High-power energy storage charging system and circuit

Abstract

The invention relates to the technical field of battery management, and discloses a high-power energy storage charging system and circuit, which comprises a plurality of battery function sub-modules BSM, each battery function sub-module BSM is provided with a state acquisition circuit, a multi-port solid-state switch matrix and a system master controller SMC, and the control circuit is used for acquiring the state information, measured by the state acquisition circuit, of the plurality of battery function sub-modules BSM, running a dynamic topology scheduling algorithm based on the state information, determining an optimal topology path formed by part or all of the battery function sub-modules BSM, and generating a topology reconstruction instruction. The dynamic topology scheduling algorithm is operated through the system master controller SMC, and the problem that although an existing battery management system can monitor the state differences, an active and dynamic control strategy is lacked to intervene and balance the health state of each module from the system topology level is solved.

Description

Technical Field

[0001] This invention relates to the field of battery management technology, specifically to a high-power energy storage charging system and circuit. Background Technology

[0002] With the increasing popularity of outdoor activities and self-driving camping, as well as the growing demand for home emergency backup power, outdoor portable power banks have become important devices for ensuring portable power supply and achieving efficient energy conversion. Existing portable power bank systems of this type consist of a certain number of battery modules connected in a fixed series-parallel configuration and equipped with a battery management system.

[0003] This existing battery management system focuses on monitoring the battery's state (e.g., voltage, current, temperature) and providing basic passive protection (e.g., overcharge, over-discharge, over-temperature protection). However, because the system uses a fixed electrical topology, the current path cannot be changed once it is set. During the long-term operation of the energy storage system, there will inevitably be differences in manufacturing processes, operating conditions, and temperature distribution among the various battery modules that make up the system. This leads to inconsistencies in their internal resistance, capacity decay rate, and aging characteristics.

[0004] In a fixed topology, this inconsistency caused by individual differences is continuously amplified, causing modules in poor condition to age faster, while the performance of modules in better condition cannot be fully utilized. The overall available capacity and cycle life of the entire energy storage system are prematurely limited by the worst-performing battery module in the system. In addition, this fixed connection method has low fault tolerance. Once an unrecoverable failure occurs in a battery module, the entire battery cluster containing that module will be forced to stop working, thereby affecting the reliability and availability of the system. Although existing battery management systems can detect these state differences, they lack a proactive and dynamic control strategy to intervene and balance the health status of each module at the system topology level. Summary of the Invention

[0005] The purpose of this invention is to provide a high-power energy storage charging system and circuit that solves the problem that although existing battery management systems can detect these state differences, they lack an active and dynamic control strategy to intervene in and balance the health status of each module at the system topology level.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] The first aspect of this invention provides a high-power energy storage and charging system, comprising:

[0008] A plurality of battery functional sub-modules (BSMs), each of which is configured with a status acquisition circuit and a multi-port solid-state switch matrix;

[0009] The system main controller (SMC) is used to acquire the status information of the plurality of battery functional sub-modules (BSMs) measured by the status acquisition circuit.

[0010] Based on the state information, a dynamic topology scheduling algorithm is run to determine an optimal topology path consisting of some or all of the battery functional sub-modules (BSMs); and a topology reconfiguration instruction is generated and sent to the battery functional sub-modules (BSMs) involved in the optimal topology path to control the multi-port solid-state switch matrix within the battery functional sub-modules (BSMs) to reconfigure and form the optimal topology path.

[0011] In one embodiment, the status information includes real-time status data and aging status data maintained by the system main controller (SMC). The real-time status data specifically includes at least one of the voltage, current, and temperature of the battery functional submodule (BSM), and the aging status data includes at least one of the effective DC internal resistance (DCR), capacity health status, and cumulative ampere-hour throughput of the battery functional submodule (BSM).

[0012] In one embodiment, the dynamic topology scheduling algorithm determines the optimal topology path by calculating a multi-objective comprehensive cost function, which is constructed as a weighted sum of thermal cost components, resistivity cost components, and aging cost components, which are calculated based on the real-time status data and the aging status data.

[0013] In one embodiment, the aging cost component is calculated based on the capacity health status and cumulative ampere-hour throughput of the battery functional submodule (BSM), and is used to preferentially select the battery functional submodule (BSM) with a lower degree of aging when selecting the optimal topology path.

[0014] In one embodiment, the system main controller (SMC) is further configured to dynamically adjust the weighting factors corresponding to the thermal cost component, the resistance cost component, and the aging cost component based on a preset operating mode and received external inputs.

