Battery endurance estimation method, device, equipment, storage medium and program product

Abstract

The application provides a battery endurance estimation method, device, equipment, storage medium and program product. It relates to the technical field of batteries. The method comprises: obtaining battery parameters of a battery on a power-consuming device, determining battery residual energy based on the battery parameters; obtaining working parameters corresponding to the power-consuming device under the current working condition, the working parameters comprising instantaneous power; and estimating the first endurance time of the battery based on the battery residual energy and the working parameters. In the application, the first endurance time is estimated based on the battery residual energy and the instantaneous power under the current working condition. The battery residual energy can more accurately reflect the residual available energy of the battery, and the instantaneous power can reflect the power consumption of the power-consuming device. Therefore, the estimation of the battery endurance based on the battery residual energy and the instantaneous power can improve the accuracy of the battery endurance time.

Description

Technical Field

[0001] This application relates to the field of battery technology, and more specifically, to a battery range estimation method, apparatus, device, storage medium, and program product. Background Technology

[0002] Battery life measures the length of time an electrical device can continue to operate after a single charge. It reflects the duration for which the device uses battery power, that is, the time the device can operate normally without an external power supply.

[0003] Currently, most methods for estimating the battery life of battery-powered devices rely on the average value of historical power consumption, resulting in low accuracy in these estimations. Summary of the Invention

[0004] The purpose of this application is to provide a battery life estimation method, apparatus, device, storage medium, and program product to improve the accuracy of battery life estimation.

[0005] In a first aspect, embodiments of this application provide a battery life estimation method, including:

[0006] Obtain the battery parameters of the battery on the electrical device, and determine the remaining energy of the battery based on the battery parameters;

[0007] Obtain the operating parameters of the electrical equipment under the current operating conditions. The operating parameters include real-time power.

[0008] The initial battery life is estimated based on the remaining battery energy and operating parameters.

[0009] The embodiments of this application estimate the first battery life based on the battery's remaining energy and the instantaneous power under the current operating conditions. The remaining battery energy can more accurately reflect the battery's remaining usable power, and the instantaneous power can reflect the power consumption of the electrical device. Therefore, estimating the battery life based on the remaining battery energy and instantaneous power can improve the accuracy of the battery life.

[0010] In any embodiment, the battery parameters include the battery's voltage-state-of-charge curve; determining the remaining energy of the battery based on the battery parameters includes:

[0011] The remaining energy of the battery is obtained by integrating the battery voltage based on the voltage-state-of-charge curve.

[0012] In this embodiment, since the relationship between the remaining energy of the battery and the state of charge is not a simple linear one, but rather the voltage changes synchronously with the change of the state of charge, the change of the remaining energy of the battery is nonlinear. Therefore, by integrating the battery voltage based on the voltage-state of charge relationship curve, the remaining energy of the battery can be calculated more accurately.

[0013] In any embodiment, the battery parameters also include battery health status and battery rated capacity;

[0014] The remaining energy of the battery is obtained by integrating the battery voltage based on the voltage-state-of-charge curve, including:

[0015] The voltage-state-of-charge curve is corrected based on the battery health status and the battery rated capacity to obtain the corrected curve.

[0016] The remaining energy of the battery is obtained by integrating the battery voltage based on the corrected curve.

[0017] In this embodiment of the application, considering that the battery will gradually age and its performance will gradually decline during use, the voltage-state of charge relationship curve is corrected by the battery health status and the battery rated capacity to improve the accuracy of the remaining battery energy estimation.

[0018] In any embodiment, the battery parameters further include battery temperature; integrating the battery voltage based on the voltage-state-of-charge curve to obtain the remaining battery energy includes:

[0019] Determine the temperature compensation factor based on the battery temperature.

[0020] The voltage-state-of-charge curve is corrected based on the temperature compensation factor to obtain the corrected curve.

[0021] The remaining energy of the battery is obtained by integrating the battery voltage based on the corrected curve.

[0022] In this embodiment, since battery temperature affects its internal chemical reaction rate and internal resistance, it affects the battery's voltage-state-of-charge curve. Therefore, the battery's voltage-state-of-charge curve will shift at different temperatures. By correcting these shifts, the remaining energy of the battery can be estimated more accurately.

[0023] In any embodiment, battery parameters include battery state of charge, battery state of health, battery voltage, and battery temperature; determining the remaining battery energy based on these parameters includes:

[0024] The battery's state of charge, state of health, voltage, and temperature are input into the battery energy prediction model to obtain the remaining battery energy. The battery energy prediction model is pre-trained based on training samples, which include historical battery parameters and their corresponding remaining battery energy.

[0025] This application incorporates a machine learning model, which can be used to quickly and accurately obtain the remaining battery energy.

[0026] In any embodiment, estimating the first battery life based on the remaining battery energy and operating parameters includes:

[0027] For electrical equipment in the initial startup phase, the ratio of remaining battery energy to instantaneous battery power is used as the first driving range; where instantaneous power is pre-calibrated or predicted based on the startup time and historical operating conditions of the electrical equipment.

[0028] In this embodiment of the application, when the electrical equipment is in the initial stage of startup, the first battery life is estimated for the electrical equipment based on the pre-calibrated power or the power predicted based on the user's behavior habits, so as to plan the power consumption and charging schedule in advance.

[0029] In any embodiment, the operating parameters further include load resistance and cell internal resistance; estimating the battery's first driving range based on the remaining battery energy and operating parameters includes:

[0030] When the electrical equipment is in operation, the energy that the battery can perform external work is determined based on the remaining energy of the battery, the load resistance, and the internal resistance of the battery cell.

[0031] The initial driving range is estimated based on the energy the battery can perform externally and its instantaneous power.

[0032] The embodiments of this application estimate the first battery life based on the real-time power corresponding to the real-time operating conditions of the electrical equipment, thereby improving the accuracy of the first battery life estimation.

[0033] In any embodiment, the method further includes:

[0034] If the first battery life is longer than the first alarm duration and shorter than the second alarm duration, then the first alarm message is issued and the new load command is refused to be executed; the first alarm duration is shorter than the second alarm duration.

[0035] If the first battery life is less than the first alarm duration, a second alarm message will be issued, and the current working state will be exited, entering a low-load mode.

[0036] This application embodiment reduces the risk of sudden shutdown of electrical equipment due to depleted power by implementing tiered alarms based on a first alarm duration and a second alarm duration. Furthermore, in scenarios where tasks are being performed, it reduces the risk of task failure or data loss due to insufficient power. With sufficient power, the equipment can continuously and stably perform tasks, improving task completion rate and efficiency. When power is insufficient, by promptly exiting the current working state and entering a low-load mode, enough power can be reserved to complete more important tasks or allow the equipment to safely return to a charging station.

