Traction battery control system that can be operated to control a battery based on adaptive sampled battery measurements by electrochemical impedance spectroscopy

Abstract

A system, such as an electrified vehicle, includes a battery, such as a traction battery. The controller manages the battery based on adaptive sampled measurements of the battery using electrochemical impedance spectroscopy (EIS). The controller can perform the adaptive sampled EIS measurements. The controller can use the adaptive sampled EIS measurements to identify the values ​​of parameters in a model, such as an equivalent circuit model (ECM), of the battery. The controller can use the model with the identified parameter values ​​to detect the internal state of the battery, based on the adaptive sampled EIS measurements.

Description

FIELD OF TECHNOLOGY

[0001] The present disclosure relates to detecting an internal state of a traction battery of an electrified vehicle. GENERAL STATE OF THE ART

[0002] An electrified vehicle includes a traction battery for providing power to a vehicle's motor to propel the vehicle. Electrochemical impedance spectroscopy (EIS) is used to detect the internal state of the traction battery. The detected internal state of the traction battery is used to control the operation of the traction battery and / or the vehicle. SUMMARY

[0003] A system includes a battery and a controller. The battery can consist of individual battery cells of subcomponents or virtual battery cells consisting of battery cells connected in parallel. The controller is configured to control the battery based on adaptive sampled measurements of the battery using electrochemical impedance spectroscopy (EIS).

[0004] The controller may be configured to perform the adaptively sampled EIS measurements. In this case, the controller may perform the adaptively sampled EIS measurements until the adaptively sampled EIS measurements indicate an impedance spectrum of the battery within a given tolerance. The controller may perform a first set of the adaptively sampled EIS measurements to detect magnitude components of the adaptively sampled EIS measurements and then perform a second set of the adaptively sampled EIS measurements to detect phase components of the adaptively sampled EIS measurements. The controller may perform the adaptively sampled EIS measurements to iteratively detect magnitude and phase components of the adaptively sampled EIS measurements.

[0005] The controller may use the adaptively sampled EIS measurements to identify parameter values ​​of a model of the battery, such as resolved to its subcomponent battery cells. In this case, the controller may use the model with the identified parameter values ​​to detect an internal state of the battery, wherein the controller detects the internal state of the battery based on the adaptively sampled EIS measurements.

[0006] The internal state of the battery may be a performance of the battery, wherein the controller controlling the battery based on the adaptively sampled EIS measurements includes the controller controlling the battery based on the performance of the battery and / or the subcomponent battery cells.

[0007] The internal state of the battery may be a state of charge of the battery, wherein the controller controlling the battery based on the adaptively sampled EIS measurements includes the controller controlling the battery based on the state of charge of the battery and / or the subcomponent battery cells.

[0008] The battery can be a traction battery. In certain variations, both the battery and the controller are onboard a vehicle. In other variations, such as charging stations, the battery is onboard a vehicle and the controller is located outside the vehicle, such as at the charging station.

[0009] A method is also provided for a vehicle having a traction battery and a controller. The method includes performing, by the controller, adaptively sampled EIS measurements of the traction battery. The method further includes controlling the traction battery and / or another component of the vehicle with the controller based on the adaptively sampled EIS measurements of the traction battery.

[0010] The method may further include detecting an estimate of a distance to empty of the vehicle based on the internal state of the traction battery and controlling the traction battery and / or another vehicle component according to the distance to empty estimate.

[0011] A vehicle is also provided, including a traction battery and a controller. The controller is configured to charge and / or discharge the traction battery based on adaptively sampled EIS measurements of the traction battery. BRIEF DESCRIPTION OF THE DRAWINGS Fig. 1 illustrates a block diagram of a battery electric vehicle (BEV) having a traction battery; Fig. 2 illustrates a block diagram of a traction battery controller arrangement for monitoring the traction battery; Fig. Figure 3 illustrates a schematic representation of an equivalent circuit model (ECM) of the traction battery; Fig. 4 illustrates a flowchart depicting a process performed by the traction battery controller for detecting the internal state of the traction battery using adaptive sampling (or adaptive learning) for a reduced time from electrochemical impedance spectroscopy (EIS) measurements of the traction battery over a frequency range of interest; Fig. 5A illustrates a Nyquist graph including a plot of the magnitude component of measured samples of the traction battery impedance over a frequency range of interest and a plot of the phase component of measured samples of the traction battery impedance over the frequency range of interest; Fig. Figure 5B illustrates a Nyquist graph including a plot of the real and imaginary components of measured samples of the traction battery impedance over the frequency range of interest; Fig. 6A illustrates a graph comprising first, second, and third magnitude component variance percentages of a surrogate traction battery impedance model obtained by the traction battery controller using first, second, and third adaptive sampling algorithms for initially learning magnitude component variance percentages relative to a uniform, high-density sample as a function of the number of measured impedance samples; Fig. Figure 6B illustrates an enlarged version of a section of the graph from Fig. 6A; Fig. 6C illustrates a graph comprising first, second, and third traces of measurement time spent obtaining the magnitude component of the surrogate model of the traction battery impedance using the first, second, and third adaptive sampling algorithms as a function of the number of measured samples of the impedance, the graph further comprising a fourth trace of measurement time spent obtaining the magnitude component of the traction battery impedance using the uniform high-density sampling; Fig. 6D illustrates a graph comprising first, second, and third traces of the percentage of variance of the phase component of the surrogate model of the traction battery impedance as obtained by the traction battery controller using the first, second, and third adaptive sampling algorithms for subsequently learning the traces of the phase components relative to a uniform high-density sample as a function of the number of additional measured impedance samples; Fig. 6E illustrates a graph comprising first, second, and third traces of measurement time spent obtaining the phase component of the equivalent model of the traction battery impedance as a function of the number of additional measured samples of the impedance, the graph further comprising a fourth trace of measurement time spent obtaining the phase component of the traction battery impedance using the uniform high-density sampling; Fig. 7A illustrates a graph comprising first, second, and third magnitude component variance percentages of a surrogate traction battery impedance model obtained by the traction battery controller using the first, second, and third adaptive sampling algorithms for iteratively learning magnitude component variance percentages relative to a uniform, high-density sample as a function of the number of measured impedance samples; Fig. 7B illustrates a graph comprising first, second, and third traces of the percentage of variance of the phase component of the surrogate model of the traction battery impedance, as obtained by the traction battery controller using the first, second, and third adaptive sampling algorithms for iteratively learning the traces of the phase components relative to a uniform, high-density sample as a function of the number of measured impedance samples; and Fig. 7C illustrates a graph comprising first, second, and third traces of measurement time spent obtaining the magnitude and phase components of the surrogate model of the traction battery impedance using the first, second, and third adaptive sampling algorithms as a function of the number of measured samples of the impedance, the graph further comprising a fourth trace of measurement time spent obtaining the magnitude and phase components of the traction battery impedance using the uniform high-density sampling. DETAILED DESCRIPTION

