A safety assurance system for a compartmentalized power battery

Abstract

The application discloses a kind of compartmentalized power battery safety guarantee system, it is related to new energy power battery safety protection and intelligent control technical field.The system includes compartmentalized battery bearing module, multiple parameter state sensing network module, intelligent analysis and risk prediction module, digital twin battery package module, cabin isolation linkage execution module and central control module.A plurality of mutually independent cabin units are constructed in the interior of power battery package, and physical blocking is carried out using multi-layer composite isolation wall, to realize the accurate isolation protection of single battery module;When abnormal state occurs, the system can automatically link power off, isolation, strengthen cooling and inert gas inhibition device according to risk level, realize local accurate protection and emergency treatment, effectively block the heat runaway spread path from structural source and intelligent control level, greatly improve the overall safety and reliability of power battery system.

Description

Technical Field

[0001] This invention relates to the field of safety protection and intelligent control technology for new energy power batteries, specifically a compartmentalized power battery safety protection system. Background Technology

[0002] In recent years, with the rapid development of the new energy vehicle industry, the requirements for the energy density of power batteries have been continuously increasing, and new battery structures such as high-energy-density lithium-ion batteries and solid-liquid hybrid batteries have been widely used. However, while pursuing high energy density and compact structure, the safety risks of battery systems have also increased significantly.

[0003] Power batteries are susceptible to the following factors during operation: Overcharging and over-discharging can damage the electrode structure; high-rate charging and discharging generate a large amount of heat; manufacturing defects can cause internal short circuits; and external impacts and pressure can cause the separator to rupture. When a single battery cell malfunctions, its temperature rises sharply and releases a large amount of flammable gas, triggering a thermal runaway reaction. Currently, most power battery packs adopt an integrated structural layout, with small spaces between battery modules and rapid heat conduction. Once thermal runaway occurs, it can easily spread to the entire battery system in a short period of time.

[0004] Existing technologies mainly adopt the following measures: Forced liquid cooling or air cooling systems for temperature reduction; Add simple insulation panels; Install an integrated fire extinguishing system.

[0005] However, the above solutions have shortcomings such as delayed response, broad protection range, and inability to accurately isolate problem areas, making it difficult to meet the increasingly stringent safety requirements of high-energy-density power batteries.

[0006] Therefore, there is an urgent need for an innovative power battery safety system that can isolate and block at the structural source and is supplemented by an intelligent active protection mechanism. Summary of the Invention

[0007] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a compartmentalized power battery safety protection system, which solves the problems of existing technologies such as slow response, coarse protection range, and inability to accurately isolate problem areas, making it difficult to meet the increasingly stringent safety requirements of high-energy-density power batteries.

[0008] (II) Technical Solution The purpose of this invention is to provide a safety protection system for a separated power battery, which is achieved through the following technical solution: including, The compartmentalized battery carrier module has multiple independent compartments inside, which are physically separated by composite isolation walls. At least one power battery module is installed in each compartment. A multi-parameter state-sensing network module is deployed in each compartment to collect data on temperature, voltage, current, gas concentration, and pressure changes. The intelligent analysis and risk prediction module, based on multi-parameter fusion algorithms, AI self-evolution models and thermal runaway mechanism models, performs real-time assessment and trend prediction of the operating status of each compartment; The digital twin battery pack module is used to build a virtual model that is synchronously mapped to the actual power battery pack, so as to realize the simulation of safety status and the prediction of risk evolution; The compartment isolation linkage execution module includes a power-off device, a controllable isolation mechanism, a cooling suppression device, and an inert gas release device; The central control module controls the actions of the isolation and linkage actuators according to the risk level, so as to achieve local isolation, protection and emergency response.

[0009] As a further preferred embodiment of the present invention, the isolation wall is formed by stacking the following structures in sequence: a high-strength metal support layer, a porous ceramic flame-retardant heat insulation layer, a nano aerogel heat insulation layer, a high-temperature resistant polymer sealing layer, and an expansion-type fire-resistant sealing ring is provided around the isolation wall, which automatically expands and fills the gaps under high-temperature conditions to form a completely sealed heat insulation barrier.

