System and method for adaptive battery parameter optimization for state of charge estimation

Abstract

The present disclosure provides a system and method for adaptive battery parameter optimization for estimating state of charge. A method for controlling an electrically powered vehicle (EV) having a battery pack includes transmitting, by the EV, a plurality of battery operating characteristics to a remote server during periods during which the battery pack experiences a plurality of charge-discharge operations; and charging and discharging the battery pack according to a power limit defined using updated battery parameters estimated by the remote server using the plurality of battery operating characteristics.

Description

Technical Field

[0001] This disclosure generally relates to monitoring parameters used to estimate the state of charge of battery packs in electrified vehicles. Background Technology

[0002] Electric vehicles (EVs) include a battery pack, sometimes called a traction battery, which provides power to an electric motor to propel the EV. One or more operating characteristics of the battery pack (such as, but not limited to, voltage, current, state of charge (SOC), and / or electrical limits) can be detected using sensors and / or estimated using sophisticated algorithms. Utilizing these operating characteristics, the vehicle control system controls the EV's charging and discharging operations. Summary of the Invention

[0003] In one form, this disclosure relates to a method for controlling an electric vehicle (EV) having a battery pack. The method includes: the EV sending multiple battery operating characteristics to a remote server during a time period in which the battery pack undergoes multiple charge-discharge operations; and charging and discharging the battery pack according to a power limit defined using updated battery parameters estimated by the remote server using the multiple battery operating characteristics.

[0004] In one form, this disclosure relates to a vehicle system for an electric vehicle (EV) having a battery pack. The vehicle system includes a communication system and a vehicle controller. The communication system is configured to send multiple battery operating characteristics to a remote server during a period in which the battery pack undergoes multiple charge-discharge operations. The vehicle controller is configured to charge and discharge the battery pack according to a power limit defined using updated battery parameters estimated by the remote server using the multiple battery operating characteristics and based on previous battery parameters used during the multiple charge-discharge operations.

[0005] In one form, this disclosure relates to a system for an electric vehicle (EV) having a battery pack. The system includes a remote server and a vehicle system having a communication system and a vehicle controller. The remote server includes one or more computing devices configured to output updated battery parameters of the EV. The communication system is configured to communicate with the remote server to send multiple battery operating characteristics to the remote server at least during a period in which the battery pack undergoes multiple charge-discharge operations. The vehicle controller is configured to charge and discharge the battery pack according to a power limit defined using the updated battery parameters, which are estimated by the remote server using the multiple battery operating characteristics based on previous battery parameters used during the multiple charge-discharge operations. Attached Figure Description

[0006] Figure 1 This is a block diagram of an example EV based on this disclosure;

[0007] Figure 2 This is a block diagram of the battery management module of the EV and EV support server according to this disclosure; and

[0008] Figure 3 It is an example adaptive battery parameter evaluation procedure based on this disclosure. Detailed Implementation

[0009] Detailed embodiments of the invention are disclosed herein as needed; however, it should be understood that the disclosed embodiments are merely examples of the invention that can be embodied in various forms and alternative forms. The drawings are not necessarily drawn to scale; some features may be enlarged or minimized to show details of specific components. Therefore, the specific structural and functional details disclosed herein are not to be construed as limiting, but only as representative bases for teaching those skilled in the art to employ the invention in various ways.

[0010] The EV's Battery Management Module (BMM) is configured to use a predefined model / algorithm with one or more battery parameters to estimate selected operating characteristics, such as, but not limited to, the battery pack's State of Charge (SOC), electrical limits, open-circuit voltage (OCV), and / or State of Health (SOH). Over time and with use, the battery cells in the battery pack age, causing changes in their electrical properties (e.g., capacity and / or resistance). While battery parameters indicative of some of these electrical properties are used to estimate operating characteristics such as SOC, the values ​​of these battery parameters remain constant, which can lead to inaccurate or drifting SOC values.