[0015] In one embodiment, the system main controller (SMC) is also used to run a fault diagnosis and management module to identify battery function submodules (BSMs) in a faulty state and not select the battery function submodules (BSMs) in a faulty state when running the dynamic topology scheduling algorithm.

[0016] In one embodiment, the multi-port solid-state switch matrix includes multiple bidirectional switches made of power semiconductor devices for bidirectional blocking and conduction control of current.

[0017] The present invention also provides a control method for a high-power energy storage charging system, applied to the aforementioned high-power energy storage charging system, comprising the following steps:

[0018] Obtain the status information of the plurality of battery functional submodules (BSMs);

[0019] Based on the aforementioned state information, a dynamic topology scheduling algorithm is run to determine an optimal topology path consisting of some or all of the aforementioned battery functional submodules (BSMs).

[0020] And control the reconstructing of the multi-port solid-state switch matrix within the plurality of battery functional submodules (BSM) to form the optimal topology path.

[0021] In one embodiment, the step of obtaining status information includes:

[0022] Real-time status data is obtained from the battery functional submodule BSM, the real-time status data including at least one of voltage, current and temperature, and aging status data corresponding to the battery functional submodule BSM is updated, the aging status data including at least one of equivalent DC internal resistance DCR, capacity health status and cumulative ampere-hour throughput.

[0023] In one embodiment, the step of running the dynamic topology scheduling algorithm includes determining the optimal topology path by calculating a multi-objective comprehensive cost function;

[0024] The multi-objective integrated cost function is constructed as a weighted sum of thermal cost components, electrical resistance cost components, and aging cost components.

[0025] In one embodiment, the weighting factors corresponding to the thermal cost component, the resistance cost component, and the aging cost component are dynamically adjusted according to a preset working mode or received external input.

[0026] A second aspect of the present invention provides a high-power energy storage charging circuit, the circuit being a battery functional submodule (BSM) including a cell assembly; a multi-port solid-state switch matrix coupled to the cell assembly and providing multiple power ports for electrical connection with other circuits; a status acquisition circuit for measuring the status data of the cell assembly; and a local microcontroller for acquiring the status data and controlling the on / off state of the multi-port solid-state switch matrix according to topology reconfiguration instructions received from an external source.

[0027] In summary, the present invention has at least one of the following beneficial technical effects:

[0028] 1. This invention utilizes a dynamic topology scheduling algorithm run by the system main controller (SMC). When deciding on the optimal topology path, it introduces an aging cost component calculated based on capacity health status and cumulative ampere-hour throughput. This enables the system main controller (SMC) to proactively and preferentially select battery functional sub-modules (BSMs) with lower aging levels, avoiding overuse of specific modules and achieving loss balance among modules in the entire system, thereby extending the overall operating life of the high-power energy storage charging system.

[0029] 2. The system main controller (SMC) in this invention calculates the thermal cost component by acquiring real-time temperature data. Combined with the functions of the fault diagnosis and management module, when a high-temperature or faulty battery functional sub-module (BSM) is detected, the algorithm will automatically avoid selecting these high-temperature or faulty BSMs when making topology decisions. This achieves the avoidance of thermal runaway risk and online isolation of faulty units, ensuring that the high-power energy storage charging system can continue to operate even when partially failed, thus improving the operational reliability and safety of the high-power energy storage charging system.

[0030] 3. The dynamic topology scheduling algorithm in this invention also evaluates the resistance cost component calculated based on the equivalent DC internal resistance (DCR) when making decisions. By selecting the path with the lowest total equivalent internal resistance from among many candidate topology paths, it can minimize the internal power loss of the high-power energy storage charging system during operation, thereby improving the energy conversion efficiency of the high-power energy storage charging system in the charge and discharge cycle. Attached Figure Description

[0031] Figure 1 This is a schematic diagram of the overall architecture of the high-power energy storage and charging system in this invention;

[0032] Figure 2 This is a flowchart illustrating the overall workflow of the high-power energy storage and charging system in this invention.

[0033] Figure 3 This is a circuit diagram of the BSM multi-port solid-state switch matrix in this invention. Detailed Implementation

[0034] The following is in conjunction with the appendix Figure 1 -Appendix Figure 3 The present invention will be further described in detail below.

[0035] This invention provides a high-power energy storage charging system and circuit, including a system main controller (SMC), a battery functional submodule (BSM) array, and a high-speed communication network for connecting the system main controller (SMC) and the battery functional submodule (BSM) array.