[0012] Detailed embodiments of the present disclosure are disclosed herein; however, it should be understood that the disclosed embodiments are merely exemplary of the present disclosure, which may be embodied in various and alternative forms. The figures are not necessarily to scale; some features may be greatly exaggerated or reduced to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present disclosure.

[0013] Now with reference to Fig. 1 shows a block diagram of an electrified vehicle 12 in the form of a battery electric vehicle (BEV). The BEV 12 includes a powertrain having one or more traction motors ("electric machine(s)") 14, a traction battery ("battery" or "battery pack") 24, and a power electronics module 26 (e.g., an inverter). In the BEV configuration, the traction battery 24 provides all propulsion power, and the vehicle does not include an internal combustion engine. In other variations, the electrified vehicle may be a plug-in hybrid electric vehicle (PHEV) further including an internal combustion engine.

[0014] The traction motor 14 is part of the drivetrain of the BEV 12 for driving the BEV's motion. In this regard, the traction motor 14 is mechanically connected to a transmission 16 of the BEV 12. The transmission 16 is mechanically connected to a drive shaft 20, which is mechanically connected to wheels 22 of the BEV 12. The traction motor 14 can provide propulsion capability to the BEV 12 and is capable of operating as a generator.

[0015] The traction battery 24 stores electrical energy that can be used by the traction motor 14 to power the BEV 12. The traction battery 24 typically provides a high-voltage (HV) direct current (DC) output. The traction battery 24 is electrically connected to the power electronics module 26. The traction motor 14 is also electrically connected to the power electronics module 26. The power electronics module 26, such as an inverter, provides the ability to transfer power bidirectionally between the traction battery 24 and the traction motor 14. For example, a traction battery 24 may provide a DC voltage, while the traction motor 14 may require three-phase alternating current (AC) to operate. The inverter 26 can convert the DC voltage into a three-phase AC current to operate the traction motor 14.In a regeneration mode, the inverter 26 can convert the three-phase AC current from the traction motor 14 acting as a generator into DC voltage compatible with the traction battery 24.

[0016] In addition to providing electrical energy to propel the BEV 12, the traction battery 24 may provide electrical energy for use by other electrical systems of the BEV, such as HV loads, such as electric heating and air conditioning systems, and low-voltage loads (LV loads), such as an auxiliary battery.

[0017] The traction battery 24 is rechargeable by an external power source 36 (e.g., the grid). The external power source 36 may be electrically connected to an electric vehicle supply equipment (EVSE) 38. The EVSE 38 provides circuitry and controls for controlling and managing the transfer of electrical energy between the external power source 36 and the BEV 12. The external power source 36 may provide DC or AC electrical power to the EVSE 38. The EVSE 38 may include a charging connector 40 for plugging into a charging port 34 of the BEV 12.

[0018] A power conversion module 32 of the BEV 12, such as an on-board charger including a DC / DC converter, may condition power supplied by the EVSE 38 to provide the proper voltage and current levels to the traction battery 24. The power conversion module 32 may interface with the EVSE 38 to coordinate the delivery of power to the traction battery 24.

[0019] The various components described above may have one or more associated controllers for controlling and monitoring the operation of the components. The controllers may be microprocessor-based devices. The controllers may communicate via a serial bus (e.g., a Controller Area Network (CAN)) or via separate conductors.

[0020] For example, a system controller 48 ("vehicle controller") is provided to coordinate the operation of the various components. The controller 48 includes electronics, software, or both to perform the necessary control functions to operate the BEV 12. In embodiments, the controller 48 may be a combination of a vehicle system controller and a powertrain control module (VSC / PCM). Although the controller 48 is depicted as a single device, the controller 48 may include multiple controllers in the form of multiple hardware devices or multiple software controllers with one or more hardware devices. In this regard, reference to a "controller" herein may refer to one or more controllers.

[0021] The controller 48 implements a battery energy control module (BECM) 50. The BECM 50 is in communication with the traction battery 24. The BECM 50 is a traction battery controller operable to monitor operating characteristics (e.g., an internal state) of the traction battery 24 and to manage the charging and discharging of the traction battery 24. The BECM 50 implements algorithms to measure and / or estimate the operating characteristics of the traction battery 24. The BECM 50 controls the operation and performance of the traction battery 24 based on the operating characteristics of the traction battery. The BECM 50 or other controllers may control the operation and performance of other systems and components of the BEV 12 based on operating characteristics of the traction battery 24.

[0022] The operating characteristics of the traction battery 24 include the charge capacity and the state of charge (SOC) of the traction battery 24. The charge capacity of the traction battery 24 indicates the maximum amount of electrical energy the traction battery can store. The SOC of the traction battery 24 indicates a current amount of electrical charge stored in the traction battery. The SOC of the traction battery 24 can be represented as a percentage of the maximum amount of electrical charge that can be stored in the traction battery.

[0023] Another operating characteristic of the traction battery 24 is the traction battery capacity. The traction battery capacity 24 is a measure of the maximum amount of power the traction battery can supply or receive for a given period of time. Thus, the traction battery capacity 24 corresponds to a discharge and a charge power limit, which define the amount of electrical power that can be supplied by or to the traction battery at a given time. These limits can be provided to other vehicle controllers, for example, by a vehicle system controller (VSC), so that the information can be used by systems that can draw power from or supply power to the traction battery 24.Vehicle control systems need to know how much power the traction battery 24 can provide (discharge) or receive (charge) to meet driver demand and optimize energy consumption. Thus, knowing the performance of the traction battery 24 allows electrical loads and sources to be managed so that the requested power remains within the limits the traction battery can handle.

