Online characterization of battery model parameters with enhanced dynamic stimulation

Abstract

A method for intelligently generating a stimulus for characterizing a battery model parameter may include dynamically analyzing a current drawn by a load from a battery based on an analysis of the current, determining an enhancement current for enhancing the current drawn by the load, and generating the enhancement current based on a determination that the parameter needs to be updated.

Description

Technical Field

[0001] The present disclosure relates generally to circuits for electronic devices, including but not limited to personal portable devices such as wireless phones and media players, and more particularly to online characterization of battery model parameters with enhanced dynamic stimulation and estimating equivalent circuit model parameters of a battery. Background Art

[0002] Portable electronic devices are widely used, including wireless phones, such as mobile / cellular phones, tablet computers, cordless phones, mp3 players, and other consumer devices. Such portable electronic devices may include a battery (e.g., a lithium-ion battery) for powering components of the portable electronic device.

[0003] In operation, the terminal voltage of the battery may drop under load current due to the internal output impedance of the battery. This output impedance can be modeled in a number of suitable ways, including using an equivalent circuit model of a series of parallel coupled resistors and capacitors. Knowledge of the detailed impedance of the battery may be useful to fuel gauging algorithms (e.g., for determining battery open circuit voltage and state of charge, predicting power limits, and / or deriving safety limits or safe operating limits of the battery (e.g., maximum voltage and maximum current at the battery terminals)).

[0004] There may be advantages to using the system load current drawn from the battery to perform in situ characterization of the parameters of the equivalent circuit model, as this approach avoids the time-consuming and computationally expensive offline characterization of measuring the battery impedance over a frequency range. However, spectrally rich stimuli may be required to accurately estimate the equivalent circuit model parameters, and there is no guarantee that the system load current always contains spectrally rich content. Summary of the invention

[0005] According to the teachings of the present disclosure, one or more disadvantages and problems associated with existing methods of modeling batteries using equivalent circuit models may be reduced or eliminated.

[0006] According to an embodiment of the present disclosure, a method for intelligently generating stimulation for characterizing battery model parameters may include dynamically analyzing a current drawn by a load from a battery based on an analysis of the current, determining an enhanced current for enhancing the current drawn by the load, and generating the enhanced current based on determining that the parameters need to be updated.

[0007] According to these and other embodiments of the present disclosure, a system for intelligently generating stimuli for characterizing battery model parameters may include circuitry for dynamically analyzing current drawn by a load from a battery based on an analysis of the current, determining a boost current for boosting the current drawn by the load, and generating the boost current based on a determination that a parameter update is needed.

[0008] Those skilled in the art can easily see the technical advantages of the present disclosure from the drawings, descriptions and claims included herein. The objects and advantages of the embodiments will be realized and completed at least by the elements, features and combinations specifically pointed out in the claims.

[0009] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are not restrictive of the present disclosure, as claimed. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] A more complete understanding of the present embodiments and their advantages may be obtained by referring to the following description in conjunction with the accompanying drawings, wherein like reference numerals represent like features, and wherein:

[0011] Figure 1A A block diagram illustrating selected components of an example power delivery network according to an embodiment of the present disclosure;

[0012] Figure 1B A block diagram illustrating selected components of another example power delivery network according to an embodiment of the present disclosure;

[0013] Figure 1C A block diagram illustrating selected components of yet another example power delivery network according to an embodiment of the present disclosure;

[0014] Figure 2 shows an example graph of battery open circuit voltage versus battery state of charge according to an embodiment of the present disclosure;

[0015] Figure 3 A circuit diagram showing selected components of a battery equivalent circuit model according to an embodiment of the present disclosure;

[0016] Figure 4 A block diagram of a system for online characterization of an equivalent circuit model of a battery using sub-band and enhanced dynamic current stimulation according to an embodiment of the present disclosure is shown;

[0017] Figure 5A and Figure 5B A block diagram showing an example architecture of an algorithm for calculating parameters of an equivalent circuit model of a battery according to an embodiment of the present disclosure;

[0018] Figure 6 A block diagram illustrating an example architecture of a tracker for calculating parameters of an equivalent circuit model of a battery according to an embodiment of the present disclosure; and

[0019] Figure 7 A block diagram showing selected components of a system for online characterization of an equivalent circuit model of a battery using sub-band and enhanced dynamic current stimulation according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0020] Figure 1A A block diagram showing selected components of an example power delivery network 10A according to an embodiment of the present disclosure. In some embodiments, the power delivery network 10A may be implemented within a portable electronic device, such as a smartphone, tablet computer, game controller, and / or other suitable device.

[0021] like Figure 1A As shown, the power delivery network 10A may include a battery 12 and a load 18. Figure 1A As shown, when the load 18 is loaded, the battery 12 can generate a battery voltage V across its terminals. CELL , and the battery current I CELL The power delivery network 10A is transmitted to the load 18. In some embodiments, the battery 12 may include a lithium-ion battery. The load 18 may represent any electrical component, electronic component, and / or combination thereof. For example, the load 18 may include any suitable functional circuit or device of the power delivery network 10A, including but not limited to a power converter, a processor, an audio encoder / decoder, an amplifier, a display device, etc. In addition, although Figure 1A Although not explicitly shown, the power delivery network 10A may also include control circuitry for controlling the operation of the battery 12 and / or the load 18 .

