Turn-off control method and system of new energy battery system

Abstract

The invention relates to the technical field of new energy, and discloses a turn-off control method and system for a new energy battery system, and the method comprises the following steps: an initialization stage: setting initial state parameters of the system, including the voltage and current of the battery system, and the initial temperature and thermal diffusion rate of a battery unit, and configuring an optimization target weight and a system safety threshold; real-time state monitoring: acquiring voltage, current, temperature and thermal diffusion rate data of the battery system through a sensor, and detecting whether abnormity exceeding a safety threshold exists or not; dynamic optimization calculation: constructing an optimal turn-off strategy based on a system state and an optimization objective function after an anomaly is detected; and executing turn-off control: adjusting an electromagnetic relay or other control components according to the optimal turn-off strategy. According to the invention, through real-time monitoring, dynamic optimization calculation, thermal diffusion suppression and staged recovery, rapid response, accurate control, efficient recovery and cooperative stable operation effects among modules of the new energy battery system are realized.

Description

Technical Field

[0001] The present invention relates to the field of new energy technology, and in particular to a shutdown control method and system for a new energy battery system. Background Art

[0002] As an important technology for energy storage, new energy battery systems are widely used in electric vehicles, home energy storage devices, and distributed energy management systems. In these application scenarios, battery systems need to provide stable power output and be able to adapt to complex operating environments. However, battery systems often face abnormal conditions such as overload, short circuit, and thermal runaway in actual operation. These problems not only threaten the safety of the equipment itself, but may also cause serious systemic failures. Therefore, in order to ensure the safety and reliability of new energy battery systems and extend their service life, there is an urgent need for a technical solution that can monitor abnormalities in real time, accurately control, and dynamically recover, so as to achieve the dual goals of safety protection and efficient operation.

[0003] In the prior art, new energy battery systems mainly use battery management systems (BMS) to achieve abnormal monitoring and protection functions. Existing BMS technology relies on fixed thresholds to monitor key parameters such as voltage, current and temperature. Once the parameters are detected to be out of the safe range, the system will trigger an alarm or perform a simple shutdown operation. This technical solution has certain practicality and can effectively reduce the probability of major safety accidents during the operation of the battery system. In addition, the prior art also uses a heat dissipation system to cool the battery module as a whole to delay the occurrence of thermal runaway. These technical solutions have improved the operating safety of new energy battery systems to a certain extent and provided a basic guarantee for the stable operation of the battery.

[0004] Although the existing technology has a certain effect in the protection of new energy battery systems, it also has many shortcomings; first, the existing technology relies on a fixed threshold trigger mechanism and cannot dynamically adapt to complex working conditions, resulting in a slow response speed, especially in short circuit or rapid thermal runaway scenarios, which is easy to delay the processing time; second, in response to the spread of thermal runaway, the existing technology more often adopts an overall cooling method, lacks the ability to accurately control local high-risk areas, and makes the management efficiency of thermal diffusion low; in addition, in terms of system recovery, the existing technology usually adopts a static mode and fails to dynamically adjust the recovery strategy according to the real-time status, which is easy to cause system shock or even secondary failure during the recovery process; finally, the existing technology has obvious deficiencies in multi-module collaboration, and there is a lack of efficient communication and coordination mechanisms between modules, making it difficult to achieve system balance and stability when abnormalities occur. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a shutdown control method and system for a new energy battery system, which solves the problems of slow response speed, weak heat diffusion suppression capability, low system recovery efficiency and insufficient multi-module coordination in the prior art.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A shutdown control method and system for a new energy battery system, comprising the following steps: Initialization phase: Set the initial state parameters of the system, including the voltage, current, initial temperature and heat diffusion rate of the battery system, and configure the optimization target weight and system safety threshold; Real-time status monitoring: The sensors collect the voltage, current, temperature and heat diffusion rate data of the battery system to detect whether there are any abnormalities exceeding the safety threshold; Dynamic optimization calculation: After detecting an abnormality, the optimal shutdown strategy is constructed based on the system status and optimization objective function; Execute shutdown control: adjust electromagnetic relays or other control components according to the optimal shutdown strategy to cut off related circuit modules; heat diffusion suppression and collaborative operation: dynamically adjust the shutdown strategy based on the heat diffusion rate and priority, give priority to cutting off high-risk areas, and notify other modules to adjust the operating status; System recovery: After the status parameters return to a safe range, the disconnected circuit connections are gradually restored.

[0007] Preferably, the initialization phase includes: Setting initial state parameters, including total voltage, total current, initial temperature of battery cells and thermal diffusion rate; Configure optimization target weights, including power consumption, temperature priority, and control signal strength weights; Set system safety thresholds, including maximum voltage, maximum current, and maximum allowable temperature.

