A method, device, vehicle, medium and product for predicting battery power

Abstract

The present application discloses a method, device, vehicle, medium and product for predicting battery power, and relates to the field of new energy technology. The method includes: when it is detected that the battery assembly of the vehicle is switched to the power-on state, obtaining the initial state of charge of the battery assembly; determining the confidence level of the initial state of charge; determining the charge correction amount and the gain value of the Lumberg observer according to the confidence level; correcting the initial state of charge based on the charge correction amount to obtain the target state of charge; and using the Lumberg observer to predict the remaining battery power of the vehicle during driving based on the target state of charge and the gain value. The technical solution provided by the present application can realize a simple hardware structure and accelerate the convergence speed of the Lumberg observer, and can accurately predict the remaining battery power of the vehicle during driving.

Description

Technical Field

[0001] The present application relates to the field of new energy technology, and in particular to a method, device, vehicle, medium and product for predicting battery power. Background Art

[0002] With the rapid development of intelligent technology, new energy vehicles (such as electric vehicles or hybrid vehicles) are becoming more and more common. Accurately predicting the remaining battery power of a vehicle during driving is of great significance to the reliability of vehicle operation.

[0003] At present, the main methods for predicting the battery state of charge (SOC) in new energy vehicles are: ampere-hour integration method, open circuit voltage method, Lumberg observer, Kalman filter algorithm and particle filter algorithm. The ampere-hour integration is simple and easy, but the initial error cannot be determined, and there is a cumulative error over time; the open circuit voltage method requires the battery to be stationary for a long time, which is difficult to meet the actual vehicle operating conditions; the Lumberg observer has a simple structure, small amount of calculation but slow convergence; the Kalman filter method has a large amount of calculation, fast convergence but cannot overcome the problem of data saturation; and more complex algorithms such as particle filtering often have higher accuracy, but their calculation amount is difficult to rely on commercial battery management system (BMS) hardware. Therefore, designing a SOC prediction method with simple hardware structure, fast convergence speed and improved accuracy has become an urgent problem to be solved. Summary of the invention

[0004] The present application provides a battery power prediction method, device, vehicle, medium and product, which can realize a simple hardware structure and accelerate the convergence speed of the Lumberg observer, and can accurately predict the remaining battery power of the vehicle during driving.

[0005] In a first aspect, the present application provides a method for predicting battery power, the method comprising:

[0006] When detecting that the battery assembly of the vehicle is switched to a power-on state, obtaining an initial charge state of the battery assembly;

[0007] determining a confidence level of the initial state of charge;

[0008] Determining a charge correction amount and a gain value of a Lumberg observer according to the confidence level;

[0009] Correcting the initial state of charge based on the charge correction amount to obtain a target state of charge;

[0010] The remaining battery power of the vehicle during driving is predicted using a Romberg observer based on the target state of charge and the gain value.

[0011] Furthermore, determining the confidence level of the initial state of charge includes: obtaining self-discharge data of the battery assembly during the current parking period; determining the battery performance of the battery assembly; and determining the confidence level based on the self-discharge data and the battery performance.

[0012] Furthermore, the self-discharge data of the battery assembly during this parking period is obtained, including: obtaining the parking time between the current power-on time and the last power-off time of the vehicle; obtaining the ambient temperature of the vehicle during this parking period; and calculating the self-discharge data based on the parking time and the ambient temperature.

[0013] Furthermore, determining the battery performance of the battery assembly includes: determining whether the battery assembly has been effectively corrected within a preset time period before the current time, the effective correction including at least one of a charging end correction, a discharging end correction and an initial state of charge correction; if it has been effectively corrected, determining that the battery performance of the battery assembly is not in a state of charge-open circuit voltage SOC-OCV plateau period; if it has not been effectively corrected, determining that the battery performance of the battery assembly is in a SOC-OCV plateau period.

