SOF estimation method, device and vehicle for vehicle

Abstract

The present invention provides a method, device and vehicle for estimating the SOF of a vehicle. The method relates to the technical field of new energy vehicles. The method includes: combining the characteristics of dynamic working conditions and static working conditions to comprehensively determine a more accurate SOF, and the model used to estimate the SOF can be obtained by training based on historical data on the cloud side, and the vehicle side sends the real-time acquired data to the cloud side so that the cloud side can update the model in time and better adapt to the current state of the vehicle battery to obtain a more accurate SOF.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy vehicles, and in particular to a SOF estimation method and device for a vehicle and a vehicle. Background Art

[0002] At present, new energy vehicles are developing rapidly, among which new energy vehicles with electricity as the power source and motor as the driving device have developed significantly. Power batteries and battery management systems are key components of electric vehicles. Battery SOF (State of Function) is an important parameter for state estimation of the battery management system. The estimation accuracy of SOF is not high, which may easily lead to battery undervoltage or overcharge, affecting the safety and use of the battery, or the energy is too conservative to affect the driving experience. Accurately estimating the maximum charge and discharge capacity of the battery has always been the focus and difficulty of research at home and abroad.

[0003] At present, the mainstream SOF estimation method for new energy electric vehicles is the table lookup method, that is, after a large number of tests, test data is obtained, and the temperature and current SOC state are considered, and the maximum charge and discharge capacity of the current battery is obtained by looking up the two-dimensional power current table. However, the battery is constantly changing with use, and this method is relatively rigid to deal with this situation. Summary of the invention

[0004] The object of the present invention is to provide a method, device and vehicle for estimating SOF of a vehicle, so as to alleviate the technical problem of low SOF estimation accuracy existing in the prior art.

[0005] In a first aspect, the present invention provides a method for estimating SOF of a vehicle, further comprising:

[0006] Determine a first limiting current value based on battery operating state data under current operating conditions and a predetermined dynamic current limiting prediction model;

[0007] Determine the second limiting current value based on the battery working state data under the current working condition and the predetermined charge and discharge power curve; wherein the dynamic current limiting prediction model and the charge and discharge power curve are obtained by global optimization in the cloud based on the historical battery working state data and the SOF of the battery system in advance;

[0008] The SOF of the battery system is determined based on a target limit current value which is the smallest of the first limit current value and the second limit current value.

[0009] In an optional embodiment, the method further comprises:

[0010] Based on the battery working status data under the current working condition and the SOF of the battery system, the dynamic current limiting prediction model and the charge and discharge power curve are locally optimized locally, so that before receiving the globally optimized dynamic current limiting prediction model and the globally optimized charge and discharge power curve sent by the cloud, the SOF of the battery system under the next working condition is estimated based on the locally optimized dynamic current limiting prediction model and the locally optimized charge and discharge power curve.

[0011] In an optional implementation, the predetermined dynamic current limit prediction model is determined based on a limit condition obtained by simulating a limit operating condition using a Monte Carlo algorithm.

[0012] In an optional embodiment, the battery operating status data includes one or more of a current measurement value, a temperature measurement value, a power capability, a SOC parameter, and a SOH parameter.

[0013] In an optional implementation, the charge-discharge power curve includes a first charge-discharge power curve related to the SOC parameter and temperature, and a second charge-discharge power curve related to the SOH.

[0014] In an optional implementation, during the optimization of the charge and discharge power curve, optimization is performed based on a charge and discharge strategy, where the charge and discharge strategy includes a healthy charge and discharge strategy and a fast charge and discharge strategy.

[0015] In an optional implementation manner, determining the SOF of the battery system based on a minimum target limiting current value between the first limiting current value and the second limiting current value includes:

[0016] Determining a first limiting voltage value based on the target limiting current value, the first limiting current value and a prediction factor;

[0017] The SOF of the battery system is determined based on the target limit current value and the first limit voltage value.

