Method and system for shutting down a new energy battery system

Abstract

The application relates to the field of new energy technology, and discloses a new energy battery system shutdown control method and system, which comprises the following steps: an initialization stage: setting initial state parameters of the system, including voltage, current, initial temperature of a battery unit and heat diffusion rate of the battery system, and configuring an optimization target weight and a system safety threshold; real-time state monitoring: collecting voltage, current, temperature and heat diffusion rate data of the battery system through a sensor, and detecting whether an abnormality exceeding the safety threshold exists; dynamic optimization calculation: after detecting the abnormality, constructing an optimal shutdown strategy based on the system state and an optimization target function; and executing shutdown control: adjusting an electromagnetic relay or other control components according to the optimal shutdown strategy. Through real-time monitoring, dynamic optimization calculation, heat diffusion inhibition and staged recovery, the application realizes the effects of fast response, precise control, efficient recovery and collaborative stable operation among modules of the new energy battery system.

Description

TECHNICAL FIELD

[0001] The application relates to the field of new energy technology, in particular to a new energy battery system shutdown control method and system. BACKGROUND

[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, the battery system needs to provide stable power output and be able to adapt to complex operating environments. However, the battery system often faces abnormal situations such as overload, short circuit, and thermal runaway in actual operation. These problems not only threaten the safety of the device itself, but also can cause serious systemic failures. Therefore, in order to ensure the safety and reliability of the new energy battery system and prolong its service life, a technology solution that can monitor abnormalities in real time, accurately control and dynamically recover is urgently needed to achieve the dual goals of safety protection and efficient operation.

[0003] In the prior art, the new energy battery system mainly realizes abnormal monitoring and protection functions through the battery management system (BMS). The existing BMS technology relies on fixed threshold monitoring of voltage, current, and temperature and other key parameters. 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 a certain practicality and can effectively reduce the probability of major safety accidents in the battery system during operation. In addition, the existing technology also cools the battery modules as a whole through a heat dissipation system to delay the occurrence of thermal runaway. These technical solutions have improved the operational safety of the new energy battery system to a certain extent and provide a basic guarantee for the stable operation of the battery.

[0004] Although the existing technology has certain effects in the protection of new energy battery systems, it also has many shortcomings. First, the existing technology relies on a fixed threshold triggering mechanism and cannot dynamically adapt to complex working conditions, resulting in slow response speed, especially in short circuit or rapid thermal runaway scenarios, which can delay the handling time. Second, for the problem of thermal runaway diffusion, the existing technology mainly adopts the overall cooling method, which lacks the ability to accurately control the local high-risk area, making 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 state, which can easily cause system impact and even secondary failure during the recovery process. Finally, the existing technology has obvious shortcomings in multi-module cooperation, and lacks efficient communication and coordination mechanism between modules, making it difficult to achieve balance and stability of the system when an abnormality occurs. SUMMARY

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

[0006] To achieve the above object, the present application is implemented by the following technical solutions: a new energy battery system shutdown control method and system, comprising the following steps:

[0007] Initialization stage: set the initial state parameters of the system, including the voltage, current, initial temperature of the battery unit and heat diffusion rate of the battery system, and configure the optimization target weight and system safety threshold;

[0008] Real-time state monitoring: collect the voltage, current, temperature and heat diffusion rate data of the battery system through sensors, and detect whether there is an abnormality exceeding the safety threshold;

[0009] Dynamic optimization calculation: after detecting the abnormality, based on the system state and the optimization target function, an optimal shutdown strategy is constructed;

[0010] Execution of shutdown control: adjust the electromagnetic relay or other control components according to the optimal shutdown strategy to cut off the relevant circuit modules; heat diffusion inhibition and cooperative operation: based on the heat diffusion rate and priority, dynamically adjust the shutdown strategy, preferentially cut off the high-risk area, and notify other modules to adjust the operating state;

[0011] System recovery: after the state parameters return to the safety range, gradually restore the disconnected circuit connection.

[0012] Preferably, the initialization stage comprises:

[0013] Setting the initial state parameters, including the total voltage, total current, initial temperature of the battery unit and heat diffusion rate;

[0014] Configuring the optimization target weight, including the weight of power consumption, temperature priority and control signal strength;

[0015] Setting the system safety threshold, including the maximum voltage, maximum current and maximum allowable temperature.

