Intelligent optimization method, device and equipment for low-temperature endurance of new energy automobile and medium

Abstract

The invention provides an intelligent optimization method and device for low-temperature endurance of a new energy automobile, equipment and a medium. The method comprises the following steps: collecting relevant information of a battery pack in real time, wherein the relevant information at least comprises battery temperature, battery remaining capacity and battery health degree; the working condition information of the whole vehicle is collected in real time, and the working condition information at least comprises the vehicle running speed, the running mileage and the environment temperature; calculating an optimal heating strategy in a current state according to a pre-established model in combination with the battery pack information and the whole vehicle working condition information; controlling a heater to heat the battery pack according to the optimal heating strategy; and in the heating process, continuously monitoring the state change of the battery pack, and adjusting the heating strategy based on the state change. According to the method provided by the invention, the endurance mileage of the electrokinetic automobile in a low-temperature environment can be improved.

Description

Technical Field

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

[0002] With the increasing global awareness of environmental protection and the adjustment of energy structure, new energy vehicles, especially electric vehicles, have become an important development direction of the automotive industry. However, the range of electric vehicles in cold environments has always been a technical problem that needs to be solved urgently. In low temperature environments, the performance of lithium-ion batteries will decline significantly, mainly manifested in reduced battery capacity, increased internal resistance and reduced discharge efficiency. These factors together lead to a significant reduction in the range of electric vehicles in low temperature conditions.

[0003] There are currently a variety of solutions to solve this problem. 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 temperature of the battery 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] The current mainstream low-temperature endurance optimization technology solutions are usually based on simulation and testing strategies. These strategies often have certain limitations and can only provide optimal solutions under specific working conditions. They lack real-time and flexibility. Therefore, how to achieve efficient low-temperature endurance 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-mentioned 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 technical problems mentioned above. The specific solution is as follows: According to the specific implementation of the present application, in the first aspect, the present application provides a method for intelligently optimizing low-temperature endurance of a new energy vehicle, comprising: Collect relevant information of the battery pack in real time, the relevant information including at least the battery temperature, the remaining battery power and the battery health; collect the whole vehicle operating condition information in real time, the operating condition information including at least the vehicle speed, mileage and ambient temperature; calculate the best 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 best heating strategy; during the heating process, continuously monitor the state change of the battery pack and adjust the heating strategy based on the state change.

[0007] 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.

[0008] In one embodiment, the method also 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 a current battery temperature and a current state of charge of the battery pack.

[0009] 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: 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 the 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 the heating is maintained.

[0010] 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 by the following formula: ΔE=ΔE k +E 回TK -E 耗Tk ; Among them, ΔE represents the comprehensive energy value, ΔE k It represents the amount of electricity 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.

[0011] In one embodiment, the additional power of the battery pack is determined by the following formula: ΔE k =(Ah TK -Ah T（K-1） )*U 端 ; 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 capacity at temperature (TK), U 端 Indicates the terminal voltage of the battery.

[0012] In one implementation, the power consumption of the battery pack is determined using the following formula: ;Q k = ; Among them, E 耗Tk represents the power consumption of the battery pack in the kth time interval, represents the thermoelectric conversion efficiency, Q k represents the heat dissipation of the battery pack in the kth time interval, 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.

[0013] According to the specific implementation of the present application, in the second aspect, the present application provides a new energy vehicle low temperature endurance intelligent optimization device, comprising: A battery pack information acquisition module is used to collect relevant information of the battery pack in real time, and the relevant information includes at least battery temperature, remaining battery power and battery health; a vehicle operating condition information acquisition module is used to collect the vehicle operating condition information in real time, and the operating condition information includes at least vehicle speed, mileage and ambient temperature; a model calculation module is used to 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 vehicle operating condition information; a heating control module is used to control the 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.

[0014] According to the specific implementation 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 on the memory, characterized in that the processor executes the computer program to implement any one of the methods described in the first aspect.

[0015] 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.

[0016] Compared with the prior art, the above scheme of the embodiment of the present application has at least the following beneficial effects: the present application collects relevant information of the battery pack (such as battery temperature, remaining power and health) and vehicle operating condition information (such as driving speed, mileage and ambient temperature) in real time, and calculates the optimal heating strategy based on the pre-established model. This method can dynamically adjust the heating process of the battery pack to meet the needs of different driving conditions. This method not only improves the cruising range of electric vehicles in low temperature environments, but also ensures the safety and performance of the battery and improves the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flow chart of an intelligent optimization method for low-temperature endurance of new energy vehicles is shown; Figure 2 A flow chart showing another intelligent optimization method for low-temperature endurance of new energy vehicles is shown; Figure 3 A flow chart of a method for continuously monitoring the state change of a battery pack during the heating process and adjusting the heating strategy based on the state change is shown; Figure 4 A schematic diagram of a battery pack capacity table is shown; Figure 5 A schematic diagram of performing SOC correction during heating is shown; Figure 6 A unit block diagram of an intelligent optimization device for low-temperature endurance of a new energy vehicle according to an embodiment of the present application is shown.

