Controller employing battery model efficiently representing complex battery diffusion kinetics

Abstract

The present disclosure provides a controller employing a battery model that efficiently represents complex battery diffusion kinetics. A system, such as a motorized vehicle, includes a battery, such as a traction battery. The system also includes a controller configured to control the battery based on (i) a value of a first parameter of a model of the battery obtained from an electrical measurement of the battery and (ii) a value of a second parameter of the model obtained from the value of the first parameter, the value of the second parameter dependent on the value of the first parameter.

Description

Technical Field

[0001] The present invention relates to detecting operating characteristics of a traction battery of an electrified vehicle. Background Art

[0002] An electrified vehicle includes a traction battery for providing power to a motor of the vehicle to propel the vehicle. Operating characteristics of the traction battery, such as the power capability of the traction battery and the state of charge of the traction battery, may be monitored when controlling operation of the traction battery and / or the vehicle. Summary of the invention

[0003] A system includes a battery and a controller configured to control the battery based on (i) a value of a first parameter of a model of the battery obtained from electrical measurements of the battery and (ii) a value of a second parameter of the model obtained from the value of the first parameter, the value of the second parameter being dependent on the value of the first parameter.

[0004] The controller may be further configured to control the battery based on (i) the value of the first parameter, (ii) the value of the second parameter, and (iii) values ​​of one or more additional parameters of a model derived from the value of the first parameter, the values ​​of the one or more additional parameters being dependent on the value of the first parameter.

[0005] The first parameter, the second parameter, and the one or more additional parameters may at least partially indicate a Warburg impedance of the model.

[0006] The first parameter may be a circuit element of a first resistor-capacitor (RC) circuit and the second parameter may be a circuit element of a second RC circuit, in which case the first RC circuit has a time constant that is smaller than a time constant of the second RC circuit.

[0007] The first parameter may be a resistor of a first RC circuit, in which case the value of the first parameter is the value of the resistance of the resistor of the first RC circuit, and the second parameter may be a resistor of a second RC circuit, in which case the value of the second parameter is the value of the resistance of the resistor of the second RC circuit.

[0008] The first parameter may be a capacitor of a first RC circuit, in which case the value of the first parameter is the value of the capacitance of the capacitor of the first RC circuit, and the second parameter may be a capacitor of a second RC circuit, in which case the value of the second parameter is the value of the capacitance of the capacitor of the second RC circuit.

[0009] The first parameter may be a time constant of a first RC circuit, in which case the value of the first parameter is a time value of the time constant of the first RC circuit, and the second parameter may be a time constant of a second RC circuit, in which case the value of the second parameter is a time value of the time constant of the second RC circuit, in which case the time constant of the RC circuit is less than the time constant of the second RC circuit.

[0010] The dependence of the value of the second parameter on the value of the first parameter may change as the temperature of the battery, the current of the battery, and / or the state of charge (SOC) of the battery changes.

[0011] The electrical measurements of the battery include voltage and / or current and / or temperature measurements of the battery.

[0012] A system for a vehicle having a traction battery includes a sensor and a controller. The sensor is configured to sense an electrical measurement of the traction battery. The controller is configured to control the traction battery based on (i) a value of a first resistor-capacitor (RC) circuit of an equivalent circuit model (ECM) of the traction battery obtained from the electrical measurement of the traction battery and (ii) a value of a second RC circuit of the ECM obtained from the value of the first RC circuit, the value of the second RC circuit being dependent on the value of the first RC circuit.

[0013] The controller may be further configured to control the traction battery based on (i) a value of the first RC circuit, (ii) a value of the second RC circuit, and (iii) values ​​of one or more additional RC circuits of the ECM derived from the value of the first RC circuit, the values ​​of the one or more additional RC circuits being dependent on a value of the first parameter.

[0014] The first RC circuit, the second RC circuit, and the one or more additional RC circuits may at least partially indicate a Warburg impedance of the ECM.

[0015] The first RC circuit may have a minimum time constant among all RC circuits. The second RC circuit may have a minimum time constant among all RC circuits except the first RC circuit.

[0016] A method includes sensing an electrical measurement of a traction battery of a vehicle. The method further includes controlling, by a controller, the traction battery based on (i) a value of a first parameter of an equivalent circuit model (ECM) of the traction battery obtained from the electrical measurement of the traction battery and (ii) a value of a second parameter of the ECM obtained from the value of the first parameter of the ECM, the value of the second parameter being dependent on the value of the first parameter. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A block diagram of a battery electric vehicle (BEV) is shown;

[0018] Figure 2 A block diagram showing an arrangement for a traction battery controller of a BEV to monitor a traction battery of the BEV;

[0019] FIG3 shows a schematic diagram of a conventional equivalent circuit model (ECM) of a traction battery;

[0020] FIG4A shows a schematic diagram of a portion of a conventional ECM;

[0021] FIG4B shows a schematic diagram of a model of Warburg impedance parameters for the portion shown in FIG4A ;

[0022] FIG4C shows a schematic diagram of a conventional ECM having a simplified version of the parts shown in FIG4A ;

[0023] FIG4D shows a schematic diagram of the conventional ECM shown in FIG4C , wherein the Warburg impedance model shown in FIG4B replaces the Warburg impedance parameters;

[0024] Figure 5 Schematic diagram showing the proposed model for Warburg impedance parameters;

[0025] Figure 6 shows a schematic diagram of the proposed ECM of a traction battery, the proposed ECM including the proposed model of Warburg impedance parameters;

[0026] Figure 7 shows a schematic diagram of the proposed ECM in the form of a stand-alone 1-RC ECM; and

[0027] Figure 8 A flow chart describing operations performed by a traction battery controller when using a separate 1-RC ECM to detect the power capability of the traction battery and / or the state of charge (SOC) of the traction battery is shown. DETAILED DESCRIPTION

[0028] Detailed embodiments of the present disclosure are disclosed herein; however, it should be understood that the disclosed embodiments are merely examples of the present disclosure that may be embodied in various forms and alternative forms. The drawings are not necessarily drawn to scale; some features may be exaggerated or minimized to show details of particular components. Therefore, the specific structural and functional details disclosed herein should not be interpreted as limiting, but merely as a representative basis for teaching those skilled in the art to adopt the present disclosure in different ways.