[0008] Preferably, the real-time status monitoring includes: Collect real-time voltage, current, temperature and heat diffusion rate data of the battery system; Compare the collected data with the set threshold to determine whether there is an abnormal state; When an abnormal state is detected, an alarm signal is sent to the dynamic optimization module.

[0009] Preferably, the dynamic optimization calculation includes: Build an optimization objective function based on real-time collected data, with the goal of minimizing power consumption, temperature rise rate, and control intensity; Constrain the variable range of the optimization objective function, including the safe range of voltage, current and temperature; The optimal shutdown strategy is calculated according to the optimality condition of the optimization objective function, and the shutdown control instruction is output.

[0010] Preferably, the executing shutdown control includes: Receiving a shutdown control instruction output by a dynamic optimization module; Adjust the triggering intensity and time of the electromagnetic relay to cut off the circuit of the faulty module; The shutdown strategy is updated in real time according to the feedback signal, and the abnormal area is gradually isolated.

[0011] Preferably, the thermal diffusion suppression and coordinated operation include: Calculate the priority of high-risk areas based on the heat diffusion rate and determine the priority shutdown modules; Trigger local hard shutdown operations for high priority areas to prevent heat spread; Send status adjustment instructions to other modules to dynamically adjust operating parameters.

[0012] Preferably, the system recovery includes: Real-time detection of whether voltage, current and temperature return to safe range; Gradually restore the circuit connection of the disconnected modules; Update status parameters to ensure normal operation of the system.

[0013] The invention also provides a shutdown control system for a new energy battery system, including: Condition monitoring module, used to collect voltage, current, temperature and heat diffusion rate of the battery system; Dynamic optimization module, used to calculate the optimal shutdown strategy based on collected data; An execution control module for adjusting electromagnetic relays or other control components according to an optimal shut-off strategy; Collaborative control module, used to dynamically adjust the operating status of other modules to prevent abnormalities from spreading; The system recovery module is used to gradually restore the disconnected circuit connection after the state parameters return to a safe range.

[0014] Preferably, the status monitoring module includes: Voltage sensor, used to monitor the total voltage of the battery system; Current sensor, used to monitor the total current of the battery system; Temperature sensor, used to detect the real-time temperature of the battery cell; The data processing unit is used to process the data collected by the sensor and compare it with the set threshold value.

[0015] Preferably, the dynamic optimization module includes: An optimization objective function construction unit, used for constructing an optimization objective function based on collected data; A constraint condition setting unit is used to set the safety range of voltage, current and temperature; The optimization solving unit is used to calculate the optimal shutdown strategy according to the optimization objective function.

[0016] The present invention provides a shutdown control method and system for a new energy battery system, which has the following beneficial effects: 1. The present invention can quickly identify anomalies and accurately calculate the optimal shutdown strategy through dynamic optimization calculation and real-time status monitoring, achieving a millisecond-level response speed. Compared with the shutdown method that relies on a fixed threshold in the prior art, the present invention effectively solves the problems of response delay and large error, and improves the overall security of the system.

[0017] 2. The present invention realizes the priority suppression of high-risk areas and dynamic adjustment of other modules through heat diffusion rate calculation, priority evaluation and multi-module collaborative operation. Compared with the technical solutions lacking real-time heat diffusion management in the prior art, the present invention shows higher robustness and coordination under complex working conditions, significantly reducing the impact of local anomalies on the whole system.

[0018] 3. The present invention designs a phased shutdown and recovery strategy, which improves the safety and efficiency of the recovery process by gradually restoring the disconnected module load and adjusting the operating parameters in real time. Compared with the prior art, which lacks dynamic adjustment in the shutdown and recovery process, the present invention avoids system shock and secondary abnormalities, while ensuring the reliability of long-term operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a flow chart of the method of the present invention; Figure 2 is a system structure diagram of the present invention; Figure 3 It is a module architecture diagram of the status monitoring module of the present invention; Figure 4 This is a module architecture diagram of the dynamic optimization module of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Please see attached Figure 1 The embodiment of the present invention provides a shutdown control method and system for a new energy battery system, comprising the following steps: S1, initialization phase: set the initial state parameters of the system, including the voltage, current, initial temperature and thermal diffusion rate of the battery system, and configure the optimization target weight and system safety threshold; The initialization phase is used to lay the parameter foundation for subsequent status monitoring, dynamic optimization calculations, and shutdown execution. This phase requires not only accurate setting of the system's initial parameters, but also configuration of optimization targets according to different working environments and application requirements. Through reasonable initialization settings, the system's response speed and accuracy in detecting anomalies and performing shutdown operations can be effectively improved.