[0016] Preferably, the real-time state monitoring comprises:

[0017] Collecting the real-time voltage, current, temperature and heat diffusion rate data of the battery system;

[0018] Comparing the collected data with the set threshold to determine whether there is an abnormal state;

[0019] When detecting the abnormal state, send an alarm signal to the dynamic optimization module.

[0020] Preferably, the dynamic optimization calculation comprises:

[0021] Based on real-time data acquisition, an optimization objective function is constructed, aiming to minimize power consumption, temperature rise rate and control intensity;

[0022] The variable range of the optimization objective function is constrained, including the safe range of voltage, current and temperature;

[0023] According to the optimality condition of the optimization objective function, the optimal shutdown strategy is calculated, and the shutdown control instruction is output.

[0024] Preferably, the execution of the shutdown control comprises:

[0025] The shutdown control instruction output by the dynamic optimization module is received;

[0026] The triggering intensity and time of the electromagnetic relay are adjusted to cut off the circuit of the fault module;

[0027] According to the feedback signal, the shutdown strategy is updated in real time, and the abnormal area is gradually isolated.

[0028] Preferably, the heat diffusion suppression and cooperative operation comprises:

[0029] Based on the heat diffusion rate, the priority of the high-risk area is calculated to determine the priority shutdown module;

[0030] Local hard shutdown operation is triggered in the high-priority area to prevent heat diffusion;

[0031] State adjustment instructions are sent to other modules to dynamically adjust the operating parameters.

[0032] Preferably, the system recovery comprises:

[0033] Real-time detection of whether voltage, current and temperature return to the safe range;

[0034] The circuit connection of the cut-off module is gradually restored;

[0035] The state parameters are updated to ensure normal operation of the system.

[0036] The invention also provides a shutdown control system for a new energy battery system, comprising:

[0037] A state monitoring module is used to acquire the voltage, current, temperature and heat diffusion rate of the battery system;

[0038] A dynamic optimization module is used to calculate the optimal shutdown strategy based on the acquired data;

[0039] An execution control module is used to adjust the electromagnetic relay or other control components according to the optimal shutdown strategy;

[0040] A coordination control module is configured to dynamically adjust the operation state of other modules and prevent abnormal expansion;

[0041] A system recovery module is configured to gradually restore the disconnected circuit connection after the state parameter returns to the safe range.

[0042] Preferably, the state monitoring module comprises:

[0043] A voltage sensor is configured to monitor the total voltage of the battery system;

[0044] A current sensor is configured to monitor the total current of the battery system;

[0045] A temperature sensor is configured to detect the real-time temperature of the battery unit;

[0046] A data processing unit is configured to process the data collected by the sensors and compare the data with the set threshold.

[0047] Preferably, the dynamic optimization module comprises:

[0048] An optimization objective function construction unit is configured to construct an optimization objective function based on the collected data;

[0049] A constraint condition setting unit is configured to set the safe range of voltage, current and temperature;

[0050] An optimization solving unit is configured to calculate the optimal shutdown strategy according to the optimization objective function.

[0051] The present application provides a new energy battery system shutdown control method and system. It has the following advantages:

[0052] 1. The present application can quickly identify abnormalities and accurately calculate the optimal shutdown strategy through dynamic optimization calculation and real-time state monitoring, achieving millisecond-level response speed. Compared with the shutdown method relying on fixed threshold in the prior art, the present application effectively solves the problems of response delay and large error, and improves the overall safety of the system.

[0053] 2. The present application realizes the preferential inhibition of high-risk areas and the dynamic adjustment of other modules through heat diffusion rate calculation, priority evaluation and multi-module coordinated operation. Compared with the technical solution lacking real-time heat diffusion management in the prior art, the present application shows higher robustness and coordination under complex working conditions, and significantly reduces the influence of local abnormalities on the whole system.

[0054] 3. The present application 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 way lacking dynamic adjustment in the shutdown and recovery process in the prior art, the present application avoids the occurrence of system impact and secondary abnormalities, and at the same time guarantees the reliability of long-time operation. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 Method flowchart of the present application;

[0056] Figure 2 System structure diagram of the present application;

[0057] Figure 3 Module architecture diagram of the state monitoring module of the present application;

[0058] Figure 4 Module architecture diagram of the dynamic optimization module of the present application. DETAILED DESCRIPTION

[0059] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the specification of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

[0060] Please refer to the accompanying drawings in the specification of the present application Figure 1 The present application provides a new energy battery system shutdown control method and system, which comprises the following steps:

[0061] S1, initialization stage: set the initial state parameters of the system, including the voltage of the battery system, the current, the initial temperature of the battery unit and the heat diffusion rate, configure the optimization target weight and the system safety threshold;

[0062] The initialization stage is used to lay the parameter foundation for subsequent state monitoring, dynamic optimization calculation and shutdown execution. This stage not only needs to accurately set the initial parameters of the system, but also needs to configure the optimization target according to different working environments and application requirements. Through reasonable initialization setting, the response speed and accuracy of the system in detecting abnormalities and executing shutdown operation can be effectively improved.