[0029] Reference now Figure 1, a block diagram of an electrified vehicle (EV) 12 in the form of a battery electric vehicle (BEV) is shown. The BEV 12 includes a powertrain having one or more traction motors ("motors") 14, a traction battery ("battery" or "battery pack") 24, and a power electronics module 26 (e.g., an inverter). In the BEV configuration, the traction battery 24 provides all propulsion power, and the vehicle does not have an engine. In other variations, the EV may be a plug-in (or conventional) hybrid electric vehicle (HEV) that also has an engine.

[0030] The traction motor 14 is part of the powertrain of the BEV 12 and is used to provide power for the movement of the BEV. In this regard, the traction motor 14 is mechanically connected to a transmission 16 of the BEV 12. The transmission 16 is mechanically connected to a drive shaft 20, which is mechanically connected to wheels 22 of the BEV 12. The traction motor 14 can provide propulsion capability to the BEV 12 and can operate as a generator. The traction motor 14 acting as a generator can recover energy that might normally be lost as heat in the friction braking system of the BEV 12.

[0031] The traction battery 24 stores electrical energy that can be used by the traction motor 14 to propel the BEV 12. The traction battery 24 typically provides a high voltage (HV) direct current (DC) output. The traction battery 24 is electrically connected to a power electronics module 26. The traction motor 14 is also electrically connected to the power electronics module 26. The power electronics module 26 (such as an inverter) provides the ability to transfer energy bidirectionally between the traction battery 24 and the traction motor 14. For example, the traction battery 24 may provide a DC voltage, while the traction motor 14 may require a three-phase alternating current (AC) current to function. The inverter 26 may convert the DC voltage into a three-phase AC current to operate the traction motor 14. In the regenerative mode, the inverter 26 may convert the three-phase AC current from the traction motor 14 acting as a generator into a DC voltage compatible with the traction battery 24.

[0032] In addition to providing electrical energy for propulsion of the BEV 12 , the traction battery 24 may also provide electrical energy for use by other electrical systems of the BEV, such as HV loads like an electric heater and air conditioning system, and low voltage (LV) loads like an auxiliary battery.

[0033] The traction battery 24 may be recharged by an external power source 36 (e.g., an electrical grid). The external power source 36 may be electrically connected to an electric vehicle supply equipment (EVSE) 38. The EVSE 38 provides circuitry and controls for controlling and managing the transfer of electrical energy between the external power source 36 and the BEV 12. The external power source 36 may provide DC or AC power to the EVSE 38. The EVSE 38 may have a charging connector 40 for plugging into the charging port 34 of the BEV 12.

[0034] A power conversion module 32 of the EV 12, such as an onboard charger having a DC / DC converter, may condition power supplied from the EVSE 38 to provide appropriate voltage and current levels to the traction battery 24. The power conversion module 32 may interface with the EVSE 38 to coordinate power delivery to the traction battery 24.

[0035] The various components described above may have one or more associated controllers to control and monitor the operation of the components. The controller may be a microprocessor-based device. The controller may communicate via a serial bus (e.g., a controller area network (CAN)) or via discrete conductors.

[0036] For example, there is a system controller 48 ("vehicle controller") to coordinate the operation of the various components. The controller 48 includes electronics, software, or both to perform the necessary control functions for operating the BEV 12. The controller 48 may be a combined vehicle system controller and powertrain control module (VSC / PCM). Although the controller 48 is shown as a single device, the controller 48 may include multiple controllers in the form of multiple hardware devices, or multiple software controllers with one or more hardware devices. In this regard, references to "controller" herein may refer to one or more controllers.

[0037] The controller 48 implements a battery energy control module (BECM) 50. The BECM 50 communicates with the traction battery 24. The BECM 50 is a traction battery controller operable to manage the charging and discharging of the traction battery 24 and to monitor the operating characteristics of the traction battery. The BECM 50 is operable to implement an algorithm for measuring (e.g., detecting or estimating) the operating characteristics of the traction battery 24. The BECM 50 controls the operation and performance of the traction battery 24 based on the operating characteristics of the traction battery. The operation and performance of other systems and components of the BEV 12 may be controlled based on the operating characteristics of the traction battery 24.

[0038] The operating characteristics of the traction battery 24 include the charge capacity and the state of charge (SOC) of the traction battery 24. The charge capacity of the traction battery 24 indicates the maximum amount of electrical energy that can be stored in the traction battery. The SOC of the traction battery 24 indicates the current amount of charge stored in the traction battery. The SOC of the traction battery 24 may be expressed as a percentage of the maximum amount of charge that can be stored in the traction battery.

[0039] Another operational characteristic of the traction battery 24 is the power capability of the traction battery. The power capability of the traction battery 24 is a measure of the maximum amount of power that the traction battery can provide (i.e., discharge) or receive (i.e., charge) in a specified time period. Thus, the power capability of the traction battery 24 corresponds to charge and discharge power limits that define the amount of electrical power that can be supplied or received by the traction battery at a given time. These limits can be provided to other vehicle controls, such as by a vehicle system controller (VSC), so that the information can be used by systems that can draw power from or provide power to the traction battery 24. The vehicle controls will know how much power the traction battery 24 can provide (discharge) or absorb (charge) in order to meet driver demand and optimize energy usage. Thus, knowing the power capability of the traction battery 24 allows management of electrical loads and sources so that the requested power is within the limits that the traction battery can handle.

