Method and system for estimating state of charge of power battery of intelligent BMS (Battery Management System)

Abstract

The invention discloses a method and system for estimating the state of charge of a power battery of an intelligent BMS, and relates to the field of battery management, and the system comprises a monitoring module which is used for monitoring the operation state information of the battery in real time, and storing the monitored operation state information of the battery as sample information; the selection module is used for traversing the stored sample information in the monitoring module and selecting the sample information from the stored sample information; according to the method, operation data such as battery residual electric quantity, current and voltage are acquired in real time in multiple dimensions, samples are intelligently screened in combination with environmental parameter similarity, a dynamic correction model of a charging / discharging scene is constructed, and the electric quantity percentage is accurately calibrated by fusing multiple parameters based on a normalization algorithm, so that the limitation of a traditional single algorithm is broken through; and error accumulation of the ampere-hour integral method under complex working conditions is effectively eliminated.

Description

Technical Field

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

[0002] The estimation of the state of charge (SOC) of a power battery refers to the real-time evaluation of the percentage of the remaining battery power through an algorithm, which is a core function of the battery management system. Common methods include the ampere-hour integration method, the open-circuit voltage method, the Kalman filter method, etc. It is necessary to comprehensively consider parameters such as current, voltage, and temperature. Accurate estimation can optimize the battery usage efficiency, extend the battery life, and ensure the accuracy and safety of the electric vehicle's cruising range.

[0003] The invention patent application with the application number 201310121490.6 discloses a method for estimating the state of charge of a power battery system. The power battery system includes a plurality of battery cells connected in series and / or in parallel. The estimation method includes the following steps: S1. Step of obtaining the open-circuit voltage and the corresponding temperature: Collect the open-circuit voltage 0CV and the core temperature T of each battery cell in the power battery system; S2. Step of obtaining the minimum state of charge S0C1 and the maximum state of charge S0C2: Obtain 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 having the minimum open-circuit voltage 0CV1; Obtain the maximum open-circuit voltage 0CV2 among the open-circuit voltages 0CV of each battery cell, and the second temperature T2 of the battery cell having the maximum open-circuit voltage 0CV2: Query the 0CV-S0C-T three-dimensional table according to the minimum open-circuit voltage 0CV1 and the first temperature T1 to obtain the minimum state of charge S0C1; Query the 0CV-S0C-T three-dimensional table according to the maximum open-circuit voltage 0CV2 and the second temperature T2 to obtain the maximum state of charge S0C2. This application aims to solve the problem that "it is inevitable that there are voltage differences between the battery cells in the battery pack, for example, causing the voltage of one or several battery cells to be too high or too low. At this time, if the average 0CV is used to estimate the SOC and report the power value as above, there will be a very large deviation."

[0004] However, for the estimation of the state of charge of a power battery in the prior art, over time, the estimation error often accumulates, and most of the relevant parameters related to the estimation of the state of charge of the battery are not fully considered during the estimation of the state of charge of the battery, resulting in a certain deviation in the estimation result.

[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] Aiming at the above-mentioned disadvantages 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 object, the present invention is implemented by the following technical solutions; The present invention discloses a state of charge estimation system for power batteries of an intelligent BMS, including: A monitoring module for real-time monitoring of battery operation status information and storing the monitored battery operation 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; a correction module for receiving the sample information selected by the selection module and correcting the percentage of remaining battery power in the sample information based on the sample information; an extraction module for receiving the corrected result of the percentage of remaining battery power in the correction module and extracting a battery remaining power correction factor based on the correction result; an application module for obtaining the current percentage of remaining battery power and correcting the current percentage of remaining battery power based on the battery remaining power correction factor extracted by the extraction module; a reset module for resetting the system operation according to a preset period.

