Battery assembly state of charge estimation

Abstract

A system for estimating a state of charge of a battery assembly includes a sensor unit and an estimator circuit. The sensor unit is coupled in series to the battery assembly. The battery assembly has an assembly battery chemistry and the sensor unit has a sensor battery chemistry, and the assembly battery chemistry is different from the sensor battery chemistry. The estimator circuit is operable to: obtain a current sensor cell state-of-charge sequence based on a sensor cell model and a sequence of sensor voltages across the sensor cells, calculate a current battery component state-of-charge sequence based on the current sensor cell state-of-charge sequence, and calculating an estimated battery component state of charge of the battery component and an estimated sensor unit state of charge of the sensor unit by filtering the current battery component state of charge sequence and the current sensor unit state of charge sequence in parallel.

Description

[0001] introduction The present disclosure relates to systems and methods for battery assembly state-of-charge estimation.

[0002] Lithium iron phosphate batteries have long life and good thermal stability. Therefore, the battery is suitable for use in electric vehicles and hybrid vehicles. However, lithium iron phosphate batteries also have a relatively flat open circuit voltage vs. state of charge curve. The relatively flat open circuit voltage makes the estimation of the battery state of charge difficult, often resulting in errors of up to 10%.

[0003] Accordingly, those skilled in the art continue to conduct research and development efforts in the field of estimating the state of charge of battery assemblies. Summary of the invention

[0004] A system for estimating a state of charge of a battery assembly is provided herein. The system includes a sensor unit and an estimator circuit. The sensor unit is coupled in series to the battery assembly. The battery assembly has an assembly battery chemistry, the sensor unit has a sensor battery chemistry, and the assembly battery chemistry is different from the sensor battery chemistry. The estimator circuit is coupled to the battery assembly and the sensor unit. The estimator circuit is operable to: obtain a current sensor unit state of charge sequence of the sensor unit based on a sensor unit model of the sensor unit and a sequence of sensor voltages across the sensor unit, calculate a current battery assembly state of charge sequence of the battery assembly based on the current sensor unit state of charge sequence, and calculate an estimated battery assembly state of charge of the battery assembly and an estimated sensor unit state of charge of the sensor unit by filtering the current battery assembly state of charge sequence and the current sensor unit state of charge sequence in parallel.

[0005] In one or more embodiments of the system, the sensor unit model of the sensor unit includes a current battery assembly state of charge sequence as an augmented state variable.

[0006] In one or more embodiments of the system, the current battery component state of charge sequence is composed of SOC BA (k+1)=(CAP SC / CAP BA )(SOC SC (k)-d%)+(Idt / CAP BA ))+ε(k). Where SOC BA (k+1) is the current battery pack state of charge at time k+1, k is a number of measurement times, CAP SC is the capacity of the sensor unit, CAP BA is the capacity of the battery pack, SOC SC(k) is the current sensor cell state of charge at time k, d% is the minimum charge offset, Idt is the sum of the currents flowing through the battery assembly, and ε(k) is the noise model at time k.

[0007] In one or more embodiments of the system, the filtering utilizes an extended Kalman filter.

[0008] In one or more embodiments of the system, the extended Kalman filter is a fast extended Kalman filter for calculating the estimated sensor cell state of charge.

[0009] In one or more embodiments of the system, the estimator circuit is further operable to estimate a capacity degradation coefficient of the sensor unit using an extended Kalman filter that executes at a slower rate than a fast extended Kalman filter.

[0010] In one or more embodiments of the system, the component battery chemistry is a lithium iron phosphate chemistry, a lithium iron manganese phosphate chemistry, or a sodium ion chemistry.

[0011] In one or more embodiments of the system, the sensor battery chemistry is a nickel manganese cobalt chemistry, a nickel cobalt aluminum chemistry, a lithium ion manganese chemistry, or a lithium cobalt chemistry.

[0012] In one or more embodiments of the system, the battery assembly is a battery pack or a battery module.

