Voltage signal adaptive filtering method and system of battery management system

Abstract

The invention provides a voltage signal adaptive filtering method and system of a battery management system, and relates to the technical field of battery management. The method comprises the following steps: calculating a voltage change rate between a current voltage sampling value and a historical filtering output voltage value; according to a comparison result of the voltage change rate and a preset threshold value, the filtering intensity is determined in a self-adaptive mode, and the voltage change rate is in negative correlation with the filtering intensity; and carrying out filtering processing on the current voltage sampling value by adopting the adaptively determined filtering intensity so as to output a smoothed voltage value. By calculating the voltage change rate between the current voltage sampling value and the historical filtering output value in real time and comparing the voltage change rate with the preset threshold value, the filtering intensity in negative correlation with the voltage change rate is adaptively determined: when the battery is in a steady state and the voltage change is slow, the system automatically adopts stronger filtering, so that the noise can be effectively inhibited; and the stability and the measurement precision of the voltage signal are ensured.

Description

Technical Field

[0001] This invention relates to the field of battery management technology, and in particular to a voltage signal adaptive filtering method and system for a battery management system. Background Technology

[0002] In battery management systems, accurate and rapid sampling of battery voltage is fundamental for achieving precise state estimation and safe control. Currently, systems commonly employ fixed-parameter filtering algorithms, such as first-order low-pass filtering or moving average filtering, to suppress high-frequency noise in the voltage sampling signal. A common characteristic of these methods is that their filtering strength, such as the cutoff frequency of the first-order low-pass filter and the window length of the moving average, is set before system operation and remains constant throughout the system's lifespan.

[0003] This fixed filtering strategy has inherent technical contradictions, making it difficult to meet the dynamic operating requirements of the battery. Firstly, when the battery is in a steady state, such as under static or low-current conditions, the voltage changes slowly. While a fixed strong filter can effectively smooth noise, it introduces significant signal lag and phase shift, affecting the real-time performance and accuracy of SOC estimation. Secondly, when battery conditions change drastically, such as during high-current charging and discharging, the voltage signal itself changes rapidly. If the fixed filter strength is set too weakly, a large amount of noise will remain in the output signal, causing voltage jumps; if it is set too strong, the filtered output cannot track the actual voltage changes, resulting in severe distortion. This contradiction between lag and insufficient dynamic tracking capability puts engineers in a dilemma when tuning parameters, often forcing them to adopt compromise solutions that cannot simultaneously achieve both steady-state accuracy and dynamic response. Summary of the Invention

[0004] The purpose of this invention is to provide a voltage signal adaptive filtering method and system for a battery management system, in order to solve the technical problem mentioned in the background art: the fixed filtering strategy has inherent technical contradictions, which makes it difficult for its performance to meet the needs of dynamic battery operation.

[0005] To achieve the above objectives, according to one aspect of the present invention, an adaptive filtering method for voltage signals in a battery management system is provided, the method comprising the following steps:

[0006] Calculate the rate of change of voltage between the current voltage sample value and the historical filtered output voltage value;

[0007] The filtering strength is adaptively determined based on the comparison result between the voltage change rate and the preset threshold, wherein the voltage change rate is negatively correlated with the filtering strength;

[0008] The current voltage sample value is filtered using the adaptively determined filtering strength to output a smoothed voltage value.

[0009] In one possible implementation, the step of calculating the rate of change of voltage between the current voltage sample value and the historical filtered output voltage value specifically includes:

[0010] Calculate the first difference between the current voltage sample value and the filtered output voltage value at the previous moment;

[0011] The voltage change rate is calculated based on the first difference.

[0012] In one possible implementation, the step of calculating the rate of change of voltage between the current voltage sample value and the historical filtered output voltage value specifically includes:

[0013] Calculate the second difference between the current voltage sample value and the voltage sample value at the previous moment;

[0014] The voltage change rate is calculated based on the second difference.

[0015] In one possible implementation, the step of adaptively determining the filter strength based on the comparison result of the voltage change rate and a preset threshold specifically includes:

[0016] If the voltage change rate is less than the preset threshold, then the first filter strength is determined;

[0017] If the voltage change rate is greater than or equal to the preset threshold, then the second filter strength is determined;

[0018] The first filter strength is greater than the second filter strength.

[0019] In one possible implementation, the filtering process employs a first-order low-pass filtering algorithm, and the filtering strength is reflected by the filtering coefficients.

