Method and system for controlling and monitoring vehicle battery pack using battery impedance model

Abstract

Methods and systems for controlling and monitoring a vehicle battery pack using a battery impedance model are provided. An electrically powered vehicle (EV) includes a battery pack, one or more sensors, and a vehicle controller. The battery pack includes a plurality of battery cells and is operable to provide at least a portion of propulsion power. The vehicle controller is configured to charge and discharge the battery pack according to a power limit defined by an output of a battery impedance model. The battery impedance model associates a battery impedance value with a frequency-dependent polarized impedance value representative of a diffusion state of the battery cell, and receives a parameter indicative of a measurement of the frequency-dependent polarized impedance value of the battery pack from the one or more sensors.

Description

Technical Field

[0001] The present disclosure generally relates to managing and / or controlling a battery pack of an electrified vehicle. Background Art

[0002] An electrified vehicle (EV) includes a battery pack, sometimes referred to as a traction battery, for providing electrical power to an electric motor to propel the EV. One or more operating characteristics of the battery pack, such as temperature, power limit, and / or state of charge (SOC), can be measured / estimated to control the charging and discharging operations of the battery pack.

[0003] In a non-limiting example, the EV includes a battery management module (BMM) and a control system. Generally, during a discharging operation (e.g., driving of the EV), the BMM is configured to estimate the SOC and / or power limit of the battery pack, and the control system is configured to control various devices / subsystems within the EV by, for example: using the operating characteristics, input from a user, power demands of devices (e.g., a motor, an air conditioning system, etc.), and / or other information to determine how much electrical power can be drawn from the battery pack. For a charging operation, the BMM is configured to provide a charging current / voltage request to the control system, which in turn controls the EV to start charging the battery pack (e.g., controlling an electric vehicle supply equipment (EVSE)). Summary of the Invention

[0004] In one form, the present disclosure relates to an electrified vehicle (EV) including: a battery pack, one or more sensors, and a vehicle controller. The battery pack includes a plurality of battery cells and is operable to provide at least a portion of the propulsion power. The vehicle controller is configured to charge and discharge the battery pack according to a power limit defined by an output of a battery impedance model. The battery impedance model correlates a battery impedance value with a polarization impedance value related to a frequency representing a diffusion state of the battery cells, and receives a parameter indicative of a measurement of the frequency-related polarization impedance value of the battery pack from the one or more sensors.

[0005] In another form, the present disclosure relates to a method of operating an electrified vehicle (EV) having a battery pack including a plurality of battery cells. The method includes: charging and discharging the battery pack according to a power limit defined by an output of a battery impedance model. The battery impedance model correlates a battery impedance value with a polarization impedance value related to a frequency representing a diffusion state of the battery cells, and receives a parameter indicative of a measurement of the frequency-related polarization impedance value of the battery pack from the one or more sensors.

[0006] In one form, the present disclosure relates to a system for an electrified vehicle (EV), the electrified vehicle (EV) including: a vehicle controller configured to charge and discharge the battery pack according to a power limit defined by an output of a battery impedance model. The battery impedance model correlates a battery impedance value with a polarization impedance value related to a frequency representing a diffusion state of the battery cells, and receives, from one or more sensors, parameters indicative of a measurement of the frequency-related polarization impedance value of the battery pack. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 is an example block diagram of an electrified vehicle (EV) in accordance with the present disclosure;

[0008] Figure 2 is a block diagram of a battery pack of an EV in accordance with the present disclosure;

[0009] Figure 3 is a block diagram of a BMM of an EV in accordance with the present disclosure; and

[0010] Figure 4 is a flowchart of a battery control routine in accordance with the present disclosure. DETAILED DESCRIPTION

[0011] As needed, detailed embodiments of the present invention are disclosed herein; however, it is to be understood that the disclosed embodiments are merely examples of the invention that may be embodied in various forms and alternative forms. The figures are not necessarily drawn to scale; some features may be exaggerated or minimized to show details of particular components. Accordingly, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present invention.

[0012] Typically, battery characteristics, such as but not limited to power capacity and energy capacity, are controlled and monitored based on voltage, current, and temperature measurements interpreted by carefully designed and calibrated models. These models and calibrations can require time-intensive development and may not capture all relevant operations, such as aging.

