A high-safety lithium ion battery thermal management system for electric vehicles

Abstract

The application belongs to the technical field of battery thermal management, and particularly relates to a high-safety-performance lithium ion battery thermal management system for electric vehicles, which comprises a multi-dimensional temperature sensing module, a thermal runaway propagation blocking module, an active cooling regulation module and a thermal isolation barrier management module. The multi-dimensional temperature sensing module constructs a real-time temperature monitoring network through a high-density temperature sensor array, realizes full-coverage sensing of the temperature field inside the battery pack, the thermal runaway propagation blocking module receives temperature field data and performs thermal runaway risk assessment and propagation path identification operation, and outputs a blocking instruction to the thermal isolation barrier management module, and the active cooling regulation module dynamically adjusts the cooling liquid flow distribution by using a model predictive control framework. Through the above technical scheme, active prevention and control of battery thermal runaway are realized, chain propagation of thermal runaway is effectively curbed, and the safety and reliability of the power battery of the electric vehicle are significantly improved.

Description

Technical Field

[0001] This invention belongs to the field of battery thermal management technology, specifically a high-safety lithium-ion battery thermal management system for electric vehicles. Background Technology

[0002] New energy vehicle power battery packs are composed of a large number of cells densely connected in series and parallel. The cells rely on conductive busbars, fixed brackets, and liquid cooling pipelines to form a dense structural heat conduction path. Abnormal temperature rise of a single cell can easily spread along the path. Precise control of battery operating temperature and effective suppression of thermal runaway chain propagation are the core technical prerequisites for ensuring the safe operation of the whole vehicle and extending the cycle life of the battery.

[0003] Currently, power battery thermal management systems generally adopt passive temperature control solutions such as liquid cooling, air cooling, and phase change material heat absorption. The overall battery temperature equalization and conventional heat dissipation are achieved solely through a single mode of medium circulation heat exchange and external heat conduction, without targeted monitoring of inter-cell thermal links or active barrier structure design.

[0004] Existing passive thermal management architectures suffer from a fundamental flaw: the lack of protection mechanisms under extreme operating conditions. They can only remove heat outwards and cannot identify subtle temperature gradient anomalies between cells or block high thermal conductivity heat transfer paths between cells. Once a single cell triggers thermal runaway, the heat will rapidly spread in a chain reaction. The system lacks pre-emptive intervention measures to prevent this chain reaction of thermal runaway, which can easily lead to catastrophic failure of the entire battery pack. This makes it difficult to meet the high safety and high reliability protection requirements of power batteries for new energy vehicles.

[0005] Therefore, the present invention provides a high-safety-performance lithium-ion battery thermal management system for electric vehicles. Summary of the Invention

[0006] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0007] The technical solution adopted by the present invention to solve its technical problem is: a high-safety lithium-ion battery thermal management system for electric vehicles, comprising a processing link consisting of a multi-dimensional temperature sensing module, a thermal runaway propagation blocking module, an active cooling regulation module, and a thermal isolation barrier management module. A high-safety-performance lithium-ion battery thermal management system for electric vehicles, the system including a processing link, the processing link comprising: Acquire real-time temperature field data inside the battery pack, and apply adaptive acquisition density adjustment based on thermal risk level to the temperature field data; When the surface temperature rise rate, peak temperature and temperature gradient distribution of a single cell trigger the preset thermal runaway critical criteria, a thermal conduction network modeling operation is performed to identify high-risk thermal conduction paths that spread from the trigger source to the surrounding cells. Perform a blocking response to the identified high-risk heat conduction paths, including driving a thermal baffle at the target location to physically cut off the heat conduction path between adjacent cells; And output thermal runaway interruption status identifiers based on the priority distribution of the high-risk heat conduction paths.

[0008] Preferably, the determination of the thermal runaway critical criterion includes: Perform multi-parameter fusion calculations on the cell surface temperature rise rate, the temperature difference between the cell and adjacent cells, the cell voltage drop rate, and the cell internal impedance change characteristics. When at least three of the four indicators exceed their respective threshold ranges simultaneously, thermal runaway status confirmation is triggered.

[0009] Preferably, the heat conduction network modeling calculation includes: The heat conduction path inside the battery pack is abstracted as a weighted directed graph, where nodes represent cells or structural components and edge weights represent thermal conductivity coefficients. Perform maximum flow path calculations to identify the set of easily propagating thermal runaway pathways from the trigger source to each target node; And a list of blocking targets ranked by priority is determined based on the calculation results of the maximum flow path.

[0010] Preferably, the processing link includes: Obtain the thermal runaway interruption status identifier and the temperature field distribution information, and calculate the temperature uniformity and peak temperature deviation of each cooling zone based on the temperature field distribution information; Perform multivariate collaborative optimization calculations, with the goal of minimizing cooling energy consumption and the temperature uniformity and peak temperature deviation as constraints, to calculate the optimal flow distribution scheme and target temperature setpoint for each cooling zone; And drive the electric flow regulating valves of each cooling zone to perform differentiated flow control according to the optimal flow distribution scheme.

