System and method for controlling a vehicle battery pack based on an estimated open circuit voltage

Abstract

A system for an electrified vehicle (EV) having a battery pack includes a vehicle controller configured to charge and discharge the battery pack according to power limits defined by an estimated open circuit voltage (OCV) when the EV is turned on. The estimated OCV is based on voltages measured over a period following a last shutdown of the EV, at least one temperature measured after the last shutdown, and a decay parameter dependent on the measured voltages and detected using a selected relaxation time and an iterative estimation of a subparameter of the decay parameter.

Description

FIELD OF TECHNOLOGY

[0001] The present disclosure generally relates to managing and / or controlling a battery pack for an electrified vehicle based at least on an open circuit voltage. BACKGROUND

[0002] An electrified vehicle (EV) includes a battery pack, sometimes referred to as a traction battery, to provide power to electric motors to propel the EV. One or more operating characteristics of the battery pack, such as power limits and state of charge (SOC), can be estimated to control the charging and discharging process of the battery pack.

[0003] As one non-limiting example, the EV includes a battery management module (BMM) and a control system. Generally, during a discharging event (e.g., driving the EV), the BMM is configured to estimate the SOC, and the control system is configured to control various devices / subsystems within the EV, for example, by determining how much power can be drawn from the battery pack using operating characteristics, inputs from a user, power demands from devices (e.g., motors, HVAC system, etc.), and / or other information. For a charging event, the BMM is configured to provide a charging current / voltage request to the control system, which in turn controls the EV to begin charging the battery pack (e.g., controlling an electric vehicle supply equipment (EVSE)). SUMMARY

[0004] In one form, the present disclosure is directed to a system for an electrified vehicle (EV) having a battery pack. The system includes a vehicle controller configured to charge and discharge the battery pack according to performance limits defined, when the EV is powered on, by an open circuit voltage (OCV) based on voltages measured over a duration following a last shutdown of the EV, at least one temperature measured following the last shutdown, and a decay parameter dependent on the measured voltages and detected using a selected relaxation time and an estimated sub-parameter of the decay parameter.

[0005] In one form, the present disclosure is directed to a method for controlling an electrified vehicle (EV) having a battery pack including a plurality of battery cells. The method includes, in response to a shutdown request, opening one or more contactors to electrically decouple the battery pack from a charging / discharging system of the EV.In response to a turn-on, the method further includes closing the one or more contactors to electrically couple the battery pack to the charging / discharging system, and charging or discharging the battery pack according to power limits defined at EV turn-on by an open circuit voltage (OCV) based on voltages measured over a duration following a last shutdown of the EV, at least one temperature measured following the last shutdown, and a decay parameter dependent on the measured voltages and detected using a selected relaxation time and an estimated sub-parameter of the decay parameter. BRIEF DESCRIPTION OF THE DRAWINGS Fig. 1 is an exemplary block diagram of an electrified vehicle (EV) according to the present disclosure; Fig. 2 is a block diagram of a battery pack of the EV according to the present disclosure; Fig. 3 is a block diagram of a battery management module of the EV according to the present disclosure; and the Fig. 4A and Fig. 4B are flow diagrams of a routine for estimating an open circuit voltage according to the present disclosure. DETAILED DESCRIPTION

[0006] As appropriate, detailed embodiments of the present invention are disclosed herein; however, it should be understood that the disclosed embodiments are merely exemplary of the invention that may be embodied in various and alternative forms. The figures are not necessarily to scale; some features may be greatly enlarged or reduced to show details of specific components. Therefore, 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.

[0007] Generally, to manage a battery pack in an electrified vehicle (EV), an EV's vehicle system needs to know a battery pack's state of charge (SOC) to estimate the battery pack's capability / performance limit. For most battery chemistries, SOC is estimated based on a battery pack's open circuit voltage (OCV), which is the battery pack's voltage at rest. As a non-limiting example, for hybrid electric vehicles (HEVs), where battery cell sizes are typically on the order of five (5) ampere-hours, the OCV can stabilize within 30 minutes, but this may take longer in colder temperatures.Specifically, stabilization occurs when the active material is evenly distributed (by diffusion) throughout the thickness of an electrode, and the time required to achieve stabilization can be referred to as the equilibrium time for the battery cell. Battery charge and discharge reactions occur at the electrode surface. As battery cells become larger (e.g., size increases), the electrodes tend to become thicker, and thus the equilibrium time increases. Some EV battery cells may require several hours (e.g., over 3 hours) for the OCV to stabilize, which is longer at colder temperatures.

[0008] In various situations, it may be difficult for the EV to remain at rest (i.e., no charging or discharging) for such long equilibrium periods. For example, in one situation, an EV user may stop at a restaurant to eat, which may only take one to two hours. In another example, the user may stop at a charging station, and the amount of time required between turning off the EV and charging the battery pack may be only a few minutes.