[0063] Generally, the initialization stage in the present embodiment includes three main contents: setting of system parameters, configuration of safety threshold and weight configuration of optimization target.

[0064] In some embodiments, the initial state parameters of the system mainly include the total voltage of the battery pack x1(0), the total current x2(0), the initial temperature of the battery cell x3(0) and the heat 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, which is 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, which is measured by a current sensor, and its unit is ampere (A). The initial temperature of the battery cell x3(0) represents the temperature of the battery module in the initial state, which is usually obtained by a high-precision temperature sensor, and its unit is Kelvin (K). The heat diffusion rate x4(0) represents the diffusion rate of heat in the battery module, which is calculated by a thermosensitive element and an algorithm analysis, and its unit is Kelvin per second (K / s).

[0065] As an option, in this embodiment, the setting of system parameters can be combined with historical data analysis and experimental measurement results. For example, in a set of battery pack with a capacity of 60 kWh, the initial voltage x1(0) can be set to 350 V, the total current x2(0) is 0 A (no load), the initial temperature x3(0) is 298 K (25℃), and the heat diffusion rate x4(0) is 0.1 K / s.

[0066] Specifically, the configuration of the system safety threshold is an important link in the initialization stage. The configuration of the safety threshold includes the maximum voltage V max , the maximum current I max and the maximum temperature I max . In a 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 may be set to 4.2 V, the maximum current I max is 300 A, and the maximum temperature T max is set to 60℃ (333K). These thresholds are used to judge whether there is an abnormal state in the subsequent state monitoring stage.

[0067] As an implementation, the weight of the optimization target also needs to be configured in this embodiment. The weight of the optimization target includes the power weight a, the temperature weight b and the control signal strength weight g. 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:

[0068]

[0069] Wherein: J is a system optimization objective function, used to describe the comprehensive influence of power loss, temperature change and control strength during the entire shutdown process; T is the total time of the system shutdown process, with units of seconds (s); x1(t) is the total voltage at time t, with units of volts (V); x2(t) is the total current at time t, with units of amperes (A); x3(t) is the battery temperature at time t, with units of kelvins (K); u(t) is the shutdown control signal strength at time t, dimensionless, and a, b, g are optimization weight parameters, respectively controlling the influence of power, temperature and control signal on the objective function.

[0070] The selection of the weights a, b, g in the above optimization objective function needs to be adjusted according to the actual application scenario. For example, in the case of a high temperature operating environment of the battery system, the temperature weight b should be set to a large value to prioritize the control of the temperature trend. Conversely, under low load conditions, the power weight a can be appropriately increased to optimize energy efficiency.

[0071] In another possible implementation, the initialization stage 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 thermal diffusion rate x4 also needs to consider the influence of air thinness.

[0072] Through the setting of the above initialization stage, accurate state parameters and optimization objectives can be provided for various working conditions, providing a reliable technical foundation for subsequent state monitoring and shutdown operations. The content of the initialization stage comprehensively considers the characteristics and complexity of the battery system, ensuring the applicability and operability of the technical solution.

[0073] S2, real-time state monitoring: collecting voltage, current, temperature and thermal diffusion rate data of the battery system through sensors to detect whether there is an abnormality exceeding the safety threshold;

[0074] Real-time state monitoring can quickly identify abnormal conditions through real-time collection and analysis of key parameters such as voltage, current, temperature and thermal diffusion rate of the battery system, and provide reliable decision basis for subsequent dynamic optimization calculation and shutdown control. In order to achieve this function, the present application designs a multi-sensor monitoring network, which combines with the data processing unit to perform real-time processing and judgment on the sensor collected data, ensuring the efficiency and accuracy of the monitoring process.

[0075] Generally, 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 state parameters of each module, which are finally analyzed comprehensively by the data processing unit. In this way, real-time monitoring not only captures abnormalities, but also dynamically reflects the overall operating state of the battery pack.