[0013] A method for estimating a state of charge of a battery assembly is provided herein. The method includes: obtaining a current sensor unit state of charge sequence of the sensor unit using an estimator circuit based on a sensor unit model of the sensor unit and a sequence of sensor voltages across the sensor unit. The sensor unit is coupled in series with a battery assembly, the battery assembly has an assembly battery chemistry, the sensor unit has a sensor battery chemistry, and the assembly battery chemistry is different from the sensor battery chemistry. The method includes: calculating a current battery assembly state of charge sequence of the battery assembly based on the current sensor unit state of charge sequence, and calculating an estimated battery assembly state of charge of the battery assembly and an estimated sensor unit state of charge of the sensor unit by filtering the current battery assembly state of charge sequence and the current sensor unit state of charge sequence in parallel.

[0014] In one or more embodiments of the method, the sensor unit model of the sensor unit includes the current battery assembly state of charge sequence as an augmented state variable.

[0015] In one or more embodiments of the method, the current battery component state of charge sequence is composed of SOC BA(k+1)=(CAP SC / CAP BA )(SOC SC (k)-d%)+(Idt / CAP BA ))+ε(k). Where SOC BA (k+1) is the current battery pack state of charge at time k+1, k is a number of measurement times, CAP SC is the capacity of the sensor unit, CAP BA is the capacity of the battery pack, SOC SC (k) is the current sensor cell state of charge at time k, d% is the minimum charge offset, Idt is the sum of the currents flowing through the battery assembly, and ε(k) is the noise model at time k.

[0016] In one or more embodiments of the method, the filtering utilizes an extended Kalman filter.

[0017] In one or more embodiments of the method, the extended Kalman filter is a fast extended Kalman filter used to calculate the estimated sensor cell state of charge.

[0018] In one or more embodiments, the method includes estimating a capacity degradation coefficient of the sensor unit using an extended Kalman filter that executes at a slower rate than a fast extended Kalman filter.

[0019] In one or more embodiments of the method, the component battery chemistry is a lithium iron phosphate chemistry, a lithium iron manganese phosphate chemistry, or a sodium ion chemistry.

[0020] In one or more embodiments of the method, the sensor cell chemistry is a nickel manganese cobalt chemistry, a nickel cobalt aluminum chemistry, a lithium ion manganese chemistry, or a lithium cobalt chemistry.

[0021] In one or more embodiments of the method, the sensor cell model includes a hysteresis transit component, a plurality of lagged current components, a plurality of resistance components, and a terminal voltage component.

[0022] A vehicle is provided herein. The vehicle includes a battery assembly, a sensor unit, and an estimator circuit. The battery assembly has an assembly battery chemistry. The sensor unit is coupled to the battery assembly in series. The sensor unit has a sensor battery chemistry, and the assembly battery chemistry is different from the sensor battery chemistry. The estimator circuit is coupled to the battery assembly and the sensor unit. The estimator circuit is operable to: obtain a current sensor unit state of charge sequence of the sensor unit based on a sensor unit model of the sensor unit and a sequence of sensor voltages across the sensor unit, calculate a current battery assembly state of charge sequence of the battery assembly based on the current sensor unit state of charge sequence, and calculate an estimated battery assembly state of charge of the battery assembly and an estimated sensor unit state of charge of the sensor unit by filtering the current battery assembly state of charge sequence and the current sensor unit state of charge sequence in parallel.

[0023] In one or more embodiments of the vehicle, the battery assembly state of charge is estimated with an accuracy within 3%.

[0024] The above features and advantages and other features and advantages of the present disclosure are readily apparent from the following detailed description of the best modes for carrying out the disclosure when taken in connection with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 is a schematic plan view illustrating a scenario of a system according to one or more exemplary embodiments.

[0026] Figure 2 is a schematic diagram of a state of charge map according to one or more exemplary embodiments.