[0040] Reference now Figure 2 ,, continue to refer to Figure 1 , a block diagram of an arrangement for the BECM 50 to monitor the traction battery 24 is shown. The traction battery 24 is composed of a plurality of battery cells 52. The battery cells 52 are physically connected together (e.g., as shown in FIG. Figure 2 connected in series as shown).

[0041] The BECM 50 is operable to monitor pack level characteristics of the traction battery 24, such as a battery current 56, a battery voltage 58, and a battery temperature 60. The battery current 56 is the current output from (i.e., discharging) or input to (i.e., charging) the traction battery 24. The battery voltage 58 is the terminal voltage of the traction battery 24.

[0042] The BECM 50 may also be operable to measure and monitor cell level characteristics of the battery cells 52 of the traction battery 24. For example, the terminal voltage, current, and temperature of one or more of the battery cells 52 may be measured. The BECM 50 may measure the cell level characteristics using battery sensors 54. The battery sensors 54 may measure characteristics of one or more battery cells 52. The BECM 50 may utilize Nc battery sensors 54 to measure characteristics of all of the battery cells 52. Each battery sensor 54 may transmit measurements to the BECM 50 for further processing and coordination. The functionality of the battery sensors 54 may be internally incorporated into the BECM 50.

[0043] The traction battery 24 may have one or more temperature sensors (such as thermistors) in communication with the BECM 50 to provide data indicative of the temperature of the battery cells 52 of the traction battery 24 for the BECM to monitor the temperature of the traction battery and / or the battery cells. The BEV 12 may also include a temperature sensor to provide data indicative of the ambient temperature for the BECM 50 to monitor the ambient temperature.

[0044] The BECM 50 controls the operation and performance of the traction battery 24 based on the monitored traction battery level characteristics and battery cell level characteristics. For example, the BECM 50 may use the monitored characteristics to measure (e.g., detect or estimate) operating characteristics of the traction battery 24 (e.g., power capability of the traction battery, SOC of the traction battery, etc.), such as for use in controlling the traction battery and / or the BEV 12.

[0045] As is known to those of ordinary skill in the art, the BECM 50 may measure the operating characteristics of the traction battery 24 by using observers, and a battery model (i.e., an "equivalent circuit model" (ECM)) is used to construct the observer using measurements of battery current, terminal voltage, and battery temperature. The BECM 50 may estimate parameter values ​​of the ECM (e.g., resistance and capacitance of circuit elements of the ECM) and state values ​​of the ECM (e.g., voltage and current across circuit elements of the ECM) by recursive estimation based on such measurements. For example, the BECM 50 may estimate the values ​​of the model parameters and model states using some adaptive estimation method, such as an extended Kalman filter (EKF).

[0046] In order for the values ​​of the operating characteristics of the traction battery 24 measured by the BECM 50 to accurately agree with the actual values ​​of the operating characteristics of the traction battery, the ECM must accurately model the traction battery. In order for the ECM to accurately model the traction battery 24, (i) the ECM must have a sufficient set of parameters (e.g., resistances and capacitances of circuit elements of the ECM), and (ii) the estimated values ​​of the model parameters and model states must be at least substantially similar to the values ​​of the parameters and states of the ECM that accurately model the traction battery (i.e., the estimated parameter and state values ​​must be at least substantially similar to the actual parameter and state values).

[0047] As explained, an accurate model of the traction battery 24 enables the BECM 50 to properly control the traction battery, which directly affects vehicle performance and range for a given full charge. Equivalent circuit models (ECMs) are widely used in electrified vehicle traction battery control systems in order to meet real-time control system requirements for computational speed and RAM / ROM usage. Specifically, n-RC ECMs where n=1 or 2 are widely used (an n-RC ECM is a type of ECM having "n" RC circuit elements, each RC circuit element including resistor ("R") parameters and capacitor ("C") parameters; where n=1, a 1-RC ECM includes one such RC circuit element; and where n=2, a 2-RC ECM includes two such RC circuit elements). As indicated, the parameters of the ECM are learned using an online learning method such as a Kalman filter or an extended Kalman filter (EKF) .

[0048] During use of the BEV 12, the load on the traction battery 24 varies over a wide frequency range. For example, during local driving or when the driver has frequent increase / decrease demands, the load on the traction battery 24 varies within seconds. During highway cruising, the load on the traction battery 24 may remain relatively constant for several minutes or longer. During DC charging of the traction battery 24, the load on the traction battery may be nearly constant for half an hour or more.

[0049] Battery diffusion is a continuous process that also includes a wide frequency range. High frequency range components mainly affect fast dynamic performance. During unidirectional current flow conditions, such as during the charge depletion (CD) vehicle operating mode or during the DC fast charging process, the impact of slow frequencies can be observed for a relatively long time.

[0050] 1-RC and 2-RC ECMs are limited in their ability to model the traction battery diffusion process over a wide frequency range. To facilitate model state and parameter learning, the input signal (usually current) needs to be a dynamic load that is sufficiently "exciting" to the traction battery (in a simpler form of explanation, the current needs to have sufficient variation in addition to a constant value). This requirement limits the use of ECMs that include low frequency components whose parameters are to be identified via online learning. For 1-RC and 2-RC ECMs, they represent higher frequency diffusion components. Due to this limitation, 1-RC and 2-RC ECMs are somewhat limited in accurately predicting the terminal voltage of the traction battery, such as during a CD vehicle operating mode or a DC fast charging process.