[0076] 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 set of 60 kWh battery packs, the voltage range is usually 300 V to 400 V. When the voltage value exceeds the set threshold V max (eg. 4.2 V / cell), the data processing unit will immediately send an alarm signal to the dynamic optimization module.

[0077] In one possible implementation, the current sensor is used to monitor the total current x2(t) of the battery system. The sensor can accurately capture the rapid change of current fluctuation, 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 300 A during charging and discharging, and the duration exceeds 2 seconds, the system determines that it is in an overload state, thus triggering an abnormal signal.

[0078] In particular, the temperature sensor in this embodiment 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 can detect subtle temperature rise, such as from 298 K (25 °C) to 305 K (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 abnormal detection. Generally, when the temperature of a certain battery cell exceeds the set threshold T max (eg. 333 K, 60 °C), the system marks this area as a high-risk area.

[0079] In some embodiments, the present application also uses a combination of thermal elements and mathematical models to calculate the heat diffusion rate x4(t). The heat diffusion rate is an important parameter describing the speed of heat propagation within the battery pack. The heat diffusion rate can be dynamically calculated by the following formula:

[0080]

[0081] where: x4(t): heat diffusion rate, unit: Kelvin per second (K / s); T(t): temperature field distribution at time t, unit: Kelvin (K); K: thermal diffusion coefficient, unit: square meter per second (m 2 / s); Laplacian of temperature, describes the rate of change of temperature distribution in space.

[0082] As an implementation, the data processing unit can identify the areas with high heat diffusion rate by real-time calculation of the above formula. These areas are usually high-risk areas of abnormal diffusion. For example, when x4(t) > 0.5 K / s, the system determines that the heat diffusion in this area has entered a dangerous stage.

[0083] In order to ensure the accuracy and real-time performance of data processing, the data processing unit in this embodiment synchronously analyzes the collected multi-channel signals. Generally, the data processing unit compares the collected data with the safety threshold set in the initialization stage. 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.

[0084] In a possible implementation, real-time state monitoring also supports fault warning function. By analyzing the change trend of multiple collected data, such as the voltage drop rate or the temperature rise rate, the data processing unit can identify potential abnormalities in advance. For example, when the temperature rise rate is greater than the threshold, even if the current temperature does not exceed the threshold, the system will start the optimization module to prepare for shutdown operation in advance.

[0085] Through the above real-time state monitoring method, multi-parameter and multi-dimensional comprehensive monitoring of the battery system is realized, and accurate data input is provided for the dynamic optimization module. The monitoring process has the characteristics of high precision and fast response, which provides guarantee for the safety and stability of the system, and also lays a reliable foundation for the implementation of subsequent shutdown control.

[0086] S3, dynamic optimization calculation: based on the system state and the optimization objective function, the optimal shutdown strategy is constructed;

[0087] The dynamic optimization calculation is based on the real-time monitored data, combined with the optimization objective and constraint conditions set in the initialization of the system, to calculate the optimal shutdown control strategy. This step directly affects the accuracy of the shutdown control and the safety of the system. Through mathematical optimization method and iterative algorithm, the balance between power loss, temperature rise rate and control signal strength is realized under multiple constraint conditions.

[0088] Generally, the dynamic optimization calculation module receives the abnormal data transmitted by the real-time state monitoring module, and combines the safety threshold and optimization weight in the initialization stage to construct the objective function and constraint conditions. On this basis, the optimal control input is calculated by optimization solving method to provide accurate instructions for the shutdown control module.

[0089] 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 strength during the shutdown process;

[0090] In this embodiment, the dynamic optimization module also needs to define the constraint conditions of the optimization problem to ensure the safety and feasibility of the shutdown strategy. The constraint conditions include the following aspects:

[0091] Limit conditions of state variables:

[0092] x1(t)≤V max ,x2(t)≤I max ,x3(t)≤T max

[0093] Where: V max is the maximum allowable voltage, unit: volt (V); I max is the maximum allowable current, unit: ampere (A); T max is the maximum allowable temperature, unit: Kelvin (K).

[0094] Range constraint of control input:

[0095] u(t)∈[0,1]

[0096] The value range of the control signal u(t) is limited to 0 to 1, which is used to adjust the triggering strength of the electromagnetic relay.

