update column n- 1 ).according to Figure 2 At block 106, the controller C is configured to determine a predicted cathode capacity. The cathode capacity may be defined as:

[0086] In time n The (proportional) reciprocal of the predicted cathode capacity.

[0087] In time n The (proportional) reciprocal of the predicted anode capacity.

[0088] In one example, Cap ref Set the value of to 1.

[0089] According to block 108, the controller C is configured to obtain a measured equilibrium voltage, which may be obtained from a sensor S or Figure 1 The battery state estimator module 160 obtains (as determined by Figure 2 The measured balancing voltage signal may come either directly from the corresponding sensor data (via the voltage sensor) when the battery cells 20 have rested for a sufficient period of time (this condition is enforced by some controller logic), or it may come continuously from the battery state estimator module 160. The battery state estimator module 160 may employ its own Kalman filter processing to estimate what the balancing voltage is, even when the battery cells 20 are in use.

[0090] Also, according to blocks 104 and 106, method 100 may include determining a predicted anode state of charge based at least in part on the predicted anode capacity, the predicted cathode capacity factor, and the accumulated current value. and predicting cathode state of charge The accumulated current value or (proportional) coulomb count can be expressed as: The ampere-hour integral between the rest voltage a , ampere-hour throughput integral a t , and the integral of the square current a 2t , which can be described as follows: , as well as , where I is the measured current, Indicates from time t n-1 Time t n All time steps The sign convention used here is that charging current is positive and discharging current is negative. Therefore, if there is charging, then a >0, and if we discharge, then a < 0. It will be appreciated that alternative approaches may be employed.

[0091] according to Figure 2 In block 110, the controller C is configured to utilize Figure 1 A Kalman filter module 150 is provided to process data from boxes 104, 106, and 108 and generate an updated set of variables and updated corresponding variance values. The Kalman filter module 150 operates in a recursive manner and runs in real time using the current state, the previously calculated state, and its uncertainty matrix. The Kalman filter module can work in two steps. For example, in the first step, the Kalman filter module 150 generates an estimate of the current state variable and its uncertainty. Once the result of the next measurement is observed (with a discrete amount of error, such as random noise), these estimates are updated using a weighted average, and a larger weight is given to the estimate with a higher certainty. The Kalman filter module 150 can incorporate multiple types of Kalman filters, such as but not limited to an extended Kalman filter, a Sigma Point Kalman filter, a square-root Kalman filter, and other variants.

[0092] according to Figure 2 At block 120 of the method 100, the method 100 includes controlling the operation of the battery unit 20 based in part on the updated set of variables and the updated corresponding variance values ​​output from block 110. For example, the controller C may be configured to transmit a message or an alert when at least one of the updated set of variables is outside a first predetermined range. The message may be sent to a user, owner, or fleet manager of the device 14 via the mobile application 56 to schedule a maintenance check or follow-up. The alert may be sent to the remote server 50 via the wireless network 54 and / or the mobile application 56.

[0093] The controller C may be configured to manage the charging and discharging cycles of the battery pack 16 with improved efficiency based on the updated set of variables. Controlling the operation of the battery unit 20 may include resting the battery unit 20 for a predetermined time when at least one of the updated set of variables is outside the second predetermined range. The term "rest" includes a stagnant state in which the battery unit 20 is neither charged nor discharged. Controlling the operation of the battery unit 20 may include charging or discharging the battery unit 20 and determining an updated charge capacity when at least one of the updated set of variables is outside the third predetermined range.

[0094] The controller C may be configured to reduce or decrease the power rating of the battery pack 16 when at least one of the updated set of variables is outside of a fourth predetermined range. The first, second, third, and fourth predetermined ranges may be different or may overlap. If the total power rating of the rechargeable energy storage unit 12 is sufficiently reduced (i.e., reaches a predetermined minimum value), the controller C may be configured to switch to an alternative operating mode, which may be a "limp-home" mode or other mode that limits the energy consumption and / or speed of the device 14.

