Multi-module battery information acquisition method based on dynamic correction and multi-module battery system

Abstract

The invention relates to the technical field of energy storage, in particular to a multi-module battery information acquisition method based on dynamic correction and a multi-module battery system.The multi-module battery information acquisition method based on dynamic correction comprises the steps that high-internal-resistance series connection lines causing errors are recognized, configuring the series connection line as a virtual battery cell, deploying an independent acquisition channel for the virtual battery cell, synchronously acquiring electrical data of all real battery cell modules and the virtual battery cell, dynamically calculating voltage drop and temperature drift errors introduced by the series connection line, shielding the electrical data of the virtual battery cell, and obtaining the voltage drop and temperature drift errors of the virtual battery cell. And real-time compensation is carried out on the voltage data of the real battery cell module associated with the virtual battery cell, and the electric quantity and the capacity of the battery cell module are calculated. According to the invention, the series connection line causing the measurement error is marked as the virtual battery cell, the data of the virtual battery cell is shielded when the data of the battery cell module is acquired, and meanwhile, the associated real battery cell module data is dynamically compensated, so that the data acquisition error can be reduced, and the reliability of the data result is improved.

Description

Technical Field

[0001] This invention relates to the field of energy storage technology, specifically to a method for acquiring information from multi-module batteries based on dynamic correction and a multi-module battery system. Background Technology

[0002] A battery (English: electric cell or battery) is a portable energy device that converts chemical energy into electrical energy. A battery generally consists of two conductive materials (positive electrode and negative electrode) and an electrolyte between them. During an internal oxidation-reduction reaction, electrons flow from the negative electrode to the positive electrode, forming a current in the external circuit, which can then power various electrical devices. Common batteries include lithium-ion batteries, sodium-ion batteries, nickel-metal hydride batteries, and lead-acid batteries. Using batteries as an energy source provides stable voltage and current, enabling long-term stable power supply, minimal susceptibility to external influences, and a relatively simple structure, making charging and discharging easy. They play a significant role in various aspects of modern life. With technological advancements, battery energy density has increased significantly, and cycle life has also grown substantially, leading to a wider range of applications, such as in new energy vehicles and home energy storage. When high battery capacity is required, multiple cell modules are often packaged together in a single enclosure to form a larger battery, also known as a battery pack. During use, it is necessary to collect information from the cells to determine their status. Currently, taking lithium batteries as an example, cell module information is usually collected using single-point sensors. This method results in data that is singular, has poor timeliness, and large errors, making it difficult to fully reflect the true state of the cell. Specifically, module data is generally collected by collecting the voltage across the positive and negative terminals of a single string of cells through a data acquisition line. When cell modules need to be connected in series with longer series lines or longer flexible connections due to space limitations or other requirements, the voltage generated in the series connection part is collected by the battery management system (BMS) when a large current is charged and discharged due to the internal resistance of the series connection part. As a result, the BMS will produce errors in the voltage information of a certain string connected in the connection part. That is, the BMS will identify the voltage of the series line and the voltage of the adjacent cell module as the voltage of the cell module, leading to errors in subsequent data processing. Summary of the Invention

[0003] To address the aforementioned problems, this invention provides a method for acquiring information from a multi-module battery based on dynamic correction, and also provides a multi-module battery system.

[0004] This invention is implemented using the following scheme: A method for collecting information from multi-module batteries based on dynamic correction includes the following steps: Step 1: The battery management system identifies and marks components in the battery that introduce acquisition errors due to internal resistance or structural characteristics, configures the marked components as virtual cells, and deploys independent acquisition channels for them. Step 2: Synchronously collect electrical data of all real battery cell modules and virtual battery cells. The electrical data includes voltage, current, temperature and environmental parameters. Step 3: Based on the electrical data collected from the virtual cell, dynamically calculate the voltage drop and temperature drift error introduced by the components, shield the electrical data of the virtual cell in the battery management system, and perform real-time compensation on the voltage data of the real cell module associated with the virtual cell to calculate the power and capacity of the corresponding real cell module. Step four: Combine historical data and environmental parameters of the actual battery cell modules to perform differentiated capacity correction for each actual battery cell module.