[0022] like Figure 1A As further shown, the power delivery network 10A may include a battery monitoring circuit 20. The battery monitoring circuit 20 may include a circuit configured to monitor a battery voltage V CELL and the battery current I CELL In addition, the battery monitoring circuit 20 may include a battery model estimator 24, which is configured to receive the monitored battery voltage V CELL and indicates the battery current I CELL The sensing voltage V across the sensing resistor 22 SNS , and based thereon estimates a battery impedance model of the battery 12, as described in more detail below. The battery model estimator 24 may be implemented with a processing device, including but not limited to a microprocessor, a digital signal processor, an application specific integrated circuit, a field programmable gate array, an electrically erasable programmable read-only memory, a complex programmable logic device, and / or other suitable processing devices. In some embodiments, the battery monitoring circuit 20 may monitor a temperature associated with the battery 12, and the battery model estimator 24 may be based on the battery voltage V CELL , sensing voltage V SNS and the sensed temperature to estimate the impedance model.

[0023] like Figure 1A As further shown, the power delivery network 10A may also include a battery model estimator 24 controlled and configured to generate an enhanced current IAUG The following describes in more detail the battery model estimator 24's dependence on the enhancement current I AUG control.

[0024] Figure 1B A block diagram showing selected components of an example power delivery network 10B according to an embodiment of the present disclosure.In some embodiments, the power delivery network 10B may be implemented within a portable electronic device, such as a smartphone, tablet computer, game controller, and / or other suitable device. Figure 1B The power delivery network 10B shown may be similar in many respects to Figure 1A The illustrated power delivery network 10A is similar, and therefore only certain differences between the power delivery network 10A and the power delivery network 10B are described below.

[0025] In particular, the power delivery network 10B may not include the dependent current source 26, but may include a power converter 28 coupled between the battery 12 and the load 18. The power converter 28 is Figure 1B The boost converter is depicted as including a boost converter and a switch control system 19, the boost converter having a power inductor 27, switches 23 and 25, and an output capacitor 29 arranged as shown in the figure, and the switch control system 19 is used to control the switching of the switches 23 and 25 to generate a desired stable output voltage V on the output capacitor 29. OUT However, power converter 28 may be implemented using any suitable power converter, including but not limited to a buck converter, a cascaded combination of a buck converter and a boost converter, or any other suitable power converter.

[0026] In operation, the switch control system 19 may receive a control signal from the battery model estimator 24 to generate an enhanced current I to or from the output capacitor 29. AUG , while still setting the output voltage V OUT regulated to approximately its desired stable voltage. Thus, to generate the enhanced stimulus, as described in more detail below, the battery model estimator 24 may cause the switch control system 19 to transfer charge from the battery 12 to the output capacitor 29 by operating the power converter 28 as a boost converter from the battery 12 to the output capacitor 29 or to transfer charge from the output capacitor 29 to the battery 12 by operating the power converter 28 as a buck converter from the output capacitor 29 to the battery 12.

[0027] Furthermore, when AC power is drawn from the battery 12, the battery current I CELLThe output capacitor 29 can be drawn unidirectionally to periodically charge, and the load 18 (which can be a DC load) can discharge the output capacitor 29. This combination of charging and discharging the output capacitor 29 can draw a combination of DC and AC power from the battery 12 while maintaining the voltage on the output capacitor 29 within an acceptable voltage range (e.g., 3V-5V).

[0028] and the power delivery network 10A for generating an enhanced current I AUG Compared with the method of the power delivery network 10B, the power delivery network 10B is used to generate the enhanced current I AUG One advantage of the approach is that the power converter 28 can generate the enhanced current I by simply moving charge back and forth between the battery 12 and the output capacitor 29. AUG , which may consume less power than sinking current through dependent current source 26 in power delivery network 10A.

[0029] Figure 1C A block diagram showing selected components of an example power delivery network 10C according to an embodiment of the present disclosure.In some embodiments, the power delivery network 10C may be implemented within a portable electronic device, such as a smartphone, tablet computer, game controller, and / or other suitable device. Figure 1C The power delivery network 10C shown may be similar in many respects to Figure 1B The illustrated power delivery network 10B is similar, and therefore only certain differences between the power delivery network 10B and the power delivery network 10C are described below.

[0030] In particular, the power delivery network 10C may include a battery charger 8 coupled to the battery 12 and configured to charge the battery 12. The power converter 8 is Figure 1C The buck converter is depicted as including a buck converter and a switch control subsystem 9, the buck converter having a power inductor 7, switches 3 and 5, and a power source 11 arranged as shown, and the switch control subsystem 9 is used to control the switching of switches 3 and 5 to control the transfer of energy from the power source 11 to the battery 12, thereby charging the battery 12.

[0031] In operation, the switch control subsystem 9 may receive a control signal from the battery model estimator 24 to generate an enhanced current I to or from the output capacitor 29. AUG, while still charging the battery 12 from the power source 11 as needed. Therefore, in order to generate the enhanced stimulus, as described in more detail below, the battery model estimator 24 can cause the switch control subsystem 9 to transfer charge from the battery 12 to the power source 11 by operating the battery charger 8 as a boost converter from the battery 12 to the power source 11, or to transfer charge from the power source 11 to the battery 12 by operating the battery charger 8 as a buck converter from the power source 11 to the battery 12.

