A lithium battery inter-cluster equalization adaptive control method and a battery management system

Abstract

The present application relates to the technical field of battery energy storage system management and control, and particularly relates to a lithium battery cluster inter-equalization adaptive control method and a battery management system, comprising: step S1, monitoring the terminal voltage of each battery cluster in the energy storage system, and obtaining the maximum voltage deviation value between each battery cluster; step S2, obtaining the average temperature and average state of charge of the energy storage system; step S3, calculating the reference charging current value of the energy storage system based on the average temperature and average state of charge; step S4, judging whether the maximum voltage deviation value is greater than the set threshold value, if not, taking the reference charging current value as the maximum safe charging current of the energy storage system; if yes, inputting the maximum deviation value and the reference current into the adaptive controller, and dynamically adjusting the maximum safe charging current of the energy storage system through control law operation. The present application dynamically adjusts the charging current through real-time monitoring and adaptive control, effectively suppresses the cluster inter-circulation, and improves the system safety, balance and life.

Description

An adaptive control method for equalization among lithium battery clusters and a battery management system Technical Field

[0001] This invention relates to the field of battery energy storage system management and control technology, specifically to a lithium battery cluster equalization adaptive control method and battery management system. Background Technology

[0002] With the continuous development of the new energy industry and the increasing demand for large-scale energy storage, energy storage system architecture is gradually evolving from the traditional single-cell configuration to a multi-cell parallel structure to adapt to the diverse requirements for energy density and dynamic power response in complex application scenarios. The multi-cell parallel architecture not only increases the overall system capacity but also enhances the system's adaptability to fluctuating loads, becoming the mainstream technology direction for medium-to-large-scale energy storage power plants and high-power applications.

[0003] In multi-cluster parallel systems, due to inherent differences in parameters such as capacity, internal resistance, and temperature among the cells within each cluster, inconsistencies in inter-cluster voltage can easily occur during system operation, leading to current imbalances in parallel branches. This problem not only causes some branch currents to exceed the safe operating limits of the battery cluster, but also accelerates the aging of local cells, affects the system's cycle life, and threatens the overall stability and safety of the system.

[0004] To address the aforementioned issues, the industry currently employs two main technical solutions: passive balancing and active balancing. However, passive balancing primarily relies on resistor energy dissipation, resulting in low balancing efficiency, slow dynamic response, and difficulty in handling significant differences between large-capacity battery clusters. Active balancing, primarily using cascaded power converters, can achieve effective balancing through energy transfer, but typically requires an independent power conversion unit for each battery cluster. This significantly increases system hardware costs and control complexity, and presents serious challenges in power density and heat dissipation management. Consequently, it is difficult to balance economic viability and engineering feasibility in large-scale energy storage applications.

[0005] Therefore, how to design an efficient, low-cost, and easy-to-manage equalization control scheme in a multi-cluster parallel architecture has become a key technical problem that urgently needs to be solved in the field of energy storage systems. Summary of the Invention

[0006] To address the above technical problems, this invention provides a technical solution for an adaptive control method for equalization among lithium battery clusters and a battery management system.

[0007] The technical problem solved by this invention can be achieved by the following technical solution: an adaptive control method for equalization between lithium battery clusters, comprising: step S1, real-time monitoring of the terminal voltage of each battery cluster in the energy storage system, and obtaining the maximum voltage deviation value between each battery cluster based on the terminal voltage of each battery cluster. Step S2: Obtain the average temperature of the energy storage system. and average state of charge Step S3, based on the average temperature and the average state of charge Calculate the reference charging current value of the energy storage system. Step S4: Determine the maximum voltage deviation value. Is it greater than a set threshold? If not, set the reference charging current value. The maximum safe charging current of the energy storage system If so, the maximum deviation value and the reference current The input adaptive controller dynamically adjusts the maximum safe charging current of the energy storage system through control law calculations. .

