Battery thermal management method, device and system and vehicle

Abstract

The invention discloses a battery thermal management method, device and system and a vehicle, and relates to the technical field of battery management. A specific embodiment of the method comprises the following steps: acquiring battery data of a battery, acquiring a maximum battery cell temperature from the battery data, and extracting battery characteristics; determining the thermal runaway probability of the battery according to the battery data and the battery characteristics; selecting a target early warning strategy matched with the maximum cell temperature and the thermal runaway probability from a preset multi-level early warning strategy and a preset hidden state early warning strategy; according to the environment information of the environment where the battery is located and the battery characteristics, rule parameters in the target early warning strategy are adjusted, and regulation and heat dissipation are conducted on the battery through the adjusted target early warning strategy. According to the embodiment, the rule parameters of the early warning strategy are adaptively adjusted according to the actual state of the battery, a fixed threshold value judgment mode is replaced, advanced recognition of potential hazards and dynamic optimization of the early warning strategy are achieved, and therefore early warning accuracy and flexibility are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle battery management, and in particular to a method, device, system and vehicle for thermally managing a battery. Background Art

[0002] With the rapid development of electric vehicles and energy storage systems, thermal runaway in power batteries has gradually attracted widespread attention. Traditional battery management systems (BMS) rely primarily on fixed thresholds and linear control strategies. However, this simple threshold-based approach, with its fixed thresholds and delayed response, makes it difficult to identify potential hazards in advance and cannot dynamically adjust optimization strategies based on the actual battery status, resulting in inaccurate and inflexible early warning and control effects. Summary of the Invention

[0003] In view of this, embodiments of the present invention provide a method, device, system and vehicle for thermally managing a battery, which can at least solve the problem in the prior art that it is difficult to identify potential hidden dangers in advance and it is impossible to dynamically adjust the optimization strategy according to the actual status of the battery.

[0004] To achieve the above-mentioned purpose, according to one aspect of an embodiment of the present invention, a method for thermal management of a battery is provided, comprising: obtaining battery data of the battery, obtaining the maximum cell temperature from the battery data, and extracting battery characteristics; determining the thermal runaway probability of the battery based on the battery data and the battery characteristics; selecting a target warning strategy that matches the maximum cell temperature and the thermal runaway probability from a preset multi-level warning strategy and a preset latent warning strategy; adjusting the rule parameters in the target warning strategy based on the environmental information of the battery environment and the battery characteristics, and regulating the heat dissipation of the battery through the adjusted target warning strategy.

[0005] To achieve the above-mentioned purpose, according to another aspect of an embodiment of the present invention, a device for thermally managing a battery is provided, comprising: a feature extraction module for obtaining battery data of a battery, obtaining a maximum cell temperature from the battery data, and extracting battery characteristics; a thermal runaway probability module for determining the thermal runaway probability of the battery based on the battery data and the battery characteristics; a strategy matching module for selecting a target warning strategy that matches the maximum cell temperature and the thermal runaway probability from a preset multi-level warning strategy and a preset latent warning strategy; a strategy optimization module for adjusting the rule parameters in the target warning strategy according to the environmental information of the battery environment and the battery characteristics, and regulating the heat dissipation of the battery through the adjusted target warning strategy.

[0006] To achieve the above objectives, according to another aspect of the embodiments of the present invention, a thermal management battery electronic device is provided.

[0007] An electronic device according to an embodiment of the present invention includes: one or more processors; and a storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement any of the above-mentioned methods for thermally managing a battery.

[0008] To achieve the above object, according to another aspect of an embodiment of the present invention, a computer-readable medium is provided, on which a computer program is stored. When the program is executed by a processor, any of the above methods for thermally managing a battery is implemented.

[0009] According to the solution provided by the present invention, one embodiment of the above invention has the following advantages or beneficial effects: by extracting multi-dimensional characteristics of the battery and combining it with dynamic thermal runaway probability assessment, the most suitable target warning strategy is matched from the preset multi-level warning strategy and the preset latent warning strategy, and the rule parameters of the target warning strategy are adaptively adjusted according to the actual state of the battery, replacing the fixed threshold judgment method, realizing early identification of potential hidden dangers and dynamic optimization of warning strategies, greatly improving the accuracy and flexibility of warnings.

[0010] The further effects of the above-mentioned non-conventional optional manner will be described below in conjunction with specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The accompanying drawings are provided for a better understanding of the present invention and are not intended to limit the present invention.

[0012] Figure 1 This is a schematic diagram of the main process of a method for thermal management of a battery according to an embodiment of the present invention;

[0013] Figure 2 is a flow chart of an optional method for thermal management of a battery according to an embodiment of the present invention;

[0014] Figure 3 is a flow chart of another optional method for thermal management of a battery according to an embodiment of the present invention;

[0015] Figure 4 1 is a schematic diagram of main modules of a device for thermal management of a battery according to an embodiment of the present invention;

[0016] FIG5( a ) is a diagram of an exemplary system architecture in which embodiments of the present invention may be applied;

[0017] FIG5( b ) is a diagram of a vehicle architecture to which an embodiment of the present invention may be applied;

[0018] Figure 6 Schematic diagram of the interaction process between the battery, battery management system and vehicle-mounted computing unit according to an embodiment of the present invention;

[0019] Figure 7 It is a schematic diagram of the structure of a computer system of a mobile device or server suitable for implementing the embodiments of the present invention. DETAILED DESCRIPTION

[0020] The following description of exemplary embodiments of the present invention is made in conjunction with the accompanying drawings, in which various details of the embodiments of the present invention are included to facilitate understanding. These details should be considered as merely exemplary. Therefore, it should be appreciated by those skilled in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0021] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned. They should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0022] The embodiments and features of the embodiments of the present invention may be combined unless they conflict. The acquisition, transmission, storage, use, and processing of data in the technical solution of the present invention comply with relevant national laws and regulations, are used for legal and reasonable purposes, are not shared, disclosed, or sold beyond these legal uses, and are subject to supervision and management by regulatory authorities.

[0023] With respect to user information, necessary measures should be taken to prevent unauthorized access to such personal information data, ensure that persons with access to such personal information data comply with relevant laws and regulations, and ensure the security of user personal information. Once such user personal information data is no longer needed, risks should be minimized by restricting or even prohibiting data collection and / or deleting the data. Where applicable, including in certain relevant applications, user privacy should be protected by de-identifying the data, for example, by removing specific identifiers (e.g., date of birth), controlling the amount or specificity of stored data (e.g., collecting location data at the city level rather than at the specific address level), controlling how the data is stored, and / or other methods of de-identification.

[0024] Traditional battery management systems (BMS) rely primarily on fixed thresholds and linear control strategies for management. For example, cooling is initiated or power output is limited only after the battery temperature exceeds a specific value. However, this approach has many limitations:

[0025] 1. Fixed thresholds and delayed responses: Measures are often taken only after the temperature rises significantly, making it impossible to identify potential hazards in advance.

[0026] 2. The strategy is single and lacks mechanism support: Most strategies usually only adopt linear power reduction or simply enhance cooling, while ignoring the internal mechanism and life status of the battery.

[0027] 3. Insufficient scalability and adaptability: It is difficult to dynamically optimize management strategies based on the actual battery aging status (such as SOC (State of Charge), SOH (State of Health)), changes in environmental conditions, and multi-parameter coupling relationships (such as voltage, current, temperature, gas byproducts and their concentration changes).

[0028] Among them, SOC indicates the percentage of the battery's current remaining charge to the total capacity. It is usually 0% to 100%, where 0% means fully discharged and 100% means fully charged. In electric vehicles, SOC directly affects the vehicle's range. SOH indicates the degree to which the battery's current performance compares to its performance when it was brand new, and is usually expressed as a percentage. It is usually 0% to 100%, where 100% means the battery is in a brand new state. As the number of uses increases and ages, SOH will gradually decrease. SOH is used to assess the degree of aging and remaining life of the battery. A decrease in SOH may affect the battery's maximum capacity and charge and discharge efficiency. Gas by-products mainly appear in the charge and discharge reactions of the battery, especially in certain types of batteries (such as lead-acid batteries and lithium-ion batteries).