[0020] The first filtering strength corresponds to the first filtering coefficient, the second filtering strength corresponds to the second filtering coefficient, and the first filtering coefficient is greater than the second filtering coefficient.

[0021] In one possible implementation, the first filter coefficient has a value range of 0.8 to 0.95, and the second filter coefficient has a value range of 0.1 to 0.4.

[0022] In one possible implementation, the preset threshold value ranges from 2mV / s to 3mV / s.

[0023] According to another aspect of the present disclosure, a battery management system is provided, the battery management system comprising:

[0024] The sampling circuit is configured to acquire the battery's voltage signal;

[0025] The processing circuit is communicatively connected to the sampling circuit and is configured to perform the steps of the voltage signal adaptive filtering method of the battery management system as described in any of the possible implementations above.

[0026] A communication interface, connected to the processing circuit, is configured to output filtered voltage data.

[0027] According to another aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the voltage signal adaptive filtering method of the battery management system as described in any of the possible implementations above.

[0028] According to another aspect of the present disclosure, an electronic device is provided, the electronic device comprising:

[0029] Memory is used to store executable instructions for a computer;

[0030] A processor is configured to execute computer-executable instructions in the memory to implement the steps of the voltage signal adaptive filtering method for a battery management system as described in any of the possible implementations above.

[0031] The above-described one or more technical solutions in the embodiments of this application have at least one or more of the following technical effects:

[0032] This invention provides an adaptive voltage signal filtering method for a battery management system. By calculating the rate of change of voltage between the current voltage sample value and the historical filtered output value in real time and comparing it with a preset threshold, the system adaptively determines the negatively correlated filtering strength. When the battery is in a steady state and the voltage changes slowly, the system automatically adopts a stronger filter, effectively suppressing noise and ensuring the stability and measurement accuracy of the voltage signal, laying the foundation for accurate SOC estimation. When the battery operating conditions change drastically and the rate of voltage change increases, the system intelligently weakens the filtering strength, enabling the filtered output to quickly track the actual voltage change, significantly reducing signal lag and phase shift, and avoiding voltage distortion and jumps during dynamic processes. This adaptive core strategy successfully achieves the optimal balance between smooth suppression of high-frequency noise in the voltage signal and agile tracking of the battery's actual voltage dynamic response. It fundamentally solves the dilemma faced by traditional methods in parameter tuning, simultaneously ensuring high accuracy in steady state and fast response in dynamic states without compromise, greatly improving the real-time performance, accuracy, and overall safety management reliability of the battery management system.

[0033] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0034] Figure 1 This is a schematic flowchart of a voltage signal adaptive filtering method for a battery management system according to an exemplary embodiment.

[0035] Figure 2 This is a schematic diagram of the composition structure of a battery management system module according to an exemplary embodiment;

[0036] Figure 3 This is a schematic diagram of raw cell voltage data provided according to an exemplary embodiment;

[0037] Figure 4 To provide a fixed filter coefficient according to an exemplary embodiment A schematic diagram illustrating the filtering effect;

[0038] Figure 5 To provide a fixed filter coefficient according to an exemplary embodiment A schematic diagram illustrating the filtering effect;

[0039] Figure 6 To provide a fixed filter coefficient according to an exemplary embodiment A schematic diagram illustrating the filtering effect;

[0040] Figure 7 To provide a fixed filter coefficient according to an exemplary embodiment A schematic diagram illustrating the filtering effect;

[0041] Figure 8 First filter coefficients provided according to an exemplary embodiment Second filter coefficient A schematic diagram of the adaptive filtering effect;

[0042] Figure 9 First filter coefficients provided according to an exemplary embodiment Second filter coefficient A schematic diagram of the adaptive filtering effect of 5;

[0043] Figure 10 First filter coefficients provided according to an exemplary embodiment Second filter coefficient A schematic diagram of the adaptive filtering effect;

[0044] Figure 11First filter coefficients provided according to an exemplary embodiment Second filter coefficient A schematic diagram of the adaptive filtering effect.

[0045] Explanation of reference numerals in the attached figures: 100, sampling circuit; 200, processing circuit; 300, communication interface. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this disclosure clearer, the embodiments of this disclosure will be described in further detail below with reference to the accompanying drawings.

[0047] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of systems and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0048] Figure 1 This is a flowchart of a voltage signal adaptive filtering method for a battery management system according to an exemplary embodiment, as shown below. Figure 1 As shown, the method includes the following steps:

[0049] In step S100, the voltage change rate between the current voltage sample value and the historical filtered output voltage value is calculated; the voltage change rate is used to characterize the degree of dynamic change of the battery voltage signal at the current sampling time, which provides a decision basis for subsequently adaptively determining the filtering strength.