[0008] Preferably, step S1 includes:

[0009] Step S11: Monitor the terminal voltage of each battery cluster in the energy storage system in real time, and calculate the global average voltage of the energy storage system. The specific formula is expressed as follows:

[0010] ,(i=1,2,……,n), where, For the first The terminal voltage of each battery cluster;

[0011] Step S12, based on the global average voltage The voltage deviation of each battery cluster is calculated using the following formula:

[0012] ,(i=1,2,……,n), where, For the first Voltage deviation of individual battery clusters;

[0013] Step S13: Obtain the terminal voltage of each battery cluster and the global average voltage. Maximum voltage deviation The specific formula is expressed as follows: .

[0014] Preferably, in step S2,

[0015] The average temperature for: ,(i=1,2,……,n), where, For the first Temperature values ​​of individual battery clusters;

[0016] The average state of charge for: ,(i=1,2,……,n), where, For the first The state of charge (SOC) value of each battery cluster.

[0017] Preferably, in step S3, the reference charging current value Calculated using the following formula:

[0018] ,

[0019] in, Rated current;

[0020] The state-of-charge compensation function is expressed by the following formula:

[0021] ;

[0022] The temperature compensation function is expressed by the following formula:

[0023] .

[0024] Preferably, in step S4, the maximum safe charging current Dynamic adjustments can be made using the following formula:

[0025] ,

[0026] in, The function is a restricted function. This is the proportionality coefficient. The integral coefficient is... This is the upper limit of the charging current. This is the lower limit of the system charging current.

[0027] Preferably, the The function will use the maximum safe charging current Limited to [ , Inside, specifically:

[0028] .

[0029] Preferably, the proportionality coefficient and the integral coefficient Dynamic values ​​are assigned based on predefined functional relationships. The specific calculation method is as follows:

[0030] proportionality coefficient The formula is expressed as follows: ,in, The critical cluster total pressure deviation that triggers overcurrent;

[0031] Integral coefficient The formula is expressed as follows: ,in, Let be the system's inertial time constant.

[0032] It also includes a battery management system based on lithium battery cluster equalization adaptive control, which implements the lithium battery cluster equalization adaptive control method described above, including: a master control module, used to execute the adaptive control method, calculate and issue system-level maximum safe charging current command; at least one cluster control module, connected to the master control module, used to manage the operating status of individual battery clusters and report cluster-level status data to the master control module; and at least one slave control module, connected to the cluster control module, used to collect basic operating parameters of the battery pack and report battery pack data to the cluster control module.

[0033] Preferably, the central control module includes: a data acquisition unit for acquiring cluster-level status data uploaded by each cluster control module, the cluster-level status data including terminal voltage, temperature, and state of charge; and a calculation and analysis unit connected to the data acquisition unit for calculating the global average voltage of the energy storage system based on the cluster-level status data. Maximum voltage deviation Average temperature and average state of charge The adaptive control unit, connected to the calculation and analysis unit, is used to perform calculations based on the maximum voltage deviation value. Average temperature and average state of charge Generate the maximum safe charging current command;

[0034] The instruction output unit, connected to the adaptive control unit, is used to send the maximum safe charging current instruction to the power conversion system.

[0035] Preferably, the cluster control module includes: a cluster status monitoring unit, used to receive and integrate battery pack data reported by the slave control module, and calculate and generate the cluster-level status data; a cluster-level management unit, connected to the cluster status monitoring unit, used to perform local charging and discharging management and fault diagnosis of the battery cluster; and a data interaction unit, connected to the cluster-level management unit, used to upload the cluster-level status data to the master control module.

[0036] Beneficial effects: This invention monitors the terminal voltage of each battery cluster, the average temperature of the system, and the state of charge in real time. Based on the maximum voltage deviation and the reference charging current, it dynamically adjusts the maximum safe charging current of the system. This enables the system to actively respond to the inconsistency problem between battery clusters, effectively suppress the current imbalance of parallel branches, and achieve both balanced efficiency and hardware cost control while ensuring the safe operation of the system. This significantly improves the dynamic adaptability and overall lifespan of the energy storage system. Attached Figure Description

[0037] Figure 1 is a flowchart of the lithium battery cluster equalization adaptive control method of the present invention.

[0038] Figure 2 is a flowchart of step S1 in the lithium battery cluster equalization adaptive control method of the present invention.

[0039] Figure 3 is a battery management system architecture diagram of the lithium battery cluster equalization adaptive control strategy of the present invention.