[0013] In some cases, Electrochemical Impedance Spectroscopy (EIS) can be used to capture additional information about chemical processes within a battery cell that affect battery characteristics. EIS involves phase-sensitive measurements of voltage and current in response to an electrical perturbation that varies according to frequency. Different battery processes, such as liquid and solid diffusion and lithium insertion charge transfer reactions, have different impedance responses at different frequencies. Additionally, EIS can potentially be implemented for on-vehicle monitoring by increasing the sampling rate of existing battery voltage and current measurements. Unfortunately, traditional EIS measurements are performed using, for example, single-frequency sinusoidal excitation or broadband excitation methods, resulting in long acquisition times for low-frequency measurements (f < 0.1 Hz).

[0014] This disclosure relates to a system that uses the output of a battery impedance model to control the operation of a battery pack, the battery impedance model being defined to estimate the low-frequency impedance behavior of the battery pack to obtain a complete characterization of the battery impedance. Specifically, the battery impedance model is defined to correlate battery impedance values with polarization impedance values that are frequency-dependent and representative of the diffusion state of the battery cells of the battery pack.

[0015] More specifically, the low-frequency impedance of a battery cell is typically dominated by diffusion, and by accurately understanding / depicting diffusion, the battery pack can be controlled more accurately. For example, direct current (DC) power delivery is dominated by diffusion, which is a limiting factor at low temperatures. Additionally, when an external electric potential is applied beyond the diffusion-limited battery capacity, internal battery polarization occurs. For example, if too much current is pushed during rapid charging, the internal polarization may cause a potential that leads to lithium plating. Lithium plating increases battery degradation (capacity loss) and can lead to thermal runaway in the case where lithium dendrites grow to cause a short circuit.

[0016] The extraction of parameters that describe physical battery processes may require deconvolution of overlapping EIS features. Typically, this means sampling over a wide range of frequencies, especially those dominated by individual processes. The low frequency is usually dominated by diffusion, but under some conditions, there is charge transfer that overlaps with diffusion at moderately low frequencies. In this case, even lower frequencies are sampled to separately characterize the contributions of diffusion and charge features.

[0017] The battery impedance model of this disclosure characterizes electrochemical battery processes to more accurately control the power and energy of the battery pack and to track the aging of the battery pack. As detailed herein, the battery management module of an EV is configured to employ the battery impedance model to define low-frequency battery impedance values for controlling the battery pack 106 and thus the EV.

[0018] Reference Figure 1 and Figure 2, in one form, the EV 100 is provided as a full battery electric vehicle (BEV) powered by an electric motor. In a non-limiting example, the EV 100 includes a powertrain having one or more electric motors 104 (i.e., electric machines), a battery pack 106 (i.e., traction battery), and a power electronics module 108. The EV 100 of the present disclosure does not include an engine, and thus, the battery pack 106 provides all of the propulsion power. In other variations, the present disclosure may be applied to other types of EVs having a high voltage battery pack, such as but not limited to hybrid electric vehicles (plug-in or non-plug-in) having an engine and fuel cell electric vehicles. Additionally, the EV is not limited to four-wheeled vehicles and may be applied to scooters, three-wheeled vehicles, aerial vehicles, and / or other electrified vehicles.

[0019] The electric motor 104 provides the motive movement of the EV 100, and in a non-limiting example, the electric motor is mechanically connected to a transmission 110, the transmission is mechanically connected to a drive shaft 112, and the drive shaft is mechanically connected to the wheels 114 of the EV 100. In addition to providing propulsion power, the electric motor 104 may also be configured to operate as a generator to recover energy that would otherwise be lost as heat in the friction braking system of the EV 100.

[0020] The battery pack 106 provides a high voltage (HV) direct current (DC) output that is used to power the electric motor 104 via the power electronics module 108. In one form, the power electronics module 108, which includes an inverter, provides a bi-directional transfer of energy between the battery pack 106 and the electric motor 104. Specifically, as is known, the power electronics module 108 converts the DC voltage to a three-phase AC current to operate the electric motor 104, and in the regenerative mode, the power electronics module 108 converts the three-phase AC current from the electric motor 104 acting as a generator to a DC voltage compatible with the battery pack 106.

[0021] The battery pack 106 may be rechargeable by an external power source 120 (e.g., the power grid), which is electrically connected to an electric vehicle supply equipment (EVSE) 122. The EVSE 122 provides the circuitry and controls to manage the transfer of electrical energy between the external power source 120 and the EV 100. The external power source 120 may supply DC or AC power to the EVSE 122. The EVSE 122 may have a charging connector 124 for insertion into a charging port 126 of the EV 100.

