A method and system for estimating the state of charge of a power battery in an intelligent BMS

Abstract

The present invention discloses a method and system for estimating the state of charge of a power battery of an intelligent BMS, which relates to the field of battery management, and includes: a monitoring module for monitoring battery operating status information in real time and storing the monitored battery operating status information as sample information; a selection module for traversing the stored sample information in the monitoring module and selecting sample information from the stored sample information; the present invention collects operating data such as the remaining power, current, and voltage of the battery in real time from multiple dimensions, intelligently screens samples based on the similarity of environmental parameters, constructs a dynamic correction model for charging / discharging scenarios, and accurately calibrates the power percentage based on the integration of multiple parameters using a normalization algorithm, breaking through the limitations of a traditional single algorithm and effectively eliminating the error accumulation of the ampere-hour integration method under complex working conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management, and in particular to a method and system for estimating the state of charge of a power battery of an intelligent BMS. Background Art

[0002] Estimating the state of charge (SOC) of a power battery involves using algorithms to assess the remaining battery charge in real time. This is a core function of a battery management system. Common methods include the ampere-hour integration method, the open-circuit voltage method, and the Kalman filter. These methods comprehensively consider parameters such as current, voltage, and temperature. Accurate estimation optimizes battery efficiency, extends battery life, and ensures the accuracy and safety of electric vehicles.

[0003] The invention patent application with application number 201310121490.6 discloses a method for estimating the state of charge of a power battery system, wherein the power battery system includes a plurality of battery cells connected in series and / or in parallel, and the estimation method includes the following steps: S1, obtaining the open circuit voltage and corresponding temperature step: collecting the open circuit voltage 0CV and the center temperature T of each battery cell in the power battery system; S2, obtaining the minimum state of charge S0C1 and the maximum state of charge S0C2 step: obtaining the minimum open circuit voltage 0CV1 among the open circuit voltages 0CV of each battery cell, and the first temperature T1 of the battery cell with the minimum open circuit voltage 0CV1; obtaining the open circuit voltage 0CV of each battery cell The maximum open circuit voltage OCV2, and the second temperature T2 of the battery cell with the maximum open circuit voltage OCV2: query the OCV-S0C-T three-dimensional table according to the minimum open circuit voltage OCV1 and the first temperature T1 to obtain the minimum state of charge S0C1: query the OCV-S0C-T three-dimensional table according to the maximum open circuit voltage OCV2 and the second temperature T2 to obtain the maximum state of charge S0C2. This application aims to solve the problem that "there is inevitably a voltage difference between the battery cell voltages in the battery pack, such as causing the voltage of one or several battery cells to be too high or too low. At this time, if the S0C is estimated and the power value is reported according to the above-mentioned average OCV, there will be a very large deviation."

[0004] However, existing technologies for estimating the state of charge of power batteries often lead to accumulated estimation errors over time, and most of the relevant parameters related to the battery state of charge estimation are not fully considered when estimating the battery state of charge, resulting in certain deviations in the estimation results.

[0005] Therefore, a method and system for estimating the state of charge of a power battery of an intelligent BMS are proposed. Summary of the Invention

[0006] In view of the above-mentioned shortcomings of the prior art, the present invention provides a method and system for estimating the state of charge of a power battery of an intelligent BMS, which can effectively solve the problems of the prior art.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0008] The present invention discloses a power battery state of charge estimation system of an intelligent BMS, comprising:

[0009] The monitoring module is used to monitor the battery operating status information in real time and store the monitored battery operating status information as sample information; the selection module is used to traverse the sample information stored in the monitoring module and select sample information from the stored sample information; the correction module is used to receive the sample information selected by the selection module and correct the battery remaining power percentage in the sample information based on the sample information; the extraction module is used to receive the battery remaining power percentage correction result in the correction module and extract the battery remaining power correction factor based on the correction result; the application module is used to obtain the current battery remaining power percentage and correct the current battery remaining power percentage based on the battery remaining power correction factor extracted by the extraction module; the reset module is used to reset the system operation according to a preset period.