[0032] and the power delivery network 10A for generating an enhanced current I AUG Compared with the method, the power delivery network 10C is used to generate an enhanced current I AUG An advantage of the method is that the battery charger 8 can generate the enhanced current I by simply moving charge back and forth between the battery 12 and the power source 11. AUG , which may consume less power than sinking current through dependent current source 26 in power delivery network 10A.

[0033] As is known, lithium-ion batteries typically operate from 4.5 V down to 3.0 V, which is referred to as the open circuit voltage V of the battery (e.g., battery 12). OC .like Figure 2 As shown, when the battery is discharged due to the current drawn from the battery, the battery's state of charge may also decrease, and the open circuit voltage V OC (which may be a function of the state of charge) or may be reduced by electrochemical reactions occurring within the battery. OC Outside the 3.0V and 4.5V range, the capacity, life and safety of lithium-ion batteries may be reduced. For example, at about 3.0V, about 95% of the energy in the lithium-ion cell may be consumed (i.e., the state of charge is 5%), and if further discharge continues, the open circuit voltage V OC may drop rapidly. Below about 2.4 V, the metal plates of a lithium-ion battery may corrode, which may result in increased internal impedance of the battery, reduced capacity, and potential short circuits. Therefore, in order to protect a battery (e.g., battery 12) from excessive discharge, many portable electronic devices may prevent operation below a predetermined end-of-discharge voltage. Knowledge of the output impedance can be used to determine the open circuit voltage V of battery 12. OC and other parameters.

[0034] Figure 3 1 is a block diagram showing selected components of an equivalent circuit model of a battery 12 according to an embodiment of the present disclosure. Figure 3As shown, the battery 12 can be modeled as having a battery cell 32 having an open circuit voltage V in series with a plurality of parallel resistor-capacitor sections 34 (e.g., parallel resistor-capacitor sections 34-1, 34-2, ..., 34-N) and also in series with an equivalent series resistance 36 of the battery 12. OC , this equivalent series resistance 36 has R 0 Resistance R 1 , R 2 ,……R N and the corresponding capacitance C 1 , C 2 ,……,C N The battery chemistry-dependent time constant τ can be modeled 1 , τ 2 , ..., τ N , these time constants can be related to the open circuit voltage V OC and the equivalent series resistance 36. The series impedance components represented by the resistance-capacitance component 34 and the equivalent series resistance 36 can represent the diffusion processes occurring at different rates within the battery 12. The cutoff frequencies of the parallel resistance-capacitance components 34 can be given by the following equations:

[0035]

[0036] …

[0037]

[0038] wherein π represents a well-known mathematical constant defined as the ratio of the circumference of a circle to its diameter, and wherein the parallel resistor-capacitor portions 34 are arranged such that f cN <… <f c2 <f c1 .

[0039] It is worth noting that Figure 3 Medium voltage V CELL-EFF The depicted electrical nodes can capture the time-varying discharge behavior of the battery 12, and the battery voltage V CELL It may be the actual voltage seen across the output terminals of the battery 12. The voltage V CELL-EFF It may not be possible to measure the battery voltage V directly. CELL = may be the only voltage associated with battery 12 that can be measured to assess the battery's state of health. It is also worth noting that when current consumption is zero (e.g., I CELL =0), the battery voltage V CELL Can be equal to the voltage V CELL-EFF , and the voltage V CELL-EFF Then it can be equal to the open circuit voltage V at a given charge state OC, provided that sufficient time has passed since the current was zero so that all the voltage on capacitor 34 has discharged to zero.

[0040] Battery behavior may change due to various factors, including temperature, state of charge, charge / discharge current amplitude and / or frequency, etc. Changes in these factors may change the electrochemical state of the battery, and these dynamic behaviors may cause corresponding changes in the parameter values ​​of the equivalent circuit model. The parameters of the equivalent circuit model can be used to evaluate the battery condition, predict future voltage, and / or other battery management tasks.

[0041] Figure 4 A block diagram of a system 50 for online characterization of an equivalent circuit model of a battery 12 using sub-band and enhanced dynamic current stimulation according to an embodiment of the present disclosure is shown. The system 50 may be implemented as a battery model estimator 24 or a portion thereof.

[0042] To estimate the impedance model of the battery 12, the battery model estimator 24 may divide the output impedance of the battery 12 into a plurality of levels or frequency sub-bands 52, each level / sub-band 52 corresponding to a respective parallel resistor-capacitor portion 34 of the equivalent circuit model, thereby decomposing the online model estimation 54 into a number of low-order identification problems. c (or time constant τ) may be separated by an order of magnitude or more, such hierarchical estimation is possible. In addition, for each specific frequency sub-band 52, the battery model estimator 24 can monitor the current stimulus I drawn from the battery 12 through a spectrum / signal-to-noise ratio (SNR) adequacy analysis 56. CELL The battery model estimator 24 may also implement a decision subsystem 58 that may analyze the results of the online model estimation 54 for each subband 52 and the spectrum / SNR analysis 56 for each subband 52 to decide whether to generate an enhanced current stimulus for a particular subband 52. If the decision subsystem 58 determines to generate an enhanced current stimulus for a particular subband 52, the enhanced stimulus signal generator 60 may generate an enhanced current I AUG to supplement the current drawn by load 18 so as to draw a battery current I with sufficient spectral content CELL To characterize the parameters of the equivalent circuit model of the battery 12. The enhancement current I AUG Can be based on the battery current I CELL The decision subsystem 58 may determine, in response to determining that no parameter update is required, whether to increase the current I AUGIn these and other embodiments, an enhanced current I AUG The decision can be in response to drawing additional current from load 18 (e.g., activating a subsystem within the load that was previously not activated).

