A method for intelligent thermal management control of power batteries

Abstract

This invention relates to an intelligent thermal management control method for power batteries, comprising: acquiring real-time operational monitoring data of the power battery, including battery temperature distribution, current, voltage, SOC, power, and vehicle operating conditions; constructing and continuously updating an adaptive dynamic thermal characteristic model of the power battery based on the real-time operational monitoring data, used to describe the mapping relationship between battery heat generation, heat dissipation, and temperature changes under the current battery aging level and vehicle operating conditions; dynamically adjusting the adaptive dynamic thermal characteristic model and combining it with predicted battery usage needs through a model predictive control algorithm to predict battery temperature changes within a preset time period and calculate the optimal thermal management control sequence; and dynamically executing thermal management actions based on the optimal thermal management control sequence, the vehicle operating conditions, and the initial temperature range of the battery, to manage battery temperature more efficiently.

Description

Technical Field

[0001] This invention belongs to the field of battery thermal management technology, and particularly relates to an intelligent thermal management control method for power batteries. Background Technology

[0002] As a core energy component of new energy equipment, the operating temperature of a power battery directly determines its energy output efficiency, cycle life, safety, and overall performance, making it a core indicator for the design and management of power battery systems. The optimal operating temperature range for power batteries is strictly defined. At excessively low temperatures, the internal lithium-ion diffusion rate decreases significantly, leading to incomplete electrochemical reactions, resulting in capacity decay and limited charging / discharging power, failing to meet the normal power demands of the equipment. At excessively high temperatures, the battery's internal resistance increases, and the heat generation rate accelerates. This not only triggers the power limiting logic of the battery management system, affecting the user experience, but also increases the risk of thermal runaway, fires, and even explosions. Furthermore, high temperatures accelerate the aging of the battery's positive and negative electrode materials and electrolyte decomposition, significantly shortening the battery's cycle life. Therefore, maintaining the power battery temperature within its optimal operating range through efficient thermal management control strategies is crucial for ensuring the safe, healthy, and stable operation of the power battery system and is a core technological support for promoting the industrialization of new energy equipment.

[0003] Currently, power battery thermal management technology has become a key research and development focus in the industry. Most existing thermal management control methods are passive threshold-triggered control. This means that after the vehicle is powered on, the battery management system only collects the current temperature data of the power battery in real time. When the temperature is below a preset low-temperature threshold, the heating device is activated; when the temperature is above a preset high-temperature threshold, the cooling device is activated. Thermal management stops immediately once the temperature returns to the threshold range. This passive control method can only provide a simple response to the current temperature state of the battery, failing to consider the nonlinear and time-varying characteristics of power battery temperature changes, and also failing to perform fine-tuning based on core influencing factors such as the actual operating conditions and performance degradation of the power battery. This results in problems such as rigid control, lack of predictive ability, and low temperature control accuracy, making it difficult to meet the usage requirements of power batteries under complex operating conditions.

[0004] To compensate for the shortcomings of passive threshold control, several improved thermal management technologies have emerged in the industry. For example, patent CN117039211A discloses an intelligent thermal management method for electric vehicles. By coupling four parameters—the state of charge of the power battery, vehicle speed, the temperature difference between the battery pack and the environment, and the remaining mileage to the destination—it performs graded heating / cooling control of the power battery during driving, reducing energy loss in the thermal management process to some extent. However, this solution is only applicable to the single operating condition of vehicle driving and does not cover the core heat-generating scenario of power battery charging. Moreover, its core consideration is energy economy, which may sacrifice the optimal operating temperature of the battery to reduce energy consumption, causing the power battery to operate in a non-optimal temperature range, affecting battery performance and lifespan. At the same time, this solution does not consider the performance degradation characteristics of the power battery throughout its entire life cycle. Using the same set of control parameters for new and old batteries can lead to problems with untimely temperature control in aging batteries due to increased internal resistance and heat generation.

[0005] For example, patent CN118054128A discloses a remote intelligent thermal management method and system for a bus power battery. By collecting battery information and user driving habits, the remote control module formulates a thermal management plan and issues control commands, realizing automated control of thermal management. However, this plan is highly dependent on the remote communication module and network signal. In areas with no signal or weak signal, the remote control function will be completely ineffective. Moreover, continuous remote communication support is required during vehicle use, and the resulting data traffic costs reduce the economic efficiency of the plan. At the same time, the thermal management strategy of this plan is formulated only based on historical driving habits and weather information, without real-time prediction of the temperature change trend of the power battery. It is still a "post-event response" type of control and cannot cope with the problem of sudden temperature rise / fall under sudden operating conditions.