[0029] 4. Lack of closed-loop optimization mechanisms and long-term evolution capabilities: Once the rule parameters are set, they are rarely fine-tuned online or iteratively updated based on data feedback, resulting in poor performance in actual scenarios.

[0030] Furthermore, existing technologies generally neglect in-depth analysis of the battery's internal electrochemical reaction mechanisms, ion transport dynamics, and thermal diffusion characteristics, further limiting the accuracy and flexibility of early warning and control. Therefore, an intelligent thermal management BMS solution is urgently needed that can proactively identify potential risks, deeply integrate electrochemical and thermodynamic mechanisms, and possess dynamic adjustment capabilities to meet the increasingly complex demands of battery thermal management.

[0031] See also Figure 1 , which shows a main flow chart of a method for thermally managing a battery provided by an embodiment of the present invention, including the following steps:

[0032] S101: Obtain battery data of a battery, obtain a maximum cell temperature from the battery data, and extract battery characteristics.

[0033] S102: Determine a thermal runaway probability of the battery based on the battery data and the battery characteristics.

[0034] S103: Selecting a target warning strategy that matches the maximum battery cell temperature and the thermal runaway probability from a preset multi-level warning strategy and a preset latent warning strategy.

[0035] S104: adjusting the rule parameters in the target warning strategy according to the environmental information of the environment in which the battery is located and the battery characteristics, and regulating the heat dissipation of the battery through the adjusted target warning strategy.

[0036] In the above implementation, for step S101, this solution achieves comprehensive monitoring of the battery status by pre-configuring sensors in the battery management system (BMS). These sensors include high-precision voltage sensors, current sensors, temperature sensors, and MEMS (Microelectromechanical System) gas sensors, such as hydrofluoric acid (HF), carbon monoxide (CO), and methane (CH4). This multi-sensor integration not only significantly improves the comprehensiveness of data collection but also effectively enhances the system's fault tolerance.

[0037] The solution's primary real-time data processing, decision-making, and control logic are primarily executed by the vehicle's Electronic Control Unit (ECU). Battery data collected by sensors in the BMS is transmitted to the ECU via the CAN (Controller Area Network) bus or automotive Ethernet for efficient processing, ensuring real-time and reliable data transmission.

[0038] The ECU extracts battery data based on battery mechanism features to achieve in-depth analysis and evaluation of the battery status. Battery features mainly include two aspects:

[0039] 1. Battery status characteristics. By combining key parameters such as SOC and SOH, indicators closely related to battery life and safety are extracted;

[0040] 2. Electrochemical reaction characteristics. Based on the electrochemical reaction mechanism within the battery, core parameters such as internal resistance and polarization voltage are extracted to reflect the battery's health status and potential risks. These characteristics can intuitively reflect the chemical reaction rate and charge transfer efficiency within the battery and are important indicators for predicting the risk of thermal runaway.

[0041] Therefore, this solution can not only extract battery status characteristics, but also deeply analyze the battery internal resistance, polarization voltage, gas generation rate, etc., to extract deep electrochemical reaction characteristics from the internal reaction mechanism of the battery, providing a basis for the subsequent dynamic determination of the thermal runaway probability and adjustment of the rule parameters in the early warning strategy.

[0042] As an optional implementation method, data preprocessing and synchronization are key steps to ensure the quality of system processing. Therefore, after the ECU obtains the battery data collected by the sensors set for the battery, it can also normalize different types of data (such as voltage, current, temperature, and gas concentration) to the same interval, such as [0,1] or [-1,1], to ensure that each data has a balanced weight in subsequent judgments and avoid deviations caused by dimensional differences. Furthermore, timestamp alignment and interpolation strategies can be used to design efficient time synchronization algorithms to ensure that battery data collected by different sensors are accurately matched in the time dimension. For data missing due to delays or transmission problems, the nearest effective value interpolation method can be used to supplement it, thereby ensuring the continuity and integrity of the data and providing a reliable basis for subsequent analysis and decision-making.

[0043] For step S102, the entire early warning system of this solution is based on two key indicators: 1) Maximum cell temperature: real-time monitoring of the highest temperature value in the battery cell, which can be directly extracted from the battery data. 2) Thermal runaway probability P TP (t+τ): Predicts the probability of thermal runaway occurring within a certain period of time τ in the future. This probability is calculated based on multiple factors such as the electrochemical reaction characteristics, thermal diffusion, and gas generation within the battery.

[0044] A common approach to building a model for predicting the probability of thermal runaway is to use the battery data collected by sensors, establish a model for the risk factor R(t), and then use a nonlinear mapping similar to the Sigmoid function to obtain P(t). TP (t+τ). For example:

[0045]

[0046] Among them, T max (t) is the maximum cell temperature at the current moment. T ref This is a preset reference temperature (e.g., a safe temperature, such as 40°C or lower) used to calculate temperature excesses. dT(t) / dt is the rate of temperature rise, reflecting the temperature trend. G(t) represents the gas sensor reading, reflecting gas generation. Imp(t) represents changes in the battery's internal impedance or polarization voltage; these parameters reflect the state of the battery's internal chemical reactions. α, β, γ, and δ are weighting coefficients calibrated based on experimental or historical data. "..." indicates that other factors may also be considered, such as SOC, SOH, and ambient temperature.

[0047] Then, a Sigmoid function is used to map the risk factor to the [0,1] interval to obtain the thermal runaway probability P TP (t+τ):

[0048]

[0049] This form of model can convert the comprehensive effect of various indicators into a probability value. By continuously collecting data online, R(t) and P are calculated in real time. TP (t+τ), the system can capture potential anomalies in advance and issue early warnings based on preset thresholds.

[0050] It should be noted that since the battery data (such as temperature, gas concentration, internal resistance) are all dynamically changing, the weight coefficient and reference temperature T in the above formula are ref It can be adjusted in real time based on factors such as battery aging, ambient temperature, and charge and discharge rates. If certain battery data is found to deviate from the prediction under specific environmental conditions, the weight coefficients in the model will be adjusted in real time to more accurately calculate the probability of thermal runaway.

[0051] Specifically, the environmental information of the battery environment, such as external temperature and humidity, is obtained, and the reference range (i.e., reference interval) of normal battery operation is determined based on the environmental information. The weight coefficient is dynamically adjusted based on the difference between the battery data and the reference range. The system continuously corrects the model so that P TP (t+τ) is closer to the actual risk level, thereby improving the reliability of the entire system. Furthermore, the relationships between various physical quantities are not linear. By introducing the Sigmoid function, multiple linear inputs can be converted into nonlinear probabilistic outputs. This not only captures subtle changes in the data, but also avoids false alarms caused by fluctuations in a single parameter.

[0052] For step S103, this solution further introduces a latent warning strategy based on the pre-set multi-level warning strategy. This strategy can detect potential risks before the formal first-level warning strategy is triggered, allowing timely fine-tuning of the cooling strategy, buying more time for the battery management system (BMS) to intervene preemptively, and effectively improving the system's response efficiency and safety.

[0053] The multi-level warning strategy here is explained with a three-level warning strategy as an example, but other multi-level warning strategies can also be used in actual applications. The three-level warning strategy targets the maximum cell temperature T max (t) and thermal runaway probability P TP (t+τ) sets the threshold condition, which is set as follows:

[0054] 1. Level 1 warning conditions: T max (t)>45℃ and P TP (t+τ)>0.3. At this point, the temperature has exceeded the safety reference value, and the risk of thermal runaway has reached a certain level. The system initiates a preliminary warning and takes gentle cooling measures, such as reducing the cooling fan speed to a low setting (such as 5000 rpm), and notifies the user through the instrument panel.

[0055] 2. Second level warning conditions: T max (t)>50℃ or there is abnormal gas reading, and P TP (t+τ)>0.5. This indicates that when the temperature is higher or the gas indicators are abnormal, the warning level increases, and the system will issue an intermediate warning and increase the cooling force, such as adopting mid-range cooling (such as 10,000 rpm). At the same time, the electronic switch activates the heat insulation unit pre-configured between the batteries, and a voice prompt is issued to ask the driver to slow down or stop.

