Battery electric quantity accurate detection method based on multi-parameter fusion

Abstract

The invention discloses a battery electric quantity accurate detection method based on multi-parameter fusion, and relates to the technical field of accurate detection. Basic data of a battery are collected in real time through a battery management system and serve as initial parameters of battery electric quantity estimation; the aging degree of the battery is monitored by using the charging and discharging period data, the cycle index data and the internal impedance data of the battery; designing an initial battery power estimation formula and a primary dynamic adjustment battery power estimation formula; according to the charging and discharging mode, a battery electric quantity estimation formula is dynamically adjusted for the second time; and outputting the electric quantity estimation result after secondary dynamic adjustment as the residual electric quantity of the battery, and displaying the residual electric quantity by a battery management system or a user interface. According to the invention, through dynamic adaptation and multi-parameter fusion strategies, the accuracy and adaptability of battery electric quantity estimation are significantly improved, and it is ensured that the remaining electric quantity of the battery can be accurately reflected all the time in the use process of the battery.

Description

Technical Field

[0001] The present invention relates to the field of precise detection technology, and in particular to a method for precise battery power detection based on multi-parameter fusion. Background Art

[0002] With the widespread application of mobile devices, electric vehicles and renewable energy systems, batteries, as core energy storage devices, have become an indispensable and important part of modern scientific and technological products; existing battery capacity estimation methods mainly rely on basic parameters such as battery voltage, current, temperature, etc., and are estimated through calculation methods such as integration method, open circuit voltage method and Ohm's law; with the advancement of technology, the accuracy of battery capacity estimation has been improved based on real-time monitoring of battery management systems (BMS) and sensor networks; however, with battery aging, temperature fluctuations and changes in load usage, traditional estimation methods often cannot accurately reflect the actual remaining battery capacity, resulting in large errors in battery capacity display, especially under complex working conditions, the nonlinear changes in battery performance have a more significant impact on capacity estimation.

[0003] The main technical methods currently include power estimation techniques based on a single parameter, such as integral calculation through the battery's voltage and current data, or estimation using the battery's open circuit voltage and charge and discharge characteristics; however, these methods have obvious limitations; first, the battery's aging process and temperature changes have a greater impact on power estimation, and traditional methods are difficult to accurately capture changes in the battery's internal state; second, due to the complexity of battery operating conditions such as charge and discharge modes and load fluctuations, estimation methods based on a single parameter cannot cope with the dynamic changes in battery performance under different operating conditions; although some improved methods optimize the estimation accuracy by introducing factors such as internal resistance and temperature, they still cannot effectively solve the error problem of batteries at different aging stages and under different workloads; in addition, most current methods lack a dynamic adjustment mechanism and cannot be adaptively corrected based on real-time charge and discharge status and aging data, resulting in unstable battery power estimation in practical applications. Summary of the invention

[0004] In view of the fact that the single-parameter battery power estimation technology in the above background technology has significant accuracy errors, especially in the problems of battery aging and load fluctuations, the present invention proposes a battery power accurate detection method based on multi-parameter fusion.

[0005] Therefore, the problem to be solved by the present invention is how to effectively deal with the problem of complex state changes of the battery.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides a method for accurately detecting battery power based on multi-parameter fusion, which includes: step S1: collecting basic data of the battery in real time through a battery management system, the basic data including voltage data, current data, temperature data and internal resistance data, and using the basic data as preliminary parameters for battery power estimation; using the battery's charge and discharge cycle data, cycle number data, and internal impedance data to monitor the battery's aging degree; step S2: designing an initial battery power estimation formula, establishing a relationship between battery aging and power detection error, and dynamically adjusting the battery power estimation formula once; step S3: fusing the preliminary parameters and analyzing the real-time charge and discharge mode of the battery, the charge and discharge mode including high load and low load, and according to the charge and discharge mode, dynamically adjusting the battery power estimation formula for a second time; step S4: outputting the power estimation result after the secondary dynamic adjustment as the remaining power of the battery for display in the battery management system or user interface.