[0010] As a further preferred embodiment of the present invention, the multi-parameter state sensing network module includes: a multi-point temperature detection probe, a single cell voltage sampling unit, a module current monitoring unit, a pressure change sensor, a combustible gas concentration detection sensor, and all sensing data are transmitted to the central control module through a high-speed communication bus.

[0011] As a further preferred embodiment of the present invention, the intelligent analysis and risk prediction module includes: The data preprocessing unit is used to filter, denoise, and normalize the collected temperature, voltage, current, gas concentration, and pressure data. The multi-parameter fusion analysis unit is used to perform weighted fusion calculations on multi-source data to generate comprehensive security status indicators. Thermal runaway mechanism modeling unit, used to construct the thermal diffusion and energy release evolution model of power battery; The AI ​​self-evolution prediction unit is used to dynamically learn and predict the risk level of abnormal development trends based on historical operating data and real-time status data.

[0012] As a further preferred embodiment of the present invention, the intelligent analysis and risk prediction module employs a multi-parameter coupled self-evolutionary prediction algorithm, the specific implementation of which includes the following steps: First, the system constructs a multi-dimensional state vector from the collected temperature, voltage, current, gas concentration, and pressure data: S = {T, V, I, G, P}, where T is the set of temperature parameters, V is the set of voltage parameters, I is the set of current parameters, G is the set of gas concentration parameters, and P is the set of pressure change parameters. Secondly, a dynamic weight allocation mechanism is used to assess the real-time importance of each parameter. Based on historical anomaly sample data, the system calculates the contribution of each parameter to the triggering of the thermal runaway event and forms an initial weight matrix W0. During real-time operation, the AI ​​self-evolutionary prediction unit continuously corrects the weight matrix based on the deviation between the prediction results and the actual state, generating a dynamic weight matrix W0. t This enables the risk assessment model to be adaptive. Subsequently, the multi-parameter fusion analysis unit generates the comprehensive risk index R in the following way: R = Σ(W i ×S i ), where W i S represents the weights of each parameter at the current time. i For the corresponding parameter value, when the comprehensive risk index R exceeds the preset dynamic safety threshold, the system enters an abnormal warning state.

[0013] As a further preferred embodiment of the present invention, the digital twin battery pack module includes: Physical structure mapping unit, used to construct a three-dimensional virtual model of the power battery pack and compartment structure; The operation status synchronization unit is used to synchronize the temperature, current, voltage and gas parameters of each compartment in real time. The thermal evolution simulation unit is used to simulate the diffusion path of abnormal states based on the heat conduction model; The risk evolution prediction unit is used to predict and assess future security situations in advance.

[0014] As a further preferred embodiment of the present invention, the compartment isolation linkage execution module includes: High-speed power-off relays are used to quickly disconnect the power supply circuit of the corresponding compartment when an anomaly occurs; Electrically controlled partition mechanism, used to form an openable and closable physical isolation barrier between compartments; Localized enhanced cooling device for rapid cooling of abnormal compartments; Inert gas release device, used to suppress combustion reactions under high-risk conditions.

[0015] As a further preferred embodiment of the present invention, the central control module includes: a risk level determination unit, used to classify safety levels based on prediction results; a linkage control decision unit, used to generate corresponding isolation, protection and emergency commands; and a communication management unit, used to perform high-speed data interaction with each module.

[0016] As a further preferred embodiment of the present invention, the operating method is as follows: after startup, all compartments enter a real-time monitoring state: Step 1: Status Acquisition Distributed sensors continuously collect temperature, voltage, current, gas, and pressure data; Step Two: Data Fusion Analysis The control system integrates and processes various data to calculate risk indicators; Step 3: Trend Forecasting Self-learning algorithms combined with thermal models predict future temperature rise trends; Step 4: Security Response When minor abnormalities occur: The system initiates enhanced cooling and adjusts the current load; when a moderate anomaly occurs: the system cuts off the power supply to the corresponding compartment and activates the isolation baffle; when a severe anomaly occurs: the system seals the compartment and releases inert gas to extinguish the fire, while simultaneously performing depressurization.

