New energy vehicle low-temperature endurance intelligent optimization method and device, equipment and medium

Abstract

The application provides a new energy vehicle low-temperature endurance intelligent optimization method, device, equipment and medium. The method comprises the following steps: collecting related information of a battery pack in real time, wherein the related information at least comprises battery temperature, battery remaining capacity and battery health degree; collecting vehicle working condition information in real time, wherein the working condition information at least comprises vehicle driving speed, driving mileage and environmental temperature; calculating an optimal heating strategy under a current state according to a pre-established model and in combination with the battery pack information and the vehicle working condition information; controlling a heater to heat the battery pack according to the optimal heating strategy; and continuously monitoring state changes of the battery pack in the heating process and adjusting the heating strategy based on the state changes. The method provided by the application can improve the endurance mileage of an electric vehicle in a low-temperature environment.

Description

Technical Field

[0001] The present application relates to the field of new energy vehicles, and specifically to a method, device, equipment and medium for intelligent optimization of low-temperature endurance of new energy vehicles. Background Art

[0002] With growing global awareness of environmental protection and adjustments to energy structures, new energy vehicles, particularly electric vehicles, have become a key development direction for the automotive industry. However, the range of electric vehicles in cold environments remains a pressing technical challenge. Lithium-ion battery performance degrades significantly in low-temperature environments, primarily manifesting as reduced battery capacity, increased internal resistance, and decreased discharge efficiency. These factors collectively result in a significant reduction in the range of electric vehicles in cold conditions.

[0003] To solve this problem, there are currently a variety of solutions. A common method is to improve the performance of the battery at low temperatures by improving battery materials, but this is usually accompanied by increased costs and increased technical complexity. Another method is to improve the working state of the battery by optimizing the battery management system (BMS), but this also requires a high level of technical investment. In addition, there are methods to maintain the battery temperature through physical isolation or heating devices, such as using insulation materials or isolating the battery pack from the external environment to maintain the battery in a more ideal temperature range. Although this method can alleviate the impact of low temperatures on the battery to a certain extent, it increases the mass and cost of the entire vehicle and cannot dynamically adapt to different driving conditions and environmental changes.

[0004] Current mainstream low-temperature battery life optimization technologies are typically based on strategies derived from simulation and testing. These strategies often have limitations, only providing optimal solutions under specific operating conditions and lacking real-time and flexibility. Therefore, achieving efficient low-temperature battery life optimization while ensuring battery safety has become an important research topic.

[0005] Therefore, the present application provides a method, device, equipment and medium for intelligent optimization of low-temperature endurance of new energy vehicles to solve one of the above technical problems. Summary of the Invention

[0006] The purpose of this application is to provide a method and device for intelligently optimizing the low-temperature endurance of new energy vehicles, which can solve at least one of the above-mentioned technical problems. The specific solution is as follows:

[0007] According to the specific implementation of the present application, in a first aspect, the present application provides a method for intelligently optimizing low-temperature endurance of a new energy vehicle, comprising:

[0008] Collect relevant information of the battery pack in real time, the relevant information including at least battery temperature, remaining battery power and battery health; collect whole vehicle operating condition information in real time, the operating condition information including at least vehicle speed, mileage and ambient temperature; calculate the optimal heating strategy under the current state based on a pre-established model and in combination with the battery pack information and the whole vehicle operating condition information; control the heater to heat the battery pack according to the optimal heating strategy; during the heating process, continuously monitor the state changes of the battery pack and adjust the heating strategy based on the state changes.

[0009] In one embodiment, the model is a prediction model constructed based on the heat dissipation of the battery system, the discharge characteristic diagram, the feedback power, the temperature-state of charge-open circuit voltage table and the battery pack capacity, and is used to predict the vehicle range under different heating strategies; the optimal heating strategy satisfies the requirement of maximizing the vehicle range.

