Method and device for determining remaining capacity of battery, medium, equipment and program product

Abstract

The invention relates to a battery remaining capacity determination method and device, a medium, equipment and a program product, and relates to the technical field of battery management. The method comprises the steps that an ampere-hour integral method is adopted, a first reference value is obtained according to the historical value of the remaining capacity of a battery, the rated capacity of the battery and the charging / discharging current of the battery, and the historical value is the remaining capacity of the battery determined in the previous calculation period; adopting an extended Kalman filtering method and a Thevenin equivalent circuit model to obtain a second reference value according to the historical value, the rated capacity, the charging / discharging current and the voltage of the battery; and performing weighted summation on the first reference value and the second reference value to obtain the remaining capacity of the battery. According to the method, the accuracy of estimating the remaining capacity of the battery can be improved, and the defects of an ampere-hour integral method and an extended Kalman filtering method are overcome.

Description

Technical Field

[0001] This disclosure relates to the field of battery management technology, specifically to a method, apparatus, medium, device, and program product for determining the remaining charge of a battery. Background Technology

[0002] Accurate estimation of remaining battery capacity is crucial for battery management systems, but the ampere-hour integration method and extended Kalman filter (EKF) in related technologies both have limitations. The ampere-hour integration method (AHI) is simple and intuitive to calculate. However, it relies on real-time measurement data and is easily affected by sensor accuracy and sampling frequency. Therefore, over time, accumulated errors occur, leading to a decrease in the accuracy of remaining battery capacity (SOC) estimation. While the extended Kalman filter (EKF) can effectively handle noise and uncertainty, there are significant ranges of dynamic changes during battery charging / discharging. The EKF's state-space model struggles to accurately capture battery behavior, resulting in substantial estimation errors. Therefore, both the ampere-hour integration method and the extended Kalman filter exhibit significant errors in estimating remaining battery capacity across the entire range. Summary of the Invention

[0003] To overcome the problems existing in related technologies, this application provides a method, apparatus, medium, device and program product for determining the remaining power of a battery.

[0004] According to a first aspect of this application, a method for determining the remaining battery power is provided, comprising: Using the ampere-hour integration method, a first reference value is obtained based on the historical value of the battery's remaining capacity, the battery's rated capacity, and the battery's charging / discharging current. The historical value is the battery's remaining capacity determined in the previous calculation cycle. Using the extended Kalman filter and the Thevenin equivalent circuit model, a second reference value is obtained based on the historical value, the rated capacity, the charging / discharging current, and the battery voltage. The remaining battery power is obtained by weighted summation of the first reference value and the second reference value.

[0005] Optionally, the method further includes: Substituting the open-circuit voltage of the battery at the start of use into a preset function yields the initial value of the remaining battery capacity. The initial value of the remaining battery capacity is the remaining battery capacity determined when the battery is in the first calculation cycle.

[0006] Optionally, the step of weighted summing of the first reference value and the second reference value to obtain the remaining battery power includes: Based on the derivative value of the preset function at the historical value, the first weight corresponding to the first reference value and the second weight corresponding to the second reference value are determined, wherein the preset function is a mapping function between the remaining battery power and the open circuit voltage; The remaining battery power is obtained by weighting and summing the first reference value and the second reference value according to their respective weights.

[0007] Optionally, determining the first weight corresponding to the first reference value and the second weight corresponding to the second reference value based on the derivative value of the preset function at the historical value includes: The first weight is obtained by dividing the derivative value by the maximum value of the derivative of the preset function, and the second weight is one minus the first weight.

[0008] Optionally, the preset function is determined through the following steps: Obtain the remaining battery charge and open-circuit voltage after multiple discharges; The preset function is obtained by fitting the remaining battery capacity and open-circuit voltage after multiple discharges.

[0009] Optionally, the method further includes: The charging / discharging current is obtained using a current sensor; The voltage is obtained by a voltage sensor.

[0010] According to a second aspect of this application, a device for determining the remaining battery power is provided, comprising: The first determining unit is used to obtain a first reference value by using the ampere-hour integration method based on the historical value of the remaining battery capacity, the rated capacity of the battery, and the charging / discharging current of the battery. The historical value is the remaining battery capacity determined in the previous calculation cycle. The second determining unit is used to obtain a second reference value based on the historical value, the rated capacity, the charging / discharging current, and the battery voltage using the extended Kalman filter and the Thevenin equivalent circuit model. The third determining unit is used to perform a weighted summation of the first reference value and the second reference value to obtain the remaining battery power.