[0097] Time boundary condition:

[0098] x(0)=x0,x(T)=x f

[0099] Where: x(0) is the initial state, x(T) is the terminal state, which represents the starting point and the end point of the shutdown control process respectively.

[0100] In one possible implementation, the dynamic optimization module solves the above optimization problem by Pontryagin’s Minimum Principle (PMP). The specific steps are as follows:

[0101] Construct Hamiltonian function H:

[0102]

[0103] Where: H: Hamiltonian function; λ i: co-state 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; a: power weight coefficient, dimensionless; b: temperature weight coefficient, dimensionless; g: control signal strength weight coefficient, dimensionless; State variable x i The rate of change of the state variable x

[0104] According to the Hamilton function, the co-state equation is calculated:

[0105]

[0106] Through the above conditions, the optimal control input u * (t) is calculated:

[0107]

[0108] As an option, the dynamic optimization module can use an iterative algorithm to numerically approximate the above solving process, such as the gradient descent method or the Newton iteration method. Through multiple iterations, the optimal control strategy u * (t) is gradually converged.

[0109] In some embodiments, the present application also considers the optimization processing in special scenarios. For example, when the system has multiple module abnormalities, the dynamic optimization module needs to coordinate the optimization objectives of each module through a distributed algorithm.

[0110] Through the above dynamic optimization calculation method, the optimal shutdown control strategy can be calculated in real time, providing high-precision decision basis for subsequent execution of shutdown control, while effectively balancing the safety, energy efficiency and hardware protection needs of the system.

[0111] S4, execute shutdown control: adjust the electromagnetic relay or other control components according to the optimal shutdown strategy to shut down the relevant circuit modules;

[0112] The execution of shutdown control adjusts the electromagnetic relay or other execution devices according to the optimal control instructions output by the dynamic optimization module to achieve rapid disconnection or adjustment of the circuit. After the abnormal state is detected, the execution of shutdown operation can effectively isolate the abnormal area, prevent fault propagation and reduce system risk. This step directly connects the dynamic optimization calculation module, and its accuracy and execution efficiency are crucial to the overall safety of the system.

[0113] Generally, the execution shutdown control module is responsible for receiving the control instructions u *(t) and convert them into specific operations of the execution device. For example, by adjusting the trigger strength and duration of the electromagnetic relay, the breaking behavior of the control circuit is controlled. In addition, the execution shutdown module dynamically adjusts the subsequent operation according to the feedback data after the shutdown operation to ensure safety.

[0114] In this embodiment, the specific implementation of the execution shutdown control includes the following contents.

[0115] As an option, the core of the shutdown control in this embodiment is to drive the electromagnetic relay to achieve circuit shutdown based on the dynamically optimized optimal control variable u * (t) to the electromagnetic relay. The input u * (t) of the shutdown control 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 * (t) = 1, the relay triggers with maximum strength and completely disconnects the circuit; when u * (t) = 0.5, the relay acts with medium strength and partially limits the current.

[0116] Specifically, the trigger response of the electromagnetic relay can be described by the following formula:

[0117] I coil = K·u * (t)

[0118] Where: I coil : relay coil current, unit: ampere (A); K: proportional coefficient, related to the physical characteristics of the relay, unit: ampere; u * (t): control variable output by the dynamic optimization module, dimensionless.

[0119] In some embodiments, the action duration t on and t off of the relay are also determined by the control variable u * (t). Their relationship can be expressed as:

[0120] t on = T max ·u * (t), t off = T max ·(1-u * (t))

[0121] Where: t on : the duration of the relay in the closed state, unit: seconds (s); t off : the duration of the relay in the open state, unit: seconds (s); T maxMaximum action period of the relay, in seconds (s).

[0122] As an implementation, the execution 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 preferentially triggers partial shutdown operation, and only the circuit of part of the battery module is cut off; while 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.

[0123] Generally, the execution shutdown control in this embodiment also involves multi-module cooperative operation. In a group of battery systems with a capacity of 60 kWh, each module communicates through a 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℃, 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.

[0124] In a possible implementation, the feedback mechanism of the execution module can dynamically adjust the subsequent shutdown operation. For example, when the abnormal parameters (such as voltage or current) are detected after shutdown and do not return to the safe range, the system will trigger the hard shutdown operation again until the parameters are stable. This feedback adjustment process can be described by the following formula:

[0125] u new (t)=u prev (t)+Δu(t)

[0126] Where: u new (t): adjusted shutdown control variable; u prev (t): previous shutdown control variable; Δu(t): feedback correction value, calculated according to the severity of the abnormality.