[0022] The EV 100 may also include a power conversion module 128, which is an on-vehicle charger with a DC / DC converter to regulate the power supplied from the EVSE 222 and provide an appropriate voltage level and current level to the battery pack 106. The power conversion module 228 may dock with the EVSE 222 to coordinate the power delivery to the battery pack 106.

[0023] In addition to providing electrical energy for propulsion, the battery pack 106 may also provide electrical energy for other electrical systems in the EV 100, such as HV loads like electric heaters and air conditioning systems, and LV loads like auxiliary batteries. In some variations, the battery pack 106 is configured to have bidirectional power transfer capability to provide power to systems external to the EV 100 (i.e., external systems), such as but not limited to homes, businesses, and / or microgrids. In a non-limiting example, the battery pack 106 uses the EVSE connector 224 to electrically couple to an external system and is operable to provide energy based on the transient load recommended for the external system and the amount of energy available from the battery pack 106.

[0024] In one form, the EV 100 includes a control system 130 to coordinate the operation of various components. The control system 130 includes electronics, software, or both to perform the necessary control functions for operating the EV 100. The control system 130 may be a combined vehicle control system and powertrain control module (VSC / PCM). Although the control system 130 is shown as a single device, the control system 130 may also include multiple controllers in the form of multiple hardware devices, or multiple software controllers with one or more hardware devices. In this regard, the reference to "controller" herein may refer to one or more controllers.

[0025] In one form, the EV 100 includes a battery management module (BMM) 132, which is configured to estimate one or more operating characteristics of the battery pack 106. The BMM 132 communicates with one or more sensors 134 provided with the battery pack 106 to detect characteristics of the battery pack 106, such as but not limited to current, voltage, and / or temperature. The BMM 132 may form part of the vehicle control system together with the control system 130, and although shown separate from the control system 130, may be integrated with the control system 130. Using the operating characteristics of the battery pack 106, the control system 130 is configured to draw power from the battery pack 106 based on driver demand and other considerations. In one form, the BMM 132 and the control system 130 may be referred to as vehicle controllers.

[0026] The BMM 132 is further configured to operate a contactor (not shown) based on commands from the control system 130 to electrically couple / decouple the battery pack 106 from the charge-discharge system of the EV. The charge-discharge system of the EV includes components that charge the battery pack 106 or act as a load to draw power from the battery pack 106, and may thus include a charge port 126, a power electronics module 108, and / or a transmission 110, among other components.

[0027] Among other components, the battery pack 106 includes a plurality of battery arrays 202A and 202B (collectively referred to as "array 202"), where each array 202 includes a plurality of battery cells 204-1 to 204-N (collectively referred to as "cells 204") connected in series ( Figure 2 ). The arrays 202 are connected to a positive power bus 206A and a negative power bus 206B (collectively referred to as "power buses 206"). Although two arrays 202 are provided, the battery pack 106 may include one or more arrays 202 and should not be limited to the examples provided herein. Additionally, although the arrays 202 are shown in parallel, the arrays 202 may be configured to operate in parallel and / or in series using power switches.

[0028] The sensor 134 includes one or more sensors 134A and 134B for the array 202. In one form, the sensor 134 measures various parameters related to the operation / performance of the battery pack 106 (i.e., measured parameters). In non-limiting examples, the measured parameters may include: electrical characteristics (e.g., voltage and / or current) of the array 202 and / or the battery cells 204 of the battery pack 106 and / or the temperature of the battery pack. Thus, the sensor 134 may include voltage sensors, current sensors, temperature sensors, and other sensing devices.

[0029] Reference Figure 3 , in one form, the BMM 132 includes a battery characteristic estimator (BCE) 302 having a battery impedance model 304 and a contactor control module (CCM) 306 configured to operate a contactor in a closed / open position based on commands from the control system 130.

[0030] The BCE 302 is configured to estimate various operating characteristics of the battery pack 106, such as but not limited to the electrical characteristics of the battery pack 106 (e.g., the electrical characteristics of each battery cell 204, the electrical characteristics of each array 202, and the electrical characteristics of the battery pack 106 as a whole), the OCV of each battery cell, the SOC of the battery pack 106, the power limit of the battery pack 106, and the temperature of the battery pack 106.