[0006] In addition, existing power battery thermal management technologies generally suffer from the following defects: First, they do not consider the temperature consistency of power batteries. The temperature difference between cells and sampling points within the battery box is not included in the control scope. Excessive temperature difference can easily lead to inconsistent charging and discharging of cells, accelerate the aging of local cells, and thus affect the performance and safety of the entire battery system. Second, they lack prediction and protection mechanisms for extreme thermal events. They can only handle normal high / low temperatures and cannot identify early signs of thermal runaway such as local temperature anomalies and abnormal voltage changes. Once a sudden thermal failure occurs, they can only respond passively, posing serious safety hazards. Third, thermal management control is disconnected from the actual power demand of the power battery. The start / stop of heating / cooling and power adjustment are not dynamically adapted to the driving and charging power demands of the equipment, which can easily lead to the problem of "conflict between temperature control and energy demand," affecting the normal use of the equipment.

[0007] Therefore, an intelligent thermal management control method that can break through fixed threshold limitations and combine the operating conditions, performance degradation status, and temperature change trends of power batteries for refined, dynamic, and predictive control has become an urgent need to solve the shortcomings of existing technologies and promote the upgrading of power battery thermal management technology. Summary of the Invention

[0008] In view of this, the present invention provides a smart thermal management control method for power batteries, which solves the technical problems of passive response and failure to adapt to battery degradation in existing power battery thermal management.

[0009] To achieve the above objectives, in a first aspect, the technical solution of the present invention to solve the technical problem is to provide a power battery intelligent thermal management control method, comprising: acquiring real-time operation monitoring data of the power battery, wherein the operation monitoring data includes battery temperature distribution, current, voltage, SOC, power and vehicle operating conditions; constructing and continuously updating an adaptive dynamic thermal characteristic model of the power battery based on the real-time operation monitoring data, for describing the mapping relationship between battery heat generation, heat dissipation and temperature change under the current battery aging level and vehicle operating conditions; dynamically adjusting the adaptive dynamic thermal characteristic model and combining it with predicted battery usage needs through a model predictive control algorithm to predict battery temperature changes within a preset time period in the future, and calculating the optimal thermal management control sequence; and dynamically executing thermal management actions based on the optimal thermal management control sequence, and based on the vehicle operating conditions and the temperature range where the initial battery temperature is located.

[0010] In one specific embodiment, the temperature range in which the initial temperature of the battery is located includes a first temperature range, a second temperature range, and a third temperature range.

[0011] In one specific embodiment, when the vehicle is in driving mode, the following thermal management actions are performed according to the initial temperature range of the battery: First temperature range: Heating mode is activated, and the driving power demand and regenerative braking power demand are determined based on the driving status. When the allowable power of the power battery is less than the power demand of the vehicle, heating is continuously performed and the heating power is adjusted. When the allowable power of the power battery is greater than or equal to the power demand of the vehicle, the heating mode is immediately deactivated until the battery temperature rises above the first threshold. Second temperature range: No thermal management actions are performed when the battery temperature is stable / changing slowly. When the battery temperature rises sharply and continues to approach the second threshold, cooling is activated and the power is adjusted. When the battery temperature drops sharply and continues to approach the first threshold, heating is activated and the power is adjusted. Third temperature range: Cooling mode is activated, and the cooling demand is determined based on the driving mode and the cooling power is adjusted. When the battery temperature continues to drop and approaches the second threshold, the cooling mode is deactivated. When the battery temperature continues to rise / remains unchanged, the cooling power is increased until the battery temperature drops below the second threshold.

[0012] In one specific embodiment, when the vehicle is in charging mode, the following thermal management actions are performed according to the initial temperature range of the battery: First temperature range: Heating mode is activated, the maximum charging power is determined based on the charging demand, heating continues and the heating power is adjusted when the allowable power of the power battery is less than the maximum charging power, and the heating mode is immediately deactivated when the allowable power of the power battery is greater than or equal to the maximum charging power, until the battery temperature rises above the first threshold; Second temperature range: No thermal management actions are performed when the charging power meets the demand and the battery temperature is stable / gradually rising and falling, cooling is activated and the power is adjusted only when the battery temperature rises sharply and continues to approach the second threshold; Third temperature range: Cooling mode is activated, cooling demand is determined based on the charging power and the cooling power is adjusted, cooling mode is deactivated when the battery temperature continues to fall and approaches the second threshold, and cooling power is increased when the battery temperature continues to rise / remains unchanged, until the battery temperature drops below the second threshold.