[0050] In step S200, the filtering strength is adaptively determined based on the comparison result between the voltage change rate and a preset threshold, wherein the voltage change rate is negatively correlated with the filtering strength. After calculating the voltage change rate, the filtering strength to be used in the current sampling period is adaptively determined based on its comparison result with at least one preset threshold. The filtering strength is negatively correlated with the voltage change rate; that is, when the voltage change rate increases, the filtering strength is weakened; when the voltage change rate decreases, the filtering strength is strengthened.

[0051] Preferably, the filter coefficients of the first-order low-pass filter are used. As a direct representation of the filtering strength. The value range is between 0 and 1, and The value of is positively correlated with the filter strength, that is The larger the value, the greater the weight of historical values, the stronger the filtering effect, and the smoother the output, but the greater the signal lag. The smaller the value, the greater the weight of the current sampled value, the weaker the filtering effect, the more sensitive the output, but the smaller the lag.

[0052] In step S300, the current voltage sample value is filtered using the adaptively determined filter strength to output a smoothed voltage value. After determining a filter strength that matches the current battery operating condition, this strength is used to perform filtering calculations on the current voltage sample value, ultimately outputting a smooth, delayed voltage signal for state estimation and safety monitoring of the battery management system.

[0053] Preferably, this step is performed using a first-order low-pass filtering algorithm as the framework.

[0054] Specifically, the filter coefficients adaptively determined in step S200 Substitute the values ​​into the first-order low-pass filter formula. Simultaneously, obtain the current sampling time. The original voltage sample value and the historical filtered output voltage value at the previous sampling time .

[0055] The current filtered output voltage value is calculated using the following formula. :

[0056]

[0057] in:

[0058] This is the original voltage value collected by the current AFE.

[0059] This is the historical filtered output voltage value from the previous moment.

[0060] The filter coefficients are adaptively determined based on the voltage change rate in step S200.

[0061] The calculation obtained This is the smoothed voltage value output at the end of this cycle. The system outputs this value to subsequent modules such as SOC estimation and equalization control. Simultaneously, this... Value storage, as the next sampling period ( (At any given time) calculate the rate of voltage change and the historical filter output voltage value required to perform the filtering. This completes a full processing cycle.

[0062] By calculating the rate of change of voltage between the current voltage sample value and the historical filtered output value in real time and comparing it with a preset threshold, the system adaptively determines the filtering strength that is negatively correlated with the voltage. When the battery is in a steady state and the voltage changes slowly, the system automatically adopts a stronger filter, which can effectively suppress noise and ensure the stability and measurement accuracy of the voltage signal, laying the foundation for accurate SOC estimation. When the battery operating conditions change drastically and the rate of change of voltage increases, the system intelligently weakens the filtering strength, enabling the filtered output to quickly track the real voltage change, significantly reducing signal lag and phase shift, and avoiding voltage distortion and jumps in the dynamic process. This adaptive core strategy successfully achieves the optimal balance between smooth suppression of high-frequency noise in the voltage signal and agile tracking of the battery's real voltage dynamic response. It fundamentally solves the dilemma faced by traditional methods in parameter tuning, simultaneously ensuring high accuracy in steady state and fast response in dynamic state without compromise, greatly improving the real-time performance, accuracy, and overall safety control reliability of the battery management system.

[0063] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0064] In an exemplary embodiment, the step of calculating the rate of change of voltage between the current voltage sample value and the historical filtered output voltage value specifically includes:

[0065] Calculate the first difference between the current voltage sample value and the filtered output voltage value at the previous moment;

[0066] The voltage change rate is calculated based on the first difference.

[0067] Specifically, obtain the current sampling period voltage sampling value and the previous sampling period Historical filtered output voltage values The historical filtered output voltage value It is the smoothed voltage value that was finally output by this adaptive filtering method in the previous cycle.

[0068] Based on the current voltage sample value and the historical filtered output voltage value, the difference between the two is calculated; to eliminate the influence of the direction of change on the filtering decision, the absolute value of this difference is taken to obtain the absolute difference of voltage change. :

[0069]

[0070] Furthermore, to normalize the absolute difference into a standard quantity independent of the sampling frequency, it is divided by the system's fixed sampling time interval. Thus, the rate of change of voltage, expressed as the change in voltage per unit time, is obtained. :

[0071]

[0072] The voltage change rate The unit can be volts per second (V / s). Those skilled in the art will understand that the sampling time interval... These are the pre-defined known parameters in the BMS.