[0056] 3. Level 3 warning conditions: T max (t)>55℃ and the temperature continues to rise, while P TP (t+τ)>0.7. At this point, the risk is already very high, and the system immediately triggers a high-level warning and implements emergency cooling and protection measures to ensure the safe and stable operation of the battery system. For example, high-speed cooling (such as 15,000 rpm) and emergency power outage measures are adopted, while alerts are issued through all channels.

[0057] It should be noted that the various thresholds in the above three-level warning strategy (such as temperatures of 45°C, 50°C, and 55°C and probabilities of 0.3, 0.5, and 0.7) and their interrelationships are all obtained using knowledge of battery mechanisms. These thresholds are empirical values ​​obtained through a large number of experiments and data statistics. These values ​​must take into account both safety redundancy and the need to avoid overly frequent warnings. Specific thresholds can be further refined and personalized based on battery type, usage scenario, and environmental conditions. For example, a comprehensive evaluation can be conducted in combination with multi-dimensional indicators such as temperature, voltage, current, and gas concentration to ensure the comprehensiveness and accuracy of the warning conditions.

[0058] For step S104, the latent state warning strategy in this solution is based on the battery mechanism drive design. Specifically, at the maximum cell temperature T max (t) Before reaching the first-level warning condition of 45°C, potential anomalies are captured in advance by detecting nonlinear changes in the gas characteristic curve (such as the transition from low-frequency random disturbances to high-frequency mutations) and ion transmission hysteresis based on online impedance testing. That is, when the first-level warning conditions have not yet been fully met, the system can pre-increase the cooling power slightly (such as 5%-10%) and mark a "latent warning" at the internal strategy level to buy response time for subsequent formal warnings. This latent state is not directly displayed to the user, but it can enable the battery management system (BMS) to have a more proactive response capability, thereby optimizing overall safety and efficiency.

[0059] Furthermore, the dynamic threshold adjustment of the latent warning strategy is closely tied to battery aging. The warning threshold can be adaptively fine-tuned based on the battery's SOH, SOC, and aging characteristics. For example, for severely aged batteries, power reduction can be implemented at the earliest stages of abnormal gas readings to avoid delaying warnings due to waiting for excessive temperatures. For new batteries, the threshold setting can be appropriately relaxed to minimize unnecessary performance loss. This refined dynamic adjustment mechanism further enhances the flexibility and reliability of warnings.

[0060] When the target warning strategy is a latent warning strategy, the rule parameters can be adjusted through the above operations. However, when the target warning strategy is any warning strategy in a multi-level warning strategy, other processing methods need to be used to adjust the rule parameters. It should be noted that the rule parameters in this scheme mainly refer to all key thresholds and mapping relationships set in the entire warning and control strategy, such as: temperature thresholds (such as 45°C, 50°C, 55°C), thermal runaway probability thresholds (such as 0.3, 0.5, 0.7), various mapping points or coefficients in the nonlinear power limit curve, gas concentration anomaly judgment thresholds, and other auxiliary parameters related to electrochemistry, thermal diffusion, ion transport, etc. Therefore, the rule parameters in this scheme basically cover all the parameters involved, and can also be extended to other parameters that are found to need tuning in actual applications.

[0061] In the first embodiment, the target early warning strategy is a first-level early warning strategy, which adopts a nonlinear power limitation strategy and a hierarchical load management strategy.

[0062] 1. Nonlinear Power Limiting Strategy: Unlike existing simple linear power reduction strategies (e.g., -10% for level 1 warning, -20% for level 2, and -30% for level 3), this solution introduces a multidimensional mapping table that comprehensively considers parameters such as SOC, SOH, internal resistance, and gas concentration change rate to dynamically calculate the battery's power reduction ratio. For example, for severely aged batteries, power output can be reduced by 15% at level 1 warning, while for newer batteries, the reduction can be as low as 5%, thus achieving personalized power regulation based on battery status awareness.

[0063] 2. Tiered Load Management Strategy: When the vehicle enters the warning level, not only is overall power reduced uniformly, but different types of electrical loads can also be treated differently. For example, while maintaining the power of power steering and basic safety systems, power allocation to transient acceleration and non-essential air conditioning loads can be significantly reduced. This approach enhances battery safety while also maintaining minimum comfort and controllability.

[0064] The above-mentioned implementation method of the first-level warning strategy can realize a nonlinear and personalized power limiting mechanism based on the battery status (SOC, SOH), aging degree and real-time gas parameters, combined with a multi-dimensional mapping table, and treat different electrical load types differently, giving priority to battery safety and basic driving functions.

[0065] In the second embodiment, the target warning strategy is a two-level warning strategy, which adopts a multi-physical field coordinated cooling and insulation strategy to achieve efficient thermal management and avoid thermal runaway.

[0066] 1. Dynamic thermal insulation material regulation: The battery is divided into multiple independent batteries, and heat-resistant and thermal insulation materials are used to build thermal insulation units between the batteries. For example, deformable aerogels or electric field tunable thermal insulation materials can be used to build thermal insulation units. In the second-level warning, the thermal insulation unit can be activated by an electronic switch (such as a relay, circuit breaker, etc.), so that the thermal insulation unit changes its microstructure density to reduce thermal conductivity by 10%-20%, thereby effectively blocking heat diffusion. At the same time, in low-risk situations, some heat dissipation channels are retained to prevent heat from being closed and accumulated, ensuring the thermal balance of the system.

[0067] 2. Application of Advanced Cooling Media and Phase Change Materials: For battery management systems equipped with liquid cooling, a high-specific-heat coolant containing phase-change microcapsules is injected into the battery during an alert upgrade. This coolant absorbs large amounts of heat in a short period of time, significantly smoothing the rate of temperature rise. High-specific-heat coolants are substances with high specific heat capacity. Specific heat capacity refers to the amount of heat required to raise a substance's temperature per unit mass. The higher the specific heat capacity, the smaller the temperature change when absorbing or releasing the same amount of heat.

[0068] In an optional embodiment, the timing and proportion of coolant injection can be precisely controlled based on real-time thermal model calculations to minimize the spread of thermal runaway. Specifically, temperature sensors are first deployed to collect data in real time and predict temperature trends. When the battery temperature reaches the preset temperature threshold in the secondary warning strategy or the temperature is predicted to exceed the safe range in a short period of time, coolant injection is initiated. In addition, the coolant flow rate is dynamically adjusted based on the difference between the current battery temperature and the target battery temperature, the coolant efficiency, and the battery thermal capacity to maintain temperature stability. If the temperature drops too quickly, the coolant flow rate can be appropriately reduced; if the temperature still rises, the flow rate is increased.

[0069] Assuming that the current temperature of the battery is 55°C, the target temperature is 45°C, the cooling efficiency of the coolant is 1000W / L (that is, each liter of coolant can take away 1000 watts of heat per second), the thermal capacity of the battery is 500J / (kg·°C), and the coolant flow rate is calculated using a preset formula. Assuming that the calculation result is 41.86L / s, it means that the coolant needs to be injected at a flow rate of 41.86L / s. In actual applications, the coolant flow rate usually does not reach such a high value, and the parameters need to be adjusted according to actual conditions. Assuming that the maximum coolant flow rate is 10L / s, the coolant is injected at a flow rate of 10L / s, and the temperature change can be monitored subsequently until the target temperature of 45°C is reached. Among them, the preset formula can be a cooling power formula or other formula known to those skilled in the art, and is not limited here.

[0070] The above-mentioned implementation method of the secondary warning strategy dynamically activates the insulation unit and actively reconstructs the heat conduction path to transfer heat to a safe area in an orderly manner, effectively avoiding the problem of single-point overheating accumulation and improving overall thermal safety.

[0071] In the third embodiment, the target warning strategy is a three-level warning strategy, which adopts active heat conduction path management, layered power off and cell-level protection mechanism to achieve more efficient and safer thermal management and fault isolation.