[0011] According to a third aspect of this application, a non-transitory computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of any of the methods provided in the first aspect of this application.

[0012] According to a fourth aspect of this application, an electronic device is provided, comprising: A memory on which computer programs are stored; A processor for executing the computer program in the memory to implement the steps of any of the methods provided in the first aspect of this application.

[0013] According to a fifth aspect of this application, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the steps of the method described in any of the first aspects of this application.

[0014] By combining the ampere-hour integration method and the extended Kalman filter method, the accuracy and reliability of battery remaining capacity estimation can be improved. The ampere-hour integration method utilizes historical data and real-time charging / discharging current to provide a relatively accurate estimate of remaining battery capacity during calculation periods with drastic changes in battery dynamic characteristics. The extended Kalman filter method, combined with the Thevenin equivalent circuit model, considers the nonlinear characteristics and dynamic changes of the battery, thus providing a relatively accurate estimate of remaining battery capacity during calculation periods with stable battery dynamic characteristics. By weighted fusion of the two methods, their respective advantages can be balanced, reducing the error of a single method, and the weights of the two methods can be dynamically adjusted to adapt to changes in battery operating state, thereby improving the accuracy of battery remaining capacity estimation across the entire range.

[0015] Other features and advantages of this disclosure will be described in detail in the following detailed description section. Attached Figure Description

[0016] The accompanying drawings are provided to further illustrate the present disclosure and form part of the specification. They are used together with the following detailed description to explain the present disclosure, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart illustrating a method for determining the remaining battery power according to an exemplary embodiment; Figure 2 It is a curve showing the derivative of a preset function changing with discharge time, according to an exemplary embodiment; Figure 3 This is a flowchart illustrating a method for determining the remaining battery power according to another exemplary embodiment; Figure 4 This is a comparison chart of the estimated remaining battery capacity and the actual value of the extended Kalman filter method, the ampere-hour integration method, and the present disclosure, according to an exemplary embodiment. Figure 5 This is a magnified comparison diagram of the estimated remaining battery capacity and the actual value of the extended Kalman filter method, the ampere-hour integration method, and the present disclosure, according to an exemplary embodiment. Figure 6 This is a comparison chart showing the errors in estimating the remaining battery capacity using the extended Kalman filter, the ampere-hour integral method, and the present disclosure, according to an exemplary embodiment. Figure 7 A block diagram illustrating a device for determining the remaining battery power according to an exemplary embodiment; Figure 8 This is a block diagram illustrating an electronic device according to an exemplary embodiment. Detailed Implementation

[0017] The specific embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit this disclosure.

[0018] It should be noted that all actions involving the acquisition of signals, information, or data in this disclosure are carried out in compliance with the relevant data protection laws and policies of the country where the location is situated, and with the authorization of the owner of the relevant device.

[0019] In this article, the terms "first," "second," etc., are used only to distinguish one element from another, without requiring or implying any actual relationship or order between these elements. In fact, the first element can also be called the second element, and vice versa.

[0020] There are generally two techniques for estimating the remaining battery capacity: the ampere-hour integration method and the extended Kalman filter method.

[0021] The ampere-hour integration method is a method that estimates the real-time remaining battery capacity by monitoring the real-time charging / discharging current of the battery and integrating it. Its specific principle is as follows: First, obtain the initial remaining battery capacity at startup; second, monitor the charging / discharging current in real time; then, integrate the charging / discharging current to obtain the total charging / discharging capacity; based on the initial remaining battery capacity and the total charging / discharging capacity, calculate the real-time remaining battery capacity. However, this method has the following problems: (1) The initial value of the remaining battery capacity at startup must be accurately obtained, otherwise errors will occur. (2) Due to the influence of current sampling accuracy and sampling rate, it is difficult to obtain accurate remaining battery capacity values ​​under complex operating conditions. (3) Due to limitations in sensor speed and sampling accuracy, there is a difference between the actual current value and the sampled current value, leading to an accumulation of remaining battery capacity estimation errors over time. Therefore, the ampere-hour integration method has certain limitations and cannot provide high-precision remaining battery capacity estimation across the entire remaining battery capacity range.