[0051] The limitations of the 1-RC and 2-RC ECMs can affect the overall performance of the traction battery control function. For example, in a battery model-based SOC estimate, the unmodeled portion of the diffusion (as designated as the higher numbered RC pairs) can ultimately distort the SOC estimate due to a bias toward higher diffusion voltages (positive) or lower diffusion voltages (negative). Using a similar argument, the effects of unmodeled diffusion dynamics can lead to overestimation or underestimation of charge and discharge capabilities, particularly at lower battery temperatures and / or higher / lower SOC values.

[0052] According to the present disclosure, the BECM 50 employs an equivalent circuit model (“proposed ECM”) of the traction battery 24 that efficiently represents the complex battery diffusion dynamics of the traction battery. The proposed ECM can model the battery wide range frequency diffusion dynamics. In addition, the number of parameters of the proposed ECM is less than the number of parameters of a multi-RC pair ECM having three or more RC circuit elements, and the parameters of the proposed ECM can be learned using an EKF or similar method at reasonable BECM capabilities (such as CPU utilization ratio and RAM / ROM availability).

[0053] The proposed ECM is capable of accurately representing battery cell dynamics at low SOC and / or low temperature. In addition, the proposed ECM is capable of accurately estimating discharge power capability estimation at low SOC and / or low temperature, and is capable of accurately estimating charge power capability estimation at high SOC and / or low temperature.

[0054] The proposed ECM is different from a multi-RC pair ECM having more parameters that need to be identified or learned online, which is an impractical task for a multi-RC pair ECM (i.e., n-RC ECM, where n≥3) having three or more pairs of RC circuit elements. The proposed ECM only needs to learn the first RC pair of key parameters that describe the diffusion process, rather than learning so many pairs of RC parameters. In other words, the proposed ECM has a complex structure that is functionally comparable to an n-RC pair ECM (where n>>2), but the parameters to be learned by the proposed ECM can be the same as those used in the 1-RC model and do not exceed the parameters used in the 2-RC model. In this way, the proposed ECM efficiently represents complex battery diffusion dynamics.

[0055] Now refer to Figure 3, and continue to refer to Figure 1 , shows a schematic diagram of a conventional ECM 80 for the traction battery 24. According to the conventional ECM 80, the traction battery 24 is modeled as a circuit having a voltage source (OCV / (SOC)) 82, a resistor R0 84, a first RC pair 86 having a first resistor R1 88 and a first capacitor C1 90 in series, and one or more such additional RC pairs 87. Thus, the conventional ECM 80 is an n-RC ECM, where n≥2.

[0056] Voltage source 82 represents the open circuit voltage (OCV) of traction battery 24. The OCV of traction battery 24 depends on the state of charge (SOC) of the traction battery and the temperature of the traction battery. Resistor R0 84 represents the internal resistance of traction battery 24. The RC pair represents the diffusion process of traction battery 24. Therefore, the diffusion process of traction battery 24 in conventional ECM 80 is represented by RC pairs R1 and C1, ..., R n and C nto describe.

[0057] Voltage V0 92 is the voltage drop across resistor R0 84 due to battery current I 94 flowing across resistor R0 84. Voltage V1 96 is the voltage drop across first RC pair 86 due to battery current IR1 flowing across resistor R1 88. Voltage drops are across each additional RC pair 87. Voltage V t 98 is the voltage across the terminals of the traction battery 24 (ie, the terminal voltage).

[0058] The parameters of the conventional ECM 80 include resistors (ie, resistor R0, resistor R1, and resistor R n ) and capacitors (i.e., capacitor C1 and capacitor C n ). The parameters will have values ​​such that the calculated output of the conventional ECM 80 in response to a hypothetical given input represents the actual output of the traction battery 24 in response to an actual given input. Therefore, the values ​​of the parameters of the conventional ECM 80 must be accurate so that the ECM accurately models the behavior of the traction battery 24.

[0059] The values ​​of the parameters may be learned online by the BECM 50, such as using an EKF. Understandably, it is much easier for the BECM 50 to learn the values ​​of a few parameters than to learn the values ​​of many parameters. Therefore, due to the limitations of the BECM hardware capabilities, conventional ECMs 80 in conventional use are either 1-RC ECMs or 2-RC ECMs.

[0060] Referring now to FIGS. 4A , 4B, 4C, and 4D, with continued reference to FIG. 3 , further details of the conventional ECM 80 will be described.

[0061] FIG4A shows a schematic diagram of a portion 100 of a conventional ECM 80. As indicated, the ECM RC parameters can be linked to different cell physical properties, as shown in the ECM portion 100 of FIG4A. In the ECM portion 100, the resistor R s In relation to the solid and electrolyte resistance of the traction battery 24, the resistor R ct Related to the charge transfer resistance (across the double layer), C dl is related to the double layer capacitance and the impedance Z W It is the Warburg impedance used to describe the dispersion of the cell in a wide frequency range.

[0062] FIG. 4B shows the Warburg impedance parameter (Z W). Since there is no simple differential equation to represent the Warburg impedance, multiple RC pairs are used to approximate the effect of the Warburg impedance, as shown in the Warburg impedance model 110.

[0063] FIG4C shows a schematic diagram of a conventional ECM 80 having a simplified version of the ECM portion 100. In the case where the traction battery 24 is a lithium-ion traction battery, the double layer capacitor C dl It may be considered small enough that the ECM portion 100 shown in FIG. 4A is simplified, resulting in the simplified conventional ECM 120 shown in FIG. 4C .

[0064] 4D shows a schematic diagram of the simplified conventional ECM 120 shown in FIG4C, wherein the Warburg impedance model 110 shown in FIG4B replaces the Warburg impedance parameters. In the case of using multiple RC pairs to represent the Warburg impedance, the simplified conventional ECM 120 shown in FIG4C leads to the expanded conventional ECM 130 shown in FIG4D.