[0024] Now with reference to Fig. 2 and with continued reference to Fig. 1 is a block diagram of an arrangement for the BECM 50 to monitor the traction battery 24. The traction battery 24 consists of a plurality of battery cells 52. The battery cells 52 are physically connected to one another (e.g., connected in series, as shown in Fig. 2). In variations, the traction battery 24 consists of individual battery cells of subcomponents or virtual battery cells consisting of battery cells connected in parallel.

[0025] The BECM 50 is operable to monitor pack-level characteristics of the traction battery 24, such as battery current 56, battery voltage 58, and battery temperature 60. The battery current 56 is the current output from (i.e., discharged) or input to (i.e., charged) the traction battery 24. The battery voltage 58 is the terminal voltage of the traction battery 24.

[0026] The BECM 50 may also be operated to measure and monitor battery cell-level characteristics of the battery cells 52 of the traction battery 24. For example, the terminal voltage, current, and temperature of one or more battery cells 52 may be measured. The BECM 50 may utilize a battery sensor 54 to measure the battery cell level characteristics. The battery sensor 54 may measure the characteristics of one or more battery cells 52. The BECM 50 may utilize Nc battery sensors 54 to measure the characteristics of all battery cells 52. Each battery sensor 54 may transmit the measurements to the BECM 50 for further processing and coordination. The functionality of the battery sensor 54 may be integrated internally within the BECM 50.

[0027] The traction battery 24 may include one or more temperature sensors, such as thermistors, in communication with the BECM 50 to provide data indicative of the temperature of battery cells 52 of the traction battery 24 so that the BECM can monitor the temperature of the traction battery and / or the battery cells. The BEV 12 may further include a temperature sensor to provide data indicative of an ambient temperature to the BECM 50 to monitor the ambient temperature.

[0028] The BECM 50 controls the operation and performance of the traction battery 24 based on the monitored characteristics at the traction battery and battery cell level. For example, the BECM 50 may use the monitored characteristics to detect an internal condition (e.g., charge capacity, SOC, and / or power capability) of the traction battery 24, such as for use in controlling the traction battery and / or the BEV 12.

[0029] As known to those of ordinary skill in the art, the BECM 50 may detect an internal state of the traction battery 24 using an observer, wherein a battery model (i.e., an "equivalent circuit model" (ECM)) is used to construct the observer, with measurements of battery current, battery terminal voltage, and battery temperature. Values ​​of ECM parameters may be identified by recursive estimation based on such measurements.

[0030] The BECM 50 is configured to derive an estimate of an internal state of the traction battery 24 using the ECM. For the internal state estimate to be accurate, the ECM must accurately model the traction battery 24. For the ECM to accurately model the traction battery 24, (i) the ECM must have a set of parameters that is at least substantially similar to the parameters of an ECM that accurately models the traction battery 24 (i.e., a "correct" ECM that is fairly characteristic of the traction battery must be initially selected from a set of candidate ECMs), and (ii) the estimated values ​​of the ECM's parameters must be at least substantially similar to the values ​​of the parameters of the ECM that accurately models the traction battery (i.e., the estimated parameter values ​​must be at least substantially similar to the actual parameter values).

[0031] The BECM 50 is configured to implement electrochemical impedance spectroscopy (EIS) to estimate the ECM's parameter values ​​for use by the ECM to detect an internal condition of the traction battery 24. The EIS is a non-invasive technique for measuring the internal condition of the traction battery 24. The EIS is sensitive to internal condition factors of the traction battery 24, including state of charge, state of health, temperature, and the like.

[0032] The BECM 50 implements EIS to measure the impedance of the traction battery 24 by applying a relatively small-amplitude current or sine-wave stimulus to the traction battery over a range of frequencies and then measuring the voltage or current response of the traction battery. The impedance response can be taken as a property of the battery assembly and can also be resolved into parallel groups of individual battery cells. The impedance data of the traction battery 24 (i.e., the EIS measurements) are then "fitted" to the ECM to estimate the values ​​of the ECM's parameters. The goal is to find the ECM parameters that cause the best match between an impedance spectrum from the ECM and the measured impedance data of the traction battery 24.

[0033] With reference now to Fig. 3 and with continued reference to the Fig. 1 and Fig. 2, a schematic diagram of an ECM 70 of the traction battery 24 is shown. The traction battery 24 may, as indicated, be modeled as a circuit, and the ECM 70 is one possible traction battery ECM. An ECM includes one or more circuit elements (i.e., one or more "parameters"). According to the ECM 70, the traction battery 24 is modeled as a circuit that includes, in series, a voltage source ("V OC ”) (open-circuit voltage (OCV)) 72, an inductor (“L”) 74, a resistor (“Rs”) 76, one or more RC circuit elements 78, each circuit element comprising a resistor (“Rx”) 80 and a capacitor (“Cx”) 82 connected in parallel, and a constant phase element (CPE) (“A”) 84. The voltage (“V t “) 86 is the voltage across the terminals of the traction battery 24 (ie the terminal voltage).

[0034] An equation for the impedance (Z) of the ECM 70 is as follows: Z=iωL+Rs+(11Rx+Qx(iω)n)+Aω+Aiω

[0035] The parameters of the ECM 70 include L, R S , R X , C X and A. These parameters should have values ​​at which the calculated impedance of the ECM matches the measured impedance of the traction battery 24. Accordingly, the ECM 70 requires that it be an ECM that has a correct set of parameters consistent with the traction battery 24, and that the values ​​of the ECM 70 parameters be accurate so that the ECM 70 accurately models the behavior of the traction battery 24. This algorithmic process can be repeated with circuit models for each component battery cell in the traction battery 24. The combined behavior of the component battery cells comprises the internal state of the traction battery 24.