[0018] In an optional implementation manner, determining the SOF of the battery system based on the target limit current value and the first limit voltage value includes:

[0019] Based on the first limit voltage value and the third charge and discharge power curve, perform voltage limit derating to determine a second limit voltage value after derating;

[0020] The SOF of the battery system is determined based on the target limit current value and the second limit voltage value.

[0021] In a second aspect, the present invention provides a BMS, further comprising:

[0022] A dynamic module, used to determine a first limiting current value based on battery operating state data under current operating conditions and a predetermined dynamic current limiting prediction model;

[0023] A static module, used to determine a second limiting current value based on the battery working state data under the current working condition and a predetermined charge and discharge power curve; wherein the dynamic current limiting prediction model and the charge and discharge power curve are obtained by global optimization in the cloud based on the historical battery working state data and the SOF of the battery system in advance;

[0024] The integration module is used to determine the SOF of the battery system based on a minimum target limiting current value between the first limiting current value and the second limiting current value.

[0025] In an optional embodiment, it also includes: an optimization module, which is used to locally optimize the dynamic current limiting prediction model and the charge and discharge power curve based on the battery working status data under the current working condition and the SOF of the battery system, so that before receiving the globally optimized dynamic current limiting prediction model and the globally optimized charge and discharge power curve sent by the cloud, the SOF of the battery system under the next working condition is estimated based on the locally optimized dynamic current limiting prediction model and the locally optimized charge and discharge power curve.

[0026] In an optional implementation, the predetermined dynamic current limit prediction model is determined based on a limit condition obtained by simulating a limit operating condition using a Monte Carlo algorithm.

[0027] In an optional embodiment, the battery operating status data includes one or more of current measurement values, temperature measurement values, power capability, SOC parameters, and SOH parameters.

[0028] In an optional implementation, the charge-discharge power curve includes a first charge-discharge power curve related to the SOC parameter and temperature, and a second charge-discharge power curve related to the SOH.

[0029] In an optional implementation, during the optimization of the charge and discharge power curve, optimization is performed based on a charge and discharge strategy, where the charge and discharge strategy includes a healthy charge and discharge strategy and a fast charge and discharge strategy.

[0030] In an optional implementation manner, the integrated module is specifically used for:

[0031] Determining a first limiting voltage value based on the target limiting current value, the first limiting current value and a prediction factor;

[0032] The SOF of the battery system is determined based on the target limit current value and the first limit voltage value.

[0033] In an optional implementation manner, the integrated module is specifically used for:

[0034] Based on the first limit voltage value and the third charge and discharge power curve, perform voltage limit derating to determine a second limit voltage value after derating;

[0035] The SOF of the battery system is determined based on the target limit current value and the second limit voltage value.

[0036] In a third aspect, the present invention provides a vehicle, including a battery management system BMS, wherein the BMS includes a memory and a processor, wherein the memory stores a computer program executable on the processor, and when the processor executes the computer program, the steps of the method described in any one of the aforementioned embodiments are implemented.

[0037] The present invention provides a method, device and vehicle for estimating the SOF of a vehicle. The first limiting current value is determined based on the battery working state data under the current working condition and a predetermined dynamic current limiting prediction model; the second limiting current value is determined based on the battery working state data under the current working condition and a predetermined charge and discharge power curve; the battery function state SOF of the battery system is determined based on the smallest target limiting current value of the first limiting current value and the second limiting current value; the battery working state data under the current working condition and the SOF of the battery system are uploaded to the cloud, so that the cloud performs global optimization on the dynamic current limiting prediction model and the charge and discharge power curve based on the battery working state data under the current working condition and the SOF of the battery system, and sends the globally optimized dynamic current limiting prediction model and the globally optimized charge and discharge power curve to the vehicle end; the SOF of the battery system under the next working condition is estimated based on the globally optimized dynamic current limiting prediction model and the globally optimized charge and discharge power curve. In this way, the characteristics of dynamic and static working conditions can be combined to comprehensively determine a more accurate SOF, and the model used to estimate SOF can be trained by the cloud based on historical data. The vehicle side sends the real-time data to the cloud so that the cloud can update the model in time and better adapt to the current state of the vehicle battery to obtain a more accurate SOF. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 A block diagram of a vehicle SOF estimation system provided by an embodiment of the present invention;