[0010] In one embodiment, the method further includes: obtaining a discharge characteristic diagram of the battery system, a first temperature threshold and a first state of charge threshold for ending heating, a second temperature threshold and a second state of charge threshold for starting heating, and the current battery temperature and current state of charge of the battery pack.

[0011] In one embodiment, during the heating process, the state changes of the battery pack are continuously monitored, and the heating strategy is adjusted based on the state changes, including: if the current battery temperature is greater than the first temperature threshold and the current state of charge is better than the first state of charge threshold, then heating is terminated; if the current battery temperature is less than the first temperature threshold, or the current state of charge is worse than the first state of charge threshold, then heating is maintained.

[0012] In one embodiment, during the heating process, the state change of the battery pack is continuously monitored, and the heating strategy is adjusted based on the state change, including: determining the increased power of the battery pack; determining the feedback power of the battery pack; determining the power consumption of the battery pack; determining the energy comprehensive value of the battery pack based on the increased power, feedback power, and power consumption of the battery pack; if the energy comprehensive value is greater than the energy comprehensive threshold, heating is maintained; if the energy comprehensive value is less than the energy comprehensive threshold, heating is terminated; wherein the energy comprehensive value of the battery pack is determined using the following formula: ΔE = ΔE k +E 回TK -E 耗Tk ; Among them, ΔE represents the comprehensive energy value, ΔE k Indicates the amount of power added to the battery pack when heated from temperature T(K-1) to temperature TK, E 回TK Indicates the feedback power at temperature TK, E 耗Tkrepresents the power consumption of the battery pack in the kth time interval.

[0013] In an embodiment, the increased power of the battery pack is determined by the following formula: ΔE k = (Ah TK - Ah T(K-1) ) * U 端 ; wherein, ΔE k represents the increased power of the battery pack, Ah TK represents the remaining power at temperature TK, Ah T(K-1) represents the remaining power at temperature (T-K), and U 端 represents the terminal voltage of the battery.

[0014] In an embodiment, the power consumption of the battery pack is determined by the following formula: E 耗Tk = η2* Q k ; wherein, E 耗Tk represents the power consumption of the battery pack in the kth time interval, η2 represents the thermoelectric conversion efficiency, Q k represents the heat dissipation of the battery pack in the kth time interval, and P Tk (t) represents the heat dissipation power of the battery pack at time t; wherein, the heat dissipation power of the battery pack is calculated based on the battery pack heat dissipation table, the current vehicle speed, and the ambient temperature.

[0015] According to the specific embodiments of the present application, in a second aspect, the present application provides a new energy vehicle low-temperature endurance intelligent optimization device, comprising:

[0016] a battery pack information acquisition module, configured to collect relevant information of the battery pack in real time, wherein the relevant information at least includes battery temperature, battery remaining power, and battery health degree; a vehicle working condition information acquisition module, configured to collect vehicle working condition information in real time, wherein the working condition information at least includes vehicle driving speed, driving mileage, and ambient temperature; a model calculation module, configured to calculate the optimal heating strategy under the current state according to a pre-established model and in combination with the battery pack information and the vehicle working condition information; a heating control module, configured to control the heater to heat the battery pack according to the optimal heating strategy; and a state monitoring and adjustment module, configured to continuously monitor the state change of the battery pack during the heating process and adjust the heating strategy based on the state change.

[0017] According to the specific embodiments of the present application, in a third aspect, the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the method of any one of the first aspect.

[0018] According to the specific implementation of the present application, in a fourth aspect, the present application provides a computer-readable storage medium having a computer program / instruction stored thereon, characterized in that when the computer program / instruction is executed by a processor, the method described in any one of the first aspects is implemented.