[0031] In one form, the battery impedance model 304 correlates battery impedance values with polarization impedance values that are frequency-dependent and representative of the diffusion state of the battery cell, and receives from the sensor 134 a parameter indicative of a measured frequency-dependent polarization impedance value of the battery pack 106. Specifically, the frequency-dependent polarization impedance value is typically the low-frequency polarization impedance of the battery cell 204, which is typically diffusion-dominated. The battery impedance at low frequencies can be obtained by: (1) measuring the electrical characteristics of the battery pack 106, (2) obtaining the OCV, (3) calculating the polarization voltage, (4) calculating the polarization resistance, and (5) estimating the complex impedance at low frequencies using the battery impedance model 304 and the polarization resistance. The battery impedance can be set for the battery pack 106 as a whole, for multiple arrays of battery cells 204 within the battery pack 106, and / or for individual battery cells 204 based on individual measurements, depending on, for example, the needs of the control system 130 and diagnostics.

[0032] The electrical characteristics of the battery pack 106 include the DC voltage and current of the battery pack 106 measured by the sensor 134. In a non-limiting example, the current is measured across the entire battery pack 106, and the voltage is measured across each set of parallel battery cells 204. Additionally, the current and voltage of the battery pack 106 can be sampled at 500 Hz, and the voltage of the battery cell 204 can be sampled at 10 Hz. The sampling rate of the current can be selected to meet the Nyquist sampling criterion to integrate the current within a selected accuracy criterion. In one form, the DC voltage is significantly different from the OCV, and the current should be large enough to form a polarization, but may depend on the battery chemistry, temperature, and / or life of the battery pack 106 / cell 204. Thus, the electrical characteristics measured during charging / discharging of the EV100 can be used for low-frequency battery impedance estimation. In some applications, the current and voltage difference measurements may also require a specific amount of time and / or stability (rate of change) to ensure the accuracy of the low-frequency battery impedance estimation.

[0033] The polarization impedance varies with the OCV, which is the voltage of the battery pack 106 when at rest. When the EV 100 is not operating (i.e., no charging and no discharging), known techniques can be used to determine the OCV, which can include measuring the voltage of each battery cell and using known models / algorithms to estimate the OCV. When the EV 100 is charging or discharging, the OCV is estimated based on the state of charge (SOC) of the battery and a defined correlation between multiple battery SOC values and OCV values, which can be referred to as the SOC-OCV correlation. The SOC-OCV correlation is typically found through a careful calibration process with a long waiting time across a range of SOCs. For example, in large cells, a four-hour equilibration time may be required, but this value varies with cell construction, chemistry, SOC, temperature, and current history. It is assumed that the SOC-OCV correlation is accurate and suitable for the state of the battery (i.e., power limits, SOC, temperature, capacity at full charge of the battery pack 106, and / or other variables that describe the health / ability of the battery pack 106) and the conditions under which the battery pack 106 operates. The accuracy of the SOC-OCV can depend on the type of EV and can include, but is not limited to, 1% accuracy for BEVs and 5% accuracy for HEVs. Known techniques can be used to determine the SOC, such as but not limited to SOC algorithms / models and the completion of a standard charge corresponding to the SOC. In one form, in addition to the SOC, the temperature of the battery pack 106 or the battery cell 204 is used to estimate the OCV, and thus the SOC-OCV correlation is defined as correlating the OCV with the SOC and temperature.

[0034] The polarization voltage is determined using Equation 1 below, where "V P " is the polarization voltage, "V DC " is the DC voltage, and "V OC " is the open-circuit voltage. The polarization voltage is the absolute value of the difference between the OCV of the battery pack 106 and the measured DC voltage.

[0035]

[0036] Using the polarization voltage, Equation 2 is used to calculate the polarization resistance (R P ), where I DC is the current. Alternatively, an empirically calibrated non-linear equation (such as the Butler-Volmer equation) can be used to relate the voltage, current, and zero-current impedance.