[0072] 1. Active heat conduction path management: By pre-configuring controllable thermal channels in the battery (such as shape memory alloy structures or micro thermal switches), the heat from the local overheating area of ​​the battery is systematically diverted to specific heat dissipation areas on the vehicle body. This method allows the high heat in the battery compartment to be more evenly distributed, significantly reducing the risk of single-point overheating. Compared to traditional passive methods that rely solely on "lowering battery temperature" or "isolating heat sources," this method actively reconstructs the heat flow path at the system level, providing a more flexible and efficient thermal management strategy.

[0073] 2. Cell-level quick-break unit: A cell-level quick-break unit, such as a thermal fuse or solenoid valve, is installed between individual cells. Before globally disconnecting the high-voltage circuit, this cell-level quick-break unit is activated to prioritize the detection of abnormal cells through local electronic isolation, minimizing thermal runaway. This cell-level rapid response mechanism not only effectively curbs the spread of thermal runaway but also minimizes the impact of a complete vehicle power outage, providing drivers and passengers with more time for safety.

[0074] 3. Phased Voltage Reduction and Soft Power-Off Strategy: Before the final power outage, the soft switching circuit gradually reduces the voltage in steps of 0.5-1 second (for example only, the value is adjustable), smoothly transitioning from a high-voltage state to a safe low-voltage state. This strategy ensures that the vehicle can still briefly perform a safe stop or enter emergency mode in an emergency, further improving the user experience and safety.

[0075] In addition to the heat conduction strategy, this three-level warning strategy also utilizes cell-level quick-disconnect units to isolate the problem cell, followed by a phased voltage reduction strategy to gradually disconnect the high-voltage circuit. This entire process ensures system safety while also balancing user experience, achieving a balance between efficient thermal management and fault isolation.

[0076] In addition to the above-mentioned strategy adjustments, this solution also introduces adaptive rule correction and data-driven optimization mechanisms to further enhance the intelligence level and operational efficiency of the system.

[0077] 1. Online Model-Assisted Tuning: During vehicle operation, the system dynamically fine-tunes the current rule parameters within the pre-set multi-level warning strategy using a built-in simplified online thermal-electric coupling model. If warnings are excessive or insufficient under certain environmental conditions, the system automatically optimizes various thresholds and power limit curves, achieving more accurate risk assessment and more balanced performance.

[0078] Assume that during vehicle operation, the system discovers that a certain environmental condition frequently triggers the Level 1 warning strategy, but no subsequent safety incidents actually occur. This indicates that the warning threshold of the Level 1 warning strategy may be set too sensitively. The simplified thermal-electric coupling model can be fine-tuned in a data-driven manner as follows:

[0079] 1) Temperature threshold adjustment. The temperature threshold of the original first-level warning strategy was set at 45°C, corresponding to a thermal runaway probability threshold of 0.3. However, under low ambient temperature conditions, the battery cell temperature often fluctuates between 44°C and 46°C, causing the system to frequently trigger the first-level warning strategy. Based on historical data analysis, the model recommends raising the temperature threshold in the first-level warning strategy to 47°C, and adjusting the thermal runaway probability threshold to 0.35. The increase here is determined based on environmental information, and the adjustment range and direction are different for different environments. For example, in a high-temperature environment, the temperature threshold can be appropriately lowered to provide early warning of potential risks. This adjustment strategy can effectively reduce the frequency of false alarms and avoid unnecessary interference. This idea is also applicable to warning strategies with insufficient triggering, and will not be repeated here.

[0080] 2) Power limit curve adjustment. The original power reduction rule is: 10% power reduction for level 1 warning, 20% for level 2 warning, and 30% for level 3 warning. However, in certain environmental conditions with frequent minor warnings, continuous power reduction has a significant impact on driving performance, while the actual safety risk is not significant. In this case, the system can adjust the power reduction ratio of the level 1 warning strategy to 5% to 8% based on the environmental information, balancing safety and performance and preventing overly aggressive power limiting from affecting normal vehicle operation.

[0081] 3) Dynamic mapping relationship adjustment: The system continuously collects real-time data under multiple environmental conditions to evaluate the accuracy and response effect of the early warning strategy.

[0082] Through online model-assisted tuning, the system continuously learns and optimizes during vehicle operation, dynamically adjusting warning thresholds, power reduction ratios, and other factors. This adaptive rule-based correction and data-driven optimization mechanism not only improves warning accuracy but also effectively balances safety and driving performance, providing users with a more intelligent and reliable vehicle operation experience.

[0083] 2. Iterative Optimization Based on Historical Data: Manufacturers regularly collect operational data from the fleet and, through cloud-based big data analysis, determine the optimal adjustment range for warning thresholds under different environments. Subsequently, updated policy rule parameters are pushed to vehicles via Over-the-Air (OTA) technology, continuously approaching the optimal safety-performance balance and enabling continuous evolution and intelligent adaptation of the system throughout its lifecycle.

[0084] Looking at the above description, this solution deeply optimizes the existing rule-based multi-level warning strategy and control execution mechanism, and expands its functions from a forward-looking perspective. By integrating the underlying electrochemical mechanism of the battery, thermodynamic models, material science innovations, and vehicle usage scenario analysis, the entire decision-making and control system is comprehensively upgraded. The new strategy breaks through the limitations of traditional threshold triggering and linear response, introduces dynamic monitoring and analysis of gas generation, ion transport, and microstructure thermal diffusion, and combines multi-dimensional tuning such as power output regulation, cooling medium injection, insulation material property adjustment, and power-off strategy optimization to achieve more intelligent and refined comprehensive management and control, thereby significantly improving the safety performance and operating efficiency of the system.

[0085] The method provided in the above embodiment extracts multi-dimensional characteristics of the battery, combines it with dynamic thermal runaway probability assessment, matches the most suitable target warning strategy from the preset multi-level warning strategy and the preset latent warning strategy, and adaptively adjusts the rule parameters of the target warning strategy according to the actual state of the battery, replacing the fixed threshold judgment method, realizing early identification of potential hidden dangers and dynamic optimization of warning strategies, thereby greatly improving the accuracy and flexibility of battery heat dissipation control.

[0086] See also Figure 2 , shows a schematic flow chart of an optional method for thermal management of a battery according to an embodiment of the present invention, comprising the following steps:

[0087] S201: Obtain the operating status of the battery and perform risk assessment based on the operating status of the battery.

[0088] S202: Determine a data acquisition frequency according to the risk assessment result, and use the determined data acquisition frequency as the acquisition frequency of the battery data.

[0089] In this solution, battery data is collected through sensors. The battery management system (BMS) is pre-configured with multiple sensor types to enable comprehensive monitoring of the battery status. The ECU's risk assessment of the battery's operating status allows it to adjust the frequency with which it acquires sensor-collected battery data. Furthermore, the data acquisition frequency can be adjusted if abnormal changes in monitored battery data are detected (such as a rapid temperature rise). In high-risk situations, a preset high-frequency data acquisition frequency can be used; in low-risk situations, a preset low-frequency data acquisition frequency can be used.

[0090] The above operations can reduce ECU resource overhead. In addition to the above methods, this solution also supports adaptive sampling of sensors (RTM, Real-Time Monitoring). The sensor can collect multi-dimensional battery data according to a preset sampling frequency, but this sampling frequency is not fixed but can be adjusted dynamically.

[0091] Specifically, the sensor can initially collect data at a fixed sampling frequency. After collecting the battery's operating status, the BMS transmits this information to the vehicle's onboard computing unit (ECU). The ECU performs a risk assessment based on the received battery operating status and determines an appropriate sampling frequency based on the risk assessment results. This sampling frequency is then fed back to the BMS to dynamically adjust the sensor's sampling frequency. Alternatively, data can be transmitted directly to the sensor without passing through the BMS, allowing the sensor to directly perform frequency modulation processing. For example, in high-risk conditions, the sampling frequency can be increased (e.g., set to sample once every 200ms) to capture more detailed data; in normal conditions, such as low-risk conditions, the sampling frequency can be reduced to conserve system resources. Both high-frequency and low-frequency sampling frequencies can be pre-set, such as the sampling frequency for preset high-frequency sampling mode and the sampling frequency for preset low-frequency sampling mode, to ensure efficient system operation under different environmental conditions.