[0007] As a preferred solution of the battery power accurate detection method based on multi-parameter fusion of the present invention, the specific implementation process of step S2 includes: The initial battery capacity estimation formula is designed based on the battery aging degree and historical data, as follows: in, Represents the initial battery charge estimate, Indicates the battery time The current, Indicates the battery time The voltage, Indicates a time interval.

[0008] Under the influence of battery aging, a correction factor based on historical charge and discharge cycle data and battery internal resistance change trend is introduced, as follows: in, Indicates the corrected estimated power value, represents the correction factor, Indicates the current internal resistance of the battery. Indicates the initial internal resistance of the battery.

[0009] By analyzing the error detection results of different battery aging stages and combining the battery usage scenarios, a battery power estimation strategy is established, as follows: in, Indicates the estimated power value after phased adjustment. represents the aging stage correction factor, Indicates the current number of charge and discharge cycles of the battery. Indicates the initial charge and discharge cycle number of the battery. The power exponent representing the phase adjustment.

[0010] As a preferred solution of the battery power accurate detection method based on multi-parameter fusion of the present invention, the specific implementation process of step S2 also includes: According to the staged adjustment characteristics, an intelligent feedback mechanism is designed to estimate the battery aging state according to the changes in real-time charging data, so as to dynamically adjust the power estimation formula, as follows: in, Represents the estimated power value after feedback adjustment, Represents the preset feedback adjustment coefficient, Indicates the current battery charging voltage. Indicates the rated voltage of the battery.

[0011] As a preferred solution of the battery power accurate detection method based on multi-parameter fusion of the present invention, the specific implementation process of step S3 includes: The classification and analysis of charging and discharging modes are carried out as follows: in, Indicates the battery load power, Indicates the battery time The current, Indicates the battery time The voltage, Indicates the rated capacity of the battery.

[0012] Carry out load mode classification and power correction factor design, as follows: in, It represents the estimated power value after load mode adjustment. Represents the initial battery charge estimate, Indicates the battery load power, Indicates the preset load correction factor, Indicates the rated load power of the battery.

[0013] As a preferred solution of the battery power accurate detection method based on multi-parameter fusion of the present invention, the specific implementation process of step S3 also includes: Perform secondary dynamic adjustment based on real-time charging and discharging mode, as follows: in, Indicates the estimated power value after secondary dynamic adjustment. It represents the estimated power value after load mode adjustment. represents the current feedback correction coefficient, Indicates the maximum charge and discharge current of the battery. Indicates the current influence correction index.

[0014] As a preferred solution of the battery power accurate detection method based on multi-parameter fusion of the present invention, the specific implementation process of step S4 includes: Calculate the remaining power based on the revised power estimate as follows: in, Indicates the remaining battery power. Indicates the estimated power value after secondary dynamic adjustment. Indicates that the battery has been used. Indicates the maximum capacity of the battery.

[0015] As a preferred solution of the battery power accurate detection method based on multi-parameter fusion of the present invention, the specific implementation process of step S4 also includes: The remaining battery power As output, it is displayed on the user interface, and the remaining power output and feedback mechanism are designed as follows: in, Indicates the remaining battery power that is finally output on the user interface. Indicates the current status of the battery. represents the preset feedback adjustment factor, Indicates the charging status. Indicates the discharge state, and idle indicates the idle state.

[0016] In a second aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the method for accurate battery power detection based on multi-parameter fusion as described in the first aspect of the present invention are implemented.

[0017] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the method for accurate battery power detection based on multi-parameter fusion as described in the first aspect of the present invention are implemented.

[0018] The beneficial effects of the present invention are as follows: through multi-step dynamic adjustment and multi-parameter fusion, the accuracy of battery power estimation is significantly improved; first, the basic data such as voltage, current, temperature and internal resistance are collected in real time through the battery management system, and the performance and aging of the battery are comprehensively monitored to provide reliable reference data for subsequent estimation; secondly, an initial power estimation formula is designed according to the aging degree and historical data of the battery, and the relationship between aging and power detection error is established, so as to realize dynamic adjustment of the power estimation formula, thereby solving the performance degradation problem caused by battery aging and maintaining a high estimation accuracy; finally, through real-time analysis of the battery's charging and discharging mode (such as high load, low load), the power estimation formula is further dynamically adjusted to adapt to changes in battery performance under different working conditions, so as to ensure that accurate power prediction can be provided under various load conditions; through the strategy of dynamic adaptation and multi-parameter fusion, the accuracy and adaptability of battery power estimation are significantly improved, ensuring that the remaining power of the battery can always be accurately reflected during the use of the battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 Schematic diagram of the steps of a method for accurate battery charge detection based on multi-parameter fusion. DETAILED DESCRIPTION

