A thermal management control system and method for a new energy vehicle battery

Abstract

The embodiment of the application discloses a new energy automobile battery thermal management control system and method, the thermal management control system includes: the acquisition module is used for collecting and storing real-time monitoring data; the monitoring data includes temperature data and charge-discharge data; the processing module is used for transmitting the current monitoring data and the preset number of historical monitoring data to the temperature prediction model which is trained in advance to process, so as to obtain the predicted temperature; the control module is used for judging whether the predicted temperature exceeds the set temperature interval, if yes, the preset control strategy is called to realize the effective control of the battery temperature rise; its beneficial effect is: overcome the current only involves a single category of temperature data for prediction, realize the combination application of temperature data and charge-discharge data; while realizing the construction of more accurate battery temperature prediction, based on the called control strategy, realizing the effective control of the battery temperature rise, so as to also achieve the purpose of thermal management control.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile battery management, and in particular to a thermal management control system and method for a new energy automobile battery. Background Art

[0002] With the development of green energy, new energy vehicles have seen significant growth and adoption. As a core component of new energy vehicles, the performance and lifespan of batteries significantly impact the overall performance and service life of the vehicle. Battery thermal management and control systems are crucial for ensuring proper battery operation and extending battery life.

[0003] Temperature monitoring and control are crucial components in the design of thermal management systems for new energy vehicle batteries. However, under complex operating conditions, the electrochemical reactions, temperature fields, and internal resistance within lithium-ion batteries can fluctuate significantly, exacerbating nonlinear battery polarization. This can lead to significant temperature fluctuations in lithium-ion batteries. While existing temperature prediction models exist, these approaches only utilize a single type of temperature data, ignoring operational charge and discharge data. This can lead to significant temperature deviations in predicted temperatures.

[0004] Therefore, how to build a more accurate battery temperature prediction for thermal management control is an urgent problem to be solved. Summary of the Invention

[0005] In order to overcome the deficiencies in the prior art, the present invention aims to provide a thermal management control system and method for new energy vehicle batteries, so as to construct a more accurate battery temperature prediction for thermal management control.

[0006] The technical solution provided by the present invention is:

[0007] In a first aspect, a thermal management control system for a new energy vehicle battery is provided, the thermal management control system comprising:

[0008] An acquisition module, configured to acquire and store real-time monitoring data, wherein the monitoring data includes temperature data and charge and discharge data;

[0009] A processing module is used to transmit the current monitoring data and a preset number of historical monitoring data to a pre-trained temperature prediction model for processing to obtain a predicted temperature;

[0010] The control module is used to determine whether the predicted temperature exceeds a set temperature range. If so, a preset control strategy is called to effectively control the temperature rise of the battery.

[0011] Preferably, when collecting the temperature data, the corresponding sampling period is first divided into at least three sampling time points;

[0012] When the sampling time reaches the first sampling time point, the voltage signal is acquired according to the new sampling interval until the sampling times accumulate to the preset times, and the voltage signal of the preset times is stored in the temporary storage area as a temporary variable;

[0013] When the sampling time reaches the second sampling time point, calculating the average value of the temporary variable;

[0014] When the sampling time reaches the third sampling time point, the voltage signal is converted into a temperature signal based on the average value and according to the relationship between the resistance voltage and the temperature, thereby obtaining the temperature data.

[0015] Preferably, the temperature prediction model is trained using a gated recurrent unit recurrent neural network, and the output information is controlled by combining input parameters, hidden states, and previous states by updating gates and resetting gates.

[0016] Preferably, the temperature prediction model also utilizes an optimization algorithm to automatically adjust the learning rate factor to ensure convergence speed.

[0017] Preferably, the control strategy adopts a method of limiting the motor torque threshold, by adjusting the maximum output torque of the motor and thereby limiting the current to reduce heat accumulation in the battery.

[0018] In a second aspect, the present invention further provides a thermal management control method for a new energy vehicle battery, which is applied to the thermal management control system for a new energy vehicle battery described in the first aspect, and the method comprises:

[0019] Collect and store real-time monitoring data; wherein the monitoring data includes temperature data and charge and discharge data;

[0020] The current monitoring data and a preset number of historical monitoring data are transmitted to a pre-trained temperature prediction model for processing to obtain a predicted temperature;

[0021] It is determined whether the predicted temperature exceeds a set temperature range. If so, a preset control strategy is called to achieve effective control of the battery temperature rise.