[0022] Generally, the initialization phase in this embodiment includes three main contents: setting of system parameters, configuration of safety thresholds, and weight configuration of optimization targets.

[0023] In some embodiments, the initial state parameters of the system mainly include the total voltage x1(0) of the battery pack, the total current x2(0), the initial temperature x3(0) of the battery cell, and the thermal diffusion rate x4(0). These parameters are used to define the initial state of the system. The total voltage x1(0) is the terminal voltage of the battery pack in the initial state, usually measured in real time by a voltage sensor, and its unit is volt (V). The total current x2(0) represents the current intensity of the system in the initial state, measured by a current sensor, and its unit is ampere (A). The initial temperature x3(0) of the battery cell represents the temperature of the battery module in the initial state, usually obtained by a high-precision temperature sensor, and its unit is Kelvin (K). The thermal diffusion rate x4(0) represents the diffusion rate of heat in the battery module, which is calculated by thermistor and algorithm analysis, and its unit is Kelvin per second (K / s).

[0024] As an option, in this embodiment, the setting of system parameters can be performed in combination with historical data analysis and experimental measurement results. For example, in a battery pack with a capacity of 60 kWh, the initial voltage x1(0) can be set to 350 V, the total current x2(0) to 0 A (no load), the initial temperature x3(0) to 298 K (25° C.), and the heat diffusion rate x4(0) to 0.1 K / s.

[0025] Specifically, the configuration of the system safety threshold is an important part of the initialization phase. The configuration of the safety threshold includes the maximum voltage V max , Maximum current I max and the maximum temperature I max In one possible implementation, the safety threshold is set according to the battery characteristics and environmental requirements. For example, for a group of lithium-ion batteries, the maximum voltage V max Can be set to 4.2V, maximum current I max is 300A, and the maximum temperature T maxThese thresholds are used to determine whether there is an abnormal state in the subsequent state monitoring stage.

[0026] As an implementation method, the weight of the optimization target needs to be configured in this embodiment. The weight of the optimization target includes power weight α, temperature weight β and control signal strength weight γ. These weights correspond to the influence of power consumption, temperature change and control signal on the system respectively. In some embodiments, the optimization target can be described by the following objective function: Where: J is the system optimization objective function, which is used to describe the comprehensive influence of power loss, temperature change and control intensity of the entire shutdown process; T is the total time of the system shutdown process, in seconds (s); x1(t) is the total voltage at time t, in volts (V); x2(t) is the total current at time t, in amperes (A); x3(t) is the battery temperature at time t, in Kelvin (K); u(t) is the shutdown control signal strength at time t, dimensionless, α, β, γ: optimization weight parameters, respectively controlling the influence of power, temperature and control signal on the objective function.

[0027] The selection of weights α, β, and γ in the above optimization objective function needs to be adjusted according to the actual application scenario. For example, when the operating environment temperature of the battery system is high, the temperature weight β should be set to a larger value to give priority to controlling the temperature change trend. On the contrary, under low load conditions, the power weight α can be appropriately increased to optimize energy efficiency.

[0028] In another possible implementation, the initialization phase can also configure dynamic parameters according to the working environment of the battery system. For example, in a high-altitude environment, the measurement range and accuracy of the temperature sensor need to be adjusted, and the calculation model of the heat diffusion rate x4 also needs to consider the influence of air thinness.

[0029] The settings in the above initialization phase can provide accurate status parameters and optimization targets for a variety of working conditions, providing a reliable technical basis for subsequent status monitoring and shutdown operations. The content of the initialization phase fully considers the characteristics and complexity of the battery system to ensure the applicability and operability of the technical solution.

[0030] S2. Real-time status monitoring: The voltage, current, temperature and heat diffusion rate data of the battery system are collected through sensors to detect whether there are abnormalities exceeding the safety threshold; Real-time status monitoring can quickly identify abnormal situations through real-time collection and analysis of key parameters such as voltage, current, temperature and heat diffusion rate of the battery system, and provide a reliable decision-making basis for subsequent dynamic optimization calculations and shutdown control. In order to achieve this function, the present invention designs a multi-sensor monitoring network, which combines a data processing unit to process and judge the sensor collected data in real time to ensure the efficiency and accuracy of the monitoring process.

[0031] Generally speaking, the real-time monitoring of this step mainly relies on hardware devices such as voltage sensors, current sensors and temperature sensors. These sensors are distributed at key nodes of the battery system to collect the status parameters of each module, which are finally analyzed by the data processing unit. In this way, real-time monitoring can not only capture anomalies, but also dynamically reflect the overall operating status of the battery pack.