[0065] The extended conventional ECM 130 shown in FIG4D accurately represents battery diffusion over a wide frequency range when the values ​​of the parameters for multiple RC pairs can be accurately determined. However, because of the computational requirements due to the relatively large number of parameters, the parameters cannot be reliably calibrated even in an offline optimization. Therefore, it is impractical to use the extended conventional ECM 130 to determine the values ​​of the RC parameters in an operating vehicle using an EKF.

[0066] According to the present disclosure, the proposed ECM is based on defining the RC parameters in a plurality of RC pairs representing the Warburg impedance (shown in the Warburg impedance model 110 of FIG. 4B ) as depending on the first RC pair with the lowest time constant (τ). The first RC pair with the lowest time constant (τ) is an RC pair consisting of a resistor R1 and a capacitor C1, wherein the time constant (τ1) is equal to the product of the resistance and capacitance of the RC pair according to the equation τ1=R1*C1. The resistors of the other RC pairs are functions of the resistor R1 of the first RC pair, expressed as fs(). The time constants (τ) of the other RC pairs are functions of the time constant (τ1), expressed as gs().

[0067] The physics behind the proposed ECM is simple. Instead of trying to find n pairs of lower frequency RC pairs to describe the cell diffusion dynamics, which is ultimately an approximation, a simpler representation is used to discretely cover the expected frequency domain while ensuring that the ECM is accurate enough to be at least comparable to the full n-RC pair model with complex model structure and parameters.

[0068] The functions fs() and gs() linking the parameters in the n-RC pair are determined off-line. The relationships remain constant during real-time control of the traction battery 24 by the BECM 50. These functions may be updated, such as after extended use of the traction battery 24.

[0069] When using the Warburg impedance in the extended conventional ECM 130 of FIG. 4D , the values ​​of only one pair of RC parameters (ie, the first RC pair) need to be determined because the RC parameters in the other pairs of the Warburg impedance depend on the parameters of the first RC pair.

[0070] Functions f1(), f2(), ..., and f n () Determine how the resistance values ​​of the other RC pairs depend on the resistance value of the resistor R1 of the first RC pair. The resistance values ​​in the other RC pairs can be set to be the same, or can be assumed to be different. The dependence can also be a function of temperature and battery current, which means that R2 = f1 (R1, T, I), R3 = f2 (R1, T, I), ..., and R n+1 =f n (R1,T,I).

[0071] In more detail, in the conventional ECM 80 shown in FIG. 3 , resistors R1, R2, . . . , R n In contrast, in the proposed ECM, it is specified that only one of the resistors (e.g., R1) is an independent parameter, with the remaining resistors (e.g., R2, ..., R n ) is a parameter that depends on the resistor. In this example, resistors R2, ..., R n The dependence on resistor R1 is given by the functions f1(), f2(), ..., and f n ()(that is, R2 = f1(R1, T, I), R3 = f2(R1, T, I), ..., and R n+1 =f n (R1,T,I)) is explained.

[0072] Functions g1(), g2(), ..., and g n () determines how to extend the frequency range. These functions ensure that each RC pair covers a frequency range and that they do not overlap each other. These relationships are used to ensure that τ2>3τ1, τ3>3τ2, ..., τ n >3τ n-1 The dependence can also be a function of temperature and battery current, which means that C2 = g1(τ1,T,I), C3 = g2(τ1,T,I), ..., C n+1=g n (τ1,T,I).

[0073] For controlling current type of traction batteries with the proposed ECM, n=5 or 6 are viable options.

[0074] Reference now Figure 5 Continuing with the previous figure, the Warburg impedance parameter (Z W ) is a schematic diagram of the proposed model 140. In the proposed ECM, the proposed Warburg impedance model 140 replaces the Warburg impedance model 110 shown in FIG. 4B.

[0075] The proposed Warburg impedance model 140 can be referred to as an independent 2-RC pair because the proposed Warburg impedance model has only two RC pairs with parameters independent of the other pairs. As an example, assume that the function f1=f2=f3=…, which makes R1=R2=R3=…, and τ2=[γ(T,I)*τ1]*τ1=γ(T,I)τ1 2 ,γ3=αγ(T,I)τ1 2 , τ4=α 2 γ(T,I)τ1 2 ,τ5=α 3 γ(T,I)τ1 2 ,......,τ n =α n-2 γ(T,I)τ1 2 , where α is a constant parameter greater than 3, and γ(T,I) is a function of temperature (T) and current (I). γ can also be a constant with units of 1 / s. α and γ are identified offline using test data and are not changed during battery operation, but can be updated after extensive battery use.

[0076] Reference now Figure 6 , continue to refer to Figure 5 , shows a schematic diagram of the proposed ECM 150 for the traction battery 24. When the above relationship is applied and γ=1 (1 / second), the independent 2-RC model becomes Figure 6 The proposed ECM 150 is shown. Figure 6 As shown, the proposed ECM 150 has only five RC parameters R s , R ct , C dl (or τ dl =R ct C dl ), R1 and τ lfor the BECM 50 to learn. The number of five RC parameters is the same as the number of parameters of a conventional 2-RC pair ECM. Another advantage of the proposed ECM 150 is that the values ​​of the parameters can be learned using dynamic loads even if the parameters describe the low frequency properties of the battery cells.

[0077] exist Figure 6 In the proposed ECM 150 shown, the first RC pair describes the battery charge transfer properties of the double layer capacitor. Its frequency response is much faster than the frequency response of the first RC pair representing the Warburg impedance of the cell diffusion properties. Therefore, during the model parameter determination, τ dl Apply τ dl The upper limit of the limit, and for τ l Apply τ l The lower limit of τ may be temperature dependent. dl The upper limit should be significantly smaller than τ l The lower limit of .