[0043] Figure 5A and Figure 5B A block diagram illustrating an example architecture of an algorithm for calculating parameters of an equivalent circuit model of battery 12 according to an embodiment of the present disclosure. Figure 5A and Figure 5B The functions of the various blocks depicted in the figure can be realized Figure 4 The sub-bands 52 and the online model estimation 54 are shown in FIG.

[0044] according to Figure 5A and Figure 5B According to the algorithm shown in the figure, the battery model estimator 24 can measure the battery voltage V CELL and the battery current I CELL The battery model estimator 24 can also apply different time constants τ that characterize the temporal behavior of the battery 12 to convert the measured battery voltage V CELL and the battery current I CELL The battery model estimator 24 may also continuously track the resistance R within each sub-band 52 and the resistance R associated therewith, as well as continuously track the open circuit voltage V of the battery 12. OC The time constant can be fixed or can be adjusted based on the battery voltage V CELL and the battery current I CELL Dynamic estimation.

[0045] like Figure 5A and Figure 5B As shown, the example architecture includes a multi-rate filter bank 62 including a plurality of decimators 66, a pre-filter 64, a signal selector 68, and a tracker 70 arranged as shown.

[0046] The multi-rate filter bank 62 is as follows Figure 5A As shown, it has a full frequency band of 40KHz, but can have any suitable full frequency band range. In addition, each of the extractors 66 can have Figure 5A decimation ratios shown, but each may have Figure 5A Each decimator 66 can be configured to convert the battery voltage V CELL and the battery current I CELLThe signal is decimated into a low frequency signal. Although specific exemplary frequencies associated with the decimators 66 are shown, any suitable frequency may be used. The output of each decimator 66 may be communicated to a corresponding pre-filter 64, and the output of each pre-filter 64 may represent various sub-bands 52, the battery voltage V CELL and the battery current I CELL The full frequency band of the signal is divided into these sub-bands. For example, Figure 5B The full frequency band is shown divided into sub-bands of 8-20 KHz, 5-8 KHz, 2-5 KHz, 400-2500 Hz, 800-400 Hz, 8-80 Hz, 0.8-8 Hz, 0.008-0.8 Hz, and 0-0.008 Hz, although the various sub-bands 52 may use any suitable range.

[0047] Signal selector 68 may receive a fixed time constant or an adaptive time constant for each parallel resistor-capacitor section 34. The fixed time constant may be based on a set of pre-stored values. The adaptive time constant may be determined by using the battery voltage V CELL and the battery current I CELL The adaptive algorithm dynamically calculates and / or may select the adaptive time constant from a lookup table, where an index of the lookup table is selected based on the battery charge state and / or temperature.

[0048] like Figure 5B As shown, the tracker 70 may perform inductor tracking 72 based on the output of the pre-filter 64 representing the highest frequency sub-band 52 to estimate the inductance value L 0 (not shown in Figure 5), the inductance value L 0 The equivalent resistance in series with the equivalent series resistance 36 of the battery 12 and the equivalent circuit model of the battery 12 are modeled. The tracker 70 may also include an adaptive block 74 (e.g., a recursive least squares method, a total least squares method, a normalized least mean square method, a total least squares cost function, and / or a gradient descent-based algorithm of different variations) that outputs a resistance value R of the equivalent circuit model of the battery 12 based on the output of the pre-filter 64 representing the highest frequency sub-band 52. 0 .

[0049] The output of the remaining prefilter 64 may be provided as an input to a signal selector 68. Figure 5A and Figure 5BAs shown, the signal selector 68 can receive a plurality of different time constants that can be distributed to cover the entire spectral range of interest and the total time constant value. The signal selector 68 can select a signal from the output of the pre-filter 64 based on the time constant for each transform 76 / resistor update 78 pair of the tracker 70. Specifically, the signal selector 68 can select an appropriate signal from the multi-rate filter bank 62 so that the spectral coverage of the selected signal includes the frequencies corresponding to the corresponding time constants.

[0050] Tracker 70 may also include a plurality of transform 76 / resistor update 78 pairs that may calculate corresponding resistance values ​​R of an equivalent circuit model of battery 12. 1 ,……,R N For example, in some embodiments, the battery model estimator 24 may calculate the corresponding resistance value R of the equivalent circuit model of the battery 12 in a manner similar to that disclosed in U.S. patent application Ser. No. 17 / 463,980, filed Sept. 1, 2021, and incorporated herein by reference. 1 ,……,R N . For example, the resistor update block 78 may use an adaptive algorithm implemented by the adaptive control block 80 of the tracker 70 to continuously update the resistance representing each sub-band 52 using recursive least squares, total least squares, normalized least mean squares, or other suitable methods. In some embodiments, the parameters of the equivalent circuit model of the battery 12 may be updated by the resistor update block 78: (a) when the full-band current rms level is above a threshold, (b) to minimize the sub-band error; or (c) to minimize the full-band error together. The learning rate of the update algorithm may be based on the sub-band rms current level. In some embodiments, the resistor update block 78 may be constrained to calculate only positive resistance values.