[0014] Furthermore, the confidence level is determined based on the self-discharge data and the battery performance, including: determining whether the self-discharge data meets a first data standard, and determining whether the battery performance meets a second data standard, the second data standard being that the battery assembly is in a SOC-OCV plateau period exceeding a preset time; if both the first data standard and the second data standard are met, the confidence level is determined to be a low level; if the first data standard is met and the second data standard is not met, or the first data standard is not met and the second data standard is met, the confidence level is determined to be a medium level; if both the first data standard and the second data standard are not met, the confidence level is determined to be a high level.

[0015] Furthermore, before determining the charge correction amount according to the confidence level, it also includes: determining the environmental impact constant of the vehicle during this parking period, and the environmental impact constant is determined by the parking time and ambient temperature during this parking period; accordingly, determining the charge correction amount according to the confidence level, including: determining a charge correction coefficient according to the confidence level, the higher the confidence level, the smaller the corresponding charge correction coefficient; and multiplying the environmental impact constant and the charge correction coefficient to obtain the charge correction amount.

[0016] In a second aspect, the present application provides a battery power prediction device, the device comprising:

[0017] A state of charge acquisition module, used to acquire the initial state of charge of the battery assembly when detecting that the battery assembly of the vehicle is switched to a power-on state;

[0018] a first data processing module, configured to determine a confidence level of the initial state of charge;

[0019] A second data processing module, used for determining a charge correction amount and a gain value of a Lumberg observer according to the confidence level;

[0020] a state of charge correction module, configured to correct the initial state of charge based on the charge correction amount to obtain a target state of charge;

[0021] The remaining power prediction module is used to predict the remaining power of the battery of the vehicle during driving by using a Lumberg observer based on the target state of charge and the gain value.

[0022] In a third aspect, the present application provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the battery charge prediction method described in any embodiment of the present application.

[0023] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the battery power prediction method described in any embodiment of the present application when executed.

[0024] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the battery power prediction method described in any embodiment of the present application.

[0025] In order to solve the defects of the prior art in the background technology, an embodiment of the present application provides a method for predicting battery power. Execution of the method can bring the following beneficial effects: from the perspective of the cause of the initial state of charge error, the present application determines the confidence level of the initial state of charge during the power-on initialization process through a pre-set confidence judgment condition, thereby improving the estimation accuracy of the state of charge; in addition, by adding a correction module for the initial state of charge and adjusting the Lumberg correction gain combination method, the charge correction amount and gain value are determined based on the confidence level, which can achieve a simple hardware structure and accelerate the convergence speed of the observer, and accurately predict the remaining battery power of the vehicle during driving.

[0026] It should be noted that the above computer instructions may be stored in whole or in part on a computer-readable storage medium. The computer-readable storage medium may be packaged together with the processor of the battery power prediction device, or may be packaged separately from the processor of the battery power prediction device, which is not limited in this application.

[0027] The description of the second, third, ..., and fifth aspects in this application can refer to the detailed description of the first aspect; and the beneficial effects of the description of the second, third, ..., and fifth aspects can refer to the beneficial effect analysis of the first aspect, which will not be repeated here.

[0028] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description.

[0029] It is understandable that before using the technical solutions disclosed in the embodiments of this application, the type, scope of use, and usage scenarios of the personal information involved in this application should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0031] Figure 1 A schematic diagram of a first process flow of a method for predicting battery power provided in an embodiment of the present application;

[0032] Figure 2 A schematic diagram of an environmental impact constant data table provided in an embodiment of the present application;

[0033] Figure 3 A schematic diagram of an architecture for predicting the remaining battery power based on a Lumberg observer provided in an embodiment of the present application;

[0034] Figure 4 A second flow chart of a method for predicting battery power provided in an embodiment of the present application;

[0035] Figure 5 A schematic diagram of the structure of a battery power prediction device provided in an embodiment of the present application;

[0036] Figure 6The block diagram is a block diagram of an electronic device used to implement a battery power prediction method according to an embodiment of the present application. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0038] It should be noted that the terms "first", "second", "target", and "original" in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including", "having", and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0039] Figure 1 This is a first flow chart of a method for predicting battery power provided in an embodiment of the present application. This embodiment is applicable to the situation where the remaining power of a battery assembly is predicted during the driving process of a new energy vehicle. The method for predicting battery power provided in this embodiment can be executed by a battery power prediction device provided in an embodiment of the present application. The device can be implemented in software and / or hardware and integrated in an electronic device that executes the method. Preferably, the electronic device in the embodiment of the present application can be a new energy vehicle that is completely or mainly driven by new energy. The executor of the method of the present application is the vehicle control unit (VCU) of the new energy vehicle.