[0011] In one form, this disclosure relates to a system / method for providing updated battery parameters using a Battery Model Evaluation (BME) module configured to evaluate the battery operating characteristics of a given EV and provide updated battery parameters when transmission conditions are met. For example, transmission conditions may include the elapsed duration of a defined time period or the detection of parameter differences. That is, the estimation performed by the BMM of a given EV can be provided as an inner loop while the EV is charging / discharging, and an EV support server with a BME module is provided as an outer loop to provide updated battery parameters as applicable. The updated parameters are based on the evaluation of one or more battery operating characteristics of a particular EV and are adjusted based on operational differences detected between the estimated operating characteristics provided by the BME module and the operating characteristics provided by the EV. Using the operational differences, the battery parameters are adjusted to obtain updated battery parameters for the BMM.

[0012] refer to Figure 1 In one form, EV 100 is provided as a fully battery electric vehicle (BEV) powered by an electric motor. In a non-limiting example, EV 100 includes a powertrain having one or more motors 104 (i.e., electric motors), a battery pack 106, and a power electronics module 108. The EV 100 of this disclosure does not include an engine, and therefore the battery pack 106 provides all propulsion power. In other variations, this disclosure can be applied to other types of EVs, such as hybrid electric vehicles (plug-in or non-plug-in) with an engine, fuel cell electric vehicles (FCEVs), and is therefore not limited to pure battery electric EVs. Furthermore, EVs are not limited to four-wheeled automobiles and can be applied to scooters, vehicles with one or more wheels, aircraft, and / or other vehicles.

[0013] Motor 104 provides powered movement to EV 100, and in a non-limiting example, the motor is mechanically connected to transmission 110, which is mechanically connected to drive shaft 112, which is mechanically connected to wheels 114 of EV 100. In addition to providing propulsive power, motor 104 can also be configured to operate as a generator to recover energy that would typically be lost as heat in the friction braking system of EV 100.

[0014] Battery pack 106 provides a high-voltage (HV) direct-current (DC) output for powering motor 104 via power electronics module 108. While one battery pack 106 is shown, EV 100 may include multiple battery packs. In one embodiment, power electronics module 108, including an inverter, provides bidirectional energy transfer between battery pack 106 and motor 104. Specifically, power electronics module 108 is known to convert DC voltage to three-phase AC current to operate motor 104, and in regenerative mode, power electronics module 108 converts three-phase AC current from motor 104, which acts as a generator, to a DC voltage compatible with battery pack 106.

[0015] EV 100 may also include a power conversion module 128, which is an on-board charger with a DC / DC converter to regulate power supplied from an external power source (e.g., an electric grid / network) via charging port 126 and to provide the correct voltage and current levels to battery pack 106. In an exemplary example, charging port 126 is connected to an electric vehicle power supply unit (not shown) that draws power from a power source and supplies power to the EV via charging port 126 and power conversion module 128.

[0016] In one embodiment, EV 100 includes a control system 130 to coordinate the operation of various components. Control system 130 includes electronics and software to perform the necessary control functions for operating EV 100. Control system 130 may be a combined vehicle control system and powertrain control module (VSC / PCM). Although control system 130 is shown as a single device, it 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.

[0017] In one configuration, the BMM 132 communicates with one or more sensors 134 (also referred to as battery sensors (BS)) provided with the battery pack 106 to detect one or more operating characteristics of the battery pack 106, such as, but not limited to, current, voltage and / or temperature.

[0018] EV 100 may also include a battery management module (BMM) 132, which is configured to estimate one or more operating characteristics of the battery pack 106 using, for example, data from BS 134 and a series of algorithms or battery models. For example, BMM 132 is configured to estimate the open-circuit voltage and state of charge (SOC) of the battery pack 106 as additional operating characteristics of the battery pack.

[0019] In one form, the BMM 132 provides the State of Charge (SOC) or a power limit defined using the SOC 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 a non-limiting example, during driving operation, the BMM 132 provides operating characteristics, such as, but not limited to, power limits and / or SOC, to the control system 130, which determines how much power to draw from the battery pack 106. During charging operation, the BMM 132 informs the control system 130 how much power is needed to charge the battery pack 106. Although illustrated separately from the control system 130, the BMM 132 can be integrated with the control system 130. In one form, the BMM 132 and the control system 130 can be referred to as a vehicle controller.

