Electrode voltage real-time estimation and self-adaptation for direct current fast charge control

Abstract

An electric vehicle includes a system for charging a battery of the electric vehicle. The system includes a sensor for obtaining a reference measurement of the battery during charging and a processor. The processor is configured to calculate an enhancement state from the reference measurement, determine an anode voltage from the enhancement state, compare the anode voltage to a threshold, and adjust a charge rate based on the comparison of the anode voltage to the threshold.

Description

Technical Field

[0001] The subject disclosure relates to charging electric vehicles, and in particular, to systems and methods for estimating electrode voltage during a charging operation and adjusting parameters of the charging operation based on the estimated electrode voltage. Background Art

[0002] Batteries used in electric vehicles require periodic charging. During charging, the charging rate may need to be adjusted to prevent damage to the battery, for example by preventing lithium plating from occurring at the battery. Adjustment can be made based on the anode voltage or the cathode voltage. However, these voltages are measured between the anode and a reference electrode or between the cathode and a reference electrode of the battery. There is no direct method for accessing the reference electrode in order to estimate the anode voltage and / or cathode voltage. Therefore, it is desirable to provide a system and method for determining the anode voltage and / or cathode voltage during a charging operation. Summary of the invention

[0003] In one exemplary embodiment, a method of charging a battery is disclosed. A reference measurement of the battery is obtained during charging. An enhancement state is calculated from the reference measurement. An anode voltage is determined from the enhancement state. The anode voltage is compared to a threshold. A charging rate is adjusted based on the comparison of the anode voltage to the threshold.

[0004] In addition to one or more features described herein, the method also includes determining the anode voltage from the enhancement state using at least one of a regression model and a neural network. The method also includes adjusting coefficients of the regression model or parameters of the neural network to obtain estimates of the anode voltage and the cathode voltage. The method also includes calculating the enhancement state by applying a nonlinear transformation to a reference measurement. The method also includes adjusting coefficients of the nonlinear transformation based on historical data. In an embodiment, the reference measurement is adjusted for the age of the battery. The method further includes assigning a severity metric when the anode voltage is less than a threshold, determining degradation of the battery based on the severity metric, and adjusting a charging rate based on the degradation of the battery.

[0005] In another exemplary embodiment, a system for charging a battery of an electric vehicle is disclosed. The system includes a sensor for obtaining a reference measurement of the battery during charging, and includes a processor. The processor is configured to calculate an enhancement state based on the reference measurement, determine an anode voltage based on the enhancement state, compare the anode voltage to a threshold, and adjust a charging rate based on the comparison of the anode voltage to the threshold.

[0006] In addition to one or more features described herein, the processor is further configured to determine the anode voltage from the enhancement state using at least one of a regression model and a neural network. The processor is further configured to adjust coefficients of the regression model or parameters of the neural network to obtain estimates of the anode voltage and the cathode voltage. The processor is further configured to apply a nonlinear transformation to a reference measurement to calculate the enhancement state. The processor is further configured to adjust coefficients of the nonlinear transformation based on historical data. The processor is further configured to adjust the reference measurement for the age of the battery. The processor is further configured to assign a severity metric when the anode voltage is less than a threshold, determine degradation of the battery based on the severity metric, and adjust a charging rate based on the degradation of the battery.

[0007] In yet another exemplary embodiment, an electric vehicle is disclosed. The electric vehicle includes a sensor for obtaining a reference measurement of a battery of the electric vehicle during charging, and includes a processor. The processor is configured to calculate an enhancement state based on the reference measurement, determine an anode voltage based on the enhancement state, compare the anode voltage to a threshold, and adjust a charging rate based on the comparison of the anode voltage to the threshold.

[0008] In addition to one or more features described herein, the processor is further configured to determine the anode voltage from the enhancement state using at least one of a regression model and a neural network. The processor is further configured to apply a nonlinear transformation to a reference measurement to calculate the enhancement state. The processor is further configured to adjust coefficients of the nonlinear transformation based on historical data. The processor is further configured to adjust the reference measurement for the age of the battery. The processor is further configured to assign a severity metric when the anode voltage is less than a threshold, determine degradation of the battery based on the severity metric, and adjust a charge rate based on the degradation of the battery.

