An electric vehicle, power battery cooling method, device and storage medium

Abstract

This application discloses a method, apparatus, and storage medium for cooling electric vehicles and power batteries. By configuring an independent cooling control unit and corresponding temperature acquisition sensor for each battery module, real-time and accurate monitoring of the operating temperature of each module is achieved. Based on the prediction model's assessment of the vehicle's future operating conditions and battery aging status, the system can proactively calculate the differentiated coolant flow rate required for each module and precisely control the flow rate of each cooling control unit through independent flow rate regulating valves. This allows modules with higher temperatures to achieve stronger cooling effects, while modules with suitable temperatures avoid unnecessary cooling, thus effectively solving the problems of uneven temperature, local overheating, or undercooling within the battery pack caused by traditional overall cooling methods. This method ultimately achieves differentiated and precise cooling for each battery module, significantly improving the overall temperature uniformity, operational safety, and system energy efficiency of the battery pack.

Description

Technical Field

[0001] This invention relates to the field of automotive technology, and more particularly to an electric vehicle, a power battery cooling method, apparatus, and storage medium. Background Technology

[0002] Electric vehicle power batteries typically employ an integrated cooling system, with coolant flowing through all battery modules at a uniform rate. Due to differences in physical parameters and heat dissipation conditions among the modules within the battery pack, temperature distribution is often uneven. With integrated cooling, the system cannot adjust to the actual temperature of each module, easily leading to insufficient cooling of some warmer modules while overcooling others with suitable temperatures. This not only affects battery performance consistency, lifespan, and safety but also results in unnecessary energy waste. Therefore, achieving differentiated and precise cooling of battery modules is a pressing issue that needs to be addressed. Summary of the Invention

[0003] This application provides a method, apparatus, and storage medium for cooling electric vehicles and power batteries. It solves the technical problem in the prior art where, when using an overall cooling method, the system cannot make differentiated adjustments based on the actual temperature of each module, which easily leads to insufficient cooling of some modules with higher temperatures, while some modules with suitable temperatures are over-cooled. This achieves the technical effect of differentiated and precise cooling of battery modules.

[0004] In a first aspect, this application provides a method for cooling a power battery, used to control a cooling plate in an electric vehicle. The cooling plate includes multiple independent cooling control units, each of which is equipped with an independent coolant flow rate regulating valve and a temperature acquisition sensor. The power battery includes multiple battery modules, each corresponding to a cooling control unit. The method includes:

[0005] During the operation of the electric vehicle after powering on, the current operating temperature of each battery module in the power battery is acquired by the temperature acquisition sensor.

[0006] The process involves performing a conventional coolant flow rate determination step, which includes: acquiring historical operating condition data of the electric vehicle over a preset historical period; acquiring the actual battery parameters of the power battery at the current moment; inputting the historical operating condition data into a pre-trained vehicle operating condition prediction model to obtain expected operating condition data of the electric vehicle over a preset future period; inputting the actual battery parameters into a pre-trained battery aging prediction model to obtain the actual aging coefficient of the power battery at the current moment; based on the expected operating condition data and the actual aging coefficient, obtaining the expected heat generation of the power battery over the preset future period; and based on the expected heat generation, the target operating temperature of the power battery, the current operating temperature of each battery module, and the coolant inlet / outlet temperature difference of each cooling control unit, determining the target coolant flow rate of each cooling control unit.

[0007] Based on the target coolant flow rate of each of the cooling control units, the coolant flow rate regulating valve of each of the cooling control units is controlled to cool each of the battery modules.

[0008] Secondly, this application provides a power battery cooling device for controlling a cooling plate in an electric vehicle. The cooling plate includes multiple independent cooling control units, each equipped with an independent coolant flow rate regulating valve and a temperature acquisition sensor. The power battery includes multiple battery modules, each corresponding to a cooling control unit. The device includes:

[0009] The acquisition module is used to acquire the current operating temperature of each battery module in the power battery by the temperature acquisition sensor during the power-on operation of the electric vehicle.

[0010] The flow rate determination module is used to perform a conventional coolant flow rate determination step, which includes: acquiring historical operating condition data of the electric vehicle during a preset historical period, and acquiring the actual battery parameters of the power battery at the current moment; inputting the historical operating condition data into a pre-trained vehicle operating condition prediction model to obtain the expected operating condition data of the electric vehicle during a preset future period; inputting the actual battery parameters into a pre-trained battery aging prediction model to obtain the actual aging coefficient of the power battery at the current moment; based on the expected operating condition data and the actual aging coefficient, obtaining the expected heat generation of the power battery during the preset future period; and based on the expected heat generation, the target operating temperature of the power battery, the current operating temperature of each battery module, and the coolant inlet and outlet temperature difference of each cooling control unit, determining the target coolant flow rate of each cooling control unit.

[0011] A flow rate control module is used to control the coolant flow rate regulating valve of each cooling control unit based on the target coolant flow rate of each cooling control unit, so as to cool each battery module.

[0012] Thirdly, this application provides an electric vehicle, comprising:

[0013] Power battery, including multiple battery modules;

[0014] The cooling plate includes multiple cooling control units, each corresponding to a battery module. The cooling control units are arranged on the outside of the battery module and are used to cool the battery module.

[0015] The processor is connected to each of the aforementioned cooling control units;

[0016] Memory used to store the processor's executable instructions;

[0017] The processor is configured to execute a power battery cooling method as provided in the first aspect.

[0018] Fourthly, this application provides a non-transitory computer-readable storage medium that, when the instructions in the storage medium are executed by the processor of an electric vehicle, enables the electric vehicle to perform a power battery cooling method as provided in the first aspect.

[0019] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0020] This application embodiment achieves real-time and accurate monitoring of the operating temperature of each battery module by configuring an independent cooling control unit and a corresponding temperature acquisition sensor for each module. Based on the predictive model's assessment of the vehicle's future operating conditions and battery aging status, the system can proactively calculate the differentiated coolant flow rate required for each module and precisely control the flow rate of each cooling control unit through independent flow rate regulating valves. This allows modules with higher temperatures to receive stronger cooling, while modules with suitable temperatures avoid unnecessary cooling, thus effectively solving the problems of uneven temperature, localized overheating, or undercooling within the battery pack caused by traditional overall cooling methods. This method ultimately achieves differentiated and precise cooling for each battery module, significantly improving the overall temperature uniformity, operational safety, and system energy efficiency of the battery pack. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A schematic flowchart illustrating a power battery cooling method provided in an embodiment of this application;

[0023] Figure 2 A schematic diagram comparing the predicted vehicle speed and the actual vehicle speed of the vehicle condition prediction model provided in the embodiments of this application.

[0024] Figure 3 A schematic flowchart of another power battery cooling method provided in an embodiment of this application;

[0025] Figure 4 This is a schematic diagram of the architecture of an electric vehicle provided in an embodiment of this application. Detailed Implementation

[0026] This application provides a power battery cooling method that solves the technical problem in the prior art where the overall cooling method cannot be adjusted according to the actual temperature of each module, which easily leads to insufficient cooling of some modules with higher temperatures, while some modules with suitable temperatures are over-cooled.

[0027] The technical solution of this application embodiment is to solve the above-mentioned technical problems, and the general idea is as follows:

[0028] This application embodiment achieves real-time and accurate monitoring of the operating temperature of each battery module by configuring an independent cooling control unit and a corresponding temperature acquisition sensor for each module. Based on the predictive model's assessment of the vehicle's future operating conditions and battery aging status, the system can proactively calculate the differentiated coolant flow rate required for each module and precisely control the flow rate of each cooling control unit through independent flow rate regulating valves. This allows modules with higher temperatures to receive stronger cooling, while modules with suitable temperatures avoid unnecessary cooling, thus effectively solving the problems of uneven temperature, localized overheating, or undercooling within the battery pack caused by traditional overall cooling methods. This method ultimately achieves differentiated and precise cooling for each battery module, significantly improving the overall temperature uniformity, operational safety, and system energy efficiency of the battery pack.

[0029] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0030] First, it should be clarified that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0031] This application provides a method for cooling a power battery, the method including steps S11-S13, which can be found in detail below. Figure 1 As shown.

[0032] Step S11: During the power-on operation of the electric vehicle, the current operating temperature of each battery module in the power battery is acquired by the temperature acquisition sensor.

[0033] Step S12: Perform a conventional coolant flow rate determination step, which includes: acquiring historical operating condition data of the electric vehicle during a preset historical period, and acquiring the actual battery parameters of the power battery at the current moment; inputting the historical operating condition data into a pre-trained vehicle operating condition prediction model to obtain the expected operating condition data of the electric vehicle during a preset future period; inputting the actual battery parameters into a pre-trained battery aging prediction model to obtain the actual aging coefficient of the power battery at the current moment; based on the expected operating condition data and the actual aging coefficient, obtaining the expected heat generation of the power battery during the preset future period; and based on the expected heat generation, the target operating temperature of the power battery, the current operating temperature of each battery module, and the coolant inlet and outlet temperature difference of each cooling control unit, determining the target coolant flow rate of each cooling control unit.