[0036] See attached document Figure 1The system master controller (SMC) can be physically a centralized controller, such as a high-performance server or industrial control computer, or a distributed control system, such as a master-slave or multi-master architecture consisting of multiple cooperating microcontroller units or digital signal processors.

[0037] In a preferred embodiment, the system main controller (SMC) adopts a hardware platform with reliability and real-time computing capabilities. The battery functional submodule (BSM) array can be physically arranged in one or more racks and interconnected through a backplane or high-voltage wiring harness. The array in the battery functional submodule (BSM) array refers not only to the physical arrangement, but also to the potential for flexible electrical topology reconfiguration.

[0038] The "high speed" in the high-speed communication network refers to the speed relative to the low-speed CAN bus or daisy-chain communication used in the existing battery functional submodule (BSM). In this invention, since it is necessary to frequently issue topology reconfiguration commands and upload massive amounts of status data at high speed, the communication network is preferably an industrial bus with high bandwidth, low latency, and high reliability. For example, CAN-FD, FlexRay, or an industrial protocol based on RS-485 or EtherCAT real-time industrial Ethernet technology can be used. The topology of the communication network can be bus, star, ring, or redundant ring to ensure the robustness of communication.

[0039] The system main controller (SMC) integrates a state awareness and modeling module, a topology scheduling decision module, and a fault diagnosis and management module. The SMC is responsible for receiving external user commands or load requirements, executing dynamic topology scheduling algorithms, and generating topology reconfiguration commands.

[0040] Furthermore, the State Awareness and Modeling module is responsible for aggregating the raw data uploaded by all Battery Function Sub-modules (BSMs) and running state estimation algorithms, such as Extended Kalman Filtering (EPF) or Unscented Kalman Filtering (UFF), to estimate in real time the state of charge and internal states of each BSM that are difficult to measure directly, such as peak power capability. The Topology Scheduling Decision module is the core decision-making unit of this invention. It receives the accurate state model output by the State Awareness and Modeling module and the power command from the external energy management system. It generates the optimal topology by solving a multi-objective optimization problem (i.e., the dynamic topology scheduling algorithm). The Fault Diagnosis and Management module runs in parallel, continuously performing consistency checks and threshold checks on the data to achieve early fault warning and online diagnosis.

[0041] The Battery Function Submodule (BSM) array consists of multiple BSMs, each of which is an independent energy storage and execution unit. The BSM array integrates a cell assembly, a local microcontroller, a status acquisition circuit, and a multi-port solid-state switch matrix. The multi-port solid-state switch matrix forms the physical basis of the topology reconfiguration execution module.

[0042] Independent energy storage and execution units refer to the fact that each battery functional submodule (BSM) has a complete closed-loop function of autonomous energy storage (cell pack), state awareness (state acquisition circuit), local decision-making and protection (local microcontroller), and topology execution (multi-port solid-state switch matrix). This modular design allows high-power energy storage and charging systems to be expanded in capacity or maintained in a plug-and-play manner.

[0043] The battery pack can use lithium iron phosphate, ternary lithium or other chemical systems of cells, and the selection is based on the application scenario (e.g. energy type or power type). The multi-port solid-state switch matrix provides the battery functional submodule (BSM) with the physical ability to be connected in series, in parallel or by bypass with other battery functional submodules (BSM) or system power interfaces.

[0044] A high-speed communication network is used to enable bidirectional data transmission between the system main controller (SMC) and each battery function submodule (BSM) in the battery function submodule array, including uploading status information and issuing control commands.

[0045] In one embodiment, the high-speed communication network can employ a Controller Area Network (CAN)-Flexible Data Rate (CDR) communication bus, with a data field baud rate of up to 5 Mbps or higher, capable of meeting the real-time transmission requirements of high-density data. In another optional embodiment, an industrial real-time Ethernet can also be used to provide higher bandwidth and lower deterministic latency. To improve communication robustness, the high-speed communication network can be designed with a redundant topology, such as a dual-channel CAN bus or a ring Ethernet topology, ensuring that communication between the system main controller (SMC) and the battery functional submodule (BSM) remains unaffected in the event of a single-point communication link failure. The communication protocol stack can implement cyclic redundancy check and timeout retransmission mechanisms to ensure data integrity.

[0046] The high-power energy storage charging system and circuit also include a system power interface for connecting external charging equipment or electrical equipment. In addition, the high-power energy storage charging system and circuit may also include a human-machine interaction and auxiliary unit for displaying system status and receiving user input.