[0020] In addition to the components / systems for controlling the driving operation of EV 100, EV 100 also includes other systems for performing other supporting functions. In a non-limiting example, EV 100 includes a communication system 140 configured to exchange information with external devices or systems using wired / wireless communication (e.g., Bluetooth, UWB, cellular, and / or Wi-Fi). In one form, communication system 140 exchanges messages with an EV support server 150 having a Battery Model Evaluation (BME) module 152. Therefore, communication system 140 may include a router, modem, antenna, input-output interface, universal serial bus (USB) port, and / or other suitable means for supporting wireless and wired communication.

[0021] refer to Figure 2 In one form, BMM 132 is configured to include a power estimation module 202 for estimating the state of charge (SOC) and / or power limit of the battery pack 106 using one or more battery parameters 204. In a non-limiting example, the power estimation module 202 is defined by a series of algorithms based on a circuit representation of the battery pack 106 having one or more resistor-capacitor pairs. These algorithms are defined by one or more battery parameters, such as, but not limited to, the battery internal resistance (R0), one or more resistor-capacitor (RC) pairs (R1, R2, ..., RN), and / or one or more RC pairs (e.g., C1, C2, ..., CN). Input variables for the power estimation module 202 may include temperature, the voltage of one or more battery cells, the current of one or more battery cells, and / or the open-circuit voltage that can be measured or estimated by the power estimation module 202 using, for example, a lookup table or an OCV algorithm. In a non-limiting example, the SOC may be estimated using coulomb counting (utilizing initial SOC, current measurement, and known capacity) or a defined model using a circuit representation of the battery pack. The power limit refers to the capacity of the battery pack 106 to provide power over a given time period (Td) without exceeding the current and voltage limits.

[0022] In some variations, if the power estimation module 202 includes multiple RC pairs as part of a circuit representation, the power estimation module 202 is configured to use known techniques to obtain R1 and C1 from the BME module 152 and to estimate additional parameters (e.g., R0, R2, C2, SOC) using at least data from the BS134. In non-limiting examples, in some forms, the BME module 152 is configured to estimate and provide additional parameters, such as, but not limited to, R2 and C2.

[0023] In one embodiment, EV support server 150 is a cloud-based server configured to exchange information with one or more EVs 100. In a non-limiting example, in addition to BME module 152, EV support server 150 also includes server communication system 220 and EV data module 222, which is configured to store and manage data received from one or more EVs 100 in EV data storage area 224.

[0024] In one embodiment, server communication system 203 is configured to exchange information with one or more EVs 100 using wireless communication (e.g., Bluetooth, ultra-wideband, cellular, and / or Wi-Fi) and may include a router, modem, antenna, input-output interface, universal serial bus (USB) port, and / or other suitable means for supporting wireless communication. Relative to each EV 100, server communication system 203 receives messages including data indicating a vehicle identifier for uniquely identifying the EV 100, timestamped battery operating characteristics, and / or values ​​of currently used battery parameters. As detailed herein, server communication system 220, when applicable, sends updated battery parameters to the desired EV 100 based on information from BME module 152.

[0025] EV data module 222 is configured to store battery operating characteristics from each EV 100 in EV data storage area 224 and retrieve appropriate data for analysis by BME module 152. In a non-limiting example, upon receiving battery operating characteristics, EV data module 222 is configured to use a vehicle identifier to retrieve historical data associated with EV 100, the vehicle identifier being used to associate the stored battery operating characteristics with the associated EV 100. Once evaluated by BME module 152, EV data module 22 is configured to store the output and operating characteristics of BME module 152 in data storage area 224 for future use.

[0026] BME module 152 is configured to monitor battery parameters 204 adopted by power estimation module 202 during a period of time when battery pack 106 undergoes multiple charge-discharge operations. If applicable, BME module 152 provides one or more updated battery parameters to be adopted by power estimation module 202, replacing the previous battery parameters adopted during the multiple charge-discharge operations. In a non-limiting example, BME module 150 includes battery circuit model 230, change detector 232, and adaptive correction module 234.