[0021] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0022] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0023] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0024] Example 1 Reference Figure 1, which is the first embodiment of the present invention, and provides a method for accurately detecting battery power based on multi-parameter fusion, including: Step S1: The basic data of the battery is collected in real time through the battery management system (BMS), wherein the basic data includes voltage data, current data, temperature data and internal resistance data, and the basic data is used as the preliminary parameters for battery power estimation; the battery's charge and discharge cycle data, cycle number data, and internal impedance data are used to monitor the battery's aging degree, providing an important reference for subsequent power estimation.

[0025] It should be noted that in order to accurately estimate the remaining battery power, it is necessary to monitor the real-time status of the battery. These basic status data reflect the electrical performance and thermal state of the battery during operation, and are important inputs for estimating power. Obtaining this data through the battery management system (BMS) can provide the most basic information for subsequent power estimation; inputting this data (voltage, current, temperature, internal resistance) into the battery management system (BMS) to form a real-time monitoring data stream; the battery management system can use this data as basic parameters to perform preliminary power calculations and health assessments, providing necessary data support for subsequent optimization and adjustment.

[0026] Step S2: Design an initial battery power estimation formula, establish the relationship between battery aging and power detection error, and dynamically adjust the battery power estimation formula.

[0027] Specifically, the initial battery capacity estimation formula is designed based on the battery aging degree and historical data, as follows: in, Represents the initial battery capacity estimate (in Ah (ampere-hour), which represents the battery capacity obtained by integrating current and voltage), Indicates the battery time The current (in A (ampere), which indicates the current of battery charging or discharging, Indicates the battery time The voltage (in V (volts), indicating the voltage state of the battery), Indicates the time interval (in hours (h), indicating the length of time from the start of battery charging to the current time).

[0028] Furthermore, under the influence of the aging degree of the battery, a correction factor based on the historical charge and discharge cycle data and the change trend of the battery internal resistance is introduced, as follows: in, Indicates the corrected estimated power value, Represents the correction factor (unit is dimensionless, it is a constant determined by the battery model and the use environment experiment, representing the degree of correction), Indicates the current internal resistance of the battery (in Ω (ohm), indicating the change in the battery's electrical performance with aging), Indicates the initial internal resistance of the battery.

[0029] It should be noted that this formula is designed to correct the estimated power value by the change trend of internal resistance. Considering that internal resistance is an important indicator of battery aging, the correction factor It is determined through historical experimental data and can reflect the impact of battery aging on the accuracy of power estimation. This factor is compared with multiple experimental data to establish a dynamic correction model to correct the power estimation error.

[0030] Furthermore, by analyzing the error detection results at different aging stages of the battery, the aging correction coefficient in the power estimation formula is further refined, and a battery power estimation strategy with staged adjustment characteristics is established in combination with the specific battery usage scenario, as follows: in, Indicates the estimated value of power after stage adjustment (in Ah. This value indicates the power corrected considering the aging stage). represents the aging stage correction factor, Indicates the current number of charge and discharge cycles of the battery. Indicates the initial charge and discharge cycle number of the battery. The power exponent representing the phase adjustment.

[0031] Furthermore, according to the staged adjustment characteristics, an intelligent feedback mechanism is designed to estimate the battery aging state according to the changes in real-time charging data, thereby dynamically adjusting the power estimation formula to improve the accuracy of power estimation, as follows: in, Indicates the estimated power value after feedback adjustment (in Ah, this value represents the final power estimation result after adjustment by the real-time charging status). Represents the preset feedback adjustment coefficient (unit is dimensionless, it is a constant determined according to the battery model and application scenario, representing the adjustment effect of the charging state on the power estimation), Indicates the current battery charging voltage (in V (volts), which is the current charging status information of the battery). Indicates the rated voltage of the battery (in V, indicating the standard voltage for which the battery is designed).