[0005] Furthermore, the component is a series connection line. In step one, identifying and marking the series connection lines in the battery that introduce acquisition errors due to internal resistance or structural characteristics includes: measuring the dynamic internal resistance of each connection line segment during the battery assembly stage; associating the internal resistance data with the physical models of the material, length, and cross-sectional area of ​​the series connection lines to establish an internal resistance-structure mapping library; and marking a connection line segment as a virtual cell candidate when its internal resistance exceeds a set threshold of its structural prediction value.

[0006] Furthermore, the acquisition channel of the virtual battery cell adopts a differential sampling circuit with a sampling frequency of not less than 1kHz, and is hardware isolated from the acquisition channel of the real battery cell module.

[0007] Furthermore, the synchronous acquisition process adopts a master-slave control architecture. The master control unit issues acquisition commands with timestamps, and the slave control unit completes data acquisition within a set time window according to the commands, and uploads the data to the master control unit via CAN-FD or Ethernet protocol.

[0008] Furthermore, during the process of collecting electrical data, the main control unit dynamically adjusts the acquisition frequency according to the real-time working status of the actual battery cell module: when the rate of change of charging and discharging current is greater than the set value, the acquisition frequency is increased to above 500Hz.

[0009] Furthermore, in step three, the voltage drop value is calculated in real time based on the voltage and current data collected by the virtual cell and its internal resistance model; the voltage drop value is applied to the collected voltage of the downstream real cell module in a reverse superposition manner, and a temperature compensation coefficient is introduced during the calculation process to correct the temperature drift of the voltage drop value.

[0010] Furthermore, the differentiated capacity correction includes: constructing a corresponding health status score based on the historical cycle count, internal resistance growth trend, and temperature change record of each real cell module; assigning independent capacity attenuation compensation parameters to the corresponding real cell module according to the corresponding health status score; and dynamically applying these parameters in the capacity calculation.

[0011] A multi-module battery includes: multiple actual cell modules, wherein the multiple actual cell modules are connected in series via a series connection line; The battery management system is configured to execute the aforementioned multi-module battery information acquisition method based on dynamic correction; Distributed acquisition nodes are deployed on each real battery cell module and on the series connection lines marked as virtual battery cells; A high-speed communication bus is used to transmit acquired data and commands.

[0012] Furthermore, according to the multi-module battery of claim 1, the distributed acquisition node integrates multiple ADCs, temperature sensors, and humidity sensors.

[0013] Compared with the prior art, the present invention has the following advantages: This invention provides a method for acquiring information from multi-module batteries based on dynamic correction and a multi-module battery system. By identifying and configuring high-internal-resistance series connection lines as "virtual cells" and deploying independent acquisition channels for them, the voltage error introduced by these cells can be monitored and compensated in real time.

[0014] The system adopts a master-slave collaborative acquisition architecture, combining high-frequency synchronous sampling, dynamic internal resistance modeling, temperature drift compensation, and health status assessment to achieve high-precision, multi-dimensional acquisition and processing of electrical data from multiple battery modules. This invention significantly improves the data accuracy and state estimation reliability of the battery management system under dynamic operating conditions without substantially increasing hardware costs. Attached Figure Description

[0015] Figure 1 The flowchart illustrates a method for collecting information from multi-module batteries based on dynamic correction, as provided in an embodiment of the present invention.

[0016] Figure 2 This is a topology diagram of a multi-module battery system according to an embodiment of the present invention.