[0032] As an option, the voltage sensor in this embodiment is used to measure the total voltage x1(t) of the battery system. The output signal of the sensor is converted into digital form by the data processing unit. Generally, the unit of voltage is volt (V). In a battery pack with a capacity of 60kWh, the voltage range is usually 300V to 400V. When the voltage value exceeds the set threshold V max (e.g. 4.2V / cell), the data processing unit will immediately send an alarm signal to the dynamic optimization module.

[0033] In one possible implementation, a current sensor is used to monitor the total current x2(t) of the battery system. The sensor can accurately capture rapidly changing current fluctuations, and its output signal is transmitted to the data processing unit after analog-to-digital conversion. The unit of current is ampere (A). For example, when the total current suddenly reaches 300A during the charging and discharging process and lasts for more than 2 seconds, the system determines that it is in an overload state, thereby triggering an abnormal signal.

[0034] Specifically, in this embodiment, the temperature sensor is used to monitor the real-time temperature x3(t) of the battery cell. Temperature is one of the key parameters in battery operation. The sensor is able to detect slight temperature rises, such as from 298K (25°C) to 305K (32°C). As an option, the unit of temperature is Kelvin (K). The accuracy and response time of the temperature sensor play a decisive role in the effectiveness of anomaly detection. Usually, when the temperature of a battery cell exceeds the set threshold T max (such as 333K, 60℃), the system marks the area as a high-risk area.

[0035] In some embodiments, the present invention also uses a combination of a thermal element and a mathematical model to calculate the heat diffusion rate x4(t). The heat diffusion rate is an important parameter that describes the speed at which heat propagates within a battery pack. The heat diffusion rate can be dynamically calculated using the following formula: Where: x4(t): thermal diffusion rate, in Kelvin per second (K / s); T(t): temperature field distribution at time t, in Kelvin (K); κ: thermal diffusion coefficient, in square meters per second (m 2 / s); The Laplace operator of temperature describes the rate of change of temperature distribution in space.

[0036] As an implementation method, the data processing unit can identify areas with higher heat diffusion rates by performing real-time calculations on the above formula. These areas are usually high-risk areas for abnormal diffusion. For example, when x4(t)>0.5K / s, the system determines that the heat diffusion in this area has entered a dangerous stage.

[0037] In order to ensure the accuracy and real-time performance of data processing, the data processing unit in this embodiment performs synchronous analysis on the collected multi-channel signals. Generally, the data processing unit compares the collected data with the safety threshold set in the initialization phase. When a parameter exceeds its safety threshold, for example, voltage x1(t)>V max 、Current x2(t)>I max Or temperature x3(t)>T max , the system immediately sends an alarm signal to the dynamic optimization module.

[0038] In one possible implementation, real-time status monitoring also supports fault warning functions. By analyzing the changing trends of multiple collected data, such as the rate of voltage drop or the rate of temperature increase, the data processing unit can identify potential anomalies in advance. For example, when the temperature increase rate When the temperature exceeds the threshold, the system will start the optimization module to prepare for the shutdown operation in advance.

[0039] Through the above-mentioned real-time status monitoring method, comprehensive monitoring of multiple parameters and dimensions of the battery system is achieved, and accurate data input is provided for the dynamic optimization module. The monitoring process is characterized by high precision and rapid response, which provides guarantees for the safety and stability of the system, and also lays a solid foundation for the implementation of subsequent shutdown control.

[0040] S3, dynamic optimization calculation: after detecting an abnormality, the optimal shutdown strategy is constructed based on the system status and the optimization objective function; The dynamic optimization calculation is based on the real-time monitoring data, combined with the optimization goals and constraints set during system initialization, to calculate the optimal shutdown control strategy. This step directly affects the accuracy of shutdown control and the safety of the system. Through mathematical optimization methods and iterative algorithms, a balance between power loss, temperature rise rate and control signal strength is achieved under multiple constraints.

[0041] In general, the dynamic optimization calculation module receives the abnormal data transmitted by the real-time status monitoring module, and combines the safety threshold and optimization weight in the initialization phase to construct the objective function and constraint conditions. On this basis, the optimal control input is calculated through the optimization solution method to provide accurate instructions for the shutdown control module.

[0042] As an option, in this embodiment, the calculation core of the dynamic optimization module is the objective function J. The objective function is used to describe the comprehensive influence of power loss, temperature rise rate and control intensity during the shutdown process; In this embodiment, the dynamic optimization module also needs to define the constraints of the optimization problem to ensure the safety and feasibility of the shutdown strategy. The constraints include the following aspects: Restrictions on state variables: x1(t)≤V max ,x2(t)≤I max ,x3(t)≤T max Where: V max is the maximum allowable voltage, in volts (V); I max is the maximum allowable current in ampere (A); T max is the maximum allowable temperature in Kelvin (K).