[0032] It should be noted that this formula dynamically adjusts the power estimation value by the deviation between the charging voltage and the rated voltage, reflecting the important influence of the real-time charging status on the accuracy of the battery power. It is a constant determined based on experiments and battery characteristics.

[0033] Step S3: The preliminary parameters are integrated and the real-time charge and discharge mode of the battery (such as high load and low load states) is analyzed. According to the charge and discharge mode, the battery power estimation formula is dynamically adjusted for a second time to adapt to changes in battery performance under different working conditions.

[0034] Specifically, the classification and analysis of the charge and discharge modes are carried out as follows: in, Indicates the battery load power (in W (watt), indicating the current battery load level), Indicates the battery time The current (in A (ampere), which indicates the current of battery charging or discharging, Indicates the battery time The voltage (in V (volts), indicating the voltage state of the battery), Indicates the rated capacity of the battery (in Ah (ampere-hour), indicating the designed capacity of the battery when it leaves the factory).

[0035] Carry out load mode classification and power correction factor design, as follows: in, It represents the estimated power value after load mode adjustment. Represents the initial battery capacity estimate (in Ah (ampere-hour), which represents the battery capacity obtained by integrating current and voltage), Indicates the battery load power (in W (watt), indicating the current battery load level), Represents the preset load correction factor (unit is dimensionless, it is a constant obtained by fitting experimental data, indicating the influence of load power on power estimation), Indicates the rated load power of the battery (in W, indicating the maximum power output of the battery under standard working conditions).

[0036] Furthermore, a secondary dynamic adjustment is performed based on the real-time charging and discharging mode, as follows: in, Indicates the estimated value of power after secondary dynamic adjustment (in Ah, indicating the battery power after load mode correction and real-time charge and discharge mode adjustment). It represents the estimated power value after load mode adjustment. represents the current feedback correction coefficient (unit is dimensionless, it is a constant obtained by fitting experimental data, which represents the adjustment effect of battery charging and discharging current on power estimation), Indicates the maximum charge and discharge current of the battery. Represents the current impact correction index (unit is dimensionless, used to characterize the nonlinear effect of current on power estimation).

[0037] Step S4: Outputting the optimized and adjusted power estimation result as the remaining power of the battery for display in a battery management system or a user interface.

[0038] Specifically, the remaining power is calculated according to the corrected power estimate as follows: in, Indicates the remaining battery power. Indicates the estimated value of power after secondary dynamic adjustment (in Ah, indicating the battery power after load mode correction and real-time charge and discharge mode adjustment). Indicates that the battery has been used. Indicates the maximum capacity of the battery.

[0039] It should be noted that the purpose of this step is to calculate the remaining available power of the battery based on the optimized power estimation results. This is done by adjusting the remaining power according to the power consumed and the rated capacity of the battery to ensure that the displayed power reflects the actual usage of the battery.

[0040] Furthermore, the remaining battery power As output, it is displayed on the user interface, and the remaining power output and feedback mechanism are designed as follows: in, Indicates the remaining battery power that is finally output on the user interface. Indicates the current status of the battery. represents the preset feedback adjustment factor, Indicates the charging status. Indicates the discharge state, and idle indicates the idle state.

[0041] This step further optimizes the output of the remaining power by considering the impact of the battery status on the power display. When the battery is in an idle state, it may be necessary to make certain corrections to the power to compensate for factors such as battery self-discharge. By introducing the feedback adjustment coefficient , which can make the display of remaining power more in line with actual conditions.