[0095] The operation of the battery cell 20 may also be controlled based on the results obtained from blocks 112, 114, 116, and 118, as indicated by lines 111, 113, 115, and 117, respectively. According to block 112, the method 100 may include determining an updated anode capacity decay rate and an updated cathode capacity decay rate. According to block 114, the method 100 may include determining an updated full battery capacity. According to block 116, the method 100 may include determining updated values ​​of other parameters of interest based on the updated set of variables and the updated corresponding variance values.

[0096] For example, as described below, the controller C may be configured to determine an updated open circuit voltage, an updated negative positive ratio of electrochemical capacity (ratio of N to P), and an updated remaining active lithium factor. Additionally, the method 100 may include determining corresponding uncertainty values ​​associated with these parameters. For corresponding uncertainty values ​​associated with the parameters of interest, a linear approximation may be used to calculate the Kalman filter module 150. According to Figure 2 The outputs of blocks 114 and 116 may be fed to a battery state estimator module 160 accessible to controller C.

[0097] Kalman filter update

[0098] The Kalman filter module 150 is executed to update the state vector x n-1 To obtain the updated state vector x n The state transfer equation can be expressed as follows:

[0099]

[0100] The updated state vector can be expressed as follows:

[0101] Noise Vector It can be defined as: , modeled as a zero-mean random distribution with covariance given as a square matrix The noise vector is used to accommodate various unknown factors in the process, such as sensor errors in measuring current, timing errors, and electrochemical decay processes. These unknown factors either change the capacity of one or both electrodes and / or result in accumulation of errors in the coulomb count of how much lithium migrates from one electrode to the other.

[0102] Although the exact value of the noise input at the time step is unknown, it is assumed to have a usable covariance. As will be appreciated by those skilled in the art, a covariance matrix is ​​a square matrix whose diagonal elements are the variances of a single element in a given random vector, and whose off-diagonal elements are the covariances between pairs of elements of that vector. As an example, the covariance matrix is It can be considered as a block diagonal matrix and can be calibrated with respect to five parameters , , , r q , r 1 The expression is as follows:

[0103]

[0104] The covariance matrix can be used other components. The corresponding variance value of can be characterized by .

[0105] Anode and cathode degradation can be correlated, and the random walk tracks the degradation. r q = 1, the two decays are perfectly correlated, meaning that the capacities of the two electrodes develop in lockstep (although active lithium may still be lost), whereas for r q <1, the capacity of the two electrodes can decay independently to some extent. r I = 1, the errors in the updates of the two SOCs will be perfectly correlated, as if the errors were due only to coulomb counting. r 1A value of <1 indicates that some unrelated behavior may occur, such as loss of active lithium, so that the amount of lithium deintercalated from one electrode is not exactly equal to the amount of lithium intercalated into the other electrode. G The minus sign in refers to the fact that when one electrode gains lithium, the other electrode loses lithium.

[0106] The state transfer equation can be expressed in matrix form as follows: ,in:

[0107] ,and .

[0108] Measure the equilibrium voltage ( EV n ) or rest voltage based at least in part on a predicted state of charge ( S pos,n ), predict cathode state of charge ( S neg,n ) and measurement noise variables , which can be expressed as a Gaussian distribution:

[0109]

[0110] Here, is a zero-mean random variable with the corresponding variance Here, the measured voltage is not directly converted into the state of charge (SOC). The open circuit voltage curve relates the state of charge of the battery cell 20 to the open circuit voltage, such that: Open circuit voltage = U(SOC) If the equilibrium voltage ( EV n )From Figure 1 The battery state estimator module 160, the battery voltage estimator module 160 can also provide .if EV n from the sensor S after the device 14 has rested, then The voltage may be adjusted based on how long the device 14 has been at rest or how much the voltage has changed over the previous length of time (e.g., the last 10 minutes). Because the battery cell 20 may require some time to reach full balance, readings taken with the battery cell 20 resting for a shorter length of time (e.g., 20 or 30 minutes) may still provide information, but may provide less certainty as to where the measured balance voltage will ultimately stabilize. In other words, The value of can vary and can be assigned to controller C in a meaningful way depending on the operating conditions.