[0078] Reference now Figure 7 , continue to refer to Figure 6 , shows a schematic diagram of the proposed ECM 150 in the form of an independent 1-RC ECM 160. The independent 1-RC ECM 160 is derived as follows. For a lithium-ion traction battery, C dl is relatively small. Therefore, the time constant (τ dl =R ct C dl ) is much less than one second in the temperature range of normal operation of the traction battery. In extremely cold temperatures such as -30°C or colder, it can only reach about one second. When C dl The proposed ECM 150 does not affect power estimates for durations of five seconds or longer at all temperatures, and may have a small and negligible effect on one-second power estimates at -20°C or colder. Therefore, the proposed ECM 150 can be further simplified as Figure 7 A separate 1-RC ECM 160 is shown.

[0079] The standalone 1-RC ECM 160 has only three RC parameters R0, R1 and τ l for learning by the BECM 50. The number of the three RC parameters is the same as that of the conventional 1-RC pair ECM.

[0080] When ignoring C dl When τ1 is τ2, the first RC pair of the independent 1-RC ECM 160 describes the cell diffusion properties rather than the double layer behavior. During the model parameter determination, a temperature dependent lower bound limit is applied to τ1 to keep it within the cell diffusion frequency range.

[0081] When the proposed model 150 is used in the BECM 50, for a standalone 2-RC ECM, R is estimated during vehicle operation using some adaptive scheme (such as EKF). s , R ct , C dl (or τ dl ), R1 and τ1, and for an independent 1-RC ECM, an adaptive scheme is used to estimate the values ​​of R0, R1 and τ1.

[0082] As an example of the proposed ECM 150, Figure 7 An independent 1-RC ECM 160 is shown in FIG. 1 , where the defined current direction and voltage are indicated. The traction battery function can be expressed as (let γ = 1 (1 / s)):

[0083] V t =OCV-V0-V1-V2-....-V n

[0084]

[0085] …

[0086]

[0087] To facilitate online estimation, the parameters are specified as state variables that can be learned using online learning methods such as the Extended Kalman Filter. For example, the proposed independent n-RC ECM can be expressed as follows. Here, SOC and V1(t) are states, and R0, R1, τ1 are parameters to be learned.

[0088]

[0089] I(t)+w(t)

[0090] V(t)=OCV(t)-R0*I(t)-V1(t)-V2(t)-V3(t)-…V n (t)+μ(t)

[0091] =f(SOC(t))-R0*I(t)-V1(t)-V2(t)-V3(t)-…V n (t)+μ(t)

[0092] Where V1(t), V2(t), V3(t) and V n (t) are all functions of R1 and τ1, etc.

[0093] They are calculated as follows:

[0094] The above state function is as simple as that used for a conventional 1-RC ECM.

[0095] A further simplification method is to add V2, V3, ..., V n Direct open loop calculations are used as long as their initial values ​​are considered known. This is a special case and should not be extended to general vehicle operating scenarios, since the traction battery is not always at rest to have zero initial values ​​for e.g. V2, V3, ...

[0096] The BECM 50 may utilize the estimated states SOC, V1, V2, . . . , V n The power of the traction battery 24 is estimated using the learned parameters R0, R1, and τ1.

[0097] Using the proposed ECM 150 , the BECM 50 may derive the discharged battery current limit as:

[0098]

[0099] Where V t is the battery terminal voltage, V1(0), V2(0), ..., Vn(0) are Figure 7 The voltage at time t=0 or when the power estimate is updated is shown. τ2, τ3, ..., τ3 are functions of τ1, and R2, R3, ..., R n is a function of R1.

[0100] During a discharge condition (assuming the discharge current is positive), the BECM 50 determines the discharge current limit by:

[0101]

[0102] Where V tmin is the minimum voltage limit of the traction battery. The voltage at maximum discharge current will be:

[0103]

[0104] The discharge power capability after time t seconds will be:

[0105]

[0106] During charging (assuming the charge current is negative, the maximum charge current will be the minimum current):

[0107]

[0108] Where V tmax is the maximum voltage limit of the battery. The voltage at maximum charging current will be:

[0109]

[0110] The BECM 50 can obtain the charging power capability after time t seconds by:

[0111]

[0112] Reference now Figure 8 , a flowchart 200 is shown describing operations performed by the BECM 50 when using the independent 1-RC ECM 160 to detect the power capability and / or SOC of the traction battery 24 .

[0113] In process block 202, the BECM 50 receives inputs indicative of traction battery voltage (Vt), traction battery current (I), and traction battery temperature (T). The inputs are measured by sensors as described above. The inputs may also include a previously estimated SOC, as represented by the dashed lines in the flowchart 200.

[0114] In process block 204, the BECM 50 determines battery control parameters such as SOC and battery voltage and current limits (V max 、V min ,I lim_ch ,I lim_dch ). The BECM 50 may calculate the SOC using an ampere-hour integration method.

[0115] In process block 206, the BECM 50 estimates the values ​​of the parameters of the standalone 1-RC ECM 160 (i.e., the values ​​of R0, R1, and τ1) using a recursive parameter estimation algorithm with an EKF. The EKF estimates the ECM parameters using the measured voltage, current, and temperature of the traction battery 24 in the recursive parameter estimation algorithm. The SOC, which may be provided by process blocks 202 or 204, may also be used to estimate the ECM battery parameters.

[0116] In process block 208 , the BECM 50 estimates the power capability (P cap ). Knowing the estimated ECM battery parameters, the BECM 50 estimates the charge and discharge battery power capabilities, respectively, using known equations. The BECM 50 controls the traction battery 24 and / or the BEV 12 according to the estimated power capabilities of the traction battery.