[0017] The image includes: Among them, B0 to B6: voltage acquisition channels; Cell 1 to Cell 5: battery cell modules; virtual battery cell: the series connection line between Cell 4 and Cell 5, corresponding to acquisition channels B4-B5. Detailed Implementation

[0018] To facilitate understanding of the present invention by those skilled in the art, the present invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0019] Example 1

[0020] Reference Figure 1-2 The present invention provides a method for collecting information from a multi-module battery based on dynamic correction, comprising the following steps: Step 1: The battery management system identifies and marks components in the battery that introduce acquisition errors due to internal resistance or structural characteristics, configures the marked components as virtual cells, and deploys independent acquisition channels for them. Step 2: Synchronously collect electrical data of all real battery cell modules and virtual battery cells. The electrical data includes voltage, current, temperature and environmental parameters. Step 3: Based on the electrical data of the virtual cell, dynamically calculate the voltage drop and temperature drift error introduced by the components, shield the electrical data of the virtual cell in the battery management system, and compensate the voltage data of the real cell module associated with the virtual cell in real time, and calculate the power and capacity of the corresponding real cell module. Step four: Combine historical data and environmental parameters of the actual battery cell modules to perform differentiated capacity correction for each actual battery cell module.

[0021] In this embodiment, the component causing the acquisition error is the series connection line. In actual implementation, when other types of components cause acquisition errors, the acquisition method in this embodiment can also be used.

[0022] The battery management system's memory stores two types of object models: real cell objects and virtual cell objects. Both have voltage, internal resistance, and temperature attributes, but virtual cell objects are marked with a 'not included in capacity' flag, and when the dynamic judgment logic meets the conditions, the voltage data collected by them is used for correction calculations, rather than directly participating in SOC accumulation.

[0023] In step one, the acquisition channel of the virtual battery cell adopts a differential sampling circuit with a sampling frequency of not less than 1kHz, and is hardware isolated from the acquisition channel of the real battery cell module.

[0024] Step one, identifying and marking series connection lines in the battery that introduce acquisition errors due to internal resistance or structural characteristics, includes: measuring the dynamic internal resistance of each connection line during the battery assembly stage; associating the internal resistance data with physical models of the material, length, and cross-sectional area of ​​the series connection lines to establish an internal resistance-structure mapping library; and marking a connection line as a virtual cell candidate when its internal resistance exceeds a set threshold of its structural prediction value. The process of measuring the internal resistance of series connection lines needs to be accurate and can be performed using a pulse testing method. The internal resistance threshold, as a judgment standard, needs to be determined based on the battery type, specifications, and actual application requirements. For example, in a multi-module battery used in electric vehicles, during laboratory testing, if the internal resistance of the series connection lines exceeds 50 milliohms, it will significantly affect the acquisition of electrical data for the cell modules. Therefore, 50 milliohms is used as the internal resistance threshold. Subsequently, during actual execution, if the measured internal resistance of a series connection line exceeds 50 milliohms, the battery management system will mark the series connection line with excessive internal resistance.

[0025] Marking series connection lines as virtual cells can be achieved through software algorithms. Specifically, a corresponding virtual cell model is created for each marked series connection line in the battery management system. This virtual cell model has electrical characteristic parameter settings similar to those of a real cell, such as initial parameters like voltage and capacity. However, its essence is a virtual unit set up to simulate the voltage drop of the series connection line.

[0026] In step two, during the acquisition of electrical data, the main control unit dynamically adjusts the acquisition frequency based on the real-time operating status of the actual battery cell modules. When the rate of change of charging and discharging current exceeds a set value, the acquisition frequency is increased to above 500Hz. The synchronous acquisition process employs a master-slave control architecture. The master control unit issues acquisition commands with timestamps, and the slave control units complete data acquisition within a set time window according to the commands, uploading the data to the master control unit via CAN-FD or Ethernet protocol. Furthermore, the battery management system uses the voltage of the first identified healthy actual battery cell module as a dynamic benchmark. Within each synchronous acquisition cycle, it accumulates the differences between the voltages of all other actual modules and this benchmark, thereby reconstructing the total system voltage in real time. This method completely eliminates the dependence on any fixed reference point, giving the total voltage calculation adaptive dynamic characteristics.