[0111] Measuring noise variables The covariance of is a scalar. The corresponding variance values ​​represent the sensor errors as well as errors from hysteresis, diffusion, and other sources. According to the first-order approximation, the measurement matrix H is:

[0112]

[0113] The Kalman filter module 150 may be configured to incorporate the following equations:

[0114]

[0115] exist n th At the update, the state variables are:

[0116]

[0117] The covariance matrix is n th Updated at .

[0118] Update anode and cathode capacity decay rates

[0119] In another variation, the Kalman filter module 150 may be configured to determine the respective decay rates of the anode 26 and the cathode 28 as follows:

[0120] ,in is the ampere-hour throughput, and γ is an empirically determined exponent. p It can be assumed to follow a random walk such that: ,in is with variance A zero-mean random variable. Other decay estimates can be used. The state vector can be modified as:

[0121]

[0122] In the noise covariance matrix After expanding the specification of from 4 × 4 to 6 × 6 to include two new noise variables, the unknown variation in the decay rate of the positive electrode and the similar unknown variation in the decay rate of the negative electrode, and expanding the definitions of the matrices F and G to include the new state element p pos and p neg After the state transfer equation of , as those skilled in the art will recognize, the Kalman filter equation remains in the same form as above.

[0123] Estimation of quantities as a function of state

[0124] Differentiable scalar function of state The variance of can be approximated as:

[0125]

[0126] Using this principle, formulas for calculating the functions of the various states and approximating the uncertainty in their estimation are given below. The quantities so treated are anode capacity, cathode capacity, negative-to-positive capacity ratio, open circuit voltage, full cell capacity, and lithium retention.

[0127] Anode and cathode capacity

[0128] The capacity of a single electrode can be defined as q pos,n and q neg,n The corresponding inverse of . However, the variance needs to be linearized because taking the inverse is a nonlinear transformation. Cap pos,n The variance in the estimate of t n The positive electrode capacity at can be approximated as

[0129] ,in: .

[0130] Similarly, in Cap neg,n The variance in the estimate of can be approximated as

[0131] ,in: .

[0132] Updated negative positive ratio

[0133] Updated negative positive ratio (or N / P ratio, R NP,n ) can be defined as the ratio of the anode capacity to the cathode capacity Based on the linear approximation, the corresponding variance value of the negative positive ratio is:

[0134] ,in:

[0135] Updated open circuit voltage

[0136] The open circuit voltage of the battery cell 20, referred to herein as full battery OCV, can be obtained with respect to the state of charge of the cathode as:

[0137]

[0138] The associated uncertainty of the open circuit voltage of the battery cell 20 is based on the SOC and can be specified as: .

[0139] Here:

[0140] Factor exist Evaluate at.

[0141] Updated full battery capacity

[0142] The controller C may be configured to obtain an updated full battery capacity of the battery cell 20 by solving a nonlinear system of equations satisfying the following boundary conditions, such as when SOC =0, OCV = V 0 When, and when SOC =1, OCV = V 1 As described below, the state of charge of the battery cell 20 can be obtained using Newton's method (or other root-finding methods). y 0 ,when SOC = 0 when the charge state of the cathode 28 is determined as:

[0143]

[0144] It can be rewritten as:

[0145] .

[0146] Here, x n is a current estimate, available are:

[0147] . Similarly, at the charge vertex, we make:

[0148]

[0149] In the corresponding y 0 and y 1 After solving these, the corresponding anode state of charge can be calculated as: . y 0 , y 1 , x 0 , x 1 After solving, the single battery capacity can be calculated as follows:

[0150]

[0151] Alternatively, replace the full battery voltage V 1 The charge vertex can be defined by y 1 Fixed to a constant value throughout the life cycle to redefine the charge vertex, in this case, Cap n The formula remains the same, but y 1 Specifies a constant.