[0009] The above features and advantages and other features and advantages of the present disclosure are apparent from the following detailed description when taken in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Other features, advantages and details appear, by way of example only, in the following detailed description, which refers to the accompanying drawings, in which:

[0011] Figure 1 An embodiment of a vehicle according to an exemplary embodiment is shown;

[0012] Figure 2 A battery pack suitable for use in an electric vehicle in an illustrative embodiment is shown;

[0013] Figure 3 a graph showing the relationship between anode voltage and state of charge of a battery;

[0014] Figure 4 A graph showing the relationship between cathode voltage and state of charge (SOC) of a battery;

[0015] Figure 5 is a flow chart illustrating a method for developing a virtual sensor based on an augmented state;

[0016] Figure 6 A diagram showing a charging system for adjusting a charging rate of a battery during a charging operation;

[0017] Figure 7 a flow chart showing a method of operating a charging system for charging an electric vehicle; and

[0018] Figure 8 A flow chart is shown of a method for operating a charging system based on the severity of a violation of a lithium plating threshold. DETAILED DESCRIPTION

[0019] The following description is merely exemplary in nature and is not intended to limit the present disclosure, its application, or uses. It should be understood that throughout the drawings, corresponding reference numerals indicate the same or corresponding parts and features.

[0020] According to an exemplary embodiment, Figure 1 An embodiment of a vehicle 10 is shown that includes a body 12 that at least partially defines a passenger compartment 14. The body 12 also supports various vehicle subsystems, including a propulsion system 16, and other subsystems for supporting the functions of the propulsion system 16 and other vehicle components, such as a braking subsystem, a suspension system, a steering subsystem, etc.

[0021] The vehicle 10 may be an electric vehicle (EV), a hybrid vehicle, or any other vehicle. In an embodiment, the vehicle 10 is an electric vehicle including a plurality of motors and / or a drive system. Any number of drive units may be included, such as one or more drive units for applying torque to the front wheels (not shown) and / or the rear wheels (not shown). The drive units are controllable to operate the vehicle 10 in various operating modes, such as a normal mode, a high-performance mode (in which additional torque is applied), all-wheel drive ("AWD"), front-wheel drive ("FWD"), rear-wheel drive ("RWD"), etc.

[0022] For example, the propulsion system 16 is a multi-drive system including a front drive unit 20 for driving the front wheels and a rear drive unit for driving the rear wheels. The front drive unit 20 includes a front electric motor 22 and a front inverter 24 (e.g., a front power inverter module or FPIM), as well as other components such as a cooling system. The left rear drive unit 30L includes a left rear electric motor 32L and a left rear inverter 34L. The right rear drive unit 30R includes a right rear electric motor 32R and a right rear inverter 34R. The front inverter 24, the left rear inverter 34L, and the right rear inverter 34R (e.g., a power inverter unit or PIM) each convert the direct current (DC) power from the high voltage (HV) battery system 40 into a multi-phase (e.g., two-phase, three-phase, six-phase, etc.) alternating current (AC) power to drive the front electric motor 22, the left rear electric motor 32L, and the right rear electric motor 32R.

[0023] like Figure 1 As shown, the drive system features individual electric motors. However, embodiments are not limited thereto. For example, instead of individual motors, multiple drives may be provided by a single machine having physically independent sets of windings.

[0024] Also like Figure 1 As shown, the drive system is configured so that the front electric motor 22 drives the front wheels (not shown), and the left rear electric motor 32L and the right rear electric motor 32R drive the rear wheels (not shown). However, the embodiments are not limited to this, as there can be any number of drive systems and / or motors at various locations (e.g., a motor driving each wheel, dual motors for each axle, etc.). In addition, the embodiments are not limited to dual drive systems, as the embodiments can be used with vehicles having any number of motors and / or power inverters.

[0025] In the propulsion system 16, the front drive unit 20, the left rear drive unit 30L, and the right rear drive unit 30R are electrically connected to a battery system 40. The battery system 40 may also be electrically connected to other electrical components (also referred to as "electrical loads"), such as vehicle electronics (e.g., via an auxiliary power module or APM 42), heaters, cooling systems, etc. The battery system 40 may be configured as a rechargeable energy storage system (RESS).