[0019] Compared to existing technologies, the above-described solution in the embodiments of this application has at least the following advantages: By collecting real-time battery pack information (such as battery temperature, remaining charge, and health) and vehicle operating condition information (such as driving speed, mileage, and ambient temperature), and calculating the optimal heating strategy based on a pre-established model, this method can dynamically adjust the battery pack heating process to meet the needs of different driving conditions. This method not only improves the range of electric vehicles in low-temperature environments, but also ensures battery safety and performance, enhancing the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A flow chart showing an intelligent optimization method for low-temperature endurance of new energy vehicles is shown;

[0021] Figure 2 A flow chart showing another intelligent optimization method for low-temperature endurance of new energy vehicles is shown;

[0022] Figure 3 A flow chart showing a method for continuously monitoring the state changes of a battery pack during the heating process and adjusting the heating strategy based on the state changes is shown;

[0023] Figure 4 A schematic diagram of a battery pack capacity table is shown;

[0024] Figure 5 A schematic diagram of performing SOC correction during heating is shown;

[0025] Figure 6 A unit block diagram of an intelligent optimization device for low-temperature endurance of new energy vehicles according to an embodiment of the present application is shown.

[0026] Figure 7 The present invention is a block diagram of an electronic device for intelligent optimization of low-temperature endurance of new energy vehicles according to an exemplary embodiment. DETAILED DESCRIPTION

[0027] To make the objectives, technical solutions, and advantages of this application more clear, this application will be further described in detail below with reference to the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0028] The terms used in the examples of this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms "a," "the," and "the" used in the examples of this application and the appended claims are also intended to include plural forms, and unless the context clearly indicates otherwise, "a plurality" generally includes at least two.

[0029] It should be understood that the term "and / or" as used herein is merely a description of the relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0030] It should be understood that although the terms first, second, third, etc. may be used to describe in the embodiments of the present application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, without departing from the scope of the embodiments of the present application, the first may also be referred to as the second, and similarly, the second may also be referred to as the first.

[0031] As used herein, the words "if" and "if" may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.

[0032] It should also be noted that the terms "include," "comprises," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a product or device comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such product or device. In the absence of further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the product or device comprising the element.

[0033] It should be noted in particular that any symbols and / or numbers in the specification that are not marked in the accompanying drawings are not drawing marks.

[0034] The optional embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0035] The embodiment provided in this application is an embodiment of an intelligent optimization method for low-temperature endurance of new energy vehicles.

[0036] The following combination Figure 1 The embodiments of the present application are described in detail.

[0037] Figure 1 A flowchart of a new energy vehicle low-temperature endurance intelligent optimization method is shown, as shown in the following steps. Figure 1

[0038] Step S101, real-time collection of battery pack related information, the related information at least including battery temperature, battery remaining capacity and battery health degree.

[0039] Step S102, real-time collection of vehicle working condition information, the working condition information at least including vehicle speed, driving mileage and environmental temperature.

[0040] Step S103, according to the pre-established model, and combining the battery pack information and the vehicle working condition information, the best heating strategy under the current state is calculated.

[0041] Step S104, according to the best heating strategy, the heater is controlled to heat the battery pack.

[0042] Step S105, during the heating process, the state change of the battery pack is continuously monitored, and the heating strategy is adjusted based on the state change.

[0043] The method provided in the application can dynamically adjust the heating process of the battery pack to meet the needs under different driving conditions by real-time collection of battery pack related information (such as battery temperature, remaining capacity and health degree) and vehicle working condition information (such as driving speed, mileage and environmental temperature), and calculation of the best heating strategy according to the pre-established model. This method not only improves the endurance mileage of electric vehicles in low temperature environment, but also ensures the safety and performance of the battery, and improves the user experience.

[0044] In the embodiment of the application, the model is a prediction model constructed based on battery system heat dissipation, discharge characteristic map, feedback power, temperature-charge state-open circuit voltage table (T-SoC-OCV table) and battery pack capacity, which is used to predict the vehicle endurance mileage under different heating strategies.

[0045] Among them, based on the trained model, the output best heating strategy meets the vehicle with the maximum vehicle endurance mileage.