[0027] Step four, differentiated capacity correction, includes: The battery management system, based on extensive historical data and in-depth analysis of battery performance data under different environmental conditions, constructs a corresponding health status score for each real cell module based on its historical cycle count, internal resistance growth trend, and temperature change records. Independent capacity decay compensation parameters are assigned to the corresponding real cell module according to the health status score and dynamically applied in capacity calculation. Calibration coefficient generation: Based on comparison with a "historical golden benchmark"; the system does not continuously calculate but triggers a learning cycle when a reliable resting condition is detected. The triggering conditions are: the battery pack is resting (current <0.02C) for more than 3 hours, and the maximum temperature difference between modules is less than 3℃. The golden benchmark voltage refers to: the system selects all resting voltage data for the cell module in its early life (e.g., within the first 50 cycles) from the historical database, at an ambient temperature of 25±2℃, and with a SOC in the stable range of 40%-60%. A weighted average of these data is taken to calculate the module's unique "golden benchmark voltage." This voltage is considered the most realistic voltage reference value for the module in its healthiest state.

[0028] Specifically, during each constant current charge, the voltage curves of each module are simultaneously acquired. The core lies in extracting two innovative features: curvature change integral and health quantification (i.e., health status score). The curvature change integral calculates the integral of the curvature change of the charging curve within the 20%-80% SOC range, quantifying the "steepness" of the curve and sensitively reflecting intensified polarization. By calculating the derivative of voltage with respect to SOC and combining it with current, the dynamic internal resistance reflecting overall polarization is obtained. Health quantification compares the current ICI and DDR values ​​of each module with the baseline values ​​at the beginning of its lifespan, generating a comprehensive health score. This score can accurately quantify the health differences between modules.

[0029] In capacity calculation, the dynamic application of compensation coefficients is based on HS, setting an independent SOC offset compensation amount for each module. The system maintains a compensation mapping table (HS, SOC). Modules with poorer health are assigned larger positive SOC compensation values ​​in the high and low SOC ranges, thus "virtually boosting" their usable power at the algorithm level. This guides the BMS to protect weak modules when performing balancing and limiting, delaying overall package degradation. For example, when a module's SOC is below 0.8, the system will find a positive compensation value ΔSOC when calculating its SOC. This is equivalent to "telling" the BMS at the algorithm level that the actual usable power of this weak module is slightly more than the calculated value, thereby preventing it from reaching the bottom prematurely during discharge and from reaching the full charge prematurely during charging, achieving more effective balancing and extended lifespan. In layman's terms, compensation parameters comprehensively consider various factors such as the health status of the battery cell module, its charge and discharge history, and environmental factors. For battery cell modules with poor health status, the compensation parameters in the calculation of charge and capacity are appropriately increased to more reasonably reflect their actual performance. By setting appropriate compensation parameters, capacity decay can be effectively compensated when calculating charge and capacity, making the calculation results more consistent with the actual situation. This method of setting compensation parameters based on the health status of the battery cell module can effectively improve the accuracy of charge and capacity calculations, providing a scientific basis for the rational use and maintenance of batteries.

[0030] Appendix Figure 2 This is an example of a multi-module battery topology diagram. The diagram includes 5 cells, i.e., 5 battery cell modules. B0 to B6 are data acquisition channels (i.e., data acquisition harnesses). The resistance of the series connection between cell 4 and cell 5 exceeds a set threshold; this segment of the series connection is marked as a virtual cell. In the battery management system, the data acquisition channel corresponding to this virtual cell (e.g., B4-B5) is shielded to directly eliminate voltage interference caused by the internal resistance of this series connection segment. This method does not require large-scale modifications to the hardware structure; error isolation can be achieved simply by adding a data acquisition harness and optimizing the software logic, thus reducing hardware modification costs.

[0031] When the battery management system (BMS) collects electrical and environmental data, it utilizes distributed sensor nodes deployed in each cell module. The synchronous acquisition process employs a master-slave control architecture. The master control unit issues timestamped acquisition commands, and the slave control units control the sensors to synchronously collect electrical data and cell temperature data from multiple cell modules within a set time window. The data collected from each cell module is then uploaded to the master control unit via CAN-FD or Ethernet protocols and aggregated. The sensor nodes possess high-precision data acquisition capabilities, enabling real-time monitoring of various electrical data and cell temperatures from the cell modules. This distributed, collaborative data acquisition method achieves synchronous, high-frequency acquisition of multiple parameters (such as voltage, current, and temperature) from the cell modules, significantly improving the comprehensiveness and timeliness of the collected data, and providing a more comprehensive and timely reflection of the cell's true state. The slave control units obtain key parameters such as battery voltage, current, and temperature from the sensor nodes and then upload them to the master control unit. The master control unit integrates and processes the data, reducing the computational burden on the central processing unit.