[0037] In any embodiment, the first alarm duration and the second alarm duration are determined based on the operating conditions of the electrical equipment; or, based on the instantaneous power, current and / or moving speed of the electrical equipment.

[0038] The embodiments of this application determine the first alarm duration and the second alarm duration based on the operating conditions or real-time power, which can more intelligently allocate equipment resources, maintain efficient operation when the power is sufficient, and take timely protective measures when the battery is low to reduce excessive power consumption.

[0039] In any embodiment, the method further includes:

[0040] Obtain the average power consumption of the electrical equipment from startup to the current moment;

[0041] The second driving range of the battery is estimated based on the average power and the remaining battery energy.

[0042] The embodiments of this application estimate the second battery life based on average power and remaining battery energy, which helps to more accurately grasp the power consumption trend of electrical devices and thus make more reasonable power management decisions.

[0043] In any embodiment, the method further includes:

[0044] The duration of the third alarm is determined based on the average power.

[0045] If the second battery life is less than the third alarm duration, then a third alarm message will be issued.

[0046] In this embodiment, the average power changes accordingly with the changes in equipment operating conditions or load, thereby dynamically adjusting the alarm threshold and making the alarm more accurate and targeted.

[0047] Secondly, embodiments of this application provide a battery life estimation device, comprising:

[0048] The energy calculation module is used to obtain the battery parameters of the battery on the electrical equipment and determine the remaining energy of the battery based on the battery parameters;

[0049] The working parameter acquisition module is used to acquire the working parameters of the electrical equipment under the current operating conditions. The working parameters include real-time power.

[0050] The range estimation module is used to estimate the initial battery range based on the remaining battery energy and operating parameters.

[0051] Thirdly, embodiments of this application provide an electrical device, including: a processor, a memory, and a bus, wherein...

[0052] The processor and the memory communicate with each other via a bus;

[0053] The memory stores program instructions that can be executed by the processor, and the processor can execute the method of the first aspect by calling the program instructions.

[0054] Fourthly, embodiments of this application provide a non-transitory computer-readable storage medium, comprising:

[0055] A non-transitory computer-readable storage medium stores computer instructions that cause the computer to perform the method of the first aspect.

[0056] Fifthly, embodiments of this application provide a computer program product, including computer program instructions, which, when read and executed by a processor, perform the method of the first aspect.

[0057] Other features and advantages of this application will be set forth in the following description and will be apparent in part from the description or may be learned by practicing embodiments of this application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0058] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 This is a schematic flowchart of a battery range estimation method provided in an embodiment of this application;

[0060] Figure 2 This is a voltage-state-of-charge curve of a ternary battery cell after the first number of charge-discharge cycles provided in an embodiment of this application.

[0061] Figure 3This is a voltage-state-of-charge curve of a ternary battery cell after the second charge-discharge cycle, provided in an embodiment of this application.

[0062] Figure 4 A schematic diagram of another battery life estimation method provided in this application embodiment;

[0063] Figure 5 This is a schematic diagram of a battery range estimation device provided in an embodiment of this application;

[0064] Figure 6 This is a schematic diagram of the physical structure of the electrical equipment provided in the embodiments of this application. Detailed Implementation

[0065] The embodiments of the technical solution of this application will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of this application and are therefore merely examples, and should not be used to limit the scope of protection of this application.

[0066] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0067] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0068] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0069] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0070] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0071] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0072] Batteries can include power batteries and energy storage batteries. Taking power batteries as an example, they provide power to electrical equipment, enabling the equipment to operate or perform tasks. With the rapid development of new energy batteries, the applications of power batteries are becoming increasingly widespread, for example, in automobiles, drones, and service robots.

[0073] To ensure the safe and stable operation of electrical equipment and reduce the safety risks caused by battery depletion, it is necessary to estimate the remaining battery life of the equipment to determine whether it can complete the user's task. Currently, battery life estimation methods combine the power consumption of the equipment under various operating conditions to calculate the average power consumption, and then estimate the remaining battery life based on this average consumption. However, the power consumption of electrical equipment varies significantly under different operating conditions, resulting in low accuracy when using average power consumption for estimation. For example, for mobile service robots, power consumption is highly uneven during service, with significant differences in power consumption under different operating conditions. For instance, scenarios involving voice dialogue with the robot have relatively low power consumption, while scenarios involving the robot lifting heavy objects have relatively high power consumption. Similar to digital products, robot products also display the remaining battery percentage to users, allowing them to determine their usage method based on the remaining battery level. However, if users cannot reasonably estimate the remaining battery life based on the remaining battery power, and under certain working conditions (such as helping someone move, lifting heavy objects, hiking, etc.), a sudden lack of battery power may pose a safety risk.

[0074] To address this technical problem, embodiments of this application provide a battery life estimation method, apparatus, device, storage medium, and program product. Based on battery parameters, the remaining battery energy is determined, and based on the remaining battery energy and the operating parameters of the electrical device under current conditions, a first battery life duration is estimated, improving the accuracy of battery life estimation.

[0075] It is understood that the electrical equipment in the embodiments of this application can be any device powered by a battery, such as a mobile service robot, an industrial robot, a vehicle, etc. The embodiments of this application will be described below using a mobile service robot as an example.

[0076] The execution entity of this method can be a battery management system, a processor or controller of the electrical device, etc. If the execution entity is the processor or controller of the electrical device, then the processor or controller is connected to the battery management system and can obtain relevant battery parameters. Alternatively, the execution entity of this method can also be a cloud-based management platform that is communicatively connected to the electrical device. For ease of description, this application embodiment uses the controller of the electrical device as the execution entity. Figure 1 This is a schematic flowchart of a battery range estimation method provided in an embodiment of this application, as shown below. Figure 1 As shown, the method includes:

[0077] Step 101: Obtain the battery parameters of the battery on the electrical device, and determine the remaining energy of the battery based on the battery parameters.

[0078] Step 102: Obtain the operating parameters of the electrical equipment under the current operating conditions. The operating parameters include the instantaneous power.

[0079] Step 103: Estimate the battery's first operating time based on the remaining battery energy and operating parameters.

[0080] In step 101, battery parameters are various indicators describing battery performance and characteristics. These can be real-time data, such as current and voltage at the current moment; or parameters obtained after accumulated operation, such as the average voltage and average current of the device from the start of use to the current moment; or pre-generated standard parameters for the battery, such as the voltage-state-of-charge curves of the battery under different health states. Battery parameters can be acquired through a Battery Management System (BMS), an electronic control system used to monitor and manage the operating status of rechargeable batteries (including individual cells and / or battery packs). The BMS integrates multiple sensors to monitor battery parameters such as voltage, current, and temperature in real time.