[0092] As an optional implementation, this solution can also adopt an event-driven sampling mode to achieve more flexible data collection by designing an event trigger mechanism. For example, when an abnormality is detected in the battery data collected by the sensor, the system will automatically switch to the preset high-frequency sampling mode to trigger the sensor to capture key data in a timely manner through a higher sampling frequency, ensuring the integrity and timeliness of the battery data under abnormal conditions. For example, the data change situation hits the corresponding abnormal condition, such as the battery temperature rises rapidly in a short period of time (i.e., the first preset time length) exceeding the preset rate threshold (such as 5°C per second), or a certain battery data hits a pre-defined abnormal condition, such as the voltage value is lower than 2.5V or the current suddenly changes by more than 10A. Here, only whether the battery data is abnormal, or only whether the battery data change situation is abnormal, or a combination of the two can be considered, and there is no restriction on this. In this way, the system can respond quickly when an abnormality is discovered.

[0093] As another optional implementation, this solution can further implement intelligent sensor management. Specifically, the battery management system BMS uses an embedded microcontroller to monitor the working status of each sensor in real time and transmit it to the ECU. The ECU processes the working status through a built-in algorithm, generates calibration parameters and feeds them back to the BMS, so that the BMS can automatically calibrate the sensor, thereby ensuring the accuracy and stability of the data collected by the sensor. The embedded microcontroller here can use a high-performance, low-power processor, such as the ARM Cortex-M series. The generation of calibration parameters can be dynamically adjusted based on the data trends and historical records of the sensor, combined with filtering algorithms (such as Kalman filtering or moving average filtering) to smooth the data and reduce noise interference, and automatically correct the sensor deviation by comparing with the reference value or the average value of multiple sensors, thereby ensuring the continuous accuracy of the data.

[0094] In addition, in order to improve the reliability of the system, multiple sensors of the same or different types can be configured at key battery measurement points to form a redundant design. Even if a sensor fails, the system can still rely on other normally working sensors to continue operating to ensure the continuity of data collection. For example, the system will compare the data of each sensor in the redundant design within the second preset time period. If the data of a certain sensor is significantly different from that of other sensors (such as two groups of readings of three groups of temperature sensors are consistent, and the other group has a deviation of more than ±2°C), it is determined that the sensor may be faulty. Once a sensor failure is confirmed, the system will notify the BMS to isolate the faulty sensor to avoid abnormal data from interfering with the system's judgment. At the same time, the system will trigger an alarm mechanism to notify maintenance personnel so that the faulty sensor can be replaced or repaired in time, thereby ensuring the continuity, reliability and fault tolerance of the entire system.

[0095] The above-described embodiment provides a multi-sensor high-precision data fusion and real-time monitoring mechanism. By integrating multiple sensor types into the battery management system and employing redundant design and automatic calibration mechanisms, data acquisition quality is significantly improved. Furthermore, the combination of adaptive sampling frequency and event-driven sampling ensures timely capture and monitoring of critical battery data. This comprehensive design effectively balances performance and efficiency, providing a more reliable and intelligent solution for battery management.

[0096] See also Figure 3 , shows a schematic flow chart of another optional method for thermal management of a battery according to an embodiment of the present invention, comprising the following steps:

[0097] S301: Acquire attribute information of battery data, and determine a target cache area that matches the attribute information from a multi-level cache area.

[0098] S302: Storing battery data in a target cache area.

[0099] S303: Perform real-time compression and encryption processing on the battery data stored in the target cache area.

[0100] This solution implements separate management of data storage by introducing a multi-level cache mechanism in the ECU, thereby optimizing data access speed and storage efficiency. Multi-level cache mechanisms include L1 cache and L2 cache. The L1 cache is mainly used for short-term storage to store data that requires high-speed processing and real-time response, such as battery temperature, voltage, and other real-time monitoring indicators that are updated every few hundred milliseconds. Due to the need for high-frequency updates and low-latency access, these data are preferentially stored in the L1 cache to support fast decision-making and real-time control. The L2 cache is responsible for long-term storage and is used to store historical data and trend information, such as accumulated temperature changes, aging characteristics, SOC / SOH curves, etc. over a period of time. This type of data is updated less frequently, but is crucial for subsequent offline analysis, model correction, and OTA update optimization. Therefore, it is more suitable to be stored in the larger and more persistent L2 cache.

[0101] The determination of the cache area where data is stored is mainly based on the data's attribute information, which includes but is not limited to the following:

[0102] 1. Data timeliness and access frequency: Data with high-frequency updates and low latency requirements are allocated to the L1 cache to support real-time monitoring and decision-making. Data with low update frequency and long-term storage is stored in the L2 cache for historical trend analysis and optimization;

[0103] 2. Real-time requirements and data processing requirements: Data used for real-time control, early warning, and rapid response is placed in the L1 cache, while data used for offline calculations, historical induction, and model training is stored in the L2 cache;

[0104] 3. From the perspective of persistence and resource optimization: As a high-speed cache, the L1 cache has limited capacity and focuses on real-time performance, while the L2 cache has a larger capacity and is suitable for storing data that needs to be preserved for a long time. Technologies such as data compression and encryption can be used to further improve storage efficiency and ensure data security to meet long-term storage needs.

[0105] To further improve system performance and data security, this solution can perform real-time compression and encryption on data during the cache phase, thereby efficiently utilizing storage space while ensuring data transmission security and speed. Its advantages are mainly reflected in the following aspects:

[0106] 1. Cache space optimization: High-frequency data collection will lead to the generation of massive amounts of data. Real-time compression technology can significantly reduce the amount of data, effectively alleviate the pressure on the ECU's limited storage space, and optimize the use efficiency of cache resources.

[0107] 2. Optimizing data transmission efficiency: Although data is initially cached, some data still needs to be transmitted internally (e.g., between the ECU and modules) or externally (e.g., uploaded to a cloud server). Compressed data is smaller, significantly improving transmission efficiency and reducing latency, thereby enhancing the system's real-time responsiveness.

[0108] 3. Data Security Optimization: Cached data may be exposed to physical or network security risks. To address this, encryption of cached data prevents sensitive information leakage caused by unauthorized access. This provides comprehensive security protection, whether data is stored at rest or in transit, ensuring reliable and stable system operation.

[0109] In the method provided in the above embodiment, after receiving the battery data collected by the sensor, the on-board computing unit ECU uses a multi-level cache mechanism to determine the target cache area that matches the battery data attribute information, thereby ensuring the targeted and efficient data storage. At the same time, the stored battery data is compressed in real time to save storage space, and data security is ensured through encryption processing, thereby improving the efficiency of data management, the utilization of storage resources and the overall security of the system.

[0110] In summary, the present invention proposes a method for intelligent thermal management of batteries based on battery mechanisms. By integrating multiple types of high-precision sensors for data acquisition and combining real-time adaptive sampling technology, feature extraction based on electrochemical reaction mechanisms, and multi-dimensional rule parameter evaluation, this method implements multi-level early warning, nonlinear power limiting, and multi-physics field collaborative control, effectively improving the safety, adaptability, and reliability of the system. Furthermore, it enables continuous closed-loop optimization based on battery operating data. Furthermore, while real-time operations are performed on the ECU, the entire system can also continuously optimize and adjust early warning control strategies through cloud-based data analysis and OTA updates, thereby achieving the goal of continuous optimization.

[0111] See also Figure 4 , showing a schematic diagram of the main modules of a device 400 for thermally managing a battery provided by an embodiment of the present invention, including:

[0112] A feature extraction module 401 is used to obtain battery data of a battery, obtain a maximum cell temperature from the battery data, and extract battery features;

[0113] a thermal runaway probability module 402, configured to determine a thermal runaway probability of the battery based on the battery data and the battery characteristics;

[0114] a strategy matching module 403, configured to select a target warning strategy that matches the maximum cell temperature and the thermal runaway probability from a preset multi-level warning strategy and a preset latent warning strategy;

[0115] The strategy optimization module 404 is configured to adjust the rule parameters in the target warning strategy according to the environmental information of the environment in which the battery is located and the battery characteristics, and regulate the heat dissipation of the battery through the adjusted target warning strategy.