[0026] In one embodiment, the battery system 40 includes a plurality of individual battery assemblies, each of which can be independently charged and can be used to independently supply power to one or more drive systems. For example, the battery system 40 includes a first battery assembly, such as a first battery pack 44 connected to the front inverter 24, and includes a second battery pack 46. The first battery pack 44 includes a plurality of battery modules 48, and the second battery pack 46 includes a plurality of battery modules 50. Each battery module 48, 50 includes a plurality of individual battery cells (not shown). In various embodiments, one or more battery packs may include MODACS (Multi-Output Dynamically Adjustable Capacity) batteries, as described herein with respect to Figure 2-4 described.

[0027] Each of the front electric motor 22 and the left rear electric motor 32L and the right rear electric motor 32R is a three-phase motor having a three-phase motor winding. However, the embodiments described herein are not limited thereto. For example, the motor may be any multi-phase machine supplied by a multi-phase inverter, and the drive unit may be implemented using a single machine having an independent winding set.

[0028] The battery system 40 and / or the propulsion system 16 include a switching system having various switching devices for controlling the operation of the first battery pack 44 and the second battery pack 46 and selectively connecting the first battery pack 44 and the second battery pack 46 to the front drive unit 20, the left rear drive unit 30L, and the right rear drive unit 30R. The switching device can also be operated to selectively connect the first battery pack 44 and the second battery pack 46 to the charging system. The charging system can be used to charge the first battery pack 44 and the second battery pack 46, and / or supply power from the first battery pack 44 and / or the second battery pack 46 to charge another energy storage system (e.g., vehicle-to-vehicle (V2V) and / or vehicle-to-everything (V2X) charging). The charging system includes one or more charging modules. For example, a first onboard charging module (OBCM) 52 is electrically connected to a charging port 54 for charging and charging from an AC system or device (such as a public AC power source). A second OBCM 53 may be included for DC charging (e.g., DC fast charging or DCFC). As Figure 1 As shown, the utility AC power source is a charging station 110 connected to the charging port 54 via an electrical line 112 .

[0029] In an embodiment, the switch system includes a first switch device 60 that selectively connects the first battery pack 44 to the front inverter 24, the left rear inverter 34L, and the right rear inverter 34R, and a second switch device 62 that selectively connects the second battery pack 46 to the front inverter 24, the left rear inverter 34L, and the right rear inverter 34R. The switch system also includes a third switch device 64 (also referred to as a "battery switch device") for selectively connecting the first battery pack 44 in series to the second battery pack 46.

[0030] Any of a variety of controllers may be used to control the functions of the battery system 40, the switching system, and the drive unit. The controller includes any suitable processing device or unit, and an existing controller such as a drive system controller, a RESS controller, and / or a controller in a drive system may be used. For example, a controller 65 may be included to control the switching and drive control operations as discussed herein.

[0031] The controller 65 may include processing circuitry that may include an application specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group) and memory that executes one or more software or firmware programs, combinatorial logic circuits, and / or other suitable components that provide the described functionality. The controller 65 may include a non-transitory computer readable medium storing instructions that, when processed by one or more processors of the controller 65, implement a method of determining an anode voltage and a cathode voltage during charging and adjusting a charging rate of a vehicle based on the anode voltage and the cathode voltage according to one or more embodiments detailed herein.

[0032] The vehicle 10 also includes a computer system 55 including one or more processing devices 56 and a user interface 58. The computer system 55 can communicate with the charging system controller, for example, to provide commands thereto in response to user input. The various processing devices, modules, and units can communicate with each other via a communication device or system, such as a controller area network (CAN) or a transmission control protocol (TCP) bus.

[0033] As shown herein, the vehicle 10 is an electric vehicle. In alternative embodiments, the vehicle 10 may be an internal combustion engine vehicle, a hybrid vehicle, etc., or a system utilizing a rechargeable battery pack, such as a mobile phone, a laptop computer, etc.

[0034] Figure 2 A battery pack 200 suitable for use in a vehicle 10 in an illustrative embodiment is shown. The battery pack 200 includes an anode 202, a cathode 204, and a reference electrode 206. A 3-electrode test may be used to obtain voltage measurements of the battery. The voltage measurements may include the anode voltage V A and cathode voltage V CThe anode voltage is obtained by measuring the voltage difference between the anode 202 and the reference electrode 206, and the cathode voltage V is obtained by measuring the voltage difference between the cathode 204 and the reference electrode 206. C .