[0040] Figure 2 A schematic diagram of a BMS structure provided by an embodiment of the present invention;

[0041] Figure 3 A schematic flow chart of a method for estimating SOF of a vehicle provided by an embodiment of the present invention;

[0042] Figure 4 An architecture diagram of a SOF system provided by an embodiment of the present invention;

[0043] Figure 5 A schematic diagram of a dynamic current limiting model of a SOF system provided by an embodiment of the present invention;

[0044] Figure 6 A schematic diagram of another SOF system dynamic current limiting model provided by an embodiment of the present invention;

[0045] Figure 7 A flowchart of SOF estimation provided by an embodiment of the present invention;

[0046] Figure 8 A schematic diagram of the hardware architecture of a BMS800 provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0048] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0050] Some embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0051] Figure 1 FIG. 4 is a block diagram of a vehicle SOF estimation system according to an embodiment of the present invention. Figure 1 As shown, the SOF estimation system of the vehicle includes a cloud 120 and a battery management system BMS (Battery Management System) 111 arranged on the vehicle end 110 .

[0052] Figure 2 A schematic diagram of a BMS structure provided in an embodiment of the present application. Figure 2 As shown, the BMS 11 may include:

[0053] A dynamic module 201, configured to determine a first limiting current value based on battery operating state data under current operating conditions and a predetermined dynamic current limiting prediction model;

[0054] The static module 202 is used to determine the second limiting current value based on the battery operating state data under the current working condition and the predetermined charge and discharge power curve;

[0055] A synthesis module 203 is used to determine a battery function state SOF of the battery system based on a minimum target limiting current value of the first limiting current value and the second limiting current value;

[0056] The sending module 204 is used to upload the battery working state data under the current working condition and the SOF of the battery system to the cloud 120, so that the cloud 120 can globally optimize the dynamic current limit prediction model and the charge and discharge power curve based on the battery working state data under the current working condition and the SOF of the battery system, and send the globally optimized dynamic current limit prediction model and the globally optimized charge and discharge power curve to the BMS 11 of the vehicle end 110;

[0057] The estimation module 205 is used to estimate the SOF of the battery system under the next working condition based on the globally optimized dynamic current limit prediction model and the globally optimized charge and discharge power curve; the estimation module 205 includes a dynamic module 201 , a static module 202 and a comprehensive module 205 .

[0058] In some embodiments, it also includes: an optimization module, which is used to locally optimize the dynamic current limiting prediction model and the charge and discharge power curve based on the battery working status data under the current working condition and the SOF of the battery system, so that before receiving the globally optimized dynamic current limiting prediction model and the globally optimized charge and discharge power curve sent by the cloud, the SOF of the battery system under the next working condition is estimated based on the locally optimized dynamic current limiting prediction model and the locally optimized charge and discharge power curve.

[0059] In some embodiments, the predetermined dynamic current limit prediction model is determined based on the limit conditions obtained by simulating the extreme operating conditions using a Monte Carlo algorithm.

[0060] In some embodiments, the battery operating status data includes one or more of current measurement values, temperature measurement values, power capability, SOC parameters, and SOH parameters.

[0061] In some embodiments, the charge-discharge power curve includes a first charge-discharge power curve related to the SOC parameter and temperature, and a second charge-discharge power curve related to the SOH.

[0062] In some embodiments, during the optimization of the charge and discharge power curve, the optimization is performed based on the charge and discharge strategy, and the charge and discharge strategy includes a healthy charge and discharge strategy and a fast charge and discharge strategy.

[0063] In some embodiments, the integrated module is specifically configured to:

[0064] Determining a first limiting voltage value based on the target limiting current value, the first limiting current value, and the prediction factor;

[0065] The SOF of the battery system is determined based on the target limit current value and the first limit voltage value.