[0040] See also Figure 1 The method of this embodiment includes but is not limited to the following steps:

[0041] S110. When it is detected that the battery assembly of the vehicle is switched to a power-on state, an initial state of charge of the battery assembly is obtained.

[0042] The vehicle in this embodiment is a new energy vehicle, such as a pure electric vehicle, a hybrid electric vehicle, a plug-in hybrid electric vehicle, and a fuel cell vehicle. A battery assembly refers to a combination of one or more parts that constitute a battery system. It can be a complete battery unit or a collection of multiple battery units and related auxiliary components. The state of charge is a key parameter used to describe the amount of power remaining in the battery. It represents the ratio of the remaining power of the battery to the rated capacity of the battery, usually expressed as a percentage.

[0043] In an embodiment of the present application, some optional ways for the VCU to detect whether the battery assembly is switched from the power-off state to the power-on state may be: the first way, when the vehicle starts, the VCU can monitor the status of the high-voltage battery main negative relay, the high-voltage battery non-power pre-charge relay, the high-voltage battery power pre-charge relay, and the high-voltage battery main positive relay. When the VCU detects that these relays are closed in a predetermined order and the feedback status signal is normal, it can be determined that the battery assembly has been switched to the power-on state. The second way is that the BMS will send signals such as battery bus voltage and normal battery status to the VCU after initialization is completed and the battery assembly is powered on. After the VCU receives these signals and confirms that they meet the power-on requirements, it can be known that the battery assembly has been switched to the power-on state. The third way is that when the ignition switch is turned on and turned to the ON position, the vehicle controller will start a series of power-on operations and detections after receiving the signal. If it is subsequently detected that the various parameters are normal and the vehicle can enter the corresponding working state normally, it indicates that the battery assembly has been successfully switched to the power-on state.

[0044] The VCU and BMS usually communicate through the Controller Area Network (CAN) bus and other communication methods. The BMS monitors various parameters of the battery pack in real time, including the state of charge. When the battery pack of the vehicle is detected to be switched to the power-on state, the VCU can request the initial state of charge of the battery from the BMS according to the pre-defined CAN communication protocol. The BMS will calculate the initial state of charge (denoted as ) information is fed back to VCU.

[0045] S120: Determine a confidence level of the initial state of charge.

[0046] Specifically, determining the confidence level of the initial state of charge includes: obtaining self-discharge data of the battery assembly during the current parking period; determining the battery performance of the battery assembly; and determining the confidence level based on the self-discharge data and the battery performance.

[0047] Among them, the confidence level of the initial state of charge is a quantitative evaluation indicator of the accuracy of the battery state of charge estimation. It indicates the degree of credibility of the acquired state of charge. The higher the level, the higher the accuracy of the state of charge estimation. Self-discharge data refers to the data of natural power loss caused by various physical and chemical processes inside the battery when the battery is not connected to an external load (i.e. in an open circuit state). Battery performance is used to characterize the battery's ability in energy storage, release, and service life.

[0048] The VCU obtains the self-discharge data of the battery assembly during the parking period. In one embodiment, if the BMS has the function of directly measuring or estimating the battery self-discharge data, the VCU can directly read the self-discharge data of the battery assembly during the parking period from the BMS through communication methods such as the CAN bus.

[0049] In another embodiment, the BMS continuously monitors the voltage, current, temperature and other parameters of the battery assembly, and the VCU can obtain the battery voltage, current and other data at the parking time and the current power-on time from the BMS, and then combine the parking time of this parking to indirectly calculate the self-discharge of the battery during the parking period through a specific algorithm. For example, according to Ohm's law and the charge calculation formula, the change of charge is calculated by integrating the current over time, and then the self-discharge data is obtained.