[0010] Furthermore, the monitoring module operates synchronously with the charging or discharging operation of the battery, and the battery operating status information monitored by the monitoring module includes: battery remaining power percentage, current, voltage, temperature, internal resistance, electrochemical impedance spectrum, electrolyte concentration, charge and discharge rate, capacity decay, and charge and discharge curve slope;

[0011] After monitoring the battery operating status information, the monitoring module simultaneously performs integrity identification on the battery charge and discharge information. If the identification result is complete, the monitored battery operating status information is stored. The integrity identification logic of the battery charge and discharge information is as follows: whether all types of battery operating status information are included in the current monitoring result;

[0012] Among them, when the monitoring module stores the sample information, it simultaneously marks the sample information differently. The marking content is: monitoring under charging state, monitoring under discharging state. The monitoring module distinguishes and stores the sample information based on the marking content. The percentage of remaining battery power in the battery operating status information is obtained through the ampere-hour integration method.

[0013] Furthermore, when the monitoring module stores the sample information, it iterates the sample information stored internally based on a preset period, and the iteration target is always the earliest sample information stored in the monitoring module that meets the preset period;

[0014] The selection module is provided with a determination unit and an identification unit at the lower level. The determination unit is used to determine the storage status of the sample information in the monitoring module and trigger the corresponding selection strategy based on the storage status of the sample information. The identification unit is used to identify the similarity of the associated information to which each sample information belongs.

[0015] The associated information of the sample information is derived from the battery operation message or electronic log, and the associated information of the sample information includes: the source time of the sample information, the ambient temperature, air pressure, and humidity during the sample information monitoring stage.

[0016] Furthermore, when the selection module selects the sample information, it performs an independent selection operation based on the sample information stored and differentiated therein, that is, it performs a selection operation once for the sample information monitored in the charging state and performs a selection operation once for the sample information monitored in the discharging state;

[0017] The sample information storage status and corresponding selection strategy determined by the determination unit include:

[0018] Storage status: the number of sample information groups is unique; selection strategy: no selection operation is performed;

[0019] Storage status: sample information is not unique and is less than or equal to three groups; selection strategy: select all;

[0020] Storage status: more than three groups of sample information; selection strategy: select the three groups of sample information with the highest cumulative similarity of the associated information of each sample information;

[0021] The recognition unit is triggered to run when the storage state is that the sample information exceeds three groups.

[0022] Furthermore, the similarity of the associated information to which the sample information belongs is calculated using the following formula:

[0023] ;

[0024] Where: is the similarity between associated information a and associated information b; The source time of the sample information in the associated information a and the associated information b; The ambient temperature during the monitoring phase of the sample information in the associated information a and associated information b; is the air pressure in the associated information a and the associated information b; is the humidity in associated information a and associated information b; is the adjustment factor;

[0025] Among them, the adjustment factor The value is subject to: The numerator of the fraction is less than or equal to the denominator, then =1, The numerator of the fraction is greater than the denominator, then =-1, weight The sum is 1, and obeys , The initial default values ​​are 0.4, 0.3, 0.2, and 0.1 respectively.

[0026] Furthermore, the logic for correcting the remaining battery power percentage in the sample information in the correction module is expressed as follows:

[0027] ;

[0028] Where: The percentage of remaining battery power after correction; The percentage of remaining power of the original battery; 、 、 、 、 、 、 、 、 Respectively represent the current, voltage, temperature, internal resistance, electrochemical impedance spectrum, electrolyte concentration, charge and discharge rate, capacity decay, and charge and discharge curve slope, which are normalized and mapped to the value of [0, 1] interval;

[0029] in, 、 、 、 、 、 、 、 、 When performing normalization processing, normalization is performed based on the corresponding standard values ​​of each parameter, and a corrected battery remaining power percentage is obtained based on each group of samples.

[0030] Furthermore, the extraction module is provided with a differentiation unit at a lower level, and the differentiation unit is used to receive the battery remaining power percentage correction result from the correction module and differentiate the correction results;

[0031] The differentiation logic of the correction results by the differentiation unit is as follows: identifying the tag content of the sample information corresponding to the correction result, and differentiating the correction results based on the tag content, so that the correction results are divided into two groups, and the two groups of correction results correspond to two different tag contents respectively;

[0032] The correction factor extraction logic for the remaining battery capacity corresponds to different tag contents:

[0033] Monitoring during charging:

[0034] ;

[0035] Monitoring during discharge:

[0036] ;

[0037] Where: Correction factor for remaining battery capacity in charging state; is the total amount of correction results in the charging state; is the qth correction result; The remaining battery power percentage corresponding to the qth correction result; is the weight coefficient; Correction factor for the remaining battery capacity in the discharged state; is the total amount of correction results in the discharge state; is the remaining battery capacity percentage corresponding to the jth correction result; is the jth correction result; is the weight coefficient;

[0038] in, 、 The two groups of correction results correspond to the distinction. 、 The value is an integer in the range [1, 3]. In formula (1), The value is subject to: the larger the correction result value of each product term is, the larger the value itself is; otherwise, the smaller the value itself is, and are all positive numbers, and their sum is 1; in formula (2) The values ​​are subject to: all values ​​are equal, and They are all positive numbers, and their sum is 1.

