Real-time battery fault detection and health status monitoring

Abstract

To provide a method for automatically monitoring various aspects of battery health and operation.SOLUTION: The present invention pertains to a method for monitoring the state of a battery cell. The method includes a step of determining a current value of a state-of-health parameter of one or more battery cells, a step of measuring the values of a plurality of operation state parameters of the battery cells, a step of calculating an optimistic potential on the basis of an optimistic value corresponding to a state of health better than the current value, a step of calculating a pessimistic potential on the basis of a pessimistic value corresponding to a state of health worse than the current value, a step of determining whether or not an actual potential of the battery cell exists in an envelope defined by the optimistic potential and the pessimistic potential, and a step of generating a model fault notification when the actual potential does not actually exist in the envelope.SELECTED DRAWING: Figure 4

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Application No. 62 / 988,853, filed March 12, 2020, and U.S. Patent Application No. 17 / 196,848, filed March 9, 2021, the disclosures of which are incorporated herein by reference.

[0002] FIELD OF THE DISCLOSURE The present disclosure relates generally to battery monitoring, and more particularly to systems and methods for real-time monitoring of battery health and performance. [Background technology]

[0003] A battery is an electrochemical device that can convert stored chemical energy into electrical energy. Many examples of battery technologies are known in the art, including lithium ion, nickel metal hydride, lead acid, nickel cadmium, alkaline, etc. Batteries are made in many sizes and with a variety of operating characteristics (e.g., voltage (or potential), maximum current, charge capacity, etc.). To accommodate higher voltages and higher charge capacities, multiple battery cells can be electrically connected in series and / or parallel to create a battery pack. Depending on the technology, some types of batteries can be recharged by connecting them to a source of charging current.

[0004] Batteries, particularly lithium-ion batteries, are used in a wide variety of applications, including as portable power sources to drive motors in vehicles such as automobiles, aircraft, and ships. In some cases, a battery or battery pack may be the only power source for a vehicle. A vehicle that is powered solely by battery power may suddenly lose its driving power if the battery fails. Depending on the state of the vehicle when the battery fails, the consequences may range from inconvenient to disastrous. It may therefore be desirable to monitor battery performance and detect conditions that indicate a problem so that repair or replacement can be performed before the battery fails. Summary of the Invention

[0005] Described herein are examples (or embodiments) of battery monitoring systems and methods that can provide real-time, automated monitoring of various aspects of a battery's health and operation. In various embodiments, different aspects of a battery's health and operation can be monitored. For example, the battery monitoring system can use an equivalent cell circuit model to predict in real time a range (or "envelope") that defines the expected behavior of a battery cell (e.g., the cell's potential or voltage) under actual operating conditions (e.g., a particular load current or charging current at a particular temperature). The predicted value can be compared to the actual behavior of the cell (e.g., the cell's measured potential) to determine whether a potential problem exists, referred to herein as a "model failure" condition. As another example, the battery monitoring system can maintain estimates of the battery's state-of-health parameters, such as charge capacity and internal resistance, which are updated in real time while the battery is being discharged and / or charged, and abnormal fluctuations in the state-of-health parameters can suggest a "suspect parameter" failure. The battery monitoring system can notify of detected failures in real time, allowing for rapid corrective action.

[0006] According to some embodiments, a method for monitoring the charge capacity of a battery cell may include determining an initial state of charge of the battery cell while the battery cell is in an idle state, then monitoring a total amount of charge transferred to or from the battery cell while the battery cell is in an active state (e.g., a discharging state in which charge is transferred from the battery cell to a load, or a charging state in which charge is transferred to the battery cell from an external power source), determining a final state of charge of the battery cell after the battery cell returns to the idle state, calculating an unfiltered charge capacity value using the initial state of charge, the final state of charge, and the total amount of charge transferred, and updating a charge capacity estimate using the unfiltered charge capacity value.

[0007] In some embodiments, the magnitude of the change in charge capacity estimate relative to the previous estimate may be calculated and if the magnitude of the change in charge capacity exceeds a threshold, a cell capacity fault notification may be generated.

[0008] In some embodiments, determining the initial state of charge may include measuring an initial cell potential and an initial cell temperature of the battery cell while the battery cell is in an initial idle state, and calculating the state of charge of the battery cell based on an equivalent cell circuit model using the initial cell potential and the initial cell temperature.

[0009] In some embodiments, determining the final state of charge may include measuring a final cell potential and a final cell temperature of the battery cell when the battery cell returns to an idle state, and calculating the state of charge of the battery cell based on an equivalent cell circuit model using the final cell potential and the final cell temperature.

[0010] In some embodiments, monitoring the total amount of charge transferred while the battery cell is in an active state includes measuring the current through the battery cell at regular time intervals and adding the product of the measured current and a time step defined by the regular time interval to a cumulative value of the charge transferred.

[0011] In some embodiments, updating the charge capacity estimate may include applying an infinite impulse response filter to the unfiltered charge capacity value and the previously stored charge capacity estimate.

[0012] According to some embodiments, a method for monitoring the internal resistance of a battery cell may include, in response to detecting a transition of the battery cell from an idle state to an active state (which may be, for example, a charging state or a discharging state), initializing a running estimate of the internal resistance using a stored value; measuring the potential, current, and temperature of the battery cell while the battery cell is in the active state; iteratively updating the running estimate of the internal resistance based on the measured potential, current, and temperature; in response to detecting a transition of the battery cell from an active state to an idle state, calculating a change in the internal resistance based on the stored value and a final value of the running estimate; and updating the stored value using the final value of the running estimate.

[0013] In some embodiments, if the change in internal resistance exceeds a threshold, a cell resistance fault notification may be generated.

[0014] In some embodiments, iteratively updating the running estimate of the internal resistance based on the measured potential, current, and temperature may include determining whether the measured potential, current, and temperature are within a predetermined valid range for each iterative update. If the measured potential, current, and temperature are within a predetermined valid range, a raw estimate of the internal resistance may be calculated based on an equivalent cell circuit model, and the running estimate may be updated using the raw estimate and the previous running estimate from the immediately preceding time step. In some embodiments, the raw estimate is compared to a range of plausible values ​​and used to update the running estimate only if the raw estimate is within a range of plausible values. If one or more of the measured potential, current, or temperature are not within the predetermined valid range, the method may include waiting for the next time step without updating the running estimate.

[0015] In some embodiments, updating the running estimate may include applying an infinite impulse response filter to the raw estimate and the previous running estimate.

[0016] According to some embodiments, a method for monitoring a state of a battery cell may include determining a current value of one or more state of health parameters of the battery cell (e.g., an internal resistance of the battery cell, a charge capacity of the battery cell); measuring values ​​of a number of operational state parameters of the battery cell (e.g., a current through the battery cell, a temperature of the battery cell), including measuring an actual potential of the battery cell; calculating an optimistic potential based on a cell state model (e.g., an equivalent cell circuit model), measurements of a first subset of the operational state parameters, and optimistic values ​​of the one or more state of health parameters corresponding to a better state of health than the current values; calculating a pessimistic potential based on the cell state model, measurements of the first subset of the operational state parameters, and pessimistic values ​​of the one or more state of health parameters corresponding to a worse state of health than the current values; determining whether the actual potential of the battery cell is substantially within an envelope defined by the optimistic potential and the pessimistic potential; and generating a model fault notification if the actual potential is not substantially within the envelope. In various embodiments, the method can be performed in real-time while the battery cells are powering a load and / or while the battery cells are charging. The model fault notification can be generated in real-time while the battery is actively being used (powering a load and / or charging). In some embodiments, the predicted potential of the battery cells can also be calculated based on the cell state model, measurements of the first subset of operating state parameters, and current values ​​of one or more state-of-health parameters.