[0034] Step S13: Based on the target coolant flow rate of each cooling control unit, control the coolant flow rate regulating valve of each cooling control unit to cool each battery module.

[0035] The power battery cooling method provided in this application embodiment can be executed by a processor in the vehicle control system of an electric vehicle, such as a vehicle controller or a processing unit specifically responsible for battery management.

[0036] This application provides a power battery cooling method for controlling the cooling plate of a power battery in an electric vehicle. The cooling plate includes multiple independent cooling control units, each of which is equipped with an independent coolant flow rate regulating valve and an independent temperature acquisition sensor. The power battery includes multiple battery modules (hereinafter referred to as modules), and each battery module corresponds to one of the cooling control units.

[0037] The cooling plate provided in this embodiment includes multiple independent cooling control units (hereinafter referred to as units), each with its own independent coolant inflow and outflow paths. Specifically, the coolant pipelines of each cooling control unit are connected in parallel: the coolant flows from a shared storage container into the inlet of each unit, undergoes heat exchange through its respective flow channel, and then converges from the outlet of each unit back into the storage container.

[0038] Structurally, these cooling control units can be integrated into a single cooling plate, or they can be completely independent components. They can also be assembled into a complete cooling plate using detachable connectors (such as clips and bolts), providing flexible assembly and maintenance options. In other words, the number and distribution of cooling control units can be flexibly adjusted according to the number and layout of battery modules in the power battery, adapting to the battery pack designs of different vehicle models.

[0039] The flow rate of coolant can be adjusted in various ways, such as by using a proportional control valve for continuous stepless control, or by using a valve core driven by a stepper motor for discrete gear adjustment.

[0040] For precise control, each cooling control unit can be configured with multiple temperature acquisition sensors, such as those used to acquire the surface temperature of the corresponding battery module, the inlet temperature of the coolant in the unit, and the outlet temperature of the coolant.

[0041] The cooling plate provided in this application embodiment achieves significant technical effects in battery cooling through its parallel piping design of multiple independent cooling control units. Each unit has an independent flow regulating valve and temperature monitoring point, enabling the cooling plate to independently and precisely adjust the flow rate of coolant through each corresponding battery module based on its real-time temperature and heat load. This design ensures differentiated and localized cooling from a physical structure perspective, effectively reducing the maximum temperature difference within the battery pack and decreasing the probability of coexisting "overcooled" and "overheated" areas, thereby improving battery temperature uniformity. Simultaneously, the modular (integrated, independent, or splicable) structure gives the cooling plate good adaptability and maintainability. Overall, the cooling plate itself provides a key hardware foundation for implementing refined thermal management, directly contributing to extending battery life, ensuring safety, and optimizing the energy efficiency of the cooling system.

[0042] Based on the cooling plate provided above, the following description is provided for a power battery cooling method according to an embodiment of this application.

[0043] Regarding step S11, during the power-on operation of the electric vehicle, the current operating temperature of each battery module in the power battery is acquired by the temperature acquisition sensor.

[0044] During the operation of an electric vehicle, the system periodically acquires real-time temperature data from the temperature sensors on each battery module at a preset fixed frequency. This preset frequency is typically adjustable between 0.5Hz and 10Hz, and the specific value can be set according to the actual system's computing resources and thermal management accuracy requirements. The higher the acquisition frequency, the more timely the system's monitoring of battery temperature changes, providing a more continuous and accurate data foundation for subsequent operating condition prediction, aging assessment, and heat generation calculation, thereby improving the accuracy and response speed of cooling control. Conversely, the lower the acquisition frequency, the greater the data update delay, which may lead to insufficient capture of temperature fluctuations, thus affecting the real-time performance and effectiveness of the cooling strategy, and even increasing the risk of battery thermal runaway.

[0045] The embodiments of this application will be described below using the current operating temperature collected at a certain moment as an example.

[0046] Regarding step S12, the conventional coolant flow rate determination step is performed, which includes steps S121-S125.

[0047] Step S121: Obtain historical operating condition data of the electric vehicle during a preset historical period, and obtain the actual battery parameters of the power battery at the current moment;

[0048] Step S122: Input the historical operating condition data into the pre-trained vehicle operating condition prediction model to obtain the expected operating condition data of the electric vehicle in a preset future period.

[0049] Step S123: Input the actual battery parameters into the pre-trained battery aging prediction model to obtain the actual aging coefficient of the power battery at the current moment.

[0050] Step S124: Based on the expected operating condition data and the actual aging coefficient, obtain the expected heat generation of the power battery in the preset future period.

[0051] Step S125: Based on the expected heat generation, the target operating temperature of the power battery, the current operating temperature of each battery module, and the temperature difference between the inlet and outlet of the coolant in each cooling control unit, determine the target coolant flow rate of each cooling control unit.

[0052] Step S12 aims to calculate and determine the optimal target coolant flow rate for each cooling control unit. This step integrates historical vehicle operating data and real-time battery status, first using a predictive model to estimate future operating conditions and the current aging level of the battery, then calculating the expected heat generation of the power battery in the next stage. Finally, by combining this expected heat generation, the overall target operating temperature of the battery, the real-time temperature of each module, and the heat exchange temperature difference of each cooling unit, a differentiated target flow rate is generated for each independent cooling control unit. This series of calculations realizes a shift in cooling strategy from "passive response" to "active prediction and adaptation," aiming to make advance and precise adjustments to the cooling system based on predictions of future heat loads, thereby optimizing cooling efficiency and reducing energy consumption while maintaining a uniform and stable battery temperature.

[0053] Specifically, regarding step S121, the preset historical time period refers to a historical time period ending at the current time. The duration of the preset historical time period can be selected and adjusted according to actual conditions, for example, it can be 30 seconds. The historical operating condition data includes at least vehicle speed, acceleration, accelerator pedal opening, accelerator pedal opening change rate, brake pedal opening, brake pedal opening change rate, actual battery charge, and ambient temperature; the actual battery parameters include at least the battery cycle count, historical highest operating temperature, and actual battery charge.

[0054] Regarding step S122, the preset future time period refers to a future time period starting from the current time. The duration of the preset future time period can be selected according to the actual situation, for example, it can be 5 seconds. The preset future time period is shorter than the preset historical time period. The expected operating condition data includes at least vehicle speed.

[0055] The training process of the vehicle operating condition prediction model is illustrated in the following example of an embodiment of this application.

[0056] First, real-world road operation data, including vehicle speed, acceleration, accelerator and brake pedal signals, actual battery level, and ambient temperature, was collected. After preprocessing, the data was evenly divided into continuous 40-second data segments. These segments were then divided into training, validation, and test sets in a 7:2:1 ratio. For model construction, a Long Short-Term Memory (LSTM) neural network was used. Its input consisted of the temporal features of the first 35 seconds of each data segment (including eight parameters: vehicle speed, acceleration, accelerator pedal opening and its rate of change, brake pedal opening and its rate of change, actual battery level (State of Charge, SOC), and ambient temperature). The output was the time sequence of the last 5 seconds of the segment and the corresponding vehicle speed sequence. The network contained three stacked LSTM layers, each with 64 hidden units. During training, mean squared error was used as the loss function, and the Adam optimizer was employed with an initial learning rate of 0.001, which adaptively decayed during training. An early stopping strategy was also introduced, terminating training when the validation set loss did not decrease for five consecutive rounds to prevent overfitting. After training, the model was evaluated on an independent test set. The results showed that the average absolute error of its predicted vehicle speed was less than 1.2 km / h, and the overall accuracy of its driving condition predictions was higher than 90%. Figure 2 The diagram shows a time-speed curve, where the dashed line represents the actual vehicle speed, and the solid line represents the predicted speed based on the vehicle condition prediction model. Through the above process, a directly deployable vehicle condition prediction model is obtained, capable of accurately predicting the time-speed curve for the next 5 seconds using approximately 30 seconds of historical data as input.

[0057] Step S122 executes its core prediction function based on the specific input data obtained in step S121, namely, historical operating condition data for a preset historical period. Specifically, after collecting historical operating data in step S121, step S122 inputs this data (e.g., time series containing parameters such as vehicle speed and pedal signal) into a pre-trained vehicle operating condition prediction model (e.g., an LSTM-based neural network model). This model, after training, can learn and infer short-term evolution patterns of vehicle operating modes from historical data, thereby outputting expected operating condition data for a specific future period (e.g., the next 5 seconds), primarily the time-vehicle speed curve. In other words, step S122 uses advanced algorithms to transform historical data into a quantitative prediction of future driving conditions.