[0039] Furthermore, the application module is internally provided with an identification unit, which is used to identify the current battery status;

[0040] When the identification result of the identification unit is that the current battery is in the charging state, Correct the current remaining battery power percentage:

[0041] ;

[0042] If the identification result shows that the battery is in discharge state, Correct the current remaining battery power percentage:

[0043] ;

[0044] Where: The current percentage of remaining battery power after correction; The current remaining battery power percentage.

[0045] Furthermore, the monitoring module is interactively connected to the selection module through a wireless network, the selection module is interactively connected to a determination unit and an identification unit at a lower level through a wireless network, the selection module is interactively connected to a correction module and an extraction module through a wireless network, the extraction module is interactively connected to a distinction unit at a lower level through a wireless network, the distinction unit is interactively connected to the correction module through a wireless network, the extraction module is interactively connected to an application module and a reset module through a wireless network, and the application module is interactively connected to the identification unit through a wireless network.

[0046] On the other hand, a method for estimating the state of charge of a power battery of an intelligent BMS includes:

[0047] Monitor the battery operating status information in real time, identify the integrity of the monitored battery operating status information, store the complete battery operating status information based on the identification result, record the stored battery operating status information as sample information, set the sample information selection logic, and select sample information from the stored sample information based on the sample information selection logic; correct the battery remaining power percentage in each sample information according to the selected sample information, and extract the battery remaining power correction factor based on the correction result; identify the current battery operating status, select the battery remaining power correction factor according to the current battery operating status, and correct the current battery remaining power percentage; preset a reset cycle, and reset the battery remaining power correction factor based on the reset cycle.

[0048] Compared with the prior art, the technical solution provided by the present invention has the following beneficial effects:

[0049] The present invention provides a method and system for estimating the state of charge of a power battery of an intelligent BMS. During execution, the method and system collect operating data such as the remaining power, current, and voltage of the battery in real time through multiple dimensions, intelligently screen samples based on the similarity of environmental parameters, and construct a dynamic correction model for charging / discharging scenarios. The method and system accurately calibrate the power percentage based on the integration of multiple parameters based on a normalization algorithm, breaking through the limitations of a traditional single algorithm and effectively eliminating the error accumulation of the ampere-hour integration method under complex working conditions. The method and system realize adaptive adjustment of the estimation model under different operating conditions through the similarity calculation and group correction mechanism of environmental data (time, temperature, air pressure, humidity). At the same time, the system continuously optimizes the estimation accuracy through periodic sample iteration and automatic reset mechanism, providing a full-cycle, high-reliability state of charge reference for the power battery, helping to improve the intelligence level and safety of battery management, extend the battery life, and optimize energy management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0051] Figure 1 This is a schematic diagram of the structure of a power battery state of charge estimation system of an intelligent BMS;

[0052] Figure 2 The figure is a flow chart of a method for estimating the state of charge of a power battery of an intelligent BMS. DETAILED DESCRIPTION

[0053] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0054] The present invention will be further described below with reference to the embodiments. Example 1:

[0055] The embodiment of the present invention is a power battery state of charge estimation system of an intelligent BMS, such as Figure 1 As shown, including:

[0056] A monitoring module is used to monitor the battery operating status information in real time and store the monitored battery operating status information as sample information;

[0057] The monitoring module runs synchronously with the battery's charging or discharging operation. The battery operating status information monitored by the monitoring module includes: battery remaining power percentage, current, voltage, temperature, internal resistance, electrochemical impedance spectroscopy, electrolyte concentration, charge and discharge rate, capacity decay, and charge and discharge curve slope;

[0058] After monitoring the battery operating status information, the monitoring module will simultaneously identify the integrity of the battery charge and discharge information. If the identification result is complete, the monitored battery operating status information will be stored. The integrity identification logic of the battery charge and discharge information is: whether the operating status information of each type of battery is included in this monitoring result;

[0059] Among them, when the monitoring module stores the sample information, it simultaneously distinguishes the sample information and marks it with the following contents: monitoring in charging state and monitoring in discharging state. The monitoring module distinguishes and stores the sample information based on the marking contents. The remaining battery capacity percentage in the battery operation status information is obtained by the ampere-hour integration method.