[0081] After acquiring the battery parameters, the controller calculates the battery's current remaining energy based on these parameters. Remaining battery energy refers to the energy the battery can perform work on. Remaining battery energy is different from the battery's state of charge (SOC).

[0082] Electrical equipment operates under different conditions, and these parameters can include instantaneous power, which refers to the power consumed by the equipment at the current moment. Instantaneous power can reflect the power consumption of the equipment to a certain extent. That is, the higher the instantaneous power, the more power the equipment consumes. Instantaneous power can be obtained by detecting it using a dedicated power detection device on the equipment, or by calculating it by measuring parameters such as instantaneous voltage and instantaneous current. Alternatively, the correspondence between operating conditions and power can be built into the equipment. Knowing the current operating condition, the instantaneous power can be determined using the pre-stored correspondence.

[0083] After obtaining the remaining battery energy and the corresponding operating parameters of the electrical equipment under the current operating conditions, the first battery life is estimated based on the remaining battery energy and operating parameters, so as to estimate how much longer the electrical equipment can work.

[0084] This application embodiment estimates the first battery life by using the remaining battery energy of the device and the instantaneous power under the current operating conditions. The remaining battery energy can more accurately reflect the remaining working capacity of the battery, and the instantaneous power can reflect the power consumption of the device. Therefore, estimating the battery life based on the remaining battery energy and instantaneous power can improve the accuracy of the battery life.

[0085] Based on the above embodiments, the battery parameters include the battery voltage-state-of-charge curve. When determining the remaining energy of the battery based on the battery parameters, the following method can be used for calculation:

[0086] The remaining energy of the battery is obtained by integrating the battery voltage based on the voltage-state-of-charge curve.

[0087] In the specific implementation process, to improve the accuracy of battery remaining energy calculation, this application embodiment integrates the battery voltage based on the voltage-state-of-charge curve to obtain the battery remaining energy. The calculation formula is: Where W represents the remaining energy of the battery; V0 is the discharge cutoff voltage; V1 is the current voltage; ΔSOC is the SOC difference corresponding to a small segment intercepted from the voltage-state-of-charge curve; and V is the average voltage corresponding to ΔSOC. The voltage-state-of-charge curve can be a standard voltage-state-of-charge curve provided by the battery manufacturer, or it can be a voltage-state-of-charge curve simulated based on the battery's usage, obtaining voltage-state-of-charge curves corresponding to different cycles of charge and discharge.

[0088] This application embodiment integrates the battery voltage based on the voltage-state-of-charge curve to obtain the remaining battery energy, and then estimates the driving range based on the remaining battery energy. The principle is as follows:

[0089] Figure 2This is a voltage-state-of-charge curve of a ternary lithium battery cell provided in an embodiment of this application after the first number of charge-discharge cycles. Figure 3 The voltage-state-of-charge curve of a ternary lithium battery cell provided in this application embodiment after the second charge-discharge cycle is shown below. Figure 2 and Figure 3 As shown in the figure, the horizontal axis represents the State of Charge (SOC), and the vertical axis represents the voltage (V). The second charge-discharge cycle is greater than the first. As the SOC changes, the voltage also changes synchronously, and the voltage corresponding to the same SOC varies depending on the number of charge-discharge cycles. That is, the relationship between the battery's remaining energy and its SOC is non-linear; the change in remaining energy is greater than the change in SOC. Furthermore, battery lifespan degradation affects not only the SOC but also the battery's functional capacity. Therefore, the SOC alone cannot accurately reflect the battery's remaining energy. Instead, the remaining energy can be obtained by integrating the battery voltage using the voltage-SOC curve, as provided in the embodiments of this application.

[0090] In this embodiment, since the relationship between the remaining energy of the battery and the charge is not a simple linear one, but rather the voltage changes synchronously with the change in charge, and this change is nonlinear, the remaining energy of the battery can be calculated more accurately by integrating the battery voltage based on the voltage-state of charge curve.

[0091] Based on the above embodiments, the battery parameters also include battery health status and battery rated capacity; the remaining battery energy is obtained by integrating the battery voltage based on the voltage-state-of-charge relationship curve, including:

[0092] The voltage-state-of-charge curve is corrected based on the battery health status and the battery rated capacity to obtain the corrected curve.

[0093] The remaining energy of the battery is obtained by integrating the battery voltage based on the corrected curve.

[0094] In practical implementation, Battery Health / State of Health (SOH) is a quantitative expression of the degree of battery performance degradation, aiming to characterize the performance difference between the battery in its current state and its brand-new state. Battery rated capacity, on the other hand, refers to the amount of energy a battery can store and release; it is the time a battery can continuously supply current under specific load conditions when fully charged.

[0095] As batteries age, their internal structure and chemical composition change, leading to a gradual decline in performance, including reduced capacity, internal resistance, and voltage. The state of battery health affects both battery and voltage response characteristics. For example, when a battery is in poor health, its voltage may drop more rapidly, causing changes in the voltage-state-of-charge (VOC) curve. Therefore, by considering battery health, the VOC curve can be more accurately corrected to reflect the battery's current actual performance.

[0096] A battery's rated capacity represents the maximum energy it can store. Batteries with different rated capacities will exhibit different rates of voltage drop under the same discharge conditions. Batteries with larger rated capacities may have more stable voltage during discharge because they have more energy to sustain the process. Conversely, batteries with smaller rated capacities may experience a faster voltage drop during discharge. Therefore, by considering the battery's rated capacity, the voltage-state-of-charge (VOC) curve can be further adjusted to more accurately reflect the voltage changes of batteries with different rated capacities during discharge.

[0097] When correcting the voltage-state-of-charge curve using battery health status and battery rated capacity, the battery health status and battery rated capacity can be used as correction factors and multiplied by each point on the voltage-state-of-charge curve to obtain the corrected curve.

[0098] The formula for calculating the remaining energy of a battery is as follows:

[0099]

[0100] Where W is the remaining energy of the battery; V0 is the discharge cutoff voltage; V1 is the current voltage; and ΔSOC is the SOC difference corresponding to a small segment intercepted from the voltage-state-of-charge curve. ΔSOC represents the average voltage; Q represents the battery's rated capacity; and SOH represents the battery's health status.

[0101] It should be noted that in practical applications, voltage-state-of-charge curves for different battery health states can be generated in advance. In this way, it is no longer necessary to correct the voltage-state-of-charge curves, but the voltage-state-of-charge curves for the corresponding battery health states can be used directly for subsequent calculations.