[0047] The system power interface specifically includes positive and negative power buses for collecting the total current of the battery functional submodule (BSM) array, as well as high-voltage DC contactors, fuses, and pre-charge circuits installed on the buses. The pre-charge circuit is used to pre-charge the capacitive components of the external load through current-limiting resistors before the system main contactor closes, in order to avoid generating excessive surge current. The human-machine interface and auxiliary unit can be an industrial panel PC with a touch screen, which is connected to the system main controller (SMC) via Ethernet or an interface. The HMI's graphical interface can display the electrical topology connection diagram of the entire battery functional submodule (BSM) array, the aging status (SOH) temperature and other key status parameters of each battery functional submodule (BSM), the total power curve of the system, and historical fault logs in real time and dynamically. It also allows authorized operators to start and stop the system, switch modes, or set parameters.

[0048] See attached document Figure 2 After the high-power energy storage charging system and circuit are powered on, the initialization process begins. During the initialization process, the system main controller (SMC) performs hardware self-tests with all battery functional sub-modules (BSMs). Subsequently, the system main controller (SMC) establishes a communication connection with each battery functional sub-module (BSM) in the BSM array through a high-speed communication network and obtains the initial status information of each battery functional sub-module (BSM), including initial voltage, temperature, and identification information.

[0049] The hardware self-test includes verification and inspection of the internal flash memory and random access memory of the Battery Function Submodule (BSM) by its local microcontroller, reference voltage and zero-point drift tests on the ADC in the status acquisition circuit, and open / short circuit detection on the gate drive circuit of the multi-port solid-state switch matrix. The System Main Controller (SMC) also performs a similar self-test during startup. After passing the self-test, the SMC broadcasts a connection request frame. Upon receiving this frame, all online BSMs reply with a connection response frame within their respective preset time slots. The connection response frame contains a unique hardware ID, firmware version, and configuration parameters. Based on this, the SMC dynamically constructs a list of currently available BSM devices and the system's initial state vector, completing the initialization process.

[0050] After the initialization process is completed, the high-power energy storage charging system and circuit enter the closed-loop control process under steady-state operation. The closed-loop control process is a control loop that is executed periodically. During the control cycle, the state perception and modeling module first performs the operation, and the system main controller SMC broadcasts data acquisition instructions to all battery function sub-modules BSM.

[0051] The execution cycle of the closed-loop control process is a configurable key parameter. In one embodiment, the execution cycle of the closed-loop control process can be set to 100 milliseconds. A shorter cycle can improve the response speed of the high-power energy storage charging system to load transients, but it will increase the computational burden on the system main controller (SMC) and the load on the communication bus. The broadcast data acquisition command issued by the system main controller (SMC) can include a synchronization clock signal or timestamp to ensure that all battery functional submodules (BSMs) perform sampling at the most consistent time possible, thereby obtaining a high-fidelity system state snapshot.

[0052] After receiving an instruction, each battery functional submodule (BSM) acquires real-time voltage, current, and temperature data through its internal state acquisition circuit and uploads the data to the system main controller (SMC). The SMC updates the state model of each BSM based on the received real-time and historical data. The state model includes parameters such as the equivalent DC internal resistance (DCR) and the state of aging (SOH).

[0053] During the data upload phase, to avoid data conflicts on high-speed communication networks, the system main controller (SMC) can adopt a master polling method, sequentially sending data request frames to each battery functional submodule (BSM) in the BSM list. The battery functional submodules then respond. In systems with a large number of battery functional submodules (BSMs), a time-division multiplexing strategy can also be adopted to improve efficiency. That is, when the SMC broadcasts the acquisition command, it has already allocated a specific time slot for each battery functional submodule (BSM) to upload subsequent data. After receiving the data, the system main controller (SMC) first performs data cleaning and preprocessing, and then sends the data to the state observer to iteratively update the model parameters of the equivalent DC internal resistance (DCR) and the aging state (SOH).

[0054] Next, the topology scheduling decision module performs the operation. The system main controller (SMC) executes the dynamic topology scheduling algorithm based on the current output demand of the high-power energy storage charging system and the latest state model of all battery functional sub-modules (BSM). The topology scheduling algorithm determines the optimal topology path for the next control cycle by calculating the multi-objective comprehensive cost function.