[0022] The Extended Kalman Filter (EKF) is an algorithm for state estimation of nonlinear systems. Its specific principle includes two main steps: prediction and update. Prediction step: use the system model to predict the state and error covariance at the next moment. Update step: combine the actual measured values ​​to correct the prediction results and obtain the optimal estimate. Specifically, when estimating the remaining battery capacity, the EKF uses the Thevenin equivalent circuit model of the battery and takes the remaining battery capacity as one of the state variables; by measuring the battery's voltage, current and other parameters, and combining the equivalent model, the remaining battery capacity is estimated. However, the EKF has the following problems. (1) When the remaining battery capacity is in the inflection point region of the open circuit voltage-remaining battery capacity curve, a large estimation error will occur. This is because at the inflection point of the curve, the nonlinear characteristics of the battery model are strong, the linearization approximation effect of the EKF deteriorates, and the estimation accuracy decreases. (2) The choice of noise covariance cannot simultaneously meet the estimation accuracy requirements of different battery capacity regions. This is because the performance of the EKF largely depends on the choice of the noise covariance matrix. However, the characteristics of batteries vary greatly in different battery remaining charge regions, and a fixed noise covariance is difficult to adapt to this variation. (3) Sensitive to initial state and parameters. This is because the extended Kalman filter requires relatively accurate initial state estimation and model parameters. If the initial values ​​are not set properly, it may cause the filter to diverge or converge to the wrong result. (4) High computational complexity. The extended Kalman filter requires matrix operations, especially in high-dimensional state spaces, which has a large computational load and may affect real-time performance. (5) High requirements for model accuracy. The performance of the extended Kalman filter largely depends on the accuracy of the battery model. If the battery model is not accurate enough, it will lead to deviations in the estimation results.

[0023] To overcome the problems existing in related technologies, this application provides a method for determining the remaining battery capacity. This method is applicable to the estimation of remaining battery capacity in energy storage systems, such as those used in electric vehicles and grid-connected energy storage systems. The applicable scope of this method is as follows: all types of lithium batteries, such as ternary lithium batteries and lithium iron phosphate batteries; the full range of remaining battery capacity, i.e., 0-100%; different charge / discharge conditions, such as 1C (the battery is charged / discharged at one time its rated capacity) and 0.5C (the battery is charged / discharged at 0.5 times its rated capacity).

[0024] Figure 1 This is a flowchart illustrating a method for determining the remaining battery power according to an exemplary embodiment, including the following steps.

[0025] In step 110, the ampere-hour integration method is used to obtain a first reference value based on the historical value of the remaining battery capacity, the rated capacity of the battery, and the charging / discharging current of the battery. The historical value is the remaining battery capacity determined in the previous calculation cycle.

[0026] In step 120, an extended Kalman filter and the Thevenin equivalent circuit model are used to obtain a second reference value based on the historical value, the rated capacity, the charging / discharging current, and the battery voltage.

[0027] In step 130, the first reference value and the second reference value are weighted and summed to obtain the remaining battery power.

[0028] Here, the battery charging / discharging current refers to the current generated during charging and discharging. A battery generates a charging current when charging and a discharging current when discharging. In actual use, charging and discharging may alternate or occur independently. These current values ​​change continuously during battery use. The calculation cycle refers to the frequency at which the remaining battery capacity is calculated. For example, calculating the remaining battery capacity every minute constitutes one calculation cycle. The Thevenin equivalent circuit model is a second-order RC circuit model commonly used to describe the dynamic characteristics of a battery.

[0029] The specific calculation formula corresponding to step 110 is formula (1), where t is the current calculation cycle, t-1 is the previous calculation cycle, SOC is the remaining battery capacity, I is the real-time changing charging / discharging current from the previous calculation cycle to the current calculation cycle, when I>0, the battery discharges, and when I<0, the battery charges. n This refers to the battery's rated capacity.