[0009] Furthermore, the number of battery cells used in the EV's battery pack can also affect the detection of the OCV, which can be measured for each battery cell. In particular, some EVs have approximately 100 cells in series, and as the EV moves toward higher-power systems (e.g., 800 V to 1200 V), the number of battery cells can double or even triple, increasing the computational demands of the vehicle system.

[0010] New EV battery warranty protocols may also require the EV to detect a certified state of energy (SOCE) or state of health (SOH), which is the amount of energy a battery pack can deliver for standard drive cycles relative to when the battery pack was new. Accordingly, the OCV-derived SOC at rest should be accurate to improve capacity accuracy, with a 2% SOC error relative to the customer's SOC window for capacity estimates.

[0011] The present disclosure is generally directed to a vehicle system configured to charge / discharge a battery pack based on an estimated OCV. In particular, the OCV for the battery pack is estimated using voltage measurements, a temperature measurement, and a decay parameter that depends on the voltages since a last shutdown of the battery pack and is detected using a selected relaxation time and an iterative estimation of a sub-parameter of the decay parameter. In one non-limiting example, the OCV is estimated for each battery cell and then aggregated to determine the OCV for the battery pack. With the estimated OCV, the vehicle system can, among other actions (e.g.,Output of an SOH) estimate a SOC, provide an available energy at the beginning of a drive cycle used to predict a vehicle driving range, and / or provide a power limit estimate.

[0012] With reference to the Fig. 1 and Fig. 2, in one form, an EV 100 is provided as a pure battery electric vehicle (BEV) powered by electric motors. In one non-limiting example, the EV 100 includes a powertrain system having one or more electric motors 104 (i.e., electric machines), a battery pack 106 (i.e., a traction battery), and a power electronics module 108. The EV 100 of the present disclosure does not include an internal combustion engine, and thus, the battery pack 106 provides all of the motive power. In other variations, the present disclosure may be applied to other types of EVs, such as a hybrid electric vehicle (plug-in or non-plug-in) having an internal combustion engine, fuel cell electric vehicles (FCEV), and is therefore not limited to pure battery-powered EVs.Additionally, the EV is not limited to four-wheeled automobiles and can be applied to scooters, three-wheeled vehicles, aircraft and / or other vehicles.

[0013] The electric motor 104 provides propulsion to the EV 100 and, in one non-limiting example, is mechanically connected to a transmission 110, which is mechanically connected to a driveshaft 112, which is mechanically connected to wheels 114 of the EV 100. In addition to providing propulsion power, the electric motor 104 may be configured to operate as a generator to recover energy that may normally be lost as heat in a friction braking system of the EV 100.

[0014] The battery pack 106 provides a high-voltage direct current (HV-DC) output used to power the electric motor 104 via the power electronics module 108, and although one battery pack 106 is shown, the EV 100 may include multiple battery packs. In one form, the power electronics module 108, which includes an inverter, provides bidirectional transfer of power between the battery pack 106 and the electric motor 104. In particular, 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 a regenerative mode, the power electronics module 108 converts the three-phase AC current from the electric motor 104, acting as a generator, to DC voltage compatible with the battery pack 106.

[0015] The battery pack 106 may be rechargeable by an external power source 120 (e.g., the electrical grid / network) that is electrically connected to an electric vehicle power supply (EVSE) 122. The EVSE 122 provides circuitry and controls for managing the transfer of electrical energy between the external power source 120 and the EV 100. The external power source 120 may provide electrical power to the EVSE 122 as DC or AC. The EVSE 122 may include a charging connector 124 for plugging into a charging port 126 of the EV 100.

[0016] The EV 100 may further include a power conversion module 128, which is an on-board charger that includes a DC / DC converter to condition power supplied by the EVSE 122 and provide the appropriate voltage and current levels to the battery pack 106. The power conversion module 128 may interface with the EVSE 122 to coordinate the delivery of power to the battery pack 106.

[0017] In one form, the EV 100 includes a control system 130 to coordinate the operation of the various components. The control system 130 includes electronics, software, or both to perform the necessary control functions to operate the EV 100. The control system 130 may be a combination of a 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 include multiple controllers in the form of multiple hardware devices or multiple software controllers with one or more hardware devices. In this regard, reference to a "controller" throughout this specification may refer to one or more controllers.

[0018] In one form, the EV 100 includes a battery management module (BMM) 132 configured to estimate one or more operating characteristics of the battery pack 106 and provide one or more of the operating characteristics to the control system 130, which controls the operation of the battery pack 106 (e.g., controls the charging / discharging of the battery pack 106). In one non-limiting example, the BMM 132 provides operating characteristics, such as, among other things, a power limit and / or SOC, to the control system 130 during driving operation, which determines how much power to draw from the battery pack 106. During a charging session, the BMM 132 notifies the control system 130 of how much power is needed to charge the battery pack 106. The BMM 132 forms part of the vehicle control system with the control system 130 and, although illustrated separately from the control system 130, may be integrated into the control system 130.In one form, the BMM 132 and the control system 130 may be referred to as a vehicle controller.