[0013] In one specific embodiment, when the dynamic thermal management action is performed, the prediction and emergency handling of extreme scenarios of power battery thermal events are performed simultaneously, including: identifying the characteristics of local temperature abnormalities and voltage changes, triggering thermal event warnings, and forcibly activating the cooling function to suppress rapid changes in battery temperature through full-power cooling.

[0014] In one specific embodiment, the predicted battery usage demand includes the battery's charge and discharge power demand, battery waste heat, temperature change trend, and charging time demand within a preset future time period.

[0015] Secondly, the present invention provides an intelligent thermal management control device for a power battery, comprising: a data acquisition module for acquiring real-time operational monitoring data of the power battery, the operational monitoring data including battery temperature distribution, current, voltage, SOC, power, and vehicle operating conditions; a model building module for constructing and continuously updating an adaptive dynamic thermal characteristic model of the power battery based on the real-time operational monitoring data, for describing the mapping relationship between battery heat generation, heat dissipation, and temperature changes under the current battery aging level and vehicle operating conditions; a sequence generation module for dynamically adjusting the adaptive dynamic thermal characteristic model and combining it with predicted battery usage requirements through a model predictive control algorithm to predict battery temperature changes within a preset time period and calculate the optimal thermal management control sequence; and an execution module for dynamically executing thermal management actions based on the optimal thermal management control sequence, the vehicle operating conditions, and the temperature range in which the initial battery temperature is located.

[0016] In one specific embodiment, the device further includes an emergency handling module, which is used to simultaneously perform extreme scenario prediction and emergency handling of power battery thermal events when dynamically executing thermal management actions, including: identifying the characteristics of local temperature abnormalities and voltage changes, triggering thermal event warnings, and forcibly activating the cooling function to suppress rapid changes in battery temperature through full-power cooling.

[0017] Thirdly, the present invention provides a computer device, comprising: a memory and at least one processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method.

[0018] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows: By establishing an adaptive dynamic thermal characteristic model based on real-time current, power, SOC, and temperature data, and combining driving and charging conditions to divide temperature ranges and match corresponding thermal management logic, this approach can accurately capture the changing patterns of the power battery's thermal characteristics in real time. This avoids temperature prediction deviations caused by ambiguity in model input and output, ensuring that the timing of thermal management actions is highly compatible with the actual thermal state of the battery. Furthermore, relying on the temperature parameters output by the model, targeted regulation of thermal management actions under different operating conditions can be achieved. This avoids energy waste caused by unnecessary thermal management actions and effectively controls the battery temperature within the target range, ensuring the operational safety and cycle life of the power battery under different conditions. Simultaneously, it eliminates the need to introduce additional complex models or redundant parameters, simplifying the thermal management control process and improving the method's engineering practicality and reliability. This approach allows for more efficient management of battery temperature, preventing performance limitations at low temperatures and accelerated battery life loss at high temperatures. It ensures that the power battery operates within a suitable temperature range, maximizing the safety and lifespan of the power battery, as well as the vehicle's power and fuel economy. Attached Figure Description

[0020] Figure 1 This is a flowchart of the steps of the intelligent thermal management control method for power batteries provided in the first embodiment of the present invention; Figure 2 A schematic diagram of the module framework for battery thermal management; Figure 3 This is a curve showing the correlation between the battery degradation rate and the thermal management cooling power. Figure 4 This is a graph showing the trend of cooling power and battery temperature changes in power battery thermal management during charging mode. Detailed Implementation

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

[0022] It should be noted that all directional indications in the embodiments of this application are only used to explain the relative positional relationship and movement of each component in a specific posture. If the specific posture changes, the directional indications will also change accordingly.

[0023] Furthermore, the use of terms such as "first" and "second" in this application is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. Additionally, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of a person skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, the user should consider such a combination of technical solutions to be non-existent and not within the scope of protection claimed in this application.

[0024] like Figure 1 As shown, the intelligent thermal management control method for power batteries provided in the first embodiment of the present invention includes the following steps: S100, acquires real-time operation monitoring data of the power battery, including battery temperature distribution, current, voltage, SOC, power and vehicle operating conditions; Specifically, depending on the power battery configuration, the temperature of one or more batteries in the battery pack can be continuously collected. By analyzing the temperature data from each collection point, the temperature distribution within the battery system can be obtained. Simultaneously, the battery management system (BMS) can acquire real-time electrical parameters such as current, voltage, and state of charge (SOC), and the vehicle controller can obtain vehicle operating conditions, including driving mode and charging mode.