[0022] Through the above technical solution, the present invention can bring the following beneficial effects:

[0023] The present invention collects and stores real-time monitoring data; the monitoring data includes temperature data and charge and discharge data; then the current monitoring data and a preset number of historical monitoring data are transmitted to a pre-trained temperature prediction model for processing to obtain a predicted temperature; finally, it is determined whether the predicted temperature exceeds the set temperature range. If so, a preset control strategy is called to achieve effective control of the battery temperature rise; the technical solution overcomes the current practice of only involving a single category of temperature data for prediction, ignoring the charge and discharge data related to operation, and realizes the combined application of temperature data and charge and discharge data; while achieving the construction of a more accurate battery temperature prediction, based on the called control strategy, effective control of the battery temperature rise is achieved, thereby also achieving the purpose of thermal management control. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is an overall architecture diagram of a thermal management control system for a new energy vehicle battery provided by an embodiment of the present invention;

[0025] Figure 2 This is a flow chart of a thermal management control method for a new energy vehicle battery provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0026] Specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are intended to be illustrative only and are not intended to limit the present invention. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be apparent to those skilled in the art that these specific details are not necessarily required to practice the present invention.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] In this application, terms such as "upper," "lower," "left," "right," "front," "back," "top," "bottom," "inner," "outer," "center," "vertical," "horizontal," "transverse," and "longitudinal" indicate positions or locations based on the positions or locations shown in the accompanying drawings. These terms are primarily intended to better describe this application and its embodiments and are not intended to limit the devices, elements, or components indicated to having a specific orientation, or to being constructed or operated in a specific orientation.

[0029] Furthermore, some of the above terms may be used to express other meanings besides indicating a position or location. For example, the term "on" may also be used to indicate a dependency or connection in certain circumstances. Those skilled in the art will understand the specific meanings of these terms in this application based on the specific circumstances.

[0030] Throughout this specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment of the present invention. Thus, the appearances of the phrases "in one embodiment," "in an embodiment," "an example," or "an example" in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, or characteristics may be combined in any suitable combinations and / or subcombinations in one or more embodiments or examples.

[0031] It should be noted that, unless otherwise specified, the technical terms in this embodiment have the common meanings understood in the relevant technical field.

[0032] like Figure 1 As shown, an embodiment of the present invention provides a thermal management control system for a new energy vehicle battery, the thermal management control system comprising:

[0033] An acquisition module, configured to acquire and store real-time monitoring data, wherein the monitoring data includes temperature data and charge and discharge data;

[0034] A processing module is used to transmit the current monitoring data and a preset number of historical monitoring data to a pre-trained temperature prediction model for processing to obtain a predicted temperature;

[0035] The control module is used to determine whether the predicted temperature exceeds a set temperature range. If so, a preset control strategy is called to effectively control the temperature rise of the battery.

[0036] When applied, relevant data is collected by deploying corresponding sensors. Since the accuracy of temperature value collection is crucial when predicting temperature, temperature sensors such as thermistors are used for collection. The charge and discharge data include charge and discharge current and voltage. At the same time, the collected data is timestamped and stored in time series to obtain corresponding historical monitoring data. The preset amount of historical monitoring data includes at least data corresponding to multiple adjacent moments before the current moment.

[0037] In this embodiment, when collecting the temperature data, the corresponding sampling period is first divided into at least three sampling time points;

[0038] When the sampling time reaches the first sampling time point, the voltage signal is acquired according to the new sampling interval until the sampling times accumulate to the preset times, and the voltage signal of the preset times is stored in the temporary storage area as a temporary variable;

[0039] When the sampling time reaches the second sampling time point, calculating the average value of the temporary variable;

[0040] When the sampling time reaches the third sampling time point, the voltage signal is converted into a temperature signal based on the average value and according to the relationship between the resistance voltage and the temperature, thereby obtaining the temperature data.

[0041] Specifically, the sampling period is 100ms, the three sampling time points are 50ms, 85ms, and 95ms respectively, the new sampling interval is 2ms, and the preset number of times is 15 times. In practice, multiple sampling time points can be flexibly set, and there is no restriction here.

[0042] It should be noted that, when building the temperature prediction model, this embodiment fully combines temperature with charge and discharge data, fully considering the influence of battery charge and discharge rate and charge and discharge current, as well as the influence of the temperature at adjacent moments on the current value and future temperature values;

[0043] When applied, the battery's historical temperature, charge and discharge current, as well as the battery's current temperature, ambient temperature, voltage, charge and discharge current, and operating time are used as inputs to the model, and the output is the predicted battery temperature;

[0044] The temperature prediction model is trained using a gated recurrent unit recurrent neural network, which controls the output information by combining input parameters, hidden states, and previous states through updating gates and resetting gates.