[0043] Range constraints for control inputs: u(t)∈[0,1] The value range of the control signal u(t) is limited to between 0 and 1, and is used to adjust the trigger strength of the electromagnetic relay.

[0044] Time boundary conditions: x(0)=x0,x(T)=x f Among them: x(0) is the initial state, x(T) is the terminal state, which respectively represent the starting point and end point of the shutdown control process.

[0045] In a possible implementation, the dynamic optimization module solves the above optimization problem through Pontryagin's Minimum Principle (PMP). The specific steps are as follows: Construct the Hamiltonian function H: Where: H: Hamiltonian function; λ i : Covariate variable, used to describe the multi-objective trade-off of the optimization problem; t: time variable; x1(t): total voltage of the system at time t; x3(t): battery cell temperature of the system at time t; u(t): shutdown control input at time t; α: power weight coefficient, dimensionless; β: temperature weight coefficient, dimensionless; γ: control signal strength weight coefficient, dimensionless; State variable x i The rate of change of represents the dynamic change of the system at time t.

[0046] Calculate the costate equation based on the Hamiltonian function: Based on the above conditions, calculate the optimal control input u * (t): As an option, the dynamic optimization module can use an iterative algorithm to numerically approximate the above solution process, such as gradient descent method or Newton iteration method. Through multiple iterations, it gradually converges to the optimal control strategy u * (t).

[0047] In some embodiments, the present invention also considers optimization processing in special scenarios. For example, when there are multiple module anomalies in the system, the dynamic optimization module needs to coordinate the optimization targets of each module through a distributed algorithm.

[0048] Through the above-mentioned dynamic optimization calculation method, the optimal shutdown control strategy can be calculated in real time, providing a high-precision decision-making basis for the subsequent execution of shutdown control, while effectively balancing the system's safety, energy efficiency and hardware protection requirements.

[0049] S4, executing shutdown control: adjusting the electromagnetic relay or other control components according to the optimal shutdown strategy to cut off the relevant circuit modules; Execution shutdown control adjusts the electromagnetic relay or other actuators to achieve rapid disconnection or adjustment of the circuit according to the optimal control instructions output by the dynamic optimization module. After the abnormal state is detected, by executing the shutdown operation, the abnormal area can be effectively isolated, the fault spread can be prevented and the system risk can be reduced. This step directly connects to the dynamic optimization calculation module, and its accuracy and execution efficiency are crucial to the overall safety of the system.

[0050] Generally, the shutdown control module is responsible for receiving the control instruction u output by the dynamic optimization module. *(t) and converts it into specific operations of the actuator. For example, by adjusting the trigger strength and duration of the electromagnetic relay, the circuit breaking behavior is controlled. In addition, the execution shutdown module will dynamically adjust subsequent operations to ensure safety based on the feedback data after the shutdown operation.

[0051] In this embodiment, the specific implementation of shutdown control includes the following contents.

[0052] As an option, in this embodiment, the core of the shutdown control is the optimal control variable u based on the dynamic optimization output * (t) to drive the electromagnetic relay to cut off the circuit. The input u of the shutdown control * (t) is the control signal strength at time t, dimensionless, and the value range is 0≤u * (t)≤1. Its value determines the triggering degree of the relay action. For example, when u * When u(t)=1, the relay is triggered with maximum intensity and completely disconnects the circuit; * When (t) = 0.5, the relay operates with medium intensity and partially limits the current.

[0053] Specifically, the trigger response of the electromagnetic relay can be described by the following formula: I coil =K·u * (t) Where: I coil : Relay coil current, in amperes (A); K: Proportional coefficient, related to the physical characteristics of the relay, in amperes; u * (t): Control variable output by the dynamic optimization module, dimensionless.

[0054] In some embodiments, the relay action time t on and t off Also controlled by the variable u * (t) decision. The relationship can be expressed as: t on =T max ·u * (t),t off =T max ·(1-u * (t)) Where: t on : The duration of the relay being in the closed state, in seconds (s); t off : The duration of the relay being in the off state, in seconds (s); T max : The maximum action cycle of the relay, in seconds (s).

[0055] As an implementation method, the shutdown module can dynamically adjust the shutdown strategy according to the severity of the abnormality. For example, in the case of slow temperature rise, the system will trigger partial shutdown operations first, cutting off the circuits of only some battery modules; in the case of short circuit or thermal runaway, the relay will completely disconnect the circuit of the entire battery pack to avoid further damage caused by high current.

[0056] In general, the shutdown control in this embodiment also involves the coordinated operation of multiple modules. In a battery system with a capacity of 60kWh, each module communicates through the CAN bus. When a module triggers shutdown, the system will notify the adjacent modules to enter protection mode. For example, if a module detects that the temperature exceeds 60°C, the execution module will first disconnect the circuit of the module, and at the same time notify other modules to reduce the load or adjust the operating parameters.