[0017] In some embodiments, estimates of the charge capacity and / or internal resistance of the battery cells can be determined while the battery is in active use in accordance with the techniques described herein, and these estimates can be used in condition monitoring methods.

[0018] Any or all of the above or other methods, or any combination thereof, may be implemented in a real-time battery monitoring system (using hardware, software / firmware, or any combination thereof) that can operate whether or not the battery is being actively used, including while the battery is powering a load and / or while the battery is charging. In some embodiments, the battery monitoring system can generate an alert or take other action based on the results of any of the above or other methods.

[0019] The following detailed description, taken together with the accompanying drawings, provides a better understanding of the nature and advantages of the claimed invention. [Brief description of the drawings]

[0020] [Figure 1] FIG. 1 illustrates a high-level block diagram of an operating environment for battery monitoring according to some embodiments. [Diagram 2] FIG. 1 illustrates a simplified schematic diagram of a battery bank according to some embodiments. [Diagram 3] 1 illustrates an example of a battery monitoring system according to some embodiments. [Figure 4] FIG. 2 is a flow diagram of a process for modeling cell behavior and detecting model faults, according to some embodiments. [Diagram 5] 1 shows an example plot of cell potential as a function of time for a battery cell used to power an aircraft, according to some embodiments. [Figure 6] FIG. 2 is a flow diagram of a process for estimating a charge capacity of a cell based on a discharge event, according to some embodiments. [Figure 7] FIG. 2 is a flow diagram of a process for estimating the internal resistance of a cell based on a discharge event, according to some embodiments. [Figure 8] FIG. 1 is a flow diagram of a process for iteratively updating a running estimate of internal resistance, according to some embodiments. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0021] The following description of exemplary embodiments of the invention is presented for purposes of illustration and description. It is not intended to be exhaustive or to limit the claimed invention to the precise form described, as those skilled in the art will recognize that many modifications and variations are possible. The embodiments have been selected and described to best explain the principles of the invention and its practical application, thereby enabling those skilled in the art to best utilize the invention in various embodiments and with various modifications suited to the particular applications contemplated.

[0022] System Overview FIG. 1 illustrates a high-level block diagram of an operating environment 100 for battery monitoring according to some embodiments. The environment 100 may be, for example, an electric vehicle, such as an aircraft, a ship, a train, an automobile, a truck, an off-road vehicle, etc. The environment 100 includes a battery 102 that powers an electric load 104. The battery 102 may be any type of battery, including lithium-ion, lead-acid, nickel-metal hydride, etc. The battery 102 may be implemented as a single battery cell or as a battery pack including multiple battery cells connected together in series and / or parallel as needed. (As used herein, the term "battery cell" or "cell" may be understood to include a stand-alone battery, or in the case of a battery pack, one of several independent replaceable battery units in the battery pack.) The electric load 104 may include, for example, a motor (or engine) of the vehicle, or any other battery-powered mechanism or device. The electric load 104 may draw different amounts of power from the battery 102 at different times. For example, a vehicle's motor may consume more power when the vehicle is accelerating than when the vehicle is stationary or moving at a constant speed.

[0023] The control system 106 communicates with the load 104. For example, the control system 106 can send commands to the load 104, for example, to increase or decrease a motor speed, or to enable, disable, or change the operating state of any power consuming components. The control system 106 can also receive feedback from the load 104 indicating its operating state, possible abnormal conditions that may occur, and the like. The control system 106 can also include human interface components, such as a display screen, indicator lights, speakers, human operable controls (e.g., keyboard, mouse, touch screen or touch pad, joystick, control wheel, foot pedals, and the like). The control system 106 can be local to the load 104 (e.g., the load 104 is in a vehicle that includes a motor), or remote from the load 104 and communicate via an appropriate connection, including a short-range or long-range network connection. In some embodiments, the control system 106 can include both local and remote elements. For example, the environment 100 can be an autonomous or remotely piloted vehicle that is monitored and guided from a location other than where it operates.

[0024] In some embodiments, the battery 102 is a rechargeable battery that can be charged by connecting the battery 102 to a charging power source, such as the charger 110. The charger 110 can include any system or device that can provide the power (or charge) stored by the battery 102 from an external source (e.g., a standard wall outlet or any other power source external to the battery 102), and numerous examples are known in the art. The charger 110 can also include control circuitry that controls the operation of the charger 110, including when and how much power to provide. In some embodiments, the charger 110 is shown using dashed lines because the battery 102 can be coupled to the charger 110 at certain times and disconnected from the charger 110 at other times. Depending on the implementation, the battery 102 may or may not be able to power the load 104 while receiving power from the charger 110. In some embodiments, the control system 106 can coordinate the operation of the charger 110 with the load-powering operation of the battery 102.

[0025] The battery monitoring system 108 may be coupled to the battery 102, the control system 106, and the charger 110. The battery 102 may be equipped with sensors to measure various state parameters of the battery or individual cells, such as potential, current (which can flow in either direction depending on whether the battery is being charged or discharged), and operating temperature. The battery monitoring system 108 may receive the measured state parameters from the battery 102 in real time (e.g., while the load 104 is drawing power from the battery 102). Based on the state parameters, the battery monitoring system 108 may perform various calculations to determine values ​​of state-of-health parameters (e.g., charge capacity and / or internal resistance) of the battery or individual cells and / or to detect whether the battery performance is consistent with expected behavior. Examples of calculations that may be implemented by the battery monitoring system 108 are described below. Based on the calculation results, the battery monitoring system 108 may provide battery state information to the control system 106 and / or the charger 110. Battery status information may include, for example, any combination of measured state parameters reported by the battery 102, values ​​of state of health parameters calculated by the battery monitoring system 108, information comparing actual battery behavior to a model of expected battery behavior, a "fault" notification indicating that some aspect of the battery's performance has deviated from expectations, and / or any other information available to the battery monitoring system 108. The control system 106 and / or charger 110 may use this information to generate alerts to an operator of the environment 100, modify the operation of the load 104 based on the battery status information, modify the operation of the charger 110 (e.g., the rate at which charging power is delivered to the battery 102) based on the battery status information, maintain battery history information for the battery 102, and / or take other responsive actions that may be programmed into the control system 106.

[0026] In one example, the operating environment 100 corresponds to a battery-powered aircraft that can fly under local or remote control. The load 104 can include the aircraft's motors, and the battery 102 can be the power source for the motors. In such an environment, the reliability of the battery 102 is important to allow the aircraft to safely complete a flight. To reliably provide the required amount of power, the battery 102 can include a high-voltage battery bank made up of multiple battery packs arranged in parallel (which can provide redundancy), with each battery pack incorporating multiple cells arranged in series and / or parallel. The battery monitoring system 108 can provide real-time information on the status of each battery pack, for example, on a cell-by-cell or group of cells, allowing issues to be detected and addressed before a battery failure occurs. For example, when the battery monitoring system 108 generates a notice of failure, a service technician can be notified, and the battery 102 can be repaired or replaced before the next flight. In this example, the battery 102 can be coupled to a charger 110 for recharging between flights, and the battery monitoring system 108 can continue to provide information on the status of each battery pack during charging.