[0037]

[0038] The battery impedance model 304 and the polarization resistance are used to obtain the complex impedance at low frequencies, where the polarization resistance is the real component of the frequency-dependent complex impedance (Z) at low frequencies. The battery impedance model 304 is defined as extrapolating from the real polarization impedance to the imaginary polarization impedance and fitting the impedance data using the different relative behaviors between the real and imaginary components under low-frequency limits. In a non-limiting example, the battery impedance model is defined using at least one of a semi-infinite Warburg model, a reflective boundary Warburg model, a transmissive boundary Warburg model, or a constant phase element-based model. These models assume that the low-frequency behavior dominates and ignore the mid-frequency and high-frequency behaviors. For example, the semi-infinite Warburg impedance uses equations 3 and 4 to characterize the low-frequency impedance, where “Z W ” is the Warburg impedance, “ω” is the angular frequency, “R” is the gas constant, “T” is the temperature of the electrochemical system, “a” is the surface area, “n” is the valence, “F” is the Faraday constant, “C ox ” is the concentration of the oxidized species, “D ox ” is the diffusion coefficient of the oxidized species, “C red ” is the concentration of the reduced species, and “D red ” is the diffusion coefficient of the reduced species. The Warburg model provides the relationship between the real and imaginary components of the complex impedance. Specifically, in equation 3, is the real component, and is the imaginary component. Thus, equation 3 can be provided as equation 5, where R P is the polarization resistance.

[0039]

[0040]

[0041] The battery impedance model 304 can be calibrated during development or in-situ. For in-situ calibration, traditional single-sine excitation or broadband excitation can be used to directly measure the low-frequency impedance, and then the measured data is fitted to the desired model to extract the relationship between the real impedance and the imaginary impedance. Then, it can be assumed that this fitted relationship is as constant as the battery impedance model 304 that varies according to another state variable (such as the SOC or temperature of the battery cell / battery pack) (or stored in the on-vehicle memory of the EV 100). A look-up table is defined to implement the relationship between the real impedance and the imaginary impedance, allowing the magnitude of the imaginary impedance to be looked up for a given estimate of the magnitude of the real impedance (which is the measured polarization resistance).

[0042] Then, the output of the battery impedance model 304 is used to control the charging and / or discharging of the battery pack 106. For example, using known techniques, the battery impedance is used to determine the power limit of the battery pack 106, which is used to inhibit over-discharge of the battery pack 106 and control the life of the battery pack 106. In a non-limiting example, the Fourier transform of the measured battery impedance from the frequency to the time-dependent variable space can be used to estimate the power capacity of the battery pack 106. The time-dependent impedance combined with models related to battery current, voltage, and impedance (such as Ohm's law or the Butler-Volmer equation) enables the calculation of available power. The battery impedance can also be used to monitor factors related to lithium plating or thermal runaway.

[0043] In some variations, the battery impedance can also be used to estimate the state of health (SOH) of the battery pack or estimate the available energy of the battery pack 106. SOH is a diagnosis for monitoring the life of the battery pack 106. The available energy can be an input for calculating the remaining energy travel distance in the EV 100.

[0044] The output of the battery impedance model 304 can be used for other management / control aspects of the battery pack 106 and should not be limited to the examples herein.

[0045] Reference Figure 4 provides an example battery control routine 400 executed by the BMM 132 and the control system 130. At 402, the BMM 132 obtains the electrical characteristics (e.g., measured parameters) and OCV of the battery pack 106 as described above. At 404, the BMM 132 uses the electrical characteristics and OCV to calculate or estimate the real impedance value (i.e., the frequency-dependent polarization impedance). As described above, the real impedance at low frequencies is the polarization resistance, which is determined using the electrical characteristics and OCV from the sensor 134. At 406, the BMM 132 generates the frequency-dependent battery impedance based on the battery impedance model 304. Specifically, using the frequency-dependent polarization impedance, the battery impedance model 304 provides the low-frequency battery impedance (i.e., the frequency-dependent battery impedance representing the diffusion state of the battery cell). At 408, the control system 130 is configured to control the charging / discharging operation of the battery pack 106 based on the battery impedance. For example, the BMM 132 uses the battery impedance to determine the power limit of the battery pack 106, and the battery impedance is used to control the charging / discharging operation of the EV through the control system 130. It should be readily understood that the routine 400 is only one example of the control operation of the BMM 132 with the battery impedance model 304, and other routines can be used within the scope of the present disclosure.

[0046] While the above describes exemplary embodiments, these embodiments are not intended to describe all possible forms of the invention. Rather, the words used in the specification are descriptive words rather than limiting words, and it should be understood that various changes can be made without departing from the spirit and scope of the invention. Additionally, the features of the various implemented embodiments can be combined to form additional embodiments of the invention.