[0060] When the monitoring module stores the sample information, it iterates the internally stored sample information based on the preset period, and the iteration target is always the earliest sample information stored in the monitoring module that meets the preset period;

[0061] The selection module is provided with a determination unit and an identification unit at the lower level. The determination unit is used to determine the storage status of the sample information in the monitoring module and trigger the corresponding selection strategy based on the storage status of the sample information. The identification unit is used to identify the similarity of the associated information to which each sample information belongs.

[0062] The associated information of the sample information is derived from the battery operation message or electronic log, and includes: the source time of the sample information, the ambient temperature, air pressure, and humidity during the sample information monitoring period;

[0063] A selection module, used for traversing the sample information stored in the monitoring module and selecting sample information from the stored sample information;

[0064] When the selection module selects the sample information, it performs an independent selection operation based on the sample information stored and distinguished within it, that is, it performs a selection operation once for the sample information monitored in the charging state and another selection operation once for the sample information monitored in the discharging state;

[0065] The sample information storage status and corresponding selection strategy determined in the determination unit include:

[0066] Storage status: the number of sample information groups is unique; selection strategy: no selection operation is performed;

[0067] Storage status: sample information is not unique and is less than or equal to three groups; selection strategy: select all;

[0068] Storage status: more than three groups of sample information; selection strategy: select the three groups of sample information with the highest cumulative similarity of the associated information of each sample information;

[0069] Among them, the recognition unit is triggered to run when the storage state is that the sample information exceeds three groups;

[0070] The similarity of the associated information of the sample information is calculated using the following formula:

[0071] ;

[0072] Where: is the similarity between associated information a and associated information b; The source time of the sample information in the associated information a and the associated information b; The ambient temperature during the monitoring phase of the sample information in the associated information a and associated information b; is the air pressure in the associated information a and the associated information b; is the humidity in associated information a and associated information b; is the adjustment factor;

[0073] Among them, the adjustment factor The value is subject to: The numerator of the fraction is less than or equal to the denominator, then =1, The numerator of the fraction is greater than the denominator, then =-1, weight The sum is 1, and obeys , The initial default values ​​are 0.4, 0.3, 0.2, and 0.1 respectively;

[0074] The above logic formula is used to calculate the similarity between the associated information of the sample information, thereby providing support for the selection module to select valid sample information for the system's subsequent estimation of the battery state of charge, while also estimating the accuracy of the results;

[0075] A correction module, configured to receive the sample information selected by the selection module and correct the percentage of remaining battery power in the sample information based on the sample information;

[0076] The logic for correcting the remaining battery power percentage in the sample information in the correction module is expressed as follows:

[0077] ;

[0078] Where: The percentage of remaining battery power after correction; The percentage of remaining power of the original battery; 、 、 、 、 、 、 、 、 Respectively represent the current, voltage, temperature, internal resistance, electrochemical impedance spectrum, electrolyte concentration, charge and discharge rate, capacity decay, and charge and discharge curve slope, which are normalized and mapped to the value of [0, 1] interval;

[0079] in, 、 、 、 、 、 、 、 、 When performing normalization processing, normalization is performed based on the corresponding standard values ​​of each parameter, and a corrected battery remaining power percentage is obtained based on each group of samples;

[0080] The above logic formula is used to calculate the remaining battery power percentage, thereby providing support for the system to subsequently calculate the remaining battery power correction factor.

[0081] An extraction module is configured to receive a battery remaining power percentage correction result from the correction module and extract a battery remaining power correction factor based on the correction result;

[0082] The extraction module is provided with a distinguishing unit at the lower level, which is used to receive the battery remaining power percentage correction result from the correction module and distinguish the correction result;

[0083] The differentiation logic of the correction result differentiation unit is as follows: identifying the tag content of the sample information corresponding to the correction result, and distinguishing the correction result based on the tag content, so that the correction result is divided into two groups, and the two groups of correction results correspond to two different tag contents respectively;

[0084] The correction factor extraction logic for the remaining battery capacity corresponds to different tag contents:

[0085] Monitoring during charging:

[0086] ;

[0087] Monitoring during discharge:

[0088] ;

[0089] Where: Correction factor for remaining battery capacity in charging state; is the total amount of correction results in the charging state; is the qth correction result; The remaining battery power percentage corresponding to the qth correction result; is the weight coefficient; Correction factor for the remaining battery capacity in the discharged state; is the total amount of correction results in the discharge state; is the remaining battery capacity percentage corresponding to the jth correction result; is the jth correction result; is the weight coefficient;

[0090] in, 、 The two groups of correction results correspond to the distinction. 、 The value is an integer in the range [1, 3]. In formula (1), The value is subject to: the larger the correction result value of each product term is, the larger the value itself is; otherwise, the smaller the value itself is, and are all positive numbers, and their sum is 1; in formula (2) The values ​​are subject to: all values ​​are equal, and They are all positive numbers, and their sum is 1;

[0091] By using the above logic formula, the battery remaining power correction factor of the application in the battery charging state and the battery discharging state is defined respectively;

[0092] The application module is used to obtain the current battery remaining power percentage and correct the current battery remaining power percentage based on the battery remaining power correction factor extracted by the extraction module;

[0093] An identification unit is provided inside the application module, and the identification unit is used to identify the current battery status;

[0094] When the identification result of the identification unit is that the current battery is in the charging state, Correct the current remaining battery power percentage:

[0095] ;

[0096] If the identification result shows that the battery is in discharge state, Correct the current remaining battery power percentage:

[0097] ;

[0098] Where: The current percentage of remaining battery power after correction; The current remaining battery power percentage;

[0099] The current battery remaining power percentage is corrected using the above logic formula;

[0100] Reset module, used to reset system operation according to a preset cycle;

[0101] The monitoring module is interactively connected to the selection module through a wireless network. The selection module is interactively connected to the judgment unit and the identification unit at its lower level through a wireless network. The selection module is interactively connected to the correction module and the extraction module through a wireless network. The extraction module is interactively connected to the differentiation unit at its lower level through a wireless network. The differentiation unit is interactively connected to the correction module through a wireless network. The extraction module is interactively connected to the application module and the reset module through a wireless network. The application module is interactively connected to the identification unit through a wireless network.

[0102] In this embodiment, the monitoring module runs in real time to monitor the battery operating status information, and stores the monitored battery operating status information as sample information. The selection module runs post-processing to traverse the stored sample information in the monitoring module and select sample information from the stored sample information. The determination unit synchronously determines the storage status of the sample information in the monitoring module and triggers the corresponding selection strategy based on the storage status of the sample information. The identification unit identifies the similarity of the associated information belonging to each sample information in real time. The correction module then receives the sample information selected by the selection module and corrects the remaining battery percentage in the sample information based on the sample information. The extraction module further receives the correction result of the remaining battery percentage in the correction module and extracts the remaining battery correction factor based on the correction result. The current remaining battery percentage is obtained through the application module and corrected based on the remaining battery correction factor extracted by the extraction module. The identification unit identifies the current battery status in real time. Finally, the reset module resets the system operation according to a preset period.

[0103] Through the operation of the system in the above embodiment, a new estimation scheme is provided for estimating the state of charge of the power battery. Compared with the existing technology, this scheme has more comprehensive considerations of parameters in the estimation environment, so the estimation result is more accurate. At the same time, by setting the reset logic and estimating the battery status accordingly, the error accumulation of the estimation result is effectively avoided, thereby ensuring the reliability of the battery state of charge estimation result.

[0104] Implementation:2:

[0105] In terms of specific implementation, based on Example 1, this example refers to Figure 2 The power battery state of charge estimation system of an intelligent BMS in Example 1 is further described in detail:

[0106] A method for estimating the state of charge of a power battery of an intelligent BMS, comprising:

[0107] Step 1: monitor the battery operating status information in real time, identify the integrity of the monitored battery operating status information, store the complete battery operating status information based on the identification result, and record the stored battery operating status information as sample information;

[0108] Step 2: setting sample information selection logic, and selecting sample information from the stored sample information based on the sample information selection logic;

[0109] Step 3: Correct the remaining battery power percentage in each sample information according to the selected sample information, and extract the remaining battery power correction factor based on the correction result;

[0110] Step 4: Identify the current battery operating status, select a battery remaining power correction factor based on the current battery operating status, and correct the current battery remaining power percentage;

[0111] Step 5: Preset a reset cycle and reset the battery remaining power correction factor based on the reset cycle.