[0008] Furthermore, the monitoring module runs synchronously with the charging or discharging operation of the battery. The battery operation status information monitored by the monitoring module includes: percentage of remaining battery power, current, voltage, temperature, internal resistance, electrochemical impedance spectrum, electrolyte concentration, charge and discharge rate, capacity attenuation, slope of charge and discharge curve; After the monitoring module monitors the battery operation status information, it synchronously identifies the integrity of the battery charge and discharge information. When the identification result is complete, it stores the monitored battery operation status information. The integrity identification logic of the battery charge and discharge information is: whether each type of battery operation status information is included in the current monitoring result; Among them, when the monitoring module stores the sample information, it synchronously makes a differential mark on the sample information. The mark content is: monitored under charging state, monitored under discharging state. The monitoring module stores them separately based on the mark content of the sample information. The percentage of remaining battery power in the battery operation status information is obtained by the ampere-hour integration method.

[0009] Furthermore, when the monitoring module stores the sample information, it synchronously iterates the sample information stored internally according to a preset period, and the iteration target is always the earliest stored sample information in the monitoring module that meets the preset period; A determination unit and an identification unit are arranged at the lower level of the selection module. The determination unit is used to determine the storage status of the sample information in the monitoring module and trigger corresponding selection strategies 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; Among them, the associated information to which the sample information belongs is derived from battery operation messages or electronic logs, and the associated information to which the sample information belongs includes: the source time of the sample information, the ambient temperature, air pressure, and humidity during the monitoring stage of the sample information.

[0010] Furthermore, when the selection module selects the sample information, it performs an independent selection operation based on the sample information stored separately inside it, that is, it performs a selection operation on the sample information monitored under the charging state and a selection operation on the sample information monitored under the discharging state; The storage status of the sample information determined in the determination unit and the corresponding selection strategy include: Storage status: The number of sample information groups is unique; Selection strategy: Do not perform a selection operation; Storage status: The sample information is not unique and is less than or equal to three groups; Selection strategy: Select all; Storage status: The sample information exceeds three groups; Selection strategy: Select three groups of sample information with the highest cumulative similarity of the associated information to which each sample information belongs; Among them, when the storage status is that the sample information exceeds three groups, the recognition unit is triggered to run.

[0011] Furthermore, the similarity of the associated information to which the sample information belongs is obtained through the following formula: ; In the formula: is the similarity between associated information a and associated information b; is the source time of the sample information in associated information a and associated information b; is the ambient temperature during the monitoring stage of the sample information in associated information a and associated information b; is the air pressure in associated information a and associated information b; is the humidity in associated information a and associated information b; is the adjustment factor; Among them, the adjustment factor takes values that follow: If the numerator of the fraction where it is located is less than or equal to the denominator, then = 1, If the numerator of the fraction where it is located is greater than the denominator, then = -1, and the sum of the weights is 1 and follows , The initial default values are 0.4, 0.3, 0.2, and 0.1 respectively.

[0012] Furthermore, the logic for correcting the percentage of the remaining battery power in the sample information in the correction module is expressed as: ; Wherein: is the percentage of the remaining battery power after correction; is the percentage of the original remaining battery power; , , , , , , , , respectively represent the values mapped to the interval [0, 1] after normalization processing of current, voltage, temperature, internal resistance, electrochemical impedance spectrum, electrolyte concentration, charge-discharge rate, capacity attenuation, and charge-discharge curve slope; Among them, , , , , , , , , When performing normalization processing, normalization is performed based on the corresponding standard values of each parameter, and a corrected percentage of the remaining battery power is obtained for each set of samples.

[0013] Furthermore, a discrimination unit is provided at a lower level of the extraction module. The discrimination unit is used to receive the correction result of the percentage of the remaining battery power in the correction module and discriminate the correction result; The discrimination logic of the discrimination unit for the correction result is: identify the marked content of the sample information corresponding to the correction result, and discriminate the correction result based on the marked content, so that the correction result is divided into two groups, and the two groups of correction results respectively correspond to two different marked contents; Then the extraction logic of the correction factor of the remaining battery power corresponds to different marked contents respectively as: Monitoring under charging state: ; Monitoring under discharge state: ; Wherein: is the correction factor of the remaining battery power in the charging state; is the total amount of the correction result in the charging state; is the q-th correction result; is the percentage of the original remaining battery power corresponding to the q-th correction result; is the weight coefficient; is the correction factor of the remaining battery power in the discharge state; is the total amount of the correction result in the discharge state; is the percentage of the remaining power of the primary battery corresponding to the j-th correction result; is the j-th correction result; is the weight coefficient; where , respectively correspond to two groups of distinguished correction results, , take integer values within the range of [1, 3]. In formula (1), takes values subject to: the larger the corresponding correction result value of its respective product term, the larger its own value; conversely, the smaller its own value, and are all positive numbers and their sum is 1; in formula (2), takes values subject to: the values of each term are equal, and are all positive numbers and their sum is 1.