[0046] In the application, the prediction model constructed based on battery system heat dissipation, discharge characteristic map, feedback power, temperature-charge state-open circuit voltage table and battery pack capacity can more accurately predict the influence of different heating strategies on vehicle endurance mileage. This model makes the calculated best heating strategy maximize the vehicle endurance mileage, so as to significantly improve the practical availability of electric vehicles under low temperature conditions on the premise of ensuring safety.

[0047] ​In an example of the present application, a discharge characteristic map of the battery system, a first temperature threshold and a first state of charge threshold for ending heating, a second temperature threshold and a second state of charge threshold for starting heating, and a current battery temperature and a current state of charge of the battery pack are also required. Further, a determination of starting or ending heating is made according to the thresholds.

[0048] For example, heating can be started if the current battery temperature is less than the second temperature threshold, or the current state of charge is worse than the second state of charge threshold.

[0049] For another example, a determination of maintaining or terminating heating can be further made according to the first temperature threshold and the first state of charge threshold during heating.

[0050] Figure 2 A flowchart of another intelligent optimization method for low-temperature cruising range of a new energy vehicle is shown in FIG. 2, which includes the following steps. Figure 2

[0051] In step S201, relevant information of the battery pack is collected in real time, including at least battery temperature, battery remaining capacity, and battery health.

[0052] In step S202, vehicle working condition information is collected in real time, including at least vehicle speed, driving distance, and ambient temperature.

[0053] In step S203, the best heating strategy under the current state is calculated according to a pre-established model and in combination with the battery pack information and the vehicle working condition information.

[0054] In step S204, the heater is controlled to heat the battery pack according to the best heating strategy.

[0055] In step S205a, if the current battery temperature is greater than the first temperature threshold and the current state of charge is better than the first state of charge threshold, heating is terminated.

[0056] In step S205b, if the current battery temperature is less than the first temperature threshold, or the current state of charge is worse than the first state of charge threshold, heating is maintained.

[0057] In the present application, the discharge characteristic map of the battery system and the first and second temperature thresholds and state of charge thresholds for controlling the start and end of heating are helpful for accurate control of the heating process. The introduction of these parameters enables the system to make more precise judgments according to the specific conditions of the current battery, thereby avoiding unnecessary energy consumption and ensuring that the battery is always in the best working state, further improving the cruising range.

[0058] ​During the heating process, the battery temperature and state of charge are continuously monitored, and the heating strategy is dynamically adjusted accordingly to prevent overheating or undercharging. Heating is terminated when the battery temperature reaches a preset threshold and the state of charge meets the conditions, while continuing heating if the conditions are not met. This mechanism ensures battery safety while optimizing energy efficiency and extending battery life.

[0059] Figure 3 A flow chart of a method for continuously monitoring the state changes of the battery pack during the heating process and adjusting the heating strategy based on the state changes is shown. Figure 3 As shown, the following steps are included.

[0060] In step S301 , the added power of the battery pack is determined, the fed-back power of the battery pack is determined, and the power consumption of the battery pack is determined.

[0061] Among them, the steps of determining the increased power of the battery pack, determining the feedback power of the battery pack, and determining the power consumption of the battery pack can be performed simultaneously or in a certain order, and this application does not make any specific restrictions on this.

[0062] In step S302 , the comprehensive energy value of the battery pack is determined based on the added power, the fed-back power, and the power consumption of the battery pack.

[0063] In step S303a, if the energy comprehensive value is greater than the energy comprehensive threshold, heating is maintained.

[0064] In step S303b, if the energy comprehensive value is less than the energy comprehensive threshold, heating is terminated.

[0065] Among them, the following formula is used to determine the comprehensive energy value of the battery pack:

[0066] ΔE=ΔE k +E 回TK -E 耗Tk .

[0067] Among them, ΔE represents the comprehensive energy value, ΔE k Indicates the amount of power added to the battery pack when heated from temperature T(K-1) to temperature TK, E 回TK Indicates the feedback power at temperature TK, E 耗Tk Indicates the power consumption of the battery pack in the kth time interval.