[0066] In some embodiments, the integrated module is specifically configured to:

[0067] Based on the first limit voltage value and the third charge and discharge power curve, perform voltage limit derating to determine a second limit voltage value after derating;

[0068] The SOF of the battery system is determined based on the target limit current value and the second limit voltage value.

[0069] In this way, the characteristics of dynamic and static working conditions can be combined to comprehensively determine a more accurate SOF, and the model used to estimate SOF can be trained by the cloud based on historical data. The vehicle side sends the real-time data to the cloud so that the cloud can update the model in time and better adapt to the current state of the vehicle battery to obtain a more accurate SOF.

[0070] Figure 3 A flow chart of a method for estimating the SOF of a vehicle provided in an embodiment of the present application. Figure 3 As shown, the method may include the following steps:

[0071] S310, determining a first limiting current value based on battery operating state data under a current operating condition and a predetermined dynamic current limiting prediction model;

[0072] Among them, the battery operating status data may include one or more of current measurement values, temperature measurement values, power capability ((Uocv-Um)*I), SOC (State of Charge) parameters, and SOH (state of health) parameters.

[0073] In some embodiments, the measured values ​​of voltage, current, temperature, etc. may be sensor data collected in real time. The SOC and SOH used to calculate SOF may be estimated online based on an electrochemical model of a Thevenin second-order circuit model and a Kalman or observer.

[0074] The battery operating status data corresponding to the current working condition can be determined based on the measured value and the battery model. The battery model here can be a plurality of reference curves measured under laboratory conditions, in which the reference curve is a functional relationship curve of the influencing factors (such as current I, temperature T, state of charge SOC, state of health SOH) to the battery model parameters (battery internal resistance DCIR, resistance R0, resistance R1, capacitance C1) and battery state quantity (Cap capacity, SOC, SOH, SOP, SOE). Optionally, the reference curve can be pre-stored in the BMS when the electric vehicle leaves the factory, or it can be downloaded from the cloud.

[0075] The predetermined dynamic current limit prediction model here can be a combination of limit curves determined by simulating the limit conditions based on the Monte Carlo algorithm for the limit conditions, and can be obtained by global optimization in the cloud based on the historical battery working state data and the SOF of the battery system. The first limit current value can be obtained based on the battery working state data under the current working condition and the dynamic current limit prediction model.

[0076] like Figure 4 As shown, the dynamic current limiting prediction model can be a system dynamic current limiting model, which can include two parts, one part predicts the cell voltage U and reversely calculates the current I of the maximum capacity of the extreme operating condition, and the other part ensures that the power capacity ((Uocv-Um)*I) does not exceed the boundary.

[0077] Specifically, Figure 5 As shown, the model can be trained based on the Monte Carlo random algorithm. The worst cell voltage condition and power loss are simulated, and the original data input is obtained from the cloud platform for data training optimization to obtain a dynamic current limiting model.

[0078] like Figure 6As shown, a part of the dynamic current limiting model establishes a process equation through an equivalent circuit to predict SOC, electrochemical currents Ibv and Iwb; and according to Ohm's law U=I*R, an observation equation is established with the input current and internal resistance to obtain the predicted voltage U; the voltage limit is estimated according to conditions such as online internal resistance aging, and the charging and discharging currents are obtained by reverse calculation based on the voltage limit, and finally the first sub-limiting current value is obtained.

[0079] The other part automatically finds the maximum current under the premise of temperature limit. According to the temperature and power loss, the charging and discharging currents are calculated while ensuring that the power does not exceed the limit, and the second sub-limit current value is obtained.

[0080] The smallest one of the first sub-limit current value and the second sub-limit current value is used as the first limit current value.

[0081] S320, determining a second limiting current value based on the battery operating state data under the current operating condition and a predetermined charge and discharge power curve;

[0082] like Figure 4 The charge and discharge power curve shown may include a first charge and discharge power curve related to the SOC parameter and temperature, and a second charge and discharge power curve related to the SOH.

[0083] The third sub-limiting current value can be determined based on the first charge and discharge power curve, and the fourth sub-limiting current value can be determined based on the second charge and discharge power curve. The smallest one of the third sub-limiting current value and the fourth sub-limiting current value is used as the second limiting current value.