[0025] S200, based on real-time operation monitoring data, builds and continuously updates an adaptive dynamic thermal characteristic model of the power battery, which is used to describe the mapping relationship between battery heat generation, heat dissipation and temperature change under the current battery aging level and vehicle operating conditions. Specifically, based on real-time operational monitoring data of the power battery, a mathematical model describing the relationship between battery heat generation, heat dissipation, and temperature changes is established using system identification or deep learning algorithms. This model needs to reflect the impact of battery degradation on thermal characteristics and dynamically update parameters as the battery is used. For example, the recursive least squares (RLS) algorithm can be used to estimate the battery's equivalent internal resistance and thermal capacity online, or a convolutional neural network (CNN) / long short-term memory network (LSTM) can be used to fit nonlinear thermal behavior. The model's inputs include current, power, state of charge (SOC), and temperature, and the output is the rate of temperature change or a future short-term temperature value.

[0026] Taking a specific example, by collecting vehicle operating parameters from the past week, the RLS algorithm is used to identify the battery's internal resistance online. The initial internal resistance is 1.2mΩ, and as the battery is used, the internal resistance gradually increases to 1.5mΩ, with the model updating in real time. Simultaneously, a neural network is used to fit the heat generation coefficient under different SOCs, enabling the model to make accurate predictions.

[0027] S300, based on the adaptive dynamic thermal characteristic model and combined with the predicted battery usage demand, dynamically adjusts the battery temperature change within a preset time period through a model predictive control algorithm, predicts the battery temperature change within a preset time period, and calculates the optimal thermal management control sequence. Specifically, the predicted battery usage demand includes the battery's charging and discharging power demand, battery residual heat, temperature change trends, and charging time demand within a future preset time period. For example, the battery usage demand within a future preset time period can be predicted using a deep learning model (LSTM, trained based on historical data).

[0028] Then, starting from the current battery state, an adaptive dynamic thermal characteristic model is used to simulate the evolution of battery temperature under different control actions, such as heating / cooling power, within the MPC framework. By solving an optimization problem, for example, with the objective of minimizing energy consumption while keeping the temperature within a suitable range, and constraints such as temperature not exceeding limits and control action range, the optimal control command for each future control cycle is obtained.

[0029] To illustrate with a specific example, when a vehicle is about to enter a long uphill section, the predicted average power demand over the next 5 minutes is 200kW. The current battery temperature is 25℃ (within a suitable range), but the model predicts that without cooling, the temperature will rise to 38℃ after 5 minutes (exceeding the high-temperature threshold of 35℃). The MPC algorithm calculates that if cooling is immediately activated at 2kW, the temperature can be controlled at 34℃ after 5 minutes with lower energy consumption. Therefore, a control sequence is generated: 2kW cooling power for the first 2 minutes, and then reduced to 1kW for the next 3 minutes.

[0030] S400 dynamically executes thermal management actions based on the optimal thermal management control sequence, taking into account the vehicle operating conditions and the temperature range where the battery's initial temperature is located. Specifically, according to the optimal thermal management control sequence and combined with the current actual operating conditions of the vehicle, the thermal management system is dynamically controlled to perform corresponding actions, including heating or cooling, and the output power of the thermal management system is adjusted in real time to keep the power battery temperature within a suitable operating range, thereby realizing adaptive thermal management control based on operating conditions and model predictions.

[0031] To illustrate with a specific example: When the vehicle is in driving mode, the system dynamically adjusts the cooling power according to the optimal thermal management control sequence, combined with the vehicle speed and the heat generation intensity of the battery, so that the battery temperature is maintained within a suitable range; when the vehicle enters charging mode, the system prioritizes adjusting the battery temperature to the optimal charging temperature range according to the control sequence, and adjusts the thermal management output in real time according to the charging power, so as to achieve precise execution of thermal management actions under different operating conditions.

[0032] In one embodiment, the initial temperature range of the battery includes a first temperature range, a second temperature range, and a third temperature range.

[0033] Specifically, the first threshold T0 is the minimum optimal operating temperature of the power battery, and the second threshold T1 is the maximum optimal operating temperature of the power battery. In both driving and charging modes, the corresponding thermal management control logic is executed according to the initial temperature range of the power battery. The first temperature range is when the power battery temperature BT is less than the first threshold T0, the second temperature range is when the power battery temperature BT is greater than the first threshold T0 and less than the second threshold T1, and the third temperature range is when the power battery temperature BT is greater than the second threshold T1.