[0035] Figure 3 A graph 300 showing the relationship between anode voltage and the state of charge of a battery is shown. The state of charge is shown as a percentage (SOC%) along the abscissa, and the anode voltage is shown in volts (V) along the ordinate axis. The graph 300 includes a plurality of curves 302 obtained at different temperatures of the battery and different charging currents at the battery. A voltage threshold 304 is shown, indicating the voltage below which lithium plating occurs at the battery electrode. For illustrative purposes, the voltage threshold 304 is at zero volts. However, any suitable voltage threshold may be used.

[0036] Figure 4 A graph 400 is shown showing the relationship between the cathode voltage and the state of charge (SOC) of a battery. The state of charge is shown as a percentage (SOC%) along the abscissa and the cathode voltage is shown in volts (V) along the ordinate axis. The graph 400 includes a plurality of curves 402 obtained at different temperatures of the battery and different charging currents at the battery.

[0037] The method disclosed herein allows the determination of the anode voltage and cathode voltage when there is no access to the reference electrode. A reference measurement of a set of laboratory tests is performed on the anode voltage and cathode voltage using the reference electrode. These reference measurements include parameters such as state of charge (SOC), battery voltage, anode voltage, cathode voltage, temperature, charging current, etc., and are used to create an enhanced state. The enhanced state is a derived parameter designed from a reference measurement. The enhanced state can be derived using a nonlinear transformation and / or a linear transformation of the reference measurement. The enhanced state is used to build a regression model or train a neural network. During charging, a reference measurement is obtained and an enhanced state Z is generated. The enhanced state Z is input to a regression model or a trained neural network to output the anode voltage and cathode voltage. In this way, the regression model or the trained neural network acts as a virtual sensor of the anode voltage and cathode voltage. The anode voltage and cathode voltage can then be used to adjust or regulate the charging current applied to the battery.

[0038] Figure 5 5 is a flow chart 500 showing a method for developing a virtual sensor based on an enhanced state. The method includes developing a regression model or training a neural network based on experimental data. In block 502, experimental testing is performed in a controlled or laboratory environment using three-electrode measurements of one or more batteries. The three-electrode measurements include the anode voltage V A (measured between anode 202 and reference electrode 206) and cathode voltage V C(measured between cathode 204 and reference electrode 206). Experimental data may be obtained using one or more cells at multiple temperatures and multiple charging currents. In box 504, the three-electrode measurement is cleaned and preprocessed to output a reference measurement. In box 506, a preprocessed reference measurement is obtained. An exemplary reference measurement is labeled X and is shown in equation (1):

[0039] X=[SOC,T,C rate ,V cell ] Equation (1)

[0040] Where SOC is the state of charge of the battery, T is the temperature of the battery, C rate is the charging rate, and V cell is the voltage of the battery cell.

[0041] In block 508, a nonlinear transformation F is performed on the reference measurement X. a (X), as shown in Equation 2:

[0042] F a (X)→Z Equation (2)

[0043] In block 510, the nonlinear transformation generates an enhancement state Z. An illustrative set of enhancement states is shown in equation (3). :

[0044] Z=[SOC,T,V cell ,η,ΔΦ,OCP n ,OCP p ,V OC ,T e ,…] Equation (3)

[0045] In equation (3), SOC, T, and Vcell are the original reference measurements, and η, ΔΦ, OCP n ,OCP p ,V OC ,T e is the enhanced state due to the nonlinear transformation on X. The enhanced state is discussed in equations (4)-(9). Where:

[0046]

[0047] Where η is the current flux, T is the temperature, R is the universal gas constant, and F is the Faraday constant;

[0048] ΔΦ=K1·C rate +K2·T·log e (f(C rate )) Equation (5)

[0049] Where ΔΦ is the derived parameter, K1 and K2 are coefficients;

[0050] OCP n =c 10 +c 11 SOC+c 12 SOC 2 +… Equation (6)

[0051] Among them, OCP n is the open circuit potential of the anode, c 10 、c 11 、c 12 is the coefficient;

[0052] OCP p =c 20 +c 21 SOC+c 22 SOC 2 +… Equation (7)

[0053] Among them, OCP p is the open circuit potential of the cathode, and c 20 、c 21 、c 22 is the coefficient;

[0054] V oc =OCP p -OCP n Equation (8)

[0055] Where V oc is the open circuit voltage; and

[0056]

[0057] Where T e is the derived temperature parameter.