[0116] In the implementation device of the present invention, the feature extraction module 401 is used to: receive battery data collected by a sensor configured for the battery; wherein the sensor includes at least one of a voltage sensor, a current sensor, a temperature sensor, and a gas sensor.

[0117] In the implementation device of the present invention, the battery data includes the operating status of the battery, and the device also includes a first frequency modulation module, which is used to: perform risk assessment based on the operating status of the battery, determine the data acquisition frequency based on the risk assessment result, and use the determined data acquisition frequency as the acquisition frequency of the battery data.

[0118] The implementation device of the present invention also includes a second frequency modulation module, which is used to: determine the change of the battery data, and in response to the change of the battery data hitting a preset abnormal condition, use the data acquisition frequency of the preset high-frequency mode as the acquisition frequency of the battery data.

[0119] In the embodiment of the present invention, the battery data includes battery temperature, and the second frequency modulation module is configured to: cause a rising rate of the battery temperature in a first preset time period to exceed a preset rate threshold.

[0120] The implementation device of the present invention also includes one or more of the following situations:

[0121] Receiving the working status of each sensor transmitted by the battery management system, generating calibration parameters according to the working status and transmitting the calibration parameters to the battery management system, so that the battery management system transmits the calibration parameters to the sensor for calibration processing;

[0122] For a redundant design consisting of multiple sensors of the same or different types, the battery data collected by each sensor in the redundant design within a second preset time period is compared to determine the faulty sensor and notify the battery management system, so that the battery management system can isolate the faulty sensor.

[0123] The implementation device of the present invention also includes a cache module, which is used to: obtain attribute information of battery data, determine a target cache area that matches the attribute information from the multi-level cache area, and store the battery data in the target cache area; and perform real-time compression and encryption processing on the battery data stored in the target cache area.

[0124] In the embodiment of the present invention, the feature extraction module 401 is used to normalize the battery data to the same interval, obtain the maximum cell temperature from the normalized battery data, and extract battery features.

[0125] In the implementation device of the present invention, the feature extraction module 401 is used to: process the battery data using a preset timestamp alignment and interpolation padding strategy, obtain the maximum cell temperature from the processed battery data, and extract battery features.

[0126] In the device implemented in the present invention, the battery characteristics include battery status characteristics, and the preset latent state warning strategy includes a preset cooling strategy;

[0127] When the target early warning strategy is a preset latent early warning strategy, the strategy optimization module 404 is used to:

[0128] determining an aging condition or a health condition satisfied by the battery status characteristic, determining an adjustment range and an adjustment direction of a warning threshold corresponding to the battery data based on the determined condition satisfied by the battery status characteristic and the environmental information, and adjusting the warning threshold corresponding to the battery data in a preset cooling strategy based on the adjustment range and the adjustment direction;

[0129] In response to the maximum battery cell temperature being lower than the preset temperature threshold of the first-level warning strategy in the preset multi-level warning strategy, when the battery data reaches the adjusted warning threshold by detecting the nonlinear change of the gas characteristic curve and the ion transmission blockage phenomenon, the cooling power for the battery is increased through the adjusted preset cooling strategy.

[0130] In the embodiment of the present invention, the battery characteristics include battery state characteristics and electrochemical reaction characteristics;

[0131] When the target early warning strategy is a level one early warning strategy among the preset multi-level early warning strategies, the strategy optimization module 404 is used to:

[0132] determining a new battery power reduction ratio in a first-level warning strategy based on the environmental information, the battery state characteristics, and the electrochemical reaction characteristics, and adjusting the output power of the battery according to the new battery power reduction ratio;

[0133] Determine a new power reduction ratio corresponding to each electrical load type in the first-level warning strategy, and adjust the output power corresponding to each electrical load type in the battery according to the new power reduction ratio corresponding to each electrical load type.

[0134] In the embodiment of the present invention, when the target early warning strategy is a level 2 early warning strategy among the preset multi-level early warning strategies, the strategy optimization module 404 is used to:

[0135] The thermal insulation unit pre-configured between the batteries is activated by an electronic switch to reduce thermal conductivity by changing the microstructure density of the thermal insulation unit; wherein the thermal insulation unit is constructed using deformable aerogel or electric field adjustable thermal insulation material;

[0136] and / or,

[0137] In response to a liquid cooling device pre-configured in the battery management system, when an early warning strategy upgrade is detected, a high specific heat coolant containing phase change microcapsules is injected into the battery through the liquid cooling device to absorb heat.

[0138] In the implementation device of the present invention, the strategy optimization module 404 is used to:

[0139] Calculate the injection timing and injection ratio of coolant through real-time thermal model;

[0140] wherein the real-time thermal model determines a change trend of the battery temperature over time based on the battery data, and initiates coolant injection in response to the battery temperature reaching a preset temperature threshold of the secondary warning strategy, or predicting that the battery temperature reaches the preset temperature threshold of the secondary warning strategy based on the change trend;

[0141] The injection ratio of the coolant is determined based on the difference between the battery temperature and the target battery temperature, the cooling efficiency of the coolant, and the thermal capacity of the battery.

[0142] In the embodiment of the present invention, when the target early warning strategy is a third-level early warning strategy among the preset multi-level early warning strategies, the strategy optimization module 404 is used to:

[0143] Through the controllable heat channel pre-configured for the battery, the heat from the local overheating area of ​​the battery is diverted to the specific heat dissipation area of ​​the vehicle body for dissipation;

[0144] and / or,

[0145] Before globally disconnecting the high-voltage circuit, for a single cell detected as abnormal in the battery, activating a cell-level quick-disconnect unit pre-configured for the single cell to perform local electronic switch isolation processing on the single cell;

[0146] and / or,

[0147] Before the final power failure, the voltage of the battery is gradually reduced by a soft switching circuit according to a preset time ladder to transition from a high voltage state to a safe low voltage state.

[0148] In the implementation device of the present invention, the strategy optimization module 404 is further configured to:

[0149] The number of times each warning strategy is triggered during vehicle operation is counted, and the rule parameters in the preset multi-level warning strategy are adjusted according to the number of triggers.

[0150] In the implementation device of the present invention, the strategy optimization module 404 is used to:

[0151] For a warning strategy whose trigger count exceeds a preset first threshold, in response to no security incident occurring or the number of security incidents occurring is less than or equal to a preset second threshold, determining an adjustment range and an adjustment direction of the warning threshold based on the environmental information, adjusting the warning threshold in the warning strategy, and determining a new power reduction ratio based on the environmental information;

[0152] For a warning strategy whose trigger times are less than a preset third number threshold, the adjustment amplitude and adjustment direction of the warning threshold are determined according to the environmental information to adjust the warning threshold in the warning strategy, and a new power reduction ratio is determined according to the environmental information; wherein the preset first number threshold is greater than or equal to the preset third number threshold.

[0153] In the embodiment of the present invention, the thermal runaway probability is determined by a thermal runaway probability model, and the thermal runaway probability model is:

[0154]

[0155] Among them, R(t) is the risk factor, T max (t) is the maximum cell temperature at the current moment t, T ref is the preset reference temperature, dT(t) / dt is the temperature rise rate, G(t) is the gas sensor reading, Imp(t) is the battery internal impedance or polarization voltage change, α, β, γ, δ are weight coefficients, P TP (t+τ) is the probability of thermal runaway, and τ is a period of time in the future.

[0156] The implementation device of the present invention also includes a model optimization module for:

[0157] A reference range of the battery data is determined according to the environmental information, and in response to the battery data exceeding the reference range, a weight coefficient in the thermal runaway probability model is adjusted according to a difference between the battery data and the reference range.