[0117] In process block 210, the BECM 50 estimates the SOC of the traction battery 24. Again, knowing the estimated ECM battery parameters, the BECM 50 estimates the SOC using known equations. The BECM 50 controls the traction battery 24 and / or the BEV 12 based on the estimated SOC of the traction battery.

[0118] As described, according to the present disclosure, the BECM 50 employs a proposed ECM that efficiently represents the complex diffusion dynamics of the traction battery 24. In the proposed ECM, the Warburg impedance is represented by a plurality of RC pairs, where the parameters in each pair except one pair (i.e., the first pair) depend on the first pair with the smallest time constant (highest frequency). Specifically, the resistance of each RC pair depends on the resistance of the first RC pair. The dependence of the other RC pairs on the first RC pair may change with temperature, SOC, and / or current.

[0119] In multiple RC pairs, if necessary, each RC pair covers a frequency range from DC to essentially infinity. Such frequency ranges do not overlap with each other. The minimum time constant in the first RC pair can be at least several seconds longer than the minimum time constant in the pair consisting of the charge transfer resistance and the double-layer capacity to allow the two pairs to be clearly separated. The time constants of these nRC pairs should be different, each representing a different frequency. The frequency range they represent includes the higher frequencies of the dynamic load of the vehicle to the low frequencies of constant charge / discharge from 0% to 100% / (100% to 0%). The time constants of all RC pairs except the first pair depend on the time constant of the first RC pair. This dependent function can be a function of the time constant of the first RC pair only. It can also depend on temperature, SOC and / or current.

[0120] The BECM 50 learns only the first RC parameters. The BECM 50 calculates the parameters of the RC pairs other than the first RC pair by a predetermined relationship with the parameters of the first RC pair. The relationship between the parameters of the first RC pair and the other RC pairs is developed offline, and the dependency is fixed during vehicle operation and a relatively long vehicle usage time. Such dependency functions can be updated with battery life.

[0121] The power capability estimation method employed by the BECM 50 is provided in relation to the RC pair parameters, battery SOC and charge / discharge voltage limits, and current limit map (if any).

[0122] The proposed ECM can be extended to other battery pack control structures of battery packs with multiple cells connected in series.

[0123] Although exemplary embodiments are described above, these embodiments are not intended to describe all possible forms of the present disclosure. In fact, the words used in this specification are descriptive rather than restrictive, and it should be understood that various changes can be made without departing from the spirit and scope of the present disclosure. In addition, the features of various implemented embodiments can be combined to form other embodiments of the present disclosure.

[0124] According to the present invention, a system is provided, comprising: a battery; and a controller configured to control the battery based on (i) a value of a first parameter of a model of the battery obtained from electrical measurements of the battery and (ii) a value of a second parameter of the model obtained from the value of the first parameter, the value of the second parameter being dependent on the value of the first parameter.

[0125] According to an embodiment, the controller is further configured to control the battery based on (i) the value of the first parameter, (ii) the value of the second parameter and (iii) values ​​of one or more additional parameters of a model obtained from the value of the first parameter, the values ​​of the one or more additional parameters being dependent on the value of the first parameter.

[0126] According to an embodiment, the first parameter, the second parameter and the one or more additional parameters are at least partially indicative of a Warburg impedance of the model.

[0127] According to an embodiment, the first parameter is a circuit element of a first resistor-capacitor (RC) circuit; the second parameter is a circuit element of a second RC circuit; and the first RC circuit has a time constant that is less than a time constant of the second RC circuit.

[0128] According to an embodiment, the first parameter is a resistor of a first RC circuit, and the value of the first parameter is the value of the resistance of the resistor of the first RC circuit; and the second parameter is a resistor of a second RC circuit, and the value of the second parameter is the value of the resistance of the resistor of the second RC circuit.

[0129] According to an embodiment, the first parameter is a capacitor of a first RC circuit and the value of the first parameter is the value of the capacitance of the capacitor of the first RC circuit; and the second parameter is a capacitor of a second RC circuit and the value of the second parameter is the value of the capacitance of the capacitor of the second RC circuit.

[0130] According to an embodiment, the first parameter is a time constant of a first resistor-capacitor (RC) circuit, and a value of the first parameter is a time value of the time constant of the first RC circuit; the second parameter is a time constant of a second RC circuit, and a value of the second parameter is a time value of the time constant of the second RC circuit; and the time constant of the RC circuit is less than the time constant of the second RC circuit.

[0131] According to an embodiment, the dependency of the value of the second parameter on the value of the first parameter changes with changes in the temperature of the battery, the current of the battery and the state of charge (SOC) of the battery.

[0132] According to an embodiment, the electrical measurement of the battery comprises a voltage and / or current and / or temperature measurement of the battery.

[0133] According to the present invention, a system for a vehicle having a traction battery is provided, the system having: a sensor configured to sense an electrical measurement of the traction battery; and a controller configured to control the traction battery based on (i) a value of a first resistor-capacitor (RC) circuit of an equivalent circuit model (ECM) of the traction battery obtained from the electrical measurement of the traction battery and (ii) a value of a second RC circuit of the ECM obtained from the value of the first RC circuit, the value of the second RC circuit being dependent on the value of the first RC circuit.

[0134] According to an embodiment, the controller is further configured to control the traction battery based on (i) a value of the first RC circuit, (ii) a value of the second RC circuit and (iii) values ​​of one or more additional RC circuits of the ECM derived from the value of the first RC circuit, the values ​​of the one or more additional RC circuits being dependent on the value of the first parameter.

[0135] According to an embodiment, the first RC circuit, the second RC circuit, and the one or more additional RC circuits are at least partially indicative of a Warburg impedance of the ECM.