[0102] In this embodiment of the application, considering that the battery will gradually age and its performance will gradually decline during use, the voltage-state of charge relationship curve is corrected by the battery health status and the battery rated capacity to improve the accuracy of the remaining battery energy estimation.

[0103] Based on the above embodiments, the remaining energy of the battery is obtained by integrating the battery voltage based on the voltage-state-of-charge curve, including:

[0104] Determine the temperature compensation factor based on the battery temperature.

[0105] The voltage-state-of-charge curve is corrected based on the temperature compensation factor to obtain the corrected curve.

[0106] The remaining energy of the battery is obtained by integrating the battery voltage based on the corrected curve.

[0107] In practice, battery temperature affects the rate and efficiency of internal chemical reactions. Low temperatures reduce the activity of electrode reactions, slowing down internal chemical reactions, decreasing the potential difference, and consequently lowering the battery voltage. Low temperatures also affect charging and discharging efficiency, leading to inaccurate State of Charge (SOC) estimates. High temperatures, on the other hand, make internal chemical reactions more active, generating higher voltages. However, high temperatures also accelerate battery aging and degradation, shortening battery life. Furthermore, high temperatures can cause thermal runaway within the battery, further impacting battery performance and safety.

[0108] The temperature compensation factor is a coefficient adjusted based on changes in battery temperature to correct for variations in battery voltage and state of charge (SOC) caused by temperature changes. Introducing the temperature compensation factor allows for more accurate estimation of the battery's SOC, thereby improving the accuracy and reliability of the battery management system. The temperature compensation factor can be calculated based on measured battery temperature, combined with battery characteristics and temperature compensation coefficients or curves provided by the battery manufacturer.

[0109] Specifically, the temperature compensation factor can be determined by the following method:

[0110] 1. Collect battery temperature data

[0111] A temperature sensor is used to monitor the battery temperature in real time and record battery performance data at different temperatures. This data can include battery voltage, current, capacity, etc.

[0112] 2. Analyze the battery temperature characteristics

[0113] Based on the collected battery temperature data, the changes in battery performance at different temperatures can be analyzed. This can be achieved by plotting curves showing how battery performance parameters change with temperature.

[0114] 3. Determine the temperature compensation coefficient

[0115] Based on the battery's temperature characteristic curve, determine the temperature compensation coefficient. This coefficient is typically a temperature-dependent function used to describe the degree to which battery performance parameters change with temperature.

[0116] Temperature compensation coefficients can be obtained through experimental measurements, empirical formulas, or data fitting. For example, linear interpolation can be used to calculate the compensation coefficients at various temperatures based on the battery's performance parameters at a standard temperature and measurement data at different temperatures.

[0117] 4. Calculate the temperature compensation factor

[0118] After determining the temperature compensation coefficient, the temperature compensation factor can be calculated based on the current battery temperature. The temperature compensation factor is typically a function relating the temperature compensation coefficient and the current temperature.

[0119] The temperature compensation factor at the current temperature can be obtained by multiplying the temperature compensation coefficient by the current temperature (or by calculating it according to a specific functional relationship).

[0120] After obtaining the temperature compensation factor, it can be used to adjust the voltage value or state of charge, so that the corrected curve is closer to the actual situation.

[0121] The remaining battery energy is obtained by integrating the battery voltage based on the corrected curve.

[0122] In this embodiment, since battery temperature affects its internal chemical reaction rate and internal resistance, it affects the battery's voltage-state-of-charge curve. Therefore, the battery's voltage-state-of-charge curve will shift at different temperatures. By correcting these shifts, the remaining energy of the battery can be estimated more accurately.

[0123] Based on the above embodiments, battery parameters include battery state of charge, battery state of health, battery voltage, and battery temperature; determining the remaining battery energy based on these parameters includes:

[0124] The battery's state of charge, state of health, voltage, and temperature are input into the battery energy prediction model to obtain the remaining battery energy. The battery energy prediction model is pre-trained based on training samples, which include historical battery parameters and their corresponding remaining battery energy.

[0125] In the specific implementation process, a battery energy prediction model can be pre-trained. Specifically, historical battery performance data can be collected beforehand, including the battery's SOC, SOH, voltage, temperature, and corresponding remaining energy. This data can come from experimental tests or actual operation records. After data collection, preprocessing operations such as cleaning, denoising, and normalization can be performed to improve data quality and consistency. State parameters that significantly influence battery remaining energy prediction are selected from the preprocessed data as input features for the model. A battery energy prediction model is constructed based on the selected algorithm, and its parameters and structure are set. Then, the model is trained using preprocessed training sample data, and its parameters are adjusted through iterative optimization algorithms (such as gradient descent and backpropagation) to make the model's prediction results closer to the actual values. The trained model is validated using an independent validation dataset to evaluate its predictive performance and generalization ability. If the model's predictive performance is unsatisfactory, the previous steps can be returned for adjustment and optimization. The trained model is then deployed to the battery management system for predicting battery remaining energy.

[0126] When predicting the remaining energy of a battery, the collected battery state of charge, battery health status, battery voltage, and battery temperature are input into the battery energy prediction model to obtain the remaining battery energy output by the battery energy prediction model.

[0127] This application incorporates a machine learning model, which can be used to quickly and accurately obtain the remaining battery energy.

[0128] Based on the above embodiments, the first battery life is estimated based on the remaining battery energy and operating parameters, including:

[0129] For electrical equipment in the initial startup phase, the ratio of remaining battery energy to instantaneous battery power is used as the first driving range; where instantaneous power is pre-calibrated or predicted based on the startup time and historical operating conditions of the electrical equipment.

[0130] In practice, electrical equipment may not always be powered on. For example, a service robot may be turned off at night and turned on again the next day when it is needed to work. Therefore, the period of time after the equipment switches from the off state to the on state is called the initial startup phase. For example, it could be within 10 seconds after powering on, or within 2 minutes after powering on, or the period from powering on to the start of the task.

[0131] In the initial stage of starting up electrical equipment, since the equipment has just started working and its energy consumption mode has not yet stabilized, in order to obtain the first battery life of the equipment, the pre-calibrated power can be used as the instantaneous power. Then, based on the instantaneous power and the remaining battery energy, the first battery life can be estimated. Specifically, the ratio of the remaining battery energy to the instantaneous power can be used as the first battery life.