[0058] Regarding step S123, the battery aging factor (State of Health, or SOH for short) is defined as the ratio of the current actual usable maximum capacity of the power battery to the initial rated capacity at the factory (the value ranges from 0 to 1, and the closer it is to 1, the less aging occurs).

[0059] The training process of the battery aging prediction model is illustrated in the following example from an embodiment of this application.

[0060] First, publicly available power battery datasets from relevant databases were integrated as raw data and subjected to rigorous preprocessing, including removing voltage and temperature outliers, imputing missing data using linear interpolation, and normalizing all input features. The processed data was then divided into training, validation, and test sets in a 7:2:1 ratio. The model employs a Transformer neural network architecture, with input features including battery cycle count, historical maximum operating temperature, and actual battery capacity. The output is the current battery aging coefficient (SOH, ranging from 0 to 1). The core of the model consists of a stack of four Transformer encoder layers with eight attention heads. Each layer contains a multi-head self-attention sublayer and a 256-dimensional feedforward network (using the ReLU activation function). Learnable positional encodings are added after the input layers to fuse temporal information. During training, the mean squared error (MSE) was used as the loss function (see the first formula below for details). The Adam optimizer was employed (with an initial learning rate of 0.001 and a cosine annealing strategy for decay). A dropout rate of 0.2 was introduced after the feedforward network for regularization, and an early stopping strategy (stopping the training when the loss did not decrease for five consecutive rounds) was used to prevent overfitting. Evaluation on an independent test set showed that the model's mean absolute error in predicting SOH was less than 0.02, the root mean square error was less than 0.03, and the accuracy was higher than 95%. Thus, a battery aging prediction model capable of accurately assessing the current aging state using real-time battery parameters as input and directly applicable to cooling control processes was successfully trained.

[0061] The first formula is:

[0062] (1)

[0063] In the formula, This represents the loss function. Indicates the number of data samples. Indicates that for the first Predicted battery aging coefficient for each sample. Indicates that for the first The true value of the battery aging coefficient for each sample.

[0064] Step S123 assesses the internal health status of the power battery in real time. This step takes the current actual battery parameters (such as cycle count, temperature history, etc.) obtained in step S121 as input and feeds them into a pre-trained battery aging prediction model (e.g., a neural network model based on the Transformer architecture). This model learns the complex nonlinear mapping relationship between feature parameters and capacity decay in a large amount of battery aging data, and can dynamically calculate the battery's aging coefficient at the current moment (usually represented by the state of health, SOH). This coefficient quantifies the degree of performance degradation of the battery due to use, and its output provides a crucial correction basis for subsequent heat generation calculations. This allows the system to predict the heat load not only based on the current operating conditions but also in conjunction with the battery's actual degradation state, thus laying a state-aware foundation for achieving personalized, refined cooling control adapted to the battery's life cycle.

[0065] Regarding step S124, based on the actual aging coefficient and the rated internal resistance of the power battery, the actual internal resistance of the power battery at the current moment is determined; based on the actual aging coefficient, the aging disturbance parameter of the power battery is determined; based on the expected operating condition data, the expected operating current of the power battery is obtained; based on the expected operating current, the actual internal resistance of the battery, and the aging disturbance parameter, the expected heat generation of the power battery in the preset future period is estimated.

[0066] Rated internal resistance refers to the standard internal resistance value of the power battery at the time of factory design. Actual battery internal resistance, however, is the value obtained after correcting the rated internal resistance based on the current aging factor, reflecting the battery's current true state. See the second formula below for details. The aging disturbance parameter is a correction factor calculated based on the actual aging factor. Its meaning lies in quantifying the additional impact of battery aging on heat generation characteristics, such as changes in heat generation efficiency due to internal material degradation. See the third formula below for details.

[0067] The second formula is:

[0068]

[0069] This represents the actual internal resistance of the battery. Indicates the rated internal resistance. This represents the actual aging coefficient.

[0070] The third formula is:

[0071]

[0072] This represents the aging disturbance parameter. This represents the actual aging coefficient.

[0073] The expected operating current is calculated by inputting predicted future operating condition data (such as vehicle speed and acceleration curves) into the vehicle powertrain model. This model can deduce the current required by the battery based on driving demands. Step S124 integrates the above factors, first determining the actual internal resistance and disturbance parameters based on the aging coefficient, and then combining this with the predicted operating current to finally calculate the expected heat generation of the battery in the future period, as detailed in the fourth formula below. This process, by dynamically incorporating the battery's health status and future load, significantly improves the accuracy of heat generation prediction, thus providing a more reliable thermal load basis for subsequent differentiated cooling control.

[0074] The fourth formula is:

[0075]

[0076] In the formula, Indicates the preset future time period Expected heat production at any given time express The expected operating current of the power battery at all times. This indicates the actual internal resistance of the power battery after aging. This represents the aging disturbance parameter. Based on formulas two, three, and four, it can be seen that... The lower the value, the larger the disturbance term, and the less stable the heat generation caused by aging.

[0077] Regarding step S125, based on the target operating temperature of the power battery and the current operating temperature of each battery module, a temperature correction coefficient for each battery module is determined; based on the expected heat generation, the temperature correction coefficient for each battery module, and the temperature difference between the inlet and outlet of the cooling control unit configured in each battery module, the target coolant flow rate for each cooling control unit is determined.

[0078] The target operating temperature refers to the optimal operating temperature that the power battery as a whole needs to maintain. It is usually determined based on factors such as the battery's chemical characteristics, life cycle optimization, and safety thresholds, and is set at 25°C. Since the design requires that the temperature of each battery module be as consistent as possible, the target operating temperature of all modules is unified to this value.

[0079] The temperature correction factor is an adjustment factor calculated for each battery module based on the deviation between its current operating temperature and the target operating temperature. See Formula 5 below for details. If the module temperature is higher than the target, the factor is greater than 1 to increase coolant distribution; if it is lower than the target, the factor is less than 1 to reduce cooling, thus prioritizing cooling of overheated areas and avoiding overcooling.

[0080] The fifth formula is:

[0081]

[0082] In the formula Indicates the power battery's first Temperature correction factor for each module Indicates the power battery's first The current operating temperature of each module. This indicates the target operating temperature of the power battery. This represents the temperature sensitivity coefficient, with a preset constant of 0.4, used to adjust the weight of the current temperature on the flow rate.

[0083] The temperature difference between the inlet and outlet of the coolant refers to the temperature rise of the coolant after it flows through the cooling control unit. The maximum allowable temperature at the coolant outlet is usually set to a fixed value (e.g., 38°C) to protect the system. The coolant inlet temperature is regulated by other components of the cooling system (e.g., radiators, refrigeration units) and is usually preset to a lower range (e.g., no higher than 28°C) to ensure effective heat exchange capacity.

[0084] Step S125 calculates the target coolant flow rate required for each cooling unit by integrating the expected heat generation, the real-time temperature deviation of each module, and the heat exchange temperature difference. For details, please refer to the sixth formula below. This achieves refined cooling control based on real-time heat distribution and future heat load prediction, effectively improving battery temperature uniformity, extending lifespan, and optimizing energy consumption.

[0085] The sixth formula is:

[0086]

[0087] In the formula, Indicates the power battery's first The coolant flow rate of each module corresponds to the cooling control unit, in meters per second (m). 3 / s. This indicates the density of the coolant. When using an aqueous solution of ethylene glycol as the coolant, the density is 1050 kg / m³. 3 . This indicates the specific heat capacity at constant pressure of the coolant. When using an aqueous solution of ethylene glycol as the coolant... It is 4180 J / (kg·℃). This indicates the maximum allowable outlet temperature of the coolant at the coolant outlet of the cooling control unit. A preset fixed value, such as 38°C, can be used to ensure the heat exchange efficiency of the coolant and the safety of the system. This indicates the coolant temperature at the coolant inlet of the cooling control unit, which is not higher than 28°C. The value represents the cooling heat exchange efficiency, which is generally in the range of 0.7 to 0.95, and can be taken as 0.8 in the embodiments of this application. Indicates the power battery's first The temperature correction factor for the cooling control unit corresponding to each module.

[0088] Regarding step S13, based on the target coolant flow rate of each of the cooling control units, the coolant flow rate regulating valve of each of the cooling control units is controlled to cool each of the battery modules.

[0089] Step S13 involves converting the differentiated target coolant flow rate calculated in the complex process into actual physical control of each cooling control unit. Specifically, the system (such as a BMS or thermal management controller) generates corresponding control signals based on the target coolant flow rate calculated independently for each unit, and drives the corresponding coolant flow rate regulating valve (such as a proportional regulating valve or a stepper motor driven valve) to precisely adjust the coolant flow rate through the cooling channels of each battery module. This step achieves a complete closed loop from the predictive model and thermal state assessment to the final actuator. Step S13 allocates a higher coolant flow rate to modules with higher temperatures or larger heat loads to enhance heat dissipation, while reducing the flow rate for modules with suitable temperatures to avoid overcooling. Through this independent and precise flow allocation, the system can actively and dynamically maintain the temperature of each battery module near the target operating temperature, thereby effectively improving the overall temperature uniformity of the battery pack, ensuring battery safety, and optimizing the overall energy efficiency of the cooling system.