[0032] During the data acquisition process, a combination of hardware synchronous trigger circuit and software clock calibration algorithm is adopted, and finally the data is aggregated to the main control unit to achieve microsecond-level synchronization of multi-module data acquisition. The synchronization error is controlled within 1μs, which solves the state analysis deviation problem caused by traditional asynchronous acquisition. The data acquisition is expanded from the traditional single voltage to multi-dimensional parameters such as voltage, current and temperature, which is especially suitable for dynamic scenarios such as fast charging and discharging of electric vehicles.

[0033] The distributed sensor nodes integrate environmental sensors to monitor environmental parameters (such as temperature and humidity) in real time. These parameters are then sent to the main control unit of the battery management system. The battery management system corrects the collected electrical and cell temperature data according to a set calibration coefficient. Traditional data acquisition methods do not fully consider the impact of environmental factors (such as temperature and humidity) and individual module differences on data accuracy. This invention, through an innovative acquisition mechanism and data processing scheme, can better address the interference of environmental factors and individual module differences on data acquisition, thereby improving data reliability.

[0034] In step three, the voltage drop is calculated in real time based on the voltage and current data collected from the virtual cell and its internal resistance model. This voltage drop is then applied to the voltage of the downstream real cell module in a reverse superposition manner. In actual multi-module battery charging and discharging processes, the magnitude and direction of the charging and discharging current constantly change. When the battery is charging, current flows into the battery from the external power source, generating a voltage drop as it passes through the series connection line. When the battery is discharging, current flows out of the battery, also generating a voltage drop on the series connection line. This voltage drop can cause errors in data acquisition. Specifically, in high-current charging and discharging scenarios, the voltage drop of the series connection line is dynamically calculated using Ohm's law, combined with the real-time charging and discharging current data collected by the power management system. This voltage drop is then added to the voltage of the real cell module, enabling real-time correction of the collected cell voltage data. According to Ohm's law (U=IR), the battery management system collects real-time charging and discharging current data (I) and, combined with the pre-stored internal resistance data (R) of the series connection line in the system, accurately calculates the voltage drop (ΔU) of the series connection line. The calculated voltage drop value is superimposed in real time onto the voltage data collected by the actual battery cell module, thereby achieving real-time correction of the cell voltage. For example, if the battery's charging / discharging current is 5A and the internal resistance of a certain series connection line is 4 milliohms, the voltage drop value ΔU of this series connection line is calculated according to Ohm's law as 5A × 0.004Ω = 0.02V. Adding this 0.02V voltage drop value to the corresponding voltage data collected by the actual battery cell module makes the collected cell voltage data more accurately reflect the actual working state of the cell, improving the accuracy and reliability of the battery management system's monitoring of battery status. In addition, a temperature compensation coefficient can be introduced during the calculation process to correct for temperature drift in the voltage drop value. Laboratory data shows that in a 200A high-current charging / discharging scenario, the voltage acquisition error can be reduced from ±50mV in the traditional solution to ±5mV, significantly improving the error correction effect.

[0035] Example 2

[0036] This embodiment provides a multi-module battery, including: multiple actual cell modules, which are connected in series via a series connection line; The battery management system is configured to perform the aforementioned information collection method; Distributed acquisition nodes are deployed on each real battery cell module and the serial connection line marked as a virtual battery cell. The distributed acquisition nodes integrate multiple ADCs, temperature sensors and humidity sensors. A high-speed communication bus is used to transmit acquired data and commands.

[0037] In the description of this invention, it should be understood that the indicated orientation or positional relationship is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing this invention and simplifying the description, and is not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this invention.