[0068] This application determines the battery pack's added power, recharged power, and power consumption, and calculates a comprehensive energy value based on this data to determine whether to continue heating. This strategy can minimize energy consumption while ensuring battery performance. Using the formula ΔE = ΔEk + Ereturn Tk - Econsumption Tk to calculate the comprehensive energy value helps achieve refined energy management, thereby effectively improving the overall energy efficiency of electric vehicles.

[0069] In this application, the following formula is used to determine the added power of the battery pack:

[0070] ΔE k =(Ah TK -Ah T(K-1) )*U 端 .

[0071] Where ΔE k Indicates the added capacity of the battery pack, Ah TK Indicates the remaining capacity at temperature TK, Ah T(K-1) Indicates the remaining power at temperature (TK), U 端 Represents the terminal voltage of the battery. This application uses this formula to determine the incremental charge of the battery pack, quantifying the actual energy gain of the battery during each heating cycle. This method provides more intuitive data support, facilitating the control system to make more reasonable decisions based on actual conditions, thereby optimizing the heating process and improving the economy and efficiency of battery use.

[0072] In this application, the following formula is used to determine the power consumption of the battery pack:

[0073] E 耗Tk =η2*Q k .

[0074]

[0075] Among them, E 耗Tk represents the power consumption of the battery pack in the kth time interval, η2 represents the thermoelectric conversion efficiency, Q k P represents the heat dissipation of the battery pack in the kth time interval, Tk (t) represents the heat dissipation power of the battery pack at time t. This application uses this formula to determine the power consumption of the battery pack, and based on the heat dissipation power P Tk (t) Calculate the heat dissipation Qk. This approach can accurately assess the energy loss during the heating process. In this way, the system can better balance the gains from heating with the corresponding energy consumption, ensuring that the entire heating process is efficient and energy-saving while ensuring the safe operation of the battery.

[0076] The heat dissipation power of the battery pack is calculated based on the battery pack heat dissipation meter, the current vehicle speed, and the ambient temperature.

[0077] In some embodiments, the battery pack capacity can be obtained by reading the battery pack capacity table. Figure 4 A schematic diagram of a battery pack capacity table is shown.

[0078] Figure 5 A schematic diagram showing SOC correction during heating is shown.

[0079] For example, Figure 5 As shown in the figure, when performing intelligent optimization of low-temperature endurance of new energy vehicles, the system needs to input a series of key parameters to ensure accurate battery status monitoring and control. These input parameters include current capacity, battery temperature, battery voltage, time (including static calculation time and integration time), discharge status, and battery current. Based on these input parameters, the system can calculate the true state of charge (SoC) and make real-time adjustments based on the feedback SoC, thereby achieving accurate management and optimization of the battery status. Through this method of taking into account multiple factors, the system can dynamically adjust the heating strategy under different driving conditions and environmental changes to ensure optimal battery performance, while improving the range and safety of electric vehicles under low temperature conditions.

[0080] The following example illustrates a complete process for heating a vehicle battery pack at low temperatures, including steps 1 through 13. The first temperature threshold is represented by T1, the first state-of-charge threshold is represented by SoC1, the second temperature threshold is represented by T2, the second state-of-charge threshold is represented by SoC2, the current battery temperature of the battery pack is represented by T, and the current state of charge is represented by SoC. The current battery temperature in the initial state is represented by T0, and the current state of charge in the initial state is represented by SoC0.