[0050] The VCU obtains the capacity monitoring data of the battery module (such as the rated capacity of the battery, the current actual available capacity information), rate performance data (such as parameters such as voltage, current and temperature under different charge and discharge rates), and life data (such as the number of charge and discharge cycles, and the battery usage time) from the BMS, and analyzes these data to obtain the battery performance of the battery module.

[0051] A confidence level data table is prepared in advance to record the confidence level judgment criteria of the initial state of charge under different self-discharge data and different battery performance conditions. This confidence level data table can be stored in a preset storage unit. When the self-discharge data and battery performance of the battery assembly under the current situation are determined, the confidence level data table is retrieved from the preset storage unit, and then the table is looked up to obtain the confidence level of the initial state of charge under the current situation. Among them, the number of confidence levels can be determined according to the actual application situation, and the confidence levels can be optionally divided into three levels, such as: high level, medium level and low level.

[0052] S130: Determine a charge correction amount and a gain value of a Lumberg observer according to a confidence level.

[0053] Furthermore, before determining the charge correction amount according to the confidence level, it also includes: determining the environmental impact constant of the vehicle during the parking period, recorded as Specifically, the environmental impact constant is determined by the parking duration and ambient temperature during this parking period. Figure 2 The figure shows a schematic diagram of an environmental impact constant data table. An environmental impact constant data table can be prepared in advance to record environmental impact constants that can affect the state of charge under different parking durations and different ambient temperatures. When the parking duration and ambient temperature under the current situation are determined, the environmental impact constant of the vehicle during this parking period under the current situation is obtained by checking the environmental impact constant data table.

[0054] Accordingly, the charge correction amount is determined according to the confidence level, including: the first step, determining the charge correction coefficient according to the confidence level, denoted as a. If the confidence level of the initial charge state is higher, it means that there is no need to make too many corrections to the initial charge state, and the corresponding charge correction coefficient is smaller; if the confidence level of the initial charge state is low, it means that the deviation between the actual charge state and the charge state estimated by the BMS is large, and the corresponding charge correction coefficient is relatively high.

[0055] Exemplarily, when the confidence level of the initial charge state is high, the charge correction coefficient can be set to 0.5; when the confidence level of the initial charge state is medium, the charge correction coefficient can be set to 0.8; when the confidence level of the initial charge state is low, the charge correction coefficient can be set to 1, that is, all the table lookup values ​​are trusted.

[0056] The second step is to multiply the environmental impact constant and the charge correction coefficient to obtain the charge correction value, which is recorded as The calculation formula can be: .

[0057] In other words, the charge correction amount is determined based on the parking duration, ambient temperature, and confidence level of the initial charge state during this parking period.

[0058] For the existing Lumberg observer, if the initial charge state error is too large and exceeds the correction capability of the observer, the charge state cannot be corrected to an acceptable accuracy within one driving cycle. In extreme cases, the charge state reported by the BMS and the actual charge state will deviate too much, and the user will not receive a power shortage warning in advance, and the vehicle will suddenly lose power and stop in a dangerous situation. In addition to determining the charge correction amount according to the confidence level, the gain value of the Lumberg observer can also be determined according to the confidence level, denoted as k. Specifically, if the confidence level of the initial charge state is higher, maintaining a smaller gain coefficient can ensure a reasonable correction speed. If the confidence level of the initial charge state is lower, it means that the deviation between the actual charge state and the charge state estimated by the BMS is large. The gain coefficient is designed to be relatively high, which can ensure that the charge state converges faster.

[0059] S140: Correct the initial state of charge based on the charge correction amount to obtain a target state of charge.

[0060] In the embodiment of the present application, the initial state of charge and the charge correction amount are subtracted, and the result of the operation is determined as the target state of charge, recorded as soc, and the calculation formula is: .

[0061] S150: Predict the remaining battery power of the vehicle during driving using a Lumberg observer based on the target state of charge and the gain value.