[0036] As discussed, tracking the internal state of the traction battery is useful for controlling the charging rate, controlling the delivery of traction battery power, monitoring the health of the traction battery, including capacity estimation, etc. However, external measurement of the internal state of the traction battery is made unclear by long relaxation times and state variables defined by integration algorithms. EIS is a technique that promises to provide relatively rapid information about the internal state of the traction battery by examining the complex electrical response (via the small-amplitude sinusoidal current or voltage stimulus) over a frequency range.

[0037] As noted, the BECM 50 implements the EIS by applying a sinusoidal electrical stimulus to the traction battery 24 and measuring the electrical output of the traction battery to the stimulus. For example, the stimulus applied to the traction battery is a voltage, in which case the output of the traction battery is a current, to thereby obtain an impedance measurement (i.e., Z = V / I, where Z is the impedance, V is the voltage applied to the traction battery, and I is the current output by the traction battery). Alternatively, the stimulus applied to the traction battery is a current, in which case the output of the traction battery is a voltage, to thereby obtain an impedance measurement (i.e., Z = V / I, where Z is the impedance, V is the voltage output by the traction battery, and I is the current applied to the traction battery).Thus, the input stimulus is of one type of variable (i.e., one of voltage or current), while the output is of the other type of variable (i.e., the other of voltage or current). The BECM 50 is capable of battery pack-resolved EIS using total pack current and voltage sensors, and is capable of cell-resolved EIS using individual cell current or voltage sensors. Alternatively, application-specific integrated circuits can be used to perform EIS measurements.

[0038] Every measured impedance is a "complex impedance" because the impedance contains a magnitude and a phase. Alternatively, according to another reference framework, every measured impedance contains a real component and an imaginary component.

[0039] Each measured impedance is a "sample." The measurement process is repeated with the sinusoidal electrical stimulus at different frequencies to obtain additional impedance measurements (i.e., additional samples) at each of the frequencies. That is, the frequency of the sinusoidal electrical stimulus is varied to yield a measurement spectrum of impedance as a function of frequency. The frequency range is useful because varied frequencies can stimulate various internal processes (i.e., charge transfer processes, diffusion processes, etc.) of the traction battery 24, which can be calibrated or modeled to generate useful information about the internal state of the traction battery (e.g., state of charge, state of health, internal temperature, etc.).

[0040] In laboratory environments, the frequency range can often span several decades in log space, ranging from a highest frequency (e.g., 1.0 MHz) to a lowest frequency (e.g., 1.0 mHz). It is typical to collect ten to fifteen measurements per frequency decade. Low-frequency data takes a relatively long time to collect because the sinusoidal periods are relatively longer in time. Furthermore, multiple periods may be required for low-frequency data to average out measurement noise or compensate for voltage drift due to system polarization changes. As an example, fifteen measurements per decade (i.e., uniform, high-density sampling) over a frequency range of 0.01–1000 Hz using a commercially available EIS measurement device take approximately one hour.

[0041] One hour is an unacceptable amount of time to perform EIS measurements onboard a vehicle or at a public charging station. One hour for a full EIS scan onboard the BEV 12 is unacceptable because the traction battery 24 changes state while the BEV is being driven. One hour for a full EIS scan at a charging station is unacceptable because the user may not be charging the traction battery 24 during this time. Therefore, to utilize EIS onboard the BEV 12 or at a charging station, the EIS measurement time for a full scan must be reduced.

[0042] According to the present disclosure, the BECM 50 is operable to detect the internal state of the traction battery 24 using adaptive sampling (or adaptive learning) for a reduced EIS measurement time. That is, the BECM 50 uses adaptive sampling to reduce the EIS measurement time for a given frequency range. The EIS results using adaptive sampling are virtually equivalent to the EIS results using uniform, high-density sampling. Thus, the internal state detected by the BECM 50 using adaptive EIS sampling is virtually the same as the internal state that would be detected by the BECM using uniform EIS sampling. The reduced EIS measurement time is a result of adaptive EIS sampling, which in many cases includes a 50-75% reduction in the number of samples compared to uniform, high-density EIS sampling.

[0043] By using adaptive sampling for EIS measurements, the BECM 50 implements a method for learning the impedance of the traction battery 24 across the entire frequency range of interest in a reduced amount of time compared to uniform, high-density sampling. As is conventional, the BECM 50 adapts the impedance of the traction battery 24, learned using adaptive EIS sampling, to the traction battery ECM so that the BECM estimates the values ​​of the ECM parameters. The BECM 50, in turn, detects the internal state of the traction battery 24 using the ECM. In this manner, the BECM 50 detects the internal state of the traction battery 24 using a traction battery ECM based on adaptively sampled EIS measurement information. The adaptive sampling and the EIS measurements can resolve information down to the virtual cell level.

[0044] The method for reducing EIS data acquisition time, when implemented by the BECM 50, makes EIS useful onboard a BEV. The method for reducing EIS data acquisition time also makes EIS useful when implemented off-board, such as at BEV charging stations. For simplicity, the BECM 50 is primarily referred to as using adaptive sampling for reduced EIS measurement time.

[0045] As an overview, the BECM 50 uses adaptive sampling (or active learning) approaches to reduce the number of impedance samples required to define a meta- or surrogate model equivalent to a uniform, high-density impedance sample. To achieve this, the BECM 50 begins by taking EIS measurements on a sparse, uniform grid containing the minimum and maximum frequencies of interest. As an alternative to a uniform grid, a sparse set of frequency points from a previous measurement can be used for an initial image. This can include points based on an immediately preceding EIS measurement or based on a calibration dependent on operating parameters such as temperature and internal battery condition.The BECM 50 fits the surrogate models for the magnitude and phase components of the impedance to the measured impedance points. For example, the BECM 50 uses spline functions or other functions to fit the magnitude and phase to the points. This can also be done using the real and imaginary components of the impedance, as they are mathematically interchangeable with the magnitude and phase components of the impedance.

[0046] The BECM 50 then uses an adaptive sampling algorithm to select the next measurement point based on two competing metrics: 1) grid exploration (e.g., measuring in areas with low point density); and 2) model utilization (i.e., adding points when the model indicates that more points may be necessary due to strong curvature, rapid functional changes, etc.). These competing metrics define the nature of the adaptive sampling algorithm. For reference, many adaptive sampling (or active learning) approaches have been published in the scientific literature.