[0027] It should be understood that operating environment 100 is illustrative and not limiting: any type of battery (including any number and arrangement of battery cells) driving any type of load may be monitored using systems and methods of the type described herein.

[0028] 2 shows a simplified schematic diagram of a battery bank 200 according to some embodiments. The battery bank 200 incorporates a battery monitoring system as an embedded system within the battery bank. The battery bank 200 can be used as the battery 102 in an operating environment such as the operating environment 100 described above. In this example, the battery bank 200 includes three high voltage (HV) battery packs 202a-202c connected in parallel between a terminal 204 (which can be connected to a load) and a ground point 206. The battery packs 202a-202c are rechargeable, and charging terminals 208 are provided for connecting each battery pack 202a-202c to a charger.

[0029] Each of the battery packs 202a-202c includes two HV batteries 210a-210b connected in parallel. Each HV battery 210a-210b includes individual battery cells 212, which may be, for example, lithium-ion batteries. For example, each cell may have an operating voltage of 3-5V, an internal resistance of 1-50 mΩ, and an operating current range of 0-200 mA, although these parameters can be varied. The number of cells 212 may be very large. In this example, the battery cells 212 in each HV battery 210a-210b are connected in series to form strings with a large number of cells 212 (e.g., 144 cells per string), and the three series strings are connected in parallel with each other in each HV battery 210a-210b. Battery bank 200 can provide both a high operating voltage (e.g., total system voltage in the range of approximately 400-800 V) and a high level of redundancy so that battery bank 200 can continue to supply power even if some of cells 212 fail.

[0030] Each HV battery 210 also includes a battery monitoring system (BMS) board 220, which may be a component of the battery monitoring system 108 of FIG. 1. Each BMS board 220 may be a printed circuit board having circuitry configured to monitor the status of one or more cells in the HV battery 210. In some embodiments, the BMS board 220 may monitor all cells of each HV battery 210. For example, each HV battery 210 may include 12 BMS boards 220, with each BMS board monitoring 36 cells. Example components and operations that may be implemented on the BMS boards 220 are described below.

[0031] It should be understood that battery bank 200 is exemplary and not limiting. A battery system can include any number of batteries, and each battery can include any number of cells. The battery monitoring described herein can be performed at the individual cell level, or groups of cells can be monitored as a unit, if desired.

[0032] Figure 3 illustrates an example of a battery monitoring system 300 according to some embodiments. The battery monitoring system 300 may be used to implement, for example, the battery monitoring system 108 of Figure 1. In some embodiments, each BMS board 220 of the battery bank 200 of Figure 2 may include one or more instances of the components of the battery monitoring system 300.

[0033] The battery monitoring system 300 includes a processor 302, a memory 304, a battery interface 306, and a control system interface 308. The processor 302 can be a microprocessor, a microcontroller, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other circuitry that implements the logical operations described herein. In some embodiments, the battery monitoring system 300 can be an embedded system, and the calculations, as described below, can be performed in a manner that can be performed in real time using a small, low-power processor. The memory 304 can include semiconductor-based memory (e.g., DRAM, SRAM), flash memory, magnetic storage, optical storage, or other computer-readable storage media. The memory 304 can store information about each battery cell monitored by the battery monitoring system 300. For example, the memory 304 can store cell configuration parameters 310 and cell status parameters 312. The cell configuration parameters 310 can include parameters that change statically or gradually over the life of a particular cell, and the cell status parameters 312 can include parameters that change dynamically as the battery is used. The memory 304 can also store cell state models (e.g., equivalent cell circuit models) that can be used to iteratively predict cell state parameters. For example, the cell state model can predict the state of charge and polarization state (or voltage) of a cell at a given time step based on the current draw, temperature, internal resistance, charge capacity, and state of charge at the previous time step. In some embodiments, the same cell state model is applied to all cells, but predictions for different cells may differ due to cell-to-cell differences in configuration parameter values ​​and / or state parameter values. As another example, the cell state model can be used to estimate cell configuration parameters that may change over time, such as charge capacity and internal resistance. A specific example is given below.

[0034] The battery interface 306 may include hardware and / or software components that enable the processor 302 to obtain status information from sensors of the cells (or batteries) connected to the battery interface 306. The status information may include, for example, measurements of the cell's current, potential, and operating temperature. In some embodiments, the battery interface 306 provides the status information at regular time intervals. If desired, this interval may vary depending on the current operating mode. For example, the status information may be provided once or twice per second while the battery is in active use and three or four times per hour when the battery is idle. In some embodiments, the battery interface 306 may send a request for status information to the battery at desired intervals. Alternatively, the battery cells may have sensors that provide data continuously (e.g., as an analog signal), and the battery interface 306 may sample and digitize the analog signal at the appropriate time. The processor 302 may use the sensor data thus obtained to calculate various status parameter values, for example, as described below.

[0035] The control system interface 308 may include hardware and / or software components that enable the processor 302 to provide battery status information to a control system (e.g., control system 106 of FIG. 1). In various embodiments, the battery status information may include data received from the battery cells, state parameters calculated from the data, configuration parameters calculated from the data, and / or fault notifications or other warnings, examples of which are described below.

[0036] It should be understood that the battery monitoring system 300 is exemplary and variations and modifications are possible. In some embodiments, the battery monitoring system 300 may include separate instances of its components for each cell being monitored, or may monitor multiple cells using a single processor with associated memory device(s). In the examples described herein, battery monitoring is performed on the cells, but battery monitoring at a higher level (e.g., a group of series-connected cells, or an entire battery pack or battery bank) is not precluded. The battery monitoring system 300 may implement any number and combination of monitoring operations, including, but not limited to, any one or more of the examples described below. In some embodiments, the battery monitoring system may also accommodate other battery management operations, such as calibration, self-test, etc.

[0037] During operation, a battery monitoring system such as battery monitoring system 300 can monitor the battery condition and evaluate the battery condition in real time to determine whether the battery (or particular cell) is operating as expected, taking into account the natural aging of the battery (or particular cells) (which may cause gradual performance degradation) and other considerations that affect battery performance. An example battery monitoring process will now be described.

[0038] Model Fault Detection One type of battery monitoring process can be based on prediction and observation of battery cell behavior during normal use. Battery cells are generally electrochemical in nature, and the response of a cell to specific conditions, e.g., potential as a function of current or current at a given operating potential, can be quantitatively modeled using an equivalent cell circuit model or the like. Thus, some embodiments of a battery monitoring system can use a predictive function derived from a quantitative model of the cell being monitored (referred to herein as a "cell state model") to predict the cell potential at any given time during battery operation. The actual (measured) cell potential can be compared to the prediction, and a "model failure" notification can be generated if the actual cell potential does not match the prediction.