[0030] (1) The calculation of the second reference value using the extended Kalman filter and the Thevenin equivalent circuit model can be divided into two main steps: Prediction step: Using the state equation, the remaining battery capacity and error covariance for the current calculation cycle are predicted based on the remaining battery capacity from the previous calculation cycle. The state equation describes the change in remaining battery capacity over time. Update step: The Kalman gain is calculated, and the estimated remaining battery capacity and error covariance for the current calculation cycle are updated using the actually measured battery voltage. The observation equation describes the relationship between battery voltage and remaining battery capacity. By iterating through these two steps, the estimated remaining battery capacity, i.e., the second reference value, can be dynamically adjusted.

[0031] It should be understood that weights are typically determined based on the reliability and applicability of the two methods. For example, a more reliable method can be given a higher weight. Alternatively, a more suitable method can be selected based on changes in the battery's dynamic characteristics.

[0032] It should also be understood that the above method can determine the remaining battery capacity for the current calculation cycle. This value will be used as the historical value for the next calculation cycle. Specifically, in the next calculation cycle, the ampere-hour integration method will use the currently determined remaining battery capacity as input; the extended Kalman filter method will also use this value as input. This can compensate for the ampere-hour integration method's tendency to accumulate errors. Simultaneously, by using a weighted summation method, it can also compensate for the extended Kalman filter method's inability to accurately reflect changes in the battery's dynamic characteristics. In summary, this method can utilize the calculation results from the previous cycle, improving the accuracy and reliability of the entire remaining battery capacity determination process.

[0033] By combining the ampere-hour integration method and the extended Kalman filter method, the accuracy and reliability of battery remaining capacity estimation can be improved. The ampere-hour integration method utilizes historical data and real-time charging / discharging current to provide a relatively accurate estimate of remaining battery capacity during calculation periods with drastic changes in battery dynamic characteristics. The extended Kalman filter method, combined with the Thevenin equivalent circuit model, considers the nonlinear characteristics and dynamic changes of the battery, thus providing a relatively accurate estimate of remaining battery capacity during calculation periods with stable battery dynamic characteristics. By weighted fusion of the two methods, their respective advantages can be balanced, reducing the error of a single method, and the weights of the two methods can be dynamically adjusted to adapt to changes in battery operating state, thereby improving the accuracy of battery remaining capacity estimation across the entire range.

[0034] In one embodiment, the method further includes: substituting the open-circuit voltage of the battery when it is first used into a preset function to obtain an initial value of the remaining battery power, wherein the initial value of the remaining battery power is the remaining battery power determined when the battery is in the first calculation cycle.

[0035] Here, open circuit voltage (OCV) refers to the terminal voltage of a battery when no external current flows through it. It reflects the internal chemical state of the battery and has a certain correlation with the remaining charge of the battery.

[0036] It should be understood that when a battery is first used, by measuring its open-circuit voltage, an initial value of the remaining battery capacity can be obtained according to a pre-established function. This initial value will serve as the remaining battery capacity value for the first calculation cycle. If the initial value of the remaining battery capacity is uncertain, subsequent calculations will be based on this uncertain initial value through integration, and the error will increase over time. Determining the initial value of the remaining battery capacity by measuring the open-circuit voltage avoids this problem of accumulated error.

[0037] In one embodiment, step 130 may include: determining a first weight corresponding to the first reference value and a second weight corresponding to the second reference value based on the derivative value of the preset function at the historical value, wherein the preset function is a mapping function between the remaining battery capacity and the open circuit voltage; and weighting and summing the first reference value and the second reference value according to their respective weights to obtain the remaining battery capacity.

[0038] Here, the preset function is typically non-linear, which can accurately describe the battery's remaining charge and open-circuit voltage (SOC-OCV) characteristics. The derivative of the preset function reflects the slope of the SOC-OCV curve, that is, the sensitivity of the battery's remaining charge to the open-circuit voltage. Within this curve, the sensitivity of the battery's remaining charge to the open-circuit voltage varies across different remaining charge ranges. The derivative of the preset function characterizes the battery's dynamic characteristics, changing as the battery discharges or charges.

[0039] Figure 2 This is a curve showing the derivative of a preset function changing with discharge time, according to an exemplary embodiment. For example... Figure 2 As shown, the derivative of the preset function is larger in the range where the remaining battery power is close to 20% and 80%, while the derivative is smaller in the other range.