[0090] In summary, this application embodiment achieves real-time and accurate monitoring of the operating temperature of each battery module by configuring an independent cooling control unit and a corresponding temperature acquisition sensor for each module. Based on the predictive model's assessment of the vehicle's future operating conditions and battery aging status, the system can proactively calculate the differentiated coolant flow rate required for each module and precisely control the flow rate of each cooling control unit through independent flow rate regulating valves. This allows modules with higher temperatures to receive stronger cooling, while modules with suitable temperatures avoid unnecessary cooling, thus effectively solving the problems of uneven temperature, localized overheating, or undercooling within the battery pack caused by traditional overall cooling methods. This method ultimately achieves differentiated and precise cooling for each battery module, significantly improving the overall temperature uniformity, operational safety, and system energy efficiency of the battery pack.

[0091] Based on the same inventive concept, this application also provides an optimized method for cooling a power battery, specifically including steps S31-S37, which can be found in detail in the following reference. Figure 3 As shown.

[0092] Step S31: Obtain the current operating temperature collected by the temperature acquisition sensor of each battery module in the power battery.

[0093] Step S32: Based on the current operating temperature of each battery module and the preset conditions corresponding to each thermal runaway warning level, determine the actual thermal runaway warning level of the power battery at the current moment.

[0094] Step S33: If the actual thermal runaway warning level is no thermal runaway risk level, perform the conventional coolant flow rate determination step.

[0095] Step S34: When the actual thermal runaway warning level is a mild thermal runaway risk level, the conventional coolant flow rate determination step is executed. Furthermore, for each battery module in the power battery whose current operating temperature is higher than the first warning temperature, an enhanced coolant flow rate determination step is executed. The enhanced coolant flow rate determination step includes: determining the parameter value of a preset multiple of the target coolant flow rate obtained in the conventional coolant flow rate determination step as the updated target coolant flow rate of the cooling control unit of that battery module; the preset multiple is greater than 1 and less than 1.5.

[0096] Step S35: When the actual thermal runaway warning level is a moderate thermal runaway risk level, for each battery module in the power battery whose current operating temperature is less than or equal to the first warning temperature, the conventional coolant flow rate determination step is executed; for each battery module in the power battery whose current operating temperature is greater than the first warning temperature, the emergency coolant flow rate determination step is executed. The emergency coolant flow rate determination step includes: determining the maximum coolant flow rate of the cooling control unit of each battery module as the target coolant flow rate of the cooling control unit.

[0097] Step S36: When the actual thermal runaway warning level is a severe thermal runaway risk level, for each of the battery modules in the power battery, the emergency coolant flow rate determination step and the coolant temperature at the coolant inlet of each of the cooling control units are controlled to decrease.

[0098] Step S37: After determining the target coolant flow rate of each cooling control unit, the coolant flow rate regulating valve of each cooling control unit is controlled based on the target coolant flow rate of each cooling control unit to cool each battery module.

[0099] Regarding step S31, it is similar to the aforementioned step S11. For details, please refer to the relevant description of step S11. The embodiments of this application will not be repeated here.

[0100] Regarding step S32, based on the current operating temperature of each battery module and the preset conditions corresponding to each thermal runaway warning level, the actual thermal runaway warning level of the power battery at the current moment is determined.

[0101] Specifically, if the current operating temperature of each battery module of the power battery is greater than or equal to the second warning temperature and less than or equal to the first warning temperature, the actual thermal runaway warning level of the power battery is determined to be a no-thermal-runaway-risk level.

[0102] If the proportion of battery modules in the power battery whose current operating temperature is greater than the first warning temperature is greater than zero and less than or equal to a preset proportion, the actual thermal runaway warning level of the power battery is determined to be a mild thermal runaway risk level.

[0103] If the proportion of battery modules in the power battery whose current operating temperature is greater than the first warning temperature is greater than the preset proportion but less than 100%, the actual thermal runaway warning level of the power battery is determined to be a moderate thermal runaway risk level.

[0104] If the current operating temperature of each battery module in the power battery is greater than the first warning temperature, the actual thermal runaway warning level of the power battery is determined to be a severe thermal runaway risk level.

[0105] Step S32 assesses the overall thermal runaway risk level of the power battery based on the real-time temperature of each battery module. Its core is to classify and determine the risk level using preset temperature and percentage thresholds. The determination of the first warning temperature (e.g., 35°C) and the second warning temperature (e.g., 25°C) is typically related to the battery's chemical characteristics, safety design standards, and historical operating data. The second warning temperature represents the upper limit of the battery's ideal operating temperature, used to distinguish between normal and concerning states. The first warning temperature is the critical point where the risk of thermal runaway begins to increase significantly; exceeding this temperature may accelerate battery aging or cause safety hazards. The preset percentage (e.g., 50%) is used to quantify the degree of overheating in the modules, serving as a key proportional boundary for distinguishing different risk levels.

[0106] The differences between the various thermal runaway risk levels are mainly reflected in the scope and severity of the overheated modules: The no-risk level indicates that all module temperatures are within the safe range (25℃ to 35℃); the mild risk level indicates that only a few modules (≤50%) have temperatures exceeding 35℃, and the risk is locally manageable; the moderate risk level indicates that the overheated modules have spread to a larger area (>50%, but not all), and the risk has significantly increased; the severe risk level means that all module temperatures exceed 35℃, indicating that the battery as a whole faces a serious threat of thermal runaway, requiring immediate emergency measures. This classification mechanism enables a refined and quantitative assessment of battery thermal risk, providing a clear basis for subsequent differentiated cooling or safety intervention.

[0107] Regarding step S33, if the actual thermal runaway warning level is no thermal runaway risk level, the conventional coolant flow rate determination step is performed. Step S33 is similar to the aforementioned step S12, and the specific details can be found in the relevant description of step S12. This embodiment of the application will not repeat the details here.

[0108] Regarding step S34, when the actual thermal runaway warning level is a mild thermal runaway risk level, the conventional coolant flow rate determination step is executed. This part is similar to the aforementioned step S12, and the specific details can be found in the relevant description of step S12. This embodiment will not repeat the details here. The difference between step S34 and step S12 is that for each battery module in the power battery whose current operating temperature is higher than the first warning temperature, further processing is performed, namely, an enhanced coolant flow rate determination step is executed. This enhanced coolant flow rate determination step includes: determining the parameter value of a preset multiple of the target coolant flow rate obtained in the conventional coolant flow rate determination step as the updated target coolant flow rate of the cooling control unit of the battery module; the preset multiple is greater than 1 and less than 1.5.

[0109] The enhanced coolant flow rate determination step in step S34 is as follows: When the system determines that the battery is at a mild thermal runaway risk level, in addition to generally implementing the conventional predictive cooling strategy (i.e., step S12), enhanced cooling is additionally activated for specific battery modules whose temperatures have exceeded the first warning temperature (e.g., 35°C). Specifically, the system multiplies the target coolant flow rate calculated for these overheated modules in the conventional steps by a preset multiple greater than 1 and less than 1.5 (e.g., 1.2 times), and directly updates the resulting parameter value as the final target flow rate of the module's cooling control unit.

[0110] Compared to step S12, step S34 introduces a "risk-level response" mechanism: in the low-risk stage, instead of subjecting all modules to overall overcooling that could lead to energy waste, it precisely targets a few overheated modules and only applies targeted and limited cooling enhancements (through a gentle multiplication of 1 to 1.5 times) to these abnormal battery modules. This rapidly improves the heat dissipation capacity of high-risk modules, more effectively curbing further temperature increases and preventing local hotspots from developing into global problems. Furthermore, it maintains the original energy efficiency optimization framework of the system to the greatest extent possible, avoiding a high-energy-consumption emergency state for the entire cooling system due to the overheating of a few modules. Therefore, the advantage of step S34 lies in its seamless integration of safety and precision on top of the predictability and economy of step S12, achieving efficient synergy between safety early warning and refined thermal management strategies.

[0111] Regarding step S35, when the actual thermal runaway warning level is a moderate thermal runaway risk level, for each battery module in the power battery whose current operating temperature is less than or equal to the first warning temperature, the conventional coolant flow rate determination step is executed. This part is similar to the aforementioned step S12, and the specific details can be found in the relevant description of step S12. This application embodiment will not repeat the details here. The difference between step S35 and step S12 is that when the system determines that the battery has entered a moderate thermal runaway risk level, its handling strategy for overheated battery modules changes from "predictive optimization adjustment" to "safety-first emergency handling". The specific process is as follows: for modules whose temperature is still normal, the same conventional predictive cooling as in step S12 is continued to maintain energy efficiency; however, for all overheated modules whose current operating temperature has exceeded the first warning temperature, the emergency cooling step is immediately initiated, that is, all prediction and optimization calculations are ignored, and the valve of the cooling control unit corresponding to the module is directly opened to the maximum to force heat dissipation at the maximum coolant flow rate allowed by the system.