[0039] 4 is a flow diagram of a process 400 for modeling cell behavior and detecting model failures, according to some embodiments. The process 400 can be implemented, for example, in the battery monitoring system 300 or other battery monitoring systems described above. The process 400 can operate in real time to monitor cell states and determine whether the cell states match the predictions of the cell state model. In some embodiments, the process 400 maintains an estimated (or predicted) cell state for each cell that is dynamically updated at regular time intervals based on measurement data received from the battery. The cell states can be defined and monitored using the variables defined in Table 1, where the subscript k indicates the time step. [Table 1]

[0040] In some embodiments, using the notation in Table 1, the cell state model update function is defined as follows: [μ k , V pred、k ]=modelUpdate(μ k-1 , I k , C0, R i , T k , dt) (1) The modelUpdate() function returns the previous cell state, the measured current and temperature, the charge capacity C0 and the internal resistance R i Based on the estimate of , and the time step, calculate the SOC and predicted potential of the cell for time step k. The modelUpdate() function can be based on a conventional cell behavior model that uses an equivalent cell circuit model. Examples of such models are known in the art, and an appropriate model can be selected based on the particular type of cell.

[0041] The time step dt may be selected based on the current operating state of the cell. For example, the cell may have a finite state model that includes the following states: "discharging" (current is being drawn from the battery), "charging" (current is being applied to recharge the battery), "relaxed" (current out of (or into) the battery is below a threshold indicating inactivity), and "idle" (entered from the relaxed state when the current remains below the inactivity threshold and the voltage remains stable for an extended period of time, e.g., at least 15 minutes). In some embodiments, the process 400 may update the battery state faster (e.g., dt~1 second) when in the discharging, charging, or relaxing states, and slower (e.g., dt~900 seconds) when in the idle state. Other state models may also be used.

[0042] In some embodiments, the equivalent cell circuit model is a charge capacity C0 and an internal resistance R i C0 and R0 depend on the state of health parameters such as charge capacity, charge current, and charge current. These parameters are expected to degrade over time as the cell ages, resulting in a gradual decrease in maximum charge capacity and a gradual increase in internal resistance. In some embodiments, the battery monitoring system uses a passive real-time process based on cell sensor data to monitor C0 and R0. i can be estimated, and an example of such a process is described below. In another embodiment, C and R i can be measured from time to time (e.g., by running a test procedure when the battery is expected to be idle for long periods of time) and treated as a constant between measurements.

[0043] The process 400 may begin with initialization at block 402. Initialization may occur, for example, when the battery management system is powered on or reset, or when the battery transitions from an idle state to any other state. Initialization may include setting initial values ​​for various parameters of the cell state model based on the assumption that the cell is not in use when initialization occurs. For example, V pcan be initialized to 0. C0 and R i SOC0 can be initialized based on the last measured or estimated SOC. SOC0 can be initialized to the last estimated SOC, or if the battery is not polarized during initialization, SOC0 is initialized to V using the getSOC(V,T) function, which calculates the cell state of charge as a function of cell potential and temperature based on an equivalent cell circuit model. actual、0 and T0 measurements. In some embodiments, getSOC() can be pre-calculated for a discrete set of potential and temperature values ​​and stored in a look-up table to facilitate real-time calculation. Other parameters and flags can also be initialized. For example, Q discharge、0 can be initialized to 0.

[0044] At block 404, for each iteration of process 400 (at time step k), new values ​​of the state parameters of the cell are determined. In some embodiments, determining the new state parameters includes determining the current being drawn (I k ), the actual potential across the cell (V actual、k ), and cell temperature (T k ) and apply equation (1) to obtain SOC k and predicted potential V p , V pred、k The method may include determining:

[0045] In some embodiments, the step of determining the new state parameters also determines the cell energy (U k ) and cell discharge Q discharge、k For example, the following formula can be used:

number

[0046] In block 406, the state parameters determined in block 404 are combined into a set of “optimistic” cell parameters and a set of “pessimistic” cell parameters, in particular the optimistic cell potential (V opt ) and pessimistic cell potential (V pess ) are used to calculate the optimistic cell parameters (C0 and R i These represent the state parameters of a hypothetical cell with increased charge capacity and decreased internal resistance (compared to the current estimates of C0 and R) and undergoing the same charge or discharge events as the modeled cell. Conversely, the pessimistic cell parameters are (again, with the current C0 and R i represent the state parameters of a hypothetical cell that has a reduced charge capacity and increased internal resistance (compared to the estimated values ​​of ) and experiences the same charge or discharge events as the modeled cell. In some embodiments, the optimistic and pessimistic cell parameters are not defined as fixed tolerances around the state parameters determined in block 404; instead, they are dynamically calculated using the cell state model.

[0047] Illustratively, in some embodiments, the maximum charge capacity C of the cell is K C0、opt The internal resistance of the cell, R, is underestimated by the amount i is K Ri、opt The cell overvoltage is overestimated by a scaling factor K η、opt Based on the assumption that the cell is turned off by opt Based on this assumption, the following calculation is used to determine V optcan be determined. C 0、opt =C0+K C0、opt (4) C init、opt =C 0、opt SOC0(5) C opt、k =C init、opt -Q discharge、k (6)

number

[0048] Similarly, in some embodiments, the maximum charge capacity C of the cell is K C0、pess The cell's internal resistance R is overestimated by the amount i is K Ri、pess The cell overvoltage is underestimated by an amount of K η、pess Based on the assumption that the cell potential is switched off by a scaling factor of pessBased on this assumption, the following calculation is used to determine V pess can be determined.

number

number

[0049] The labels "optimistic" and "pessimistic" are opt、k >V pess、k Note that this is not intended to imply, and does not have to be the case. For example, according to equations (4) to (10) and (11) to (18), when the cell is discharging, V opt、k >V pess、k However, when the cell is charging, V pess、k >V opt、k In some embodiments, to ensure that the envelope has at least a minimum width, a padding potential V pad Using V opt、k and V pess、k For example, V opt、k >V pess、k , the following formula can be used for fine tuning: V opt、k =V opt、k +V pad (19) V pess、k =V pess、k -V pad (20) Similarly, V pess、k ≧V opt、k , the following formula can be used for fine tuning: V opt、k =V opt、k -V pad (twenty one) V pess、k =V pess、k +V pad (twenty two) V pad can be selected as needed, for example, V pad =0.01V.

[0050] In block 408, the optimistic and pessimistic parameters are used to define an envelope of acceptable battery performance, such as an acceptable voltage range. For example, V opt、k >V pess、k In the case of , the envelope is V opt、k ≧V k ≧V pess、k (twenty three) can be defined as V pess、k ≧V opt、k In the case of , the envelope is V pess、k ≧V k ≧V opt、k (twenty four) can be defined as follows, where V k is the cell potential at time step k.

[0051] In block 410, the actual potential V across the cell is actual、k is measured. In some embodiments, the measurement may be performed as part of determining the state parameters of the cell in block 404.

[0052] At block 412, the measured potential V actual、k can be used, for example, to determine whether lies within the range of acceptable battery performance using either equation (23) or equation (24), as appropriate. In some embodiments, additional conditions may be applied. For example, the equivalent cell circuit model used to define the envelope may be k falls below a threshold (e.g., 0.1), or the current I k If V exceeds a threshold (e.g., 70 A for some types of cells), it may become unreliable. In conditions where the cell state model is unreliable, the envelope defined by the cell state model can be ignored (e.g., the measured V actual、k can be treated as always being inside the envelope.