[0040] In the range where the battery's remaining charge is close to 20% and 80%, the slope of the battery's SOC-OCV curve changes significantly, and the battery's dynamic characteristics change markedly. In this situation, the predictive power of the extended Kalman filter method based on the Thevenin equivalent circuit model decreases, making it difficult to accurately capture the actual state changes of the battery. In contrast, the ampere-hour integration method, which directly relies on real-time measured charging / discharging current, can more directly reflect changes in the battery's remaining charge, thus making the first reference value more accurate in these ranges.

[0041] For example, when the remaining battery capacity is far from the 20% and 80% range, the slope of the battery's SOC-OCV curve is relatively gentle, and the battery's dynamic characteristics are relatively stable. In this case, the extended Kalman filter method based on the Thevenin equivalent circuit model has stronger state prediction and update capabilities and can better capture changes in the remaining battery capacity. However, the ampere-hour integration method has relatively lower estimation accuracy in these ranges due to the influence of accumulated errors. Therefore, the second reference value is more accurate in these ranges.

[0042] Therefore, based on the derivative value of the preset function, the weights of the first and second reference values ​​can be dynamically adjusted to balance the advantages and disadvantages of the two methods in different battery remaining power ranges.

[0043] In one embodiment, determining the first weight corresponding to the first reference value and the second weight corresponding to the second reference value based on the derivative value of the preset function at the historical value includes: dividing the derivative value by the maximum value of the derivative function of the preset function to obtain the first weight, and the second weight is one minus the first weight.

[0044] Here, the derivative of the preset function, i.e., the derivative of the remaining battery capacity with respect to the open-circuit voltage, reaches its maximum value at certain points corresponding to the remaining battery capacity. This maximum value can represent the region where the remaining battery capacity is most sensitive to the open-circuit voltage. Using the maximum value of the derivative as a normalization factor, the derivative value can be mapped to a weight range between 0 and 1. This ensures that the sum of the first weight and the second weight is 1.

[0045] In one embodiment, the preset function is determined by the following steps: obtaining the remaining battery charge and open-circuit voltage after multiple discharges; and fitting the remaining battery charge and open-circuit voltage after multiple discharges to obtain the preset function.

[0046] Here, the remaining battery capacity and open-circuit voltage after multiple discharges can refer to the data after the battery discharge test. These data can be obtained through the following test methods. (1) Determine the rated capacity of the battery, take 10% of the rated capacity as a test step, determine the discharge current, and calculate the discharge time. (2) After fully charging, let the battery stand for 1-2 hours to allow the internal chemical reaction of the battery to stabilize. Measure the open-circuit voltage of the battery. (3) According to the calculation results of step (1), discharge the battery through an external circuit, and stop when the remaining battery capacity decreases by 10%. (4) Let the battery stand for 1-2 hours. (5) Measure the open-circuit voltage of the battery again. (6) Repeat steps (4)-(6) until the remaining battery capacity is equal to 0, and the test ends. Through this series of tests, the open-circuit voltage corresponding to the remaining battery capacity after multiple discharges can be obtained, and a preset function can be obtained by fitting.

[0047] For example, taking the Panasonic 18650 battery as the test object, the rated capacity of this battery is 3450mAh. When discharged using a current of 345mA, 10% of the charge can be discharged in one hour. By fitting the data from one discharge test, the preset function can be obtained as shown in equation (2).

[0048] (2) The derivative of equation (2) can be expressed as equation (3).

[0049] (3) The above method can be used to obtain the battery's preset function, which can provide a reliable reference for determining the battery's remaining power.

[0050] In one embodiment, the method further includes: acquiring the charging / discharging current via a current sensor; and acquiring the voltage via a voltage sensor.

[0051] Here, real-time charging / discharging current and voltage can be obtained through current and voltage sensors, which can be used to calculate the remaining battery power in real time.