[0045] The above scheme fully takes into account the problem of the distance of the data time series during prediction; as the time series continues to lengthen, the problem of gradient vanishing or gradient exploding may occur; and the gated recurrent unit recurrent neural network, through its existing gate mechanism, does not simply delete the longer "memory" when processing it. It considers the correlation between information of long time intervals and retains useful information well. This overcomes the phenomena of gradient vanishing and gradient exploding to a certain extent and improves the accuracy of the prediction model.

[0046] Furthermore, during implementation, the temperature prediction model also uses an optimization algorithm to automatically adjust the learning rate factor to ensure the convergence speed; wherein, the optimization algorithm adopts a gradient optimization algorithm so that each iteration can ensure that the learning step size is within a certain range, and will not increase the learning step size due to a large gradient, thereby ensuring the relative stability of the parameters.

[0047] In this embodiment, the synchronization triggering is performed based on the high-frequency pulse electrical signal, specifically:

[0048] During implementation, in order to effectively control the temperature rise of the battery, the control strategy adopts a method of limiting the motor torque threshold, adjusting the maximum output torque of the motor and then limiting the current to reduce the heat accumulation of the battery.

[0049] Specifically, the battery charging and discharging process alternates continuously with actual driving, resulting in a continuous increase in temperature accumulation, and high-rate current discharge directly causes the temperature to continue to rise; at the same time, rapid discharge causes the battery SOC to drop rapidly, which will cause the battery internal resistance to change, further causing the battery temperature to rise; therefore, based on the logic of controlling the discharge current, it is difficult to directly limit the battery current; when the temperature exceeds the set range, the maximum output torque of the motor is adjusted by limiting the maximum torque of the motor, thereby limiting the current and reducing the accumulation of battery Joule heat.

[0050] The above scheme collects and stores real-time monitoring data; the monitoring data includes temperature data and charge and discharge data; then the current monitoring data and a preset number of historical monitoring data are transmitted to a pre-trained temperature prediction model for processing to obtain a predicted temperature; finally, it is determined whether the predicted temperature exceeds the set temperature range. If so, the preset control strategy is called to achieve effective control of the battery temperature rise; the technical scheme overcomes the current problem of only involving a single category of temperature data for prediction, ignoring the charge and discharge data related to operation, and realizes the combined application of temperature data and charge and discharge data; while achieving the construction of a more accurate battery temperature prediction, based on the called control strategy, effective control of the battery temperature rise is achieved, thereby also achieving the purpose of thermal management control.

[0051] Based on the same inventive concept, Figure 2A thermal management control method for a new energy vehicle battery is applied to the thermal management control system of the new energy vehicle battery described above, and the method comprises:

[0052] S101, collecting and storing real-time monitoring data; wherein the monitoring data includes temperature data and charge and discharge data;

[0053] S102, transmitting the current monitoring data and a preset number of historical monitoring data to a pre-trained temperature prediction model for processing to obtain a predicted temperature;

[0054] S103: Determine whether the predicted temperature exceeds a set temperature range. If so, invoke a preset control strategy to effectively control the battery temperature rise.

[0055] Wherein, when collecting the temperature data, first divide the corresponding sampling period into at least three sampling time points;

[0056] When the sampling time reaches the first sampling time point, the voltage signal is acquired according to the new sampling interval until the sampling times accumulate to the preset times, and the voltage signal of the preset times is stored in the temporary storage area as a temporary variable;

[0057] When the sampling time reaches the second sampling time point, calculating the average value of the temporary variable;

[0058] When the sampling time reaches the third sampling time point, the voltage signal is converted into a temperature signal based on the average value and according to the relationship between the resistance voltage and the temperature, thereby obtaining the temperature data.

[0059] Furthermore, the temperature prediction model is trained using a gated recurrent unit recurrent neural network, which controls the output information by combining input parameters, hidden states, and previous states through updating gates and resetting gates.

[0060] The temperature prediction model also utilizes an optimization algorithm to automatically adjust the learning rate factor to ensure convergence speed.

[0061] In this embodiment, the control strategy adopts a method of limiting the motor torque threshold, adjusting the maximum output torque of the motor and thereby limiting the current to reduce heat accumulation in the battery.

[0062] It should be noted that for a more specific description of the workflow of the method embodiment, please refer to the aforementioned system embodiment section, which will not be repeated here.