[0027] Battery circuit model 230 is configured to estimate the State of Charge (SOC) and predict the voltage using one or more of the battery operating characteristics from EV 100. In a non-limiting example, battery circuit model 230 is a closed-loop nRC circuit model, where "n" represents the number of RC pairs used in the model. The closed-loop nRC circuit model can receive at least one of the following as inputs: current; temperature; a reference SOC point; and a measured voltage trace, which is time-series data of voltage measurements captured by BS134. In one form, the reference SOC is the value of the SOC at time = 0 and can be used as a ground-based reference starting SOC value. The non-reference SOC can be measured at the last recorded charging time, estimated using OCV, or is the SOC last estimated by battery circuit model 230. Based on ampere-hour integrals from the reference SOC value, the SOC obtained from capacity and current measurements may be very close to the true SOC. The closed-loop nRC circuit model outputs an estimated SOC 236 for battery pack 106 and a predicted voltage 238 for battery pack 106.

[0028] For the estimation, battery circuit model 230 uses one or more of the same battery parameters as those used by power estimation module 202 (e.g., battery parameter 204). For example, for the nRC circuit model, the values ​​of the battery internal resistance (e.g., R0), one or more RC pair resistors (R1, R2, ..., RN), and one or more RC pair capacitors (C1, C2, ..., CN) are the same as the corresponding battery parameter 204.

[0029] The change detector 232 is configured to detect the operational difference between at least one selected operating characteristic provided by the EV 100 and an estimated characteristic defined by the battery circuit model 230. In one form, the selected operating characteristic and the estimated characteristic indicate at least one of the SOC of the battery pack 106 or the voltage of the battery pack 106. That is, if the battery circuit model 230 is an nRC type model, the change detector 232 compares the predicted voltage 238 and the estimated SOC 236 with the measured voltage and estimated SOC from the EV 100 to obtain a difference or drift. The change detector 232 is configured to calculate the difference or drift between two values ​​(e.g., the difference between the value from the battery circuit model 230 and the value from the EV 100). For example, "ΔV" is the voltage difference between the predicted voltage 238 and the voltage from the EV, and "ΔSOC" is the difference between the estimated SOC 236 and the SOC from the EV 100.

[0030] Using the difference / drift, the adaptive correction module 234 is configured to correct the battery parameters being adopted to reduce the difference. That is, in one form, the adaptive correction module 234 is configured to use the operational difference to define one or more estimated battery parameters. In a non-limiting example, the adaptive correction module 234 employs gradient-based optimization to reduce the difference in SOC and / or voltage, where the gradient-based optimization uses appropriate step sizes and values ​​to reduce the difference. In one form, a global optimization method (e.g., genetic algorithm, ant colony optimization, or particle swarm optimization) can be used to generate initial estimates for a gradient descent-based local optimization method (e.g., fmincon in MATLAB or neural networks).

[0031] Once the battery parameters are estimated, the adaptive correction module 234 determines whether to send the estimated battery parameters to the EV 100 as updated battery parameters. In one form, one or more updated battery parameters are sent in response to the fulfillment of a transmission condition. That is, while the adaptive correction module 234 is actively monitoring the battery parameters, the battery parameters may initially not change or may change only slightly, and therefore, when the transmission condition is met, the adaptive correction module 234 provides the estimated battery parameters as updated battery parameters.

[0032] In a non-limiting example, the sending conditions may include at least one of the following: the elapsed time period (e.g., 3 months, 6 months); the detection that the difference between the estimated battery parameters and the current battery parameters is greater than or equal to a parameter drift threshold (e.g., a 2% difference, a 5% difference); the lifespan of the battery pack 106 and a predefined time period such that the frequency of updating the battery parameters increases as the lifespan of the battery pack 106 increases; and / or the number of times the battery pack 106 is being charged or discharged (e.g., the usage rate of the EV 100 may affect the lifespan of the battery pack 106, and therefore, usage rate and other conditions (such as time or drift) may be used to initiate an update). If one or more estimated battery parameters are sent to the EV 100, the estimated SOC is used as SOC(0) in the next iteration, and the battery parameters adopted by the battery circuit model 230 are also updated.

[0033] refer to Figure 3 The present disclosure provides an example adaptive battery parameter evaluation program 300 executed by the EV support server 150. At operation 302, the EV support server 150 determines whether it has received data indicating operating characteristics from the EV 100.