[0055] Subsequently, the topology reconfiguration execution module performs the operation. The system main controller SMC sends the switching state instructions required to construct the optimal topology path to the battery function sub-modules (BSMs) involved in the path through the high-speed communication network. The local microcontrollers of each battery function sub-module (BSM) parse the instructions and precisely control the multi-port solid-state switch matrix of the battery function sub-module (BSM) to complete the reconstruction of the physical circuit.

[0056] A switch status command is a precise control command. For example, a switch status command can be a data packet containing a list of IDs of the target battery functional submodule (BSM) and the precise state that each switch in the multi-port solid-state switch matrix of each target BSM should be in. When parsing the command, the local microcontroller of the battery functional submodule (BSM) performs strict safety checks, such as checking whether the command will cause a local short circuit. During reconfiguration, the local microcontroller follows the break-before-make timing logic and manages the dead time of the gate drive to ensure that no power bus short circuit or conflict occurs during the switching process of disconnecting the old path and establishing the new path, thus ensuring a smooth and safe switching process.

[0057] At any time during the operation of the high-power energy storage charging system and circuit, the fault diagnosis and management module continuously monitors the status reported by all battery function sub-modules (BSMs). When a battery function sub-module (BSM) reports an unrecoverable fault, or the system main controller (SMC) diagnoses an abnormality in a battery function sub-module (BSM), the high-power energy storage charging system and circuit enter the fault response and self-healing process.

[0058] The monitoring of the fault diagnosis and management module is parallel and asynchronous. It not only checks the fault flags actively reported by the battery function submodule (BSM), but also actively analyzes the massive amount of time-series data in the system main controller (SMC) database. For example, by establishing a consistency model of the battery function submodule (BSM), it can diagnose the deviation of the voltage or temperature behavior of a certain battery function submodule (BSM) from the behavior of the battery function submodule (BSM), thereby identifying sensor faults or early cell anomalies. Unrecoverable faults may include short circuits inside the cell (leading to continuous voltage abnormalities), permanent open / short circuits of temperature sensors, and breakdown short circuits of solid-state switches.

[0059] In the self-healing process, the main controller (SMC) of the high-power energy storage charging system and circuit marks the faulty battery functional submodule (BSM) as permanently failed. In the subsequent topology scheduling decision, the topology scheduling decision module will automatically avoid any path containing the failed battery functional submodule (BSM), thereby achieving online fault isolation and continuous operation of the high-power energy storage charging system. The high-power energy storage charging system and circuit then return to the closed-loop control process of steady-state operation, but the set of battery functional submodules (BSM) available in the closed-loop control process has been updated.

[0060] After the system main controller (SMC) marks the faulty battery functional submodule (BSM) as permanently failed, it stores the BSM's ID in a blacklist. This blacklist is stored in the non-volatile memory of the SMC to prevent data loss after a power outage and restart of the high-power energy storage charging system. When the topology scheduling decision module generates a set of candidate topology paths, it first excludes all BSMs in the blacklist. Therefore, any subsequent optimal topology path will not include the faulty BSM, achieving physical isolation. The SMC then continues to operate based on the set of available BSMs, achieving a smooth functional degradation rather than a catastrophic shutdown.

[0061] To further clarify the internal structure and functional implementation of the present invention, the detailed implementation of the core modules of the high-power energy storage charging system and circuit, namely the battery functional submodule BSM and the system main controller SMC, will be described below.

[0062] See attached document Figure 3 In a specific physical implementation, the battery cell assembly, state acquisition circuit, multi-port solid-state switch matrix, and local microcontroller can be highly integrated on one or more tightly stacked printed circuit boards and packaged together with the battery cell assembly in a standardized metal casing to form a complete battery functional submodule (BSM). This design is conducive to achieving high energy density, high integration, and good electromagnetic compatibility and heat dissipation performance.

[0063] The status acquisition circuit is used to measure various parameters of the battery cell assembly. The status acquisition circuit includes a voltage sampling circuit, which obtains the total voltage of the battery cell assembly through high-precision voltage divider resistors and isolation amplifiers; a temperature sampling circuit, which realizes multi-point temperature monitoring through NTC thermistors placed at key locations in the battery cell assembly; and a current sampling circuit, which realizes accurate measurement of the current flowing through the battery cell assembly through a low-temperature drift, high-precision shunt and corresponding current detection amplifier connected in series in the battery cell circuit. The analog output of all sampling signals is connected to the analog-to-digital conversion port of the local microcontroller.