[0063] The above solution overcomes the current problem of only involving a single category of temperature data for prediction and ignoring the charge and discharge data related to operation, and realizes the combined application of temperature data and charge and discharge data; while achieving the construction of more accurate battery temperature prediction, based on the called control strategy, it realizes effective control of battery temperature rise, thereby achieving the purpose of thermal management control.

[0064] In the several embodiments provided in this application, it should be understood that the disclosed thermal management control system for a new energy vehicle battery can be implemented in other ways. For example, the embodiments described above are merely illustrative. For example, the division of the modules may be divided in other ways in actual implementation, such as multiple units or components may be combined or integrated into another system or device, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices or units, or may be an electrical, mechanical or other form of connection.

[0065] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and such modifications or substitutions are intended to be within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A thermal management control system for a new energy vehicle battery, characterized in that: The thermal management control system includes: An acquisition module, configured to acquire and store real-time monitoring data, wherein the monitoring data includes temperature data and charge and discharge data; A processing module is configured to transmit current monitoring data and a preset number of historical monitoring data to a pre-trained temperature prediction model for processing to obtain a predicted temperature; the collected data is timestamped and stored in a time series to obtain corresponding historical monitoring data; the preset number of historical monitoring data includes at least data corresponding to multiple moments adjacent to the current moment; the temperature prediction model is trained using a gated recurrent unit recurrent neural network, and output information is controlled by combining input parameters, hidden states, and previous states through update gates and reset gates; when building the temperature prediction model, temperature is fully combined with charge and discharge data, fully considering the influence of battery charge and discharge rates and charge and discharge currents, as well as the influence of temperatures at adjacent moments on current and future temperature values; a control module, configured to determine whether the predicted temperature exceeds a set temperature range, and if so, invoke a preset control strategy to achieve effective control of the battery temperature rise; The control strategy adopts the method of limiting the motor torque threshold. When the temperature exceeds the set temperature range, the maximum output torque of the motor is adjusted to limit the current to reduce the heat accumulation of the battery. When collecting the temperature data, first divide the corresponding sampling period into at least three sampling time points; When the sampling time reaches the first sampling time point, the voltage signal is acquired according to the new sampling interval until the sampling times accumulate to the preset times, and the voltage signal of the preset times is stored in the temporary storage area as a temporary variable; When the sampling time reaches the second sampling time point, calculating the average value of the temporary variable; When the sampling time reaches the third sampling time point, the voltage signal is converted into a temperature signal based on the average value and according to the relationship between the resistor voltage and the temperature, thereby obtaining the temperature data.

2. A thermal management control system for a new energy vehicle battery according to claim 1, characterized in that: The temperature prediction model also utilizes an optimization algorithm to automatically adjust the learning rate factor to ensure convergence speed.

3. A thermal management control method for a new energy vehicle battery, characterized in that: The thermal management control system for a new energy vehicle battery according to claim 1 comprises: Collect and store real-time monitoring data; wherein the monitoring data includes temperature data and charge and discharge data; The current monitoring data and a preset number of historical monitoring data are transmitted to a pre-trained temperature prediction model for processing to obtain a predicted temperature; the collected data are timestamped and stored in time series to obtain the corresponding historical monitoring data; the preset number of historical monitoring data includes at least the data corresponding to multiple adjacent moments before the current moment; when building the temperature prediction model, the temperature is fully combined with the charge and discharge data, and the influence of the battery charge and discharge rate and charge and discharge current is fully considered, as well as the influence of the temperature at adjacent moments on the current value and future temperature values; Determining whether the predicted temperature exceeds a set temperature range, and if so, invoking a preset control strategy to effectively control the battery temperature rise; The control strategy adopts the method of limiting the motor torque threshold. When the temperature exceeds the set temperature range, the maximum output torque of the motor is adjusted to limit the current to reduce the heat accumulation of the battery. When collecting the temperature data, first divide the corresponding sampling period into at least three sampling time points; When the sampling time reaches the first sampling time point, the voltage signal is acquired according to the new sampling interval until the sampling times accumulate to the preset times, and the voltage signal of the preset times is stored in the temporary storage area as a temporary variable; When the sampling time reaches the second sampling time point, calculating the average value of the temporary variable; When the sampling time reaches the third sampling time point, the voltage signal is converted into a temperature signal based on the average value and according to the relationship between the resistor voltage and the temperature, thereby obtaining the temperature data.

4. The thermal management control method for a new energy vehicle battery according to claim 3, characterized in that: The temperature prediction model also utilizes an optimization algorithm to automatically adjust the learning rate factor to ensure convergence speed.

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