[0084] The first charge-discharge power curve and the second charge-discharge power curve can be determined based on an OCV (Open Circuit Voltage)-SOC-SOH curve, and can be globally optimized in advance in the cloud based on historical battery operating status data and the SOF of the battery system.

[0085] S330 , determining the SOF of the battery system based on the smallest target limit current value between the first limit current value and the second limit current value.

[0086] like Figure 4 As shown, after the system dynamic current limiting module and the system steady-state current limiting module output the first limiting current value and the second limiting current value, the minimum of the first limiting current value and the second limiting current value can be determined as the target limiting current value based on the battery system current limiting module.

[0087] The SOF of the battery system can be determined based on the target limiting current value. The SOF is an important parameter for the battery management system state estimation. Simply put, it is the maximum charge and discharge current of the battery under the current operating conditions.

[0088] After obtaining the SOF of the battery system under the current working conditions, the vehicle can upload the data to the cloud to optimize the dynamic current limit prediction model and the charge and discharge power curve. This can be achieved through the following steps:

[0089] Step 1: Upload the battery working state data under the current working condition and the SOF of the battery system to the cloud, so that the cloud can globally optimize the dynamic current limit prediction model and the charge and discharge power curve based on the battery working state data under the current working condition and the SOF of the battery system, and send the globally optimized dynamic current limit prediction model and the globally optimized charge and discharge power curve to the vehicle end;

[0090] like Figure 7 As shown, in the dynamic current limiting model and the steady-state current limiting model, after obtaining the first limiting current value, the second limiting current value and the target limiting current value respectively, the battery working status data under the current working conditions, the first limiting current value, the second limiting current value and the target limiting current value can be uploaded to the cloud, and the upload cycle can be determined according to actual needs.

[0091] Based on the above data, the cloud can optimize the charge and discharge power related curves (including: the first charge and discharge power curve, the second charge and discharge power curve and the third charge and discharge power curve).

[0092] Specifically, the cloud can perform data cleaning and feature extraction based on newly uploaded data and pre-recorded historical data, find the optimal current curve for temperature limit based on the data, determine the influencing factors, perform Monte Carlo simulation based on the battery model on the cloud, and ultimately optimize the dynamic current limit prediction model and the charge and discharge power curve.

[0093] In the process of optimizing the charge and discharge power curve, optimization is performed based on the charge and discharge strategy, and the charge and discharge strategy includes a healthy charge and discharge strategy and a fast charge and discharge strategy.

[0094] Step 2: Estimate the SOF of the battery system under the next operating condition based on the globally optimized dynamic current limit prediction model and the globally optimized charge and discharge power curve.

[0095] After receiving the globally optimized dynamic current limit prediction model and the globally optimized charging and discharging power curve, the vehicle side can perform a new round of SOF estimation for the battery system.

[0096] In some embodiments, before uploading the battery operating status data based on the current working condition and the SOF of the battery system to the cloud, the method further includes:

[0097] Based on the battery working status data under the current working condition and the SOF of the battery system, the dynamic current limit prediction model and the charge and discharge power curve are locally optimized, so that before receiving the globally optimized dynamic current limit prediction model and the globally optimized charge and discharge power curve sent by the cloud, the SOF of the battery system under the next working condition is estimated based on the locally optimized dynamic current limit prediction model and the locally optimized charge and discharge power curve.

[0098] In this way, the present application can combine the characteristics of dynamic working conditions and static working conditions to comprehensively determine a more accurate SOF, and the model used to estimate the SOF can be trained by the cloud based on historical data. The vehicle side sends the real-time acquired data to the cloud so that the cloud can update the model in a timely manner and better adapt to the current state of the vehicle battery to obtain a more accurate SOF.

[0099] In some embodiments, Figure 4 As shown, the above step S330 can be specifically implemented by the following steps:

[0100] Step 1) determining a first limiting voltage value based on a target limiting current value, a first limiting current value and a prediction factor.