[0011] Preferably, the multivariate collaborative optimization operation adopts a model predictive control framework, which includes a first-principles model describing the thermodynamic characteristics of the battery and an engineering empirical model describing the hydraulic characteristics of the cooling system. The optimal cooling control sequence in the finite time domain is solved on a rolling basis within each control cycle; In addition, a temperature prediction mechanism is introduced to perform feedforward compensation.

[0012] Preferably, the blocking response further includes: After receiving the blocking command, the heat insulation baffle action mechanism is driven to push the heat insulation baffle from the storage position to the working position; And after the heat insulation baffle is in place, a high-temperature impact resistance test is performed.

[0013] Preferably, the processing link includes: Before the thermal runaway blocking response is triggered, the phase change temperature monitoring value of the phase change insulation unit is used to determine whether it is necessary to activate the phase change material to perform heat absorption and energy storage. The phase change insulation unit is filled with composite phase change material.

[0014] Preferably, the processing link includes: Obtain temperature data and duration information for the localized overheated area; When a localized overheated area is detected, the micro thermoelectric conversion array arranged in that area is driven to perform thermoelectric conversion operations to convert heat into electrical energy; The converted electrical energy is stored in a supercapacitor; In the event of an external power supply failure, the electrical energy stored in the supercapacitor is used to drive the thermal runaway prevention mechanism to perform a thermal runaway prevention response.

[0015] Preferably, the processing link includes: The temperature field data and thermal runaway risk assessment results are uploaded to the cloud server via the vehicle communication unit; Receive early warning parameters issued by the cloud server after training and updating based on massive battery pack operation data; And update the threshold range of the local thermal runaway critical criterion based on the aforementioned warning parameters.

[0016] Preferably, the processing link includes: Receive remote software upgrade instructions and parameter calibration data packets sent by the cloud server; Perform a remote software upgrade to update the computational logic of the local thermal runaway detection algorithm unit; And adjust the boundary conditions and optimization target weights of the multivariate collaborative optimization operation based on the parameter calibration data packet.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention constructs a fully covered distributed temperature monitoring network through a multi-dimensional temperature sensing module, enabling refined real-time sensing of the internal thermal state of the battery pack. Compared to existing technologies that only arrange a small number of temperature sensors on the surface of the battery module, this invention can acquire temperature gradient distribution data reflecting the heat transfer process between cells, providing a complete data foundation for early warning and propagation path identification of thermal runaway, and effectively solving the problem of delayed thermal runaway detection caused by insufficient sensing granularity in existing technologies.

[0018] 2. This invention achieves reliable determination of thermal runaway state and accurate location of propagation path by organically combining multi-parameter fusion criteria and thermal conduction network graph theory analysis method in the thermal runaway propagation blocking module. This module can complete risk confirmation and path analysis in a very short time after a single cell triggers thermal runaway, and output a priority-ranked blocking target list to the thermal isolation barrier management module. This feature enables this invention to have an active protection response capability that is lacking in existing passive thermal management solutions.

[0019] 3. This invention achieves physical disconnection of the heat conduction channel between battery cells when a threat of thermal runaway is detected by the coordinated operation of the phase change insulation unit and the electronic control blocking mechanism in the thermal isolation barrier management module. The phase change insulation unit uses high latent heat composite phase change material to absorb local abnormal heat, and the electronic control blocking mechanism drives the heat insulation baffle to form a physical barrier. The combined effect of the two methods can effectively curb the chain propagation of thermal runaway under extreme conditions and reduce the risk of catastrophic failure of the battery pack.

[0020] 4. This invention achieves refined optimization of cooling system energy consumption through a multivariable collaborative control algorithm based on a model predictive control framework in the active cooling regulation module. Under the premise of ensuring temperature balance of each cell and peak temperature constraint, the algorithm controls the power consumption of the cooling pump within a reasonable range and achieves feedforward compensation through a temperature prediction mechanism, thereby saving cooling energy consumption in high-temperature environments. Attached Figure Description

[0021] The invention will now be further described with reference to the accompanying drawings.

[0022] Figure 1 This is a structural block diagram of a high-safety-performance lithium-ion battery thermal management system for electric vehicles according to the present invention; Figure 2 This is a schematic diagram of the structural framework of the thermal isolation barrier management module in this invention. Detailed Implementation

[0023] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0024] Example 1: like Figure 1 and Figure 2 As shown, this embodiment is specifically applied to the scenario of thermal runaway protection and temperature equalization control of electric vehicle power battery packs. This application scenario deploys a complete processing link consisting of a multi-dimensional temperature sensing module, a thermal runaway propagation blocking module, an active cooling control module, a thermal isolation barrier management module, a heat reverse conduction module, and a cloud-based collaborative monitoring module.