[0019] In one form, the BMM 132 is in communication with one or more sensors 134 (also referred to as a battery sensor (BS)) provided with the battery pack 106 to estimate characteristics of the battery pack 106, such as, among others, electrical current, voltage, and / or temperature.

[0020] Among other components, the battery pack 106 includes a plurality of battery banks 202A and 202B (collectively, “battery banks 202”), each battery bank 202 including a plurality of battery cells 204-1 through 204-N (collectively, “cells 204”) connected in series ( Fig. 2). The battery banks 202 are connected to a positive power bus 206A and a negative power bus 206B (collectively, "power buses 206"). While two battery banks 202 are provided, the battery pack 106 may include one or more battery banks 202 and should not be limited to the example provided herein. Additionally, the battery banks 202 and / or cells 204 of the battery pack 106 may be configured in various suitable ways. In one non-limiting example, the battery pack 106 may be configured to have the battery banks 202 in series, and for each battery bank 202, the cells 204 are provided in parallel.

[0021] The sensors 134 include one or more sensors 134A and 134B for the battery banks 202. In one form, the sensors 134 include voltage sensors and current sensors for measuring the voltage and / or electrical current of the battery bank 202 and, in some variations, each battery cell 204. It is readily understood that the sensors 134 may include other sensors, such as, among others, temperature sensors for measuring a temperature of the battery bank 202 and / or the battery pack 106.

[0022] In one form, one or more contactors 210 are provided to prevent or permit electrical current from moving through the power buses 206 to / from the battery pack 106. In particular, the contactors 210 are operable to electrically decouple or couple the battery pack 106 to a charging / discharging system of the EV 100. The charging / discharging system of the EV includes components that either charge the battery pack 106 or act as a load to draw electrical power from the battery pack 106, and thus may include, among other components, the charging port 126, the power electronics module 108, and / or the transmission 110. While one contactor 210 is illustrated, multiple contactors 210 may be used. Furthermore, the contactors 210 may be placed in various suitable positions in the EV 100, such as, among others, between the positive power bus 206A and the power electronics module 108.In one non-limiting example, the contactors 210 may be provided as a relay or electromechanical switch.

[0023] In one form, the BMM 132 is configured to open or close the contactors 210 based on a message / request from the control system 130. In one non-limiting example, the control system 130 is configured to detect when the EV 100 is to be turned on or off based on a turn-on input (e.g., a user pressing a button associated with turning the EV 100 on / off). If the EV 100 is to be turned on, the control system 130 provides the BMM 132 with a turn-on request to close the contactors 210, thereby electrically coupling the battery pack 106 to the charging / discharging system of the EV 100. If the EV 100 is to be turned off, the control system 130 provides a shutdown request to the BMM 132 to open the contactors 210, thereby electrically decoupling the battery pack 106 from the charging / discharging system of the EV 100.Additionally, the control system 130 is configured to cause the BMM 132 to close the contactor 210 by sending the connection request when the battery pack 106 is to be charged, which may be detected by a sensor at the charging port (e.g., a sensor indicating that the EVSE 122 is connected to the charging port 126, a sensor for detecting the opening of a charging port door (not shown), and / or other suitable charging detection methods).

[0024] With reference to Fig. 3, the BMM 132 includes, in one form, an actuator 302 for operating the contactors 210 in the closed / open position and a battery characteristic estimator (BCE) 304. The BCE 304 is configured to estimate various operating characteristics of the battery pack 106, such as, among other things, the OCV of each battery cell, the SOC of the battery pack 106, the power limit of the battery pack 106, and the temperature(s) of the battery pack 106 or elsewhere in the EV 100. As described in detail herein, the BCE 304 includes an OCV estimator 308 to estimate the OCV of the battery pack 106 (estimate the OCV of each battery cell 204).

[0025] In one form, the OCV estimator 308 is configured to estimate the OCV based on voltages measured by the sensors 134 after a last shutdown of the EV 100 and a decay parameter dependent on the voltages and a duration since the last shutdown. More specifically, Equation 1 below is an algorithm employed by the BCE 304 to estimate the OCV for the battery cell 204, where "V" is the voltage of the battery cell 204 and "DP" is the decay parameter. V=OCV+DP

[0026] In one form, the decay parameter exhibits a nonlinear correlation with voltage in that after the EV 100 is shut down, the rate of change of voltage over time is not constant. The decay parameter from Equation 1 characterizes the decaying voltage using an exponential parameter that includes a square root of the duration and further includes a coefficient and a constant that depend on the voltages and battery temperature.

[0027] In one form, the waste parameter is βe−kt provided and includes subparameters such as β, k and t. For the decay parameter, "β" is a coefficient related to SOC, temperature and the magnitude of the current before the contactors open; "k" is a time constant related to a diffusion coefficient in the electrodes and likely follows an Arrhenius relationship (i.e., k = Ae -Ea / RT) follows; and “t” is the time.