[0058] In block 512, a regression analysis is performed on the enhancement state Z to generate an output anode voltage V based on the enhancement state Z. A and cathode voltage V C Alternatively, the neural network can be trained using the enhanced state Z to output the anode voltage V A and cathode voltage V C .

[0059] In the case of training a neural network, a function f(Z, θ) that generates an anode voltage and a cathode voltage based on an enhanced state is obtained. The neural network can be trained by optimizing a cost function, as shown in equation (10).

[0060]

[0061] where θ represents the parameters of the neural network, and

[0062]

[0063] in is the anode voltage measured at the 3-electrode test and is the cathode voltage measured at the 3-electrode test.

[0064] In block 514, insights from the historical operation of the method may be applied to the transformation (e.g., nonlinear transformation a (X) is converted into a coefficient), thereby refining the resulting enhanced state A. In addition, in block 516, laboratory measurements obtained at test batteries with different ages may be used to adjust the value of the reference measurement X based on the age of the battery.

[0065] Figure 6 A diagram 600 of a charging system for adjusting the charging rate of a battery during a charging operation is shown. The charging system includes a battery management system 602 and an onboard charging module 604 running on a processor 606 of the vehicle. The battery management system 602 obtains a reference measurement of an electrical parameter of a battery 608 or obtains a reference measurement from a sensor at the battery and uses the information about the battery to adjust the charging rate of a battery. Figure 5 The regression model or neural network discussed determines the anode voltage and cathode voltage from these measurements. The battery management system 602 determines the need to adjust the charging rate based on the anode voltage and / or cathode voltage. The onboard charging module 604 determines what adjustments to make and adjusts the charging rate accordingly.

[0066] Figure 7 A flowchart 700 of a method of operation of a charging system for charging an electric vehicle is shown. In box 702, an electrical parameter X of a battery is measured during a charging operation. In box 704, a transformation is applied to a reference measurement X to obtain an enhancement state Z. The transformation may be a linear transformation, a nonlinear transformation, or a combination thereof. In box 706, a regression model is applied to the enhancement state Z to output an anode voltage V A and cathode voltage V C Alternatively, the enhancement state Z is input to a trained neural network, which outputs the anode voltage V A and cathode voltage V C .

[0067] In block 708, the battery management system determines the voltage V A and cathode voltage V CA decision is made. When the anode voltage is less than a given voltage threshold, lithium plating may occur. Therefore, when the anode voltage is greater than the threshold voltage, lithium plating does not occur, and the method proceeds to box 710. When the anode voltage is equal to or less than the threshold voltage, lithium plating occurs, and the method proceeds to box 712.

[0068] In box 710, the charging strategy can be calibrated based on other considerations. For example, the original charging strategy can be maintained. The method proceeds from box 710 to box 712. In box 712, the charging strategy can be adapted to prevent or reduce the occurrence of lithium plating. Adjusting the charging strategy includes determining a charging current request I to be sent to the charging station. request .

[0069] In one embodiment, heuristic-based control is used to calculate the charge current request, as shown in equations (11) and (12):

[0070] I request =I cal +ΔI Equation (11)

[0071] in

[0072]

[0073] In another embodiment, the charging current request is calculated using equation (13) based on optimized reference tracking control:

[0074]

[0075] In yet another embodiment, the charging current request is calculated based on a predetermined control logic using equation (14):

[0076] I request =I cal +ΔI Equation (14)

[0077] where ΔI is obtained from a lookup table

[0078] ΔI=LookUpTable(V anode ,V cathode ,…) Equation (15)

[0079] In box 714, the charging current is sent to the onboard charging module. In box 716, the onboard charging module controls the charging current applied to the battery. In box 718, a decision is made based on the state of charge or whether the battery is still charging. If the battery is fully charged, the method proceeds to box 720, where the charging operation ends. Returning to box 718, if the battery is still charging, the method returns to box 702.

[0080] Figure 8A flowchart 800 is shown of a method for operating a charging system based on the severity of a violation of a lithium plating threshold. In box 802, a reference measurement X of a battery is obtained during a charging operation. In box 804, a transformation is applied to the reference measurement X to obtain an enhancement state Z. The transformation may be a linear transformation, a nonlinear transformation, or a combination thereof. In box 806, a regression model is applied to the enhancement state Z to output an anode voltage V A and cathode voltage V C Alternatively, the enhancement state Z is input to a trained neural network, which outputs the anode voltage V A and cathode voltage V C , as shown in the figure.