[0034] At operation 304, server 150 retrieves data associated with the EV 100 that provided the message. For example, using the vehicle identifier in the message, server 150 obtains battery parameters and operating characteristics sent using the received message.

[0035] At operation 306, server 150 uses battery circuit model 230 to estimate the selected operating characteristics. In a non-limiting example, battery circuit model 230 is an nRC type model whose output estimated SOC 236 and predicted voltage 238 are used as the selected operating characteristics.

[0036] At operation 308, server 150 determines whether the selected operating characteristics differ from the corresponding operating characteristics provided by EV 100 (e.g., SOC and voltage from EV 100).

[0037] If a difference exists, at operation 310, server 150 uses the operational difference (e.g., ΔV or ΔSOC) to estimate battery parameters. In a non-limiting example, server 150 adjusts the battery parameters 204 currently used by EV 100 to reduce the operational difference (e.g., ΔV and / or ΔSOC).

[0038] At operation 312, server 150 determines whether the transmission conditions for sending estimated battery parameters to EV 100 as updated battery parameters are met. In a non-limiting example, server 150 determines whether a defined time period has elapsed since the last update of the battery parameters and / or whether the difference between the estimated battery parameters and the currently used battery parameters is greater than or equal to a parameter drift threshold.

[0039] If the sending conditions are met, at operation 314, server 150 sends updated battery parameters to EV 100, which then uses these updated parameters to define the power limits of battery pack 106, which are further used for charging and discharging battery pack 106. Server 150 also uses the updated battery parameters in battery circuit model 230 and stores data such as received operating characteristics and / or operating differences for future analysis.

[0040] If there is no difference among the selected operating characteristics (e.g., ΔV = 0 or ΔSOC = 0) or the transmission conditions are not met, then at operation 316, server 150 stores the operating characteristics received in association with EV 100 for future use. Other data, such as, but not limited to, estimated selected operating characteristics and / or estimated battery parameters, may also be stored.

[0041] The EV support server 150, equipped with the BME module 152 of this disclosure, is configured to proactively adjust battery parameters used by the BMM 132 to reduce, for example, SOC or voltage variations. The BME module 152 is adapted to capture the behavior of the battery pack 106 not only throughout its operation during a trip but also as the battery pack 106 ages over time. Furthermore, because the battery parameters are adjusted for each EV 100 using data from that EV 100, the BME module 152 inherently takes into account inter-battery variations by avoiding group-level calculations (e.g., using operating characteristics or trends from other EVs).

[0042] While exemplary embodiments have been described above, these embodiments are not intended to describe all possible forms of the invention. Rather, the terms 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 invention. Furthermore, features of various embodiments can be combined to form other embodiments of the invention.

[0043] In this application, the terms “module” and / or “controller” may refer to, be part of, or include the following: application-specific integrated circuit (ASIC); digital, analog, or mixed analog / digital discrete circuit; digital, analog, or mixed analog / digital integrated circuit; composable logic circuit; field-programmable gate array (FPGA); processor circuitry (shared, dedicated, or grouped) that executes code; memory circuitry (shared, dedicated, or grouped) that stores code executed by the processor circuitry; other suitable hardware components that provide the described functionality; or combinations of some or all of the foregoing, such as in a system-on-a-chip.

[0044] 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 transient electrical or electromagnetic signals propagated through a medium (such as on a carrier wave); therefore, the term computer-readable medium can be considered tangible and non-transient. Non-limiting examples of non-transient tangible computer-readable media include 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 magnetic tape or digital magnetic tape or hard disk drives), and optical storage media (such as USB, CD, DVD, or Blu-ray discs).

[0045] The apparatus and methods described in this application can be implemented, partially or entirely, by a dedicated computer created by configuring a general-purpose computer to execute one or more specific functions embodied in a computer program. Function 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 technician or programmer.

[0046] As used herein, at least one of the phrases A, B, and C should be interpreted as using non-exclusive logic or representational logic (A or B or C), and should not be interpreted as meaning "at least one of A, at least one of B, and at least one of C".

[0047] The description in this disclosure is merely exemplary in nature, and therefore, variations thereof are intended to be made within the scope of this disclosure without departing from its spirit. Such variations should not be considered as departing from the spirit and scope of this disclosure.