[0112] Compared to step S12, the advantage of step S35 lies in the fundamental shift in the priority and intensity of its response. The core objective of step S12 is to achieve precise and economical temperature uniformity control based on prediction. However, when faced with a moderately spreading thermal risk, this optimization model may be insufficient to contain the rapid escalation of the risk due to insufficient response intensity. Step S35, on the other hand, explicitly prioritizes preventing the spread of thermal runaway, taking the most decisive and strongest cooling intervention in the overheated area, sacrificing local energy efficiency for overall safety.

[0113] Compared to step S34, the advantage of step S35 lies in its upgraded strategy for dealing with higher-level risks. Step S34's enhanced cooling only involves a limited increase (1-1.5 times) in the normal flow rate, an optimized enhancement mode suitable for mild stages where risks are initially apparent and limited in scope. Step S35's emergency cooling, however, directly activates maximum cooling capacity, a full-force suppression mode. This signifies a shift in system strategy from optimizing energy efficiency while ensuring a safety baseline (S34) to balancing safety with energy efficiency as much as possible (S35). When a moderate risk occurs, the proportion of overheated modules is already high, and localized thermal runaway may trigger a chain reaction. At this point, step S35 uses zoned processing, implementing maximum cooling capacity for the overheated area to rapidly reduce temperature, while maintaining normal cooling in the safe area to avoid unnecessary energy consumption. This achieves the most effective safety control and resource allocation balance under severe thermal threats.

[0114] Regarding step S36, when the system determines that the power battery is at a severe thermal runaway risk level, i.e., the current operating temperature of all battery modules exceeds the first warning temperature, this method will execute the highest intensity comprehensive cooling intervention. Specifically, the system will simultaneously implement two measures for all battery modules: First, it will perform an emergency coolant flow rate determination step for each cooling control unit, i.e., switching the flow rate regulating valves of all units to the fully open state to force heat dissipation at the maximum coolant flow rate allowed by the system; Second, it will synchronously control the upstream components of the cooling system (such as the refrigeration compressor or cooling fan) to actively reduce the coolant temperature flowing into the inlet of all cooling control units (for example, by 15°C) to maximize the heat exchange temperature difference and heat dissipation capacity. Step S36 no longer distinguishes the temperature differences between modules, but treats the entire battery pack as a high-risk thermal runaway unit that urgently needs overall cooling. By simultaneously calling the two ultimate cooling methods of "maximum flow rate" and "lowest temperature", the strongest cooling synergy is formed, aiming to suppress the collective runaway rise of battery temperature as quickly as possible at the cost of energy consumption, and to buy the most critical time window for the safe evacuation of passengers and the safe shutdown of the system.

[0115] Regarding step S37, it is similar to the aforementioned step S13. For details, please refer to the relevant description of step S13. The embodiments of this application will not be repeated here.

[0116] In summary, this embodiment first collects the temperature of each battery module in real time through step S31, and then evaluates the actual thermal runaway warning level into four levels: no risk, mild, moderate, and severe, based on preset thresholds and proportional conditions in step S32. Subsequently, the system dynamically selects and combines differentiated cooling strategy chains according to different warning levels: In the no-risk situation (S33), conventional predictive optimized cooling is performed; in the mild-risk situation (S34), in addition to conventional cooling, limited-rate cooling enhancement is applied only to locally overheated modules; in the moderate-risk situation (S35), a "zonal control" strategy is adopted, maintaining conventional cooling in normal areas and initiating emergency cooling at maximum flow rate in overheated areas; in the severe-risk situation (S36), the highest-intensity "maximum flow rate and reduced inlet temperature" combined emergency cooling of the entire system is activated. Finally, step S37 uniformly executes all calculated target flow rates. These steps are closely linked, forming a closed loop of "monitoring-evaluation-decision-execution". Overall, the solution achieves a seamless transition from routine refined management to emergency safety intervention through risk classification and strategy linkage. While ensuring that the battery system can obtain sufficient cooling to ensure safety under any risk level, it maximizes the system's operating efficiency and temperature uniformity, achieving the optimal balance between safety and economy.

[0117] Furthermore, based on the above scheme, after controlling the coolant flow rate regulating valve of each cooling control unit according to the target coolant flow rate of each cooling control unit to cool each battery module, the method further includes steps S41-S43.

[0118] Step S41: If the actual thermal runaway warning level is a mild thermal runaway risk level, and if it is detected that the operating temperature of each battery module in the power battery, whose current operating temperature was originally greater than the first warning temperature, is less than or equal to the first warning temperature and the duration exceeds the first preset duration, then the current operating temperature collected by the temperature acquisition sensor of each battery module in the power battery is reacquired. Based on the current operating temperature of each battery module and the preset conditions corresponding to each thermal runaway warning level, the actual thermal runaway warning level of the power battery at the current moment is determined, and then the target coolant flow rate of each cooling control unit is updated to cool each battery module.

[0119] Step S42: If the actual thermal runaway warning level is a moderate thermal runaway risk level, and it is detected that the operating temperature of each battery module in the power battery, whose current operating temperature was originally higher than the first warning temperature, is lower than or equal to the first warning temperature and the duration exceeds the second preset duration, then the current operating temperature collected by the temperature acquisition sensor of each battery module in the power battery is reacquired. Based on the current operating temperature of each battery module and the preset conditions corresponding to each thermal runaway warning level, the actual thermal runaway warning level of the power battery at the current moment is determined, and then the target coolant flow rate of each cooling control unit is updated to cool each battery module; the second preset duration is longer than the first preset duration.

[0120] Step S43: If the actual thermal runaway warning level is a severe thermal runaway risk level, and the operating temperature of each battery module in the power battery is detected to be less than or equal to the third warning temperature and the duration exceeds the third preset duration, then the current operating temperature collected by the temperature acquisition sensor of each battery module in the power battery is reacquired. Based on the current operating temperature of each battery module and the preset conditions corresponding to each thermal runaway warning level, the actual thermal runaway warning level of the power battery at the current moment is determined, and then the target coolant flow rate of each cooling control unit is updated to cool each battery module; the third preset duration is greater than the second preset duration, and the third warning temperature is less than the first warning temperature.

[0121] Step S41 is the rule for determining the state transition after the system is at a mild thermal runaway risk level. The specific process is as follows: when all abnormal modules whose original temperatures exceeded the first warning temperature are detected, and their operating temperatures have all fallen back to or below the warning temperature, and this safe state remains stable for more than a first preset duration (e.g., 10 seconds), the system will enter the normal cooling control state, that is, starting from step S31, steps S32-S37 will be executed according to the actual situation. Step S41 achieves an automatic, smooth, and reliable transition of the cooling strategy from "risk response mode" to "normal optimization mode." By introducing the "duration" condition, frequent strategy switching caused by short-term temperature fluctuations or measurement noise is effectively avoided, ensuring that the system can promptly return to the more energy-efficient normal predictive cooling state after the risk is eliminated, thereby quickly restoring the system's operational economy while ensuring safety.

[0122] Regarding step S42, the conditions for resolving the moderate thermal runaway risk level are defined. Its logic is similar to S41, but a more stringent standard is applied: the temperature of all overheated modules must drop to or below the first warning temperature, and this state must persist for more than a longer second preset duration (e.g., 20 seconds). Step S42 sets a necessary observation period for the battery system undergoing high thermal load and intensive emergency cooling. Because the battery thermal state is more severe under moderate risk, the longer stabilization time ensures that the trend of thermal diffusion has been completely contained and the internal temperature field of the battery has been fully balanced, thus avoiding premature exit from the emergency state before the risk is completely eliminated. This reflects a prudent trade-off between safety and economy in system design, namely, maintaining the necessary cooling intensity to absolutely prioritize safety until safety is confirmed to have been fundamentally restored.

[0123] Regarding step S43, the highest standard release condition for downgrading from the severe thermal runaway risk level is set. It requires not only that the temperature of all battery modules must drop to a third warning temperature (e.g., 32°C) lower than the first warning temperature, but also that this low-temperature safe state last for more than the longest third preset time (e.g., 30 seconds). Step S43 provides a rigorous mechanism to ensure a full and stable return to the safe zone for systems that have experienced extreme thermal threats and the strongest cooling intervention. The design of the dual stringent conditions (lower temperature threshold and longer stabilization time) aims to confirm that the overall high thermal risk of the battery has been completely eliminated and the possibility of a thermal runaway chain reaction has been completely interrupted. This prevents the risk of reigniting if the system rashly reduces the cooling intensity before the battery is sufficiently cooled or its state is unstable. It is a key guarantee for achieving a safe and controllable exit from the highest level of emergency state, ultimately ensuring the robustness and absolute safety of the entire thermal management closed-loop control.