[0064] Furthermore, the voltage sampling circuit needs an isolation amplifier because different battery functional sub-modules (BSMs) may operate under drastically different common-mode voltages after topology reconfiguration. Electrical isolation between the low-voltage ground of the local microcontroller and the high-voltage ground of the cell assembly must be achieved. The NTC thermistor in the temperature sampling circuit can be placed on the positive and negative terminals of the cell assembly, the center of the cell body, and the heat sink of the power switch matrix to obtain the temperature of key hot spots.

[0065] In an optional embodiment, a digital temperature sensor or a fiber Bragg grating temperature sensor may also be used to improve the electromagnetic interference resistance. The low temperature drift and high precision shunt in the current sampling circuit ensure that the accuracy drift of the ampere-hour integration method is minimized over a wide operating temperature range. In an optional embodiment, a closed-loop Hall effect sensor or a giant magnetoresistive sensor may also be used to achieve non-invasive, self-isolated current measurement.

[0066] A multi-port solid-state switch matrix is ​​an actuator for dynamic reconfiguration of circuit topology. In one embodiment, the multi-port solid-state switch matrix has four independent power ports around the battery pack. Each power port consists of at least two back-to-back connected power semiconductor devices, such as silicon carbide MOSFETs or gallium nitride HEMTs, which are wide bandgap semiconductor devices used to achieve bidirectional blocking and conduction control of current. Each power semiconductor device is equipped with an independent high-speed gate drive circuit with electrical isolation. The control signal terminal of the high-speed gate drive circuit is connected to the general-purpose input / output port of the local microcontroller.

[0067] The local microcontroller is the local control core of the Battery Function Submodule (BSM). Its firmware performs the following functions: data acquisition and preprocessing; periodically activating the analog-to-digital converter for sampling and performing digital filtering and physical dimension conversion on the acquired raw data; instruction parsing and execution; receiving instructions from the System Main Controller (SMC) via the communication interface, parsing the instruction content, and converting it into specific control timing for the high-speed gate drive circuit, thereby precisely controlling the on / off state of each switch in the multi-port solid-state switch matrix; and executing local basic protection strategies. Independent, non-bypassable hardware-level protection thresholds are set in the firmware. When the temperature or voltage exceeds the safety limit, even without receiving instructions from the SMC, it can autonomously perform emergency operations, such as disconnecting all ports and isolating the entire electrical system formed by interconnecting all BSMs through the multi-port solid-state switch matrix for carrying and transmitting high-power current.

[0068] The hardware platform of the system main controller (SMC) can be a system-on-a-chip with powerful computing capabilities and rich peripheral interfaces. The hardware platform of the system main controller (SMC) must have sufficient data processing capabilities to run the dynamic topology scheduling algorithm in subsequent chapters, and have physical interfaces that support high-speed communication networks.

[0069] The software portion of the system main controller (SMC) adopts a layered and decoupled architecture to ensure stability and maintainability. The software architecture of the SMC, from bottom to top, can include:

[0070] Operating System and Driver Layer: Runs a real-time operating system, providing deterministic task scheduling and resource management for upper-layer applications. The operating system and driver layer also includes board support packages for the hardware platform and drivers for all peripherals, such as communication controller drivers and timer drivers.

[0071] Service Layer: The service layer provides a series of public service components, such as the communication service component, which is responsible for implementing the protocol stack for communication with all battery function submodules (BSM); the data management service component, which is responsible for caching, processing, and persistently storing massive amounts of uploaded status data; and the diagnostic service component, which is responsible for system fault detection and logging.

[0072] Application Algorithm Layer: The application algorithm layer is the implementation layer of the core decision-making logic of this invention. It mainly runs the relevant algorithms of the state perception and modeling module, the topology scheduling decision module, and the fault diagnosis and management module. The application algorithm layer calls the interface provided by the service layer to obtain data and send instructions, and performs online identification of state parameters, calculation of multi-objective comprehensive cost function, and decision of optimal topology path.

[0073] The core of the intelligent control of the high-power energy storage and charging system of this invention lies in a series of core algorithms running at the application layer. The following section will elaborate on the online modeling method of key state parameters and the detailed principles and implementation process of the dynamic topology scheduling algorithm.