[0132] Furthermore, in addition to pre-calibrating, real-time power can also be obtained by analyzing the historical operating conditions of the device to understand the user's behavioral habits. Based on the startup time and the user's behavioral habits, the system can predict the commands the user will soon issue to the device. For example, taking a service robot as an example, if a user habitually turns on the service robot at 9:00 AM every morning to accompany them grocery shopping, then when the service robot is turned on around 8:50 AM, it can predict that the robot will accompany the user grocery shopping, and the power corresponding to this grocery shopping scenario can be used as the real-time power. Then, the initial battery life is calculated based on the remaining battery energy and the real-time power.

[0133] After calculating the initial battery life, if it is determined that the initial battery life is insufficient to complete the next task using the device, such as grocery shopping, the system can prompt the user via voice: "Hello, the battery is currently low and we may not be able to accompany you grocery shopping."

[0134] It should be noted that user behavior patterns are obtained by the controller in the electrical equipment based on historical operating condition analysis of the equipment. The specific method is as follows:

[0135] Data collection: Collect operating parameters of electrical equipment over a historical period. These operating parameters include the operating condition of the electrical equipment, the duration of the operating condition, the start time of the operating condition, and the power of the electrical equipment under the operating condition.

[0136] Model Analysis: The collected operating parameters are input into the prediction model, which can be a logistic regression model, decision tree model, random forest, neural network, etc. By analyzing the operating parameters through the prediction model, the behavioral habits of the user corresponding to the electrical equipment are obtained.

[0137] In practical applications, when electrical equipment is turned on, the start-up time is input into the prediction model based on the start-up time of the electrical equipment. The prediction model can output the power corresponding to the work that the user wants the electrical equipment to do next, which is the instantaneous power.

[0138] In this embodiment of the application, when the electrical equipment is in the initial stage of startup, the first battery life is estimated for the electrical equipment based on the pre-calibrated power or the power predicted based on the user's behavior habits, so as to plan the power consumption and charging schedule in advance.

[0139] Based on the above embodiments, the operating parameters also include load resistance and cell internal resistance; the remaining energy of the base battery and the operating parameters are used to estimate the battery's first driving time, including:

[0140] When the electrical equipment is in operation, the energy that the battery can perform external work is determined based on the remaining energy of the battery, the load resistance, and the internal resistance of the battery cell.

[0141] The initial driving range is estimated based on the energy the battery can perform externally and its instantaneous power.

[0142] In practical implementation, the term "operating state" for electrical equipment refers to the state in which the equipment receives instructions from the user and executes the corresponding tasks accordingly. Load resistance affects the battery's discharge current. Under the condition that the output voltage and battery internal resistance remain constant, changes in load resistance directly lead to changes in output current. When the load resistance decreases, the output current increases; conversely, when the load resistance increases, the output current decreases. Therefore, when determining the energy that a battery can perform external work, the influence of load resistance on the discharge current can be considered to more accurately estimate the battery's energy output under a specific load.

[0143] If a battery consists of only one cell, then the internal resistance of that cell is the internal resistance of the corresponding cell in the battery. If a battery consists of multiple cells, then the sum of the internal resistances of all the cells is the internal resistance of the battery. The internal resistance of the cell increases the heat generated during battery discharge and causes a drop in terminal voltage, thus affecting the battery's output power and energy. Therefore, when estimating the energy that a battery can perform external work, the internal resistance of the cell should be taken into account.

[0144] The load resistance and cell internal resistance of electrical equipment vary depending on its operating state. After obtaining the battery's remaining energy, load resistance, and cell internal resistance, the energy that the battery can perform external work can be calculated based on these parameters. The specific calculation method is as follows: Where W' is the energy that the battery can perform work on; W is the remaining energy of the battery; R 负 r is the load resistance value. 内 This represents the internal resistance of the battery cell.

[0145] After obtaining the energy that the battery can perform external work, the first driving range can be estimated based on the energy that the battery can perform external work and the instantaneous power. Specifically, the ratio of the energy that the battery can perform external work to the instantaneous power can be used as the first driving range.

[0146] In addition, to allow for the time before the electrical equipment is deactivated, the deactivation time can be taken into account when calculating the first battery life. The specific calculation method is: (T+Y)*P=W', where T is the first battery life; Y is the deactivation time; P is the instantaneous power; and W' is the energy that the battery can do external work.

[0147] The embodiments of this application estimate the first battery life based on the real-time power corresponding to the real-time operating conditions of the electrical equipment, thereby improving the accuracy of the first battery life estimation.

[0148] Based on the above embodiments, the method further includes:

[0149] If the first battery life meets the alarm conditions, then an alarm will be triggered.

[0150] The alarm conditions are pre-set and used to determine whether an alarm is needed based on the estimated first battery life. When the first battery life of the electrical device meets the alarm conditions, the alarm handling mechanism is triggered. Specifically, the alarm conditions can be a battery life threshold. If the first battery life is less than this threshold, it indicates insufficient battery life, and an alarm is triggered.

[0151] It should be noted that the battery life threshold can be determined based on the instantaneous power of the device; for example, the higher the instantaneous power, the lower the battery life threshold should be. It can also be determined based on the device's current; for example, the higher the current, the lower the battery life threshold should be. Furthermore, it can be determined based on the device's speed; for example, the higher the speed, the lower the battery life threshold should be. Alternatively, multiple factors such as instantaneous power, current, and speed can be considered together to determine the battery life threshold. Taking the combination of instantaneous power and current as an example, weights for instantaneous power and current can be pre-set, and the battery life threshold can be determined by a weighted average.

[0152] This application embodiment sets up an alarm process to issue an early warning when the battery life of the electrical device is insufficient, so as to remind the user to charge the electrical device in time.

[0153] Based on the above embodiments, the embodiments of this application can set hierarchical alarms, specifically including:

[0154] If the first battery life is longer than the first alarm duration and shorter than the second alarm duration, it indicates that the first battery life is insufficient, but the deficiency is minor. Therefore, a first alarm message can be issued, and the execution of additional load-bearing commands can be refused (e.g., refusing to lift more heavy objects, refusing to accelerate walking, etc.). For example, the first alarm message could be "Current battery level is low; additional load-bearing commands have been refused." The first alarm duration is shorter than the second alarm duration.

[0155] If the first battery life duration is less than the first alarm duration, it indicates that the battery life is severely insufficient. In this case, a second alarm message can be issued, and the current working state can be exited (e.g., putting down the heavy object being lifted, stopping movement, etc.) to enter low-load mode. For example, the second alarm message could be: "Current battery level is severely low, about to enter low-load mode."

[0156] It should be noted that the first and second alarm messages can be displayed and / or audio-based.