[0127] In some embodiments, the present application also introduces a redundant shutdown mechanism to improve the fault tolerance of the system. The core of the redundant mechanism is to design through parallel relays to ensure that even if one relay fails, the system can still perform shutdown operation. For example, when the main relay cannot be completely disconnected, the standby relay will immediately trigger to further isolate the circuit.

[0128] Through the above-mentioned execution shutdown control method, fast, accurate and flexible circuit disconnection operation is realized, which effectively prevents abnormality from spreading and guarantees the overall safety of the system.

[0129] S5, heat diffusion inhibition and cooperative operation: dynamically adjust the shutdown strategy based on the heat diffusion rate and priority, preferentially cut off the high-risk area, and notify other modules to adjust the operating state;

[0130] The heat diffusion suppression and the collaborative operation suppress the diffusion 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 the shutdown control is performed, this step continues to dynamically adjust the temperature field of the battery system, avoiding the influence of local thermal runaway on global operation.

[0131] Generally, the target of the heat diffusion suppression is to prioritize the high-risk areas based on the heat diffusion rate and the priority. Through dynamic calculation of the heat diffusion rate and combination with the spatial temperature distribution model, the present application can realize accurate control of heat diffusion. At the same time, the multi-module collaborative operation ensures the safety and coordination of adjacent modules through information sharing and real-time communication.

[0132] In this embodiment, the specific implementation of the heat diffusion suppression and the collaborative operation includes the following contents.

[0133] As an option, in this embodiment, the heat diffusion suppression identifies the high-risk areas by calculating the heat diffusion rate x4(t) in real time

[0134] Generally, when the system calculates the heat diffusion rate x4(t) of a certain position to be greater than 0.5 K / s, it is determined that there is an abnormal diffusion trend in the position. At this time, the system will prioritize local hard shutdown for the position and trigger cooling measures to reduce the diffusion rate.

[0135] Specifically, in this embodiment, the heat diffusion suppression also combines a priority calculation method to dynamically adjust the execution order of the shutdown strategy. The calculation formula of the priority is:

[0136]

[0137] Wherein: Priority: the priority of the heat diffusion suppression of a certain area, dimensionless; Temperature rise rate, unit: Kelvin per second (K / s); d: distance between the area and the heat source, unit: meter (m).

[0138] As an implementation manner, the d in the priority calculation formula can be dynamically adjusted through a heat source positioning algorithm. For example, when a module temperature sensor detects that the temperature exceeds 333 K (60°C), the system will locate the heat source according to the sensor array and calculate the priority of the adjacent module to determine the shutdown order.

[0139] In some embodiments, the multi-module collaborative operation is also implemented in this embodiment, and the real-time communication between the modules is realized 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 triggers shutdown due to temperature rise, the adjacent modules can reduce the current load or enable auxiliary cooling devices to reduce heat accumulation.

[0140] As an option, the multi-module cooperative operation also includes dynamic distribution of current. After the thermal diffusion region is turned off, the system re-distributes the current of the remaining modules. For example, the total system load is 300A, and after a module is turned off, the remaining modules will distribute the current according to their current temperature and load capacity, so that the temperature rise of each module is kept within a controllable range.

[0141] Specifically, the thermal diffusion suppression and cooperative operation in this embodiment also includes a feedback adjustment mechanism. For example, after a module triggers a shutdown, the system continuously monitors its temperature. If it is detected that the temperature starts to drop and returns to the safe range, the system will gradually restore the connection of the module, and notify other modules to adjust the parameters. The feedback adjustment process can be described by the following formula:

[0142] T new (t)=T old (t)+ΔT(t)

[0143] 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).

[0144] In a possible implementation, the thermal diffusion suppression and cooperative operation module also supports hierarchical processing. For example, for low-priority regions, the system will first trigger a soft shutdown, only reducing the current or output power, rather than completely disconnecting the circuit. This hierarchical processing mode can reduce the impact on the overall performance of the system, while ensuring the safety of the abnormal region.

[0145] Through the above thermal diffusion suppression and cooperative operation method, and through the cooperative effective control of the thermal diffusion range of multiple modules, the abnormality is avoided from expanding to the whole system.

[0146] S6, system recovery: after the state parameter returns to the safe range, gradually restore the disconnected circuit connection.