[0074] In the state awareness and modeling module, the system main controller (SMC) establishes and dynamically maintains a state model for each battery functional submodule (BSM). First, regarding the equivalent DC internal resistance (DCR), the SMC leads the process and interacts with the relevant BSM to perform the DCR identification task. In one embodiment, when the high-power energy storage charging system's operating conditions permit, the SMC injects a short-duration current pulse into the specific BSM. Simultaneously measure the voltage change at the terminals of this battery function submodule (BSM). , No. Each battery function submodule (BSM) in Equivalent DC internal resistance at time It can be calculated using the following formula:

[0075] ;

[0076] in, for Each battery function submodule (BSM) in The equivalent DC internal resistance at time t. During the application of the current pulse, the first The terminal voltage change of each battery functional submodule (BSM). To apply to the first The amplitude of the short-time current pulse of the battery function submodule BSM.

[0077] The online identification process is executed periodically to ensure the timeliness of internal resistance data.

[0078] Secondly, regarding the aging state, the system main controller (SMC) conducts a comprehensive evaluation from two dimensions: capacity and cumulative damage.

[0079] No. Capacity health status of each battery functional submodule (BSM) Define the current available capacity of this battery function submodule (BSM). With this battery function submodule BSM rated capacity The ratio:

[0080] ;

[0081] in, For the first Each battery function submodule (BSM) in Real-time capacity health status For the first Each battery function submodule (BSM) in The current actual available capacity at any given moment. For the first The rated capacity or factory capacity of each battery functional submodule (BSM).

[0082] Calibration updates are performed using the ampere-hour integration method combined with periodic complete charge-discharge cycles. Simultaneously, the system's main controller (SMC) records the... Each battery function submodule BSM cumulative ampere-hour throughput Cumulative ampere-hour throughput reflects the total usage load of the Battery Function Submodule (BSM):

[0083] ;

[0084] in, For the first The battery functional submodule BSM from its commissioning to the present moment Cumulative ampere-hour throughput, For a from 0 to definite integral, The variable is the integral variable, representing past time. In the past Time flows through the first The absolute value of the current of each battery function submodule (BSM).

[0085] The dynamic topology scheduling algorithm runs in the topology scheduling decision module, and its core is to solve the optimal path problem with the multi-objective comprehensive cost function as the optimization objective.

[0086] Multi-objective integrated cost function Used to quantitatively evaluate any candidate topology path To assess the overall performance, in one embodiment, the multi-objective integrated cost function is constructed as a weighted sum of different cost components:

[0087] ;

[0088] in, Candidate topology paths The overall cost For a specific candidate topology path, , , These represent the thermal cost, resistance cost, and aging cost of the path, respectively. , , The corresponding non-negative weighting factor, and satisfying .

[0089] Thermal cost component Thermal risk and thermal cost components used to assess the path The thermal cost component is positively correlated with the highest temperature of the battery functional submodule (BSM) in the path, in one embodiment. The calculation method is as follows:

[0090] ;

[0091] in, To construct the path From all the battery function submodules (BSM) set, take the maximum value of the calculation result of the subsequent expression. For the first in the path Real-time temperature of the battery functional submodule (BSM). The preset safe temperature threshold, This refers to the maximum allowable temperature of the system's main controller (SMC).

[0092] Resistor cost component The power loss of the path is used to evaluate the resistance cost component. The resistance cost component is positively correlated with the total equivalent internal resistance of the path. The calculation method is as follows:

[0093] ;

[0094] in, It is based on the series and parallel connections of each battery functional submodule (BSM) in the path and their respective... The calculated total equivalent internal resistance of the path, This is the reference internal resistance value for high-power energy storage charging systems, used for normalization.

[0095] Aging cost component The aging cost component is used to evaluate the impact of the path on the overall lifespan of the battery array, which consists of all battery functional submodules (BSMs). The design prioritizes the use of battery functional modules (BSMs) with higher health and lower load, and in one embodiment, the aging cost component... The calculation method is the average of the BSM aging indexes of all battery function sub-modules in the path:

[0096] ;

[0097] in, This represents the number of Battery Function Submodules (BSMs) in the path. For the first The capacity health status of each battery function submodule (BSM). and For adjustment coefficients, It is the value after normalizing the cumulative ampere-hour throughput.

[0098] Weighting factors , , It is not a fixed value, but is dynamically adjusted by the system's main controller (SMC) based on the macroscopic state of the high-power energy storage and charging system, so that the topology decision can better adapt to the current task requirements.

[0099] One adjustment strategy is based on the system's preset operating mode. For example, in high-performance mode, high-power energy storage charging systems will increase the weight of resistor costs. In pursuit of the lowest possible power loss, long-lifespan modes will increase the weight of aging costs. To achieve loss balance, when the overall temperature of a high-power energy storage charging system is too high, the weight of thermal costs will increase. Safety should be the top priority.