[0040] Figure 4 is the main circuit topology diagram of the lithium battery cluster equalization adaptive control strategy of the present invention. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0043] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but this is not intended to limit the scope of the invention.

[0044] Referring to Figure 1, the present invention provides an adaptive control method for equalization among lithium battery clusters, comprising: step S1, real-time monitoring of the terminal voltage of each battery cluster in the energy storage system, and obtaining the maximum voltage deviation value between each battery cluster based on the terminal voltage of each battery cluster. Step S2: Obtain the average temperature of the energy storage system. and average state of charge Step S3, based on the average temperature and the average state of charge Calculate the reference charging current value of the energy storage system. Step S4: Determine the maximum voltage deviation value. Is it greater than a set threshold? If not, set the reference charging current value. The maximum safe charging current of the energy storage system If so, the maximum deviation value and the reference current The input adaptive controller dynamically adjusts the maximum safe charging current of the energy storage system through control law calculations. .

[0045] Specifically, in this embodiment of the invention, to address the issues of inter-cluster voltage inconsistency and circulating current caused by differences in battery parameters in a multi-cluster parallel system, the average temperature of the system is fused. Average state of charge In addition to real-time voltage deviation and other multi-dimensional information, it introduces conditional judgment and adaptive control law based on a set threshold to dynamically and smoothly limit the maximum safe charging current of the system. It avoids the high cost and complexity problems caused by the need to add independent power conversion hardware in traditional solutions, and realizes efficient, low cost and easy engineering management of inter-cluster adaptive equalization through software algorithms without changing the main circuit topology.

[0046] Specifically, this solution transforms the system's equalization control into optimized management of the global maximum safe charging current. During system operation, when the inter-cluster voltage deviation does not exceed a set threshold, the system operates based on average temperature. and average state of charge The calculated reference charging current The system operates in a way that ensures efficient energy conversion while also taking into account the natural equilibrium trend among the battery clusters. If the voltage deviation exceeds a set threshold, the system automatically switches to current suppression mode, dynamically reducing the maximum safe charging current through an adaptive controller. By mitigating the uneven current distribution among parallel branches from the energy input source, a balance between safety and equilibrium is achieved at the system level.

[0047] As a preferred embodiment of the present invention, referring to FIG2, step S1 includes:

[0048] Step S11: Monitor the terminal voltage of each battery cluster in the energy storage system in real time, and calculate the global average voltage of the energy storage system. The specific formula is expressed as follows:

[0049] ,(i=1,2,……,n), where, For the first The terminal voltage of each battery cluster;

[0050] Step S12, based on the global average voltage The voltage deviation of each battery cluster is calculated using the following formula:

[0051] ,(i=1,2,……,n), where, For the first Voltage deviation of individual battery clusters;

[0052] Step S13: Obtain the terminal voltage of each battery cluster and the global average voltage. Maximum voltage deviation The specific formula is expressed as follows: .

[0053] Specifically, considering that voltage inconsistency between battery clusters is a key factor leading to circulating current and energy loss, and that the maximum voltage difference directly determines the severity of system imbalance, this embodiment of the invention establishes a complete voltage deviation quantification and evaluation process to accurately capture the real-time operating status of the system. The specific implementation steps are as follows: First, a high-precision voltage sensor is used to synchronously collect the terminal voltage values ​​of all online battery clusters at a fixed sampling period (e.g., once per second). Subsequently, all the collected voltage values ​​are summed, and the global average voltage of the system is accurately calculated. This value represents the central tendency of the system voltage level at the current moment; next, the terminal voltage of each battery cluster is calculated separately. With global average voltage The difference, i.e. This yields a set of positive and negative deviations that accurately reflect the deviations of each cluster from the average level; finally, by comparing all... The absolute value of the value is used to quickly identify and pinpoint the battery cluster with the most severe voltage deviation; the corresponding absolute value is determined as the maximum voltage deviation. .

[0054] Through this series of precise and continuous calculation steps, the system can grasp the extreme conditions of internal imbalance in real time and quantitatively, providing an accurate and reliable basis for subsequent adaptive adjustment of charging current, thereby effectively preventing the risk of overcharging or over-discharging caused by excessively high or low voltage of individual battery clusters.