[0158] In addition, the specific implementation content of the device described in the embodiment of the present invention has been described in detail in the aforementioned method, so the repeated content will not be described again here.

[0159] Figure 5(a) shows an exemplary system architecture diagram to which embodiments of the present invention may be applied, including a battery 501, a battery management system 502 (not shown), and a device 503 for thermally managing the battery. The device for thermally managing the battery is an ECU. The execution logic of the ECU has been described in detail in the aforementioned method and will not be repeated here. Figure 5(b) shows a schematic diagram of the architecture of a vehicle 504 to which embodiments of the present invention may be applied.

[0160] The battery management system 502 includes a sensor 5021 , a liquid cooling device 5022 , a heat insulation unit 5023 , a controllable heat channel 5024 , a cell-level quick-break unit 5025 , a soft switching circuit 5026 , and an embedded microcontroller 5027 .

[0161] There can be multiple sensors 5021, such as at least one of a voltage sensor, a current sensor, a temperature sensor, and a gas sensor. Sensor 5021 is directly connected to battery 501 to collect multi-dimensional data from battery 501. Furthermore, multiple sensors of the same or different types can be deployed to create a redundant design to ensure the accuracy of the collected data. An embedded microcontroller 5027 monitors the operating status of each sensor 5021 in real time.

[0162] The liquid cooling device 5022 is used to inject a high specific heat coolant containing phase change microcapsules into the battery 501 to absorb heat.

[0163] The battery is divided into multiple independent cells, and heat-resistant, insulating materials are used to construct insulation units 5023 between the cells. Deformable aerogel or electric field-adjustable insulation materials can be used to construct insulation units 5023. When insulation units 5023 are needed, the microstructure density of the insulation units can be changed to reduce thermal conductivity.

[0164] Controllable thermal channels 5024 are used to divert heat from localized overheating areas of battery 501 to specific cooling areas on the vehicle body for dissipation. Cell-level quick-break units 5025 are used to implement local electronic switch isolation of individual cells. Soft-switching circuits 5026 are used to gradually reduce the voltage of battery 501, transitioning from a high-voltage state to a safe low-voltage state.

[0165] Figure 6 The interaction process between the battery, battery management system and ECU is shown:

[0166] 1. The battery management system BMS collects battery data through sensors pre-configured for the battery.

[0167] 2. The battery management system BMS transmits battery data to the ECU.

[0168] 3. After receiving the battery data transmitted by the battery management system BMS, the ECU normalizes the battery data and uses the preset timestamp alignment and interpolation padding strategies.

[0169] 4. Perform a risk assessment based on the operating status of the battery in the battery data, determine the data acquisition frequency based on the risk assessment result, and use the determined data acquisition frequency as the acquisition frequency of the battery data.

[0170] 5. Determine the change in the battery data. In response to the change in the battery data hitting a preset abnormal condition, use the preset high-frequency mode data acquisition frequency as the battery data acquisition frequency; wherein, for steps 4 and 5, adaptive adjustment of the sensor sampling frequency can also be achieved.

[0171] 6. Extract the maximum cell temperature and battery characteristics from the processed battery data.

[0172] 7. Determine the probability of thermal runaway of the battery based on battery data and battery characteristics.

[0173] 8. From the preset multi-level warning strategies and the preset latent warning strategies, select the target warning strategy that matches the maximum cell temperature and thermal runaway probability.

[0174] 9. Adjust the rule parameters in the target warning strategy based on the battery's environmental information and battery characteristics. The adjusted target warning strategy transmits specific heat dissipation control information to the battery management system (BMS). During vehicle operation, the number of times each warning strategy is triggered can be counted, and the rule parameters in the pre-set multi-level warning strategy can be adjusted based on the number of triggers.

[0175] 10. The battery management system BMS regulates the heat dissipation of the battery according to the heat dissipation control information.

[0176] Reference below Figure 7 , which shows a schematic structural diagram of a computer system 700 of a terminal device suitable for implementing an embodiment of the present invention. Figure 7 The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0177] like Figure 7 As shown, the computer system 700 includes a central processing unit (CPU) 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage unit 708 into a random access memory (RAM) 703. Various programs and data required for the operation of the system 700 are also stored in the RAM 703. The CPU 701, ROM 702, and RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0178] The following components are connected to the I / O interface 705: an input section 706 including a keyboard, a mouse, and the like; an output section 707 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage section 708 including a hard disk; and a communication section 709 including a network interface card such as a LAN card or a modem. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the I / O interface 705 as needed. A removable medium 711, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 710 as needed, so that computer programs read therefrom can be installed into the storage section 708 as needed.

[0179] In particular, according to the embodiments disclosed in the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 709, and / or installed from a removable medium 711. When the computer program is executed by the central processing unit (CPU) 701, the above-mentioned functions defined in the system of the present invention are executed.

[0180] It should be noted that the computer-readable medium described in the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media can include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. This propagated data signal can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wireline, optical fiber cable, RF, or any suitable combination thereof.

[0181] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0182] The modules described in the embodiments of the present invention may be implemented in software or hardware. The modules described may also be located within a processor. For example, they may be described as comprising a feature extraction module, a thermal runaway probability module, a strategy matching module, and a strategy optimization module. The names of these modules do not, in some cases, constitute limitations on the modules themselves. For example, the thermal runaway probability module may also be described as a "probability calculation module."

[0183] As another aspect, the present invention further provides a computer-readable medium, which may be included in the device described in the above embodiments, or may exist independently and not incorporated into the device. The computer-readable medium carries one or more programs, and when executed by the device, the device executes any of the above-described methods for thermally managing a battery.

[0184] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may occur depending on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for thermal management of a battery, characterized in that: include: obtaining battery data of the battery, obtaining a maximum cell temperature from the battery data, and extracting battery characteristics; determining a thermal runaway probability of the battery based on the battery data and the battery characteristics; Selecting a target warning strategy that matches the maximum battery cell temperature and the thermal runaway probability from a preset multi-level warning strategy and a preset latent state warning strategy; According to the environmental information of the environment in which the battery is located and the battery characteristics, the rule parameters in the target early warning strategy are adjusted, and the heat dissipation of the battery is regulated by the adjusted target early warning strategy.

2. The method according to claim 1, characterized in that The acquiring of battery data of the battery includes: receiving battery data collected by a sensor configured for the battery; wherein the sensor includes at least one of a voltage sensor, a current sensor, a temperature sensor, and a gas sensor.

3. The method according to claim 1 or 2, characterized in that The battery data includes an operating status of the battery, and the method further includes: A risk assessment is performed according to the operating status of the battery, a data acquisition frequency is determined according to the risk assessment result, and the determined data acquisition frequency is used as the acquisition frequency of the battery data.

4. The method according to claim 1 or 2, characterized in that The method further comprises: A change in the battery data is determined, and in response to the change in the battery data matching a preset abnormal condition, a data acquisition frequency in a preset high-frequency mode is used as an acquisition frequency for the battery data.

5. The method according to claim 4, characterized in that The battery data includes battery temperature, and a change in the battery data hits a preset abnormal condition, including: a rising rate of the battery temperature in a first preset time period exceeds a preset rate threshold.

6. The method according to claim 2, characterized in that The method also includes one or more of the following: Receiving the working status of each sensor transmitted by the battery management system, generating calibration parameters according to the working status and transmitting the calibration parameters to the battery management system, so that the battery management system transmits the calibration parameters to the sensor for calibration processing; For a redundant design consisting of multiple sensors of the same or different types, the battery data collected by each sensor in the redundant design within a second preset time period is compared to determine the faulty sensor and notify the battery management system, so that the battery management system can isolate the faulty sensor.

7. The method according to claim 1, characterized in that After acquiring the battery data of the battery, the method further includes: Acquire attribute information of the battery data, determine a target cache area that matches the attribute information from the multi-level cache area, and store the battery data in the target cache area; The battery data stored in the target cache area is compressed and encrypted in real time.

8. The method according to claim 1, characterized in that The obtaining of the maximum cell temperature from the battery data and the extraction of battery characteristics include: The battery data is normalized to the same interval, the maximum cell temperature is obtained from the normalized battery data, and battery characteristics are extracted.