[0014] Furthermore, an identification unit is provided inside the application module, and the identification unit is used to identify the current battery state; When the identification result of the identification unit is that the current battery is in the charging state, the percentage of the remaining power of the current battery should be corrected: ; When the identification result is that the current battery is in the discharging state, the percentage of the remaining power of the current battery should be corrected: ; In the formula: is the corrected percentage of the remaining power of the current battery; is the percentage of the remaining power of the current battery.

[0015] Furthermore, the monitoring module is interconnected with the selection module through a wireless network. The lower level of the selection module is interconnected with a determination unit and an identification unit through a wireless network. The selection module is interconnected with a correction module and an extraction module through a wireless network. The lower level of the extraction module is interconnected with a discrimination unit through a wireless network. The discrimination unit is interconnected with the correction module through a wireless network. The extraction module is interconnected with an application module and a reset module through a wireless network. Inside the application module, it is interconnected with the identification unit through a wireless network.

[0016] On the other hand, a method for estimating the state of charge of a power battery of an intelligent BMS includes: Real-time monitor the battery operating status information, 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. Set the sample information selection logic, and select sample information from the stored sample information based on the sample information selection logic; correct the percentage of remaining battery power 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 percentage of the current battery remaining power; preset a reset period, and reset the battery remaining power correction factor based on the reset period.

[0017] Adopting the technical solution provided by the present invention, compared with the known prior art, it has the following beneficial effects: The present invention provides a method and system for estimating the state of charge of a power battery of an intelligent BMS. During the execution of the method and system, by multi-dimensionally and real-time collecting operation data such as the remaining battery power, current, voltage, etc., intelligently screening samples in combination with the similarity of environmental parameters, constructing a dynamic correction model for charging / discharging scenarios, and accurately calibrating the percentage of power based on a normalization algorithm to fuse multiple parameters, breaking through the limitations of traditional single algorithms, effectively eliminating the error accumulation of the ampere-hour integration method under complex working conditions, and realizing the adaptive adjustment of the estimation model under different operating states through the similarity calculation and grouping 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, provides a full-cycle and highly reliable state of charge reference for the power battery, helps to improve the intelligent level and safety of battery management, extends the battery service life and optimizes the energy management efficiency. Description of the Drawings

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings without creative efforts based on these drawings.

[0019] Figure 1 It is a structural schematic diagram of a system for estimating the state of charge of a power battery of an intelligent BMS; Figure 2 It is a flowchart of a method for estimating the state of charge of a power battery of an intelligent BMS. Detailed Embodiments

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0021] The following further describes the present invention in conjunction with embodiments. Embodiment 1:

[0022] An estimation system for the state of charge of a power battery of an intelligent BMS in this embodiment, as Figure 1 shown, includes: A monitoring module, configured to monitor the battery operation status information in real time and store the monitored battery operation status information as sample information; The monitoring module operates synchronously with the charging or discharging operation of the battery. The battery operation status information monitored by the monitoring module includes: the percentage of remaining battery power, current, voltage, temperature, internal resistance, electrochemical impedance spectrum, electrolyte concentration, charge-discharge rate, capacity attenuation, and the slope of the charge-discharge curve; After the monitoring module monitors the battery operation status information, it synchronously identifies the integrity of the battery charge-discharge information. When the identification result is complete, it stores the monitored battery operation status information. The integrity identification logic of the battery charge-discharge information is: whether each type of battery operation status information is included in the current monitoring result; Among them, when the monitoring module stores the sample information, it synchronously makes a distinction mark on the sample information. The mark content is: monitored under the charging state, monitored under the discharging state. The monitoring module stores them separately based on the mark content of the sample information. The percentage of remaining battery power in the battery operation status information is obtained by the ampere-hour integration method; When the monitoring module stores the sample information, it synchronously iterates the sample information stored internally 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; A determination unit and an identification unit are provided at the lower level of the selection module. The determination unit is used to determine the storage status of the sample information in the monitoring module and trigger corresponding selection strategies 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; Among them, the associated information to which the sample information belongs comes from the battery operation message or the electronic log. The associated information to which the sample information belongs includes: the source time of the sample information, the ambient temperature, air pressure, and humidity during the monitoring stage of the sample information; A selection module, configured to traverse the stored sample information in the monitoring module and select sample information from the stored sample information; When the selection module selects sample information, it performs independent selection operations based on the sample information stored separately inside it, that is, it performs one selection operation on the sample information monitored under the charging state and one selection operation on the sample information monitored under the discharging state; The storage status of the sample information determined in the determination unit and the corresponding selection strategies include: Storage status: The number of groups of sample information is unique; Selection strategy: Do not perform selection operations; Storage status: The sample information is not unique and is less than or equal to three groups; Selection strategy: Select all; Storage status: The sample information exceeds three groups; Selection strategy: Select three groups of sample information with the highest cumulative similarity of the associated information to which each sample information belongs; Among them, the recognition unit is triggered to run when the storage status is that the sample information exceeds three groups; The similarity of the associated information to which the sample information belongs is obtained through the following formula: ; In the formula: is the similarity between associated information a and associated information b; is the sample information source time in associated information a and associated information b; is the ambient temperature during the sample information monitoring phase in associated information a and associated information b; is the air pressure in associated information a and associated information b; is the humidity in associated information a and associated information b; is the adjustment factor; Among them, the adjustment factor takes values that obey: If the numerator of the fraction where it is located is less than or equal to the denominator, then = 1, If the numerator of the fraction where it is located is greater than the denominator, then = -1, and the weights sum to 1 and obey ; The initial default values are 0.4, 0.3, 0.2, and 0.1 respectively; Through the above logical formula calculation, the similarity between the associated information to which the sample information belongs is calculated, so as to provide support for the selection module to select effective sample information for the subsequent estimation of the state of charge of the battery by the system, and at the same time improve the accuracy of the estimation result; A correction module, configured to receive the sample information selected by the selection module and correct the percentage of the remaining battery power in the sample information based on the sample information; The logic for correcting the percentage of remaining battery power in the sample information in the correction module is expressed as: ; In the formula: is the corrected percentage of remaining battery power; is the original percentage of remaining battery power; 、 、 、 、 、 、 、 、 respectively represent the values mapped to the interval [0, 1] after normalization processing of current, voltage, temperature, internal resistance, electrochemical impedance spectroscopy, electrolyte concentration, charge-discharge rate, capacity attenuation, and charge-discharge curve slope; Among them, 、 、 、 、 、 、 、 、 When performing normalization processing, normalization is performed based on the corresponding standard values of each parameter, and a corrected percentage of remaining battery power is obtained for each group of samples; Through the above logical formula calculation, the percentage of remaining battery power is corrected, providing support for the system to subsequently obtain the battery remaining power correction factor; The extraction module is used to receive the correction result of the percentage of remaining battery power in the correction module and extract the battery remaining power correction factor based on the correction result; A discrimination unit is set under the extraction module. The discrimination unit is used to receive the correction result of the percentage of remaining battery power in the correction module and discriminate the correction result; The discrimination logic of the discrimination unit for the correction result is: identify the marked content of the sample information corresponding to the correction result, and discriminate the correction result based on the marked content, so that the correction result is divided into two groups, and the two groups of correction results respectively correspond to two different marked contents; Then the correction factor extraction logic for the remaining battery power corresponds to different marked contents respectively as: Monitoring in the charging state: ; Monitoring in the discharging state: ; In the formula: is the battery remaining power correction factor in the charging state; is the total amount of correction results in the charging state; is the q-th correction result; is the percentage of the remaining battery power of the primary battery corresponding to the q-th correction result; is the weight coefficient; is the correction factor for the remaining battery power in the discharging state; is the total amount of correction results in the discharging state; is the percentage of the remaining battery power of the primary battery corresponding to the j-th correction result; is the j-th correction result; is the weight coefficient; where , correspond to two groups of distinguished correction results respectively, , take integer values in the range of [1, 3]. In formula (1), takes values subject to: the larger the corresponding correction result value of its respective product term, the larger its own value; conversely, the smaller its own value, and are all positive numbers and their sum is 1; in formula (2), takes values subject to: each value is equal, and are all positive numbers and their sum is 1; Through the above logical formula, the correction factor for the remaining battery power applied in the charging state and discharging state of the battery is respectively defined; An application module is used to obtain the current percentage of the remaining battery power and correct the current percentage of the remaining battery power based on the correction factor for the remaining battery power extracted by the extraction module; An identification unit is set inside the application module, and the identification unit is used to identify the current battery state; When the identification result of the identification unit is that the current battery is in the charging state, the current percentage of the remaining battery power should be corrected: ; When the identification result is that the current battery is in the discharging state, the current percentage of the remaining battery power should be corrected: ; In the formula: is the corrected current percentage of the remaining battery power; is the current percentage of the remaining battery power; The current percentage of the remaining battery power is corrected through the above logical formula; A reset module is used to reset the system operation according to a preset period; The monitoring module is interconnected with the selection module through a wireless network. The lower level of the selection module is interconnected with a determination unit and an identification unit through a wireless network. The selection module is interconnected with a correction module and an extraction module through a wireless network. The lower level of the extraction module is interconnected with a discrimination unit through a wireless network. The discrimination unit is interconnected with the correction module through a wireless network. The extraction module is interconnected with an application module and a reset module through a wireless network. Inside the application module, there is an interactive connection with an identification unit through a wireless network.