[0038] Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0039] In this invention, unless otherwise explicitly specified and limited, the terms "connection," "fixed," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0040] Although the invention has been described in conjunction with the specific embodiments described above, it will be apparent to those skilled in the art that many substitutions, modifications, and variations can be made based on the foregoing. Therefore, all such substitutions, modifications, and variations are included within the scope of the appended claims.

Claims

1. A method for acquiring information from multi-module batteries based on dynamic correction, characterized in that, Includes the following steps: Step 1: The battery management system identifies and marks components in the battery that introduce acquisition errors due to internal resistance or structural characteristics, configures the marked components as virtual cells, and deploys independent acquisition channels for them. Step 2: Synchronously collect electrical data of all real battery cell modules and virtual battery cells. The electrical data includes voltage, current, temperature and environmental parameters. Step 3: Based on the electrical data of the virtual cell, dynamically calculate the voltage drop and temperature drift error introduced by the components, shield the electrical data of the virtual cell in the battery management system, and compensate the voltage data of the real cell module associated with the virtual cell in real time, and calculate the power and capacity of the corresponding real cell module. Step four: Combine historical data and environmental parameters of the actual battery cell modules to perform differentiated capacity correction for each actual battery cell module.

2. The method for acquiring multi-module battery information based on dynamic correction according to claim 1, characterized in that, The components are series connecting lines. In step one, identifying and marking series connecting lines in the battery that introduce acquisition errors due to internal resistance or structural characteristics includes: measuring the dynamic internal resistance of each connecting line during the battery assembly stage; associating the internal resistance data with the physical models of the material, length, and cross-sectional area of ​​the series connecting lines to establish an internal resistance-structure mapping library; and marking a connecting line whose internal resistance exceeds a set threshold of its structural prediction value as a virtual cell candidate.

3. The method for acquiring multi-module battery information based on dynamic correction according to claim 1, characterized in that, The virtual battery cell's acquisition channel uses a differential sampling circuit with a sampling frequency of no less than 1kHz, and is hardware isolated from the acquisition channel of the real battery cell module.

4. The method for acquiring multi-module battery information based on dynamic correction according to claim 3, characterized in that, The synchronous acquisition process adopts a master-slave control architecture. The master control unit issues acquisition commands with timestamps, and the slave control unit completes data acquisition within a set time window according to the commands and uploads the data to the master control unit via CAN-FD or Ethernet protocol.

5. The method for acquiring multi-module battery information based on dynamic correction according to claim 4, characterized in that, During the process of collecting electrical data, the main control unit dynamically adjusts the acquisition frequency according to the real-time working status of the actual battery cell module: when the rate of change of charging and discharging current is greater than the set value, the acquisition frequency is increased to above 500Hz.

6. The method for acquiring multi-module battery information based on dynamic correction according to claim 1, characterized in that, In step three, the voltage drop value is calculated in real time based on the voltage and current data collected by the virtual cell; the voltage drop value is applied to the collected voltage of the downstream real cell module in a reverse superposition manner, and a temperature compensation coefficient is introduced at the same time during the calculation process to correct the temperature drift of the voltage drop value.

7. The method for acquiring multi-module battery information based on dynamic correction according to claim 1, characterized in that, The differentiated capacity correction includes: constructing a corresponding health status score based on the historical cycle count, internal resistance growth trend, and temperature change record of each real cell module; assigning independent capacity attenuation compensation parameters to the corresponding real cell module according to the corresponding health status score; and dynamically applying them in capacity calculation.

8. A multi-module battery system, characterized in that, include: Multiple real battery cell modules are connected in series via a series connection line; A battery management system configured to perform the dynamic correction-based multi-module battery information acquisition method as described in any one of claims 1-7; Distributed acquisition nodes are deployed on each real battery cell module and on the series connection lines marked as virtual battery cells; A high-speed communication bus is used to transmit acquired data and commands.

9. The multi-module battery system according to claim 8, characterized in that, The distributed acquisition node integrates multiple ADCs, temperature sensors, and humidity sensors.

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