[0025] This system addresses the complex thermal management challenges posed by the integration of hundreds to thousands of cells in a series-parallel manner within the power battery pack of electric vehicles. It provides a complete technical solution encompassing real-time temperature monitoring, early warning of thermal runaway, propagation path identification, active blocking response, and cloud-based collaborative optimization.

[0026] A multi-dimensional temperature sensing module is configured to build a real-time temperature monitoring network inside the battery pack. This module synchronously collects temperature data from various points within the battery pack using a high-density array of temperature sensors deployed on the cell surfaces, module gaps, busbar connectors, and cooling pipe surfaces. The module employs a distributed sensing architecture to achieve full coverage sensing of the internal temperature field of the battery pack. The temperature acquisition frequency is adaptively adjusted according to the thermal risk level of each area, with a minimum acquisition frequency of 10 times per second in high-risk areas and a minimum of 2 times per second in low-risk areas. The sensed data is transmitted in real-time to the thermal management control unit to construct a dynamic temperature field model reflecting the internal heat distribution of the battery pack.

[0027] In the specific sensor configuration, the high-density temperature sensor array employs a heterogeneous sensing scheme combining miniature thin-film thermocouples and distributed fiber optic temperature measurement technology. The miniature thin-film thermocouples have a temperature measurement accuracy of ±0.5 degrees Celsius and a response time of no more than 100 milliseconds. The distributed fiber optic temperature measurement technology has a spatial resolution of no more than 5 millimeters and a temperature measurement range covering -40 to 150 degrees Celsius. The miniature thin-film thermocouple array is connected to the main control chip via a flexible circuit board. At least two thermocouple nodes are arranged on the surface of each battery cell to accurately capture the temperature field on the cell surface. The distributed fiber optic temperature measurement unit is laid along the edge of the battery module and the cooling pipes, utilizing the Raman scattering principle to achieve distributed temperature measurement along the length of the fiber. The temperature measurement data from the two sensing methods are cross-validated and fused using a data fusion algorithm to eliminate sensing blind spots caused by single-point sensor failure or measurement noise. The uncertainty of the pre-processed sensing data is no more than ±1 degree Celsius.

[0028] The thermal runaway propagation blocking module is electrically connected to the multi-dimensional temperature sensing module. This module is configured to receive real-time temperature field data and perform thermal runaway risk assessment and propagation path identification calculations. Internally, this module integrates a thermal runaway discrimination algorithm unit. This unit quantitatively determines whether the battery cell has entered a critical thermal runaway state based on preset temperature rise rate thresholds, peak temperature thresholds, and temperature gradient distribution characteristics. The thermal runaway discrimination algorithm unit uses a multi-parameter fusion criterion to confirm the thermal runaway state. This criterion comprehensively considers the battery cell surface temperature rise rate, the temperature difference between the battery cell surface and adjacent battery cells, the battery cell voltage drop rate, and the battery cell internal impedance change characteristics. Only when at least three of the above four indicators simultaneously exceed their respective threshold ranges does the algorithm unit determine that the battery cell has entered a thermal runaway state. The specific threshold ranges are set as follows: the battery cell surface temperature rise rate threshold is 3 degrees Celsius per second; the temperature difference threshold between the battery cell surface and adjacent battery cells is 15 degrees Celsius per second; the battery cell voltage drop rate threshold is 50 millivolts per second; and the battery cell internal impedance change threshold is 200% of the initial impedance value.

[0029] When a single battery cell triggers the thermal runaway detection condition, the thermal runaway propagation blocking module automatically activates the thermal conduction path analysis algorithm. This algorithm employs a graph-based thermal conduction network modeling method, abstracting the internal thermal conduction paths of the battery pack into a weighted directed graph. Nodes in the graph represent battery cells or structural components, and edge weights represent thermal conductivity coefficients. The algorithm identifies the set of channels most prone to thermal runaway propagation by calculating the maximum flow path from the trigger source to each target node. In its implementation, the set of nodes in the weighted directed graph includes all battery cell nodes, busbar connector nodes, module bracket nodes, and cooling pipe nodes. Edge weights are calculated based on the actual measured thermal conductivity coefficients and contact areas. The maximum flow path calculation uses either the Edmonds-Karp algorithm or the Dinic algorithm, calculating the maximum flow value between the trigger source node and all other nodes, and generating a priority list based on the flow values. This priority list serves as the blocking target list, outputting blocking commands and target channel coordinate information to the thermal isolation barrier management module.