[0057] In one possible implementation, the feedback mechanism of the execution module can dynamically adjust the subsequent shutdown operation. For example, when an abnormal parameter (such as voltage or current) is detected after shutdown and does not return to the safe range, the system will trigger the hard shutdown operation again until the parameter stabilizes. This feedback adjustment process can be described by the following formula: u new (t) = u prev (t)+Δu(t) Where: u new (t): adjusted shutdown control variable; u prev (t): previous shutdown control variable; Δu(t): feedback correction value, calculated according to the degree of abnormality.

[0058] In some embodiments, the present invention also introduces a redundant shutdown mechanism to improve the fault tolerance of the system. The core of the redundant mechanism is to ensure that the system can still perform the shutdown operation even if one relay fails through the parallel relay design. For example, when the main relay cannot be completely disconnected, the backup relay will immediately supplement the trigger to further isolate the circuit.

[0059] By executing the shutdown control method described above, a fast, accurate and flexible circuit disconnection operation is achieved, effectively preventing the spread of abnormalities and ensuring the overall safety of the system.

[0060] S5, heat diffusion suppression and collaborative operation: dynamically adjust the shutdown strategy based on the heat diffusion rate and priority, give priority to cutting off high-risk areas, and notify other modules to adjust the operating status; Thermal diffusion suppression and collaborative operation suppress the spread of abnormal heat in the battery system through real-time monitoring and control, and ensure the stable operation of the overall system through multi-module collaboration. After executing the shutdown control, this step continues to dynamically adjust the temperature field of the battery system to prevent local thermal runaway from affecting global operation.

[0061] In general, the goal of heat diffusion suppression is to prioritize high-risk areas based on heat diffusion rate and priority. By dynamically calculating the heat diffusion rate and combining the spatial temperature distribution model, the present invention can achieve precise control of heat diffusion. At the same time, multi-module collaborative operation ensures the safety and coordination of adjacent modules through information sharing and real-time communication.

[0062] In this embodiment, the specific implementation of heat diffusion suppression and coordinated operation includes the following contents.

[0063] As an option, in this embodiment, heat diffusion suppression identifies high risk areas by calculating the heat diffusion rate x4(t) in real time. In general, when the system calculates that the heat diffusion rate x4(t) at a certain location is greater than 0.5K / s, it is determined that there is an abnormal diffusion trend at that location. At this time, the system will give priority to local hard shutdown of that location and trigger cooling measures to reduce the diffusion rate.

[0064] Specifically, in this embodiment, the heat diffusion suppression is also combined with a priority calculation method to dynamically adjust the execution order of the shutdown strategy. The priority calculation formula is: Among them: Priority: the priority of heat diffusion suppression in a certain area, dimensionless; The rate of temperature rise is measured in Kelvin per second (K / s); d is the distance between the area and the heat source, measured in meters (m).

[0065] As an implementation method, d in the priority calculation formula can be dynamically adjusted through the heat source location algorithm. For example, when a module temperature sensor detects that the temperature exceeds 333K (60°C), the system will locate the heat source based on the sensor array and calculate the priority of adjacent modules to determine the shutdown order.

[0066] In some embodiments, this embodiment also implements multi-module collaborative operation, and realizes real-time communication between modules through the CAN bus. Specifically, when a module triggers the heat diffusion suppression operation, the system will notify the adjacent modules to adjust the operating parameters. For example, when a module is triggered to shut down due to temperature rise, the adjacent modules can reduce the current load or enable auxiliary cooling devices to reduce heat accumulation.

[0067] As an option, multi-module collaborative operation also includes dynamic current distribution. After the heat diffusion area is turned off, the system will redistribute the current of the remaining modules. For example, if the total system load is 300A, when a module is turned off, the remaining modules will distribute current according to their current temperature and load capacity, so that the temperature rise of each module remains within a controllable range.

[0068] Specifically, the thermal diffusion suppression and coordinated operation in this embodiment also includes a feedback adjustment mechanism. For example, when a module triggers shutdown, the system will continue to monitor its temperature. If it detects that the temperature begins to drop and returns to a safe range, the system will gradually restore the connection of the module and notify other modules to adjust parameters. The feedback adjustment process can be described by the following formula: T new (t) = T old (t)+ΔT(t) Where: T new (t): real-time temperature of the module during the recovery process, in Kelvin (K); T old (t): temperature of the module before shutdown, in Kelvin (K); ΔT(t): temperature change at time t, in Kelvin (K).