[0047] The BECM 50 can apply the adaptive sampling algorithm to the two traces (i.e., the magnitude trace and the phase trace) in series (i.e., magnitude first, followed by phase), iteratively (i.e., magnitude, phase, magnitude, phase, etc.), or by another method (e.g., using Kramers-Kronig relationships to derive one trace from the other). The BECM 50 repeats the process of adding points until a stopping criterion is reached. The stopping criterion could be a fixed number of samples, a difference between the two-norm of the surrogate functions between successive iterations, etc. The stopping criteria can depend on the overall pack behavior or the behavior of individual or groups of battery cells.

[0048] More detailed information is available with reference to Fig. 4, a flowchart 100 is shown illustrating a process performed by the BECM 50 for detecting the internal state of the traction battery 24 using adaptive sampling for reduced EIS measurement time. The process begins with the BECM 50 measuring an initial set of impedance samples of the traction battery 24 over a frequency range of interest according to process block 102. As noted above, each impedance sample measured by the BECM 50 results in the BECM applying a sinusoidal electrical stimulus at a given frequency to the traction battery 24 and measuring the electrical output of the traction battery to the stimulus.Accordingly, the initial set of impedance samples is measured by the BECM 50 applying a sinusoidal electrical stimulus to the traction battery 24 at a corresponding set of different frequencies, and the BECM measuring the electrical output of the traction battery at each stimulus. The different frequencies are, for example, at respective decades of frequency (or a smaller interval) across the frequency range of interest. As an example, the number of impedance samples in the initial set of samples measured in process block 102 may be on the order of 5-15% of the number of samples measured in the uniform high-density EIS scan.

[0049] The BECM 50 generates a surrogate model of the impedance spectrum of the traction battery 24 based on the initial set of impedance samples according to process block 104. As noted above, each measured impedance sample includes a magnitude component and a phase component (or alternatively, a real component and an imaginary component). Therefore, the surrogate model of the impedance spectrum includes a trace of the magnitude components of the initial set of samples and a trace of the phase components of the initial set of samples. Because each trace is based on, for example, 5-15% of the number of samples measured during the uniform high-density EIS sampling, the surrogate model traces are, at best, a rough approximation of the magnitude and phase components of the impedance spectrum of the traction battery 24.

[0050] The BECM 50 may further consider generalized information regarding the expected behavior of the impedance spectrum of the traction battery 24 when generating the surrogate model. For example, such expected attributes of the impedance spectrum of the traction battery 24 include that no discontinuities are expected in the surrogate model's waveforms, that the surrogate model's waveforms are expected to be neat, smooth waveforms, and that the surrogate model's waveforms are expected to increase / decrease over certain frequency intervals, etc. Thus, when the BECM 50 further considers the expected behavior of the impedance spectrum of the traction battery 24, the surrogate model's waveforms are a better approximation of the magnitude and phase components of the impedance spectrum of the traction battery 24.

[0051] The BECM 50 then performs adaptive sampling of the impedance of the traction battery 24 according to process block 106. Adaptive sampling involves the BECM 50 taking a sample of the impedance of the traction battery 24 at a selected frequency within the frequency range of interest. Thus, the BECM 50 applies a sinusoidal electrical stimulus to the traction battery 24 at the selected frequency and measures the electrical output of the traction battery to the stimulus, thereby obtaining the measured impedance sample.

[0052] "Adaptive" sampling refers to the selection of the selected frequency for the measured impedance sample. Specifically, as mentioned above, the BECM 50 selects the selected frequency (i.e., selects the nearest impedance measurement point) based on grid exploration (measuring in a region of the frequency domain with low point density—i.e., the number of impedance samples in that region of the frequency domain is less than the number of impedance samples in other regions of the frequency domain) and model utilization (measuring in a region of the frequency domain where the surrogate model indicates that more points may be desired—i.e., the approximation of the magnitude and phase components of the impedance spectrum of the traction battery 24 appears relatively poorer in that region of the frequency domain than in other regions of the frequency domain).

[0053] Upon completion of process block 106, the total number of measured impedance samples includes (i) the number of measured impedance samples in the initial set according to process block 102 and (ii) the adaptively sampled measured impedance sample according to process block 106. The BECM 50 then further updates the surrogate model to include the adaptively sampled measured impedance sample according to process block 108. Thus, the magnitude component trace of the surrogate model further includes the magnitude component of the adaptively sampled measured impedance sample, and the phase component trace of the surrogate model further includes the phase component of the adaptively sampled measured impedance sample. Accordingly, the surrogate model traces are now a better approximation of the magnitude and phase components of the impedance spectrum of the traction battery 24 by including one additional measurement in each trace.

[0054] The BECM 50 then decides whether the surrogate impedance model is sufficiently equivalent to the impedance spectrum of the traction battery 24 (i.e., whether the surrogate model sample is sufficiently equivalent to the uniform high-density EIS sample), as indicated by decision block 110.

[0055] If the surrogate impedance model is not sufficiently equivalent to the impedance spectrum of the traction battery 24, then another cycle of the adaptive sampling process of process block 106, the surrogate model update process of process block 108, and the comparison decision process of process block 110 are performed. Specifically, according to process block 106, the BECM 50 performs another cycle of adaptive sampling of the impedance of the traction battery 24. The adaptive sampling in this cycle includes the BECM 50 measuring another sample of the impedance of the traction battery 24 at a different selected frequency within the frequency range of interest. The BECM 50 selects this selected frequency based on grid exploration and model utilization.Now, the total number of measured impedance samples includes (i) the number of measured impedance samples in the initial set according to process block 102, (ii-a) the initial adaptively sampled measured impedance sample according to the initial iteration of process block 106, and (iib) the additional adaptively sampled measured impedance sample according to the additional iteration of process block 106. The BECM 50 then further updates the surrogate model by taking into account the additional adaptively sampled measured impedance sample according to process block 108. Thus, the magnitude component trace of the surrogate model further includes the magnitude component of the additional adaptively sampled measured impedance sample, and the phase component trace of the surrogate model further includes the phase component of the additional adaptively sampled measured impedance sample.Accordingly, the curves of the equivalent model are now an even better approximation of the magnitude and phase components of the impedance spectrum of the traction battery 24, as each curve includes one additional measurement. The BECM 50 then decides, according to decision block 110, whether the equivalent impedance model is now sufficiently equivalent to the impedance spectrum of the traction battery 24.