[0124] In addition, after the system determines that the power battery has a mild, moderate or severe risk level of thermal runaway, it can also record relevant fault data during the fault process and the fault recovery process, so as to provide data for subsequent troubleshooting and repair of the power battery.

[0125] This application provides a specific example to further illustrate the power battery cooling method provided above.

[0126] First, we take a certain pure electric vehicle as the research object. The vehicle has a range of about 550km, the power battery is a 21700 cylindrical battery module, the total mass of the power battery is 380kg, and the specific heat capacity of the power battery is 900J / (kg·℃).

[0127] Actual road driving data of the research subjects were collected to support the embodiments of this application. The data collection period was about one year, with a total mileage of about 151,000 kilometers. The vehicle operating conditions in the data covered various scenarios such as high speed, fast charging, climbing, and low-speed cruising. The collected data parameters included: vehicle speed, acceleration, accelerator pedal opening and its rate of change, brake pedal opening and its rate of change, power battery SOC, ambient temperature, etc.

[0128] Based on the vehicle operating condition prediction model, the predicted vehicle speed sequence for the next 5 seconds is [55.2, 58.5, 60.1, 57.4, 55.9] (in km / h). Then, using the vehicle dynamics model (refer to formula seven below), the required power output from the propulsion battery is calculated. .

[0129] The seventh formula is:

[0130]

[0131] In the formula, Indicates power battery Power required for output at any time This indicates the total vehicle weight, 1800 kg. This represents the acceleration due to gravity, 9.8 m / s². 2 ; This represents the rolling resistance coefficient, taken as 0.015; To represent air density, take 1.225 kg / m³. 3 , This represents the drag coefficient, taken as 0.28. The windward area is taken as 2.2m². 2 ; This represents the rotational mass conversion factor, taken as 1.05. This represents the motor efficiency, taken as 0.92. Indicates electric vehicles The speed of time.

[0132] Substituting the previously obtained 5-second vehicle speed sequence, the power output demand of the battery in the next 5 seconds can be calculated. The values ​​are [13.91, 10.55, 0, 2.46, 2.31], and the unit is kW.

[0133] Then, based on the power output demand of the power battery in the next 5 seconds, calculate the terminal voltage and operating current of the power battery.

[0134] The terminal voltage needs to consider the dynamic coupling between the open-circuit voltage and the internal resistance voltage drop. The calculation formula is: Terminal Voltage equal to open circuit voltage Subtract the product of current and internal resistance. Construct the characteristic curve of the power battery's SOC versus open-circuit voltage using polynomial fitting, obtaining the open-circuit voltage when SOC ∈ [20%, 100%]. Here, the coefficients are obtained by fitting the battery through 1000 charge-discharge cycles. Based on the open-circuit voltage... and the power output demand of the power battery The discharge current can be calculated. That is, it includes The operating current of the power battery at all times.

[0135] Internal resistance is affected by the state of charge (SOC) of the power battery and the discharge current. The internal resistance calculation formula, obtained by fitting the data from the above 1000 charge-discharge experiments of the battery, is as follows: Internal Resistance .

[0136] Based on this, and taking the predicted aging coefficient SOH of the power battery as 0.8 as a basis, the heat generation sequence for the next 5 seconds can be calculated according to the discharge current and the internal resistance of the power battery as [528.70, 529.06, 258.14, 257.01, 219.15], in J, with a total heat generation of 1792.06 J.

[0137] By utilizing the independent temperature acquisition sensor equipped in each cooling control unit, the current temperature of different modules of the electric vehicle power battery is measured and obtained. Combined with the future heat generation of the electric vehicle power battery, the coolant flow rate of each cooling control unit in the integrated cooling plate is calculated.

[0138] In this embodiment of the application, taking a certain module as an example, if the current operating temperature of the module is 35℃, then the coolant flow rate of the corresponding cooling control unit controlled by this embodiment of the application should be [0.58, 0.40, 0.16, 0.17, 0.17], in L / min.

[0139] A four-level cooling warning system is set: Cooling warning level 0 is defined as: the actual temperature of all power battery modules is between 25℃ and 35℃. At cooling warning level 0, there is no risk of thermal runaway of the electric vehicle power battery, and no warning is issued. Cooling warning level 1 is defined as: less than half of the power battery modules have an actual temperature higher than 35℃, triggering a mild risk of thermal runaway. At cooling warning level 2, more than half of the power battery modules have an actual temperature higher than 35℃, triggering a moderate risk of thermal runaway. At cooling warning level 3, the actual temperature of all power battery modules is higher than 35℃, triggering a severe risk of thermal runaway. The power battery modules with an actual temperature higher than 35℃ are designated as target modules.

[0140] When an early warning is triggered, a coordinated control strategy is executed, specifically including: when no warning is issued, the cooling system is controlled directly according to the previously calculated coolant flow rate. When a mild thermal runaway risk is triggered, the coolant flow rate of the corresponding cooling control unit of the target module is increased by an additional 10% on top of the original value. When a moderate thermal runaway risk is triggered, the coolant flow rate of the corresponding cooling control unit of the target module is increased to the maximum limit. When a severe thermal runaway risk is triggered, the coolant flow rate of all cooling control units in the integrated cooling plate is increased to the maximum limit, and at the same time, the coolant temperature at the cooling plate inlet drops to 15°C.

[0141] The reset conditions after the warning is triggered are as follows:

[0142] Reset conditions for mild warning: The target zone temperature drops below 35℃ and remains there for 10 seconds before resetting; Reset conditions for moderate warning: The temperature of all zones drops below 35℃ and remains there for 20 seconds before resetting; Reset conditions for severe warning: The temperature of all zones drops below 32℃ and remains there for 30 seconds before resetting, while recording fault data (for after-sales troubleshooting).

[0143] In addition to the above description, this application embodiment also selects two typical operating conditions to verify the effectiveness of the method. In the first typical operating condition, the vehicle travels at a constant speed of 60 km / h in a -10℃ low-temperature environment. This application embodiment can control the power battery temperature between 28.1-30.8 ℃, with the cooling system energy consumption accounting for approximately 5.8% of the total energy consumption. In the second typical operating condition, the vehicle accelerates rapidly from 0 km / h to 100 km / h and then continues driving. This application embodiment can control the power battery temperature between 29.0-31.5 ℃, with the cooling system energy consumption accounting for approximately 5.1% of the total energy consumption. The above results verify the application effect of this application embodiment, achieving differentiated and precise cooling for each battery module, significantly improving the overall temperature uniformity, operational safety, and system energy efficiency of the battery pack.

[0144] Based on the same inventive concept, this application provides a power battery cooling device for controlling the cooling plate of a power battery in an electric vehicle. The cooling plate includes multiple independent cooling control units, each of which is equipped with an independent coolant flow rate regulating valve and a temperature acquisition sensor. The power battery includes multiple battery modules, and each battery module corresponds one-to-one with a cooling control unit. The device includes:

[0145] The acquisition module is used to acquire the current operating temperature of each battery module in the power battery by the temperature acquisition sensor during the power-on operation of the electric vehicle.

[0146] The flow rate determination module is used to perform a conventional coolant flow rate determination step, which includes: acquiring historical operating condition data of the electric vehicle during a preset historical period, and acquiring the actual battery parameters of the power battery at the current moment; inputting the historical operating condition data into a pre-trained vehicle operating condition prediction model to obtain the expected operating condition data of the electric vehicle during a preset future period; inputting the actual battery parameters into a pre-trained battery aging prediction model to obtain the actual aging coefficient of the power battery at the current moment; based on the expected operating condition data and the actual aging coefficient, obtaining the expected heat generation of the power battery during the preset future period; and based on the expected heat generation, the target operating temperature of the power battery, the current operating temperature of each battery module, and the coolant inlet and outlet temperature difference of each cooling control unit, determining the target coolant flow rate of each cooling control unit.

[0147] A flow rate control module is used to control the coolant flow rate regulating valve of each cooling control unit based on the target coolant flow rate of each cooling control unit, so as to cool each battery module.

[0148] Furthermore, the flow rate determination module includes:

[0149] The internal resistance determination submodule is used to determine the actual internal resistance of the power battery at the current moment based on the actual aging coefficient and the rated internal resistance of the power battery.

[0150] The parameter determination submodule is used to determine the aging disturbance parameters of the power battery based on the actual aging coefficient.

[0151] The current determination submodule is used to obtain the expected operating current of the power battery based on the expected operating condition data.

[0152] The heat determination submodule is used to estimate the expected heat generation of the power battery in the preset future period based on the expected operating current, the actual battery internal resistance, and the aging disturbance parameters.