[0073] This approach effectively distinguishes between real voltage dynamics and sampling noise by comparing the current raw sampled value with smoothed historical state estimates, providing a stable rate of change assessment. It is particularly suitable for battery management systems in noisy environments.

[0074] In another embodiment, the step of calculating the rate of change of voltage between the current voltage sample value and the historical filtered output voltage value specifically includes:

[0075] Calculate the second difference between the current voltage sample value and the voltage sample value at the previous moment;

[0076] The voltage change rate is calculated based on the second difference.

[0077] Specifically, obtain the current sampling time The original voltage sample value and the original voltage sample value at the previous sampling time. Calculate the absolute difference between these two as the second difference. :

[0078]

[0079] Based on the second difference Then, the rate of change of voltage is calculated:

[0080]

[0081] This scheme calculates the instantaneous rate of change directly based on continuous raw sampled values, which has higher detection sensitivity and shorter response delay for voltage surges. It is suitable for application scenarios with extremely high requirements for dynamic response speed and low noise levels.

[0082] In typical battery management systems, noise interference is unavoidable in the sampled signals. The voltage change rate calculation scheme based on historical filtered output values, due to its better noise immunity and system stability by referencing smoothed historical states, is considered a preferred implementation. The voltage change rate calculation scheme based on continuous raw sampled values, on the other hand, provides another feasible technical option for those skilled in the art in specific application requirements. These two implementation schemes together enrich the technical content of this invention and expand its application adaptability.

[0083] In an exemplary embodiment, the step of adaptively determining the filter strength based on the comparison result of the voltage change rate and a preset threshold specifically includes:

[0084] If the voltage change rate is less than the preset threshold, then the first filter strength is determined;

[0085] If the voltage change rate is greater than or equal to the preset threshold, then the second filter strength is determined;

[0086] The first filter strength is greater than the second filter strength.

[0087] Specifically, a preset voltage change rate threshold is set as the preset threshold. This threshold is used to distinguish between a stable state and a dynamically changing state of battery voltage. Its value is determined through experimental calibration and is usually a small positive value. For example, the preset threshold ranges from 2mV / s to 3mV / s to effectively distinguish between high-frequency noise and real voltage changes.

[0088] The calculated real-time voltage change rate With the preset threshold Comparison:

[0089] If the voltage change rate Less than the preset threshold If the signal is stable, the battery is determined to be in a stable state, and the voltage signal fluctuations at this point are mainly due to sampling noise. Based on this, the system determines and employs a strong first filter strength to effectively suppress noise and output a highly stable voltage value.

[0090] If the voltage change rate Greater than or equal to the preset threshold If the system detects a real and rapid dynamic change in battery voltage, such as during high-current charging / discharging or nearing the voltage cutoff point, it determines that the battery voltage is undergoing a change. Based on this, the system determines and applies a weaker second filter strength. This setting significantly weakens the filtering effect, allowing the filter output to closely track the actual voltage changes and effectively avoiding significant signal lag caused by excessive filtering.

[0091] Finally, the filtered output voltage value at the current moment will be calculated based on the determined filter coefficients. Output is provided for use by upper-level applications of the battery management system.

[0092] This embodiment achieves rapid two-state switching through a single threshold, with simple logic and low computational load, making it very suitable for implementation in embedded systems. It explicitly divides the battery operating state into steady-state and dynamic modes and matches them with distinctly different filtering strategies, thus technically resolving the core contradiction that fixed-parameter filtering cannot simultaneously achieve noise suppression and dynamic tracking.

[0093] Preferably, the filtering process employs a first-order low-pass filtering algorithm, and the filtering strength is reflected by the filtering coefficients;

[0094] The first filtering strength corresponds to the first filtering coefficient, the second filtering strength corresponds to the second filtering coefficient, and the first filtering coefficient is greater than the second filtering coefficient.

[0095] Where the filter coefficients The value range is from 0 to 1, and the filter coefficient The value of is negatively correlated with the filter strength, that is The larger the value, the weaker the filtering effect and the smaller the hysteresis. The smaller the value, the stronger the filtering effect, but the greater the lag.