[0017] (III) Beneficial Effects This invention provides a segmented power battery safety protection system, which has the following beneficial effects: The power battery pack is divided into multiple independent compartments by a segmented battery carrier module, with composite isolation walls to physically isolate individual battery modules, effectively suppressing the rapid spread of heat, flames, and flammable gases, thus addressing the problem of thermal runaway chain reactions in the integral battery pack from a structural perspective. A multi-parameter state perception network module is employed to monitor key safety indicators such as temperature, voltage, current, gas concentration, and pressure in real time with high precision, forming a multi-dimensional safety perception system that significantly improves the timeliness and accuracy of abnormal state identification. An intelligent analysis and risk prediction module integrates multi-parameter data, thermal runaway mechanism models, and AI self-evolution algorithms to achieve trend prediction and level assessment of battery operation risks, upgrading safety protection from passive response to proactive early warning. A digital twin battery pack module is introduced to map the actual power battery system to a virtual model in real time, enabling thermal diffusion path simulation and risk evolution prediction, providing visualized and forward-looking basis for control decisions.

[0018] The system utilizes a compartmentalized isolation linkage module to achieve coordinated actions of power outage, physical isolation, enhanced cooling, and inert gas suppression. This allows for precise protective measures tailored to different risk levels, avoiding the resource waste and response delays associated with traditional overall fire suppression or crude cooling methods. The overall system employs a modular design, facilitating integration into various types of power battery packs and demonstrating excellent versatility and engineering application value. In summary, this invention significantly outperforms existing technologies in terms of safety protection accuracy, response speed, risk prediction capabilities, and system reliability, making it particularly suitable for high-energy-density new energy vehicle power battery systems. Attached Figure Description

[0019] Fig. 1 : A schematic diagram of the system principle framework of the present invention.

[0020] Fig. 2 : A schematic diagram of the system operation assurance method of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Please see Figs. 1-2 This invention provides a technical solution: a separated power battery safety protection system, comprising, The compartmentalized battery carrier module has multiple independent compartments inside, which are physically separated by composite isolation walls. At least one power battery module is installed in each compartment. A multi-parameter state-sensing network module is deployed in each compartment to collect data on temperature, voltage, current, gas concentration, and pressure changes. The intelligent analysis and risk prediction module, based on multi-parameter fusion algorithms, AI self-evolution models and thermal runaway mechanism models, performs real-time assessment and trend prediction of the operating status of each compartment; The digital twin battery pack module is used to build a virtual model that is synchronously mapped to the actual power battery pack, so as to realize the simulation of safety status and the prediction of risk evolution; The compartment isolation linkage execution module includes a power-off device, a controllable isolation mechanism, a cooling suppression device, and an inert gas release device; The central control module controls the actions of the isolation and linkage actuators according to the risk level, so as to achieve local isolation, protection and emergency response.

[0023] The power battery pack contains multiple independent compartments, each housing one or more power battery modules. The compartments are physically separated by composite partition walls.

[0024] The composite isolation wall, from the inside out, comprises: A high-strength metal support layer is used to provide structural strength and impact resistance; Porous ceramic flame-retardant and heat-insulating layer is used to block the spread of flames and absorb some of the heat; Nano-aerogel insulation layer, used to significantly reduce heat conduction efficiency; A high-temperature resistant polymer sealing layer is used to prevent the leakage of high-temperature gases. An intumescent fire-resistant sealing ring is installed around the perimeter of the isolation wall. When the temperature exceeds a preset threshold, it automatically expands to fill the gaps, forming a completely sealed thermal insulation barrier, thereby further improving the isolation effect.

[0025] Multiple temperature detection probes are installed inside each compartment to monitor temperature changes at key locations in the cells and modules; a single cell voltage sampling unit is set up to accurately capture cell voltage anomalies. The system is equipped with a module current monitoring unit to monitor the charging and discharging status in real time; pressure change sensors are installed to detect pressure changes inside the chamber caused by abnormal gas release; and combustible gas concentration sensors are installed to identify electrolyte decomposition gases or signs of impending combustion. All sensor data is transmitted in real time to the central control module via a high-speed communication bus.