[0028] In a form where the decay parameter is that of equation 2, β at time ‘t’ (i.e. β t ) can be defined as Equation 3A, where V(t) is a voltage measurement at time "t" and "V(0)" is the voltage measured at t = 0 seconds. In particular, when t = 0, Equation 3 becomes V(0) = OCV +β, where OCV = V(0) - β. By replacing OCV in Equation 3 with "V(0) - β", β is then represented by Equation 3A. As a non-limiting example, Equation 3B provides β at t = 60. The sign of "β" depends on the direction of the current just before the battery pack 106 is decoupled. That is, if the battery pack 106 was (mostly) discharged just before shutdown, the sign of β is negative, indicating that the voltage is lower than the OCV. If the battery pack 106 has been (mostly) charged, β is positive. βe−kt βt=V(t)−V(0)e−k*t−1 β60Sec=V(t=60)−V(0)e−k*60−1

[0029] In some example systems, the decay parameter, and in particular β and k, are estimated using complex regression models using voltage measurements taken over a selected duration, such as one minute. However, such estimation techniques may require computational power that may exceed the hardware limitations of the BMM 132.

[0030] As described in detail in this paper, k is defined in terms of β and β is calculated using a selected relaxation time (t RELAX) from a variety of calibrated relaxation times and by comparing predicted β (i.e., βpred) over a range of candidate β (βcand). In particular, at a relaxation time, which occurs some time after a relaxation process has begun, and when the accuracy of the voltage measurement is less than or equal to a voltage sensor error (V SE ), then at the relationship time (ie, t=t RELAX ), |V(t RELAX ) - OCV| ≤ V SEIn one form, the relaxation time is estimated based on a temperature of the battery pack 106, an absolute delta voltage (i.e., absolute voltage change) estimated using at least a portion of the measured voltages, and relaxation time correlation data that associates selected inputs (e.g., the temperature and the absolute delta voltage) with associated relaxation times. In one non-limiting example, the relaxation correlation data is provided as one or more lookup tables.

[0031] By setting time as the relaxation time in Equation 3A, k becomes dependent on β, V SE and the relaxation time (t RELAX ) as provided in Equation 4. k=ln[VSEβcand]2tRELAX

[0032] With reference to the Fig. 4A and Fig.4B, an example OCV estimation routine 400 is provided by and executable by the BMM 132 as part of the OCV estimator 308. As described in detail herein, the BMM 132 estimates the OCV based on voltages measured over a period following a last EV shutdown and a decay parameter dependent on the measured voltages and detected using a selected relaxation time and an iterative estimation of a sub-parameter of the decay parameter. The control system 130 is configured to charge and discharge the battery pack 106 according to power limits defined by the estimated OCV upon EV startup.

[0033] At operation 402, the BMM 132 obtains a plurality of voltage measurements over a specified duration and at least one temperature measurement (T) of the battery pack 106 after shutdown. Specifically, when the BMM 132 receives a shutdown request from the control system 130 to electrically disconnect the battery pack 106 from the charge-discharge system of the EV 100, the contactor 210 is opened and the sensors 134 measure the voltage of the battery cells 204 for a selected duration, such as, but not limited to, 25 seconds, 30 seconds, 60 seconds, or 90 seconds. In one form, the duration is shorter than a stabilization time to allow active material of each of the battery cells 204 to evenly distribute across an electrode of the battery cell 204. In one form, at least one temperature measurement is taken at the end of the selected duration after the contactor 210 is opened.

[0034] Next, the BMM 132 determines whether the battery pack 106 was significantly charged or discharged prior to shutdown. Specifically, at operation 404, the BMM 132 calculates a plurality of delta voltages to assess whether the voltage is substantially decreasing or increasing. In one non-limiting example, the BMM 132 calculates delta voltage values ​​ΔV1, ΔV2, and ΔVD using ΔV1=V(t D )-V(t1), ΔV2=V( t1)-V(0) and ΔVD = |V(t D )-V(0)|, where: V(t D ) is the voltage measured at the end of the duration; V(t1) is the voltage measured at time = t1, where t1 is a time between zero (0) and the duration (e.g., if the duration is 30 seconds, t1 is the time = 15 sec.); and V(0) is the voltage measured at time zero (0) when the EV 100 is switched off.

[0035] At operation 406, the BMM 132 is configured to determine whether the voltage is in relaxation, or in other words, whether the measured voltage is the OCV. More specifically, at operation 406, the BMM 132 determines whether the ΔVD is less than or equal to a voltage delta threshold (V RT ) is (ie |V(t D )-V(0)| ≤ V RT). If this is the case, the BMM 132 estimates the OCV, for example, by averaging the voltage measured at t1 for the battery cells 204. This can occur in various scenarios, such as, among others, the EV 100 being turned off for 6 hours, then turned on for a few minutes, and then turned off without significant charging or discharging. In such a case, the battery pack 106 has reached the relaxation time, and the measured voltage indicates an OCV. The voltage delta threshold is selected to detect a significant voltage increase or decrease using the voltage measured for the duration. In one non-limiting example, the voltage delta threshold is defined as 4*V SE provided.