[0056] Additional cycles of the adaptive sampling process of process block 106, the surrogate model update process of process block 108, and the comparison decision process of decision block 110 are performed until the BECM 50 decides, according to decision block 110, that the surrogate impedance model is sufficiently equivalent to the impedance spectrum of the traction battery 24. The BECM 50 may decide that the surrogate impedance model is sufficiently equivalent to the impedance spectrum of the traction battery 24 after a predetermined number of impedance samples have been measured, differences between the immediately previous version of the surrogate model and the current version of the surrogate model are sufficiently small, etc.

[0057] Once the surrogate impedance model is sufficiently equivalent to the impedance spectrum of the traction battery 24, the BECM 50 terminates any further adaptive sampling. The BECM 50, in turn, uses the surrogate model to update the ECM parameters of the traction battery 24 according to process block 112. The BECM 50 uses the ECM, which has the updated parameters, to detect the internal state of the traction battery 24 according to process block 114.

[0058] The BECM 50 controls the traction battery 24 and / or other components of the BEV 12 based on the internal state of the traction battery according to process block 116. For example, the internal state of the traction battery 24 may be a capability of the traction battery 24, a state of charge (SOC) of the traction battery, and / or a distance-to-empty (DTE) estimate of the BEV 12, and the BECM 50 controls the traction battery and / or a component of the BEV 12 according to the internal state of the traction battery.

[0059] To illustrate the benefits of the adaptive scanning process used by the BECM 50 to reduce the EIS measurement time, reference is now made to the Fig. 5A and Fig. 5B is referred to. Fig. 5A illustrates a Nyquist graph 120 that includes a trace 122 of the magnitude component of measured samples of the impedance of the traction battery 24 over a frequency range of interest and a trace 124 of the phase component of measured samples of the impedance of the traction battery over the frequency range of interest. As shown, the measured impedance data of the traction battery 24 takes the form of two curves (i.e., traces 122 and 124) when plotted. (For completeness, illustrated Fig. 5B shows a Nyquist plot 130 including a plot 132 of the real and imaginary components of measured samples of the traction battery impedance over the frequency range of interest. As indicated, Nyquist plots 120 and 130 depict identical data. The magnitude plot 122 and phase plot 124 are experimental data from a traction battery pouch cell acquired at 0% SOC and at 15°C. This cell state was arbitrarily chosen, and similar results would be achieved at different SOCs and temperatures. In this example, the experimental data is shown for a 76-point scan (i.e., 76 impedance samples) with a uniform, high-density sampling of fifteen points per frequency decade of the frequency range of interest. The frequency range of interest is f = 0.01 - 1000 Hz.

[0060] The adaptive sampling process serves to obtain a surrogate impedance model that is sufficiently accurate for the impedance spectrum of the traction battery. The magnitude curve 122 and the phase curve 124 represent the impedance spectrum of the traction battery 24. Thus, the magnitude curve 122 and the phase curve 124 are to be learned using the adaptive sampling process. That is, a curve of the magnitude components of the surrogate model must be sufficiently similar to the magnitude curve 122, and a curve of the frequency components of the surrogate model must be sufficiently similar to the phase curve 124. In this example, the goal is for the adaptive sampling process to learn the magnitude and phase curves in significantly fewer than the 76-point scan impedance measurements.

[0061] The Fig. 6A, Fig. 6B, Fig. 6C, Fig. 6D and Fig. Figure 6E concerns the results for sequentially (serially) learning the size and phase curves in series using any one of three different adaptive sampling algorithms. Learning the size and phase curves in series means that the size curve is first learned using adaptive sampling, and then the phase curve is learned using adaptive sampling. (Similarly, learning the phase and size curves means that the phase curve is first learned using adaptive sampling, and then the size curve is learned using adaptive sampling.)

[0062] Fig. 6A illustrates in more detail a graph 140 comprising first, second, and third traces 142, 144, and 146 of the percentage of variance of the magnitude component of a surrogate model of the impedance of the traction battery 24, as obtained by the BECM 50 using first, second, and third adaptive sampling algorithms for initially learning the traces of the magnitude components relative to a uniform, high-density sample as a function of the number of measured impedance samples. The first adaptive sampling algorithm is the Voronoi diagram / cross-validation sampling algorithm; the second adaptive sampling algorithm is the mixed adaptive sampling algorithm; and the third sampling algorithm is the Lipschitz criterion sampling algorithm. These sampling algorithms are known to those of ordinary skill in the art. These sampling algorithms are provided as examples, and other sampling algorithms could be used.

[0063] Fig. Figure 6B illustrates an enlarged version of a portion of graph 140. As in Fig. As shown in Figure 6B, each of the three adaptive sampling algorithms achieves near parity (i.e., R 2 > 0.9999) with the magnitude 122 of the experimental 76-point scan after only about fourteen impedance scans.

[0064] Fig. 6C illustrates a graph 150 comprising first, second, and third traces 152, 154, and 156 of the measurement time spent obtaining the magnitude component of the surrogate model of the impedance of the traction battery 24 using the first, second, and third adaptive sampling algorithms as a function of the number of measured impedance samples. The graph 150 also includes a fourth trace 158 of the measurement time spent obtaining the magnitude component of the impedance of the traction battery 24 using uniform, high-density sampling (namely, fifteen samples per frequency decade). As shown in the graph 150, each of the three adaptive sampling algorithms requires approximately 2x-4x less time than the time spent obtaining the impedance measurements of the 76-point scan.