[0053] The measured potential V outside the envelope actual、k may indicate a problem with the cell and may result in the generation of a model fault notification. In some embodiments, a measured potential V that is outside the envelope actual、k Any instance of the envelope voltage may result in generating a model fault notification. In other embodiments, transient variations outside the envelope may be ignored as insubstantial variations, and a fault counter may be used to determine when a variation outside the envelope is considered to be substantially outside the envelope. Thus, in block 414, if the measured potential is not within the envelope, a fault counter may be incremented, and in block 416, if the measured potential is within the envelope, the fault counter may be reset. In block 418, it is determined whether the fault counter exceeds a threshold, and if so, in block 420, a model fault notification is generated. In some embodiments, generating a model fault notification in a battery management system, such as the battery management system 108 of FIG. 1, may include sending a notification to the control system 106 (and / or other system components as appropriate). In various embodiments, the model fault notification may include other actions, such as illuminating a fault light on board the battery, sending a message to a maintenance service to have the battery serviced at the next opportunity, etc.

[0054] The counter threshold for generating a model fault notification may be a constant defined based on a trade-off between sensitivity (ability to detect a problem) and specificity (avoiding generating a fault notification in the absence of an actual problem). In one example, the determination of whether the actual cell potential is within the envelope is performed at a rate of 1 Hz and the fault counter threshold is set to 30, so that if the actual potential remains outside the envelope for more than 30 seconds, a model fault event is generated. Other thresholds may also be selected.

[0055] To further illustrate the operation of process 400, FIG. 5 shows an example plot of cell potential as a function of time for a battery cell used to power an aircraft, according to some embodiments. In this example, the aircraft is idle until shortly before time 1000 seconds, when the aircraft climbs to cruising altitude and begins cruising. Shortly before time 3000 seconds, the aircraft descends. A solid line 502 plots the measured cell potential V actual、k Line 504 corresponds to the predicted potential V predicted using the cell state model applied to the actual cell parameters. pred、k (In this example, line 504 closely tracks the measured potential.)

[0056] Line 506 is the predicted potential V predicted using optimistic cell parameters as described above (using the same cell state model as line 504). opt、k , and line 508 corresponds to the predicted potential V predicted using pessimistic cell parameters as described above (using the same cell state model as line 504). pess、k As can be seen, the width of the envelope defined by lines 506, 508 varies as a function of time. The width is affected by the current cell behavior (e.g., how much current is being drawn) and hysteresis. In this example, the actual potential (line 502) remains within the envelope defined by lines 506, 508 or for the measured duration, so no model fault event is generated.

[0057] It will be understood that process 400 is exemplary and variations and modifications thereof are possible. Where logic permits, operations described as sequential can be performed in parallel, or operations can be performed in a different order. Other operations not specifically described can be performed, or operations specifically described can be omitted, if desired. The cell state model and other parameter values ​​(e.g., parameters for determining optimistic and pessimistic potentials) can be optimized for a particular implementation based on the type and characteristics of the battery cells and the particular sensitivity and specificity desired. The monitoring process can be performed in parallel or sequentially for any number of cells. Monitoring using process 400 or a similar process can be performed while the battery is in any state. Alternatively, if desired, monitoring can be performed only during certain states (e.g., only during discharge events or only during charge events).

[0058] Cell State of Health Estimation As mentioned above, model fault detection concerns the detection of abnormal behavior of a battery or cell in active use. Apart from abnormalities, the overall performance of a battery or battery cell (particularly a rechargeable battery or cell) can be expected to gradually degrade over time due to electrochemistry and thermodynamics, reaching a point where the battery or battery cell becomes unusable. Thus, in addition to or instead of detecting abnormal behavior, some embodiments of the battery monitoring system can also monitor the "state of health" of a battery or individual cells of a multi-cell battery. In the examples herein, the state of health is determined by the maximum charge capacity C0 of the cell and the internal resistance R of the cell. i As the cell ages, the internal resistance tends to increase while the maximum charge capacity decreases. In other embodiments, other parameters may be considered, such as C0 and R i Additionally or alternatively, the health status may be associated with the

[0059] In some systems, C0 and R iThe monitoring of may be part of an active test process that is performed while the battery is idle and connected to a charger or load. Examples of such active test processes are known in the art. However, active test processes typically require the battery to remain idle and connected to a charger or load for extended periods of time (which may be hours). For batteries that operate at high duty cycles, an alternative process may be preferable.

[0060] Therefore, some embodiments of the battery monitoring system use a "passive" process to measure the C and R of the cell. i The passive process relies on real-time monitoring of voltage, current, and temperature (the same state parameters measured during the model fault detection process described above) and monitors C0 and R i can be quantitatively estimated from the measured parameters. In some embodiments, C and R i Estimating C0 and R0 may also include smoothing fluctuations in the estimate using a filtering function (e.g., a moving average). This process is called "passive" because it can be performed while the battery is in use, without affecting the battery's performance. Here, C0 and R0 are i An example of a passive process for estimating

[0061] In the example described herein, C0 is the current that the cell is at when it is at a reference current (e.g., 1 A) and a reference temperature (e.g., T ref = 25°C). In some embodiments, the raw charge capacity value C is defined as the maximum charge that can be discharged at a C0 temperature of 25°C. If the discharge event meets certain criteria for the reliability and stability of the C0 estimate, then at the end of the discharge event, 0、raw For example, the raw C 0、raw can be defined as follows:

number

[0062] 6 is a flow diagram of a process 600 for estimating a cell's C based on a discharge event, according to some embodiments. Process 600 can be implemented, for example, in battery monitoring system 300 or other battery monitoring systems described above. Process 600 can perform C estimation while the battery is in an idle state. Process 600 is an example of a passive monitoring process because it does not include battery activity other than normal operation (i.e., powering and / or charging a load).

[0063] Process 600 can begin while the battery is in an idle state. In block 602, process 600 determines the initial SOC of the cell (SOC init ) can be determined. In some embodiments, the initial SOC can be calculated as follows: SOC init =getSOC(V h , T h ) (26) Here, V h is the measured cell potential at initialization, and T h is the measured temperature at initialization, and getSOC() is the same function described above with reference to FIG.

[0064] In some embodiments, the SOC init is the cell potential V h and temperature T h is established only if it is within a certain range. This range can be selected based on the range of potentials and temperatures for which the getSOC() function is a reliable model of the cell behavior. h Or T h If the SOC is outside the appropriate range, init is not established.

[0065] SOC init In addition to establishing Q, the process at block 602 may also include initializing other parameters used in C monitoring. For example, the cumulative value of the charge discharged from the cell (Q, defined above) discharge、k ) can be initialized to 0, and the runtime parameters related to C0 monitoring (t run、C0 ) can also be reset to 0.