[0062] like Figure 3 The figure shows a schematic diagram of the architecture for predicting the remaining battery capacity based on the Lumberg observer. The Lumberg observer is a method that uses a battery mathematical model (such as an equivalent circuit model, including parameters such as battery internal resistance and capacitance) and measurable parameters during vehicle driving (such as battery voltage, current, vehicle speed, etc.) to estimate the remaining battery capacity through continuous iterative calculations. In this process, the gain value is used to correct the deviation between the model prediction value and the actual measurement value, and optimize the prediction process of the remaining battery capacity, thereby making the predicted remaining battery capacity more accurate. If the gain value is set to a larger value, the Lumberg observer will respond faster to changes in the battery state, but may be more sensitive to measurement noise; conversely, a smaller gain value will make the Lumberg observer more stable, but the response speed may be slower.

[0063] In the embodiment of the present application, the VCU collects real-time data of the battery through a sensor network, including at least battery voltage, current, and temperature. These data are input to the Lumberg observer and are used to update the predicted state. For example, a high-precision current sensor measures the discharge current of the battery in real time, a voltage sensor measures the voltage across the battery, and a temperature sensor obtains the operating temperature of the battery.

[0064] In each time step, the VCU uses the Lumberg observer to iteratively calculate the collected real-time data and the pre-established battery mathematical model based on the target state of charge and gain value. The VCU outputs the remaining battery power information predicted by the Lumberg observer to the vehicle's instrument display system for the driver's reference. It can also provide this information to other control systems of the vehicle, such as the energy management system, to optimize the vehicle's power distribution and energy recovery strategy. For example, the predicted remaining power percentage and remaining mileage are displayed on the vehicle's dashboard to help the driver arrange his trip reasonably.

[0065] The technical solution provided in this embodiment, when it is detected that the battery assembly of the vehicle is switched to the power-on state, obtains the initial state of charge of the battery assembly; determines the confidence level of the initial state of charge; determines the charge correction amount and the gain value of the Lumberg observer according to the confidence level; corrects the initial state of charge based on the charge correction amount to obtain the target state of charge; and uses the Lumberg observer to predict the remaining battery power of the vehicle during driving based on the target state of charge and the gain value. From the perspective of the cause of the initial state of charge error, this application determines the confidence level of the initial state of charge during the power-on initialization process through a pre-set confidence judgment condition, so as to improve the estimation accuracy of the state of charge; in addition, by adding a correction module for the initial state of charge and adjusting the Lumberg correction gain, the charge correction amount and the gain value are determined based on the confidence level, a simple hardware structure can be achieved and the convergence speed of the Lumberg observer can be accelerated, and the remaining battery power of the vehicle during driving can be accurately predicted.

[0066] The following further describes the battery power prediction method provided by the embodiment of the present application. Figure 4 A second flow chart of a method for predicting battery power provided in an embodiment of the present application. The embodiment of the present application is optimized on the basis of the above embodiments, and the specific optimization is as follows: the present embodiment explains in detail the process of determining the confidence level of the initial state of charge.

[0067] See also Figure 4 The method of this embodiment includes but is not limited to the following steps:

[0068] S210: Obtain the parking time between the current power-on time and the last power-off time of the vehicle, and obtain the ambient temperature of the vehicle during the current parking period.

[0069] Through experimental research and analysis, the error of the initial state of charge is mainly caused by two historical conditions: when the vehicle is parked for a long time, the battery will have obvious self-discharge phenomenon, and the battery will be in the state of charge-open circuit voltage (SOC-OCV) plateau period for a long time. Among them, steps S210-S220 explain the self-discharge phenomenon, and steps S230-S250 explain the SOC-OCV plateau period.

[0070] In the embodiment of the present application, self-discharge data refers to the data of natural power loss caused by various physical and chemical processes inside the battery when the battery is not connected to an external load (i.e., in an open circuit state). The self-discharge data is related to the parking time and ambient temperature during the current parking period of the vehicle. The VCU can obtain the vehicle's current power-on time and the last power-off time through the CAN bus, and subtract the two times to obtain the vehicle's parking time; as well as the ambient temperature of the vehicle during the current parking period.

[0071] S220: Calculate self-discharge data based on the parking time and the ambient temperature.

[0072] In the embodiment of the present application, the self-discharge rate of the battery at different temperatures can be determined in advance through experiments to obtain a curve relationship between the self-discharge rate and the temperature. The self-discharge characteristics of different types of batteries (such as lead-acid batteries, lithium-ion batteries, nickel-metal hydride batteries, etc.) can be considered.