[0030] The active cooling control module is communicatively connected to the thermal runaway propagation blocking module. This module is configured to perform differentiated control of cooling intensity in different regions. Based on the temperature field distribution information output by the thermal runaway propagation blocking module, this module dynamically adjusts the coolant circulation flow distribution ratio and the coolant inlet temperature setpoint. Specifically, this module divides the cooling circuit inside the battery pack into multiple independent and controllable cooling zones, each equipped with an independent electric flow regulating valve and temperature sensor. The module integrates a multivariable collaborative control algorithm. This algorithm aims to minimize cooling energy consumption, using the temperature uniformity of each cell and peak temperature constraints as boundary conditions, to calculate the optimal flow distribution scheme and target temperature setpoint for each cooling zone. The multivariable collaborative control algorithm employs a model predictive control framework, which includes a first-principles model describing the battery's thermodynamic characteristics and an engineering empirical model describing the hydraulic characteristics of the cooling system. The first-principles model, based on the battery's heat generation mechanism, expresses the cell's heat generation power as a function of current, internal resistance, and temperature. The engineering empirical model, based on the fluid resistance characteristics of the cooling pipes and the power characteristics of the pump, establishes a nonlinear mapping relationship between flow rate and power consumption. The algorithm iteratively solves for the optimal cooling control sequence within a finite time domain during each control cycle, ensuring that the highest temperature of the battery pack does not exceed 45 degrees Celsius and the temperature difference between any two cells does not exceed 5 degrees Celsius in the next control cycle. Simultaneously, it controls the pump power consumption of the cooling system to below 85% of the system's rated power. The algorithm incorporates a temperature prediction mechanism for feedforward compensation, with a control cycle set to 1 second and a prediction time domain set to 30 seconds.

[0031] When the thermal runaway propagation blocking module determines that a certain area has a risk of thermal runaway, the active cooling control module automatically increases the coolant flow rate and heat exchange power in the target area, while appropriately reducing the cooling intensity of adjacent safe areas to save overall energy consumption. The specific flow adjustment strategy is determined in real time through a multi-variable collaborative control algorithm: when the temperature deviation of a cooling zone exceeds 5 degrees Celsius, the target flow rate setpoint for that zone is increased to 120% of the rated flow rate; when the temperature deviation of a cooling zone is less than 2 degrees Celsius and there is no risk of thermal runaway in adjacent zones, the target flow rate setpoint for that zone is decreased to 60% of the rated flow rate. The electric flow control valve performs proportional adjustment according to the target flow rate setpoint, with an adjustment response time of no more than 500 milliseconds.

[0032] The thermal isolation barrier management module is electrically connected to both the thermal runaway propagation blocking module and the active cooling control module. This module is configured to manage the active thermal isolation barrier array embedded in the cell's thermal insulation structure. The module contains multiple phase change insulation units and electrically controlled blocking mechanisms distributed between adjacent cells. The phase change insulation units are filled with a high latent heat composite phase change material, with the phase change temperature set between 50 and 60 degrees Celsius. This temperature setting ensures the phase change material remains solid under normal operating conditions to provide passive insulation, while absorbing excess heat through its latent heat during localized temperature anomalies. The composite phase change material in the phase change insulation unit is a shaped phase change material composed of paraffin wax and a graphene thermally enhanced network. This material has an in-plane thermal conductivity of no less than 3 W / m / Kelvin and a latent heat of phase change of no less than 200 J / g. The composite phase change material is encapsulated in a thin-walled stainless steel container, the outer surface of which is coated with an infrared-reflective coating to reduce heat radiation loss.

[0033] The electrically controlled blocking mechanism is connected to the control signal output of the thermal runaway propagation blocking module. When the thermal runaway propagation blocking module identifies the thermal runaway propagation path and issues a blocking command, the electrically controlled blocking mechanism at the target location responds to the command and drives the heat insulation baffle mechanism to act, physically cutting off the heat conduction channel between adjacent cells. The response time of the electrically controlled blocking mechanism is no more than 50 milliseconds, and the heat insulation baffle mechanism can withstand instantaneous high-temperature impacts of no less than 800 degrees Celsius without deformation or failure after it has acted. The heat insulation baffle of the electrically controlled blocking mechanism is made of zirconia ceramic with a thickness of 2 mm. The action mechanism of the heat insulation baffle is driven by a linear voice coil motor with a rated thrust of 50 Newtons and a stroke of 10 mm. The heat insulation baffle is housed in the groove of the cell spacer and does not occupy additional cell arrangement space when not in operation. When the thermal runaway propagation blocking module outputs a blocking command, the control signal is amplified by the drive circuit and applied to the linear voice coil motor. The voice coil motor drives the heat insulation baffle to move rapidly from the storage position to the working position along the guide mechanism, thus physically cutting off the heat conduction channel between the cells.