[0081] 1. Obtain the discharge characteristic diagram of the battery system, the temperature at the end of heating and the SoC threshold T1, SoC1, the temperature at the start of heating and the SoC threshold T2, SoC2, the initial battery temperature T0, and the initial SoC (SoC0);

[0082] 2. Heat the battery;

[0083] 3. When the battery temperature T is lower than T2 and the SoC is lower than SoC2, T = TK;

[0084] 4. Continue heating T = TK + 1;

[0085] 5. Obtain / input the battery's T-SoC-OCV table and battery pack capacity table. Based on the battery's T-SoC-OCV table, calculate the actual SoC at the current temperature. TK(t) , at temperature Tk, the remaining capacity of the battery is Ah TK =SoCTK C TK +ΔAh TK -T(K-1);

[0086] 6. Obtain / input the battery pack heat dissipation table and calculate the battery pack heat dissipation power P according to the current vehicle speed and ambient temperature TK(t) , heat dissipation of the battery pack Power consumption E consumption Tk = η2 * Q k ;

[0087] 7. Calculate the increase in battery power when the temperature is heated from T(K-1) to TK = ΔEk = (Ah TK -Ah T(K-1) )*U 端 ;

[0088] 8. Get / input the battery pack feedback power P=f(SoC,TK) and calculate the feedback power Eback

[0089] 9. Calculate ΔE = ΔE k +E 回TK -E 耗Tk , ΔE>0, return to step 3, and repeat steps 3 to 9;

[0090] 10.ΔE k <0 or if step 3 is not satisfied, heating is terminated;

[0091] 11. Record SoC = SoC TK(t) Net power output of the battery pack E = Ah T0 *U 0端 +ΣΔE k +ΣE 回TK -Σ E耗k ;

[0092] 12. Discharge ends;

[0093] 13. Record the discharge duration t.

[0094] in, ΔAh TK-T(K-1) The capacity loss is calculated as follows:

[0095] ΔAh TK-T(K-1) =DisAh TK -ChaAh T(K-1) ;

[0096] The SOC of the battery at temperature TK and T(K-1) are SOC TK and SOC T(K-1) , the actual available capacity is C TK and C T(K-1), the full capacity loss is ΔAh TK-T(K-1) At TK temperature, the remaining battery capacity is Ah TK = SOC TK C T(K-1) .

[0097] ΔQ K = CM*1,K = 1, …, N

[0098] η1 is the coulombic efficiency, and η2 is the thermoelectric conversion efficiency.

[0099] The application also provides a device embodiment consistent with the above-mentioned embodiment for realizing the method steps of the above-mentioned embodiment, based on the same name meaning explanation as the above-mentioned embodiment, with the same technical effect as the above-mentioned embodiment, which will not be repeated here.

[0100] As Figure 6 indicated, the application provides a new energy vehicle low-temperature endurance intelligent optimization device 600, comprising:

[0101] A battery pack information acquisition module 601 is configured to collect real-time relevant information of the battery pack, and the relevant information at least includes battery temperature, battery remaining capacity and battery health. A vehicle working condition information acquisition module 602 is configured to collect real-time vehicle working condition information, and the working condition information at least includes vehicle driving speed, driving mileage and environmental temperature. A model calculation module 603 is configured to calculate the optimal heating strategy under the current state according to the pre-established model and in combination with the battery pack information and the vehicle working condition information. A heating control module 604 is configured to control the heater to heat the battery pack according to the optimal heating strategy. A state monitoring and adjustment module 605 is configured to continuously monitor the state change of the battery pack during the heating process and adjust the heating strategy based on the state change.

[0102] Regarding the device in the above-mentioned embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment related to the method, and will not be described in detail here.

[0103] Figure 7 is a block diagram of an electronic device 700 for new energy vehicle low-temperature endurance intelligent optimization according to an exemplary embodiment.

[0104] As Figure 7As shown, one embodiment of the present application provides an electronic device 700. Wherein the electronic device 700 includes a memory 701, a processor 702, an input / output (I / O) interface 703. Wherein the memory 701 is configured to store instructions. The processor 702 is configured to invoke the instructions stored in the memory 701 to execute the new energy vehicle low temperature endurance intelligent optimization method of the present application. Wherein the processor 702 is connected with the memory 701 and the I / O interface 703 respectively, for example, can be connected through a bus system and / or other forms of connection mechanism (not shown). The memory 701 can be used to store programs and data, including the programs of the new energy vehicle low temperature endurance intelligent optimization method involved in the embodiments of the present application, and the processor 702 executes the various functions of the electronic device 700 and data processing by running the programs stored in the memory 701.