[0153] Furthermore, the flow rate determination module includes:

[0154] The coefficient determination submodule is used to determine the temperature correction coefficient of each battery module based on the target operating temperature of the power battery and the current operating temperature of each battery module.

[0155] The flow rate determination submodule is used to determine the target coolant flow rate of each cooling control unit based on the expected heat generation, the temperature correction coefficient of each battery module, and the coolant inlet and outlet temperature difference of the cooling control unit configured for each battery module.

[0156] Furthermore, the device also includes:

[0157] The warning level determination module is used to determine the actual thermal runaway warning level of the power battery at the current moment based on the current operating temperature of each battery module and the preset conditions corresponding to each thermal runaway warning level after acquiring the current operating temperature of each battery module.

[0158] The flow rate determination module is used to perform the conventional coolant flow rate determination step when the actual thermal runaway warning level is no thermal runaway risk level.

[0159] Furthermore, the flow rate determination module is also used for:

[0160] When the actual thermal runaway warning level is a mild thermal runaway risk level, the conventional coolant flow rate determination step is executed. Furthermore, for each battery module in the power battery whose current operating temperature is higher than the first warning temperature, an enhanced coolant flow rate determination step is executed. This enhanced coolant flow rate determination step includes: determining the parameter value of a preset multiple of the target coolant flow rate obtained in the conventional coolant flow rate determination step as the updated target coolant flow rate of the cooling control unit for that battery module; the preset multiple is greater than 1 and less than 1.5.

[0161] When the actual thermal runaway warning level is a moderate thermal runaway risk level, for each battery module in the power battery whose current operating temperature is less than or equal to the first warning temperature, the conventional coolant flow rate determination step is performed; for each battery module in the power battery whose current operating temperature is greater than the first warning temperature, the emergency coolant flow rate determination step is performed. The emergency coolant flow rate determination step includes: determining the maximum coolant flow rate of the cooling control unit of each battery module as the target coolant flow rate of the cooling control unit.

[0162] When the actual thermal runaway warning level is a severe thermal runaway risk level, for each of the battery modules in the power battery, the emergency coolant flow rate determination step and the coolant temperature at the coolant inlet of each of the cooling control units are reduced.

[0163] The flow rate control module is used to control the coolant flow rate regulating valve of each cooling control unit after determining the target coolant flow rate of each cooling control unit, so as to cool each battery module.

[0164] Furthermore, the early warning level determination module is used for:

[0165] If the current operating temperature of each battery module of the power battery is greater than or equal to the second warning temperature and less than or equal to the first warning temperature, the actual thermal runaway warning level of the power battery is determined to be a no-thermal-runaway-risk level.

[0166] If the proportion of battery modules in the power battery whose current operating temperature is greater than the first warning temperature is greater than zero and less than or equal to a preset proportion, the actual thermal runaway warning level of the power battery is determined to be a mild thermal runaway risk level.

[0167] If the proportion of battery modules in the power battery whose current operating temperature is greater than the first warning temperature is greater than the preset proportion but less than 100%, the actual thermal runaway warning level of the power battery is determined to be a moderate thermal runaway risk level.

[0168] If the current operating temperature of each battery module in the power battery is greater than the first warning temperature, the actual thermal runaway warning level of the power battery is determined to be a severe thermal runaway risk level.

[0169] Furthermore, the device also includes a risk mitigation module for:

[0170] After controlling the coolant flow rate regulating valve of each cooling control unit based on the target coolant flow rate of each cooling control unit to cool each battery module, if the actual thermal runaway warning level is a mild thermal runaway risk level, and it is detected that the operating temperature of each battery module in the power battery that was originally above the first warning temperature is below or equal to the first warning temperature and the duration exceeds the first preset duration, then the current operating temperature collected by the temperature acquisition sensor of each battery module in the power battery is reacquired. Based on the current operating temperature of each battery module and the preset conditions corresponding to each thermal runaway warning level, the actual thermal runaway warning level of the power battery at the current moment is determined, and then the target coolant flow rate of each cooling control unit is updated to cool each battery module.

[0171] If, under the condition that the actual thermal runaway warning level is a moderate thermal runaway risk level, the operating temperature of each battery module in the power battery, whose current operating temperature was originally higher than the first warning temperature, is found to be lower than or equal to the first warning temperature and the duration exceeds the second preset duration, then the current operating temperature collected by the temperature acquisition sensor of each battery module in the power battery is reacquired. Based on the current operating temperature of each battery module and the preset conditions corresponding to each preset thermal runaway warning level, the actual thermal runaway warning level of the power battery at the current moment is determined, and then the target coolant flow rate of each cooling control unit is updated to cool each battery module; the second preset duration is longer than the first preset duration.

[0172] When the actual thermal runaway warning level is a severe thermal runaway risk level, if the operating temperature of each battery module in the power battery is detected to be less than or equal to the third warning temperature and the duration exceeds the third preset duration, the current operating temperature collected by the temperature acquisition sensor of each battery module in the power battery is reacquired. Based on the current operating temperature of each battery module and the preset conditions corresponding to each thermal runaway warning level, the actual thermal runaway warning level of the power battery at the current moment is determined, and the target coolant flow rate of each cooling control unit is updated to cool each battery module. The third preset duration is longer than the second preset duration, and the third warning temperature is less than the first warning temperature.

[0173] Based on the same inventive concept, the embodiments of this application provide, as follows: Figure 4 An electric vehicle shown includes:

[0174] Power battery 41, including multiple battery modules;

[0175] The cooling plate 42 includes multiple cooling control units, each corresponding to a battery module. The cooling control units are arranged on the outside of the battery module and are used to cool the battery module.

[0176] Processor 43 is connected to each of the aforementioned cooling control units;

[0177] Memory 44 is used to store executable instructions of the processor 43;

[0178] The processor 43 is configured to execute a power battery cooling method as described above.

[0179] Based on the same inventive concept, embodiments of this application provide a non-transitory computer-readable storage medium, which, when the instructions in the storage medium are executed by the processor 43 of an electric vehicle, enables the electric vehicle to perform a power battery cooling method as described above.

[0180] Since the electric vehicle described in this application is the electric vehicle used to implement the information processing method in this application, those skilled in the art can understand the specific implementation methods and various variations of the electric vehicle in this application based on the information processing method described in this application. Therefore, how the electric vehicle implements the method in this application will not be described in detail here. Any electric vehicle used by those skilled in the art to implement the information processing method in this application falls within the scope of protection of this application.

[0181] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0182] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0183] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0184] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0185] Although embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make further changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.

[0186] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for cooling a power battery, characterized in that, A cooling plate for controlling a power battery in an electric vehicle, the cooling plate comprising multiple independent cooling control units, each cooling control unit equipped with an independent coolant flow rate regulating valve and a temperature acquisition sensor, the power battery comprising multiple battery modules, each battery module corresponding one-to-one with a cooling control unit, the method comprising: During the operation of the electric vehicle after powering on, the current operating temperature of each battery module in the power battery is acquired by the temperature acquisition sensor. Based on the current operating temperature of each battery module and preset conditions corresponding to each preset thermal runaway warning level, the actual thermal runaway warning level of the power battery at the current moment is determined, including: when the current operating temperature of each battery module of the power battery is greater than or equal to the second warning temperature and less than or equal to the first warning temperature, the actual thermal runaway warning level of the power battery is determined to be a no-thermal-runaway-risk level; when the proportion of battery modules in the power battery whose current operating temperature is greater than the first warning temperature is greater than zero and less than or equal to a preset proportion, the actual thermal runaway warning level of the power battery is determined to be a mild thermal runaway-risk level; when the proportion of battery modules in the power battery whose current operating temperature is greater than the first warning temperature is greater than the preset proportion and less than 100%, the actual thermal runaway warning level of the power battery is determined to be a moderate thermal runaway-risk level; when the current operating temperature of each battery module in the power battery is greater than the first warning temperature, the actual thermal runaway warning level of the power battery is determined to be a severe thermal runaway-risk level. When the actual thermal runaway warning level is no risk level, a conventional coolant flow rate determination step is performed. This conventional coolant flow rate determination step includes: acquiring historical operating condition data of the electric vehicle over a preset historical period, and acquiring the actual battery parameters of the power battery at the current moment; inputting the historical operating condition data into a pre-trained vehicle operating condition prediction model to obtain the expected operating condition data of the electric vehicle over a preset future period; inputting the actual battery parameters into a pre-trained battery aging prediction model to obtain the actual aging coefficient of the power battery at the current moment; based on the expected operating condition data and the actual aging coefficient, obtaining the expected heat generation of the power battery over the preset future period; and based on the expected heat generation, the target operating temperature of the power battery, the current operating temperature of each battery module, and the coolant inlet and outlet temperature difference of each cooling control unit, determining the target coolant flow rate of each cooling control unit. When the actual thermal runaway warning level is a mild thermal runaway risk level, the conventional coolant flow rate determination step is executed. Furthermore, for each battery module in the power battery whose current operating temperature is higher than the first warning temperature, an enhanced coolant flow rate determination step is executed. This enhanced coolant flow rate determination step includes: determining the parameter value of a preset multiple of the target coolant flow rate obtained in the conventional coolant flow rate determination step as the updated target coolant flow rate of the cooling control unit for that battery module; the preset multiple is greater than 1 and less than 1.