[0034] Before the thermal runaway prevention response is triggered, the processing link determines whether the phase change material (PCM) needs to be activated to perform heat absorption and energy storage based on the phase change temperature monitoring value of the PCM insulation unit. When the monitored local temperature exceeds the phase change temperature, the PCM undergoes a solid-liquid phase change to absorb excess heat. The phase change process of the PCM is monitored in real time by miniature temperature sensors arranged inside the PCM insulation unit. When the monitored temperature is higher than the lower limit of the phase change temperature and the PCM has not completely melted, the PCM continues to absorb heat and maintains a constant temperature; when the monitored temperature is lower than the upper limit of the phase change temperature and the PCM has completely solidified, the PCM insulation unit resumes its passive insulation function.

[0035] The heat reverse conduction module is electrically connected to the multi-dimensional temperature sensing module and the thermal runaway propagation blocking module. This module is configured to convert localized overheating into electrical energy and store it in a supercapacitor via a miniature thermoelectric conversion array deployed in that area when a localized overheating region is detected. The miniature thermoelectric conversion array of the heat reverse conduction module uses bismuth telluride-based thermoelectric material with a conversion efficiency of no less than 8%, and the peak power generation of a single thermoelectric conversion array is no less than 5 watts. The generated electrical energy is used to drive the electrically controlled blocking mechanism of the thermal isolation barrier management module, achieving self-powered operation of the thermal runaway protection function. In extreme conditions of external power failure, the heat reverse conduction module can provide emergency power support for at least 30 seconds for the thermal runaway blocking response. The specific power management strategy is as follows: when the external power supply is normal, the electrical energy generated by the thermoelectric conversion array is prioritized for use by the electrically controlled blocking mechanism, and the supercapacitor is in a floating charge state; when the external power supply fails, the supercapacitor discharges to drive the electrically controlled blocking mechanism, while the thermoelectric conversion array continues to use localized waste heat to generate electricity to supplement the supercapacitor's energy.

[0036] The cloud-based collaborative monitoring module connects to the vehicle's onboard communication unit, enabling real-time data interaction with the vehicle's remote monitoring platform. This module uploads temperature field data within the battery pack, thermal runaway risk assessment results, and thermal isolation barrier trigger records to a cloud server. The cloud server trains a thermal runaway early warning model based on massive amounts of battery pack operating data and remotely sends updated warning parameters to the thermal runaway propagation blocking module of this invention. The cloud-based collaborative monitoring module also supports remote software upgrades and parameter calibration, facilitating continuous optimization and functional expansion of the thermal management system. The processing chain includes: receiving remote software upgrade instructions and parameter calibration data packets from the cloud server; performing a remote software upgrade to update the computational logic of the local thermal runaway discrimination algorithm unit; and adjusting the boundary conditions and optimization target weights of the multivariate collaborative optimization operation based on the parameter calibration data packets.

[0037] The workflow and collaborative operation of each module in this system are as follows: In response to the system startup signal, the multi-dimensional temperature sensing module begins to perform full-coverage acquisition of the internal temperature field of the battery pack. Miniature thin-film thermocouple nodes in the high-density temperature sensor array synchronously acquire temperature data from the surface of each cell, with the acquisition frequency determined according to preset thermal risk level zones. Distributed fiber optic temperature measurement units synchronously acquire cable-based temperature data distributed along cooling pipes and module borders. After cross-validation using a data fusion algorithm, the two types of sensing data generate dynamic temperature field data reflecting the internal heat distribution of the battery pack. This data is transmitted in real-time to the thermal runaway propagation blocking module via a high-speed communication bus.

[0038] After receiving dynamic temperature field data, the thermal runaway propagation blocking module performs a thermal runaway risk assessment calculation. The thermal runaway discrimination algorithm unit sequentially calculates the temperature rise rate, peak temperature, and temperature gradient distribution characteristics of each cell, and compares the calculation results with preset thresholds. When at least three of the four discrimination indicators of a cell simultaneously exceed the threshold range, the algorithm unit determines that the cell has entered a critical state of thermal runaway and outputs the trigger source identification information. Subsequently, the heat conduction path analysis algorithm is activated and executed. This algorithm constructs a weighted directed graph model of the internal heat conduction network of the battery pack and performs the maximum flow path calculation from the trigger source node to other nodes. Based on the calculation results, a priority-ranked blocking target list is generated, which includes the target channel coordinate information and the corresponding blocking priority score.

[0039] The thermal runaway propagation blocking module sends a list of blocking targets to the thermal isolation barrier management module, and simultaneously sends temperature field distribution information to the active cooling control module. Upon receiving the blocking command, the thermal isolation barrier management module identifies the electronically controlled blocking mechanism corresponding to the target channel and applies an action signal to the target electronically controlled blocking mechanism via the drive circuit. Within 50 milliseconds of receiving the action signal, the linear voice coil motor drives the heat insulation baffle from its storage position to its working position, physically cutting off the heat conduction channel. Once in position, the zirconia ceramic material ensures that the baffle maintains its structural integrity under instantaneous high-temperature impacts up to 800 degrees Celsius.