[0055] In a preferred embodiment of the present invention, in step S2,

[0056] The average temperature for: ,(i=1,2,……,n), where, For the first Temperature values ​​of individual battery clusters;

[0057] The average state of charge for: ,(i=1,2,……,n), where, For the first The state of charge (SOC) value of each battery cluster.

[0058] Specifically, since the temperature (T) and state of charge (SOC) of a battery are key intrinsic parameters that determine its ability to accept charging current, and the differences between battery clusters in the system directly affect the overall balance control effect, in this embodiment of the invention, the average temperature and average state of charge of the system are calculated to comprehensively evaluate the overall thermal state and energy state of the energy storage system, providing a core basis for subsequent dynamic calculation of the reference charging current.

[0059] Correspondingly, average temperature The specific acquisition steps are as follows: First, temperature sensors pre-distributed and installed at key sampling points (such as representative locations in the middle and ends of the cluster) in each battery cluster are used to synchronously acquire the temperature measurement values ​​of each battery cluster in real time, denoted as . Subsequently, all received temperature data are summed, and the average temperature reflecting the overall heat load level of the system is calculated. .

[0060] Similarly, average state of charge The specific steps for obtaining the SOC value are as follows: First, based on the terminal voltage, real-time charging and discharging current, and internal resistance parameters of the battery cluster, a state estimation algorithm combining the ampere-hour integration method and the voltage lookup table method is used to accurately estimate the real-time SOC value of each battery cluster online, denoted as . Subsequently, all SOC estimates are summed, and the average state of charge, representing the overall level of the system's current remaining energy, is calculated. .

[0061] By synchronously and accurately acquiring the average temperature and average state of charge This method can comprehensively perceive the overall state of the system, thus laying a solid foundation for the intelligent calculation of the reference charging current. It effectively avoids control inaccuracies caused by local battery cluster overheating or extreme state of charge, and improves the adaptability of the balancing strategy and the safety of system operation.

[0062] In a preferred embodiment of the present invention, in step S3, the reference charging current value Calculated using the following formula:

[0063] ,

[0064] in, Rated current;

[0065] The state-of-charge compensation function is expressed by the following formula:

[0066] ;

[0067] The temperature compensation function is expressed by the following formula:

[0068] .

[0069] Specifically, since the battery's available charging capacity and acceptable charging current are closely related to its overall state of charge and operating temperature, in order to prevent thermal runaway or overcharging damage caused by excessive charging current under high temperature or high SOC conditions, this embodiment of the invention introduces a piecewise linear state of charge compensation function. and temperature compensation function The average state of charge of the system With average temperature Mapped to rated current The scaling factor is used to dynamically generate a safe reference charging current that considers both the overall energy state of the system and the actual operating environment. The specific implementation process is as follows: First, the system calls the pre-stored rated current value. According to the average state of charge Query The function uses full current when the average SOC is below 80%, limits the current to 80% of the rated current when the average SOC is between 80% and 90%, and further limits it to 50% when the average SOC exceeds 90%; simultaneously, it adjusts the current based on the average temperature. Query The function is unrestricted when the average temperature is below 40℃, the current is limited to 80% of the rated current in the 40℃ to 45℃ range, and 70% of the rated current is used when the temperature reaches or exceeds 45℃. Finally, the rated current is multiplied by these two compensation coefficients to obtain the maximum reference charging current value that the system can safely accept under the current operating conditions. .

[0070] Through this dual compensation mechanism based on the macroscopic state of the system, the reference current is automatically reduced when the SOC or temperature is high, thereby achieving feedforward protection of the macroscopic operating boundary of the system. This effectively avoids the safety risks caused by using a fixed high current charging under extreme conditions, enabling the system to adaptively select a safe and efficient initial charging current reference in the entire operating range.

[0071] In a preferred embodiment of the present invention, in step S4, the maximum safe charging current... Dynamic adjustments can be made using the following formula:

[0072] ,

[0073] in, The function is a restricted function. This is the proportionality coefficient. The integral coefficient is... This is the upper limit of the charging current. This is the lower limit of the system charging current.