9. The method according to claim 1 or 8, characterized in that The obtaining of the maximum cell temperature from the battery data and the extraction of battery characteristics include: The battery data is processed using a preset timestamp alignment and interpolation padding strategy, and the maximum cell temperature is obtained from the processed battery data, and battery characteristics are extracted.

10. The method according to claim 1, characterized in that The battery characteristics include battery status characteristics, and the preset latent state warning strategy includes a preset cooling strategy; In a case where the target warning strategy is a preset latent warning strategy, adjusting the rule parameters in the target warning strategy according to the environmental information and the battery characteristics, and regulating the heat dissipation of the battery by using the adjusted target warning strategy, includes: determining an aging condition or a health condition satisfied by the battery status characteristic, determining an adjustment range and an adjustment direction of a warning threshold corresponding to the battery data based on the determined condition satisfied by the battery status characteristic and the environmental information, and adjusting the warning threshold corresponding to the battery data in a preset cooling strategy based on the adjustment range and the adjustment direction; In response to the maximum battery cell temperature being lower than the preset temperature threshold of the first-level warning strategy in the preset multi-level warning strategy, when the battery data reaches the adjusted warning threshold by detecting the nonlinear change of the gas characteristic curve and the ion transmission blockage phenomenon, the cooling power for the battery is increased through the adjusted preset cooling strategy.

11. The method according to claim 1, wherein The battery characteristics include battery state characteristics and electrochemical reaction characteristics; In a case where the target warning strategy is a first-level warning strategy in a preset multi-level warning strategy, adjusting the rule parameters in the target warning strategy according to the environmental information and the battery characteristics, and regulating the heat dissipation of the battery by using the adjusted target warning strategy, includes: determining a new battery power reduction ratio in a first-level warning strategy based on the environmental information, the battery state characteristics, and the electrochemical reaction characteristics, and adjusting the output power of the battery according to the new battery power reduction ratio; Determine a new power reduction ratio corresponding to each electrical load type in the first-level warning strategy, and adjust the output power corresponding to each electrical load type in the battery according to the new power reduction ratio corresponding to each electrical load type.

12. The method according to claim 1, characterized in that In the case where the target warning strategy is a level 2 warning strategy among the preset multi-level warning strategies, the adjusted target warning strategy is the battery controlled heat dissipation, including: The thermal insulation unit pre-configured between the batteries is activated by an electronic switch to reduce thermal conductivity by changing the microstructure density of the thermal insulation unit; wherein the thermal insulation unit is constructed using deformable aerogel or electric field adjustable thermal insulation material; and / or, In response to a liquid cooling device pre-configured in the battery management system, when an early warning strategy upgrade is detected, a high specific heat coolant containing phase change microcapsules is injected into the battery through the liquid cooling device to absorb heat.

13. The method according to claim 12, characterized in that The method further comprises: Calculate the injection timing and injection ratio of coolant through real-time thermal model; wherein the real-time thermal model determines a change trend of the battery temperature over time based on the battery data, and initiates coolant injection in response to the battery temperature reaching a preset temperature threshold of the secondary warning strategy, or predicting that the battery temperature reaches the preset temperature threshold of the secondary warning strategy based on the change trend; The injection ratio of the coolant is determined based on the difference between the battery temperature and the target battery temperature, the cooling efficiency of the coolant, and the thermal capacity of the battery.

14. The method according to claim 1, wherein In a case where the target warning strategy is a level 3 warning strategy among the preset multi-level warning strategies, regulating the heat dissipation of the battery by using the adjusted target warning strategy includes: Through the controllable heat channel pre-configured for the battery, the heat from the local overheating area of ​​the battery is diverted to the specific heat dissipation area of ​​the vehicle body for dissipation; and / or, Before globally disconnecting the high-voltage circuit, for a single cell detected as abnormal in the battery, activating a cell-level quick-disconnect unit pre-configured for the single cell to perform local electronic switch isolation processing on the single cell; and / or, Before the final power failure, the voltage of the battery is gradually reduced by a soft switching circuit according to a preset time ladder to transition from a high voltage state to a safe low voltage state.

15. The method according to claim 1, wherein The method further comprises: The number of times each warning strategy is triggered during vehicle operation is counted, and the rule parameters in the preset multi-level warning strategy are adjusted according to the number of triggers.

16. The method according to claim 15, characterized in that The adjusting of the rule parameters in the preset multi-level warning strategy according to the triggering number includes: For a warning strategy whose trigger count exceeds a preset first threshold, in response to no security incident occurring or the number of security incidents occurring is less than or equal to a preset second threshold, determining an adjustment range and an adjustment direction of the warning threshold based on the environmental information, adjusting the warning threshold in the warning strategy, and determining a new power reduction ratio based on the environmental information; For a warning strategy whose trigger times are less than a preset third number threshold, the adjustment amplitude and adjustment direction of the warning threshold are determined according to the environmental information to adjust the warning threshold in the warning strategy, and a new power reduction ratio is determined according to the environmental information; wherein the preset first number threshold is greater than or equal to the preset third number threshold.

17. The method according to claim 1, wherein The thermal runaway probability is determined by a thermal runaway probability model, which is: Among them, R(t) is the risk factor, T max (t) is the maximum cell temperature at the current moment t, T ref is the preset reference temperature, dT(t) / dt is the temperature rise rate, G(t) is the gas sensor reading, Imp(t) is the battery internal impedance or polarization voltage change, α, β, γ, δ are weight coefficients, P TP (t+τ) is the probability of thermal runaway, and τ is a period of time in the future.

18. The method according to claim 17, characterized in that The method further comprises: A reference range of the battery data is determined according to the environmental information, and in response to the battery data exceeding the reference range, a weight coefficient in the thermal runaway probability model is adjusted according to a difference between the battery data and the reference range.

19. A device for thermal management of a battery, characterized in that: include: a feature extraction module, configured to obtain battery data of the battery, obtain a maximum cell temperature from the battery data, and extract battery features; a thermal runaway probability module, configured to determine a thermal runaway probability of the battery based on the battery data and the battery characteristics; a strategy matching module, configured to select a target warning strategy that matches the maximum battery cell temperature and the thermal runaway probability from a preset multi-level warning strategy and a preset latent state warning strategy; A strategy optimization module is used to adjust the rule parameters in the target warning strategy according to the environmental information of the environment in which the battery is located and the battery characteristics, and regulate the heat dissipation of the battery through the adjusted target warning strategy.

20. A system for thermally managing a battery, characterized in that: include: A battery, a battery management system, and the device for thermally managing a battery as claimed in claim 19.

21. The system according to claim 20, wherein: The battery management system includes a sensor, a liquid cooling device, a heat insulation unit, a controllable heat channel, a cell-level quick-break unit, and a soft switching circuit; The sensor is used to collect battery data of the battery; wherein the sensor includes at least one of a voltage sensor, a current sensor, a temperature sensor, and a gas sensor; The liquid cooling device is used to inject a high specific heat coolant containing phase change microcapsules into the battery to absorb heat; The thermal insulation unit is used to reduce thermal conductivity by changing its own microstructure density; The controllable heat channel is used to divert the heat from the local overheating area of ​​the battery to a specific heat dissipation area of ​​the vehicle body for heat dissipation; The cell-level quick-break unit is used to implement local electronic switch isolation processing on the single cell; The soft switching circuit is used to gradually reduce the voltage of the battery to transition from a high voltage state to a safe low voltage state.

22. The system according to claim 21, wherein: The battery management system is configured with a redundant design by arranging a plurality of sensors of the same or different types.

23. The system according to claim 21, wherein: The battery management system further includes an embedded microcontroller for monitoring the working status of each sensor in real time.

24. The system according to claim 21, wherein: The thermal insulation unit is constructed using deformable aerogel or electric field adjustable thermal insulation material.

25. A vehicle, characterized in that: A device comprising the thermal management battery of claim 19 or a system comprising the thermal management battery of any one of claims 20-24.

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