[0051] Tracker 70 may also include a step size control block 82. If the adaptive algorithm implemented by adaptive control block 80 includes a type of gradient descent-based algorithm, step size control block 82 may dynamically calculate the learning rate or step size of such an algorithm to ensure that the parameters converge faster while not deviating from the true value.

[0052] The tracker 70 may also include a regularization block 84. Due to the dynamic nature of the signal and the changes in signal-to-noise conditions, it is important that the adaptive algorithm does not deviate too much from the true solution. The regularization block 84 may avoid the problem of overfitting, which may occur when the adaptive algorithm adapts during low signal-to-noise conditions and attempts to minimize the modeling error, even when the background noise dominates the signal.

[0053] The algorithm may also include an open circuit voltage (OCV) tracking block 86 configured to generate an open circuit voltage (OCV) signal based on the input received from the pre-filter 64 representing the lowest frequency sub-band 52 and various resistance values ​​R of the equivalent circuit model of the battery 12. 1 ,……,R N Adaptively estimates the open circuit voltage V OC For example, the OCV tracking block 86 may continuously update the estimated open circuit voltage V based on the output of the lowest frequency prefilter 64 and the tracking resistance of the equivalent circuit model of the battery 12 using an adaptive algorithm (e.g., recursive least squares, total least squares, normalized least mean squares method). OC In some embodiments, the OCV may be provided by another estimator or algorithm, such as from a fuel gauge solution.

[0054] Figure 6 A block diagram illustrating an example architecture of a tracker 70 for calculating parameters of an equivalent circuit model of a battery 12 according to an embodiment of the present disclosure. Figure 6 The architecture shown can be implemented Figure 5A and Figure 5B A transformation block 76 and a resistor update block 78 are shown.

[0055] Figure 6 The battery voltage V is depicted as being decomposed into sub-bands 52 as described above. CELL and the battery current I CELL 1 to M paths for the signal. Each processing path may include Figure 6 The arrangement shown is a cascaded current and voltage block 90, a time constant dependent filter block 92, a resistor estimation block 94, a control block 96, and an adder 98. Each cascaded current and voltage block 90 may receive a battery current I CELL The current and past sampled values ​​and the battery voltage V CELL The time constant dependent filter block 92 may receive the output from its corresponding cascaded current and voltage block 90 and the time constant T of a given subband 52. i . This filtering can be performed in order to convert a nonlinear optimization problem into a linear optimization problem. The original equivalent circuit model identification problem involves estimating either resistors and capacitors or resistors and time constants. Estimating them together is a nonlinear optimization problem that is not suitable to be done in embedded applications. Therefore, by assuming a priori knowledge of the time constant, this filtering can reduce the problem to a linear problem. The time constant can be determined by offline characterization of the battery or it can be estimated dynamically by a separate time constant estimation block.

[0056] The control block 96 may be configured to provide the adaptive algorithm with signal conditions sufficient to update the resistance value (e.g., R1 ,……,R N ) indication. Each resistor estimation block 94 can provide a battery voltage V for its corresponding sub-band 52 CELL The combiner 98 can compare the actual sample with the predicted current sample to generate a prediction error. This prediction error can then be used in the parameter update equation of the adaptive filter. The controller 96 can control the update algorithm to control the resistor estimation block 94 to minimize the prediction error.

[0057] Figure 7 1 is a block diagram of selected components of a system 50 according to an embodiment of the present disclosure. Specifically, Figure 7 Further details showing the operability between the spectrum / SNR adequacy analysis block 56, the decision subsystem 58 and the enhanced stimulation signal generator 60 are depicted.

[0058] When system load current is present (e.g., when load 18 draws current from battery 12), boost current I may be performed on a sub-band basis. AUG The decision subsystem 58 may determine the total stimulus requirement within each subband based on the state of charge (SOC) and temperature conditions during the last update of the model parameters for the corresponding subband compared to the current SOC and temperature conditions (i.e., determine whether any changes in SOC and / or temperature have occurred that require a parameter update). In addition, the decision subsystem 58 may also receive a noise distribution from the spectrum / SNR sufficiency analysis block 56, which may continuously evaluate the current within each subband for spectrum sufficiency and noise. Therefore, the decision subsystem 58 may also determine the total stimulus requirement within each subband based on such noise distribution, the SNR requirement of the subband, the duration of the signal in the subband required to calculate the parameters associated with such subband (which may vary between subbands), the amount of time that has passed since the previous parameter update of the subband, and the estimated confidence of the subband. For example, the spectrum / SNR sufficiency analysis block 56 may analyze the battery current I CELL The decision subsystem 58 may estimate the current SNR in each subband, and the decision subsystem 58 may use this SNR information to modify the stimulation level of such subband accordingly. The decision subsystem 58 may also determine the total stimulation requirement within each subband based on the state of health (SOH) of the battery 12. The decision subsystem 58 may also employ one or more energy conservation schemes in the decision process of determining the total stimulation requirement. For example, model parameters within certain frequency ranges may not change due to SOC and / or temperature changes, and therefore, the decision subsystem 58 may limit the enhanced current generation of such subbands to conditions other than SOC and / or temperature changes.