[0074] Specifically, since inter-cluster voltage deviation is a direct manifestation of system imbalance, an excessively large value will lead to increased circulating current and the risk of local overcharging. In this embodiment of the invention, the reference charging current determined in step S3 based on the system average state is used... Based on this, an adaptive controller based on a proportional-integral (PI) structure is introduced to control the maximum voltage deviation value. As the input feedback signal of the controller.

[0075] The core of this control law lies in the fact that when a voltage deviation is detected to exceed a threshold, the controller outputs a value based on... and current correction amount and from the reference current Subtracting this correction amount allows for the dynamic calculation of the maximum safe charging current that balances charging efficiency and inter-cluster balance under the current operating conditions. Among them, the proportional term Provides fast transient current suppression to quickly curb imbalance trends; integral term This is used to eliminate static deviations and ensure thorough equilibrium.

[0076] Since the controller's calculation results may exceed the system's safe operating boundaries, further... The limiting function acts as a final safety barrier, thereby limiting the maximum safe charging current. Limited to [ , [Inside]. This function is implemented through a simple conditional judgment logic, specifically: comparing the initial current x before limiting with the system's preset current safety upper and lower limits. and Perform real-time comparisons. If x is below the lower limit... Then the function output If x is higher than the upper limit Then the function output The function outputs x as is only if x is within the closed interval; its specific expression is as follows:

[0077] .

[0078] This design provides a reliable dual protection mechanism for the system current command, ensuring the final output charging current. It always operates within the safe operating range allowed by the battery and power hardware, eliminating the risk of overcharging or thermal runaway caused by current exceeding the limit. At the same time, the limiting structure frees up the current synthesis algorithm in the front stage, so that it does not need to deal with nonlinear problems such as output saturation, and can directly perform linear calculations based on the control law. This not only reduces the design difficulty of the controller, but also improves the computing efficiency.

[0079] Through this architecture, the system achieves a deep integration of security and algorithm simplicity at the logical level, significantly improving the engineering applicability and operational stability of the overall control scheme.

[0080] Furthermore, in order to overcome the lack of flexibility of fixed parameter controllers in dealing with different operating conditions, the proportional coefficient is... and the integral coefficient Dynamic values ​​are assigned based on predefined functional relationships. The specific calculation method is as follows:

[0081] proportionality coefficient The formula is expressed as follows: ,in, The critical cluster total pressure deviation that triggers overcurrent;

[0082] Integral coefficient The formula is expressed as follows: ,in, Let be the system's inertial time constant.

[0083] It can be seen that by using the proportionality coefficient Compared with the system's current reference current and critical deviation This correlation enables self-tuning of the control gain. This means that the stronger the system's reference charging capability (…), the better the self-tuning of the control gain. (The larger the deviation) or the critical cluster total pressure deviation The smaller the setting, the stronger the controller's ability to suppress voltage deviations, thus ensuring that when the voltage deviation reaches a critical value, the charging current can be precisely suppressed to the lower limit. This enables rapid protection; simultaneously, the integral coefficient... and System time constant The linkage mechanism allows the strength of the integral action to adapt to the proportional action and the dynamic characteristics of the system, ensuring both rapid control and avoiding integral saturation, thus guaranteeing system stability. This parameter adaptation mechanism enables the controller to adapt to different battery types, system sizes, and aging conditions, exhibiting excellent adaptability to various operating conditions.

[0084] Referring to Figure 3, the present invention also includes a battery management system based on adaptive control for equalization among lithium battery clusters, implementing the adaptive control method for equalization among lithium battery clusters as described above, comprising: a master control module, used to execute the adaptive control method, calculate and issue a system-level maximum safe charging current command; at least one cluster control module, connected to the master control module, used to manage the operating status of individual battery clusters and report cluster-level status data to the master control module; and at least one slave control module, connected to the cluster control module, used to collect basic operating parameters of the battery pack and report battery pack data to the cluster control module.

[0085] Specifically, in this embodiment of the invention, the battery management system adopts a hierarchical distributed architecture. It is physically deployed inside an energy storage container and logically forms a complete management system together with a higher-level cloud platform and a station-level energy management system (EMS).