[0073] When the vehicle starts to park, the BMS reads or measures the initial charge of the battery pack; the temperature sensor records the temperature change during the parking period and calculates the average temperature during the parking period. Based on the predetermined curve relationship between the self-discharge rate and temperature and the parking time, the self-discharge rate at the average temperature is calculated using a preset calculation formula, and then the self-discharge amount is calculated based on the initial charge and the self-discharge rate.

[0074] S230: Determine whether the battery assembly has been effectively corrected within a preset time period before the current time.

[0075] In the embodiment of the present application, when the battery is in the SOC-OCV plateau period for a long time, the system error will gradually accumulate in this stage. Since the state of charge is an estimated value, it is impossible to accurately evaluate whether it is used in the plateau period. On the actual vehicle, it can only be confirmed whether there is a system error accumulated by whether an effective correction has not been made for a long time; the definition of effective correction depends on the design of the BMS system, and can include at least one of the charging end correction, the discharging end correction and the initial state of charge correction.

[0076] When the VCU determines that the battery assembly has been effectively corrected within the preset time before the current time, step S240 is executed; when the VCU determines that the battery assembly has not been effectively corrected within the preset time before the current time, step S250 is executed.

[0077] S240: If the correction is effective, it is determined that the battery performance of the battery assembly is not in the SOC-OCV plateau period.

[0078] S250: If it is not effectively corrected, it is determined that the battery performance of the battery assembly is in the SOC-OCV plateau period.

[0079] S260: Determine whether the self-discharge data meets the first data standard, and determine whether the battery performance meets the second data standard.

[0080] In the embodiment of the present application, the optional first data standard may be the self-discharge amount corresponding to the vehicle parking time exceeding Y days obtained by experiment, and the second data standard may be that the battery assembly is in the SOC-OCV plateau period for more than a preset time, such as X days. The confidence level of the initial state of charge can be determined by looking up the following Table 1:

[0081] Table 1 Confidence levels of initial state of charge

[0082]

[0083] S270: If both the first data standard and the second data standard are satisfied, determine the confidence level as a low level.

[0084] S280: If the first data standard is met and the second data standard is not met, or if the first data standard is not met and the second data standard is met, determine the confidence level as a medium level.

[0085] S290: If both the first data standard and the second data standard are not met, determine the confidence level as a high level.

[0086] The technical solution provided in this embodiment obtains the ambient temperature of the vehicle during the current parking period by obtaining the parking time between the current power-on time and the last power-off time of the vehicle; calculates the self-discharge data based on the parking time and the ambient temperature; determines whether the battery assembly has been effectively corrected within a preset time before the current time; if it has been effectively corrected, determines that the battery performance of the battery assembly is not in the SOC-OCV plateau period; if it has not been effectively corrected, determines that the battery performance of the battery assembly is in the SOC-OCV plateau period; determines whether the self-discharge data meets the first data standard, and determines whether the battery performance meets the second data standard; if both the first data standard and the second data standard are met, determines the confidence level as a low level; if the first data standard is met and the second data standard is not met, or the first data standard is not met and the second data standard is met, determines the confidence level as a medium level; if both the first data standard and the second data standard are not met, determines the confidence level as a high level.

[0087] The present application takes into account the self-discharge phenomenon caused by long-term parking of the vehicle and the impact of the battery being in the SOC-OCV plateau period for a long time on the battery state of charge. The self-discharge data is calculated by the parking time and ambient temperature during the parking of the vehicle. Whether the battery performance is in the SOC-OCV plateau period is determined by judging whether the battery assembly has been effectively corrected before. Then, whether the self-discharge data meets the first data standard and whether the battery performance meets the second data standard are determined respectively. Finally, the confidence level of the initial state of charge during the power-on initialization process is obtained by looking up a table. In this way, the estimation accuracy of the state of charge can be improved, and the remaining battery power of the vehicle during driving can be accurately predicted.