[0081] In block 808, the battery management system determines the voltage V A and cathode voltage V C A decision is made. Thus, when the anode voltage is greater than the threshold voltage, the method returns to block 802. When the anode voltage is equal to or less than the threshold voltage, the method proceeds to block 810.

[0082] In block 810, a severity metric is assigned based on the amount by which the anode voltage exceeds the threshold voltage. For example, when the amount by which the anode voltage exceeds the threshold voltage is 0.1 volt, a first severity metric S1 is assigned, when the amount is 0.5 volt, a second severity metric S2 is assigned, when the amount is 1 volt, a third severity metric S3 is assigned, and so on. The severity metric may be a number between 1 and 10.

[0083] In box 812, the severity metric is stored in a memory. Multiple severity metrics obtained at multiple times can be stored. In box 814, a battery degradation metric is assigned based on the violation metric. The battery degradation metric can indicate the degree of degradation of the battery based on the amount of damage or plating at the battery recorded by the severity metric. In box 816, the battery degradation is sent to an operator or engineer via an uplink or cloud connection. From box 814 or box 816, the method proceeds to box 818. In box 818, parameters of the battery management system are updated based on the degradation of the battery. In box 820, the updated parameters are sent to the battery management system. The method returns to box 802 from box 820.

[0084] The terms "one" and "an" do not indicate a limitation of quantity, but rather indicate the presence of at least one of the referenced item. Unless the context clearly indicates otherwise, the term "or" means "and / or". References to "aspects" throughout the specification mean that a particular element (e.g., a feature, structure, step, or characteristic) described in conjunction with that aspect is included in at least one aspect described herein, and may or may not be present in other aspects. In addition, it should be understood that the described elements may be combined in any suitable manner in the various aspects.

[0085] When an element such as a layer, film, region, or substrate is referred to as being "on" another element, it can be directly on the other element or intervening elements may also be present. In contrast, when an element is referred to as being "directly on" another element, there are no intervening elements present.

[0086] Unless otherwise indicated herein, all test standards are the most current standards in effect as of the filing date of this application or, if priority is claimed, the filing date of the earliest priority application in which the test standards appear.

[0087] Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

[0088] Although the above disclosure has been described with reference to exemplary embodiments, it will be appreciated by those skilled in the art that various changes may be made and equivalents may be substituted for its elements without departing from its scope. In addition, many modifications may be made to adapt specific circumstances or materials to the teachings of the disclosure without departing from the basic scope of the disclosure. Therefore, it is intended that the disclosure is not limited to the specific embodiments disclosed, but will include all embodiments falling within its scope.

Claims

1. A method for charging a battery, comprising: obtaining a reference measurement of the battery during charging; calculating an enhancement state based on the reference measurement; determining an anode voltage from said enhanced state; comparing the anode voltage to a threshold value; as well as A charging rate is adjusted based on a comparison of the anode voltage to the threshold.

2. The method of claim 1, further comprising determining the anode voltage from the enhancement state using at least one of: (i) a regression model; and (ii) a neural network.

3. The method of claim 1, further comprising calculating the enhancement state by applying a nonlinear transformation to the reference measurement.

4. The method according to claim 1, wherein: The reference measurement is adjusted for the age of the battery.

5. The method according to claim 1, further comprising: A severity metric is assigned when the anode voltage is less than the threshold, degradation of the battery is determined based on the severity metric, and the charge rate is adjusted based on the degradation of the battery.

6. A system for charging a battery of an electric vehicle, comprising: a sensor for obtaining a reference measurement of the battery during charging; Processor, configured as: calculating an enhancement state based on the reference measurement; determining an anode voltage from said enhanced state; comparing the anode voltage to a threshold value; as well as A charging rate is adjusted based on a comparison of the anode voltage to the threshold.

7. The system according to claim 6, wherein: The processor is also configured to determine the anode voltage from the enhancement state using at least one of: (i) a regression model; and (ii) a neural network.

8. The system according to claim 6, wherein: The processor is further configured to apply a non-linear transformation to the reference measurement to calculate the enhancement state.

9. The system according to claim 6, wherein: The processor is also configured to adjust the reference measurement for the age of the battery.

10. The system according to claim 6, wherein: The processor is further configured to assign a severity metric when the anode voltage is less than the threshold, determine degradation of the battery based on the severity metric, and adjust the charge rate based on the degradation of the battery.

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