[0047] In this application, the term "module" may refer to, be part of, or include the following: an application specific integrated circuit (ASIC); a digital, analog, or mixed analog / digital discrete circuit; a digital, analog, or mixed analog / digital integrated circuit; a combinational logic circuit; a field programmable gate array (FPGA); a processor circuit (shared, dedicated, or group) that executes code; a memory circuit (shared, dedicated, or group) that stores code executed by the processor circuit; other suitable hardware components that provide the described functionality; or a combination of some or all of the above, such as in a system-on-chip.

[0048] The term memory or memory device is a subset of the term computer-readable medium. As used herein, the term computer-readable medium does not cover transitory electrical or electromagnetic signals propagated through a medium (such as on a carrier wave); thus, the term computer-readable medium can be considered tangible and non-transitory. Non-limiting examples of non-transitory tangible computer-readable media are non-volatile memory circuits (such as flash memory circuits, erasable programmable read-only memory circuits, or mask read-only circuits), volatile memory circuits (such as static random access memory circuits or dynamic random access memory circuits), magnetic storage media (such as analog or digital magnetic tape or hard disk drives), and optical storage media (such as CDs, DVDs, or Blu-ray discs).

[0049] The devices and methods described in this application can be partially or fully implemented as one or more special-purpose computers created by configuring a general-purpose computer to execute one or more specific functions embodied in a computer program. Additionally, computing devices (such as one or more special-purpose computers) can communicate with each other and with other devices in a vehicle (such as sensors) using a communication network (e.g., CAN and / or Ethernet and other networks). Functional blocks, flowchart components, and other elements described above serve as software specifications that can be translated into a computer program by routine work of a technician or programmer.

[0050] As used herein, the phrase "at least one of A, B, and C" should be interpreted to represent the logical (A or B or C) using non-exclusive logic "or", and should not be interpreted to mean "at least one of A, at least one of B, and at least one of C".

[0051] The description of the present disclosure is merely exemplary in nature and, thus, variations that do not depart from the essence of the present disclosure are intended to be within the scope of the present disclosure. Such variations should not be regarded as departing from the spirit and scope of the present disclosure.

[0052] According to the present invention, there is provided an electrified vehicle (EV) having: a battery pack including a plurality of battery cells and operable to provide at least a portion of the propulsion power; one or more sensors; and a vehicle controller configured to charge and discharge the battery pack according to a power limit defined by an output of a battery impedance model that correlates a battery impedance value with a polarization impedance value related to a frequency representing a diffusion state of the battery cells, and to receive from the one or more sensors a parameter indicative of a measurement of the frequency-related polarization impedance value of the battery pack.

[0053] According to one embodiment, the frequency-related polarization impedance value varies at least with an open circuit voltage (OCV) that is detected based on a state of charge (SOC) of the battery and a defined correlation between a plurality of battery SOC values and OCV values.

[0054] According to one embodiment, the measured parameter includes a DC voltage and a current of the battery pack.

[0055] According to one embodiment, the frequency-related polarization impedance value varies with the current and a polarization voltage estimated using the DC voltage and the open circuit voltage (OCV).

[0056] According to one embodiment, the battery impedance model is defined using at least one of a semi-infinite Warburg model, a reflecting boundary Warburg model, a transmitting boundary Warburg model, or a constant phase element-based model.

[0057] According to one embodiment, the battery impedance model is defined using a Warburg-type model.

[0058] According to the present invention, a method of operating an electrified vehicle (EV) having a battery pack including a plurality of battery cells includes: charging and discharging the battery pack according to a power limit defined by an output of a battery impedance model that correlates a battery impedance value with a polarization impedance value related to a frequency representing a diffusion state of the battery cells, and receiving from one or more sensors a parameter indicative of a measurement of the frequency-related polarization impedance value of the battery pack.

[0059] In one aspect of the present invention, the frequency - dependent polarization impedance value varies at least with the open - circuit voltage (OCV), which is detected based on the state of charge (SOC) of the battery and a defined correlation between multiple battery SOC values and OCV values.

[0060] In one aspect of the present invention, the measured parameters include the DC voltage and current of the battery pack.

[0061] In one aspect of the present invention, the frequency - dependent polarization impedance value varies with the current and the polarization voltage estimated using the DC voltage and the open - circuit voltage (OCV).