[0027] Figure 3 is a flow chart of a method for determining an estimated sensor unit state of charge and an estimated battery assembly state of charge according to one or more exemplary embodiments.

[0028] Figure 4 is a graph of estimated errors for battery assembly state of charge according to one or more exemplary embodiments.

[0029] Figure 5 is a flow chart of a method for determining an estimated sensor cell state of charge and a capacity degradation factor of a sensor cell according to one or more exemplary embodiments. DETAILED DESCRIPTION

[0030] Embodiments of the present disclosure provide a sensor unit connected in series with a battery assembly (e.g., a battery pack or a battery module) as a sensing unit for estimating the state of charge of the battery assembly. The sensor unit and the battery pack have different chemical substances, thereby forming a mixed-chemistry system. By incorporating the state of charge of the battery assembly into the unit model of the sensor unit as an extended state variable, an extended Kalman filter (EKF) can be used to estimate the state of charge of both the sensor unit and the battery assembly in parallel. A closed-loop observer can minimize the capacity uncertainty in the two chemical substances to increase the accuracy of the state of charge estimation of the mixed chemical substance system. The extended Kalman filter can be a nonlinear version of the Kalman filter that linearizes the data about the current mean estimate.

[0031] refer to Figure 1 , a schematic plan view of a scenario of an illustrated system is shown according to one or more exemplary embodiments. The system can implement a vehicle 80. The vehicle 80 generally includes a motor 90 and a system 100. The system 100 includes a rechargeable energy storage system 102 and a controller 104. The rechargeable energy storage system 102 includes one or more sensor units 106 (one shown) and one or more battery assemblies 108 (one shown). The controller 104 includes an estimator circuit 110.

[0032] The rechargeable energy storage system 102 generates a system voltage 120 (e.g., Vt) when being charged. The system voltage 120 may be measured by the estimator circuit 110. A sensor voltage 122 (e.g., Vs) is generated by the sensor unit 106 and measured by the estimator circuit 110. When in a discharge mode, a current 124 (e.g., I) may be presented by the rechargeable energy storage system 102. When in a charge mode, the current 124 may flow into the rechargeable energy storage system 102. A correction signal 126 is generated by the battery assembly 108 and presented to the estimator circuit 110. The correction signal 126 conveys the temperature of the battery assembly 108, the system voltage 120, and the current 124 to the estimator circuit 110.

[0033] The vehicle 80 may include, but is not limited to, a mobile object such as a car, truck, motorcycle, boat, train, and / or aircraft. In various embodiments, the vehicle 80 may be an electric vehicle. In other embodiments, the vehicle 80 may be a hybrid vehicle. In some embodiments, the vehicle 80 may include a stationary object such as a billboard, a kiosk, and / or a marquee. Other types of vehicles 80 may be implemented to meet the design criteria of a particular application.

[0034] The motor 90 implements an electric motor. The motor 90 is generally operable to provide rotation and torque to drive the wheels of the vehicle 80. The electrical power consumed by the motor 90 may be provided by the rechargeable energy storage system 102 and / or the alternator of the vehicle 80 under the control of the controller 104.

[0035] The system 100 implements a high voltage storage system configured to store electrical energy. The system 100 is electrically coupled to the motor 90. The system 100 is generally operable to provide electrical power to the motor 90 to move the vehicle 80. The system 100 is also operable to be recharged from a power source (e.g., a charging station and / or an onboard alternator). In various embodiments, the system 100 can provide a DC (direct current) potential of approximately 400 to 800 volts. Other battery voltages can be implemented to meet the design criteria of a specific application.

[0036] The rechargeable energy storage system 102 implements a hybrid chemistry battery pack. The rechargeable energy storage system 102 is electrically coupled to the controller 104. When in a discharge mode, the rechargeable energy storage system 102 is operable to provide electrical power to the controller 104 to operate the motor. When in a charge mode, the rechargeable energy storage system 102 is operable to receive electrical power through the controller 104 and store the electrical power for later use.