[0042] This embodiment also provides a computer device, which is suitable for the case of a battery power accurate detection method based on multi-parameter fusion, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to implement the battery power accurate detection method based on multi-parameter fusion as proposed in the above embodiment. The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0043] This embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for accurately detecting battery power based on multi-parameter fusion as proposed in the above embodiment is implemented. In summary, the present invention significantly improves the accuracy of battery power estimation through multi-step dynamic adjustment and multi-parameter fusion; first, the battery management system collects basic data such as voltage, current, temperature and internal resistance in real time, comprehensively monitors the performance and aging of the battery, and provides reliable reference data for subsequent estimation; secondly, the initial power estimation formula is designed according to the aging degree and historical data of the battery, and the relationship between aging and power detection error is established, so as to realize dynamic adjustment of the power estimation formula, thereby solving the performance degradation problem caused by battery aging and maintaining a high estimation accuracy; finally, by real-time analysis of the battery's charging and discharging mode (such as high load, low load), the power estimation formula is further dynamically adjusted to adapt to changes in battery performance under different working conditions, ensuring that accurate power prediction can be provided under various load conditions; through the strategy of dynamic adaptation and multi-parameter fusion, the accuracy and adaptability of battery power estimation are significantly improved, ensuring that the remaining power of the battery can always be accurately reflected during the use of the battery.

[0044] Example 2 In this embodiment 2, a method for accurately detecting battery power based on multi-parameter fusion is provided. In order to verify the beneficial effects of the present invention, a scientific demonstration is carried out through simulation experiments.

[0045] This experiment uses a common lithium battery with a capacity of 50Ah, a rated voltage of 3.7V, and an initial internal resistance of 0.05Ω. During the experiment, the effects of different charging states, battery aging stages, and load modes on power estimation were tested, and data collection was performed through an integrated BMS system.

[0046] Five lithium battery samples with different charge and discharge cycle times were selected, corresponding to different aging stages: New battery (number of cycles: 100 times).

[0047] Slightly aged battery (number of cycles: 500).

[0048] Moderately aged battery (number of cycles: 1000).

[0049] Severely aged battery (number of cycles: 2000 times).

[0050] Extremely aged battery (number of cycles: 3000 times).

[0051] The battery voltage, current, temperature and internal resistance data are collected in real time as basic data input for power estimation. Two charging and discharging load modes are set in the experiment: high load (discharge power 100W) and low load (discharge power 20W).

[0052] In the preliminary stage, a preliminary charge calculation is performed using a charge estimation formula based on the battery's voltage, current, temperature and internal resistance data.

[0053] By analyzing the historical charge and discharge cycle data and the internal resistance change trend, correction factors are introduced for correction. When analyzing different aging stages, a phased adjustment coefficient based on the number of charge and discharge cycles is further introduced. This coefficient is obtained by fitting multiple experimental data to reflect the impact of battery aging on the accuracy of power estimation.

[0054] According to the deviation between the real-time charging voltage and the rated voltage, the power estimation formula is dynamically adjusted to further improve the accuracy of power estimation.

[0055] Table 1 Battery capacity estimation optimization experimental data It can be clearly seen from Table 1 that the degree of battery aging has a significant impact on the accuracy of power estimation; the initial power estimation value of a new battery is very close to the actual value, and the corrected power value and the power value after staged adjustment can more accurately reflect the remaining power of the battery; however, as the battery ages, the internal resistance increases, and the power estimation error gradually increases; in moderately aged and severely aged batteries, the significant increase in internal resistance leads to an increase in the deviation of the initial power estimation value. After correction by the internal resistance correction formula, the power estimation accuracy is significantly improved.

[0056] Furthermore, by introducing a phased adjustment coefficient and combining it with the number of charge and discharge cycles of the battery, the power estimation can be further optimized and the error caused by aging can be reduced; in extremely aged batteries, although there are still errors in the power estimation value after correction, the accuracy of the corrected power estimation value is significantly improved compared to the situation without any correction.

[0057] As for the impact of the charge and discharge mode, it can be seen that in high load mode, the battery discharge rate is higher and the power estimation value will be affected by the load power change; therefore, when dynamically adjusting the power estimation formula, by introducing the load correction factor, it can better reflect the changes in battery performance under different load conditions and ensure the accuracy of the power estimation results.

[0058] In general, the power estimation optimization scheme proposed in the present invention effectively improves the accuracy of battery power estimation through the correction and dynamic adjustment of multiple factors, especially in the complex environment of battery aging and charging and discharging load changes, and can significantly reduce the estimation error; compared with the prior art, when facing aging batteries, this technology can provide more accurate power estimation results through real-time data feedback and internal resistance correction, which is of great significance for optimizing the battery management system and extending the battery life.