[0147] System recovery is the last important step in the technical solution of the present application, which aims to gradually return the system that has completed the shutdown operation to the normal working state through real-time detection and phased recovery strategy. This step is closely connected to the thermal diffusion suppression and cooperative operation module, ensuring that after the abnormal state is removed, the system can safely and stably recover and run. The recovery process needs to consider the real-time state of parameters such as temperature, voltage and current, and at the same time dynamically adjust the recovery strategy through the feedback mechanism to avoid new abnormalities caused by the impact during the recovery process.

[0148] Generally, system recovery includes three main links of state detection, recovery sequence determination and step-by-step recovery operation. Through accurate parameter monitoring and dynamic adjustment, this step can minimize energy loss during the recovery process and ensure the safety of the entire battery system.

[0149] In this embodiment, the specific implementation of system recovery includes the following contents.

[0150] As an option, the first step of system recovery in this embodiment is state detection. Through the state monitoring module, real-time acquisition of key parameters such as voltage x1(t), current x2(t) and temperature x3(t) is performed to determine whether the system meets the recovery condition. For example, when the temperature x3(t) decreases below the safety threshold, and the voltage and current fluctuation amplitudes are both less than the preset range, the system determines that the recovery condition has been met.

[0151] Specifically, in this embodiment, state detection also needs to be analyzed in combination with the recovery dynamic model. For example, the state recovery process of the battery unit is described by the following relationship:

[0152] Δx(t) = |x(t) - x ref |

[0153] Where: Δx(t): parameter deviation at time t, unit consistent with the corresponding parameter; x(t): real-time monitoring parameter, including voltage, current and temperature; x ref : reference value, i.e. the parameter value of the target recovery state.

[0154] In some embodiments, when the deviation Δx(t) of all monitoring parameters is less than a certain threshold, the system enters the recovery state.

[0155] As an implementation, the second step of system recovery in this embodiment is the determination of the recovery sequence. Generally, the system will preferentially recover the module with the slowest temperature rise and the smallest load to reduce the impact of the recovery process on the overall system.

[0156] Generally, the higher the priority of the module, the more preferentially it is recovered. This priority calculation method based on temperature deviation and load power can ensure the stability of the recovery process.

[0157] Specifically, the third step of system recovery in this embodiment is step-by-step recovery operation. Step-by-step recovery includes two parts of phased recovery circuit connection and dynamic adjustment of load distribution. For example, when a module meets the recovery condition, the execution module first gradually recovers 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 high current on the module.

[0158] As an option, the load adjustment of step-by-step recovery in this embodiment can be realized by the following relationship:

[0159] I new =I old +ΔI

[0160] wherein: I new : the load current of the module after recovery, in ampere (A); I old : the load current of the module before recovery, in ampere (A); ΔI: the load adjustment increment, in ampere (A), dynamically calculated by the system.

[0161] In some embodiments, the system recovery also includes the cooperative operation with other modules. For example, when a certain module recovers, its load current increases, and the system dynamically adjusts the current of the adjacent module to maintain the load balance of the overall system. The load adjustment of the adjacent module can be achieved through real-time communication of the CAN bus.

[0162] Specifically, the system recovery in this embodiment also combines a state feedback mechanism for real-time monitoring of abnormal conditions during the recovery process. For example, if a certain module detects temperature abnormalities again during the recovery process, the system will immediately interrupt the recovery operation and re-enter the thermal diffusion suppression state.

[0163] Through the above-mentioned system recovery method, the application can efficiently and safely recover the system operating state after the abnormality is resolved, and the strategy of gradual recovery combined with dynamic adjustment not only effectively reduces the risk of impact during the recovery process, but also improves the overall stability of the system.

[0164] The shutdown control system of the new energy battery system described below can be mutually referred to the shutdown control method of the new energy battery system described above.

[0165] Please refer to the attached Figure 2 -attached Figure 4 , the application also provides a shutdown control system of a new energy battery system, comprising:

[0166] a state monitoring module for collecting the voltage, current, temperature and thermal diffusion rate of the battery system;

[0167] a dynamic optimization module for calculating the optimal shutdown strategy based on the collected data;

[0168] an execution control module for adjusting the electromagnetic relay or other control components according to the optimal shutdown strategy;

[0169] a cooperative control module for dynamically adjusting the operating state of other modules to prevent abnormal diffusion;

[0170] a system recovery module for gradually recovering the disconnected circuit connection after the state parameters return to the safe range.