[0052] Figure 3 This is a flowchart illustrating a method for determining the remaining battery power according to another exemplary embodiment. Figure 3 As shown, the initial value of the remaining battery capacity is first calculated based on the open-circuit voltage as the remaining battery capacity for the first calculation cycle. Then, real-time charging / discharging current and voltage are acquired using the aforementioned current and voltage sensors. Next, the remaining battery capacity for this calculation cycle is calculated using the ampere-hour integration method as the first reference value, and the remaining battery capacity for this calculation cycle is calculated using an extended Kalman filter based on the Thevenin equivalent circuit model as the second reference value. Then, the derivative of the function of remaining battery capacity minus open-circuit voltage for this calculation cycle is calculated, and the weights of the first and second reference values ​​are determined based on this derivative value. Finally, the weighted sum of the first and second reference values ​​is calculated to obtain the remaining battery capacity for this calculation cycle and output. This remaining battery capacity is then used as the calculation reference data for the next calculation cycle. By repeating the above process, the remaining battery capacity can be determined in real-time during battery use.

[0053] For the method disclosed herein, a Panasonic 18650 battery was selected as the test object and subjected to constant current discharge at a 1C discharge rate. The results were compared with those obtained by using the ampere-hour integration method alone, the extended Kalman filter method alone, and the actual remaining battery capacity. Figure 4 This is a comparison chart of the remaining battery capacity estimated by the extended Kalman filter method, the ampere-hour integration method, and the present disclosure, with the actual value, according to an exemplary embodiment. Figure 5 This is a magnified comparison diagram of the estimated remaining battery capacity and the actual value, based on an exemplary embodiment of the extended Kalman filter method, the ampere-hour integration method, and the present disclosure. Figure 6 This is a comparison chart illustrating the errors in estimating remaining battery capacity using the extended Kalman filter, the ampere-hour integral method, and the method provided in this disclosure, according to an exemplary embodiment. It can be seen that within the 80% and 20% neighborhood of remaining battery capacity, the method provided in this disclosure has the smallest error in determining the remaining battery capacity. Furthermore, the method provided in this disclosure achieves the highest accuracy in determining the remaining battery capacity throughout the entire discharge process of the battery.

[0054] According to a second aspect of this application, a battery remaining power determination device 700 is provided, comprising: The first determining unit 710 is used to obtain a first reference value by using the ampere-hour integration method based on the historical value of the remaining battery power, the rated capacity of the battery, and the charging / discharging current of the battery. The historical value is the remaining battery power determined in the previous calculation cycle. The second determining unit 720 is used to obtain a second reference value based on the historical value, the rated capacity, the charging / discharging current, and the battery voltage using the extended Kalman filter and the Thevenin equivalent circuit model. The third determining unit 730 is used to perform a weighted summation of the first reference value and the second reference value to obtain the remaining battery power.

[0055] Optionally, the battery remaining power determining device 700 further includes a fourth determining unit for: Substituting the open-circuit voltage of the battery at the start of use into a preset function yields the initial value of the remaining battery capacity. The initial value of the remaining battery capacity is the remaining battery capacity determined when the battery is in the first calculation cycle.

[0056] Optionally, the third determining unit 730 includes: The fifth determining unit is used to determine the first weight corresponding to the first reference value and the second weight corresponding to the second reference value based on the derivative value of the preset function at the historical value, wherein the preset function is a mapping function between the remaining battery power and the open circuit voltage; The sixth determining unit is used to perform a weighted summation of the first reference value and the second reference value according to their respective weights to obtain the remaining battery power.

[0057] Optionally, the sixth determining unit is specifically used for: The first weight is obtained by dividing the derivative value by the maximum value of the derivative of the preset function, and the second weight is one minus the first weight.

[0058] Optionally, the battery remaining power determining device 700 further includes a seventh determining unit, used for: Obtain the remaining battery charge and open-circuit voltage after multiple discharges; The preset function is obtained by fitting the remaining battery capacity and open-circuit voltage after multiple discharges.

[0059] Optionally, the battery remaining power determining device 700 further includes an eighth determining unit, used for: The charging / discharging current is obtained using a current sensor; The voltage is obtained by a voltage sensor.

[0060] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0061] Figure 8 This is a block diagram illustrating an electronic device 800 according to an exemplary embodiment. For example... Figure 8 As shown, the electronic device 800 may include a processor 801 and a memory 802. The electronic device 800 may also include one or more of a multimedia component 803, an input / output (I / O) interface 804, and a communication component 805.