[0040] The active cooling control module performs multivariate collaborative optimization calculations based on the received temperature field distribution information. The algorithm calculates the temperature uniformity and peak temperature deviation of each cooling zone, aiming to minimize cooling energy consumption, and using temperature uniformity and peak temperature deviation as constraints to solve for the optimal flow distribution scheme and target temperature setpoint for each cooling zone. The model predictive control framework continuously solves for the optimal control sequence within a finite time domain in each control cycle, with a prediction time domain of 30 seconds. The calculated optimal flow distribution scheme is sent to the electric flow regulating valves of each cooling zone via control commands to execute differentiated flow control. When a region has a risk of thermal runaway, the algorithm automatically increases the coolant flow rate and heat exchange power in that region, while simultaneously reducing the cooling intensity of adjacent safe zones to achieve energy savings.

[0041] During the aforementioned workflow, the thermal isolation barrier management module synchronously monitors the phase change temperature of the phase change insulation unit. When a local temperature exceeds the lower limit of the phase change temperature but falls below the upper limit, the composite phase change material inside the phase change insulation unit undergoes a solid-liquid phase change process, absorbing the localized abnormal heat through the latent heat of phase change. The graphene thermal conductivity enhancement network of the phase change material ensures rapid heat transfer to the interior of the material, promoting a uniform phase change process. The infrared reflective coating of the thin-walled stainless steel container reduces heat radiation loss and improves phase change energy storage efficiency.

[0042] The reverse heat conduction module continuously monitors the temperature distribution inside the battery pack. When a localized overheating area is detected, it drives a miniature thermoelectric conversion array located in that area to perform thermoelectric conversion calculations. Bismuth telluride-based thermoelectric materials convert temperature differences into electrical energy based on the Seebeck effect, with a conversion efficiency of no less than 8%. The generated electrical energy is rectified and regulated by the power management unit and stored in a supercapacitor. When the external power supply is normal, the electrical energy generated by the thermoelectric conversion array is prioritized for use by the electronically controlled shutdown mechanism. In the event of an external power failure, the supercapacitor discharges, driving the electronically controlled shutdown mechanism to operate. The thermoelectric conversion array continues to utilize localized waste heat to generate electricity to supplement the supercapacitor's energy, ensuring an emergency power supply support time of no less than 30 seconds for thermal runaway shutdown response.

[0043] The cloud-based collaborative monitoring module uploads temperature field data, thermal runaway risk assessment results, and thermal isolation barrier trigger records from inside the battery pack to the cloud server via the vehicle-mounted communication unit. The data upload refresh frequency is no less than once every 10 seconds, and the transmission protocol uses TLS encryption to ensure data security. After receiving the data, the cloud server continuously trains and optimizes the thermal runaway early warning model based on massive amounts of battery pack operating data. Once the early warning model parameters are updated, the cloud server remotely sends the updated early warning parameters to the thermal runaway propagation blocking module. After receiving the early warning parameters, the thermal runaway propagation blocking module adjusts the threshold range of the thermal runaway critical criterion and the boundary conditions and optimization target weights of the multivariate collaborative optimization operation according to the parameter calibration data packet. Simultaneously, the cloud-based collaborative monitoring module supports remote software upgrades. When a new algorithm version or function update is available, it receives a remote software upgrade command via the vehicle-mounted communication unit to remotely update the computational logic of the local thermal runaway discrimination algorithm unit.

[0044] Example 2: This embodiment describes a thermal runaway protection scenario for an electric vehicle's power battery pack under extreme operating conditions. This scenario includes a high-temperature operating environment where the external ambient temperature consistently exceeds 40 degrees Celsius, a high-power operating environment where the battery pack experiences continuous high-current discharge during continuous mountain road driving, and a fault operating environment where the cooling system partially fails due to an external collision. This scenario deploys a system with the same architecture as in Embodiment 1, with corresponding enhanced control logic added for the aforementioned extreme conditions.

[0045] In the system's workflow under high-temperature operating conditions, when the multi-dimensional temperature sensing module detects that the ambient temperature sensor data consistently exceeds 40 degrees Celsius, it automatically increases the temperature acquisition frequency inside the battery pack. The acquisition frequency in high-risk areas increases from 10 times per second to 20 times per second, and the acquisition frequency in low-risk areas increases from 2 times per second to 5 times per second. Simultaneously, the thermal runaway propagation blocking module adjusts the threshold range of the thermal runaway critical criteria, reducing the cell surface temperature rise rate threshold from 3 degrees Celsius per second to 2 degrees Celsius per second, and the peak temperature threshold from 45 degrees Celsius to 42 degrees Celsius, to achieve more sensitive early warning of thermal runaway.