[0037] The controller 104 implements a battery controller. The controller 104 is electrically coupled to the motor 90 and the rechargeable energy storage system 102. The controller 104 is generally operable to deliver electrical power to the rechargeable energy storage system 102 in a charging mode to charge the rechargeable energy storage system 102. The controller 104 can draw electrical power from the rechargeable energy storage system 102 in a discharging mode. The electrical power received from the rechargeable energy storage system 102 can be used to power the motor 90 and / or other loads within the vehicle 80.

[0038] The sensor cell 106 implements a battery cell having a sensor battery chemistry. The sensor cell 106 has a sensor cell type. In various embodiments, the sensor cell type of the battery can be a nickel manganese cobalt battery, a nickel cobalt aluminum battery, a lithium ion manganese battery, and / or a lithium cobalt battery. The various sensor cell battery types have corresponding battery cell chemistries, including nickel manganese cobalt chemistry 106a, nickel cobalt aluminum chemistry 106b, lithium ion manganese chemistry 106c, and / or lithium cobalt chemistry 106d. Other battery cell types and / or battery cell chemistries can be implemented to meet the design criteria of a specific application.

[0039] The battery assembly 108 implements a battery module and / or a battery pack. The battery module generally includes a plurality of battery cells. The battery pack includes a plurality of battery models or a plurality of battery cells. The battery assembly 108 has a battery assembly type. In various embodiments, the battery assembly type of the battery generally includes a lithium iron phosphate battery, a lithium iron manganese phosphate battery, and / or a sodium ion battery. The battery assembly type may have a corresponding battery assembly chemistry, including a lithium iron phosphate chemistry 108a, a lithium iron manganese phosphate chemistry 108b, and / or a sodium ion chemistry 108c. Other battery assembly types and / or battery assembly chemistries may be implemented to meet the design criteria of a specific application.

[0040] The estimator circuit 110 implements measurement circuitry and computation circuitry. The estimator circuit 110 is electrically coupled to the battery assembly 108 and the sensor unit 106. The estimator circuit 110 is operable to obtain a current sensor unit state of charge sequence of the sensor unit 106 based on a sensor unit model of the sensor unit 106 and a sequence of sensor voltages 122 across the sensor unit 106. The estimator circuit 110 may also calculate a current battery assembly state of charge sequence of the battery assembly 108 based on the current sensor unit state of charge sequence, and calculate an estimated battery assembly state of charge of the battery assembly 108 and an estimated sensor unit state of charge of the sensor unit 106 by filtering the current battery assembly state of charge sequence and the current sensor unit state of charge sequence in parallel. Parallel filtering generally means concurrent filtering, overlapping filtering, filtering in unison, and / or filtering at approximately the same time.

[0041] The system voltage 120 may be a voltage across the rechargeable energy storage system 102. The system voltage 120 is the sum of a sensor voltage 122 of the sensor unit 106 and an assembly voltage of the battery assembly 108. The sensor voltage 122 is a voltage across the sensor unit 106. Both the system voltage 120 and the sensor voltage 122 are measured by the estimator circuit 110.

[0042] refer to Figure 2 , a schematic diagram of an example state of charge map 140 is shown according to one or more exemplary embodiments. The state of charge map 140 illustrates a sensor cell state of charge 142 of the sensor cell 106 relative to a battery cell state of charge 146 of a battery cell 144 having the same battery chemistry as the battery assembly 108. The sensor cell 106 has a sensor cell capacity CAP SC The battery cell 144 has a battery cell capacity CAP BC Sensor unit capacity CAP SC By equation 1 and the battery cell capacity CAP BC Related, as follows: Where S% is the span of the entire battery cell capacity CAP BC (e.g., from 0% battery cell SOC BC To 100% battery cell SOC BC ) sensor unit capacity CAP SC percentage.