[0157] This application embodiment reduces the risk of sudden shutdown of electrical equipment due to depleted power by implementing tiered alarms based on a first alarm duration and a second alarm duration. Furthermore, in scenarios where tasks are being performed, it reduces the risk of task failure or data loss due to insufficient power. With sufficient power, the equipment can continuously and stably perform tasks, improving task completion rate and efficiency. When power is insufficient, by promptly exiting the current working state and entering a low-load mode, enough power can be reserved to complete more important tasks or allow the equipment to safely return to a charging station.

[0158] Based on the above embodiments, the first alarm duration and the second alarm duration can be determined according to the operating conditions of the electrical equipment. Specifically, the electrical equipment can maintain a correspondence table between operating conditions, the first alarm duration, and the second alarm duration. The contents of this correspondence table may include the possible operating conditions and the corresponding first and second alarm durations stored by the electrical equipment manufacturer before the equipment leaves the factory. This correspondence table can also store the first and second alarm durations for other operating conditions obtained through statistics during the use of the electrical equipment. For example, if the electrical equipment is a service robot, and the user of the service robot accompanies the robot to buy groceries every day, then after a period of data collection, the battery life required for accompanying the robot to buy groceries can be determined, and then the first and second alarm durations can be set based on this battery life. Furthermore, users often ask service robots to serve dishes. After a period of time, the robot can determine the battery life required to carry a dish from the kitchen to the table, and then set the first alarm duration and the second alarm duration based on the battery life.

[0159] When determining the first alarm duration and the second alarm duration based on the required battery life, one-quarter of the required battery life can be used as the second alarm duration, and one-fifth of the required battery life can be used as the first alarm duration. The figures of one-quarter and one-fifth are merely examples, and this application does not limit the scope of the alarm.

[0160] In addition, the first and second alarm durations can also be determined based on the instantaneous power, current, and / or movement speed of the electrical equipment. Instantaneous power, current, and / or movement speed can all reflect the rate at which the electrical equipment consumes power to some extent. For scenarios with rapid power consumption, the first and second alarm durations can be set slightly longer to reduce the risk of the electrical equipment running out of power quickly after an alarm. Conversely, for scenarios with slow power consumption, the first and second alarm durations can be set slightly shorter to allow the electrical equipment to perform as many tasks as possible while maintaining safe operation. The method for determining the first and second alarm durations based on instantaneous power, current, and / or movement speed can be found in the above embodiments.

[0161] The embodiments of this application determine the first alarm duration and the second alarm duration based on the operating conditions or real-time power, which can more intelligently allocate equipment resources, maintain efficient operation when the power is sufficient, and take timely protective measures when the battery is low to reduce excessive power consumption.

[0162] Based on the above embodiments, the method further includes:

[0163] Obtain the average power consumption of the electrical equipment from startup to the current moment;

[0164] The second driving range of the battery is estimated based on the average power and the remaining battery energy.

[0165] In practical implementation, average power refers to the ratio of the total energy consumed by electrical equipment over a period of time to the energy consumed during that period. Average power reflects the average power demand of electrical equipment over a period of time and is typically used to calculate the runtime under average load conditions. It can provide a more macroscopic and long-term runtime forecast, helping users to understand and plan the overall operation of electrical equipment.

[0166] The average power can be calculated using the following method:

[0167] 1. Record total energy consumption: First, it is necessary to record the total electrical energy consumed by the equipment from startup to the current moment. This can usually be obtained through the equipment's energy consumption monitoring system.

[0168] 2. Record time: The total running time of the electrical equipment from startup to the current moment needs to be recorded.

[0169] 3. Calculate the average power: Divide the total energy consumption by the total operating time to obtain the average power of the equipment.

[0170] Another method for calculating average power is as follows: the electrical equipment can collect the instantaneous power of the electrical equipment at preset time intervals, and then average the collected instantaneous power to obtain the average power.

[0171] After obtaining the average power, the ratio of the remaining battery energy to the average power can be used as the second range.

[0172] During operation, in addition to estimating the initial battery life based on real-time power, electrical equipment can also estimate a second battery life based on average power, which can supplement the initial estimate. This allows users to know the remaining usage time of electrical equipment in advance, thus enabling them to plan their usage more rationally.

[0173] This application embodiment uses two methods to estimate battery life: instantaneous power and average power. This can obtain battery life information at different time scales, which helps users or managers to have a more comprehensive understanding of the device's battery life and thus make more reasonable decisions.

[0174] Based on the above embodiments, the method further includes:

[0175] The duration of the third alarm is determined based on the average power.

[0176] If the second battery life is less than the third alarm duration, then a third alarm message will be issued.

[0177] In the specific implementation process, after obtaining the second battery life, it can be determined whether the second battery life meets the alarm conditions. Specifically, the third alarm duration can be determined based on the average power. It can be understood that the correspondence between power and the third alarm duration can be preset, and then the third alarm duration corresponding to the average power can be determined from the correspondence.

[0178] If the second battery life duration is less than the third alarm duration, it indicates that the battery is currently low on power, and a third alarm message can be issued. The third alarm message can be issued through text display or voice.

[0179] In this embodiment, the average power changes accordingly with the changes in equipment operating conditions or load, thereby dynamically adjusting the alarm threshold and making the alarm more accurate and targeted.

[0180] Figure 4 This is a schematic diagram of another battery life estimation method provided in an embodiment of this application, as shown below. Figure 4As shown, this method includes three stages: battery life estimation during the initial startup stage, battery life estimation during the task execution stage, and battery life estimation under comprehensive operating conditions. If the battery life is insufficient during the initial startup stage, the subsequent battery life estimation for the task execution stage and comprehensive operating conditions will not proceed. Furthermore, the battery life estimation during the task execution stage and the battery life estimation under comprehensive operating conditions are not sequential. During task execution, the electrical equipment can perform both the battery life estimation for the task execution stage and the battery life estimation for comprehensive operating conditions. The task execution stage can be divided into predictable specific operating conditions and unpredictable real-time operating conditions. The purpose of distinguishing between these two scenarios is to differentiate how alarm durations are obtained. For predictable specific operating conditions, the alarm duration is stored in the electrical equipment and can be obtained by looking up a table. Unpredictable real-time operating conditions refer to situations where the corresponding alarm duration is not stored in the table; in this case, the alarm duration needs to be determined based on the real-time power. The following are the battery life estimation methods for each stage:

[0181] (1) Initial startup:

[0182] Calculate the remaining battery energy: After the electrical equipment is started, the remaining battery energy can be calculated first. The method for calculating the remaining battery energy of the electrical equipment in the initial stage of startup can be found in the above embodiment.