[0034] In one embodiment, when the vehicle is in driving mode, the following thermal management actions are performed based on the initial temperature range of the battery: First temperature range: Heat mode is turned on. The driving power demand and braking energy recovery power demand are determined according to the driving status. When the power battery's allowable power is less than the vehicle's operating power demand, heating is continued and the heating power is adjusted. When the power battery's allowable power is greater than or equal to the vehicle's operating power demand, the heating mode is immediately exited until the battery temperature rises above T0. Third temperature range: Cooling mode is activated. Cooling demand is determined and cooling power is adjusted according to driving mode. Cooling mode is deactivated when the battery temperature continues to drop and approaches T1. Cooling power is increased when the battery temperature continues to rise or remains unchanged until the battery temperature drops below T1. Second temperature range: No thermal management action is performed when the battery temperature is stable / changes slowly; cooling is activated and power is adjusted when the battery temperature rises sharply and continues to approach T1; heating is activated and power is adjusted when the battery temperature drops sharply and continues to approach T0. Specifically, when the initial temperature of the power battery is in the first temperature range: the heating mode is activated, and the driving power demand and braking energy recovery power demand are determined based on the driving conditions; as the power battery power gradually increases, the trend of power battery temperature rise and residual heat change is determined, and if it is continuously determined that the allowable power of the power battery is less than the power demand of the vehicle, the thermal management continues to activate the heating mode and control the heating power; as the power battery power gradually increases, the trend of power battery temperature rise and residual heat change is determined, and if it is continuously determined that the allowable power of the power battery is greater than or equal to the power demand of the vehicle, the thermal management immediately exits the heating mode.

[0035] When the initial temperature of the power battery is in the second temperature zone: Predicting future temperature changes, if the current battery temperature is higher than T0 but significantly lower than T1, thermal management does not perform any processing, and basic operating data is being accumulated and calculated. When a change in the battery temperature change rate characteristic parameter is detected, tracking begins. Battery load characteristic parameters are extracted based on driving conditions. If a sudden and sustained rise in battery temperature occurs, approaching T1, thermal management activates cooling mode and controls cooling power to balance the temperature rise. If a sustained and decreasing trend in battery temperature occurs, approaching T0, thermal management activates heating mode and controls heating power to balance the temperature drop. If the power battery temperature is within the second temperature zone, and the temperature consistency deviation is too large, exceeding the inter-battery temperature difference characteristic (ΔT) and surpassing the set threshold Tx, thermal management activates temperature balancing function to improve battery temperature consistency. Ultimately, the battery temperature is controlled above T0, and heating mode is exited.

[0036] When the initial temperature of the power battery is in the third temperature zone: the cooling mode is activated, and the cooling activation requirement is determined based on the driving conditions, the cooling power is controlled, and the shutdown time is predicted; when the power battery temperature is in the cooling mode after the thermal management is activated, the battery load characteristic parameters are extracted based on the driving conditions, and the battery temperature shows a downward trend and continues to decrease. When it approaches T1, the thermal management exits the cooling mode; when the battery temperature still shows an upward trend or remains unchanged and continues, the thermal management continues to activate the cooling mode and controls the increase of cooling power; finally, the battery temperature is controlled below T1, and the cooling mode is exited.

[0037] In one embodiment, when the vehicle is in charging mode, the following thermal management actions are performed based on the initial temperature range of the battery: First temperature range: Heat mode is turned on. The maximum charging power is determined according to the charging demand. When the allowable power of the power battery is less than the maximum charging power, heating continues and the heating power is adjusted. When the allowable power of the power battery is greater than or equal to the maximum charging power, the heating mode is immediately exited until the battery temperature rises above T0. Third temperature range: Cooling mode is turned on. Cooling demand is determined and the cooling power is adjusted according to the charging power. When the battery temperature continues to drop and approaches T1, cooling mode is turned off. When the battery temperature continues to rise or remains unchanged, the cooling power is increased until the battery temperature drops below T1. Second temperature range: When the charging power meets the requirements and the battery temperature is stable / gradually rising and falling, no thermal management action is performed. Cooling is activated and the power is adjusted only when the battery temperature rises sharply and continues to approach T1. Specifically, when the initial temperature of the power battery is in the first temperature zone: the heating mode is activated, and the maximum power demand is determined based on the charging requirements; as the power battery power gradually increases, the trend of power battery temperature rise and residual heat change is determined, and it is continuously determined that the allowable power of the power battery is less than the maximum charging power, so the thermal management continues to activate the heating mode and control the heating power; as the power battery power gradually increases, the trend of power battery temperature rise and residual heat change is determined, and it is continuously determined that the allowable power of the power battery is greater than or equal to the maximum charging power, so the thermal management immediately exits the heating mode; finally, the battery temperature is controlled above T0, and the heating mode is exited.