[0036] If the voltage is not a relaxation (ie ΔVD > V RT), the BMM 132 determines whether the EV 100 was charging or discharging prior to shutdown. Specifically, at operation 410, the BMM 132 determines whether the delta voltage values ​​are greater than zero (i.e., ΔV1>0 and ΔV2>0). If the delta voltage values ​​are both greater than zero, the EV 100 was discharging prior to shutdown, and if both delta voltage values ​​are less than zero, as determined at operation 411, the EV 100 was charging prior to shutdown. Otherwise, the BMM 132 is unable to determine either charging or discharging, and the process ends without determining the OCV.

[0037] After operations 410 and 411, the BMM 132 establishes a candidate range at a defined iteration step size for β based on whether the EV 100 was discharging or charging. Specifically, for the iterative estimation, the BMM 132 establishes a first sub-parameter candidate range of values ​​for β, a sub-parameter, in response to detecting that the battery pack 106 was discharging before the last shutdown, and a second sub-parameter candidate range of values, different from the first range of values ​​for β, in response to detecting that the battery pack 106 was charging before the last shutdown.

[0038] As described above, β is a negative value when the EV 100 has been discharged and is a positive value when the EV 100 has been charged. At operation 412, the BMM 132 sets the β candidate range to a discharge event range, where the β candidate range is provided as follows: V(0) - OCV(100) ≤ βcand≤0, where OCV(100) is the OCV when the SOC is at 100%, which can be defined and stored by the BMM 132, and V(0) is the voltage measured at time zero. At operation 413, the BMM 132 sets the β candidate range to a charge event range, where the β candidate range is provided as follows: 0≤ βcand≤V(0)−OCV(0), where OCV(0) is the OCV when the SOC is at 0%. The discharge event range for β may be referred to as a first sub-parameter candidate range of values, and the charge event range for β may be referred to as a second sub-parameter candidate range of values.

[0039] At operation 414 and as described above, the BMM 132 is configured to determine the relaxation time (t RELAX ) using the relaxation time correlation data with inputs including a temperature (T) and an absolute delta voltage estimated using at least a portion of the measured voltages (e.g., ΔVD). In one form, the temperature and voltage measurements used are taken at approximately the same time, which can be determined using a timestamp associated with the measurements.

[0040] At operation 416, the BMM 132 is configured to perform the iterative estimation to estimate β (i.e., the subparameter) within the candidate range at a defined iteration step size. In particular, at operation 416A, the BMM 132 is configured to determine a value for a predicted β (β PRED) to the minimum possible value of β defined by the β candidate range defined at operation 412 or 414. For example, for the discharge event range β PRED = V(0) - OCV(100) and for the loading event range β PRED = 0 V

[0041] At operation 416B, the BMM 132 is calculated using β PRED and t RELAX configured to select a k-candidate (k CAND ) and an OCV candidate (OCV CAND ). In a non-limiting example, the k-candidate is calculated using equation 4 and the OCV candidate is given by OCV=V(0)- β PRED calculated.

[0042] At operation 416C, the BMM 132 is configured to select a β candidate at a first time index (β CAND-TI1 ) and to a second time index (β CAND-TI2) using the k-candidate, the OCV candidate, and voltage measurements associated with the first time index and the second time index. In particular, the BMM 132 calculates β at least at two selected points in time (i.e., time indices). In one non-limiting example, the BMM 132 employs Equation 5 below to estimate the β-candidate at a selected time index (i.e., β CAND-TI ), where V(t) is the voltage measured at the time index and t is the time index. The time indices can be predefined and selected based on the duration. For example, if the duration is 60 seconds, the first time index is 30 seconds and the second time index is 60 seconds. Accordingly, β CAND-TI1 calculated using data referring to t=30 seconds, and β CAND-TI2 is calculated using data referring to t=60 seconds. βCAND−TI=V(t)−OCVCANDe−kCAND*t

[0043] At operation 416D, the BMM 132 is configured to detect an error, or in other words, a difference between β CAND-TI1 and β CAND-TI2 In general, β should be constant and thus the smaller the difference between β CAND-TI1 and β CAND-TI2 is, the more accurate β CAND with respect to a true β at OCV. In a non-limiting example, the BMM 132 calculates a percentage error or difference (i.e., %β DIFF ) using equation 6. %βDIFF=|((βCAND−TI1−βCAND−TI2)βCAND−TI1)|

[0044] At operation 416E, the BMM 132 is configured to determine whether the predicted β is greater than or equal to a maximum possible value of β (i.e., β MAX), which is defined by the β candidate range defined at operation 412 or 413. For example, for the discharge event range, the maximum possible value of β is zero, and for the charge event range, the maximum possible value of β is V(0)-OCV(0)), where OCV(0) = OCV at SOC = 0%.

[0045] If the predicted β is not greater than or equal to the maximum possible value of β, the BMM 132 is configured to PRED based on the iteration step size at operation 416F. That is, the BMM 132 increments the value of the predicted β based on a selected step size to estimate β candidates at the time indices across the range of β candidates. In a non-limiting example, the step size is set to 0.001 or 0.01 V. A small iteration step size provides a more refined evaluation of β, but also increases the computational load compared to a larger iteration step size.