[0065] Fig. 6D illustrates a graph 160 comprising first, second, and third traces 162, 164, and 166 of the percentage of variance of the phase component of the surrogate model of the impedance of the traction battery 24 as obtained by the BECM 50 using the first, second, and third adaptive sampling algorithms for subsequently learning the traces of the phase components relative to a uniform, high-density sample as a function of the number of additional measured impedance samples. Fig. 6E illustrates a graph 170 including first, second, and third traces 172, 174, and 176 of the measurement time spent obtaining the phase component of the equivalent model of the impedance of the traction battery 24 as a function of the number of additional impedance samples measured. The graph 170 also includes a fourth trace 158 of the measurement time spent obtaining the phase component of the impedance of the traction battery 24 using the uniform, high-density sampling.

[0066] In operation, the already sampled points after the first resolution according to the size curve (the subject of the Fig. 6A, Fig. 6B and Fig. 6C) a relatively high R 2in the phase curve. Five additional impedance samples were measured to improve phase curve agreement. Thus, there is a net reduction in sampling time for learning the magnitude and phase curves of 2x-3x compared to the time required to obtain the experimental 76-point scan.

[0067] As described, the Fig. 6A, Fig. 6B, Fig. 6C, Fig. 6D and Fig. 6E to the magnitude and phase curves versus frequency, predicted separately and in series (magnitude first, phase second) relative to a case where fifteen impedance samples were acquired per frequency decade. The R 2 is used as a comparison between the prediction of adaptive sampling and the case of uniform high-density sampling. Graph 170 from Fig. 6E indicates the accumulated sampling time when each measurement is taken.

[0068] The Fig. 7A, Fig. 7B and Fig. Figure 7C concerns the results for iteratively learning the magnitude and phase curves using any of the three different adaptive sampling algorithms. Iterative learning of the magnitude and phase curves means that the magnitude curve is updated following an adaptively sampled measurement, then the phase curve is updated following the next adaptively sampled measurement, then the magnitude curve is updated further, then the phase curve is updated further, and so on, until the magnitude and phase curves are learned.

[0069] Fig. 7A illustrates in more detail a graph 180 comprising first, second, and third traces 182, 184, and 186 of the percentage of variance of the magnitude component of a surrogate model of the impedance of the traction battery 24 as obtained by the BECM 50 using the first, second, and third adaptive sampling algorithms for iteratively learning the traces of the magnitude components relative to a uniform, high-density sample as a function of the number of measured impedance samples. Fig. 7B illustrates a graph 190 comprising first, second, and third traces 192, 194, and 196 of the percentage of variance of the phase component of the surrogate model of the impedance of the traction battery 24 as obtained by the BECM 50 using the first, second, and third adaptive sampling algorithms for iteratively learning the traces of the phase components relative to a uniform, high-density sample as a function of the number of measured impedance samples. Fig. 7C illustrates a graph 200 comprising first, second, and third traces 202, 204, and 206 of the measurement time spent obtaining the magnitude and phase components of the equivalent model of the impedance of the traction battery 24 using the first, second, and third adaptive sampling algorithms as a function of the number of measured impedance samples. The graph 200 further includes a fourth trace 208 of the measurement time spent obtaining the magnitude and phase components of the impedance of the traction battery 24 using the uniform, high-density sampling.

[0070] As described, the Fig. 7A, Fig. 7B and Fig. 7C on frequencies for the magnitude and phase, which are sampled sequentially in an iterative manner. In general, the results of iterative learning of the magnitude and phase curves according to the Fig. 7A, Fig. 7B and Fig. 7C similar to the results of sequential learning of the size and phase curves according to the Fig. 6A, Fig. 6B, Fig. 6C, Fig. 6D and Fig. 6E. For iterative learning, the magnitude and phase curves of the impedance were calculated with respect to the curve of the uniform 76-point high-density sampling to within R 2 > 0.9999 after about fifteen samples for all three adaptive sampling algorithms. Fig. The measurement time reduction shown in Figure 7C is approximately 2x-3x faster than the time required for uniform, high-density 76-point scanning. Further time reductions may be possible by optimizing the algorithms and exploiting relationships between the magnitude and phase components. Significant time reductions are also possible with a reduced frequency range of interest, particularly reduced low-frequency conditions.

[0071] With adaptive sampling, it is possible to significantly reduce the time spent acquiring spectrally resolved EIS data, enabling applications onboard vehicles or at charging stations. This is advantageous because EIS does not require significant additional hardware in a vehicle and can perform spatially distributed measurements within vehicles by using voltage as the detection variable, as the voltage is resolved to the individual battery cell level.

[0072] As described herein, a conventional method for collecting EIS data involves measuring across a uniform, high-density grid spanning from low to high frequencies of interest. This can be time-consuming. Therefore, as the condition of the traction battery 24 changes, and thus the impedance of the traction battery changes, the BECM 50 operating according to the present disclosure attempts to minimize the number of measurements needed to fully resolve the complex impedance curves. In this way, a robust algorithmic path to achieving a desired result is provided, and the number of sample points changes as the complexity of the curves increases or decreases.

[0073] While exemplary embodiments are described above, these embodiments are not intended to describe all possible forms of the disclosure. The words used in the description are words of description rather than limitation, and it is understood that various changes may be made without departing from the spirit and scope of the disclosure. In addition, the features of various implementing embodiments may be combined to form further embodiments of the present disclosure.

[0074] According to the present invention, a system is provided, comprising: a battery; and a controller configured to control the battery based on adaptive sampled measurements of the battery by electrochemical impedance spectroscopy.

[0075] According to one embodiment: the controller is further configured to perform the adaptively sampled measurements by electrochemical impedance spectroscopy.

[0076] According to one embodiment: the controller is further configured to perform the adaptively sampled electrochemical impedance spectroscopy measurements until the adaptively sampled electrochemical impedance spectroscopy measurements indicate an impedance spectrum of the battery within a given tolerance.

[0077] According to one embodiment: the controller is configured to perform a first set of the adaptively sampled electrochemical impedance spectroscopy measurements to detect magnitude components of the adaptively sampled electrochemical impedance spectroscopy measurements, and then perform a second set of the adaptively sampled electrochemical impedance spectroscopy measurements to detect phase components of the adaptively sampled electrochemical impedance spectroscopy measurements.