[0023] In this embodiment, the monitoring module runs to monitor the battery operation status information in real time, stores the monitored battery operation status information as sample information. The selection module runs later and traverses the stored sample information in the monitoring module, selects sample information from the stored sample information. The determination unit synchronously determines the storage status of the sample information in the monitoring module, triggers corresponding selection strategies based on the sample information storage status. The identification unit identifies the similarity of the associated information to which each sample information belongs in real time. Then the correction module receives the sample information selected by the selection module, corrects the percentage of the remaining battery power in the sample information based on the sample information. The extraction module further receives the correction result of the percentage of the remaining battery power in the correction module, extracts the battery remaining power correction factor based on the correction result, and obtains the current percentage of the remaining battery power through the application module. The current percentage of the remaining battery power is corrected based on the battery remaining power 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.

[0024] Through the system operation in the above embodiment, a new estimation scheme is provided for the state of charge estimation of power batteries. Compared with the prior art, since the estimation environment considers more comprehensive parameters, the estimation result is more accurate. At the same time, through the setting of the reset logic and the corresponding estimation of the battery state differentiation, the error accumulation of the estimation result is effectively avoided, ensuring the reliability of the battery state of charge estimation result. Implementation: 2:

[0025] At the specific implementation level, on the basis of Embodiment 1, this embodiment refers to Figure 2 to further specifically describe a power battery state of charge estimation system of an intelligent BMS in Embodiment 1: A method for estimating the state of charge of a power battery of an intelligent BMS, including: Step 1: Monitor the battery operation status information in real time, identify the integrity of the monitored battery operation status information, store the complete battery operation status information based on the identification result, and record the stored battery operation status information as sample information; Step 2: Set the sample information selection logic, and select sample information from the stored sample information based on the sample information selection logic; Step 3: Correct the percentage of remaining battery power in each sample information according to the selected sample information, and extract the correction factor of the remaining battery power based on the correction result; Step 4: Identify the current battery operating state, select the correction factor of the remaining battery power according to the current battery operating state, and correct the percentage of the current remaining battery power; Step 5: Preset a reset period, and reset the correction factor of the remaining battery power based on the reset period.