[0152] Battery capacity variance

[0153] In order to determine the associated uncertainty of the battery capacity, the controller C is configured to first determine the covariance of the three quantities used to calculate the uncertainty, namely y 0,n , y 1,n , q pos,n .make , where A( y ) is the above A (s pos ) Given a 1×4 array of partial derivatives, where In addition, let .Then,

[0154] .

[0155] Next, due to y 1 < y 0 , the partial derivative array of capacity is

[0156] .

[0157] This yields the final result: .

[0158] In the above case, due to y 1 is pre-specified, so y 1 There is no uncertainty in . This simplifies the above formula: from C The formula is deleted B 1 And in calculation Before Delete D The middle element of .

[0159] Updated lithium retention

[0160] The controller C may be configured to determine the updated remaining active lithium factor as follows : .

[0161] The following linear approximation can be used to calculate the associated uncertainty:

[0162] ,in: .

[0163] In summary, the controller C can be configured to determine estimated individual electrode capacities and state of charge, as well as their uncertainties, to quantify the uncertainty in full battery capacity, battery open circuit voltage, negative-to-positive ratio, and loss of active lithium. The controller C can be configured to accurately reconstruct the open circuit voltage of an aged battery, improve the regression of other parameters (such as state of charge). The method 100 allows for estimation and covariance estimation of derived quantities, which helps make decisions about battery control, management, and maintenance. Accordingly, execution of the method 100 (via the controller C) improves the functionality of the device 14.

[0164] Controller C includes computer-readable media (also referred to as processor-readable media), including non-temporary (e.g., tangible) media that participate in providing data (e.g., instructions) that can be read by a computer (e.g., by a processor or computer). Such media can take a variety of forms, including, but not limited to, non-volatile media and volatile media. Non-volatile media can include, for example, optical or magnetic disks and other permanent memories. Volatile media can include, for example, dynamic random access memory (DRAM), which can constitute a main memory. Such instructions can be transmitted by one or more transmission media, including coaxial cables, copper wires, and optical fibers, including lines containing system buses connected to processors of computers. Some forms of computer-readable media include, for example, floppy disks, magnetic disks, hard disks, tapes, other magnetic media, CD-ROMs, DVDs, other optical media, and physical media with hole patterns, RAMs, PROMs, EPROMs, FLASH-EEPROMs, other memory chips or cartridges, or other media from which other computers can read.

[0165] The lookup tables, databases, data repositories, or other data storage described herein may include various types of mechanisms for storing, accessing, and retrieving various types of data, including hierarchical databases, file collections in a rechargeable energy storage system, application databases in proprietary formats, relational database energy management systems (RDBMS), and the like. Each such data storage may be included in a computing device (such as one of those mentioned above) using a computer operating system and may be accessed via a network in one or more of a variety of ways. The file system may be accessible from a computer operating rechargeable energy storage system and may include files stored in a variety of formats. In addition to the language used to create, store, edit, and execute stored procedures, the RDBMS may utilize a structured query language (SQL), such as the PL / SQL language mentioned above.

[0166] Figure 2 The flowcharts in illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each box in a flowchart or block diagram may represent a module, fragment, or portion of code, which includes one or more executable instructions for implementing a specified logical function. It should also be noted that each box in the block diagram and / or flowchart, as well as the combination of boxes in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based rechargeable energy storage system that performs a specified function or action, or a combination of dedicated hardware and computer instructions. These computer program instructions may also be stored in a computer-readable medium that may instruct a controller or other programmable data processing device to act in a specific manner so that the instructions stored in the computer-readable medium produce an article including instructions to implement the functions / actions specified in the flowchart and / or block diagram blocks.