[0046] If the predicted β is greater than the maximum possible value of β, the BMM 132 is configured to use the value of β as the value of the β- CAND , which has the smallest %β DIFF For example, the BMM 132 uses the value of β CAND-TI1 , which has the lowest %β DIFF than the value of β. In one form, there is a value of all β search values ​​that gives the nearest estimate of OCV and lies between β CAND-TI1 and β CAND-TI2 Accordingly, the BCE 304 can be configured to set the β value between β CAND-TI1 and β CAND-T2using various techniques, such as, but not limited to, interpolation. Using the selected β, which may be referred to as the predicted first sub-parameter, the BMM 132 estimates the OCV using OCV=V(0)-β at operation 420. With the OCV, the BMM 132 is configured to estimate an initial state of charge of the battery pack based on the estimated OCV, where the performance limits are defined in part by the initial state of charge.

[0047] The OCV estimation routine 400 may be configured to perform other operations within the scope of the present disclosure and should not be limited to the example described herein. By way of non-limiting example, the following provides some variations that may be performed individually or in combination.

[0048] In one form, the BCE 304 is configured to be used instead of 416D %β DIFF to calculate, %β DIFF to be calculated after all search steps have been completed (ie after 416E).

[0049] In one form, β may be estimated using multiple iteration step sizes, where the iteration step size is changed from a large value to a small value to provide a refined estimate. In other words, the iteration step size may be set to a first value for a first set of estimates until a refinement condition that produces a finer analysis of β is satisfied. After the refinement condition is satisfied, the iteration step size is set to a second value that is smaller than the first value. In a non-limiting example, the refinement condition detects whether %β DIFFis less than or equal to a refinement threshold (e.g., 90%). Accordingly, if β CAND-TI1 and B CanD-TI2 are almost equal (e.g., 90%), the iteration step size is reduced to the second value. With multiple iteration step sizes, the computational requirements of the BMM 132 can be reduced. For example, instead of performing the iteration estimation at a step size of 0.001, the iteration estimation is first performed at a step size of 0.01 and refined to 0.001.

[0050] In one form, the BMM 132 is configured to estimate multiple β for a variety of relaxation times. In particular, measurement errors associated with sensors used to measure temperature and stress can affect the accuracy of the estimated relaxation time. Instead of estimating one relaxation time, a set of relaxation times is estimated, and β is estimated for each relaxation time.

[0051] In particular, the set of relaxation times is defined using a base relaxation time estimated as described above, and with the base relaxation time, a set of relaxation times is defined to include relaxation times provided before and after the base relaxation time. In one form, if a set of relaxation times includes 5 relaxation times, where the base relaxation time is one, the other relaxation values ​​are calculated by multiplying the base relaxation time (t relax ) is estimated using a set of relaxation coefficients. The relaxation coefficients include [a1, a2, a3, a4, a5], where a3 = 1 is the base relaxation time. The set of relaxation times is denoted as t RELAX-1 , t RELAX-2 , t RELAX-3 , t RELAX-4 , and t RELAX-5 where: t RELAX-1 =a1 *t relax ; t RELAX-2 = a2*t relax ; tRELAX-3= a3*t relax ; t RELAX-4=a4*t relax and t RELAX-5 = a5*t relax . In a non-limiting example, the relaxation coefficients are defined as follows: [a1, a2, a3, a4, a5] = [0.6, 0.8, 1, 1.2, 1.5]. The subparameter β is estimated for each relaxation time, and then the βs are averaged to define a final estimated β for the OCV. While the set of relaxation times is provided to include five relaxation times, the set of relaxation times may include two or more relaxation times. Additionally, the relaxation coefficients may be defined to other suitable values ​​and should not be limited to the example provided in this paper.

[0052] Although exemplary embodiments are described above, these embodiments are not intended to describe all possible forms of the invention. Rather, the terms used in the description are terms of description rather than limitation, and it is understood that various changes may be made without departing from the spirit and scope of the invention. Furthermore, the features of various implementing embodiments may be combined to form further embodiments of the invention.

[0053] In this application, the terms "module" and / or "controller" may refer to, be part of, or include: 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 foregoing, such as in a system-on-chip.

[0054] The term "memory" or "storage device" is a subset of the term "computer-readable medium." As used in this document, the term "computer-readable medium" does not include transitory electrical or electromagnetic signals propagating through a medium (such as a carrier wave); therefore, the term "computer-readable medium" can be considered tangible and non-transitory.Non-limiting examples of a non-transitory, tangible computer-readable medium include non-volatile memory circuits (such as a flash memory circuit, an erasable programmable read-only memory circuit, or a mask read-only memory circuit), volatile memory circuits (such as a static random access memory circuit or a dynamic random access memory circuit), magnetic storage media (such as an analog or digital magnetic tape or a hard disk drive), and optical storage media (such as a CD, a DVD, or a Blu-ray Disc).