[0062] The processor 801 controls the overall operation of the electronic device 800 to complete all or part of the steps in the method for determining the remaining battery power. The memory 802 stores various types of data to support the operation of the electronic device 800. This data may include, for example, instructions for any application or method operating on the electronic device 800, and application-related data such as contact data, sent and received messages, pictures, audio, video, etc. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and audio components. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in memory 802 or transmitted via communication component 805. The audio component also includes at least one speaker for outputting audio signals. I / O interface 804 provides an interface between processor 801 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual or physical buttons. Communication component 805 is used for wired or wireless communication between the electronic device 800 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IoT, eMTC, or other 5G technologies, or a combination thereof, is not limited here. Therefore, the corresponding communication component 805 may include: a Wi-Fi module, a Bluetooth module, an NFC module, etc.

[0063] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method for determining the remaining battery power.

[0064] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the method for determining the remaining battery power described above. For example, the computer-readable storage medium may be the memory 802 including the program instructions described above, which may be executed by the processor 801 of the electronic device 800 to complete the method for determining the remaining battery power described above.

[0065] In another exemplary embodiment, a computer program product is also provided, the computer program product comprising a computer program executable by a programmable device, the computer program having a code portion for performing the above-described method for determining the remaining battery power when executed by the programmable device.

[0066] The preferred embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, various simple modifications can be made to the technical solutions of this disclosure, and these simple modifications all fall within the protection scope of this disclosure.

[0067] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, this disclosure will not describe the various possible combinations separately.

[0068] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.

Claims

1. A method for determining the remaining capacity of a battery, characterized in that, The method includes: The ampere-hour integration method is used to obtain a first reference value based on the historical value of the remaining battery capacity, the rated capacity of the battery, and the charging / discharging current of the battery. The historical value is the remaining battery capacity determined in the previous calculation cycle. Using the extended Kalman filter and the Thevenin equivalent circuit model, a second reference value is obtained based on the historical value, the rated capacity, the charging / discharging current, and the battery voltage. The remaining battery power is obtained by weighted summation of the first reference value and the second reference value.

2. The method according to claim 1, characterized in that, The method further includes: Substituting the open-circuit voltage of the battery at the start of use into a preset function yields the initial value of the remaining battery capacity. The initial value of the remaining battery capacity is the remaining battery capacity determined when the battery is in the first calculation cycle.

3. The method according to claim 2, characterized in that, The step of weighted summing of the first reference value and the second reference value to obtain the remaining battery power includes: Based on the derivative value of the preset function at the historical value, the first weight corresponding to the first reference value and the second weight corresponding to the second reference value are determined, wherein the preset function is a mapping function between the remaining battery power and the open circuit voltage; The remaining battery power is obtained by weighting and summing the first reference value and the second reference value according to their respective weights.

4. The method according to claim 3, characterized in that, The step of determining the first weight corresponding to the first reference value and the second weight corresponding to the second reference value based on the derivative value of the preset function at the historical value includes: The first weight is obtained by dividing the derivative value by the maximum value of the derivative of the preset function, and the second weight is one minus the first weight.

5. The method according to any one of claims 2-4, characterized in that, The preset function is determined through the following steps: Obtain the remaining battery charge and open-circuit voltage after multiple discharges; The preset function is obtained by fitting the remaining battery capacity and open-circuit voltage after multiple discharges.

6. The method according to any one of claims 1-4, characterized in that, The method further includes: The charging / discharging current is obtained using a current sensor; The voltage is obtained by a voltage sensor.

7. A device for determining the remaining battery power, characterized in that, include: The first determining unit is used to obtain a first reference value by using the ampere-hour integration method based on the historical value of the remaining battery capacity, the rated capacity of the battery, and the charging / discharging current of the battery. The historical value is the remaining battery capacity determined in the previous calculation cycle. The second determining unit is used to obtain a second reference value based on the historical value, the rated capacity, the charging / discharging current, and the battery voltage using the extended Kalman filter and the Thevenin equivalent circuit model. The third determining unit is used to perform a weighted summation of the first reference value and the second reference value to obtain the remaining battery power.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method described in any one of claims 1-6.

9. An electronic device, characterized in that, include: A memory on which computer programs are stored; A processor for executing the computer program in the memory to implement the steps of the method according to any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-6.

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