[0112] In summary, during the execution of the methods and systems in the above embodiments, operating data such as the remaining battery power, current, and voltage are collected in real time from multiple dimensions, and samples are intelligently screened based on the similarity of environmental parameters to construct a dynamic correction model for charging / discharging scenarios. The normalization algorithm is used to fuse multiple parameters to accurately calibrate the power percentage, breaking through the limitations of the traditional single algorithm and effectively eliminating the error accumulation of the ampere-hour integration method under complex working conditions. The similarity calculation and group correction mechanism of environmental data (time, temperature, air pressure, and humidity) are used to achieve adaptive adjustment of the estimation model under different operating conditions. At the same time, the system continuously optimizes the estimation accuracy through periodic sample iteration and automatic reset mechanism, providing a full-cycle, highly reliable state of charge reference for the power battery, helping to improve the intelligence and safety of battery management, extend battery life, and optimize energy management efficiency.

[0113] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A power battery state of charge estimation system for an intelligent BMS, characterized in that: include: A monitoring module is used to monitor the battery operating status information in real time and store the monitored battery operating status information as sample information; A selection module, used for traversing the sample information stored in the monitoring module and selecting sample information from the stored sample information; A correction module, configured to receive the sample information selected by the selection module and correct the percentage of remaining battery power in the sample information based on the sample information; An extraction module is configured to receive a battery remaining power percentage correction result from the correction module and extract a battery remaining power correction factor based on the correction result; The application module is used to obtain the current battery remaining power percentage and correct the current battery remaining power percentage based on the battery remaining power correction factor extracted by the extraction module; Reset module, used to reset system operation according to a preset cycle; When storing the sample information, the monitoring module iterates the internally stored sample information synchronously based on a preset period, and the iteration target is always the earliest stored sample information in the monitoring module that meets the preset period; The selection module is provided with a determination unit and an identification unit at the lower level. The determination unit is used to determine the storage status of the sample information in the monitoring module and trigger the corresponding selection strategy based on the storage status of the sample information. The identification unit is used to identify the similarity of the associated information to which each sample information belongs. The associated information of the sample information is derived from the battery operation message or electronic log, and includes: the source time of the sample information, the ambient temperature, pressure and humidity during the sample information monitoring period; When the selection module selects the sample information, it performs an independent selection operation based on the sample information stored and distinguished within it, that is, it performs a selection operation once for the sample information monitored in the charging state and another selection operation once for the sample information monitored in the discharging state; The sample information storage status and corresponding selection strategy determined by the determination unit include: Storage status: the number of sample information groups is unique; selection strategy: no selection operation is performed; Storage status: sample information is not unique and is less than or equal to three groups; selection strategy: select all; Storage status: more than three groups of sample information; selection strategy: select the three groups of sample information with the highest cumulative similarity of the associated information of each sample information; The recognition unit is triggered to run when the storage state is that the sample information exceeds three groups.

2. The power battery state of charge estimation system of the intelligent BMS according to claim 1 is characterized in that: The monitoring module runs synchronously with the charging or discharging operation of the battery. The battery operating status information monitored by the monitoring module includes: battery remaining power percentage, current, voltage, temperature, internal resistance, electrochemical impedance spectrum, electrolyte concentration, charge and discharge rate, capacity decay and charge and discharge curve slope; After monitoring the battery operating status information, the monitoring module simultaneously performs integrity identification on the battery charge and discharge information. If the identification result is complete, the monitored battery operating status information is stored. The integrity identification logic of the battery charge and discharge information is as follows: whether all types of battery operating status information are included in the current monitoring result; Among them, when the monitoring module stores the sample information, it simultaneously marks the sample information differently. The marking content is: monitoring under charging state, monitoring under discharging state. The monitoring module distinguishes and stores the sample information based on the marking content. The percentage of remaining battery power in the battery operating status information is obtained through the ampere-hour integration method.

3. The power battery state of charge estimation system of the intelligent BMS according to claim 1, characterized in that: The similarity of the associated information to which the sample information belongs is calculated using the following formula: ； Where: is the similarity between associated information a and associated information b; The source time of the sample information in the associated information a and the associated information b; The ambient temperature during the monitoring phase of the sample information in the associated information a and associated information b; is the air pressure in the associated information a and the associated information b; is the humidity in associated information a and associated information b; is the adjustment factor; Among them, the adjustment factor The value is subject to: The numerator of the fraction is less than or equal to the denominator, then =1, The numerator of the fraction is greater than the denominator, then =-1, weight The sum is 1, and obeys , The initial default values ​​are 0.4, 0.3, 0.2, and 0.1 respectively.