[0055] The devices and methods described in this application may be implemented partially or entirely by a special-purpose computer created by configuring a general-purpose computer to perform one or more specific functions embodied in computer programs. The functional blocks, flowchart components, and other elements described above serve as software specifications that can be translated into computer programs through the routine work of a skilled technician or programmer.

[0056] As used in this document, the phrase "at least one of A, B, and C" should be interpreted to mean a logical (A OR B OR C) using a non-exclusive logical OR, and should not be interpreted to mean "at least one of A, at least one of B, and at least one of C."

[0057] The description of the disclosure is purely exemplary in nature, and thus, examples that do not depart from the substance of the disclosure are intended to be within the scope of the disclosure. Such variations should not be considered a departure from the spirit and scope of the disclosure.

[0058] According to the present invention, a system for an electrified vehicle (EV) having a battery pack is provided, comprising: a vehicle controller configured to charge and discharge the battery pack according to power limits defined, when the EV is turned on, by an open circuit voltage (OCV) based on voltages measured over a duration following a last shutdown of the EV, at least one temperature measured following the last shutdown, and a decay parameter dependent on the measured voltages and detected using a selected relaxation time and an estimated sub-parameter of the decay parameter.

[0059] According to one embodiment, the estimated sub-parameter uses a first sub-parameter candidate range of values ​​for the sub-parameter in response to detecting that the battery pack was discharging before the last shutdown, and a second sub-parameter candidate range of values ​​different from the first value range for the sub-parameter in response to detecting that the battery pack was charging before the last shutdown.

[0060] According to one embodiment, the vehicle controller is configured to detect the selected relaxation time based on a temperature and a delta voltage using at least a portion of the measured voltages.

[0061] According to one embodiment, the estimated subparameter falls within a candidate range at a defined iteration step size.

[0062] According to one embodiment, the defined iteration step size is set to a first value for a first set of estimates and to a second value that is smaller than the first value for a second set of estimates.

[0063] According to one embodiment, the decay parameter has a non-linear correlation with the measured voltages.

[0064] According to one embodiment, the vehicle controller is configured to: detect a plurality of selected relaxation times based on a temperature and a delta voltage using at least a portion of the measured voltages, for each selected relaxation time, estimate the sub-parameter using the estimate, and estimate the decay parameter based on the sub-parameters estimated for the plurality of selected relaxation times.

[0065] According to one embodiment, the duration is shorter than a stabilization time so that active material of each battery cell of the battery pack is evenly distributed over an electrode of the battery cell.

[0066] According to one embodiment, the vehicle controller is configured to estimate an initial state of charge of the battery pack based on the OCV, wherein the performance limits are defined in part by the initial state of charge.

[0067] According to one embodiment, the sub-parameter includes a first sub-parameter and a second sub-parameter defined with respect to the first sub-parameter.

[0068] According to one embodiment, the vehicle controller is configured to: for estimation, define a first sub-parameter candidate range, iteratively select a first sub-parameter candidate based on an iteration step size and the first sub-parameter candidate range, and for each first sub-parameter candidate, estimate the second sub-parameter using the first sub-parameter candidate and the selected relaxation time, estimate an OCV candidate using the first sub-parameter candidate and calculate a first sub-parameter error using first sub-parameter values ​​estimated using the second sub-parameter and the OCV candidate for a first time index and a second time, and estimate the estimated OCV using a predicted first sub-parameter associated with the first sub-parameter error,which is smaller than the calculated other first subparameter error.,

[0069] According to the present invention, a method for controlling an electrified vehicle (EV) having a battery pack including a plurality of battery cells includes: in response to a shutdown request, opening one or more contactors to electrically decouple the battery pack from a charging / discharging system of the EV;in response to a turn-on, closing the one or more contactors to electrically couple the battery pack to the charging / discharging system, and charging or discharging the battery pack according to performance limits defined when the EV is turned on by an open circuit voltage (OCV) based on voltages measured over a duration following a last shutdown of the EV, at least one temperature measured following the last shutdown, and a decay parameter dependent on the measured voltages and detected using a selected relaxation time and an estimated sub-parameter of the decay parameter;

[0070] In one aspect of the invention, the method includes: detecting whether the battery pack was discharging or charging prior to the last shutdown, using, for the estimation, a first sub-parameter candidate range of values ​​for the sub-parameter in response to detecting that the battery pack was discharging prior to the last shutdown; and using, for the estimation, a second sub-parameter candidate range of values ​​different from the first value range for the sub-parameter in response to detecting that the battery pack was charging prior to the last shutdown.

[0071] In one aspect of the invention, the method includes detecting the selected relaxation time based on a temperature and a delta voltage using at least a portion of the measured voltages.

[0072] In one aspect of the invention, the estimated subparameter falls within a candidate region at a defined iteration step size.

[0073] In one aspect of the invention, the defined iteration step size is set to a first value for a first set of estimates and to a second value that is smaller than the first value for a second set of estimates.