[0059] Based on the total stimulus requirement, system power requirement, battery management system requirement, battery linear mode requirement, and / or other factors determined by decision subsystem 58, boost stimulus signal generator 60 may generate boost current I for each specific subband. AUG Generally speaking, the boost stimulus signal generator 60 (working in conjunction with the decision subsystem 58) can track the absolute current and, if the current is above a certain threshold, can cause the adaptation of the equivalent circuit model parameters to be disabled. However, if the absolute current requirement is not met over time, the boost stimulus signal generator 60 can cause the generation of a low level boost current I AUG , thereby estimating the equivalent circuit model parameters within the linear range of the operating conditions of the battery 12.

[0060] As briefly mentioned above, the enhancement current I AUG The generation of the enhanced current I may be affected by various factors and conditions. For example, in some embodiments, one or more of the following conditions may be required to exist within a sub-band in order to trigger the generation of the enhanced current I for such sub-band: AUG :

[0061] The SOC changes significantly, and one of the sub-bands or the full band does not have sufficient load current within a preset period of time;

[0062] The temperature of a sub-band or full-band has changed significantly since the last verification of the equivalent circuit model parameter update, without significant load current;

[0063] Continuous tracking model fitting performance drifts in sub-band or full-band within a preset time period without significant load current;

[0064] The RMS value of the current changes significantly within a preset time period;

[0065] During the preset time period, the average battery voltage V CELL The rate of change of

[0066] The RMS value of the current is below the threshold for a period of time.

[0067] As used herein, when two or more elements are referred to as being “coupled” to each other, such term means that the two or more elements are in electrical or mechanical communication, as applicable, whether indirectly or directly connected, with or without intervening elements.

[0068] The present disclosure covers all changes, substitutions, changes, alterations and modifications to the example embodiments herein that will be understood by a person of ordinary skill in the art. Similarly, where appropriate, the appended claims cover all changes, substitutions, changes, alterations and modifications to the example embodiments herein that will be understood by a person of ordinary skill in the art. In addition, in the appended claims, references to devices or systems or components of devices or systems that are suitable for, arranged to, capable of, configured to, enabled to, operable to, or operable to perform a specific function cover the device, system or component, regardless of whether it or the specific function is activated, turned on or unlocked, as long as the device, system or component is so adapted, arranged, capable, configured, enabled, operable or operable. Therefore, without departing from the scope of the present disclosure, the systems, devices and methods described herein may be modified, added or omitted. For example, the components of the systems and devices may be integrated or separated. In addition, the operations of the systems and devices disclosed herein may be performed by more, less or other components, and the described methods may include more, less or other steps. In addition, the steps may be performed in any suitable order. As used in this application, "each" refers to each member in a set or each member in a subset of a set.

[0069] Although exemplary embodiments are shown in the drawings and described below, the principles of the present disclosure may be implemented using any number of techniques, whether currently known or not. The present disclosure should in no way be limited to the exemplary implementations and techniques shown in the drawings and described above.

[0070] Unless specifically noted otherwise, items depicted in the drawings are not necessarily drawn to scale.

[0071] All examples and conditional language described herein are intended for teaching purposes to help readers understand the present disclosure and the concepts contributed by the inventors to further develop the art, and are interpreted as not being limited to these specifically cited examples and conditions. Although the embodiments of the present disclosure have been described in detail, it should be understood that various changes, substitutions and modifications may be made to the present disclosure without departing from the spirit and scope of the present disclosure.

[0072] Although specific advantages are listed above, various embodiments may include some, none or all of the listed advantages. In addition, after reading the above drawings and descriptions, those of ordinary skill in the art may easily see other technical advantages.

[0073] To assist the Patent Office and any reader of any patent that issues from this application in interpreting the appended claims, applicants wish to note that unless the words "means for" or "step for" are expressly used in a particular claim, they do not intend any of the appended claims or claim elements to invoke 35 U.S.C. § 112(f).

Claims

1. A method for intelligently generating stimuli for parameters of a model for characterizing a battery, include: dynamically analyzing the current drawn from the battery by a load; determining a boost current for boosting the current drawn by the load based on the analysis of the current; as well as The boost current is generated based on determining that the parameter needs to be updated.

2. The method according to claim 1, in, The parameters correspond to model parameters of an equivalent circuit model of the battery.

3. The method according to claim 1 or 2, in, Determining the boost current includes determining the boost current based on a signal-to-noise ratio requirement of an algorithm used to estimate the parameter.

4. The method according to any one of claims 1 to 3, in, Determining the boost current includes determining the boost current based on a spectral requirement of an algorithm used to estimate the parameter.

5. The method according to any one of claims 1 to 4, in, Dynamically analyzing the current includes measuring a battery current drawn from the battery and a battery voltage across terminals of the battery.

6. The method according to any one of claims 1 to 5, in, Dynamically analyzing the current includes determining whether each of a plurality of frequency sub-bands has sufficient spectral content required to estimate the parameter associated with the frequency sub-band, as required by an algorithm for estimating the parameter.

7. The method according to any one of claims 1 to 6, in, Determining that the parameters need to be updated is based on a change in temperature associated with the battery since a previous update of one or more of the parameters.

8. The method according to any one of claims 1 to 6, in, Determining that the parameter needs to be updated is based on a comparison of a root mean square value of the current over a period of time with a preset threshold.

9. The method according to any one of claims 1 to 6, in, Determining that the parameter needs to be updated is based on whether a rate of change of a battery voltage across terminals of the battery exceeds a preset threshold.