[0183] The driving time T is calculated based on the rated power: The rated power here is the pre-calibrated power, and the specific determination method can be determined according to the relevant description in the above embodiments. In this embodiment, the rated power is taken as the instantaneous power.

[0184] User Identification: A single electrical device can have multiple users, each with different usage patterns. Therefore, upon startup, the device can first identify the user. This can be done by recognizing the currently logged-in account, capturing images of people near the device, or having the user register their fingerprint upon startup. If the user is identified, the device's behavior can be assessed for predictability; otherwise, it proceeds to the next task.

[0185] Predictable user behavior: Based on the power-on time of the device and the user's behavior habits, it can be determined what task the user needs the device to perform next, i.e., the expected behavior. Then, the step of calculating the battery life is executed; otherwise, the next task execution stage is entered.

[0186] The battery life T is calculated based on the expected behavior: The method for calculating the battery life based on the expected behavior is described in the above embodiment and will not be repeated here.

[0187] Determine if T≤Xi: Check if the battery life is less than the alarm duration. If it is less, output an alarm indicating insufficient battery life; otherwise, proceed to the next task execution stage.

[0188] Output a warning indicating insufficient battery life.

[0189] (2) Predictable specific operating conditions:

[0190] Extract the preset alarm time Xi, Yi: The alarm time Xi, Yi can be determined by the correspondence between the operating conditions and alarm durations stored in the electrical equipment in advance.

[0191] Calculating the instantaneous battery life T: The method for calculating the first battery life can be found in the above embodiment, and will not be repeated here.

[0192] Battery life > Xi: If the initial battery life is greater than Xi, it means that the device currently has sufficient battery life and can continue to perform the task. Furthermore, the initial battery life can be re-estimated according to the preset duration.

[0193] If Yi ≤ first battery life ≤ Xi; if Yi ≤ first battery life ≤ Xi, it means the current battery life of the device is not sufficient. In this case, an alarm can be issued and new load commands can be rejected. If the first battery life ≤ Yi, it means the current battery life of the device is seriously insufficient. In this case, an alarm can be issued, and the device can exit its current operation and enter a low-power mode.

[0194] (3) Unpredictable real-time operating conditions

[0195] The difference between unpredictable immediate operating conditions and predictable specific operating conditions lies in the determination of the alarm time. Other procedures are the same as those for predictable characteristic operating conditions and will not be repeated here. Furthermore, the determination of the alarm time can be found in the above-described embodiments.

[0196] (4) Comprehensive operating conditions

[0197] Based on the average power, a preset alarm time Z is extracted, and then a second battery life is calculated based on the average power. It is understood that the calculation method for the second battery life can be found in the above embodiment, and will not be repeated here. After obtaining the second battery life, it is compared with the alarm time Z. If it is greater than the alarm time Z, it indicates that the current battery life is sufficient; otherwise, it indicates that the current battery life is insufficient, and a third alarm message is issued.

[0198] Figure 5 This is a schematic diagram of a battery life estimation device provided in an embodiment of this application. The device can be a module, program segment, or code on an electronic device. It should be understood that this device is similar to the one described above. Figure 1 The method implementation corresponds to this and can be executed. Figure 1The specific functions of the device involved in the method embodiment can be found in the description above; to avoid repetition, detailed descriptions are omitted here. The device includes: an energy calculation module 501, a working parameter acquisition module 502, and a range estimation module 503; wherein:

[0199] The energy calculation module 501 is used to obtain the battery parameters of the battery on the electrical equipment and determine the remaining energy of the battery based on the battery parameters;

[0200] The working parameter acquisition module 502 is used to acquire the working parameters of the electrical equipment under the current working conditions. The working parameters include real-time power.

[0201] The range estimation module 503 is used to estimate the first range of the battery based on the remaining battery energy and operating parameters.

[0202] Based on the above embodiments, the battery parameters include the battery voltage-state-of-charge curve; the energy calculation module 501 is specifically used for:

[0203] The remaining energy of the battery is obtained by integrating the battery voltage based on the voltage-state-of-charge curve.

[0204] Based on the above embodiments, the battery parameters also include battery health status and battery rated capacity; the energy calculation module 501 is specifically used for:

[0205] The voltage-state-charge relationship curve is corrected based on the battery health status and the battery rated capacity to obtain the corrected curve.

[0206] The remaining energy of the battery is obtained by integrating the battery voltage based on the corrected curve.

[0207] Based on the above embodiments, the battery parameters further include battery temperature; the correction of the reference voltage-state-of-charge curve includes:

[0208] Determine the temperature compensation factor based on the battery temperature;

[0209] The voltage-state-of-charge curve is corrected according to the temperature compensation factor to obtain the corrected curve;

[0210] The remaining energy of the battery is obtained by integrating the battery voltage based on the corrected curve.

[0211] Based on the above embodiments, the battery parameters include battery state of charge, battery health status, battery voltage, and battery temperature; the energy calculation module 501 is specifically used for:

[0212] The battery state of charge, battery health status, battery voltage, and battery temperature are input into the battery energy prediction model to obtain the remaining battery energy. The battery energy prediction model is obtained in advance based on training samples, which include historical battery parameters and corresponding remaining battery energy.

[0213] Based on the above embodiments, the battery life estimation module 503 is specifically used for:

[0214] When the electrical device is in the initial startup phase, the ratio of the remaining battery energy to the instantaneous battery power is used as the first battery life; wherein the instantaneous power is pre-calibrated or predicted based on the startup time of the electrical device and the historical operating conditions of the electrical device.

[0215] Based on the above embodiments, the operating parameters also include load resistance and cell internal resistance; the range estimation module 503 is specifically used for:

[0216] When the electrical equipment is in operation, the energy that the battery can perform external work is determined based on the remaining energy of the battery, the load resistance, and the internal resistance of the battery cell.

[0217] The first driving range is estimated based on the energy that the battery can perform external work and the instantaneous power.

[0218] Based on the above embodiments, the device further includes a first alarm module, used for:

[0219] If the first battery life meets the alarm conditions, then alarm processing will be performed.

[0220] Based on the above embodiments, the first alarm module is specifically used for:

[0221] If the first battery life is longer than the first alarm duration and the first battery life is shorter than the second alarm duration, then a first alarm message is issued and the new load command is refused to be executed; the first alarm duration is shorter than the second alarm duration.

[0222] If the first battery life is less than the first alarm duration, a second alarm message will be issued, and the current working state will be exited, entering a low-load mode.

[0223] Based on the above embodiments, the first alarm duration and the second alarm duration are determined according to the operating conditions of the electrical equipment; or, they are determined according to the instantaneous power, current and / or moving speed of the electrical equipment.