[0074] In one aspect of the invention, the method includes: detecting a plurality of selected relaxation times based on a temperature and a delta voltage using at least a portion of the measured voltages; for each selected relaxation time, estimating the sub-parameter using the estimate; and estimating the decay parameter based on the sub-parameters estimated for the plurality of selected relaxation times.

[0075] In one aspect of the invention, the duration is shorter than a stabilization time in order to allow active material of each of the battery cells to be evenly distributed over an electrode of the battery cell.

[0076] In one aspect of the invention, the method includes estimating an initial state of charge of the battery pack based on the OCV, wherein the performance limits are defined in part by the initial state of charge.

[0077] In one aspect of the invention, the method includes: for estimation, defining a first subparameter candidate range; iteratively selecting a first subparameter candidate based on an iteration step size and the first subparameter candidate range; and for each first subparameter candidate, estimating a second subparameter using the first subparameter candidate and the selected relaxation time; estimating an OCV candidate using the first subparameter candidate; and calculating a first subparameter error using first subparameter values ​​estimated using the second subparameter and the OCV candidate for a first time index and a second time; and estimating the estimated OCV using a predicted first subparameter associated with the first subparameter error that is smaller than calculated other first subparameter errors.

Claims

[1] A system for an electrified vehicle (EV) having a battery pack, comprising: a vehicle controller configured to charge and discharge the battery pack according to power limits defined when the EV is turned on by an open circuit voltage (OCV) based on voltages measured over a duration following a last shutdown of the EV and a decay parameter dependent on the measured voltages and detected using a selected relaxation time and an estimate of a sub-parameter of the decay parameter. [2] The system of claim 1, wherein the estimation employs a first sub-parameter candidate range of values ​​for the sub-parameter in response to detecting that the battery pack was discharging prior to the last shutdown, and a second sub-parameter candidate range of values ​​different from the first range of values ​​for the sub-parameter in response to detecting that the battery pack was charging prior to the last shutdown. [3] The system of claim 1, wherein the vehicle controller is configured to estimate the selected relaxation time based on a temperature and a delta voltage using at least a portion of the measured voltages. [4] The system of claim 1, wherein the estimation estimates the sub-parameter in a candidate region with a defined iteration step size. [5] The system of claim 4, wherein the defined iteration step size is set to a first value for a first set of estimates and to a second value less than the first value for a second set of estimates. [6] The system of claim 1, wherein the decay parameter has a non-linear correlation with the measured voltages. [7] The system of claim 1, wherein the vehicle controller is configured to: Estimating a plurality of selected relaxation times based on a temperature and a delta voltage using at least a portion of the measured voltages, for each selected relaxation time, estimating the sub-parameter using the estimate and Estimating the decay parameter based on the subparameters estimated for the plurality of selected relaxation times. [8] The system of claim 1, wherein the duration is less than an equilibrium time for active material of each of the battery cells to be evenly distributed across an electrode of the battery cell. [9] The system of claim 1, wherein the vehicle controller is configured to estimate an initial state of charge of the battery pack based on the estimated OCV, wherein the performance limits are defined in part by the initial state of charge. [10] The system of claim 1, wherein the sub-parameter includes a first sub-parameter and a second sub-parameter defined with respect to the first sub-parameter. [11] The system of claim 10, wherein the vehicle controller is configured to: for the estimate, Defining a first sub-parameter candidate range, iteratively selecting a first sub-parameter candidate based on an iteration step size and the first sub-parameter candidate range, and for each first subparameter candidate, Estimating the second sub-parameter using the first sub-parameter candidate and the selected relaxation time, Estimating an OCV candidate using the first subparameter candidate and Calculating a first subparameter error using first subparameter values ​​estimated using the second subparameter and the OCV candidate for a first time index and a second time, and Estimating the estimated OCV using a predicted first sub-parameter associated with the first sub-parameter error that is smaller than the calculated other first sub-parameter errors. [12] A method for controlling an electrified vehicle (EV) having a battery pack including a plurality of battery cells, comprising: in response to a shutdown request, opening one or more contactors to electrically decouple the battery pack from an EV charging / discharging system; in response to a connection, Closing the one or more contactors to electrically couple the battery pack to the charging / discharging system, and Charging or discharging the battery pack according to power limits defined when the EV is turned on by an open circuit voltage (OCV) based on voltages measured over a duration following a last EV shutdown and a decay parameter dependent on the measured voltages and detected using a selected relaxation time and an estimate of a sub-parameter of the decay parameter. [13] The method of claim 12, further comprising: Detect whether the battery pack was discharged or charged before the last shutdown; Substituting, for the estimation, a first sub-parameter candidate range of values ​​for the sub-parameter in response to detecting that the battery pack was discharged before the last shutdown; and Substituting, for the estimation, a second sub-parameter candidate range of values ​​different from the first range of values ​​for the sub-parameter in response to detecting that the battery pack was charged before the last shutdown. [14] The method of claim 12, further comprising estimating the selected relaxation time based on a temperature and a delta voltage using at least a portion of the measured voltages. [15] The method of claim 12, wherein the estimation estimates the sub-parameter in a candidate region with a defined iteration step size.

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