[0105] The processor 702 in the embodiments of the present application can be realized in at least one of the hardware forms of a digital signal processor (DSP), a field programmable gate array (FPGA), a programmable logic array (PLA), and the processor 702 can be a central processing unit (CPU) or a combination of one or several of other forms of processing units with data processing and / or instruction execution capabilities.

[0106] The memory 701 in the embodiments of the present application can include one or more computer program products, which can include various forms of computer readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory, etc. The non-volatile memory may, for example, include read only memory (ROM), flash memory, hard disk (HDD) or solid state disk (SSD), etc.

[0107] In the embodiment of the present application, the I / O interface 703 can be used to receive input instructions (such as digital or character information, and generate key signal input related to user settings and function control of the electronic device 700), and can also output various information to the outside (such as images or sounds). In the embodiment of the present application, the I / O interface 703 can include one or more of a physical keyboard, function keys (such as volume control keys, power keys, etc.), a mouse, a joystick, a trackball, a microphone, a speaker, and a touch panel.

[0108] In some embodiments, the present application provides a computer-readable storage medium storing computer-executable instructions. When the computer-executable instructions are executed by a processor, any of the methods described above is performed.

[0109] In some embodiments, the present application provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it performs any of the methods described above.

[0110] Although operations are described in a particular order in the drawings, this should not be understood as requiring that the operations be performed in the particular order shown or in serial order, or that all shown operations be performed to obtain the desired results. In certain circumstances, multitasking and parallel processing may be advantageous.

[0111] The methods and apparatus of the present application can be implemented using standard programming techniques, using rule-based logic or other logic to implement the various method steps. It should also be noted that the terms "means" and "module" as used herein and in the claims are intended to include implementations using one or more lines of software code and / or hardware implementations and / or devices for receiving input.

[0112] Any steps, operations or procedures described herein may be performed or implemented using one or more hardware or software modules, either alone or in combination with other devices. In one embodiment, the software modules are implemented using a computer program product comprising a computer-readable medium containing computer program code, which can be executed by a computer processor to perform any or all of the steps, operations or procedures described.

[0113] The foregoing description of the implementation of the present application has been provided for purposes of illustration and description. The foregoing description is not intended to be exhaustive or to limit the present application to the precise form disclosed, and various variations and modifications are possible in accordance with the above teachings or may result from the practice of the present application. These embodiments have been selected and described in order to illustrate the principles of the present application and its practical application, so as to enable those skilled in the art to utilize the present application in various embodiments and modifications as appropriate for the particular use contemplated.

[0114] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0115] It is further understood that, unless otherwise specified, “connection” includes a direct connection where there are no other components between the two elements, and also includes an indirect connection where there are other elements between the two elements.

[0116] It should be further understood that although operations are described in a particular order in the drawings in the embodiments of the present application, this should not be construed as requiring that these operations be performed in the particular order shown or in a serial order, or that all of the illustrated operations be performed to obtain the desired results. In certain circumstances, multitasking and parallel processing may be advantageous.

[0117] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. This application is intended to encompass any variations, uses, or adaptations of the present application that follow the general principles of the present application and include common knowledge or customary techniques in the field of the present application that are not disclosed herein. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the scope of claims below.

[0118] It should be understood that the present application is not limited to the precise structures described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the scope of the appended claims.