[0086] Accordingly, the central control module, as the core of this system at the container level, interacts with the cloud platform via Ethernet, and simultaneously interacts with the station-level EMS and other peripherals within the container (via dry link communication) via a CAN (Controller Area Network) bus or a 485 bus. The station-level EMS is also connected to the Power Conversion System (PCS) via a CAN or 485 bus, while the central control module communicates with the PCS via its independent CAN bus, thus forming a complete system control loop.

[0087] Furthermore, the system comprises N cluster control modules, namely cluster control module 1, cluster control module 2, ..., cluster control module N. Each cluster control module is connected to the main control module via an independent CAN bus. Each cluster control module is connected in parallel to multiple slave control modules (such as slave control module 1, slave control module 2, ..., slave control module N) that manage its battery clusters via its dedicated CAN bus. Each slave control module (such as slave control module 1) directly connects to and monitors a unique battery pack (such as Pack1), forming a strict point-to-point mapping relationship. That is, slave control module 1 connects to and monitors battery pack Pack1, slave control module 2 connects to and monitors battery pack Pack2, ..., and slave control module N connects to and monitors battery pack PackN.

[0088] Specifically, in this architecture, the central control module is the core of the system, responsible for executing the adaptive control algorithm, aggregating all cluster-level data, calculating and issuing the system-level maximum safe charging current command to the PCS. Each cluster control module is responsible for the comprehensive management of its corresponding battery cluster, including calculating the cluster's total voltage, average temperature, and SOC, and reporting this cluster-level status data to the central control module. The slave control modules, as the underlying data acquisition units, are responsible for high-precision monitoring and reporting the basic parameters such as voltage and temperature of the individual cells within their unique battery pack.

[0089] As can be seen, the master control module, cluster control module, and slave control module are connected via a bus, forming a complete mapping relationship between the three-level management structure of "master control module - cluster control module - slave control module" and the three-layer physical object of "system - battery cluster - battery pack". Simultaneously, the master control module acts as a gateway, uploading critical data to the cloud platform and EMS, and receiving their scheduling instructions. This provides a precise, reliable, and hierarchically structured data acquisition, processing, and instruction execution architecture for the inter-cluster equalization adaptive control method. This architecture ensures smooth data flow and rigorous control logic, providing a solid foundation for achieving efficient and secure inter-cluster equalization.

[0090] In a preferred embodiment of the present invention, the central control module includes: a data acquisition unit, used to acquire cluster-level status data uploaded by each cluster control module, the cluster-level status data including terminal voltage, temperature, and state of charge; and a calculation and analysis unit, connected to the data acquisition unit, used to calculate the global average voltage of the energy storage system based on the cluster-level status data. Maximum voltage deviation Average temperature and average state of charge The adaptive control unit, connected to the calculation and analysis unit, is used to perform calculations based on the maximum voltage deviation value. Average temperature and average state of charge The system generates a maximum safe charging current command; the command output unit is connected to the adaptive control unit and is used to send the maximum safe charging current command to the power conversion system.

[0091] Specifically, in this embodiment of the invention, the central control module constructs a complete closed-loop control link through the coordinated operation of its internal units. The data acquisition unit, as the information entry point, ensures the real-time nature and integrity of the status data; the calculation and analysis unit, as the data processing core, accurately quantifies the system's imbalance and operating benchmark; the adaptive control unit, as the decision-making brain, transforms the analysis results into intelligent control commands; and the command output unit, as the execution terminal, ensures the accurate delivery of control commands.

[0092] This highly integrated modular design not only clarifies functional boundaries and reduces system coupling, but also solidifies the inter-cluster equilibrium adaptive control method into stable and reliable hardware logic, thereby achieving efficient, accurate and robust execution of the control algorithm at the engineering level.

[0093] In a preferred embodiment of the present invention, the cluster control module includes: a cluster status monitoring unit, used to receive and integrate battery pack data reported by the slave control module, and calculate and generate the cluster-level status data; a cluster-level management unit, connected to the cluster status monitoring unit, used to perform local charging and discharging management and fault diagnosis of the battery cluster; and a data interaction unit, connected to the cluster-level management unit, used to upload the cluster-level status data to the master control module.