[0026] In summary, in the execution process of the method and system in the above embodiments, by collecting operation data such as the remaining battery power, current, and voltage in real time in multiple dimensions, intelligently screening samples in combination with the similarity of environmental parameters, constructing a dynamic correction model for the charging / discharging scenario, and accurately calibrating the power percentage by fusing multiple parameters based on the normalization algorithm, the limitations of traditional single algorithms are broken through, the error accumulation of the ampere-hour integration method under complex working conditions is effectively eliminated, and the adaptive adjustment of the estimation model under different operating states is realized through the similarity calculation and grouped correction mechanism of environmental data (time, temperature, air pressure, humidity). At the same time, the system continuously optimizes the estimation accuracy through the periodic sample iteration and automatic reset mechanism, provides a full-cycle and highly reliable state-of-charge reference for the power battery, helps to improve the intelligent level and safety of battery management, extends the battery service life, and optimizes the energy management efficiency.

[0027] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements will not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A power battery state of charge estimation system for an intelligent BMS, characterized in that, Including: A monitoring module, which is used to monitor the battery operation status information in real time and store the monitored battery operation status information as sample information; A selection module, which is used to traverse the stored sample information in the monitoring module and select sample information from the stored sample information; A correction module, which 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; An extraction module, which 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; An application module, which 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; A reset module, which is used to reset the system operation according to a preset period.

2. The battery charge state estimation system of an intelligent BMS according to claim 1, wherein The monitoring module runs synchronously with the charging or discharging operation of the battery. The battery operation 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 attenuation, charge and discharge curve slope; After the monitoring module monitors the battery operation status information, it synchronously identifies the integrity of the battery charge and discharge information. When the identification result is complete, it stores the monitored battery operation status information. The integrity identification logic of the battery charge and discharge information is: whether each type of battery operation status information is included in the current monitoring result; Among them, when the monitoring module stores the sample information, it synchronously makes a differential mark on the sample information. The mark content is: monitored under the charging state, monitored under the discharging state. The monitoring module stores them separately based on the mark content of the sample information. The battery remaining power percentage in the battery operation status information is obtained by the ampere-hour integration method.

3. The power battery state of charge estimation system of an intelligent BMS according to claim 1, characterized in that, When the monitoring module stores the sample information, it synchronously iterates the sample information stored internally according to a preset period, and the iteration target is always the earliest stored sample information that meets the preset period in the monitoring module; A determination unit and an identification unit are arranged at the lower level of the selection module. The determination unit is used to determine the storage status of the sample information in the monitoring module and trigger corresponding selection strategies 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; Among them, the associated information to which the sample information belongs comes from the battery operation message or the electronic log. The associated information to which the sample information belongs includes: the source time of the sample information, the ambient temperature, air pressure, and humidity during the monitoring stage of the sample information.

4. The battery charge state estimation system of an intelligent BMS according to claim 3, characterized in that, When the selection module selects the sample information, it performs an independent selection operation based on the sample information stored separately inside it, that is, it performs a selection operation on the sample information monitored under the charging state and a selection operation on the sample information monitored under the discharging state; The storage status of the sample information determined in the determination unit and the corresponding selection strategies include: Storage status: The number of sample information groups is unique; Selection strategy: Do not perform a selection operation; Storage status: The sample information is not unique and is less than or equal to three groups; Selection strategy: Select all; Storage status: There are more than three groups of sample information; Selection strategy: Select three groups of sample information with the highest cumulative similarity of the associated information to which each sample information belongs; Among them, the recognition unit is triggered to run when the storage status is that there are more than three groups of sample information.

5. The state of charge estimation system for power batteries of an intelligent BMS according to claim 3, characterized in that, The similarity of the associated information to which the sample information belongs is obtained by the following formula: ； In the formula: is the similarity between associated information a and associated information b; is the sample information source time in associated information a and associated information b; is the ambient temperature during the sample information monitoring stage in associated information a and associated information b; is the air pressure in associated information a and associated information b; is the humidity in associated information a and associated information b; is the adjustment factor; Among them, the adjustment factor takes values that follow: If the numerator of the fraction where it is located is less than or equal to the denominator, then = 1, If the numerator of the fraction where it is located is greater than the denominator, then = -1, and the weights sum to 1 and follow , The initial default values are 0.4, 0.3, 0.2, and 0.1 respectively.