[0119] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A new energy vehicle low-temperature endurance intelligent optimization method, characterized in that: include: Collecting relevant information of the battery pack in real time, the relevant information including at least battery temperature, remaining battery power and battery health; Collecting vehicle operating condition information in real time, the operating condition information including at least vehicle speed, mileage and ambient temperature; Calculating the optimal heating strategy for the current state based on a pre-established model, combined with the battery pack information and the vehicle operating condition information; the model is a prediction model constructed based on the battery system heat dissipation, discharge characteristic diagram, regenerative power, temperature-state of charge-open circuit voltage table, and battery pack capacity, and is used to predict the vehicle range under different heating strategies; The optimal heating strategy satisfies the requirement that the vehicle has a maximum vehicle range; controlling a heater to heat the battery pack according to the optimal heating strategy; During the heating process, the state change of the battery pack is continuously monitored, and the heating strategy is adjusted based on the state change.

2. The method according to claim 1, characterized in that The method further comprises: Acquire a discharge characteristic diagram of the battery system, a first temperature threshold and a first state of charge threshold for ending heating, a second temperature threshold and a second state of charge threshold for starting heating, and a current battery temperature and a current state of charge of the battery pack.

3. The method according to claim 2, characterized in that During the heating process, continuously monitoring the state change of the battery pack and adjusting the heating strategy based on the state change includes: If the current battery temperature is greater than the first temperature threshold and the current state of charge is better than the first state of charge threshold, terminating heating; If the current battery temperature is less than the first temperature threshold, or the current state of charge is worse than the first state of charge threshold, heating is maintained.

4. The method according to claim 1, wherein During the heating process, continuously monitoring the state change of the battery pack and adjusting the heating strategy based on the state change includes: determining an increased charge of the battery pack; Determining the feedback power of the battery pack; determining power consumption of the battery pack; Determining a comprehensive energy value of the battery pack based on the added power, the fed-back power, and the power consumption of the battery pack; If the energy comprehensive value is greater than the energy comprehensive threshold, then keep heating; If the energy comprehensive value is less than the energy comprehensive threshold, terminating the heating; The following formula is used to determine the comprehensive energy value of the battery pack: ； Among them, ΔE represents the comprehensive energy value, ΔE k Indicates the amount of power added to the battery pack when heated from temperature T(K-1) to temperature TK, E TK Indicates the feedback power at temperature TK, E 耗Tk Indicates the power consumption of the battery pack in the kth time interval.

5. The method according to claim 4, characterized in that The following formula is used to determine the added capacity of the battery pack: ； Where ΔE k Indicates the added capacity of the battery pack, Ah TK Indicates the remaining capacity at temperature TK, Ah T（K-1） Indicates the remaining power at temperature (TK), U 端 Indicates the terminal voltage of the battery.

6. The method according to claim 4, characterized in that The power consumption of the battery pack is determined using the following formula: ； Q k = ； in, represents the power consumption of the battery pack in the kth time interval, represents the thermoelectric conversion efficiency, represents the heat dissipation of the battery pack in the kth time interval, Indicates the heat dissipation power of the battery pack at time t; The heat dissipation power of the battery pack is calculated based on the battery pack heat dissipation table, the current vehicle speed, and the ambient temperature.

7. A new energy vehicle low-temperature endurance intelligent optimization device, characterized in that: include: A battery pack information acquisition module is used to collect relevant information of the battery pack in real time, the relevant information including at least battery temperature, remaining battery power and battery health; A vehicle operating condition information acquisition module is used to collect vehicle operating condition information in real time, wherein the operating condition information includes at least vehicle speed, mileage and ambient temperature; a model calculation module for calculating an optimal heating strategy for the current state based on a pre-established model and in combination with the battery pack information and the vehicle operating condition information; the model is a prediction model constructed based on the battery system heat dissipation, discharge characteristic diagram, regenerative power, temperature-state of charge-open circuit voltage table, and battery pack capacity, and is used to predict the vehicle range under different heating strategies; The optimal heating strategy satisfies the requirement that the vehicle has a maximum vehicle range; a heating control module, configured to control a heater to heat the battery pack according to the optimal heating strategy; A state monitoring and adjustment module is used to continuously monitor the state changes of the battery pack during the heating process and adjust the heating strategy based on the state changes.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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