[0026] The intelligent analysis and risk prediction module is implemented by first filtering, denoising, and normalizing the raw sensor data through a data preprocessing unit. Subsequently, a multi-parameter fusion analysis unit uses a weighted fusion algorithm to comprehensively calculate various data types, generating a comprehensive risk index characterizing the safety status. The thermal runaway mechanism modeling unit constructs a thermal diffusion evolution process based on battery thermal conduction and chemical reaction exothermic models. The AI ​​self-evolution prediction unit continuously trains the prediction model using historical operating data, dynamically learning the development trend of abnormal states to achieve early judgment of future risk levels.

[0027] The intelligent analysis and risk prediction module employs a multi-parameter coupled self-evolutionary prediction algorithm, which is implemented through the following steps: First, the system constructs a multi-dimensional state vector from the collected temperature, voltage, current, gas concentration, and pressure data: S = {T, V, I, G, P}, where T is the set of temperature parameters, V is the set of voltage parameters, I is the set of current parameters, G is the set of gas concentration parameters, and P is the set of pressure change parameters. Second, a dynamic weight allocation mechanism is used to assess the real-time importance of each parameter. Based on historical anomaly sample data, the system calculates the contribution of each parameter to the triggering of the thermal runaway event and forms an initial weight matrix W0. During real-time operation, the AI ​​self-evolutionary prediction unit continuously corrects the weight matrix based on the deviation between the prediction results and the actual state, generating a dynamic weight matrix W0. t This enables the risk assessment model to be adaptive. Subsequently, the multi-parameter fusion analysis unit generates the comprehensive risk index R in the following way: R = Σ(W i ×S i ), where W i S represents the weights of each parameter at the current time. i For the corresponding parameter value, when the comprehensive risk index R exceeds the preset dynamic safety threshold, the system enters an abnormal warning state.

[0028] The implementation of the digital twin battery pack module involves constructing a three-dimensional virtual model that is completely consistent with the actual power battery pack through a physical structure mapping unit; a running status synchronization unit receives sensor data in real time and synchronizes it to the virtual model; a thermal evolution simulation unit simulates the heat diffusion path in the virtual environment; and a risk evolution prediction unit predicts the future safety situation based on the simulation results.

[0029] The implementation method of the compartment isolation linkage execution module is as follows: when the system determines that an abnormality has occurred: the high-speed power-off relay quickly cuts off the power supply to the corresponding compartment; the electrically controllable isolation mechanism automatically closes to form a physical barrier; the local enhanced cooling device performs directional cooling of the abnormal compartment; and in high-risk conditions, the inert gas release device releases nitrogen or carbon dioxide to inhibit the combustion reaction.

[0030] The system operation procedure involves the following steps: After system startup, all compartments enter real-time monitoring mode. First, various operating parameters are continuously collected; then, multi-parameter fusion analysis is performed to generate risk indicators; next, a predictive model is used to determine future temperature rise trends; finally, corresponding safety response measures are implemented based on the risk level to achieve tiered protection.

[0031] In Example 1, the power battery pack is equipped with six independent compartments, each containing a power battery module, and the modules are isolated from each other by a composite isolation wall.

[0032] During system operation, the multi-parameter status sensing network module collects real-time data on temperature, voltage, current, gas concentration, and pressure changes of the battery modules in each compartment.

[0033] When a single cell in the third compartment experiences abnormal heating due to an internal short circuit, the temperature detection probe first detects the abnormal rate of temperature rise at that location, while the gas concentration detection sensor detects the release of trace amounts of combustible gas.

[0034] The intelligent analysis and risk prediction module constructs a multi-dimensional state vector S={T,V,I,G,P} from the collected data and calculates the comprehensive risk index R through a dynamic weight coupling algorithm. Because the weights of the temperature rise rate and gas concentration are dynamically increased in the self-evolutionary model, the comprehensive risk index rapidly approaches the dynamic safety threshold.