4. The power battery state of charge estimation system of the intelligent BMS according to claim 1, characterized in that: The logic of correcting the remaining battery power percentage in the sample information in the correction module is expressed as follows: ； Where: The percentage of remaining battery power after correction; The percentage of remaining power of the original battery; 、 、 、 、 、 、 、 、 Respectively represent the current, voltage, temperature, internal resistance, electrochemical impedance spectrum, electrolyte concentration, charge and discharge rate, capacity decay, and charge and discharge curve slope, which are normalized and mapped to the value of [0, 1] interval; in, 、 、 、 、 、 、 、 、 When performing the normalization process, the normalization process is performed based on the corresponding standard values ​​of each parameter, and a corrected percentage of the remaining battery power is obtained based on each group of samples.

5. The power battery state of charge estimation system of the intelligent BMS according to claim 2, characterized in that: The extraction module is provided with a distinguishing unit at a lower level, which is used to receive the battery remaining power percentage correction result from the correction module and distinguish the correction results; The differentiation logic of the correction results by the differentiation unit is as follows: identifying the tag content of the sample information corresponding to the correction result, and differentiating the correction results based on the tag content, so that the correction results are divided into two groups, and the two groups of correction results correspond to two different tag contents respectively; The correction factor extraction logic for the remaining battery capacity corresponds to different tag contents: Monitoring during charging: ； Monitoring during discharge: ； Where: Correction factor for remaining battery capacity in charging state; is the total amount of correction results in the charging state; is the qth correction result; The remaining battery power percentage corresponding to the qth correction result; is the weight coefficient; Correction factor for the remaining battery capacity in the discharged state; is the total amount of correction results in the discharge state; is the remaining battery capacity percentage corresponding to the jth correction result; is the jth correction result; is the weight coefficient; in, 、 The two groups of correction results correspond to the distinction. 、 The value is an integer in the range [1, 3]. In formula (1), The value is subject to: the larger the correction result value of each product term is, the larger the value itself is; otherwise, the smaller the value itself is, and are all positive numbers, and their sum is 1; in formula (2) The values ​​are subject to: all values ​​are equal, and They are all positive numbers, and their sum is 1.

6. The power battery state of charge estimation system of the intelligent BMS according to claim 1 is characterized in that: The application module is internally provided with an identification unit, which is used to identify the current battery status; When the identification result of the identification unit is that the current battery is in the charging state, the application Correct the current remaining battery power percentage: ； When the identification result shows that the battery is in discharge state, the application Correct the current remaining battery power percentage: ； Where: The current percentage of remaining battery power after correction; The current remaining battery power percentage.

7. The power battery state of charge estimation system of the intelligent BMS according to claim 1 is characterized in that: The monitoring module is interactively connected to the selection module via a wireless network, the selection module is interactively connected to a determination unit and an identification unit at a lower level via a wireless network, the selection module is interactively connected to a correction module and an extraction module at a lower level via a wireless network, the extraction module is interactively connected to a differentiation unit at a lower level via a wireless network, the differentiation unit is interactively connected to the correction module via a wireless network, the extraction module is interactively connected to an application module and a reset module via a wireless network, and the application module is interactively connected to the identification unit internally via a wireless network.

8. A method for estimating the state of charge of a power battery of an intelligent BMS, wherein the method is an implementation method of the power battery state of charge estimation system of an intelligent BMS according to any one of claims 1 to 7, characterized in that: include: Step 1: monitor the battery operating status information in real time, identify the integrity of the monitored battery operating status information, store the complete battery operating status information based on the identification result, and record the stored battery operating status information as sample information; Step 2: setting sample information selection logic, and selecting sample information from the stored sample information based on the sample information selection logic; Step 3: Correct the remaining battery power percentage in each sample information according to the selected sample information, and extract the remaining battery power correction factor based on the correction result; Step 4: Identify the current battery operating status, select a battery remaining power correction factor based on the current battery operating status, and correct the current battery remaining power percentage; Step 5: Preset a reset cycle and reset the battery remaining power correction factor based on the reset cycle.

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