[0088] Figure 5 A schematic diagram of the structure of a battery power prediction device provided in an embodiment of the present application is shown in FIG. Figure 5 As shown, the apparatus 500 may include:

[0089] The state of charge acquisition module 510 is used to acquire the initial state of charge of the battery assembly when it is detected that the battery assembly of the vehicle is switched to the power-on state;

[0090] A first data processing module 520, configured to determine a confidence level of the initial state of charge;

[0091] A second data processing module 530, configured to determine a charge correction amount and a gain value of a Lumberg observer according to the confidence level;

[0092] A state of charge correction module 540, configured to correct the initial state of charge based on the charge correction amount to obtain a target state of charge;

[0093] The remaining power prediction module 550 is used to predict the remaining power of the battery of the vehicle during driving by using a Lumberg observer based on the target state of charge and the gain value.

[0094] Furthermore, the first data processing module 520 may be specifically used to: obtain the self-discharge data of the battery assembly during the parking period; determine the battery performance of the battery assembly; and determine the confidence level based on the self-discharge data and the battery performance.

[0095] Furthermore, the first data processing module 520 can also be used to: obtain the parking time between the current power-on time and the last power-off time of the vehicle; obtain the ambient temperature of the vehicle during the current parking period; and calculate the self-discharge data based on the parking time and the ambient temperature.

[0096] Furthermore, the above-mentioned first data processing module 520 can also be specifically used to: determine whether the battery assembly has been effectively corrected within a preset time period before the current time, and the effective correction includes at least one of the charging end correction, the discharging end correction and the initial state of charge correction; if it has been effectively corrected, it is determined that the battery performance of the battery assembly is not in the state of charge-open circuit voltage SOC-OCV plateau period; if it has not been effectively corrected, it is determined that the battery performance of the battery assembly is in the SOC-OCV plateau period.

[0097] Furthermore, the above-mentioned first data processing module 520 can also be specifically used to: determine whether the self-discharge data meets the first data standard, determine whether the battery performance meets the second data standard, the second data standard being that the battery assembly is in the SOC-OCV plateau period for more than a preset time; if both the first data standard and the second data standard are met, the confidence level is determined to be a low level; if the first data standard is met and the second data standard is not met, or the first data standard is not met and the second data standard is met, the confidence level is determined to be a medium level; if both the first data standard and the second data standard are not met, the confidence level is determined to be a high level.

[0098] Furthermore, the above-mentioned second data processing module 530 can also be specifically used for: before determining the charge correction amount according to the confidence level, determining the environmental impact constant of the vehicle during this parking period, the environmental impact constant being determined by the parking time and the ambient temperature during this parking period; determining the charge correction coefficient according to the confidence level, the higher the confidence level, the smaller the corresponding charge correction coefficient; and performing a product operation on the environmental impact constant and the charge correction coefficient to obtain the charge correction amount.

[0099] The battery power prediction device provided in this embodiment can be applied to the battery power prediction method provided in any of the above embodiments, and has corresponding functions and beneficial effects.

[0100] Figure 6 It is a block diagram of an electronic device for implementing a method for predicting battery power in an embodiment of the present application. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0101] like Figure 6 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0102] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0103] The processor 11 may be a variety of general and / or dedicated processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a method for predicting battery power.

[0104] In some embodiments, the battery power prediction method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the battery power prediction method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the battery power prediction method in any other appropriate manner (e.g., by means of firmware).

[0105] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0106] The computer programs for implementing the methods of the present application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer programs are executed by the processor, the functions / operations specified in the flow charts and / or block diagrams are implemented. The computer programs may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0107] In the context of the present application, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or apparatus. A computer-readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media may include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0108] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0109] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server) or a computing system that includes middleware components (e.g., an application server) or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein) or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0110] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0111] Note that the above are only preferred embodiments of the present application and the technical principles used. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and that various obvious changes, readjustments, and substitutions can be made by those skilled in the art without departing from the scope of protection of the present application. For example, those skilled in the art can use the various forms of processes shown above to reorder, add, or delete steps; the steps recorded in the present application can be performed in parallel, sequentially, or in different orders, as long as the desired results of the technical solution of the present application can be achieved, and this document does not limit them here.