[0035] The AI ​​self-evolution prediction unit, combined with historical anomalous samples, predicts that the compartment will enter a critical state of thermal runaway within 2 minutes and synchronizes the prediction results to the central control module.

[0036] The central control module immediately determined the situation to be of medium risk and initiated the following coordinated protective measures: Disconnect the power supply circuit to the third compartment; The electrically controlled partition mechanism is driven to close, forming a physical isolation barrier; The localized enhanced cooling device was activated to cool the compartment in a targeted manner.

[0037] Because the heat conduction path was blocked between the compartments by composite isolation walls, the abnormal heat did not spread to adjacent compartments. After cooling, the temperature of the compartment gradually dropped back to a safe range, and the system deactivated its warning status.

[0038] This embodiment demonstrates that the system of the present invention can achieve accurate early warning and local isolation protection in the early stage of thermal runaway, thus preventing the abnormal expansion.

[0039] Example 2: Prediction and Early Intervention of Multi-Compartment Thermal Runaway Chain Propagation In this embodiment, the power battery pack is provided with eight compartment units, which form a matrix arrangement.

[0040] During system operation, the battery module in the fifth compartment experienced a continuous temperature increase due to overcharging.

[0041] The multi-parameter state perception network module continuously collects relevant data and uploads it to the intelligent analysis and risk prediction module.

[0042] The thermal runaway mechanism modeling unit combines the structural parameters of the compartment, the thermal resistance coefficient of the isolation wall, and the current temperature change rate to construct a thermal diffusion evolution simulation model in the digital twin battery pack module.

[0043] Simulation results show that, without intervention, heat will be conducted through the isolation wall to the adjacent fourth compartment within about 5 minutes, potentially triggering a chain thermal runaway reaction.

[0044] The AI ​​self-evolution prediction unit compares and analyzes simulation results with historical transmission cases to predict the risk transmission path and time nodes.

[0045] Based on the forecast results, the central control module enters a severe risk protection state in advance and implements the following measures: Immediately cut off power to the fifth compartment; Simultaneously, enhanced cooling devices were activated for the fifth compartment and the adjacent fourth compartment. Close the controllable partition mechanism between the compartments; Inert gas is released inside the fifth compartment to suppress the combustion reaction.

[0046] Through early intervention, the temperature of the fifth compartment was quickly suppressed, and no significant temperature rise was observed in adjacent compartments.

[0047] This embodiment demonstrates that the system of the present invention can not only cope with single-point anomalies, but also prevent the thermal runaway chain propagation process in advance through intelligent prediction, thus significantly improving the overall battery pack safety.

[0048] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or basic characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the same elements of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0049] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A separated power battery safety protection system, characterized in that: include: The compartmentalized battery carrier module has multiple independent compartments inside, which are physically separated by composite isolation walls. At least one power battery module is installed in each compartment. A multi-parameter state-sensing network module is deployed in each compartment to collect data on temperature, voltage, current, gas concentration, and pressure changes. The intelligent analysis and risk prediction module, based on multi-parameter fusion algorithms, AI self-evolution models and thermal runaway mechanism models, performs real-time assessment and trend prediction of the operating status of each compartment; The digital twin battery pack module is used to build a virtual model that is synchronously mapped to the actual power battery pack, so as to realize the simulation of safety status and the prediction of risk evolution; The compartment isolation linkage execution module includes a power-off device, a controllable isolation mechanism, a cooling suppression device, and an inert gas release device; The central control module controls the actions of the isolation and linkage actuators according to the risk level, so as to achieve local isolation, protection and emergency response.

2. The safety protection system for a separated power battery according to claim 1, characterized in that: The compartmentalized battery support module is formed by stacking the following structures in sequence: a high-strength metal support layer, a porous ceramic flame-retardant and heat-insulating layer, a nano-aerogel heat-insulating layer, a high-temperature resistant polymer sealing layer, and an expansion-type fire-resistant sealing ring around the isolation wall, which automatically expands and fills the gaps under high-temperature conditions to form a completely sealed heat-insulating barrier.