[0048] According to the present invention, a method for controlling an electric vehicle (EV) having a battery pack includes: the EV sending a plurality of battery operating characteristics to a remote server during a time period in which the battery pack undergoes a plurality of charge-discharge operations; and charging and discharging the battery pack according to a power limit defined by updated battery parameters, the updated battery parameters being estimated by the remote server using the plurality of battery operating characteristics.

[0049] In one aspect of the invention, the method includes sending the updated battery parameters to the EV by the remote server in response to the elapsed time of a defined period of time, or because the difference between the updated battery parameters and previous battery parameters used during the plurality of charge-discharge operations is greater than or equal to a parameter drift threshold.

[0050] In one aspect of the invention, the method includes: the remote server defining estimated battery parameters using the plurality of battery operating characteristics in response to a detected operating difference between at least one selected operating characteristic among the plurality of battery operating characteristics and an estimated operating characteristic defined by the remote server.

[0051] In one aspect of the invention, the at least one selected operating characteristic and the estimated operating characteristic indicate at least one of the state of charge of the battery pack or the voltage of the battery pack.

[0052] In one aspect of the invention, the estimated operating characteristics are detected using a resistor-capacitor circuit representation of the battery pack.

[0053] In one aspect of the invention, the plurality of battery operating characteristics include at least one of the battery pack voltage, the battery pack current, open-circuit voltage, state of charge, or temperature.

[0054] In one aspect of the invention, the remote server is configured to store the plurality of battery operating characteristics from each of a plurality of EVs, and to define the updated battery parameters using only the plurality of battery operating characteristics of the EVs.

[0055] In one aspect of the invention, the updated battery parameters include at least one of the following: battery internal resistance, one or more resistor-capacitor (RC) pairs, or one or more RC pairs.

[0056] According to the present invention, a vehicle system for an electric vehicle (EV) having a battery pack is provided, comprising: a communication system configured to transmit a plurality of battery operating characteristics to a remote server during a period in which the battery pack undergoes a plurality of charge-discharge operations; and a vehicle controller configured to charge and discharge the battery pack according to a power limit defined by updated battery parameters, the updated battery parameters being estimated by the remote server using the plurality of battery operating characteristics and based on previous battery parameters used during the plurality of charge-discharge operations.

[0057] According to one embodiment, the communication system is configured to obtain the updated battery parameters from the remote server in response to at least one of the elapsed time of a defined period of time, or the difference between the previous battery parameters and the updated battery parameters being greater than or equal to a parameter drift threshold.

[0058] According to one embodiment, the plurality of battery operating characteristics include at least one of the battery pack voltage, the battery pack current, open-circuit voltage, state of charge, or temperature.

[0059] According to one embodiment, the updated battery parameters are defined using only the plurality of battery operating characteristics from the EV.

[0060] According to one embodiment, the updated battery parameters include at least one of the following: battery internal resistance, one or more resistor-capacitor (RC) pairs, or one or more RC pairs.

[0061] According to the present invention, a system for an electric vehicle (EV) having a battery pack is provided, comprising: a remote server including one or more computing devices configured to output updated battery parameters for the EV; and a vehicle system configured to control the EV and including: a communication system configured to communicate with the remote server to send a plurality of battery operating characteristics to the remote server at least during a period in which the battery pack undergoes a plurality of charge-discharge operations; and a vehicle controller configured to charge and discharge the battery pack according to a power limit defined by the updated battery parameters, the updated battery parameters being estimated by the remote server using the plurality of battery operating characteristics based on previous battery parameters used during the plurality of charge-discharge operations.

[0062] According to one embodiment, the one or more computing devices of the remote server are configured to define estimated battery parameters using the plurality of battery operating characteristics in response to an operational difference detected between at least one selected operating characteristic among the plurality of battery operating characteristics and an estimated operating characteristic defined by the remote server.

[0063] According to one embodiment, the at least one selected operating characteristic and the estimated operating characteristic indicate at least one of the state of charge of the battery pack or the voltage of the battery pack.