[0224] Based on the above embodiments, the device further includes a comprehensive range estimation module, used for:

[0225] Obtain the average power of the electrical equipment from startup to the current time;

[0226] The second driving time of the battery is estimated based on the average power and the remaining energy of the battery.

[0227] Based on the above embodiments, the device further includes a second alarm module, used for:

[0228] The third alarm duration is determined based on the average power.

[0229] If the second battery life is less than the third alarm duration, then a third alarm message is issued.

[0230] Figure 6 This is a schematic diagram of the physical structure of the electrical equipment provided in the embodiments of this application, such as... Figure 6 As shown, the electrical device includes: a processor 601, a memory 602, and a bus 603; wherein,

[0231] The processor 601 and the memory 602 communicate with each other through the bus 603;

[0232] The processor 601 is used to call program instructions in the memory 602 to execute the methods provided in the above method embodiments, such as: obtaining battery parameters of the battery on the power device, determining the remaining energy of the battery based on the battery parameters; obtaining the operating parameters of the power device under the current operating conditions, the operating parameters including instantaneous power; and estimating the first battery life based on the remaining battery energy and the operating parameters.

[0233] Processor 601 can be an integrated circuit chip with signal processing capabilities. The processor 601 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor.

[0234] The memory 602 may include, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0235] This embodiment discloses a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, and when the program instructions are executed by a computer, the computer can execute the methods provided in the above-described method embodiments, such as: obtaining battery parameters of a battery on an electrical device, determining the remaining energy of the battery based on the battery parameters; obtaining the operating parameters of the electrical device under the current operating conditions, the operating parameters including instantaneous power; and estimating a first battery life based on the remaining battery energy and the operating parameters.

[0236] This embodiment provides a non-transitory computer-readable storage medium storing computer instructions that cause the computer to execute the methods provided in the above-described method embodiments, such as: acquiring battery parameters of a battery on an electrical device, determining the remaining energy of the battery based on the battery parameters; acquiring operating parameters of the electrical device under the current operating conditions, the operating parameters including instantaneous power; and estimating a first battery life based on the remaining battery energy and the operating parameters.

[0237] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0238] Furthermore, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0239] Furthermore, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0240] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0241] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for estimating battery life, characterized in that, include: Obtain the battery parameters of the battery on the electrical device, and determine the remaining energy of the battery based on the battery parameters; Obtain the operating parameters of the electrical equipment under the current operating conditions, including the real-time power; The first battery life is estimated based on the remaining battery energy and the operating parameters.

2. The method according to claim 1, characterized in that, The battery parameters include the battery's voltage-state-of-charge curve; determining the remaining battery energy based on the battery parameters includes: The remaining energy of the battery is obtained by integrating the battery voltage based on the voltage-state-of-charge curve.

3. The method according to claim 2, characterized in that, The battery parameters also include battery health status and battery rated capacity; The step of integrating the battery voltage based on the voltage-state-of-charge curve to obtain the remaining energy of the battery includes: The voltage-state-charge relationship curve is corrected based on the battery health status and the battery rated capacity to obtain the corrected curve. The remaining energy of the battery is obtained by integrating the battery voltage based on the corrected curve.

4. The method according to claim 2, characterized in that, The battery parameters also include battery temperature; the step of integrating the battery voltage based on the voltage-state-of-charge curve to obtain the remaining energy of the battery includes: Determine the temperature compensation factor based on the battery temperature; The voltage-state-of-charge curve is corrected according to the temperature compensation factor to obtain the corrected curve; The remaining energy of the battery is obtained by integrating the battery voltage based on the corrected curve.

5. The method according to claim 1, characterized in that, The battery parameters include battery state of charge, battery state of health, battery voltage, and battery temperature; determining the remaining battery energy based on the battery parameters includes: The battery state of charge, battery health status, battery voltage, and battery temperature are input into the battery energy prediction model to obtain the remaining battery energy. The battery energy prediction model is obtained in advance based on training samples, which include historical battery parameters and corresponding remaining battery energy.

6. The method according to claim 1, characterized in that, The estimation of the first battery range based on the remaining battery energy and the operating parameters includes: When the electrical device is in the initial startup phase, the ratio of the remaining battery energy to the instantaneous battery power is used as the first battery life; wherein the instantaneous power is pre-calibrated or predicted based on the startup time of the electrical device and the historical operating conditions of the electrical device.

7. The method according to claim 1, characterized in that, The operating parameters also include load resistance and cell internal resistance; estimating the first battery life based on the remaining battery energy and the operating parameters includes: When the electrical equipment is in operation, the energy that the battery can perform external work is determined based on the remaining energy of the battery, the load resistance, and the internal resistance of the battery cell. The first driving range is estimated based on the energy that the battery can perform external work and the instantaneous power.

8. The method according to claim 1, characterized in that, The method further includes: If the first battery life is longer than the first alarm duration and the first battery life is shorter than the second alarm duration, then a first alarm message is issued and the new load command is refused to be executed; the first alarm duration is shorter than the second alarm duration. If the first battery life is less than the first alarm duration, a second alarm message will be issued, and the current working state will be exited, entering a low-load mode.

9. The method according to claim 8, characterized in that, The first alarm duration and the second alarm duration are determined based on the operating conditions of the electrical equipment; or, based on the instantaneous power, current and / or moving speed of the electrical equipment.

10. The method according to any one of claims 1-9, characterized in that, The method further includes: Obtain the average power of the electrical equipment from startup to the current time; The second driving time of the battery is estimated based on the average power and the remaining energy of the battery.

11. The method according to claim 10, characterized in that, The method further includes: The third alarm duration is determined based on the average power. If the second battery life is less than the third alarm duration, then a third alarm message is issued.

12. A battery life estimation device, characterized in that, include: An energy calculation module is used to obtain the battery parameters of the battery on the electrical device and determine the remaining energy of the battery based on the battery parameters; The working parameter acquisition module is used to acquire the working parameters of the electrical equipment under the current working conditions, including real-time power. The battery life estimation module is used to estimate the first battery life based on the remaining battery energy and the operating parameters.

13. An electrical appliance, characterized in that, include: Processor, memory, and bus, among which, The processor and the memory communicate with each other via the bus; The memory stores program instructions that can be executed by the processor, and the processor can invoke the program instructions to perform the method as described in any one of claims 1-11.

14. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions that, when executed by a computer, cause the computer to perform the method as described in any one of claims 1-11.

15. A computer program product, characterized in that, It includes computer program instructions, which, when read and executed by a processor, perform the method as described in any one of claims 1-11.

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