[0096] Specifically, the calculated real-time voltage change rate With the preset threshold Comparison:

[0097] like If the battery is in a stable state, then the first filter coefficient is applied. ;

[0098] like If the battery is in a dynamic state, then the second filter coefficient is used. 2;

[0099] Wherein, the first filter coefficient Greater than the second filter coefficient 2. This relationship ensures that when the battery is in a stable state, the system uses a relatively large filter coefficient to provide a smooth output; while when the battery is in a dynamically changing state, the system uses a relatively small filter coefficient to quickly track the actual voltage changes.

[0100] To verify the effectiveness of the adaptive filtering algorithm described above, we conducted multiple sets of comparative experiments. The experiments used actual battery cell voltage data as the test subjects, and examined the performance of fixed-parameter filtering and adaptive filtering respectively.

[0101] Figure 3 The raw cell voltage data used in the test are shown. Figures 4 to 7 To use different fixed filter coefficients The simulation results after filtering are shown in the diagram. From the simulation results above, the filtering coefficients... , The larger the value, the stronger the filtering effect, but the greater the lag; if the software... If the value is fixed, the voltage output by the BMS will be distorted under complex changes in external environmental factors.

[0102] In one specific embodiment, the first filter coefficient The value range is from 0.8 to 0.95; the second filter coefficient The value range is from 0.1 to 0.4. Specifically, when At this time, it is considered to be interference caused by noise. Adaptive, using the first filter coefficient , the first filter coefficient The value is set between 0.8 and 0.95 for strong filtering to remove clutter. At this point, it is assumed that the voltage is indeed changing. Adaptive, using the second filter coefficient ,Will Setting the value between 0.1 and 0.4 weakens the filtering effect, allowing the filtered value to closely follow the changes in the actual voltage and avoiding the introduction of large hysteresis.

[0103] Please see Figures 7 to 10 , Figures 7 to 10 The simulation results show the effects of adaptive filtering with different parameter combinations. Comparative analysis reveals that when the first filter coefficient... Choose a value in the range of 0.8 to 0.95 for the second filter coefficient. The optimal filtering effect is achieved when the value is selected within the range of 0.1 to 0.4. Within this parameter range, the system can effectively suppress noise interference in steady state and quickly track the real voltage signal during dynamic changes. The adaptive filtering algorithm successfully achieves the best balance between noise smoothing and dynamic tracking, ensuring that the output signal is neither distorted by excessive filtering nor contains too much noise by insufficient filtering.

[0104] Please see Figure 2 This disclosure also provides a battery management system, including:

[0105] The sampling circuit 100 is configured to acquire the voltage signal of the battery; specifically, the sampling circuit 100 may include an analog front-end acquisition chip and related signal conditioning circuitry, used to synchronously or time-divisionally sample the voltage of each battery cell in the battery pack, and convert the analog voltage signal into a digital sample value. .

[0106] Processing circuit 200, communicatively connected to sampling circuit 100, is configured to perform the steps of the voltage signal adaptive filtering method of the battery management system as described in any of the above example embodiments; processing circuit 200 may be a microcontroller unit, digital signal processor, application-specific integrated circuit, or any other form of processing device with data processing capabilities. Specifically, processing circuit 200 is configured to:

[0107] Receive the raw voltage sample value from the sampling circuit 100;

[0108] Calculate the rate of change of voltage between the current voltage sample value and the historical filtered output voltage value;

[0109] The filtering strength is adaptively determined based on the comparison between the voltage change rate and the preset threshold.

[0110] The current voltage sample value is filtered using the adaptively determined filter strength to obtain a smoothed voltage value. .

[0111] A communication interface 300, connected to the processing circuit 200, is configured to output filtered voltage data. The communication interface 300 may include at least one of a controller area network bus interface, a serial peripheral interface, and an Ethernet interface, for transmitting the filtered voltage data to a host system such as a vehicle controller, a display device, or a data storage device.

[0112] During system operation, the sampling circuit 100 continuously acquires battery voltage signals and transmits them to the processing circuit 200. The processing circuit 200 executes the adaptive filtering method, dynamically adjusting the filtering intensity in real time according to the voltage change rate to obtain an optimized voltage value that effectively suppresses noise while quickly tracking real changes. Finally, this optimized voltage data is output through the communication interface 300, providing a high-quality data foundation for accurate battery state estimation and reliable safety management. By implementing the aforementioned adaptive filtering method in hardware, this system effectively resolves the contradiction between steady-state accuracy and dynamic response in traditional fixed-parameter filtering while maintaining low hardware costs, significantly improving the overall performance of the battery management system.