[0167] The terms "calibration," "calibrated," and related terms refer to the result or process of comparing an actual or standard measurement associated with a device or system to a perceived or observed measurement or commanded position about the device or system. Calibration as described herein may be reduced to a storable parameter table, a plurality of executable equations, or another suitable form that may be used as part of a measurement or control routine. A parameter is defined as a measurable quantity that represents a physical characteristic of a device or other element discernible using one or more sensors and / or a physical model. A parameter may have discrete values, e.g., "1" or "0," or may be infinitely variable in value.

[0168] The detailed description and drawings or figures are supportive and descriptive of the present disclosure, but the scope of the present disclosure is limited only by the claims. Although some of the best modes and other embodiments for implementing the claimed disclosure have been described in detail, there are multiple alternative designs and embodiments for implementing the disclosure defined in the appended claims. In addition, the embodiments shown in the drawings or the features of the various embodiments mentioned in the specification do not have to be understood as independent embodiments of each other. On the contrary, each of the features described in one example of the embodiment can be combined with one or more other desired features from other embodiments to obtain other embodiments that are not described in words or described with reference to the drawings. Accordingly, such other embodiments fall within the framework of the scope of the appended claims.

Claims

1. A method for tracking a battery cell in a device having one or more sensors and a controller with a processor and tangible non-transitory memory, the battery cell having an anode and a cathode, the method comprising: acquiring, via the one or more sensors, corresponding sensor data about the anode and the cathode; determining, via the controller, a predicted anode capacity and a predicted cathode capacity based on corresponding sensor data, the predicted anode capacity and the predicted cathode capacity each having a corresponding variance value; selectively executing, via the controller, a Kalman filter module; generating, via the Kalman filter module, an updated set of variables and updated corresponding variance values ​​based in part on the predicted anode capacity, the predicted cathode capacity, and a measured equilibrium voltage, the updated set of variables including an updated anode capacity and an updated cathode capacity; as well as controlling, via the controller, operation of the battery cell based in part on the updated set of variables and the updated corresponding variance values, determining, via the controller, an updated anode capacity decay rate and an updated cathode capacity decay rate and corresponding associated uncertainties based in part on the updated set of variables and the updated corresponding variance values, The Kalman filter module determines the updated anode capacity decay rate and the updated cathode capacity decay rate in the following manner: ， in, is the ampere-hour throughput, γ is an empirically determined exponent, The decay rate coefficient p is assumed to follow a random walk such that ,in is with variance is a zero-mean random variable.

2. The method according to claim 1, further comprising: determining, via the controller, a cumulative current value based on the coulomb count and the corresponding sensor data; determining, via the controller, a predicted anode state of charge and a predicted cathode state of charge based at least in part on the predicted anode capacity, the predicted cathode capacity, and the accumulated current value; and The updated variable set includes an updated anode state of charge and an updated cathode state of charge.

3. The method according to claim 1, wherein: The controller is configured to obtain the measured balancing voltage based on at least one of a battery state estimator module and corresponding sensor data; and Controlling operation of the battery unit includes sending a message when at least one of the updated sets of variables is outside of a predetermined range.

4. The method according to claim 1, wherein: Controlling the operation of the battery unit includes: When at least one of the updated variable sets is outside a predetermined range, the battery unit is rested for a predetermined time.

5. The method according to claim 1, wherein: Controlling the operation of the battery unit includes: When at least one of the updated variable sets is outside a predetermined range, the battery cell is charged or discharged and an updated charge capacity is determined.

6. The method according to claim 1, further comprising: An updated full battery capacity and associated uncertainty for the battery cell is determined, via the controller, based in part on the updated set of variables and the updated corresponding variance values.

7. The method according to claim 1, further comprising: An updated open circuit voltage and associated uncertainty of the battery cell is determined, via the controller, based in part on the updated set of variables and the updated corresponding variance values.

8. The method according to claim 1, further comprising: An updated proportionality of electrochemical capacity and an associated uncertainty are determined, via the controller, based in part on the updated set of variables and the updated corresponding variance values.