[0078] According to one embodiment: the controller is configured to perform the adaptively sampled electrochemical impedance spectroscopy measurements to iteratively detect magnitude and phase components of the adaptively sampled electrochemical impedance spectroscopy measurements.

[0079] According to one embodiment: the controller is further configured to use the adaptively sampled electrochemical impedance spectroscopy measurements to identify values ​​of parameters of a model of the battery.

[0080] According to one embodiment: the controller is further configured to use the model with the identified values ​​of the parameters to detect an internal state of the battery, wherein the controller detects the internal state of the battery based on the adaptively sampled measurements by electrochemical impedance spectroscopy.

[0081] According to one embodiment: the internal state of the battery is a performance of the battery, wherein the controller controlling the battery based on the adaptively sampled electrochemical impedance spectroscopy measurements includes the controller controlling the battery based on the performance of the battery.

[0082] According to one embodiment: the internal state of the battery is a state of charge of the battery, wherein the controller controlling the battery based on the adaptively sampled electrochemical impedance spectroscopy measurements includes the controller controlling the battery based on the state of charge of the battery.

[0083] According to one embodiment: the battery is a traction battery.

[0084] According to one embodiment: the battery and the controller are on board a vehicle.

[0085] According to one embodiment: the battery is on board a vehicle and the controller is outside the vehicle.

[0086] According to the present invention, a method for a vehicle having a traction battery and a controller includes: performing, by the controller, adaptively sampled measurements of the traction battery by electrochemical impedance spectroscopy; and controlling, with the controller, the traction battery and / or another component of the vehicle based on the adaptively sampled measurements of the traction battery by electrochemical impedance spectroscopy.

[0087] In one aspect of the invention, the method includes: using the adaptively sampled electrochemical impedance spectroscopy measurement to identify values ​​of parameters of a model of the traction battery; and detecting an internal state of the traction battery based on the model.

[0088] In one aspect of the invention, the method includes: detecting an estimate of a distance to empty of the vehicle based on the internal state of the traction battery; and controlling the traction battery and / or another vehicle component according to the estimate of the distance to empty of the vehicle.

[0089] According to the present invention, a vehicle is provided, comprising: a traction battery; and a controller configured to charge and / or discharge the traction battery based on adaptive sampled measurements of the traction battery by electrochemical impedance spectroscopy.

[0090] According to one embodiment: the controller is further configured to perform the adaptively sampled measurements by electrochemical impedance spectroscopy.

[0091] According to one embodiment: the controller is further configured to use the adaptively sampled electrochemical impedance spectroscopy measurements to identify values ​​of parameters of a model of the traction battery.

[0092] According to one embodiment: the controller is further configured to use the model with the identified values ​​of the parameters to detect an internal state of the traction battery, wherein the controller detects the internal state of the traction battery based on the adaptively sampled measurements by electrochemical impedance spectroscopy.

[0093] According to one embodiment, the vehicle is a battery electric vehicle.

Claims

[1] System comprising: a battery; and a controller configured to control the battery based on adaptive sampled measurements of the battery by electrochemical impedance spectroscopy. [2] The system of claim 1, wherein: the controller is further configured to perform the adaptively sampled measurements by electrochemical impedance spectroscopy. [3] The system of claim 2, wherein: the controller is further configured to perform the adaptively sampled electrochemical impedance spectroscopy measurements until the adaptively sampled electrochemical impedance spectroscopy measurements indicate an impedance spectrum of the battery within a given tolerance. [4] The system of claim 2, wherein: the controller is configured to perform a first set of the adaptively sampled electrochemical impedance spectroscopy measurements to detect magnitude components of the adaptively sampled electrochemical impedance spectroscopy measurements, and then perform a second set of the adaptively sampled electrochemical impedance spectroscopy measurements to detect phase components of the adaptively sampled electrochemical impedance spectroscopy measurements. [5] System according to claim 2, wherein: the controller is configured to perform the adaptively sampled electrochemical impedance spectroscopy measurements to iteratively detect magnitude and phase components of the adaptively sampled electrochemical impedance spectroscopy measurements. [6] The system of claim 1, wherein: the controller is further configured to use the adaptively sampled measurements by electrochemical impedance spectroscopy to identify values ​​of parameters of a model of the battery. [7] System according to claim 6, wherein: the controller is further configured to use the model with the identified values ​​of the parameters to detect an internal state of the battery, wherein the controller detects the internal state of the battery based on the adaptively sampled measurements by electrochemical impedance spectroscopy. [8] System according to claim 7, wherein: the internal state of the battery is a performance capability of the battery, wherein the controller controlling the battery based on the adaptively sampled electrochemical impedance spectroscopy measurements includes the controller controlling the battery based on the performance capability of the battery. [9] The system of claim 7, wherein: the internal state of the battery is a state of charge of the battery, wherein the controller controlling the battery based on the adaptively sampled electrochemical impedance spectroscopy measurements includes the controller controlling the battery based on the state of charge of the battery. [10] The system of claim 1, wherein: the battery and the control system are on board a vehicle. [11] The system of claim 1, wherein: the battery is on board a vehicle and the control is outside the vehicle. [12] A method for a vehicle having a traction battery and a controller, the method comprising: Performing, by the controller, adaptive sampled measurements of the traction battery by electrochemical impedance spectroscopy; and Controlling, with the controller, the traction battery and / or another component of the vehicle based on the adaptively sampled measurements of the traction battery by electrochemical impedance spectroscopy. [13] The method of claim 12, further comprising: Using the adaptively sampled measurements by electrochemical impedance spectroscopy to identify values ​​of parameters of a model of the traction battery; and Detecting an internal state of the traction battery based on the model. [14] The method of claim 13, further comprising: Detecting an estimate of a distance to empty state of the vehicle based on the internal state of the traction battery; and Controlling the traction battery and / or another vehicle component according to the estimated distance to empty vehicle. [15] Vehicle comprising: a traction battery; and a controller configured to charge and / or discharge the traction battery based on adaptive sampled measurements of the traction battery by electrochemical impedance spectroscopy.

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