[0038] When the initial temperature of the power battery is in the second temperature zone: predicting future changes in the power battery temperature, and the current power battery temperature is higher than T0 but much lower than T1, and the charging power meets the requirements, the thermal management control does not perform any processing, and the basic operating data is being accumulated and calculated; when it is determined that the characteristic parameter of the battery charging temperature change rate changes, tracking is started, and the battery charging load characteristic parameter is extracted based on the charging power. If the battery temperature rises sharply and continues, when it approaches T1, the thermal management starts the cooling mode and controls the cooling power to balance the temperature rise; if the battery temperature shows a slow rising or falling trend and continues, the thermal management control does not perform any processing, and the basic operating data is being accumulated and calculated.

[0039] When the initial temperature of the power battery is in the third temperature zone: the cooling mode is activated, and the cooling activation requirement is determined based on the charging power, the cooling power is controlled, and the shutdown time is predicted; when the power battery temperature is in the cooling mode after the thermal management is activated, the battery load characteristic parameters are extracted based on the charging power, and the battery temperature shows a downward trend and continues to decrease. When it approaches T1, the thermal management exits the cooling mode; if the battery temperature still shows an upward trend or remains unchanged and continues to decrease, the thermal management continues to activate the cooling mode and controls the increase of cooling power; finally, the battery temperature is controlled below T1, and the cooling mode is exited.

[0040] In one embodiment, when dynamically executing thermal management actions, extreme scenario prediction and emergency handling for power battery thermal events are performed simultaneously, including: It identifies characteristics of local temperature anomalies and voltage changes, triggers thermal event warnings, and forcibly activates the cooling function to suppress rapid changes in battery temperature through full-power cooling. Specifically, by obtaining characteristics such as local temperature anomalies and voltage changes, it is possible to predict power battery thermal events in advance from multiple dimensions, so as to achieve early warning and forced activation of cooling function through abnormal characteristics. Forced cooling can buy time for safe vehicle transfer and disposal.

[0041] It should be noted that localized temperature anomalies refer to a significant deviation between the temperature of a single battery cell / cell group and the average temperature of the battery pack, or a rapid temperature rise of a single cell / cell group outside of normal operating conditions, or a temperature difference between different cells / cell groups within the battery pack exceeding the normal operating temperature range of the power battery. Abnormal voltage changes refer to sudden increases or decreases in the voltage of a single battery cell that are not in accordance with charging and discharging patterns, or a significant deviation in the voltage of a single cell from the average voltage of cells in the same batch, and such voltage fluctuations are continuous. The criteria for determining the above anomalies can be adaptively adjusted according to the model, capacity, and aging degree of the power battery.

[0042] like Figure 2 As shown, in this embodiment, the power battery thermal management adopts a liquid-cooled hardware topology architecture. The core includes a battery management system (BMS), multiple power battery boxes (BOX1-BOXn), a liquid cooling circuit inlet and outlet, and integrates auxiliary execution components such as a liquid cooling circulation pump, a PTC heater, and a heat dissipation unit. Each component achieves dual-layer collaborative control with the fluid circulation circuit through a signal control bus, forming a complete intelligent thermal management hardware carrier. The BMS serves as the core of the system control, establishing bidirectional signal interaction with BOX1-BOXn via the CAN bus. It collects real-time temperature, voltage, current, and SOC data at multiple points within each battery box, and simultaneously acquires the flow rate and temperature parameters of the coolant at the inlet and outlet. BOX1-BOXn are the power battery energy storage and temperature acquisition units, each equipped with liquid cooling pipes and temperature sensors. They are the core temperature control objects for thermal management, and the liquid cooling pipes of each battery box adopt a parallel design to ensure uniform coolant distribution. The inlet is the coolant input terminal. The coolant, after being regulated by the PTC heater / heat dissipation unit, is delivered to the liquid cooling pipes of each battery box through the inlet. After heat exchange, the coolant flows back through the outlet, forming a closed liquid cooling circulation loop.

[0043] The BMS and liquid cooling circuit actuators work together to control the system. Based on the collected battery status, fluid parameters, and vehicle operating conditions, the system issues commands for heating, cooling, and flow regulation. Through the circulation and regulation of the coolant at the inlet and outlet, it achieves precise temperature control of BOX1-BOXn. At the same time, it can activate temperature balancing or forced cooling functions in case of temperature difference between battery boxes exceeding the threshold or local temperature anomalies, ensuring that each power battery box is always in the optimal operating temperature range, adapting to the intelligent thermal management control requirements under driving, charging, and extreme scenarios.