[0046] The active cooling control module adjusts the boundary conditions of multivariate collaborative optimization calculations under high-temperature operating conditions, reducing the maximum battery pack temperature constraint from 45 degrees Celsius to 40 degrees Celsius and the temperature difference constraint between any two cells from 5 degrees Celsius to 3 degrees Celsius. The prediction time domain of the model predictive control framework is extended from 30 seconds to 60 seconds to accommodate the increased thermal inertia under high-temperature conditions. The coolant inlet temperature setpoint is reduced from 25 degrees Celsius to 20 degrees Celsius, and the upper limit of cooling pump power consumption is adjusted from 85% to 95% of rated power to meet the heat dissipation requirements under high-temperature conditions.

[0047] In high-power operation scenarios involving continuous mountain road travel, the thermal runaway discrimination algorithm unit of the thermal runaway propagation blocking module, in addition to performing the multi-parameter fusion criterion calculation described in Example 1, also introduces the temporal characteristics of the cell discharge depth as an auxiliary discrimination criterion. When the cell discharge depth is detected to continuously exceed 80% and the temperature rise rate shows an accelerating trend, even if only two of the four main discrimination indicators exceed the threshold, the algorithm unit will issue a thermal runaway warning signal to initiate a protective response in advance.

[0048] In a fault operation scenario where the cooling system partially fails, the multi-dimensional temperature sensing module identifies the failed cooling zone through fault diagnosis logic and automatically increases the temperature acquisition frequency of the failed zone to 30 times per second. The active cooling control module recalculates the flow distribution scheme for the remaining normal cooling zones, distributing the cooling load of the failed zone to adjacent normal zones. The thermal runaway propagation blocking module synchronously adjusts the heat conduction network model, increasing the edge weight of the heat conduction coefficient corresponding to the failed cooling pipe by a factor of 2 to reflect the actual situation of accelerated heat conduction after cooling failure. Upon detecting a cooling system failure signal, the heat reverse conduction module automatically enters emergency power supply mode, pre-charging the supercapacitor to 100% of its rated capacity to reserve sufficient emergency power for a possible thermal runaway blocking response.

[0049] Example 3: This embodiment describes a cloud-based collaborative optimization and remote operation and maintenance scenario for electric vehicle power battery packs. This scenario includes a fleet operation scenario consisting of multiple electric vehicles, a factory calibration and lifespan assessment scenario for power battery packs, and a predictive maintenance and proactive recall scenario for power battery packs.

[0050] In fleet operation scenarios, the cloud-based collaborative monitoring module collects and aggregates battery pack temperature field data, thermal runaway risk assessment results, and thermal isolation barrier trigger records from all vehicles. Each electric vehicle acts as an independent data acquisition node, uploading raw data to the cloud server via its onboard communication unit. The cloud server then constructs a fleet-level thermal runaway early warning model based on the aggregated massive amounts of data. This model comprehensively considers the thermal runaway characteristics and patterns of different vehicle models, usage habits, and ambient temperatures. Updated warning parameters are simultaneously pushed to the thermal runaway propagation blocking modules of all connected vehicles via remote distribution.

[0051] In the factory calibration scenario, the cloud-based collaborative monitoring module receives battery pack thermal characteristic calibration data before the new vehicle rolls off the production line. This data includes a baseline model of the internal temperature field distribution of the battery pack, edge weight parameters of the heat conduction network, and phase change characteristic curves of the phase change insulation unit. After verification by the cloud server, the calibration data is written to the corresponding vehicle's thermal management system storage unit. During vehicle use, the thermal management system continuously compares the actual operating data with the factory calibration data. When the deviation exceeds a preset threshold, it automatically triggers a parameter self-calibration process to update the local model parameters.

[0052] In predictive maintenance scenarios, a cloud server trains a lifespan prediction model for the phase change insulation unit (PCI) based on historical data from long-term vehicle operation. This model predicts the remaining effective lifespan of the PCI based on the actual number of phase changes and the cumulative enthalpy loss of the PCI. When the predicted lifespan falls below a preset threshold, the cloud server sends a maintenance warning to the corresponding vehicle and simultaneously pushes a proactive recall notification to the after-sales service system. This function ensures that the PCI is replaced in time before failure, avoiding a decrease in thermal runaway protection capability due to the degradation of the PCI's performance. The above description illustrates the basic principles, main features, and advantages of this invention. Those skilled in the art should understand that this invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made without departing from the spirit and scope of the invention, and all such changes and modifications fall within the scope of the invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A high-safety-performance lithium-ion battery thermal management system for electric vehicles, characterized in that, The system includes a processing link, which includes: Acquire real-time temperature field data inside the battery pack, and apply adaptive acquisition density adjustment based on thermal risk level to the temperature field data; When the surface temperature rise rate, peak temperature and temperature gradient distribution of a single cell trigger the preset thermal runaway critical criteria, a thermal conduction network modeling operation is performed to identify high-risk thermal conduction paths that spread from the trigger source to the surrounding cells. Perform a blocking response to the identified high-risk heat conduction paths, including driving a thermal baffle at the target location to physically cut off the heat conduction path between adjacent cells; And output thermal runaway interruption status identifiers based on the priority distribution of the high-risk heat conduction paths.