[0066] In block 604, the battery enters a discharge state, and process 600 can monitor for the discharge event. (If process 400 is also implemented, this monitoring can occur as part of determining the state parameters in block 404.) For example, the total charge discharged, Q discharge、k is updated at each time step (e.g., every second) according to equation (3) above, and the runtime parameter t run、C0 can be incremented every time step. In block 606, the discharge event ends and the battery re-enters the idle state (at which point Q discharge、k and the runtime parameter t run、C0 (You may stop updating the

[0067] At block 608, the process 600 calculates the final SOC of the cell (SOC final ) can be established. In some embodiments, the final SOC can be calculated as follows: SOC final =getSOC(V l , T l ) (27) Here, V l is the cell potential measured at the end of the discharge event, and T l is the temperature measured at the end of the discharge event. V l and / or T l Generally, V measured before discharge h and T h Since θ is different from θ, the results of equations (26) and (27) will, in general, differ from each other.

[0068] At block 610, the process 600 final It can be determined whether all measurements are within a range such that the SOC is considered reliable. For example, in some embodiments, final is the cell potential V l and temperature T l is established only if it is within a certain range. This range can be selected based on the range of potentials and temperatures for which the getSOC() function is a reliable model of the cell behavior. l Or T l If the SOC is outside the appropriate range, final is not established, instead, the calculations can be reset at block 612 and process 600 can start over again at block 602.

[0069] SOC final Other requirements regarding the reliability of may also be applied. For example, in some embodiments, the runtime parameter t run、C0 If exceeds the upper limit, the result is (for example, Q discharge、k ) and the process 600 may stop at block 612 and start over at block 602.

[0070] As another example, block 610 may be configured to detect a change in SOC, defined as: ΔSOC=|SOC init -SOC final | (28) A minimum reliability threshold may be required to be exceeded. The threshold may be selected to require that a discharge event consumes a significant percentage of the cell's charge capacity, for example, the threshold may be 0.4 or 0.5, etc. If not, the calculations may be reset at block 612 and process 600 may start over at block 602.

[0071] At block 614, the process 600 calculates the unfiltered C value (C0、raw ) can be calculated, where Q discharge is the Q from the discharge event in block 604 discharge、k where ΔSOC is given by equation (25). In block 616, the process 600 may calculate a filtered C value using an infinite impulse response filter that approximates a moving average. C 0、filt = γC 0、raw +(1-γ)C 0、prev (29) Here, C 0、prev is a stored estimate of C0 (e.g., from a previous iteration of process 600), and γ is a filter damping constant that can be selected based on the desired sensitivity to updates. In one example, γ=0.05, but other values ​​can be selected. 0、raw Other techniques for combining C with previous estimates of C can be used. In some embodiments, a statistical analysis of the distribution of recent C estimates for a cell can be performed, e.g., to determine the most recent C 0、raw It may be possible to determine how far the value deviates from the expected distribution and smooth out random measurement noise. In some embodiments, before the first iteration of process 600 for a new cell, the charge capacity C is calculated using a charge capacity measured during testing of the cell, a nominal value (e.g., based on the design specifications of the cell), or another value, if desired. 0、prev can be initialized.

[0072] At block 618, the process 600 continues by, for example, 0、filt By comparing with a threshold, C 0、filtis implausibly large. This threshold can be set to correspond to (or exceed) the maximum charge capacity that the cell can be expected to have. In some embodiments, the maximum charge capacity can be determined based on the design specifications of the cell, with some tolerance for performance better than designed. In other embodiments, the maximum charge capacity of a particular cell can be determined by actively measuring the charge capacity of the cell during pre-installation testing, assuming that the cell's charge capacity does not increase with use. C 0、filt If C exceeds the threshold, then in block 620, 0、filt is discarded and process 600 can reset and return to block 602. In some embodiments, 0、filt is assumed to be a numerical artifact and is simply ignored. In other embodiments, the process 600 can generate a C0 error notification. In yet other embodiments, the process 600 can generate a C0 error notification. 0、filt It can track whether occurs repeatedly and, if so, generate a C0 error notification.

[0073] In some embodiments, the C estimate C 0、filt A cell capacity fault notification may be triggered using, for example, C0 is expected to change gradually over time, and an unexpectedly sudden change may indicate a problem. Thus, in block 622, process 600 may use, for example, ΔC0=|C 0、prev -C 0、filt | (30) A change in C0 may be calculated and if ΔC0 exceeds a threshold, the result is treated as suspicious. For example, in block 624, process 600 may generate a "cell capacity failure" notification. In some embodiments, a threshold(s) may be defined and different failure notifications may be generated based on which threshold is exceeded. For example, if ΔC0 exceeds a first threshold (e.g., 0.4 Ah), a "cell capacity suspicious" failure notification may be generated, and if ΔC0 exceeds a second, larger threshold (e.g., 0.8 Ah), a "cell capacity very suspicious" failure notification may be generated. Once a cell capacity failure notification is generated in block 624, process 600 may reset, discard the result of the C0 calculation, and return to block 602.

[0074] At block 626, the stored C 0、prev The value may be, for example, the stored value calculated in block 616, C 0、filt The updated C 0、prev The value may be reported to the control system 106 or other system components. In some embodiments, the control system 106 may incorporate the estimated charge capacity into a battery status report (e.g., for review by a service technician).

[0075] The process 600 can be repeated for each discharge event, assuming the battery enters an idle state between discharge events. In some embodiments, the battery management system performs an effective SOC high is currently stored. If not, block 602 may be executed, and if so, block 608 and subsequent blocks may be executed. In some embodiments, process 600 or a similar process may also be used to estimate C based on measurements during a charging event. (This may be undesirable, for example, for battery system designs where measurements of current or other relevant parameters are less reliable during a charging event than during a discharging event.)

[0076] It will be understood that the process 600 is illustrative and that variations and modifications are possible. Operations described as sequential can be performed in parallel, or operations can be performed in a different order, where logic permits. Other operations not specifically described can be performed, or operations specifically described can be omitted, if desired. For example, while the described example uses single values ​​representing the current and previous estimates, in other implementations previous estimates from multiple iterations of the process 600 can be stored and statistical analysis of the set of estimates can be performed. (Such statistical analysis can increase accuracy and reduce variance, but also increases the amount of memory required to store the previous estimates.) The cell state model and other parameter values ​​in a particular implementation can be selected to provide optimal results based on the type and characteristics of the battery cells, and the particular sensitivity and specificity desired. The C0 estimation process can be performed in parallel or sequentially for any number of cells. In some embodiments, if the estimated C0 (after filtering) falls below a lower limit, a low C0 fault notification can be generated, which may be an indication that the cell is due for replacement.

[0077] In some embodiments, the internal resistance R i can be estimated in addition to or instead of C0. The internal resistance R i can be defined as the resistive (ohmic) component of the impedance of a cell at standard SOC, current, and temperature, and without cell polarization. The resistive component of impedance can be understood as the instantaneous change in potential with respect to current, but instantaneous measurement of change may not be practical for an operating cell. Also, because internal resistance typically depends on SOC, temperature, and discharge current, simply measuring ΔV / ΔI over short periods of time may not provide a reliable estimate. Thus, some embodiments use R i A compensation factor is introduced to improve the estimate.

[0078] FIG. 7 illustrates a graph of the R i 7 is a flow diagram of a process 700 for estimating R. Process 700 can be implemented, for example, in battery monitoring system 300 or other battery monitoring systems described above. Process 700 iterates (runs) R while the battery is actively operating (e.g., in a discharging or charging state). i It can perform calculations and use running calculations when the battery goes into idle state. i The estimates can be updated.Process 700 is another example of a passive monitoring process that does not involve any battery activity outside of normal operation.