10. The method according to any one of claims 1 to 6, in, Determining whether the parameter needs to be updated is based on whether the current is below a preset threshold for a preset period of time.

11. The method according to any one of claims 1 to 6, in, Determining that the parameter needs to be updated is based on a confidence level associated with the parameter.

12. The method according to claim 1 or 2, in: Dynamically analyzing the current drawn by the load from the battery includes dynamically analyzing spectral content of the current present in each of a plurality of frequency sub-bands; and Determining the boost current for boosting the current drawn by the load includes determining a boost current required for each sub-band based on analyzing spectral content of current present in each of the plurality of frequency sub-bands to satisfy requirements for estimating parameters associated with that sub-band according to an estimation algorithm.

13. The method according to any one of claims 1 to 12, in, Generating the boost current includes generating the boost current with a power converter.

14. The method according to claim 13, in, Generating the enhanced current includes bidirectionally operating the power converter as a buck converter and a boost converter to transfer charge from the battery to an energy storage device and vice versa to generate the enhanced current.

15. The method according to claim 14, in, The energy storage device includes an output capacitor of the power converter.

16. The method according to claim 14 or 15, in, The power converter implements a battery charger for charging the battery, and the energy storage device comprises an input capacitor or an output capacitor of the battery charger.

17. The method according to any one of claims 13 to 16, in, The power converter is a boost converter.

18. The method according to claim 17, in, When AC power is drawn from the battery, the current is drawn unidirectionally to periodically charge the output capacitor of the boost capacitor and the load discharges the output capacitor such that the charging and discharging of the output capacitor draws a combination of DC and AC power from the battery while maintaining the voltage on the output capacitor within an acceptable voltage range.

19. The method of any one of claims 1-18, further comprising generating the boost current to zero in response to determining that updating the parameter is not required.

20. The method according to any one of claims 1 to 19, in, The determined need can be determined in response to additional current being drawn from the load.

21. A system for intelligently generating stimuli for characterizing parameters of a model of a battery, comprising circuitry for: dynamically analyzing the current drawn from the battery by a load; determining a boost current for boosting the current drawn by the load based on the analysis of the current; as well as The boost current is generated based on determining that the parameter needs to be updated.

22. The system according to claim 21, in, The parameters correspond to model parameters of an equivalent circuit model of the battery.

23. The system according to claim 21 or 22, in, Determining the boost current includes determining the boost current based on a signal-to-noise ratio requirement of an algorithm used to estimate the parameter.

24. A system according to any one of claims 21 to 23, in, Determining the boost current includes determining the boost current based on a spectral requirement of an algorithm used to estimate the parameter.

25. The system according to any one of claims 21 to 24, in, Dynamically analyzing the current includes measuring a battery current drawn from the battery and a battery voltage across terminals of the battery.

26. The system according to any one of claims 21 to 25, in, Dynamically analyzing the current includes determining whether each of a plurality of frequency sub-bands has sufficient spectral content required to estimate the parameter associated with the frequency sub-band, as required by an algorithm for estimating the parameter.

27. The system according to any one of claims 21 to 26, in, Determining that the parameters need to be updated is based on a change in temperature associated with the battery since a previous update of one or more of the parameters.

28. The system according to any one of claims 21 to 26, in, Determining that the parameter needs to be updated is based on a comparison of a root mean square value of the current over a period of time with a preset threshold.

29. The system according to any one of claims 21 to 26, in, Determining that the parameter needs to be updated is based on whether a rate of change of a battery voltage across terminals of the battery exceeds a preset threshold.

30. The system according to any one of claims 21 to 26, in, Determining whether the parameter needs to be updated is based on whether the current is below a preset threshold for a preset period of time.

31. A system according to any one of claims 21 to 26, in, Determining that the parameter needs to be updated is based on a confidence level associated with the parameter.

32. The system according to claim 21 or 22, in: Dynamically analyzing the current drawn by the load from the battery includes dynamically analyzing spectral content of the current present in each of a plurality of frequency sub-bands; and Determining the boost current for boosting the current drawn by the load includes determining a boost current required for each sub-band based on analyzing spectral content of current present in each of the plurality of frequency sub-bands to satisfy requirements for estimating parameters associated with that sub-band according to an estimation algorithm.

33. A system according to any one of claims 21-22, in, Generating the boost current includes generating the boost current with a power converter.

34. The system according to claim 33, in, Generating the enhanced current includes bidirectionally operating the power converter as a buck converter and a boost converter to transfer charge from the battery to an energy storage device and vice versa to generate the enhanced current.

35. The system according to claim 34, in, The energy storage device includes an output capacitor of the power converter.

36. A system according to claim 34 or 35, in, The power converter implements a battery charger for charging the battery, and the energy storage device comprises an input capacitor or an output capacitor of the battery charger.

37. A system according to any one of claims 33 to 36, in, The power converter is a boost converter.

38. The system according to claim 37, in, When AC power is drawn from the battery, the current is drawn unidirectionally to periodically charge the output capacitor of the boost capacitor and the load discharges the output capacitor such that the charging and discharging of the output capacitor draws a combination of DC and AC power from the battery while maintaining the voltage on the output capacitor within an acceptable voltage range.

39. The system of any one of claims 21-38, further comprising generating the boost current to zero in response to determining that updating the parameter is not required.