[0094] Specifically, in this embodiment of the invention, the cluster control module achieves refined and autonomous management of individual battery clusters through the precise division of labor and efficient collaboration among its internal units. Specifically, the cluster status monitoring unit, acting as a data relay and processor, aggregates the dispersed battery pack parameters into cluster-level information with decision-making value; based on this information, the cluster-level management unit independently executes its own real-time monitoring and protection strategies, enabling early fault identification and localized processing; and the data interaction unit ensures seamless bidirectional communication between cluster-level status information and central control commands.

[0095] This design not only effectively reduces the computational load on the central control module, but also constructs a collaborative control architecture of "distributed perception and centralized decision-making," which significantly improves the granularity of management and the reliability of operation while ensuring the overall control accuracy of the system.

[0096] In a preferred embodiment of the present invention, the slave control module includes: a parameter acquisition unit for real-time acquisition of analog voltage and temperature signals of each individual battery cell in the battery pack; a signal processing unit connected to the parameter acquisition unit for filtering, amplifying, and performing analog-to-digital conversion on the analog signals; and a local communication unit connected to the signal processing unit for packaging the processed battery pack data and uploading it to the cluster control module.

[0097] Specifically, in this embodiment of the invention, the slave control module, as a front-end sensor network node deployed on each battery pack, forms the basis of system data acquisition. Its data acquisition unit achieves multi-channel synchronous measurement through a dedicated chip, ensuring data accuracy and simultaneity; the signal conditioning unit effectively suppresses electromagnetic interference and improves signal quality, converting the original analog signal into reliable data that can be processed by the digital system; and the local communication unit ultimately transmits standardized data frames covering "individual cell voltage and temperature monitoring" information stably to the next-level cluster control module via the CAN bus protocol.

[0098] This design enables the basic operating parameters of each battery pack to be independently, accurately, and in real time sensed and reported, providing the most basic and critical decision-making basis for the central control module to execute the inter-cluster equalization adaptive control algorithm.

[0099] As a preferred embodiment of the present invention, the main circuit topology of the lithium battery cluster equalization adaptive control strategy is shown in Figure 4. The topology adopts a distributed architecture in which battery packs and power modules correspond one-to-one and are connected in parallel. Each battery pack is directly connected in series with an independent power module to form the most basic "battery pack-power module" energy unit of the system. All energy units connect their AC output terminals in parallel to a common AC bus.

[0100] Specifically, in this embodiment of the invention, referring to Figure 4, taking N battery packs as an example, each battery pack (such as Pack1, Pack2, ... PackN) is directly connected in series with an independent power module (such as power module 1, power module 2, ... power module N) to form an independently controllable "battery pack-power module" energy unit; the output terminals of all energy units are connected in parallel to the positive DC bus Udc and the load DC bus -Udc, and power is supplied to the load through a shared filter inductor L and a supporting capacitor Uc; the system precharge circuit realizes the soft start of the DC bus capacitor through the control switches S(n / o)1, S(n / o)2).

[0101] Furthermore, this distributed main circuit and the hierarchical battery management system together constitute a hardware and software collaborative balanced control system. The main control module of the battery management system generates a system-level maximum safe charging current command by executing the adaptive control algorithm, and sends it to the power conversion system (PCS) composed of all power modules via the CAN bus; under this architecture, each power module can implement independent charging current limits on its corresponding battery pack according to the command.

[0102] This one-to-one mapping relationship enables the control strategy of this invention to surpass the traditional "inter-cluster" equalization and achieve finer-grained "inter-packet" energy management, thereby achieving higher equalization efficiency and operational safety at the system level, while completely eliminating the circulating current path between parallel battery packs.

[0103] The above description is merely a preferred embodiment of the present invention and does not limit the implementation and protection scope of the present invention. Those skilled in the art should realize that any equivalent substitutions and obvious changes made based on the description and illustrations of the present invention should be included within the protection scope of the present invention.