[0079] Process 700 may begin, at block 702, when the battery transitions from an idle state to an active state (e.g., a charging state or a discharging state). In response to the transition, at block 704, process 700 calculates a running estimate of the internal resistance (R i、run ) can be initialized. For example, the running estimate can be R determined from a previous run of process 700. i In some embodiments, during the first iteration of process 700 for a new cell, R i、run may be initialized based on the internal resistance measured during testing of the cell, a nominal value (eg, based on the design specifications of the cell), or another value as desired.

[0080] In block 706, R i、run is iteratively updated. (If process 400 is also implemented, this updating may occur as part of determining the state parameters in block 404.) FIG. i、run 8 shows a flow diagram of a process 800 for iteratively updating . Process 800 can be used, for example, to implement block 704 of process 700, and process 800 can be performed at regular time intervals (time index k) while the battery is in an active state.

[0081] At block 802, the process 800 calculates the current (I k ), potential (V k ), and temperature (T k ) may be measured. At block 804, the process 800 may check whether reliability conditions for current, potential and temperature are met. For example, the following reliability conditions may apply: |I k -I k-1 |>ΔI min (31) |V k -V k-1 |>ΔV min (32) T max ≧mean(T k , T k-1 )≧T min (33) Equations (31) and (32) require that the change in potential and current from one time step to the next be large enough to be measured. Equation (33) requires that the temperature is i、run The limiting parameter ΔI is required to be within a range that is considered reliable. min , ΔV min , T max , and T min can be selected as needed for a particular system. In one example using the embodiment of battery bank 200 of FIG. min =2A, ΔV min =0.010V, T max = 50°C, and T min = 20° C. Other conditions may also apply, such as upper limits on the change in current and / or potential, and limits on the change in temperature.

[0082] If at least one of the reliability conditions of block 804 is not met in block 806, the process 800 may wait and retry for the next time step in block 808. If all reliability conditions are met in block 806, the process 800 may calculate the raw estimate R for time step k in block 810. i、raw In some embodiments, the following calculation can be used:

number

number

[0083] In block 812, the process 800 calculates the R i、raw It can be determined whether R is within a reasonable range. In one example, the reasonable range is defined to be 0.005 Ω to 0.03 Ω, although other ranges may be used depending on the cell design. i、raw If R is not within a reasonable range, then in block 814, the process 800 i、raw The value can be discarded or ignored, and the process 800 can wait for the next time step and try again. i In yet another embodiment, the process 800 may generate an error notification. i、raw Track whether occurs repeatedly, and if so, R i Error notifications can be generated

[0084] R i、raw If R is within a reasonable range, then in block 816, the process 800 uses an infinite impulse response filter that approximates a moving average to calculate R i、run can be updated. R i、run =γR i、raw +(1-γ)R i、run (37) where γ is a filter damping constant that can be selected based on the desired sensitivity to the update value. In one example, γ=0.01, but other values ​​can be selected. In an embodiment that implements both process 600 and process 800, R in equation (37) i The decay constant of the estimate can have the same value as the decay constant of the C estimate in equation (29), but it does not have to be. Any decay constant of the newly calculated R i、raw R i Other techniques for combining with previous estimates of R for a cell may be used.i To carry out statistical analysis of the distribution of estimates of, for example, the most recent R i、raw It is possible to determine how far a value deviates from an expected distribution and to smooth out random measurement noise.

[0085] In some embodiments, process 800 (corresponding to block 704 of process 700) may be executed repeatedly as long as the battery remains in the active state. Referring again to FIG. 7, at block 708, the battery transitions to the idle state. After the battery enters the idle state, R (from process 800) i、run The final value is the new R i can be used as an estimate. In some embodiments, R i The estimate can be used to trigger a fault notification. For example, R i is expected to vary gradually over time, and an unexpectedly sudden change may indicate a problem. Thus, in block 710, process 700 may calculate R using, for example, i The change in can be calculated. ΔR i =|R i、run -R i | (38) Here, R i is R from block 702 i The value is ΔR i If ΔR exceeds a threshold, the result is treated as suspicious. For example, in block 714, process 700 may generate a "cell resistance failure" notification. In some embodiments, a threshold(s) may be defined and different failure notifications may be distinguished based on which threshold(s) are exceeded. For example, ΔR i If ΔR exceeds a first threshold (e.g., 0.002 Ω), a “Cell Resistance Suspect” fault notification can be generated, and ΔR i If R exceeds a second, larger threshold (e.g., 0.01 Ω), a “Cell Resistance Very Suspect” fault notification may be generated. Once a cell resistance fault notification is generated in block 714, process 700 continues by computing the resulting R i、runThe value can be discarded and a return can be made to block 702 to await the next transition to the active state.

[0086] In block 716, for example, the stored R i Value R i、run By replacing i You can update the value. Updated R i The value may be reported to the control system 106 or other system components. In some embodiments, the control system 106 may incorporate the estimated internal resistance into a battery status report (e.g., for review by a service technician).

[0087] Process 700 generates a new R i、run Calculate R when the battery is idle (if the condition is met). i In some embodiments, process 700 or a similar process can be used to update R based on either a charging event or a discharging event. i can be estimated, or process 700 can be selectively invoked only in connection with discharging events (or only in connection with charging events), if desired.

[0088] It will be understood that the processes 700 and 800 are illustrative and that variations and modifications are possible. Operations described as sequential can be performed in parallel, or operations can be performed in a different order, where logic permits. Other operations not specifically described can be performed, or operations specifically described can be omitted, if desired. For example, while the described example uses single values ​​representing the current and previous estimates, in other implementations previous estimates from multiple iterations of the process 800 can be stored, and statistical analysis of the set of estimates can be performed. (Such statistical analysis can increase accuracy and reduce variance, but also increase the amount of memory required to store the previous estimates.) The cell state model and other parameter values ​​in a particular implementation can be selected to obtain optimal results based on the type and characteristics of the battery cell, and the particular sensitivity and specificity desired. R i The estimation process can be performed in parallel or sequentially for any number of cells. In some embodiments, the estimated R i If rises above the upper limit, high R i A failure notification can be generated, which may be an indication that the cell is due for replacement.

[0089] Additional Embodiments Although the present invention has been described with reference to specific embodiments, those skilled in the art having access to this disclosure will appreciate that variations and modifications are possible. Battery monitoring systems and processes of the type described herein can be used to monitor any number of batteries or any number of battery cells, and the systems and processes can be adapted to cells implemented using various battery technologies. Notifications generated during battery monitoring are not limited to the above examples, and the use of notifications is not limited to the above use cases. Any combination of monitoring processes can be implemented in a particular system that includes any one or more of the above processes. Functions described as being based on a cell state model (e.g., an equivalent cell circuit model) can be implemented by providing a lookup table that is keyed to the inputs of the function with an appropriate accuracy based on the resolution of the measurement of the cell state parameters (e.g., the resolution of the potential, current, temperature sensors). In some embodiments, the determination of the monitoring parameters can be conditional on the inputs to the function being within a range covered by the lookup table.