[0112] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A method for predicting battery power, characterized in that: The method comprises: When detecting that the battery assembly of the vehicle is switched to a power-on state, obtaining an initial charge state of the battery assembly; Determining a confidence level of the initial state of charge, wherein the confidence level is used to represent the confidence level corresponding to the initial state of charge under the current self-discharge data of the battery assembly and the battery performance, wherein the battery performance is whether the battery assembly is in a SOC-OCV plateau period; Determining a charge correction amount and a gain value of a Lumberg observer according to the confidence level; Correcting the initial state of charge based on the charge correction amount to obtain a target state of charge; The remaining battery power of the vehicle during driving is predicted using a Romberg observer based on the target state of charge and the gain value.

2. The method for predicting battery power according to claim 1, characterized in that: Determining a confidence level of the initial state of charge includes: Obtaining self-discharge data of the battery assembly during this parking period; determining a battery performance of the battery assembly; The confidence level is determined based on the self-discharge data and the battery performance.

3. The method for predicting battery power according to claim 2, characterized in that: Obtaining the self-discharge data of the battery assembly during the parking period, including: Obtain the parking time between the current power-on time and the last power-off time of the vehicle; Obtaining the ambient temperature of the vehicle during the current parking period; The self-discharge data is calculated based on the parking time and the ambient temperature.

4. The method for predicting battery power according to claim 2, characterized in that: Determining a battery performance of the battery assembly, including: Determining whether the battery assembly has been effectively corrected within a preset time period before the current time, the effective correction comprising at least one of a charging end correction, a discharging end correction, and an initial state of charge correction; If the correction is effective, it is determined that the battery performance of the battery assembly is not in the state of charge-open circuit voltage SOC-OCV plateau period; If it is not effectively corrected, it is determined that the battery performance of the battery assembly is in the SOC-OCV plateau phase.

5. The method for predicting battery power according to claim 4, characterized in that: Determining the confidence level based on the self-discharge data and the battery performance includes: Determine whether the self-discharge data meets a first data standard, and determine whether the battery performance meets a second data standard, wherein the second data standard is that the battery assembly is in a SOC-OCV plateau period for more than a preset time; If both the first data standard and the second data standard are satisfied, determining the confidence level as a low level; If the first data standard is satisfied and the second data standard is not satisfied, or if the first data standard is not satisfied and the second data standard is satisfied, determining the confidence level as a medium level; If neither the first data criterion nor the second data criterion is satisfied, the confidence level is determined to be a high level.

6. The method for predicting battery power according to claim 1, characterized in that: Before determining the charge correction amount according to the confidence level, the method further includes: Determining an environmental impact constant of the vehicle during the current parking period, wherein the environmental impact constant is determined by the parking duration and the ambient temperature during the current parking period; Accordingly, determining the charge correction amount according to the confidence level includes: Determining a charge correction coefficient according to the confidence level, wherein the higher the confidence level, the smaller the corresponding charge correction coefficient; The charge correction amount is obtained by multiplying the environmental impact constant and the charge correction coefficient.

7. A battery power prediction device, characterized in that: The device comprises: A charge state acquisition module, used to acquire the initial charge state of the battery assembly when detecting that the battery assembly of the vehicle is switched to a power-on state; a first data processing module, configured to determine a confidence level of the initial state of charge, wherein the confidence level is used to indicate a confidence level corresponding to the initial state of charge under the current self-discharge data of the battery assembly and battery performance, wherein the battery performance is whether the battery assembly is in a SOC-OCV plateau period; A second data processing module, used for determining a charge correction amount and a gain value of a Lumberg observer according to the confidence level; a state of charge correction module, configured to correct the initial state of charge based on the charge correction amount to obtain a target state of charge; The remaining power prediction module is used to predict the remaining power of the battery of the vehicle during driving by using a Lumberg observer based on the target state of charge and the gain value.

8. A vehicle, characterized in that: The vehicle comprises: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores a computer program executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the battery power prediction method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the battery power prediction method according to any one of claims 1 to 6 when executed. 10 . A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method for predicting battery power according to claim 1 .

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