3. The safety protection system for a separated power battery according to claim 1, characterized in that: The multi-parameter state sensing network module includes: a multi-point temperature detection probe, a single cell voltage sampling unit, a module current monitoring unit, a pressure change sensor, and a combustible gas concentration detection sensor. All sensing data are transmitted to the central control module via a high-speed communication bus.

4. The safety protection system for a separated power battery according to claim 1, characterized in that: The intelligent analysis and risk prediction module includes: The data preprocessing unit is used to filter, denoise, and normalize the collected temperature, voltage, current, gas concentration, and pressure data. The multi-parameter fusion analysis unit is used to perform weighted fusion calculations on multi-source data to generate comprehensive security status indicators. Thermal runaway mechanism modeling unit, used to construct the thermal diffusion and energy release evolution model of power battery; The AI ​​self-evolution prediction unit is used to dynamically learn and predict the risk level of abnormal development trends based on historical operating data and real-time status data.

5. The safety protection system for a separated power battery according to claim 1, characterized in that: The intelligent analysis and risk prediction module adopts a multi-parameter coupled self-evolutionary prediction algorithm, the specific implementation of which includes the following steps: First, the system constructs a multi-dimensional state vector from the collected temperature, voltage, current, gas concentration, and pressure data: S = {T, V, I, G, P}, where T is the set of temperature parameters, V is the set of voltage parameters, I is the set of current parameters, G is the set of gas concentration parameters, and P is the set of pressure change parameters. Secondly, a dynamic weight allocation mechanism is used to assess the real-time importance of each parameter. Based on historical anomaly sample data, the system calculates the contribution of each parameter to the triggering of the thermal runaway event and forms an initial weight matrix W0. During real-time operation, the AI ​​self-evolutionary prediction unit continuously corrects the weight matrix based on the deviation between the prediction results and the actual state, generating a dynamic weight matrix W0. t This enables the risk assessment model to be adaptive. Subsequently, the multi-parameter fusion analysis unit generates the comprehensive risk index R in the following way: R = Σ(W i ×S i ), where W i S represents the weights of each parameter at the current time. i For the corresponding parameter value, when the comprehensive risk index R exceeds the preset dynamic safety threshold, the system enters an abnormal warning state.

6. The safety protection system for a separated power battery according to claim 1, characterized in that: The digital twin battery pack module includes: Physical structure mapping unit, used to construct a three-dimensional virtual model of the power battery pack and compartment structure; The operation status synchronization unit is used to synchronize the temperature, current, voltage and gas parameters of each compartment in real time. The thermal evolution simulation unit is used to simulate the diffusion path of abnormal states based on the heat conduction model; The risk evolution prediction unit is used to predict and assess future security situations in advance.

7. The safety protection system for a separated power battery according to claim 1, characterized in that: The compartment isolation linkage execution module includes: High-speed power-off relays are used to quickly disconnect the power supply circuit of the corresponding compartment when an anomaly occurs; Electrically controlled partition mechanism, used to form an openable and closable physical isolation barrier between compartments; Localized enhanced cooling device for rapid cooling of abnormal compartments; Inert gas release device, used to suppress combustion reactions under high-risk conditions.

8. The safety protection system for a separated power battery according to claim 1, characterized in that: The central control module includes: a risk level determination unit, used to classify safety levels based on prediction results; a linkage control decision unit, used to generate corresponding isolation, protection and emergency commands; and a communication management unit, used to perform high-speed data interaction with each module.

9. The operation method of the separated power battery safety protection system according to claim 1, characterized in that: Upon startup, all compartments enter real-time monitoring mode: Step 1: Status Acquisition Distributed sensors continuously collect temperature, voltage, current, gas, and pressure data; Step Two: Data Fusion Analysis The control system integrates and processes various data to calculate risk indicators; Step 3: Trend Forecasting Self-learning algorithms combined with thermal models predict future temperature rise trends; Step 4: Security Response When minor abnormalities occur: The system initiates enhanced cooling and adjusts the current load; when a moderate anomaly occurs: the system cuts off the power supply to the corresponding compartment and activates the isolation baffle; when a severe anomaly occurs: the system seals the compartment and releases inert gas to extinguish the fire, while simultaneously performing depressurization.

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