[0101] Step 2), determining the SOF of the battery system based on the target limit current value and the first limit voltage value.

[0102] Among them, Figure 4 As shown, the first limiting voltage value can be determined based on the maximum and minimum voltage prediction module of the battery system. Specifically, after obtaining the first limiting current value based on the dynamic current limiting prediction model and obtaining the target limiting current value based on the dynamic current limiting prediction model and the charge and discharge power curve, as shown in FIG. Figure 6 As shown, the first limiting voltage value can be calculated based on the observation equation of the dynamic current limiting prediction model, according to the first limiting current value, the target limiting current value, the prediction factor and the input internal resistance in combination with Ohm's law.

[0103] In some embodiments, Figure 4 As shown, the above step 2) can be specifically implemented by the following steps:

[0104] Step a), based on the first limit voltage value and the third charge and discharge power curve, voltage limit derating is performed to determine a second limit voltage value after derating;

[0105] Step b) determining the SOF of the battery system based on the target limit current value and the second limit voltage value.

[0106] The third charge-discharge power curve may be a SOF voltage limit derating curve. Figure 4As shown, the voltage limit derating can be performed based on the SOF voltage limit derating curve and the first limit voltage value obtained by the battery system maximum and minimum voltage prediction module to determine the second limit voltage value after derating.

[0107] The BMS provided in the embodiment of the present application has the same technical features as the SOF estimation method of the vehicle provided in the above embodiment, so it can also solve the same technical problems and achieve the same technical effects.

[0108] Figure 8 The hardware architecture diagram of the BMS800 provided in the embodiment of the present invention is shown in FIG. Figure 8 As shown, the electronic device 800 includes: a machine-readable storage medium 801 and a processor 802, and may also include a non-volatile storage medium 803, a communication interface 804 and a bus 805; wherein the machine-readable storage medium 801, the processor 802, the non-volatile storage medium 803 and the communication interface 804 communicate with each other through the bus 805. The processor 802 can execute the method described in the above embodiment by reading and executing the machine-executable instructions in the machine-readable storage medium 801.

[0109] The machine-readable storage medium mentioned in this article can be any electronic, magnetic, optical or other physical storage device that can contain or store information, such as executable instructions, data, etc. For example, the machine-readable storage medium can be: RAM (Radom Access Memory), volatile memory, non-volatile memory, flash memory, storage drive (such as hard disk drive), any type of storage disk (such as CD, DVD, etc.), or similar storage medium, or a combination thereof.

[0110] The non-volatile medium may be a non-volatile memory, a flash memory, a storage drive (such as a hard drive), any type of storage disk (such as a CD, DVD, etc.), or a similar non-volatile storage medium, or a combination thereof.

[0111] It can be understood that the specific operation methods of each functional module in this embodiment can refer to the detailed description of the corresponding steps in the above method embodiment, and will not be repeated here.

[0112] Corresponding to the SOF estimation method for the above-mentioned vehicle, an embodiment of the present application also provides a computer-readable storage medium, which stores machine-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to execute the steps of the SOF estimation method for the above-mentioned vehicle.

[0113] The BMS provided in the embodiment of the present application can be specific hardware on the device or software or firmware installed on the device. The device provided in the embodiment of the present application has the same implementation principle and technical effects as the aforementioned method embodiment. For the sake of brief description, the parts not mentioned in the device embodiment can refer to the corresponding contents in the aforementioned method embodiment. It can be clearly understood by technicians in the relevant field that for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above can all refer to the corresponding processes in the aforementioned method embodiment, and will not be repeated here.

[0114] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0115] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0116] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and a part of the module, program segment or code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0117] In addition, each functional unit in the embodiments provided in the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0118] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can essentially or in other words, the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for a computer device to execute all or part of the steps of the mobile control method of each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0119] It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description and are not to be understood as indicating or implying relative importance.

[0120] Finally, it should be noted that the above embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solutions of the present application, rather than to limit them. The protection scope of the present application is not limited thereto. Although the present application is described in detail with reference to the above embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above embodiments within the technical scope disclosed in the present application, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application. They should all be covered within the protection scope of the present application.