40. The system according to any one of claims 21 to 39, in, The determined need can be determined in response to drawing additional current from the load.

41. A method for estimating parameters of an equivalent circuit model of a battery, include: measuring a battery voltage across terminals of the battery and a battery current drawn from the battery; decomposing the battery voltage and the battery current into a plurality of sub-bands, each of the plurality of sub-bands being based on a time constant characterizing a temporal behavior of the battery; For each sub-band of the plurality of sub-bands, estimating an equivalent resistance of the sub-band based on spectral contents of a battery voltage and a battery current of the sub-band; as well as An open circuit voltage of the battery is estimated based at least on spectral content of a battery voltage and a battery current present in one of the plurality of sub-bands and equivalent resistances of the plurality of sub-bands.

42. The method according to claim 41, in, The time constant is fixed.

43. The method according to claim 41, in, The time constant is adaptive.

44. The method according to any one of claims 41 to 43, in, Decomposing includes decomposing the battery voltage and the battery current into the plurality of sub-bands using a multi-rate filter bank.

45. The method according to any one of claims 41 to 44, in, Estimating the equivalent resistance of each subband includes adjusting the equivalent resistance using an adaptive algorithm including one of an adaptive recursive least squares method, a total least squares method, a normalized least mean squares method, a total least squares cost function, or a gradient descent based algorithm.

46. ​​The method of claim 45, further comprising updating the equivalent resistance of each sub-band only when the root mean square level of the battery current is above a threshold.

47. The method of claim 45 or 46, further comprising updating the equivalent resistance of each sub-band to minimize the error of that sub-band.

48. The method of claim 45 or 46, further comprising updating the equivalent resistance of each sub-band to minimize full-band error.

49. The method according to any one of claims 45 to 48, in, The learning rate of the adaptive algorithm is based on the RMS level of the current in the sub-band.

50. The method according to claim 49, in, The adaptive algorithm includes an updating step of updating the equivalent resistance based on the error.

51. The method according to any one of claims 45 to 50, in, A regularization step is applied during the parameter updating step of the adaptive algorithm.

52. The method according to any one of claims 41-51, further comprising: include: determining spectral adequacy of battery current in each of the sub-bands; as well as When it is determined that spectral deficiency exists in one or more sub-bands, a boost current is generated to be drawn from the battery.

53. The method of any one of claims 41-52, further comprising estimating an open circuit voltage of the battery based at least on spectral content of the battery voltage and battery current present in one of the plurality of sub-bands and equivalent resistances of the plurality of sub-bands.

54. The method of any of claims 41-53, further comprising estimating an inductance of an equivalent circuit model based at least on spectral content of a battery voltage and a battery current present in one of the plurality of sub-bands.

55. A system for estimating parameters of an equivalent circuit model of a battery, comprising circuitry for: measuring a battery voltage across terminals of the battery and a battery current drawn from the battery; decomposing the battery voltage and the battery current into a plurality of sub-bands, each of the plurality of sub-bands being based on a time constant characterizing a temporal behavior of the battery; For each sub-band of the plurality of sub-bands, estimating an equivalent resistance of the sub-band based on spectral contents of a battery voltage and a battery current of the sub-band; as well as An open circuit voltage of the battery is estimated based at least on spectral content of a battery voltage and a battery current present in one of the plurality of sub-bands and equivalent resistances of the plurality of sub-bands.

56. The system according to claim 55, in, The time constant is fixed.

57. The system according to claim 55, in, The time constant is adaptive.

58. A system according to any one of claims 55-57, in, Decomposing includes decomposing the battery voltage and the battery current into the plurality of sub-bands using a multi-rate filter bank.

59. The system according to claim 55, in, Estimating the equivalent resistance of each subband includes adjusting the equivalent resistance using an adaptive algorithm including one of an adaptive recursive least squares method, a total least squares method, a normalized least mean squares method, a total least squares cost function, or a gradient descent based algorithm.

60. The system of claim 59, the circuit further configured to update the equivalent resistance of each sub-band only when the root mean square level of the battery current is above a threshold.

61. The system of claim 59 or 60, wherein the circuit is further configured to update the equivalent resistance of each sub-band to minimize the error of the sub-band.

62. The system of claim 59 or 60, wherein the circuit is further configured to update the equivalent resistance of each sub-band to minimize full-band error.

63. The system according to any one of claims 59 to 62, in, The learning rate of the adaptive algorithm is based on the RMS level of the current in the sub-band.

64. The system according to claim 63, in, The adaptive algorithm includes an updating step of updating the equivalent resistance based on the error.

65. The system of any one of claims 59-64, wherein the circuit is further configured to apply a regularization step during a parameter updating step of the adaptive algorithm.

66. The system of any one of claims 55-65, wherein the circuit is further configured to: determining spectral adequacy of battery current in each of the sub-bands; and When it is determined that spectral deficiency exists in one or more sub-bands, a boost current is generated to be drawn from the battery.

67. The system of any one of claims 55-66, the circuit further configured to estimate an open circuit voltage of the battery based at least on spectral content of the battery voltage and battery current present in one of the plurality of sub-bands and equivalent resistances of the plurality of sub-bands.

68. The system of any of claims 55-67, the circuit further configured to estimate an inductance of the equivalent circuit model based at least on spectral content of a battery voltage and a battery current present in one of the plurality of sub-bands.

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