[0044] In a specific example, the longer a power battery is used in a vehicle, the more its degradation accelerates, leading to increased internal resistance and heat generation. Under the same operating conditions, increased battery degradation results in increased heat generation and a corresponding increase in temperature. To maintain the battery temperature within a suitable range, the cooling capacity of the thermal management system must also be increased accordingly. Figure 3 As shown, comparing new batteries and used batteries, with the increase of degradation, under the same operating conditions and at the same time, in order to keep the temperature within the same range, the cooling power of thermal management needs to be increased accordingly. Otherwise, as the battery degradation increases, under the same conditions, the battery temperature will not be able to be controlled within a suitable temperature range.

[0045] like Figure 4 As shown, during the charging process, intelligent thermal management of the power battery is activated. Towards the end of charging, the heat generated during the process changes accordingly. As charging approaches full capacity, the charging power begins to decrease, the accumulated temperature rise also decreases, and the temperature gradually drops. Near the end of charging, the battery temperature begins to drop significantly. Therefore, through dynamic characteristics and prediction, the thermal management cooling actively shuts off. The coolant in the thermal management system continues to exchange heat with the battery, sufficiently covering the heat generated at the end of charging and meeting cooling requirements. With the thermal management cooling function shutting off, the energy consumption of thermal management also disappears, and the final charge amount also decreases. According to statistics, thermal management shuts off approximately 10 minutes earlier each time charging, and the charge amount also decreases. With an increase in the number of charging cycles, certain charging costs will be saved, making intelligent charging more economical.

[0046] A second embodiment of the present invention provides a smart thermal management control device for a power battery, comprising: The acquisition module is used to acquire real-time operation monitoring data of the power battery, including battery temperature distribution, current, voltage, SOC, power and vehicle operating conditions. The model building module, based on real-time operation monitoring data, builds and continuously updates an adaptive dynamic thermal characteristic model of the power battery, which is used to describe the mapping relationship between battery heat generation, heat dissipation and temperature change under the current battery aging level and vehicle operating conditions. The sequence generation module, based on the adaptive dynamic thermal characteristic model and combined with the predicted battery usage demand, dynamically adjusts the module through a model predictive control algorithm to predict the battery temperature change within a preset time period and calculate the optimal thermal management control sequence. The execution module is used to dynamically execute thermal management actions based on the optimal thermal management control sequence, the vehicle operating conditions, and the temperature range in which the battery's initial temperature is located.

[0047] In one embodiment, the device further includes an emergency handling module, which is used to simultaneously perform extreme scenario prediction and emergency handling of power battery thermal events when dynamically performing thermal management actions, including: identifying the characteristics of local temperature abnormalities and voltage changes, triggering thermal event warnings, and forcibly activating the cooling function to suppress rapid changes in battery temperature through full-power cooling.

[0048] The third embodiment of this application provides a computer device, which includes a memory and at least one processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0049] The fourth embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described method embodiments.

[0050] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one of relational and non-relational databases. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these. The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described; however, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0051] Compared with existing technologies, the intelligent thermal management control method for power batteries provided by this invention establishes an adaptive dynamic thermal characteristic model based on real-time current, power, SOC, and temperature data. It also divides temperature ranges and matches corresponding thermal management logic to driving and charging conditions. On the one hand, it can accurately capture the changing patterns of power battery thermal characteristics in real time, avoiding temperature prediction deviations caused by fuzzy model inputs and outputs, and ensuring a high degree of compatibility between the timing of thermal management actions and the actual thermal state of the battery. On the other hand, relying on the temperature parameters output by the model, it can achieve targeted adjustment of thermal management actions under different operating conditions. This avoids energy waste caused by unnecessary thermal management actions and effectively controls the battery temperature within the target range, ensuring the operational safety and cycle life of the power battery under different operating conditions. Furthermore, it eliminates the need for additional complex models or redundant parameters, simplifying the thermal management control process and improving the engineering practicality and reliability of the method. It can manage battery temperature more efficiently, avoiding performance limitations at low temperatures and accelerated battery life loss at high temperatures, ensuring the power battery operates within a suitable temperature range, and maximizing the safety, lifespan, vehicle power, and economy of the power battery.

[0052] The specific embodiments of the present invention described above do not constitute a limitation on the scope of protection of the present invention. Any other corresponding changes and modifications made in accordance with the technical concept of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A method for intelligent thermal management control of a power battery, characterized in that, include: Real-time acquisition of power battery operation monitoring data, including battery temperature distribution, current, voltage, SOC, power and vehicle operating conditions; Based on real-time operation monitoring data, an adaptive dynamic thermal characteristic model of the power battery is constructed and continuously updated to describe the mapping relationship between battery heat generation, heat dissipation and temperature change under the current battery aging level and vehicle operating conditions. Based on the adaptive dynamic thermal characteristic model and combined with the predicted battery usage demand, the model predictive control algorithm is used to dynamically adjust the battery temperature change within a preset time period and calculate the optimal thermal management control sequence. Based on the optimal thermal management control sequence, thermal management actions are dynamically executed according to the vehicle operating conditions and the temperature range of the battery's initial temperature.