6. The battery charge state estimation system of an intelligent BMS according to claim 1, characterized in that, The logic for correcting the percentage of remaining battery power in the sample information in the correction module is expressed as: ； Wherein: is the percentage of the remaining battery power after correction; is the original percentage of the remaining battery power; , , , , , , , , respectively represent the values mapped to the interval [0, 1] after normalization processing of current, voltage, temperature, internal resistance, electrochemical impedance spectrum, electrolyte concentration, charge and discharge rate, capacity attenuation, and charge and discharge curve slope; Among them, , , , , , , , , When performing normalization processing, normalization is performed based on the standard values corresponding to each parameter, and a corrected percentage of the remaining battery power is obtained for each set of samples.

7. The state of charge estimation system for power batteries of an intelligent BMS according to claim 1, characterized in that A discrimination unit is provided at the lower level of the extraction module. The discrimination unit is used to receive the correction result of the percentage of remaining battery power in the correction module and discriminate the correction result; The discrimination logic of the discrimination unit for the correction result is: Identify the marked content of the sample information corresponding to the correction result, and based on the marked content, distinguish the correction result so that the correction result is divided into two groups, and the two groups of correction results respectively correspond to two different marked contents; Then the extraction logic of the correction factor for the remaining battery power corresponds to different marked contents respectively as: Monitoring under charging status: ； Monitoring under discharging status: ； Wherein: is the correction factor for the remaining battery charge in the charging state; is the total amount of the correction result in the charging state; is the q-th correction result; is the percentage of the original remaining battery charge corresponding to the q-th correction result; is the weight coefficient; is the correction factor for the remaining battery charge in the discharging state; is the total amount of the correction result in the discharging state; is the percentage of the original remaining battery charge corresponding to the j-th correction result; is the j-th correction result; is the weight coefficient; Among them, , correspond to two groups of corrected results that are distinguished respectively, , take integer values within the range of [1, 3]. In formula (1), takes values subject to: the larger the value of the corresponding corrected result of its respective product term, the larger its own value; conversely, the smaller its own value, and are all positive numbers and their sum is 1; in formula (2), takes values subject to: the values of each term are equal, and are all positive numbers and their sum is 1.

8. An estimation system for the state of charge of a power battery of an intelligent BMS according to claim 1, characterized in that, An identification unit is provided inside the application module. The identification unit 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, it should correct the percentage of the remaining power of the current battery: ； When the recognition result indicates that the current battery is in the discharging state, it should correct the percentage of the remaining power of the current battery: ； Wherein: is the percentage of the remaining battery power after correction; is the percentage of the current remaining battery power.

9. The battery charge state estimation system of an intelligent BMS according to claim 1, wherein The monitoring module is connected to the selection module through a wireless network. The lower level of the selection module is connected to a determination unit and a recognition unit through a wireless network. The selection module is connected to a correction module and an extraction module through a wireless network. The lower level of the extraction module is connected to a discrimination unit through a wireless network. The discrimination unit is connected to the correction module through a wireless network. The extraction module is connected to an application module and a reset module through a wireless network. The identification unit inside the application module is connected to the application module through a wireless network.

10. A method for estimating the state of charge of a power battery of an intelligent BMS, which is an implementation method of a system for estimating the state of charge of a power battery of an intelligent BMS according to any one of claims 1-9, characterized in that, Including: Step 1: Monitor the battery operation status information in real time, identify the integrity of the monitored battery operation status information, store the complete battery operation status information based on the identification result, and record the stored battery operation status information as sample information; Step 2: Set the sample information selection logic, and select sample information from the stored sample information based on the sample information selection logic; Step 3: Correct the percentage of remaining battery power in each sample information according to the selected sample information, and extract the correction factor for the remaining battery power based on the correction result; Step 4: Identify the current battery operation status, select the correction factor for the remaining battery power according to the current battery operation status, and correct the percentage of the current remaining battery power; Step 5: Preset a reset period, and reset the correction factor for the remaining battery power based on the reset period.

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