Claims

1. A lithium battery cluster inter-cluster equalization adaptive control method, characterized in that, include: Step S1: Monitor the terminal voltage of each battery cluster in the energy storage system in real time, and obtain the maximum voltage deviation value between each battery cluster based on the terminal voltage of each battery cluster. ； Step S2: Obtain the average temperature of the energy storage system. and average state of charge Step S3, based on the average temperature and the average state of charge Calculate the reference charging current value of the energy storage system. Step S4: Determine the maximum voltage deviation value. If the current value is greater than a set threshold, then the reference charging current value will be used. The maximum safe charging current of the energy storage system ； If so, the maximum voltage deviation value and the reference charging current value The input adaptive controller dynamically adjusts the maximum safe charging current of the energy storage system through control law calculations. In step S3, the reference charging current value Calculated using the following formula: ,in, Rated current; The state-of-charge compensation function is expressed by the following formula: ； The temperature compensation function is expressed by the following formula: In step S4, the maximum safe charging current Dynamic adjustments can be made using the following formula: ,in, The function is a restricted function. This is the proportionality coefficient. The integral coefficient is... This is the upper limit of the charging current. This is the lower limit of the system charging current.

2. The lithium battery cluster inter-cluster equalization adaptive control method according to claim 1, characterized in that, Step S1 includes: Step S11, real-time monitoring of the terminal voltage of each battery cluster in the energy storage system, and calculation of the global average voltage of the energy storage system. The specific formula is expressed as follows: , (i=1,2,……,n), where, For the first The terminal voltage of each battery cluster; Step S12, based on the global average voltage The voltage deviation of each battery cluster is calculated using the following formula: , (i=1,2,……,n), where, For the first The voltage deviation of each battery cluster; Step S13, obtain the terminal voltage of each battery cluster and the global average voltage. Maximum voltage deviation The specific formula is expressed as follows: 。 3. The lithium battery cluster inter-cluster equalization adaptive control method according to claim 1, characterized in that, In step S2, the average temperature for: , (i=1,2,……,n), where, For the first Temperature values ​​of individual battery clusters; average state of charge for: , (i=1,2,……,n), where, For the first The state of charge (SOC) value of each battery cluster.

4. The lithium battery cluster inter-cluster equalization adaptive control method according to claim 1, characterized in that, The The function will use the maximum safe charging current Limited to [ , Inside, specifically: 。 5. The lithium battery cluster inter-cluster equalization adaptive control method according to claim 1, characterized in that, The proportionality coefficient and the integral coefficient Dynamically assign values ​​based on predefined functional relationships. The specific calculation method is as follows: Proportional coefficient The formula is expressed as follows: ,in, The critical cluster total pressure deviation that triggers overcurrent; integral coefficient The formula is expressed as follows: ,in, Let be the system's inertial time constant.

6. A battery management system based on adaptive control for equalization among lithium battery clusters, characterized in that, An application implementation of the lithium battery cluster equalization adaptive control method as described in any one of claims 1-4 includes: a master control module, used to execute the adaptive control method, calculate and issue a system-level maximum safe charging current command; at least one cluster control module, connected to the master control module, used to manage the operating status of a single battery cluster and report cluster-level status data to the master control module; and at least one slave control module, connected to the cluster control module, used to collect basic operating parameters of the battery pack and report battery pack data to the cluster control module.

7. A battery management system based on adaptive control of inter-cluster equalization of lithium battery cells according to claim 6, characterized in that, The overall control module includes: a data acquisition unit for acquiring cluster-level status data uploaded by each cluster control module, the cluster-level status data including terminal voltage, temperature, and state of charge; and a calculation and analysis unit connected to the data acquisition unit for calculating the global average voltage of the energy storage system based on the cluster-level status data. Maximum voltage deviation Average temperature and average state of charge The adaptive control unit, connected to the calculation and analysis unit, is used to perform calculations based on the maximum voltage deviation value. Average temperature and average state of charge The system generates a maximum safe charging current command; the command output unit is connected to the adaptive control unit and is used to send the maximum safe charging current command to the power conversion system.

8. A battery management system based on adaptive control of inter-cluster equalization of lithium battery cells according to claim 6, characterized in that, The cluster control module includes: a cluster status monitoring unit, used to receive and integrate battery pack data reported by the slave control module, and calculate and generate the cluster-level status data; a cluster-level management unit, connected to the cluster status monitoring unit, used to perform local charging and discharging management and fault diagnosis of the battery cluster; and a data interaction unit, connected to the cluster-level management unit, used to upload the cluster-level status data to the master control module.

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