Claims

1. A method for estimating SOF of a vehicle, characterized in that: Also includes: Determine a first limiting current value based on battery operating state data under current operating conditions and a predetermined dynamic current limiting prediction model; Determine the second limiting current value based on the battery working state data under the current working condition and the predetermined charge and discharge power curve; wherein the dynamic current limiting prediction model and the charge and discharge power curve are obtained by global optimization in the cloud based on the historical battery working state data and the SOF of the battery system in advance; determining the SOF of the battery system based on a target limit current value which is the smallest of the first limit current value and the second limit current value; The method further includes: locally optimizing the dynamic current limit prediction model and the charge-discharge power curve based on the battery operating state data under the current working condition and the SOF of the battery system, so as to estimate the SOF of the battery system under the next working condition based on the locally optimized dynamic current limit prediction model and the locally optimized charge-discharge power curve before receiving the globally optimized dynamic current limit prediction model and the globally optimized charge-discharge power curve sent by the cloud; Determining the SOF of the battery system based on a target limiting current value that is the smallest of the first limiting current value and the second limiting current value includes: determining a first limiting voltage value based on the target limiting current value, the first limiting current value, and a prediction factor; determining the SOF of the battery system based on the target limiting current value and the first limiting voltage value; Determining the SOF of the battery system based on the target limiting current value and the first limiting voltage value includes: performing voltage limiting derating based on the first limiting voltage value and a third charge and discharge power curve to determine a second limiting voltage value after derating; determining the SOF of the battery system based on the target limiting current value and the second limiting voltage value.

2. The method according to claim 1, characterized in that: The predetermined dynamic current limit prediction model is determined based on the limit conditions obtained by simulating the extreme operating conditions using a Monte Carlo algorithm.

3. The method according to claim 1, characterized in that The battery operating status data includes one or more of current measurement values, temperature measurement values, power capability, SOC parameters, and SOH parameters.

4. The method according to claim 3, characterized in that The charge-discharge power curve includes a first charge-discharge power curve related to the SOC parameter and temperature, and a second charge-discharge power curve related to the SOH.

5. The method according to claim 1, characterized in that In the process of optimizing the charge and discharge power curve, optimization is performed based on the charge and discharge strategy, and the charge and discharge strategy includes a healthy charge and discharge strategy and a fast charge and discharge strategy.

6. A battery management system BMS, characterized in that: Also includes: A dynamic module, used to determine a first limiting current value based on battery operating state data under current operating conditions and a predetermined dynamic current limiting prediction model; A static module, used to determine a second limiting current value based on the battery working state data under the current working condition and a predetermined charge and discharge power curve; wherein the dynamic current limiting prediction model and the charge and discharge power curve are obtained by global optimization in the cloud based on the historical battery working state data and the SOF of the battery system in advance; A comprehensive module, configured to determine the SOF of the battery system based on a minimum target limiting current value between the first limiting current value and the second limiting current value; It also includes: an optimization module, which is used to locally optimize the dynamic current limit prediction model and the charge and discharge power curve based on the battery working state data under the current working condition and the SOF of the battery system, so as to estimate the SOF of the battery system under the next working condition based on the locally optimized dynamic current limit prediction model and the locally optimized charge and discharge power curve before receiving the globally optimized dynamic current limit prediction model and the globally optimized charge and discharge power curve sent by the cloud; The integrated module is specifically used to: determine a first limiting voltage value based on the target limiting current value, the first limiting current value and a prediction factor; determine the SOF of the battery system based on the target limiting current value and the first limiting voltage value; The integrated module is specifically used to: perform voltage limit derating based on the first limit voltage value and the third charge and discharge power curve to determine the second limit voltage value after derating; and determine the SOF of the battery system based on the target limit current value and the second limit voltage value.

7. A vehicle, characterized in that: It comprises a battery management system BMS, the BMS comprises a memory and a processor, the memory stores a computer program that can be run on the processor, and the processor implements the steps of the method described in any one of claims 1 to 5 when executing the computer program.

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