2. The high-safety-performance lithium-ion battery thermal management system for electric vehicles according to claim 1, characterized in that, The determination of the critical criterion for thermal runaway includes: Perform multi-parameter fusion calculations on the cell surface temperature rise rate, the temperature difference between the cell and adjacent cells, the cell voltage drop rate, and the cell internal impedance change characteristics. When at least three of the four indicators exceed their respective threshold ranges simultaneously, thermal runaway status confirmation is triggered.

3. A high-safety-performance lithium-ion battery thermal management system for electric vehicles according to claim 1, characterized in that, The heat conduction network modeling calculations include: The heat conduction path inside the battery pack is abstracted as a weighted directed graph, where nodes represent cells or structural components and edge weights represent thermal conductivity coefficients. Perform maximum flow path calculations to identify the set of easily propagating thermal runaway pathways from the trigger source to each target node; And a list of blocking targets ranked by priority is determined based on the calculation results of the maximum flow path.

4. A high-safety-performance lithium-ion battery thermal management system for electric vehicles according to claim 2, characterized in that, The processing chain includes: Obtain the thermal runaway interruption status identifier and the temperature field distribution information, and calculate the temperature uniformity and peak temperature deviation of each cooling zone based on the temperature field distribution information; Perform multivariate collaborative optimization calculations, with the goal of minimizing cooling energy consumption and the temperature uniformity and peak temperature deviation as constraints, to calculate the optimal flow distribution scheme and target temperature setpoint for each cooling zone; And drive the electric flow regulating valves of each cooling zone to perform differentiated flow control according to the optimal flow distribution scheme.

5. A high-safety-performance lithium-ion battery thermal management system for electric vehicles according to claim 4, characterized in that, The multivariate collaborative optimization operation adopts a model predictive control framework, which includes a first-principles model describing the thermodynamic characteristics of the battery and an engineering empirical model describing the hydraulic characteristics of the cooling system. The optimal cooling control sequence in the finite time domain is solved on a rolling basis within each control cycle; In addition, a temperature prediction mechanism is introduced to perform feedforward compensation.

6. A high-safety-performance lithium-ion battery thermal management system for electric vehicles according to claim 1, characterized in that, The blocking response also includes: After receiving the blocking command, the heat insulation baffle action mechanism is driven to push the heat insulation baffle from the storage position to the working position; And after the heat insulation baffle is in place, a high-temperature impact resistance test is performed.

7. A high-safety-performance lithium-ion battery thermal management system for electric vehicles according to claim 6, characterized in that, The processing chain includes: Before the thermal runaway blocking response is triggered, the phase change temperature monitoring value of the phase change insulation unit is used to determine whether it is necessary to activate the phase change material to perform heat absorption and energy storage. The phase change insulation unit is filled with composite phase change material.

8. A high-safety-performance lithium-ion battery thermal management system for electric vehicles according to claim 1, characterized in that, The processing chain includes: Obtain temperature data and duration information for the localized overheated area; When a localized overheated area is detected, the micro thermoelectric conversion array arranged in that area is driven to perform thermoelectric conversion operations to convert heat into electrical energy; The converted electrical energy is stored in a supercapacitor; In the event of an external power supply failure, the electrical energy stored in the supercapacitor is used to drive the thermal runaway prevention mechanism to perform a thermal runaway prevention response.

9. A high-safety-performance lithium-ion battery thermal management system for electric vehicles according to claim 1, characterized in that, The processing chain includes: The temperature field data and thermal runaway risk assessment results are uploaded to the cloud server via the vehicle communication unit; Receive early warning parameters issued by the cloud server after training and updating based on massive battery pack operation data; And update the threshold range of the local thermal runaway critical criterion based on the aforementioned warning parameters.

10. A high-safety-performance lithium-ion battery thermal management system for electric vehicles according to claim 9, characterized in that, The processing chain includes: Receive remote software upgrade instructions and parameter calibration data packets sent by the cloud server; Perform a remote software upgrade to update the computational logic of the local thermal runaway detection algorithm unit; And adjust the boundary conditions and optimization target weights of the multivariate collaborative optimization operation based on the parameter calibration data packet.

Images