[0090] Computational operations of the type described herein can be implemented in computer systems of generally conventional design, such as desktop computers, laptop computers, tablet computers, mobile devices (e.g., smartphones), etc. Such systems can include one or more processors (e.g., general-purpose microprocessors usable as central processing units (CPUs), and / or special-purpose processors such as graphics processors (GPUs) that can provide extensive parallel processing capabilities) for executing program code, memory and other storage devices for storing program code and data, user input devices (e.g., keyboards, pointing devices such as mice or touchpads, microphones), user output devices (e.g., display devices, speakers, printers), multiple input / output devices (e.g., touch screen displays), signal input / output ports, network communication interfaces (e.g., wired network interfaces such as Ethernet interfaces and / or wireless network communication interfaces such as Wi-Fi), etc. Computer programs incorporating various features of the claimed invention may be encoded and stored on various computer-readable storage media, suitable media including magnetic disks or tapes, optical storage media such as compact disks (CDs) or DVDs (digital versatile disks), flash memories, and other non-transitory media. (It should be understood that "storing" data is distinct from propagating data using a transitory medium such as a carrier wave.) A computer-readable medium encoding the program code may be packaged with a compatible computer system or other electronic device, or the program code may be provided separately from the electronic device (e.g., via Internet download or as a separately packaged computer-readable storage medium).

[0091] It should be understood that all numerical values ​​used herein are for illustrative purposes and are subject to change, and ranges may be given to provide a sense of scale, but do not exclude values ​​outside the disclosed ranges.

[0092] It should also be understood that all figures herein are schematic. Unless otherwise specified, the figures do not imply a particular physical arrangement of the elements shown therein, or that all elements shown are required. Those skilled in the art with access to this disclosure will understand that elements shown in the figures or otherwise described in this disclosure may be modified or omitted, and other elements not shown or described may be added.

[0093] The above description is illustrative and not limiting. Many variations of the invention will become apparent to those skilled in the art upon review of this disclosure. Accordingly, the scope of patent protection should not be determined with reference to the above description, but instead with reference to the following claims along with their full scope or equivalents.

Claims

1. 1. A method for monitoring a condition of a battery cell, comprising: determining a current value of one or more state of health parameters of the battery cells; measuring the values ​​of a plurality of operating state parameters of the battery cell, the measuring step including the step of measuring an actual potential of the battery cell; calculating an optimistic potential based on a cell state model, the measurements of a first subset of the operational state parameters, and optimistic values ​​of the one or more health state parameters corresponding to a better state of health than the current values; calculating a pessimistic potential based on the cell state model, the measurements of a first subset of the operational state parameters, and pessimistic values ​​of the one or more health state parameters corresponding to a worse state of health than the current values; determining whether the actual potential of the battery cell is substantially within an envelope defined by the optimistic potential and the pessimistic potential; generating a model fault notification if the actual potential is not substantially within the envelope; A method comprising:

2. The method of claim 1 , wherein the method is performed in real-time while the battery cells are powering a load.

3. The method of claim 1 , wherein the method is performed in real time while the battery cells are charging.

4. The method of claim 1 , wherein the model fault notification is generated in real-time while the battery cell is actively in use.

5. The method of claim 1 , wherein the operating condition parameters include a current through the battery cell and a temperature of the battery cell.

6. The one or more health state parameters: An internal resistance of the battery cell; The charge capacity of the battery cell. The method of claim 1 , comprising:

7. estimating one or more values ​​of said state of health parameters while the battery is in active use; storing the estimated value as the current value of the health state parameter; The method of claim 6 further comprising:

8. The method of claim 1 , wherein the cell state model is an equivalent cell circuit model.

9. 2. The method of claim 1, further comprising: calculating a predicted potential of the battery cell based on a cell state model, the measured values ​​of a first subset of the operating state parameters, and the current values ​​of the one or more state of health parameters.

10. 1. A battery monitoring system, comprising: a battery interface for receiving sensor data from a battery sensor of the battery cell; a control system interface for providing output data to a control system; Memory, a processor coupled to the memory, the battery interface, and the control system, Determining current values ​​of one or more state parameters of the battery cells; receiving sensor data including values ​​of a plurality of operating state parameters of the battery cell, including an actual potential; calculating an optimistic potential based on a cell state model, the values ​​of a first subset of the operational state parameters, and optimistic values ​​of the one or more health state parameters that correspond to a better state of health than the current values; calculating a pessimistic potential based on the cell state model, the values ​​of the first subset of operational state parameters, and pessimistic values ​​of the one or more health state parameters corresponding to a worse state of health than the current values; determining whether the actual potential of the battery cell is substantially within an envelope defined by the optimistic potential and the pessimistic potential; Generate a model fault notification if the actual potential is not substantially within the envelope. A processor configured as A battery monitoring system comprising:

11. 11. The battery monitoring system of claim 10, wherein the processor is configured to receive the sensor data, calculate the optimistic potential, calculate the pessimistic potential, determine whether the actual potential of the battery cell is substantially within the envelope, and generate the model fault notification in real time while the battery cell is in active use.

12. The battery monitoring system of claim 10 , wherein the sensor data includes a current through the battery cell and a temperature of the battery cell.

13. The one or more health state parameters: The internal resistance of the battery cell; The charge capacity of the battery cell; The battery monitoring system of claim 10 .

14. 14. The battery monitoring system of claim 13, wherein the processor is further configured to estimate values ​​of one or more of the state parameters using the sensor data while the battery cell is supplying power to a load, and to store the estimated values ​​as the current values ​​of the state parameters.

15. When executed by a processor in a battery monitoring system, the processor: determining a current value of one or more state of health parameters of the battery cells; measuring the values ​​of a plurality of operating state parameters of the battery cell, the measuring step including the step of measuring an actual potential of the battery cell; calculating an optimistic potential based on a cell state model, the values ​​of a first subset of the operational state parameters, and optimistic values ​​of the one or more health state parameters that correspond to a better state of health than the current values; calculating a pessimistic potential based on the cell state model, the values ​​of a first subset of the operational state parameters, and pessimistic values ​​of the one or more health state parameters corresponding to a worse state of health than the current values; determining whether the actual potential of the battery cell is substantially within an envelope defined by the optimistic potential and the pessimistic potential; generating a model fault notification if the actual potential is not substantially within the envelope; A computer readable storage medium having stored therein program instructions for performing a method comprising:

16. 16. The computer-readable storage medium of claim 15, wherein the method is performed in real-time while the battery cells are actively in use.

17. The one or more health state parameters: An internal resistance of the battery cell; The charge capacity of the battery cell; 20. The computer readable storage medium of claim 15, comprising:

18. The method further comprising: estimating one or more values ​​of the state of health parameters while the battery is in active use; storing the estimated value as the current value of the health state parameter; 20. The computer-readable storage medium of claim 17, further comprising:

19. 16. The computer-readable storage medium of claim 15, wherein the method further comprises calculating a predicted potential of the battery cell based on a cell state model, the measured values ​​of a first subset of the operating state parameters, and the current values ​​of the one or more state of health parameters.

20. The computer-readable storage medium of claim 15 , wherein the operating condition parameters include a current through the battery cell and a temperature of the battery cell.

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