9. The method according to claim 1, further comprising: An updated remaining active lithium factor and associated uncertainty is determined, via the controller, based in part on the updated set of variables and the updated corresponding variance values.

10. A device comprising: a battery cell with an anode and a cathode; one or more sensors configured to acquire respective sensor data regarding the anode and the cathode; a controller in communication with the one or more sensors, the controller configured to selectively execute a Kalman filter module; The controller includes a processor and a tangible non-transitory memory, and instructions are recorded in the tangible non-transitory memory. The controller executes the instructions so that the controller: acquiring, via the one or more sensors, corresponding sensor data about the anode and the cathode; determining, via the controller, a predicted anode capacity and a predicted cathode capacity based on corresponding sensor data, the predicted anode capacity and the predicted cathode capacity each having a corresponding variance value; generating, via the Kalman filter module, an updated set of variables and updated corresponding variance values ​​based in part on the predicted anode capacity, the predicted cathode capacity, and a measured equilibrium voltage, the updated set of variables including an updated anode capacity and an updated cathode capacity; and controlling, via the controller, operation of the battery cell based in part on the updated set of variables and the updated corresponding variance values, Wherein, the controller is configured as follows: determining, via the controller, an updated anode capacity decay rate and an updated cathode capacity decay rate and corresponding associated uncertainties based in part on the updated set of variables and the updated corresponding variance values, The Kalman filter module determines the updated anode capacity decay rate and the updated cathode capacity decay rate in the following manner: ， in, is the ampere-hour throughput, γ is an empirically determined exponent, The decay rate coefficient p is assumed to follow a random walk such that ,in is with variance is a zero-mean random variable.

11. The device according to claim 10, wherein: The battery cell is part of a battery pack, and wherein: Controlling the operation of the battery cells includes reducing the power rating of the battery pack.

12. The apparatus of claim 10, wherein: The controller is configured to obtain the measured balancing voltage based on at least one of a battery state estimator module and corresponding sensor data; and Controlling operation of the battery unit includes sending a message when at least one of the updated sets of variables is outside of a predetermined range.

13. The apparatus according to claim 10, wherein: Controlling the operation of the battery unit includes: When at least one of the updated variable sets is outside a predetermined range, the battery unit is rested for a predetermined time.

14. The apparatus according to claim 10, wherein: Controlling the operation of the battery unit includes: When at least one of the updated variable sets is outside a predetermined range, the battery cell is charged or discharged and an updated charge capacity is determined.

15. The apparatus according to claim 10, wherein: The controller is configured as follows: determining, via the controller, an updated full battery capacity and associated uncertainty for the battery cell based in part on the updated set of variables and the updated corresponding variance values; An updated open circuit voltage and associated uncertainty of the battery cell is determined, via the controller, based in part on the updated set of variables and the updated corresponding variance values.

16. The apparatus according to claim 10, wherein: The controller is configured as follows: determining, via the controller, a cumulative current value based on the coulomb count and corresponding sensor data; determining, via the controller, a predicted anode state of charge and a predicted cathode state of charge based at least in part on the predicted anode capacity, the predicted cathode capacity, and the accumulated current value; and The updated variable set includes an updated anode state of charge and an updated cathode state of charge.

17. The apparatus according to claim 10, wherein: The controller is configured as follows: determining, via the controller, an updated negative proportionality and associated uncertainty of an electrochemical capacity based in part on the updated set of variables and the updated corresponding variance value; and An updated remaining active lithium factor and associated uncertainty is determined, via the controller, based in part on the updated set of variables and the updated corresponding variance values.

18. The apparatus according to claim 10, wherein: The one or more sensors include: a voltage sensor configured to generate a voltage signal representative of a voltage between the anode and the cathode during equilibrium conditions; a current sensor configured to generate a current signal representative of a current associated with the battery cell; and A temperature sensor is configured to generate respective temperature signals associated with the anode and the cathode.

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