[0043] The sensor cell 106 has a sensor cell state of charge 142 when measuring (or observing) k, 142 = SOC SC (k). The battery cell 144 has a battery cell state of charge 146 when measuring (or observing) k, 146 = SOC BC (k) Sensor unit state of charge 142 (SOC SC ) is combined with the battery cell state of charge 146 (SOC BC ) are as follows: Where d% is the state of charge (SOC) of the sensor cell across zero SC To zero battery cell state of charge SOC BC Sensor unit capacity CAP SC percentage.

[0044] The battery cell 144 can be scaled up to the battery assembly 108, and the battery cell state of charge (SOC BC ) can be scaled up to the battery module state of charge (SOC BA ). In various embodiments, the expanded state space equation of the battery assembly 108 is expressed by Equation 3 as follows: Among them, SOC BA (k+1) is the current battery component state of charge at time k+1, k is the measurement time sequence, CAP SC is the capacity of the sensor unit, CAP BA is the capacity of the battery pack, SOC SC (k) is the current sensor cell state of charge at time k, d% is the minimum charge offset, Idt is the total current flowing through the battery assembly 108, and ε(k) is the noise model at time k.

[0045] The sensor cell model of the sensor cell 106 may be modeled as a hysteresis transition component, a delay current component, and a resistance component. A shorthand notation may be used for the state of charge (SOC) and is defined in Equation 4 as follows: θ=SOC Equation (4) Sensor unit state of charge (SOC SC ) It can be expressed by equation 5 as follows: Hysteresis transition component It can be expressed by Equation 6 as follows: Delay current component (u i ) can be expressed by Equation 7 as follows: where τ i is the delay time constant, I is the current flowing through the sensor unit 106, and I* is the proportionality factor.

[0046] Resistance component It can be expressed by equation 8 as follows: Where T is the temperature. Sensor unit 106 (see Figure 1 ) can be expressed by Equation 9 as follows: Where OCV(θ SC ) is the open circuit voltage, is the lag, is the diffusion / transport factor, is the ohmic / reaction kinetics, and Vgap is the remaining voltage difference after relaxation of the sensor cell 106 .

[0047] Since the observation equation 3 is modeled, the uncertainty in the battery component capacity is modeled as process noise ε(k). The process noise can be rejected by the extended Kalman filter (EKF). The extended Kalman filter is generally applied to estimate the sensor unit state of charge SOC in parallel (or concurrently, overlapping, consistent and / or nearly simultaneously). SC and battery component state of charge SOC BA .

[0048] In the general nonlinear form of the sensor unit model, Equation 5 for the sensor unit 106 can be written as Formula 10 is as follows: in For i = ,…, n (hysteresis, diffusion, electrode / solid electrolyte interphase (SEI) potential, etc.), the terminal voltage of equation 9 can be written as equation 11 as follows: The general nonlinear model may include various battery models, equivalent circuits, or electrochemical models.

[0049] In the reference Figure 1 Reference Figure 3 , a flow chart of an example method 160 for determining an estimated sensor unit state of charge and an estimated battery assembly state of charge is shown according to one or more exemplary embodiments. The method 160 can be implemented in an estimator circuit. As illustrated, the method (or process) 160 generally includes steps 162 to 172. The sequence of steps is shown as a representative example. Other orders of steps can be implemented to meet the criteria of a specific application.

[0050] In step 162, a sensor cell model may be determined. In step 164, the system voltage 120 (Vs), the sensor voltage 122 (Vs), and the current 124 (I) may be acquired multiple times (e.g., k observations). In step 166, the sensor voltage 122 is used to determine the current sensor cell state of charge SOC at each observation. SC And the current battery component state of charge SOC BA In step 168, the expansion state is calculated.

[0051] In step 170, the extended Kalman filter is applied in parallel to the current sensor cell state of charge SOC at the time of observation. SC And the current battery component state of charge SOC BA to calculate the estimated sensor cell state of charge and estimate battery pack state of charge In step 172, the estimated sensor unit state of charge may be and estimate battery pack state of charge is stored in the estimator circuit 110 for subsequent use.