[0136] According to an embodiment, the first RC circuit has a smallest time constant among all the RC circuits.

[0137] According to an embodiment, the second RC circuit has a smallest time constant among all RC circuits except the first RC circuit.

[0138] According to an embodiment, the value of the first RC circuit is the value of a resistance of the first RC circuit; and the value of the second RC circuit is the value of a resistance of the second RC circuit.

[0139] According to an embodiment, the value of the first RC circuit is the value of the capacitance of the first RC circuit; and the value of the second RC circuit is the value of the capacitance of the second RC circuit.

[0140] According to an embodiment, the value of the first RC circuit is the value of a time constant of the first RC circuit; and the value of the second RC circuit is the value of the time constant of the second RC circuit.

[0141] According to the present invention, a method comprises: sensing electrical measurements of a traction battery of a vehicle; and controlling, by a controller, the traction battery based on (i) a value of a first parameter of an equivalent circuit model (ECM) of the traction battery obtained from the electrical measurements of the traction battery and (ii) a value of a second parameter of the ECM obtained from the value of the first parameter of the ECM, the value of the second parameter being dependent on the value of the first parameter.

[0142] In one aspect of the invention, the method comprises: controlling, by the controller, the traction battery further based on (iii) values ​​of one or more additional parameters of the ECM derived from the value of the first parameter, the values ​​of the one or more additional parameters being dependent on the value of the first parameter.

[0143] In one aspect of the invention, the first parameter is a circuit element of a first resistor-capacitor (RC) circuit; the second parameter is a circuit element of a second RC circuit; and the first RC circuit has a time constant that is less than a time constant of the second RC circuit.

Claims

1. A system comprising: Battery; as well as A controller configured to control the battery based on (i) a value of a first parameter of a model of the battery obtained from electrical measurements of the battery and (ii) a value of a second parameter of the model obtained from the value of the first parameter, the value of the second parameter being dependent on the value of the first parameter.

2. The system of claim 1, wherein: The controller is further configured to control the battery based on (i) the value of the first parameter, (ii) the value of the second parameter, and (iii) values ​​of one or more additional parameters of the model obtained from the value of the first parameter, the values ​​of the one or more additional parameters being dependent on the value of the first parameter.

3. The system of claim 2, wherein: The first parameter, the second parameter, and the one or more additional parameters are at least partially indicative of a Warburg impedance of the model.

4. The system of claim 1, wherein: The first parameter is a circuit element of a first resistor-capacitor (RC) circuit; The second parameter is a circuit element of a second RC circuit; and The first RC circuit has a time constant that is smaller than a time constant of the second RC circuit.

5. The system of claim 4, wherein: The first parameter is a resistor of the first RC circuit, and the value of the first parameter is a value of the resistance of the resistor of the first RC circuit; and The second parameter is a resistor of the second RC circuit, and the value of the second parameter is a value of the resistance of the resistor of the second RC circuit.

6. The system of claim 4, wherein: The first parameter is a capacitor of the first RC circuit, and the value of the first parameter is a value of a capacitance of the capacitor of the first RC circuit; and The second parameter is a capacitor of the second RC circuit, and the value of the second parameter is a value of a capacitance of the capacitor of the second RC circuit.

7. The system of claim 1, wherein: the first parameter is a time constant of a first resistor-capacitor (RC) circuit, and the value of the first parameter is a value of the time of the time constant of the first RC circuit; The second parameter is a time constant of a second RC circuit, and the value of the second parameter is a value of the time of the time constant of the second RC circuit; and The time constant of the RC circuit is smaller than the time constant of the second RC circuit.

8. The system of claim 1, wherein: The dependence of the value of the second parameter on the value of the first parameter changes with changes in the temperature of the battery, the current of the battery, and the state of charge (SOC) of the battery.

9. A system for a vehicle having a traction battery, the system comprising: a sensor configured to sense an electrical measurement of the traction battery; as well as a controller configured to control the traction battery based on (i) a value of a first resistor-capacitor (RC) circuit of an equivalent circuit model (ECM) of the traction battery obtained from the electrical measurements of the traction battery and (ii) a value of a second RC circuit of the ECM obtained from the value of the first RC circuit, the value of the second RC circuit being dependent on the value of the first RC circuit.

10. The system of claim 9, wherein: The controller is further configured to control the traction battery based on (i) the value of the first RC circuit, (ii) the value of the second RC circuit, and (iii) values ​​of one or more additional RC circuits of the ECM derived from the value of the first RC circuit, the values ​​of the one or more additional RC circuits being dependent on the value of the first parameter.

11. The system of claim 10, wherein: The first RC circuit has a smallest time constant among all the RC circuits; and The second RC circuit has a smallest time constant among all the RC circuits except the first RC circuit.

12. The system of claim 10, wherein: The value of the first RC circuit is the value of the resistance of the first RC circuit; and The value of the second RC circuit is a value of a resistance of the second RC circuit.

13. The system of claim 10, wherein: The value of the first RC circuit is the value of the capacitance of the first RC circuit; and The value of the second RC circuit is a value of a capacitance of the second RC circuit.

14. The system of claim 10, wherein: The value of the first RC circuit is a value of a time constant of the first RC circuit; and The value of the second RC circuit is a value of a time constant of the second RC circuit.

15. A method comprising: Sensing electrical measurements of the vehicle's traction battery; as well as The traction battery is controlled by a controller based on (i) a value of a first parameter of an equivalent circuit model (ECM) of the traction battery obtained from the electrical measurements of the traction battery and (ii) a value of a second parameter of the ECM obtained from the value of the first parameter of the ECM, the value of the second parameter being dependent on the value of the first parameter.

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