[0064] According to one embodiment, the updated battery parameters are sent to the EV in response to at least one of the following: the elapsed time of a defined period of time, or the difference between the previous battery parameters and the updated battery parameters is greater than or equal to a parameter drift threshold.

[0065] According to one embodiment, the plurality of battery operating characteristics include at least one of the battery pack voltage, the battery pack current, open-circuit voltage, state of charge, or temperature.

[0066] According to one embodiment, the remote server is configured to store the plurality of battery operating characteristics from each of a plurality of EVs, and to define the updated battery parameters using only the plurality of battery operating characteristics of the EVs.

[0067] According to one embodiment, the updated battery parameters include at least one of the following: battery internal resistance, one or more resistor-capacitor (RC) pairs, or one or more RC pairs.

Claims

1. A method for controlling an electric vehicle (EV) having a battery pack, comprising: The EV sends multiple battery operation characteristics to a remote server during the time period when the battery pack undergoes multiple charge-discharge operations. as well as The battery pack is charged and discharged according to power limits defined using updated battery parameters, which are estimated by the remote server using the plurality of battery operating characteristics based on previous battery parameters used during the plurality of charge-discharge operations.

2. The method of claim 1, further comprising: The remote server sends the updated battery parameters to the EV in response to at least one of the following: the elapsed time period of the defined period, or the difference between the previous battery parameters and the updated battery parameters is greater than or equal to a parameter drift threshold.

3. The method of claim 1, further comprising: The remote server uses the plurality of battery operating characteristics to define estimated battery parameters in response to an operational difference detected between at least one selected operating characteristic among the plurality of battery operating characteristics and an estimated operating characteristic defined by the remote server.

4. The method of claim 3, wherein the at least one selected operating characteristic and the estimated characteristic indicate at least one of the state of charge of the battery pack or the voltage of the battery pack.

5. The method of claim 3, wherein the estimated operating characteristics are detected using a resistor-capacitor circuit representation of the battery pack.

6. The method of claim 1, wherein the plurality of battery operating characteristics includes at least one of the battery pack voltage, the battery pack current, open-circuit voltage, state of charge, or temperature.

7. The method of claim 1, wherein the remote server is configured to store the plurality of battery operating characteristics from each of a plurality of EVs, and to define the updated battery parameters using only the plurality of battery operating characteristics of the EVs.

8. The method of claim 1, wherein the updated battery parameters include at least one of battery internal resistance, one or more resistor-capacitor (RC) pairs, or one or more RC pairs.

9. A system for an electric vehicle (EV) having a battery pack, comprising: A remote server, the remote server including one or more computing devices configured to output updated battery parameters for the EV; as well as A vehicle system configured to control the EV and including: A communication system configured to communicate with the remote server to send multiple battery operation characteristics to the remote server at least during a period in which the battery pack undergoes multiple charge-discharge operations. as well as A vehicle controller configured to charge and discharge the battery pack according to power limits defined using the updated battery parameters, which are estimated by the remote server using the plurality of battery operating characteristics based on previous battery parameters used during the plurality of charge-discharge operations.

10. The system of claim 9, wherein the one or more computing devices of the remote server are configured to define estimated battery parameters using the plurality of battery operating characteristics in response to a detected operating difference between at least one selected operating characteristic among the plurality of battery operating characteristics and an estimated operating characteristic defined by the remote server.

11. The system of claim 10, wherein the at least one selected operating characteristic and the estimated characteristic indicate at least one of the state of charge of the battery pack or the voltage of the battery pack.

12. The system of claim 9, wherein the updated battery parameters are sent to the EV in response to at least one of the elapsed time of a defined period of time, or the difference between the previous battery parameters and the updated battery parameters being greater than or equal to a parameter drift threshold.

13. The system of claim 9, wherein the plurality of battery operating characteristics include at least one of the battery pack voltage, the battery pack current, open-circuit voltage, state of charge, or temperature.

14. The system of claim 9, wherein the remote server is configured to store the plurality of battery operating characteristics from each of a plurality of EVs, and to define the updated battery parameters using only the plurality of battery operating characteristics of the EVs.

15. The system of claim 9, wherein the updated battery parameters include at least one of battery internal resistance, one or more resistor-capacitor (RC) pairs, or one or more RC pairs.

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