[0069] In a possible implementation, the heat spread suppression and coordinated operation module also supports hierarchical processing. For example, for low-priority areas, the system will first trigger a soft shutdown, only reducing the current or output power instead of completely disconnecting the circuit. This hierarchical processing mode can reduce the impact on the overall system performance while ensuring the safety of abnormal areas.

[0070] Through the above-mentioned heat diffusion suppression and collaborative operation method, and through the collaboration of multiple modules, the heat diffusion range is effectively controlled to avoid the abnormal expansion to the global level.

[0071] S6. System recovery: After the status parameters return to a safe range, the disconnected circuit connections are gradually restored.

[0072] System recovery is the last important step in the technical solution of the present invention, which aims to gradually return the system that has completed the shutdown operation to a normal working state through real-time detection and phased recovery strategy. This step is closely connected to the thermal diffusion suppression and collaborative operation module to ensure that the system can resume operation safely and stably after the abnormal state is lifted. The recovery process needs to comprehensively consider the real-time status of parameters such as temperature, voltage and current, and dynamically adjust the recovery strategy through a feedback mechanism to avoid new anomalies caused by shocks during the recovery process.

[0073] Generally speaking, system recovery includes three main steps: status detection, recovery sequence determination, and step-by-step recovery operation. Through precise parameter monitoring and dynamic adjustment, this step can minimize energy loss during the recovery process and ensure the safety of the entire battery system.

[0074] In this embodiment, the specific implementation of system recovery includes the following contents.

[0075] As an option, in this embodiment, the first step of system recovery is state detection. The state monitoring module collects key parameters such as voltage x1(t), current x2(t) and temperature x3(t) in real time to determine whether the system meets the recovery conditions. For example, when the temperature x3(t) drops below the safety threshold and the voltage and current fluctuations are both less than the preset range, the system determines that the recovery conditions have been met.

[0076] Specifically, the state detection in this embodiment needs to be analyzed in combination with the recovery dynamic model. For example, the state recovery process of the battery cell is described by the following relationship: Δx(t)=|x(t)-x ref | Where: Δx(t): parameter deviation at time t, the unit is consistent with the corresponding parameter; x(t): real-time monitoring parameters, including voltage, current and temperature; x ref : Reference value, that is, the parameter value of the target recovery state.

[0077] In some embodiments, when the deviation Δx(t) of all monitored parameters is less than a certain threshold, the system enters a recovery state.

[0078] As an implementation method, the second step of system recovery in this embodiment is to determine the recovery order. Generally, the system will give priority to recovering the modules with the slowest temperature rise and the smallest load to reduce the impact of the recovery process on the overall system.

[0079] Generally speaking, the modules with higher priority will be restored first. This priority calculation method based on temperature deviation and load power can ensure the smoothness of the recovery process.

[0080] Specifically, the third step of system recovery in this embodiment is a gradual recovery operation. Gradual recovery includes two parts: restoring circuit connection in stages and dynamically adjusting load distribution. For example, when a module reaches the recovery condition, the execution module first gradually restores the connection with a lower current, and then gradually increases the current load according to the real-time monitoring parameters. This phased recovery strategy can effectively avoid the impact of instantaneous large current on the module.

[0081] As an option, the load adjustment for gradual recovery in this embodiment can be implemented by the following relationship: I new =I old +ΔI Where: I new : Load current of the module after recovery, in ampere (A); I old : Load current of the module before recovery, in ampere (A); ΔI: Load adjustment increment, in ampere (A), dynamically calculated by the system.

[0082] In some embodiments, system recovery also includes coordinated operations with other modules. For example, when a module is restored, its load current increases, and the system dynamically adjusts the current of adjacent modules to maintain load balance of the overall system. Load adjustment of adjacent modules can be achieved through real-time communication of the CAN bus.

[0083] Specifically, the system recovery in this embodiment also combines a state feedback mechanism to monitor abnormal conditions during the recovery process in real time. For example, if a module detects temperature abnormality again during the recovery process, the system will immediately interrupt the recovery operation and re-enter the thermal diffusion suppression state.

[0084] Through the above-mentioned system recovery method, the present invention can efficiently and safely restore the system operation state after the abnormality is resolved. The strategy of combining gradual recovery with dynamic adjustment not only effectively reduces the impact risk during the recovery process, but also improves the overall stability of the system.

[0085] The shutdown control system of the new energy battery system described below and the shutdown control method of the new energy battery system described above may refer to each other.

[0086] Please see attached Figure 2 -Attached Figure 4 The present invention also provides a shutdown control system for a new energy battery system, including: Condition monitoring module, used to collect voltage, current, temperature and heat diffusion rate of the battery system; Dynamic optimization module, used to calculate the optimal shutdown strategy based on collected data; An execution control module for adjusting electromagnetic relays or other control components according to an optimal shut-off strategy; Collaborative control module, used to dynamically adjust the operating status of other modules to prevent abnormalities from spreading; The system recovery module is used to gradually restore the disconnected circuit connection after the state parameters return to a safe range.