[0059] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for accurately detecting battery power based on multi-parameter fusion, characterized in that: include: Step S1: collecting basic data of the battery in real time through the battery management system, the basic data including voltage data, current data, temperature data and internal resistance data, and using the basic data as preliminary parameters for battery power estimation; using the battery's charge and discharge cycle data, cycle number data, and internal impedance data to monitor the battery's aging degree; Step S2: designing an initial battery power estimation formula, establishing the relationship between battery aging and power detection error, and dynamically adjusting the battery power estimation formula; Step S3: integrating the preliminary parameters and analyzing the real-time charge and discharge mode of the battery, wherein the charge and discharge mode includes high load and low load, and dynamically adjusting the battery power estimation formula for a second time according to the charge and discharge mode; Step S4: Outputting the power estimation result after the secondary dynamic adjustment as the remaining power of the battery for display in a battery management system or a user interface.

2. The method for accurate battery power detection based on multi-parameter fusion according to claim 1 is characterized in that: The specific implementation process of step S2 includes: The initial battery capacity estimation formula is designed based on the battery aging degree and historical data, as follows: in, Represents the initial battery charge estimate, Indicates the battery time The current, Indicates the battery time The voltage, Indicates a time interval; Under the influence of battery aging, a correction factor based on historical charge and discharge cycle data and battery internal resistance change trend is introduced, as follows: in, Indicates the corrected estimated power value, represents the correction factor, Indicates the current internal resistance of the battery. Indicates the initial internal resistance of the battery; By analyzing the error detection results of different battery aging stages and combining the battery usage scenarios, a battery power estimation strategy is established, as follows: in, Indicates the estimated power value after stage adjustment. represents the aging stage correction factor, Indicates the current number of charge and discharge cycles of the battery. Indicates the initial charge and discharge cycle number of the battery. The power exponent representing the phase adjustment.

3. The method for accurate battery power detection based on multi-parameter fusion according to claim 2 is characterized in that: The specific implementation process of step S2 also includes: According to the staged adjustment characteristics, an intelligent feedback mechanism is designed to estimate the battery aging state according to the changes in real-time charging data, so as to dynamically adjust the power estimation formula, as follows: in, Represents the estimated power value after feedback adjustment, Represents the preset feedback adjustment coefficient, Indicates the current battery charging voltage. Indicates the rated voltage of the battery.

4. The method for accurate battery power detection based on multi-parameter fusion according to claim 3 is characterized in that: The specific implementation process of step S3 includes: The classification and analysis of charging and discharging modes are carried out as follows: in, Indicates the battery load power, Indicates the battery time The current, Indicates the battery time The voltage, Indicates the rated capacity of the battery; Carry out load mode classification and power correction factor design, as follows: in, It represents the estimated power value after load mode adjustment. Represents the initial battery charge estimate, Indicates the battery load power, Indicates the preset load correction factor, Indicates the rated load power of the battery.

5. The method for accurate battery power detection based on multi-parameter fusion according to claim 4 is characterized in that: The specific implementation process of step S3 also includes: Perform secondary dynamic adjustment based on real-time charging and discharging mode, as follows: in, Indicates the estimated power value after secondary dynamic adjustment. It represents the estimated power value after load mode adjustment. represents the current feedback correction coefficient, Indicates the maximum charge and discharge current of the battery. Indicates the current influence correction index.

6. The method for accurate battery power detection based on multi-parameter fusion according to claim 5 is characterized in that: The specific implementation process of step S4 includes: Calculate the remaining power based on the revised power estimate as follows: in, Indicates the remaining battery power. Indicates the estimated power value after secondary dynamic adjustment. Indicates that the battery has been used. Indicates the maximum capacity of the battery.

7. The method for accurate battery power detection based on multi-parameter fusion according to claim 6 is characterized in that: The specific implementation process of step S4 also includes: The remaining battery power As output, it is displayed on the user interface, and the remaining power output and feedback mechanism are designed as follows: in, Indicates the remaining battery power that is finally output on the user interface. Indicates the current status of the battery. represents the preset feedback adjustment factor, Indicates the charging status. Indicates the discharge state, and idle indicates the idle state.

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