[0113] In an exemplary embodiment, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the voltage signal adaptive filtering method of the battery management system as described in any of the exemplary embodiments above. Optionally, the storage medium is a non-transitory computer-readable storage medium, such as a ROM (Read-Only Memory), RAM (Random Access Memory), CD-ROM (Compact Disc Read-Only Memory), magnetic tape, floppy disk, and optical data storage device, etc.

[0114] In an exemplary embodiment, the present invention also provides an electronic device, characterized in that it comprises:

[0115] The memory is used to store computer-executable instructions; the memory may include read-only memory, flash memory, random access memory or any combination thereof, and is used to store program code and related parameters for implementing the adaptive filtering method, including but not limited to preset threshold, filtering coefficient range, sampling time interval, etc.

[0116] A processor is configured to execute computer-executable instructions in the memory to implement the steps of the voltage signal adaptive filtering method for a battery management system as described in any of the exemplary embodiments above. The processor may include a microcontroller, a microprocessor, a digital signal processor, an application-specific integrated circuit (ASIC), or any combination thereof, configured to execute instructions stored in the memory.

[0117] The electronic device may be the main controller in a battery management system, a vehicle control unit, or a computing device specifically used for battery state estimation.

[0118] In an exemplary embodiment, the present invention also provides a computer program product comprising computer program code stored in a computer-readable storage medium, wherein a processor of a computer device reads the computer program code from the computer-readable storage medium and executes the computer program code, causing the computer device to perform the operations performed in the voltage signal adaptive filtering method of the battery management system described above.

[0119] Any aspects of this invention not described in detail are well-known to those skilled in the art.

[0120] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. An adaptive filtering method for voltage signals in a battery management system, characterized in that, Includes the following steps: Calculate the rate of change of voltage between the current voltage sample value and the historical filtered output voltage value; The filtering strength is adaptively determined based on the comparison result between the voltage change rate and the preset threshold, wherein the voltage change rate is negatively correlated with the filtering strength; The current voltage sample value is filtered using the adaptively determined filtering strength to output a smoothed voltage value.

2. The voltage signal adaptive filtering method for a battery management system according to claim 1, characterized in that, The step of calculating the rate of change of voltage between the current voltage sample value and the historical filtered output voltage value specifically includes: Calculate the first difference between the current voltage sample value and the filtered output voltage value at the previous moment; The voltage change rate is calculated based on the first difference.

3. The voltage signal adaptive filtering method for a battery management system according to claim 1, characterized in that, The step of calculating the rate of change of voltage between the current voltage sample value and the historical filtered output voltage value specifically includes: Calculate the second difference between the current voltage sample value and the voltage sample value at the previous moment; The voltage change rate is calculated based on the second difference.

4. The voltage signal adaptive filtering method for a battery management system according to claim 1, characterized in that, The step of adaptively determining the filter strength based on the comparison result of the voltage change rate and the preset threshold specifically includes: If the voltage change rate is less than the preset threshold, then the first filter strength is determined; If the voltage change rate is greater than or equal to the preset threshold, then the second filter strength is determined; The first filter strength is greater than the second filter strength.

5. The voltage signal adaptive filtering method for a battery management system according to claim 4, characterized in that, The filtering process employs a first-order low-pass filtering algorithm, and the filtering strength is reflected by the filtering coefficients. The first filtering strength corresponds to the first filtering coefficient, the second filtering strength corresponds to the second filtering coefficient, and the first filtering coefficient is greater than the second filtering coefficient.

6. The voltage signal adaptive filtering method for a battery management system according to claim 5, characterized in that, The first filter coefficient has a value range of 0.8 to 0.95, and the second filter coefficient has a value range of 0.1 to 0.

4.

7. The voltage signal adaptive filtering method for a battery management system according to claim 4, characterized in that, The preset threshold value ranges from 2mV / s to 3mV / s.

8. A battery management system, characterized in that, include: The sampling circuit is configured to acquire the battery's voltage signal; A processing circuit, communicatively connected to the sampling circuit, is configured to perform the steps of the voltage signal adaptive filtering method of the battery management system as described in any one of claims 1 to 7; A communication interface, connected to the processing circuit, is configured to output filtered voltage data.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the voltage signal adaptive filtering method of the battery management system as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, include: Memory is used to store executable instructions for a computer; A processor for executing computer-executable instructions in the memory to implement the steps of the voltage signal adaptive filtering method of the battery management system according to any one of claims 1 to 7.

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