[0052] refer to Figure 4 , a graph 180 of estimated errors for an example of a battery assembly state of charge is shown according to one or more exemplary embodiments. Graph 180 generally includes a time axis 182 and an error axis 184. Time axis 182 may be in units of seconds. Error axis 184 may be in units of error percentage. Curve 186 illustrates the estimated error in the battery assembly state of charge estimate over time. Curve 186 generally illustrates that the error percentage is less than 1.5% of the error from zero.

[0053] In the reference Figure 1 Reference Figure 5 , a flow chart of an example method 200 for determining an estimated sensor cell state of charge and a capacity degradation coefficient of a sensor cell is shown according to one or more exemplary embodiments. The method 200 may be implemented in the estimator circuit 110. As illustrated, the method (or process) 200 generally includes steps 162 to 168 and 202 to 208. The sequence of steps is shown as a representative example. Other orders of steps may be implemented to meet the criteria of a particular application.

[0054] Steps 162 to 168 may be the same as the steps illustrated in method 160. Once the signal has been measured over a plurality of observations, in step 202, a fast extended Kalman filter is applied to the observed measurements. In step 204, the fast extended Kalman filter and equations 12, 13, and 14 may be used to obtain an estimated sensor cell state of charge as follows: q(k+N)=q(k)+ε(k) Equation (12) Where q is the capacity degradation coefficient and N is the elapsed time interval. In step 206, a slow extended Kalman filter is applied to estimate the sensor unit capacity degradation coefficient q. A "slow" extended Kalman filter means that one or a few observations are used in each filtering cycle (or duration). A "fast" extended Kalman filter means that observations in the elapsed time interval are used in each filtering cycle. In other words, a slow extended Kalman filter is executed at a lower rate than a fast extended Kalman filter. In step 208, the estimated sensor unit state of charge and the sensor unit capacity degradation coefficient are stored in the estimator circuit 110 for later use.

[0055] Various embodiments of the system 100 generally utilize the sensor unit 106 as a sensing unit to estimate the battery assembly state of charge of the LFP. The unit model of the sensor unit 106 is formulated using the battery assembly state of charge as an extended state variable in the plant model. The system 100 applies an extended Kalman filter to simultaneously (or in parallel) estimate the state of charge of both the sensor unit 106 and the battery assembly. The system 100 covers various types of battery cell models and applies an extended Kalman filter to estimate the state of charge of mixed chemicals. A two-time scale extended Kalman filter (e.g., fast and slow observers) can be used to determine both the estimated sensor unit state of charge and the sensor unit capacity degradation coefficient. The system 100 also provides an estimated battery assembly state of charge accuracy within 3%, which has minimal software changes from existing battery management system technology. The improved accuracy enables a low-cost electric vehicle with better performance.

[0056] Embodiments of the present disclosure generally provide a system for estimating the state of charge of a battery assembly. The system includes a sensor unit, a battery assembly, and an estimator circuit. The sensor unit is coupled to the battery assembly in series. The battery assembly has an assembly battery chemistry, the sensor unit has a sensor battery chemistry, and the assembly battery chemistry is different from the sensor battery chemistry. The estimator circuit is coupled to the battery assembly and the sensor unit. The estimator circuit is operable to: obtain a current sensor unit state of charge sequence based on a sensor unit model and a sequence of sensor voltages across the sensor unit; calculate a current battery assembly state of charge sequence based on the current sensor unit state of charge sequence; and calculate an estimated battery assembly state of charge and an estimated sensor unit state of charge by filtering the current battery assembly state of charge sequence and the current sensor unit state of charge sequence in parallel.