2. The intelligent thermal management control method for power batteries as described in claim 1, characterized in that: The initial temperature range of the battery includes a first temperature range, a second temperature range, and a third temperature range.

3. The intelligent thermal management control method for power batteries as described in claim 2, characterized in that: When the vehicle is in driving mode, the following thermal management actions are performed according to the initial temperature range of the battery: First temperature range: The heating mode is turned on. The driving power demand and braking energy recovery power demand are determined according to the driving status. When the power battery's allowable power is less than the vehicle's operating power demand, the heating mode is continuously heated and the heating power is adjusted. When the power battery's allowable power is greater than or equal to the vehicle's operating power demand, the heating mode is immediately exited until the battery temperature rises above the first threshold. Second temperature range: No thermal management action is performed when the battery temperature is stable / slowly changing; when the battery temperature rises sharply and continues to approach the second threshold, cooling is activated and power is adjusted; when the battery temperature drops sharply and continues to approach the first threshold, heating is activated and power is adjusted. Third temperature range: Cooling mode is activated, and the cooling power is adjusted according to the driving mode. When the battery temperature continues to drop and approaches the second threshold, the cooling mode is deactivated. When the battery temperature continues to rise or remains unchanged, the cooling power is increased until the battery temperature drops below the second threshold.

4. The intelligent thermal management control method for power batteries as described in claim 2, characterized in that: When the vehicle is in charging mode, the following thermal management actions are performed according to the initial temperature range of the battery: First temperature range: Heating mode is turned on. The maximum charging power is determined according to the charging demand. When the allowable power of the power battery is less than the maximum charging power, heating is continued and the heating power is adjusted. When the allowable power of the power battery is greater than or equal to the maximum charging power, the heating mode is immediately exited until the battery temperature rises above the first threshold. Second temperature range: When the charging power meets the requirements and the battery temperature is stable / gradually rising and falling, no thermal management action is performed. Cooling is activated and the power is adjusted only when the battery temperature rises sharply and continues to approach the second threshold. Third temperature range: Cooling mode is activated. Cooling demand is determined based on charging power and cooling power is adjusted accordingly. Cooling mode is deactivated when the battery temperature continues to drop and approaches the second threshold. Cooling power is increased when the battery temperature continues to rise or remains unchanged until the battery temperature drops below the second threshold.

5. The intelligent thermal management control method for power batteries as described in claim 1, characterized in that: When dynamically executing thermal management actions, the system simultaneously performs extreme scenario prediction and emergency handling for power battery thermal events, including: identifying characteristics of local temperature anomalies and voltage changes, triggering thermal event warnings, and forcibly activating the cooling function to suppress rapid changes in battery temperature through full-power cooling.

6. The intelligent thermal management control method for power batteries as described in claim 1, characterized in that: The predicted battery usage demand includes the battery's charging and discharging power demand, battery waste heat, temperature change trends, and charging time demand within a preset future time period.

7. A smart thermal management control device for power batteries, characterized in that, include: The acquisition module is used to acquire real-time operation monitoring data of the power battery, including battery temperature distribution, current, voltage, SOC, power and vehicle operating conditions. The model building module, based on real-time operation monitoring data, builds and continuously updates an adaptive dynamic thermal characteristic model of the power battery, which is used to describe the mapping relationship between battery heat generation, heat dissipation and temperature change under the current battery aging level and vehicle operating conditions. The sequence generation module, based on the adaptive dynamic thermal characteristic model and combined with the predicted battery usage demand, dynamically adjusts the module through a model predictive control algorithm to predict the battery temperature change within a preset time period and calculate the optimal thermal management control sequence. The execution module is used to dynamically execute thermal management actions based on the optimal thermal management control sequence, the vehicle operating conditions, and the temperature range in which the battery's initial temperature is located.

8. The intelligent thermal management control device for a power battery as described in claim 7, characterized in that: The device also includes an emergency handling module, which is used to simultaneously perform extreme scenario prediction and emergency handling of power battery thermal events when dynamically executing thermal management actions, including: identifying the characteristics of local temperature abnormalities and voltage changes, triggering thermal event warnings, and forcibly activating the cooling function to suppress rapid changes in battery temperature through full-power cooling.

9. A computer device, characterized in that, include: A memory and at least one processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.

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