5. When the actual thermal runaway warning level is a moderate thermal runaway risk level, for each battery module in the power battery whose current operating temperature is less than or equal to the first warning temperature, the conventional coolant flow rate determination step is performed; for each battery module in the power battery whose current operating temperature is greater than the first warning temperature, the emergency coolant flow rate determination step is performed. The emergency coolant flow rate determination step includes: determining the maximum coolant flow rate of the cooling control unit of each battery module as the target coolant flow rate of the cooling control unit. When the actual thermal runaway warning level is a severe thermal runaway risk level, for each of the battery modules in the power battery, the emergency coolant flow rate determination step and the coolant temperature at the coolant inlet of each of the cooling control units are reduced. Based on the target coolant flow rate of each of the cooling control units, the coolant flow rate regulating valve of each of the cooling control units is controlled to cool each of the battery modules.

2. The power battery cooling method as described in claim 1, characterized in that, Based on the expected operating condition data and the actual aging coefficient, the expected heat generation of the power battery in the preset future period is obtained, including: Based on the actual aging coefficient and the rated internal resistance of the power battery, determine the actual internal resistance of the power battery at the current moment. The aging disturbance parameters of the power battery are determined based on the actual aging coefficient. Based on the expected operating condition data, the expected operating current of the power battery is obtained; Based on the expected operating current, the actual battery internal resistance, and the aging disturbance parameters, the expected heat generation of the power battery in the preset future period is estimated.

3. The power battery cooling method as described in claim 1, characterized in that, Based on the expected heat generation, the target operating temperature of the power battery, the current operating temperature of each battery module, and the coolant inlet and outlet temperature difference of each cooling control unit, the target coolant flow rate of each cooling control unit is determined, including: Based on the target operating temperature of the power battery and the current operating temperature of each battery module, a temperature correction coefficient for each battery module is determined. Based on the expected heat generation, the temperature correction coefficient of each battery module, and the coolant inlet and outlet temperature difference of the cooling control unit configured in each battery module, the target coolant flow rate of each cooling control unit is determined.

4. The power battery cooling method as described in claim 1, characterized in that, After controlling the coolant flow rate regulating valve of each cooling control unit based on the target coolant flow rate of each cooling control unit to cool each battery module, the method further includes: If the actual thermal runaway warning level is a mild thermal runaway risk level, and it is detected that the operating temperature of each battery module in the power battery, which originally had a current operating temperature greater than the first warning temperature, is less than or equal to the first warning temperature and the duration exceeds the first preset duration, then the current operating temperature collected by the temperature acquisition sensor of each battery module in the power battery is reacquired. Based on the current operating temperature of each battery module and the preset conditions corresponding to each thermal runaway warning level, the actual thermal runaway warning level of the power battery at the current moment is determined, and then the target coolant flow rate of each cooling control unit is updated to cool each battery module. If, under the condition that the actual thermal runaway warning level is a moderate thermal runaway risk level, the operating temperature of each battery module in the power battery, whose current operating temperature was originally higher than the first warning temperature, is found to be lower than or equal to the first warning temperature and the duration exceeds the second preset duration, then the current operating temperature collected by the temperature acquisition sensor of each battery module in the power battery is reacquired. Based on the current operating temperature of each battery module and the preset conditions corresponding to each preset thermal runaway warning level, the actual thermal runaway warning level of the power battery at the current moment is determined, and then the target coolant flow rate of each cooling control unit is updated to cool each battery module; the second preset duration is longer than the first preset duration. When the actual thermal runaway warning level is a severe thermal runaway risk level, if the operating temperature of each battery module in the power battery is detected to be less than or equal to the third warning temperature and the duration exceeds the third preset duration, the current operating temperature collected by the temperature acquisition sensor of each battery module in the power battery is reacquired. Based on the current operating temperature of each battery module and the preset conditions corresponding to each thermal runaway warning level, the actual thermal runaway warning level of the power battery at the current moment is determined, and the target coolant flow rate of each cooling control unit is updated to cool each battery module. The third preset duration is longer than the second preset duration, and the third warning temperature is less than the first warning temperature.

5. A power battery cooling device, characterized in that, A device for controlling a cooling plate for a power battery in an electric vehicle, the cooling plate comprising multiple independent cooling control units, each cooling control unit equipped with an independent coolant flow rate regulating valve and a temperature acquisition sensor, the power battery comprising multiple battery modules, each battery module corresponding one-to-one with a cooling control unit, the device comprising: The acquisition module is used to acquire the current operating temperature of each battery module in the power battery by the temperature acquisition sensor during the power-on operation of the electric vehicle. The warning level determination module is used to determine the actual thermal runaway warning level of the power battery at the current moment based on the current operating temperature of each battery module and the preset conditions corresponding to each preset thermal runaway warning level. The flow rate determination module is used to perform a conventional coolant flow rate determination step when the actual thermal runaway warning level is no thermal runaway risk level. The conventional coolant flow rate determination step includes: acquiring historical operating condition data of the electric vehicle over a preset historical period; acquiring the actual battery parameters of the power battery at the current moment; inputting the historical operating condition data into a pre-trained vehicle operating condition prediction model to obtain expected operating condition data of the electric vehicle over a preset future period; inputting the actual battery parameters into a pre-trained battery aging prediction model to obtain the actual aging coefficient of the power battery at the current moment; based on the expected operating condition data and the actual aging coefficient, obtaining the expected heat generation of the power battery over the preset future period; and based on the expected heat generation, the target operating temperature of the power battery, the current operating temperature of each battery module, and the coolant inlet / outlet temperature difference of each cooling control unit, determining the target coolant flow rate of each cooling control unit. The flow rate determination module is further configured to: execute the conventional coolant flow rate determination step when the actual thermal runaway warning level is a mild thermal runaway risk level, and execute an enhanced coolant flow rate determination step for each battery module in the power battery whose current operating temperature is greater than the first warning temperature. The enhanced coolant flow rate determination step includes: determining a parameter value of a preset multiple of the target coolant flow rate obtained in the conventional coolant flow rate determination step as the updated target coolant flow rate of the cooling control unit for that battery module; the preset multiple is greater than 1 and less than 1.5; and, when the actual thermal runaway warning level is a moderate thermal runaway risk level, for each battery module whose current operating temperature is greater than the first warning temperature, execute an enhanced coolant flow rate determination step. For each battery module whose current operating temperature is less than or equal to the first warning temperature, the conventional coolant flow rate determination step is executed. For each battery module in the power battery whose current operating temperature is greater than the first warning temperature, the emergency coolant flow rate determination step is executed. The emergency coolant flow rate determination step includes: determining the maximum coolant flow rate of the cooling control unit of each battery module as the target coolant flow rate of the cooling control unit; when the actual thermal runaway warning level is a severe thermal runaway risk level, for each battery module in the power battery, the emergency coolant flow rate determination step is executed and the coolant temperature at the coolant inlet of each cooling control unit is reduced. A flow rate control module is used to control the coolant flow rate regulating valve of each cooling control unit based on the target coolant flow rate of each cooling control unit, so as to cool each battery module. The warning level determination module is used to: determine the actual thermal runaway warning level of the power battery as a no-thermal-runaway-risk level when the current operating temperature of each battery module in the power battery is greater than or equal to the second warning temperature and less than or equal to the first warning temperature; determine the actual thermal runaway warning level of the power battery as a mild thermal runaway-risk level when the proportion of battery modules in the power battery whose current operating temperature is greater than the first warning temperature is greater than zero and less than or equal to a preset proportion; determine the actual thermal runaway warning level of the power battery as a moderate thermal runaway-risk level when the proportion of battery modules in the power battery whose current operating temperature is greater than the first warning temperature is greater than the preset proportion and less than 100%; and determine the actual thermal runaway warning level of the power battery as a severe thermal runaway-risk level when the current operating temperature of each battery module in the power battery is greater than the first warning temperature.

6. An electric vehicle, characterized in that, include: Power battery, including multiple battery modules; The cooling plate includes multiple cooling control units, each corresponding to a battery module. The cooling control units are arranged on the outside of the battery module and are used to cool the battery module. The processor is connected to each of the aforementioned cooling control units; Memory used to store the processor's executable instructions; The processor is configured to execute a power battery cooling method as described in any one of claims 1 to 4.

7. A non-transitory computer-readable storage medium, characterized in that, When the instructions in the storage medium are executed by the processor of the electric vehicle, the electric vehicle is able to perform a power battery cooling method as described in any one of claims 1 to 4.

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