[0171] The system of the embodiment can be used to execute the method embodiments described above, which have similar principles and technical effects, and will not be described here again.

[0172] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A method of turning off a new energy battery system, characterized by, The method comprises the following steps: Initialization stage: set the initial state parameters of the system, including the voltage of the battery system, the current, the initial temperature of the battery unit and the heat diffusion rate, configure the optimization target weight and the system safety threshold; Real-time state monitoring: collect the voltage, current, temperature and heat diffusion rate data of the battery system through sensors to detect whether there is an abnormality exceeding the safety threshold; Dynamic optimization calculation: after detecting the abnormality, based on the system state and the optimization target function, the optimal shutdown strategy is constructed; Execution shutdown control: adjust the electromagnetic relay or other control components according to the optimal shutdown strategy to cut off the relevant circuit module; Heat diffusion suppression and collaborative operation: based on the heat diffusion rate and priority, dynamically adjust the shutdown strategy, preferentially cut off the high-risk area, and notify other modules to adjust the operating state; System recovery: after the state parameters return to the safety range, gradually restore the disconnected circuit connection.

2. The method of claim 1, wherein The initialization stage comprises: Setting initial state parameters, including total voltage, total current, initial temperature of battery unit and heat diffusion rate; Configuring optimization target weight, including weight of power consumption, temperature priority and control signal strength; Setting system safety threshold, including maximum voltage, maximum current and maximum allowed temperature.

3. The method of claim 1, wherein the method further comprises: The real-time state monitoring comprises: Collecting real-time voltage, current, temperature and heat diffusion rate data of the battery system; Comparing the collected data with the set threshold to determine whether there is an abnormal state; When detecting an abnormal state, send an alarm signal to the dynamic optimization module.

4. The method of claim 1, wherein The dynamic optimization calculation comprises: Based on the real-time collected data, construct an optimization target function, the target being to minimize power consumption, temperature rise rate and strength of control signal for executing shutdown; Restrict the variable range of the optimization target function, including the safety range of voltage, current and temperature; According to the optimality condition of the optimization target function, calculate the optimal shutdown strategy and output the shutdown control instruction.

5. The method of claim 1, wherein the method further comprises: The execution shutdown control comprises: Receiving the shutdown control instruction output by the dynamic optimization module; Adjusting the trigger strength and time of the electromagnetic relay to cut off the circuit of the fault module; According to the feedback signal, update the shutdown strategy in real time to gradually isolate the abnormal area.

6. The method of claim 1, wherein The heat diffusion suppression and collaborative operation comprises: Based on the heat diffusion rate, calculate the priority of the high-risk area to determine the priority shutdown module; Trigger local hard shutdown operation on high-priority areas to prevent heat diffusion; Send state adjustment instructions to other modules to dynamically adjust the operating parameters.

7. The method of claim 1, wherein the method further comprises: The system recovery comprises: Real-time detection of whether the voltage, current and temperature return to the safety range; Gradually restore the circuit connection of the disconnected module; Update the state parameters to ensure normal operation of the system.

8. A system for controlling the shutdown of a new energy battery system, characterized by The shutdown control method of the new energy battery system according to any one of claims 1-7 comprises: A state monitoring module for collecting voltage, current, temperature and heat diffusion rate of the battery system; A dynamic optimization module for calculating the optimal shutdown strategy based on the collected data; An execution control module for adjusting the electromagnetic relay or other control components according to the optimal shutdown strategy; A collaborative control module for dynamically adjusting the operating state of other modules to prevent abnormal diffusion; A system recovery module is configured to gradually restore the disconnected circuit connection after the state parameter returns to the safe range.

9. The system for controlling the shutdown of a new energy battery system according to claim 8, wherein The state monitoring module comprises: a voltage sensor configured to monitor the total voltage of the battery system; a current sensor configured to monitor the total current of the battery system; a temperature sensor configured to detect the real-time temperature of the battery unit; a data processing unit configured to process the data collected by the sensors and compare the data with the set threshold.

10. The system for controlling the shutdown of a new energy battery system of claim 8, wherein The dynamic optimization module comprises: an optimization objective function construction unit configured to construct an optimization objective function based on the collected data; a constraint condition setting unit configured to set the safe range of voltage, current and temperature; an optimization solving unit configured to calculate the optimal shutdown strategy according to the optimization objective function.

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