[0087] The system of this embodiment can be used to execute the above method embodiments, and its principles and technical effects are similar, which will not be repeated here.

[0088] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A shutdown control method for a new energy battery system, characterized in that: The following steps are involved: Initialization phase: Set the initial state parameters of the system, including the voltage, current, initial temperature and heat diffusion rate of the battery system, and configure the optimization target weight and system safety threshold; Real-time status monitoring: The sensors collect the voltage, current, temperature and heat diffusion rate data of the battery system to detect whether there are any abnormalities exceeding the safety threshold; Dynamic optimization calculation: After detecting an abnormality, the optimal shutdown strategy is constructed based on the system status and optimization objective function; Execute shutdown control: adjust electromagnetic relays or other control components according to the optimal shutdown strategy to cut off related circuit modules; Heat diffusion suppression and collaborative operation: Dynamically adjust the shutdown strategy based on the heat diffusion rate and priority, prioritize cutting off high-risk areas, and notify other modules to adjust their operating status; System recovery: After the status parameters return to a safe range, the disconnected circuit connections are gradually restored.

2. The shutdown control method of the new energy battery system according to claim 1, characterized in that: The initialization phase includes: Setting initial state parameters, including total voltage, total current, initial temperature of battery cells and thermal diffusion rate; Configure optimization target weights, including power consumption, temperature priority, and control signal strength weights; Set system safety thresholds, including maximum voltage, maximum current, and maximum allowable temperature.

3. The shutdown control method of the new energy battery system according to claim 1, characterized in that: The real-time status monitoring includes: Collect real-time voltage, current, temperature and heat diffusion rate data of the battery system; Compare the collected data with the set threshold to determine whether there is an abnormal state; When an abnormal state is detected, an alarm signal is sent to the dynamic optimization module.

4. The shutdown control method of the new energy battery system according to claim 1, characterized in that: The dynamic optimization calculation includes: Build an optimization objective function based on real-time collected data, with the goal of minimizing power consumption, temperature rise rate, and control intensity; Constrain the variable range of the optimization objective function, including the safe range of voltage, current and temperature; The optimal shutdown strategy is calculated according to the optimality condition of the optimization objective function, and the shutdown control instruction is output.

5. The shutdown control method of the new energy battery system according to claim 1, characterized in that: The execution of shutdown control includes: Receiving a shutdown control instruction output by a dynamic optimization module; Adjust the triggering intensity and time of the electromagnetic relay to cut off the circuit of the faulty module; The shutdown strategy is updated in real time according to the feedback signal, and the abnormal area is gradually isolated.

6. The shutdown control method of the new energy battery system according to claim 1, characterized in that: The thermal diffusion suppression and coordinated operation include: Calculate the priority of high-risk areas based on the heat diffusion rate and determine the priority shutdown modules; Trigger local hard shutdown operations for high priority areas to prevent heat spread; Send status adjustment instructions to other modules to dynamically adjust operating parameters.

7. The shutdown control method of the new energy battery system according to claim 1, characterized in that: The system recovery includes: Real-time detection of whether voltage, current and temperature return to safe range; Gradually restore the circuit connection of the disconnected modules; Update status parameters to ensure normal operation of the system.

8. A shutdown control system for a new energy battery system, characterized in that: A shutdown control method for a new energy battery system using any one of claims 1 to 7, comprising: Condition monitoring module, used to collect voltage, current, temperature and heat diffusion rate of the battery system; Dynamic optimization module, used to calculate the optimal shutdown strategy based on collected data; An execution control module for adjusting electromagnetic relays or other control components according to an optimal shut-off strategy; Collaborative control module, used to dynamically adjust the operating status of other modules to prevent abnormalities from spreading; The system recovery module is used to gradually restore the disconnected circuit connection after the state parameters return to a safe range.

9. The shutdown control system of the new energy battery system according to claim 8, characterized in that: The status monitoring module comprises: Voltage sensor, used to monitor the total voltage of the battery system; Current sensor, used to monitor the total current of the battery system; Temperature sensor, used to detect the real-time temperature of the battery cell; The data processing unit is used to process the data collected by the sensor and compare it with the set threshold value.

10. The shutdown control system of the new energy battery system according to claim 8, characterized in that: The dynamic optimization module includes: An optimization objective function construction unit, used for constructing an optimization objective function based on collected data; A constraint condition setting unit is used to set the safety range of voltage, current and temperature; The optimization solving unit is used to calculate the optimal shutdown strategy according to the optimization objective function.

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