[0100] Another adjustment strategy is based on external input. For example, when a fast charging command is received from a user, the high-power energy storage charging system can automatically adjust the weights to balance charging speed and heat generation. When the high-power energy storage charging system is used as a backup power source for an uninterruptible power supply, the weights can be adjusted to prioritize maximizing the reliability and health of the high-power energy storage charging system.

[0101] The search and decision-making process for the optimal topology path includes two main steps: generating a set of candidate paths and optimizing the selection based on a cost function.

[0102] First, the system's main controller (SMC) determines the total voltage required by the external load. and total current In addition, the individual cell voltages of all currently available battery functional submodules (BSMs) are used to generate one or more candidate topology paths that satisfy basic electrical constraints through a combination algorithm. .

[0103] Subsequently, the system's main controller (SMC) collects... Each candidate path in Calculate the comprehensive cost For high-power energy storage and charging systems with a small number of Battery Function Submodules (BSMs), a traversal search can be used to find the lowest-cost path. For high-power energy storage and charging systems with a large number of BSMs, to ensure real-time decision-making, heuristic optimization algorithms, such as genetic algorithms or ant colony algorithms, can be used to find an approximate optimal solution, ultimately identifying the lowest-cost path. It was selected as the target topology for the next control cycle.

Claims

1. A high-power energy storage and charging system, characterized in that, include: A plurality of battery functional sub-modules (BSMs), each of which is configured with a status acquisition circuit and a multi-port solid-state switch matrix; The system main controller (SMC) is used to acquire the status information of the plurality of battery functional sub-modules (BSMs) measured by the status acquisition circuit. Based on the aforementioned state information, a dynamic topology scheduling algorithm is run to determine an optimal topology path consisting of some or all of the aforementioned battery functional submodules (BSMs). It also generates a topology reconfiguration command and sends it to the battery function submodule (BSM) involved in the optimal topology path to control the multi-port solid-state switch matrix within the battery function submodule (BSM) to reconfigure and form the optimal topology path.

2. The high-power energy storage and charging system according to claim 1, characterized in that, The status information includes real-time status data and aging status data maintained by the system main controller (SMC).

3. The high-power energy storage and charging system according to claim 2, characterized in that, The real-time status data specifically includes at least one of the voltage, current, and temperature of the battery functional submodule BSM; the aging status data includes at least one of the equivalent DC internal resistance (DCR), capacity health status, and cumulative ampere-hour throughput of the battery functional submodule BSM.

4. A high-power energy storage and charging system according to claim 2, characterized in that, The dynamic topology scheduling algorithm determines the optimal topology path by calculating a multi-objective comprehensive cost function.

5. A high-power energy storage and charging system according to claim 4, characterized in that, The multi-objective integrated cost function is constructed as a weighted sum of thermal cost components, electrical resistance cost components, and aging cost components; The thermal cost component, the electrical resistance cost component, and the aging cost component are calculated based on the real-time status data and the aging status data.

6. A high-power energy storage and charging system according to claim 5, characterized in that, The aging cost component is calculated based on the capacity health status and cumulative ampere-hour throughput of the battery functional submodule (BSM), and is used to prioritize the selection of battery functional submodules (BSMs) with lower aging levels when selecting the optimal topology path.

7. A high-power energy storage and charging system according to claim 5, characterized in that, The system main controller (SMC) is also used to dynamically adjust the weighting factors corresponding to the thermal cost component, the resistance cost component, and the aging cost component according to the preset working mode and the received external input.

8. A high-power energy storage and charging system according to claim 1, characterized in that, The system main controller (SMC) is also used to run the fault diagnosis and management module, which identifies the battery function submodule (BSM) in a fault state and does not select the battery function submodule (BSM) in a fault state when running the dynamic topology scheduling algorithm.

9. A high-power energy storage and charging system according to claim 1, characterized in that, The multi-port solid-state switch matrix includes multiple bidirectional switches made of power semiconductor devices for bidirectional current blocking and conduction control.

10. A high-power energy storage charging circuit, characterized in that, The circuit is a battery function submodule (BSM), including: Battery cell assembly; A multi-port solid-state switch matrix coupled to the battery cell assembly and providing multiple power ports for electrical connection with other circuits; A status acquisition circuit is used to measure the status data of the battery cell assembly; A local microcontroller is used to acquire the status data and control the on / off state of the multi-port solid-state switch matrix according to the topology reconfiguration instructions received from the outside.

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