[0057] The numerical values ​​of parameters (e.g., quantities or conditions) in this specification (including the appended claims) should be understood as being modified by the term "approximately" in each instance, whether or not "approximately" actually appears before the numerical value. "Approximately" indicates that the numerical value allows some slight imprecision (having a certain approximation to the exactness of the value; approximately or reasonably close to the value; nearly). If the imprecision provided by "approximately" cannot be understood in this art with this ordinary meaning in other cases, then the "approximately" used herein at least indicates the changes that may be caused by the ordinary methods of measuring and using such parameters. In addition, the disclosure of the range includes the disclosure of the values ​​within the entire range and the further separated ranges. Each value within the range and the endpoints of the range are disclosed as separate embodiments herein.

[0058] While the best modes for carrying out the disclosure have been described in detail, those familiar with the art to which this disclosure relates will recognize various alternative designs and embodiments for practicing the disclosure within the scope of the appended claims.

Claims

1. A system for estimating a state of charge of a battery assembly, the system comprising: a sensor unit coupled in series to a battery assembly, wherein the battery assembly has an assembly battery chemistry, the sensor unit has a sensor battery chemistry, and the assembly battery chemistry is different than the sensor battery chemistry; as well as an estimator circuit coupled to the battery assembly and the sensor unit, wherein the estimator circuit is operable to: obtaining a current sensor cell state of charge sequence of the sensor cell based on a sensor cell model of the sensor cell and a sequence of sensor voltages across the sensor cell; Calculating a current battery assembly state of charge sequence of the battery assembly based on the current sensor unit state of charge sequence; and An estimated battery assembly state of charge of the battery assembly and an estimated sensor unit state of charge of the sensor unit are calculated by filtering the current battery assembly state of charge sequence and the current sensor unit state of charge sequence in parallel. 2 . The system according to claim 1 , wherein the sensor unit model of the sensor unit includes a current battery assembly state of charge sequence as an extended state variable.

3. The system according to claim 2, wherein the current battery component charge state sequence is represented by the following formula: SOC BA (k+1)=(ZIP SC / POSTCODE BA )(SOC SC (k)-d%)+(Idt / CAP BA ))+ε(k), Among them, SOC BA (k+1) is the current battery pack state of charge at time k+1, k is a number of measurement times, CAP SC is the capacity of the sensor unit, CAP BA is the capacity of the battery pack, SOC SC (k) is the current sensor cell state of charge at time k, d% is the minimum charge offset, Idt is the sum of the currents flowing through the battery assembly, and ε(k) is the noise model at time k.

4. The system of claim 1, wherein the filtering utilizes an extended Kalman filter.

5. The system of claim 4, wherein the extended Kalman filter is a fast extended Kalman filter for calculating the estimated sensor cell state of charge.

6. The system of claim 5, wherein the estimator circuit is further operable to estimate the capacity degradation coefficient of the sensor unit using an extended Kalman filter that executes at a slower rate than a fast extended Kalman filter.

7. The system of claim 1, wherein the component battery chemistry is a lithium iron phosphate chemistry, a lithium iron manganese phosphate chemistry, or a sodium ion chemistry.

8. The system of claim 1, wherein the sensor battery chemistry is a nickel manganese cobalt chemistry, a nickel cobalt aluminum chemistry, a lithium ion manganese chemistry, or a lithium cobalt chemistry.

9. The system of claim 1, wherein the battery assembly is a battery pack or a battery module.

10. A method for estimating a state of charge of a battery assembly, comprising: obtaining a current sequence of sensor cell states of charge for the sensor cell using an estimator circuit based on a sensor cell model of the sensor cell and a sequence of sensor voltages across the sensor cell, wherein the sensor cell is coupled in series with a battery assembly, the battery assembly having an assembly battery chemistry, the sensor cell having a sensor battery chemistry, and the assembly battery chemistry is different from the sensor battery chemistry; Calculating a current battery assembly state of charge sequence of the battery assembly based on the current sensor unit state of charge sequence; as well as An estimated battery assembly state of charge of the battery assembly and an estimated sensor unit state of charge of the sensor unit are calculated by filtering the current battery assembly state of charge sequence and the current sensor unit state of charge sequence in parallel.

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