[0062] In one aspect of the present invention, the battery impedance model is defined using at least one of a semi - infinite Warburg model, a reflection - boundary Warburg model, a transmission - boundary Warburg model, or a constant - phase - element - based model.

[0063] In one aspect of the present invention, the frequency - dependent polarization impedance value varies with the open - circuit voltage (OCV).

[0064] In one aspect of the present invention, the battery impedance model is defined using a Warburg - type model.

[0065] According to the present invention, there is provided a system for an electrified vehicle (EV) having a battery pack including a plurality of battery cells, the system having: a vehicle controller configured to charge and discharge the battery pack according to a power limit defined by an output of a battery impedance model that associates a battery impedance value with a frequency - dependent polarization impedance value representing the diffusion state of the battery cells, and receiving measured parameters indicating the frequency - dependent polarization impedance value of the battery pack from one or more sensors.

[0066] According to one embodiment, the frequency - dependent polarization impedance value varies at least with the open - circuit voltage (OCV), which is detected based on the state of charge (SOC) of the battery and a defined correlation between multiple battery SOC values and OCV values.

[0067] According to one embodiment, the measured parameters include the DC voltage and current of the battery pack.

[0068] According to one embodiment, the frequency - dependent polarization impedance value varies with the current and the polarization voltage estimated using the DC voltage and the open - circuit voltage (OCV).

[0069] According to one embodiment, the battery impedance model is defined using at least one of a semi - infinite Warburg model, a reflection - boundary Warburg model, a transmission - boundary Warburg model, or a constant - phase - element - based model.

[0070] According to one embodiment, the frequency-dependent polarization impedance value varies with the open circuit voltage (OCV).

[0071] According to one embodiment, the battery impedance model is defined using a Warburg-type model.

Claims

1. An electric vehicle (EV), comprising: a battery pack comprising a plurality of battery cells and operable to provide at least a portion of the propulsion power; one or more sensors; as well as a vehicle controller configured to charge and discharge the battery pack according to a power limit defined by an output of a battery impedance model that relates battery impedance values ​​to frequency-dependent polarization impedance values ​​representative of a diffusion state of the battery cells, and to receive, from the one or more sensors, parameters indicative of measurements of the frequency-dependent polarization impedance values ​​of the battery pack.

2. The electrified vehicle of claim 1 , wherein the frequency-dependent polarization impedance value varies with at least an open circuit voltage (OCV), the OCV being detected based on a battery state of charge (SOC) and a defined correlation between a plurality of battery SOC values ​​and OCV values.

3. The electrified vehicle of claim 1, wherein the measured parameters include a DC voltage and a current of the battery pack.

4. The electrified vehicle of claim 3, wherein the frequency-dependent polarization impedance value varies with the current and a polarization voltage estimated using the DC voltage and an open circuit voltage (OCV).

5. The electrified vehicle of claim 1, wherein the battery impedance model is defined using at least one of a semi-infinite Warburg model, a reflective boundary Warburg model, a transmissive boundary Warburg model, or a constant phase element based model.

6. The electrified vehicle of claim 1, wherein the battery impedance model is defined using a Warburg type model.

7. A method of operating an electric vehicle (EV) having a battery pack including a plurality of battery cells, comprising: The battery pack is charged and discharged according to power limits defined by outputs of a battery impedance model that relates battery impedance values ​​to frequency-dependent polarization impedance values ​​representative of a diffusion state of the battery cells and receives measured parameters indicative of the frequency-dependent polarization impedance values ​​of the battery pack from one or more sensors.

8. The method of claim 7, wherein the frequency-dependent polarization impedance value varies with at least an open circuit voltage (OCV), the OCV being detected based on a battery state of charge (SOC) and a defined correlation between a plurality of battery SOC values ​​and OCV values.

9. The method of claim 7, wherein the measured parameters include DC voltage and current of the battery pack.

10. The method of claim 9, wherein the frequency-dependent polarization impedance value varies with the current and a polarization voltage estimated using the DC voltage and an open circuit voltage (OCV).

11. The method of claim 9, wherein the battery impedance model is defined using at least one of a semi-infinite Warburg model, a reflective boundary Warburg model, a transmissive boundary Warburg model, or a constant phase element based model.

12. The method of claim 7, wherein the frequency-dependent polarization impedance value varies with open circuit voltage (OCV).

13. The method of claim 7, wherein the battery impedance model is defined using a Warburg type model.

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