[0018] Figure 7 The present invention is a block diagram of an electronic device for intelligent optimization of low-temperature endurance of a new energy vehicle according to an exemplary embodiment. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0020] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings, and "multiple" generally includes at least two.

[0021] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0022] 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.

[0023] 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 determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0024] It should also be noted that the term "includes", "comprising" or any other variation thereof is intended to cover non-exclusive inclusion, so that a commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprising a ..." do not exclude the existence of other identical elements in the commodity or device including the elements.

[0025] It should be particularly noted that any symbols and / or numbers in the specification that are not marked in the accompanying drawings are not drawing marks.

[0026] The optional embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0027] The embodiment provided in this application is an embodiment of a method for intelligently optimizing low-temperature endurance of a new energy vehicle.

[0028] Combine the following Figure 1 The embodiments of the present application are described in detail.

[0029] Figure 1 A flow chart of an intelligent optimization method for low-temperature endurance of new energy vehicles is shown. Figure 1 As shown, the following steps are included.

[0030] Step S101, collecting relevant information of the battery pack in real time, the relevant information at least including battery temperature, battery remaining power and battery health.

[0031] Step S102, collecting vehicle operating condition information in real time, the operating condition information at least including vehicle speed, mileage and ambient temperature.

[0032] Step S103, based on the pre-established model and in combination with the battery pack information and the vehicle operating condition information, calculate the best heating strategy under the current state.

[0033] Step S104: controlling the heater to heat the battery pack according to the optimal heating strategy.

[0034] Step S105 , during the heating process, continuously monitor the state change of the battery pack and adjust the heating strategy based on the state change.

[0035] The method provided in this application collects relevant information of the battery pack (such as battery temperature, remaining power and health) and vehicle operating condition information (such as driving speed, mileage and ambient temperature) in real time, and calculates the optimal heating strategy based on a pre-established model. This method can dynamically adjust the heating process of the battery pack 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 the safety and performance of the battery and improves the user experience.

[0036] In the embodiment of the present application, 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 (T-SoC-OCV table) and the battery pack capacity, and is used to predict the vehicle range under different heating strategies.

[0037] Among them, based on the trained model, the output optimal heating strategy satisfies the requirement of maximizing the vehicle's cruising range.

[0038] In this application, the 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 can more accurately predict the impact of different heating strategies on the vehicle's cruising range. This model enables the calculated optimal heating strategy to maximize the vehicle's cruising range, thereby significantly improving the actual availability of electric vehicles under low temperature conditions while ensuring safety.

[0039] In an example of the present application, it is also necessary to obtain 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. Further, a judgment is made on whether to start or end heating based on the threshold.

[0040] For example, heating may be started when the current battery temperature is less than a second temperature threshold, or when the current state of charge is worse than a second state of charge threshold.

[0041] For another example, during the heating process, it can be further determined whether to maintain heating or terminate heating based on the first temperature threshold and the first state of charge threshold.

[0042] Figure 2 A flow chart of another intelligent optimization method for low-temperature endurance of new energy vehicles is shown. Figure 2 As shown, the following steps are included.

[0043] Step S201, collecting relevant information of the battery pack in real time, the relevant information at least including battery temperature, battery remaining power and battery health.

[0044] Step S202, collecting vehicle operating condition information in real time, the operating condition information at least including vehicle speed, mileage and ambient temperature.

[0045] Step S203, based on the pre-established model and in combination with the battery pack information and the vehicle operating condition information, calculate the best heating strategy under the current state.

[0046] Step S204: controlling the heater to heat the battery pack according to the optimal heating strategy.

[0047] 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, then the heating is terminated.

[0048] Step S205b: if the current battery temperature is lower than the first temperature threshold, or the current state of charge is lower than the first state of charge threshold, heating is maintained.

[0049] In this application, obtaining the discharge characteristic diagram of the battery system and the first and second temperature thresholds and the state of charge thresholds for controlling the start and end of heating helps to accurately control the heating process. The introduction of these parameters enables the system to make more precise judgments based on the current specific conditions of the battery, thereby avoiding unnecessary energy consumption and ensuring that the battery is always in the best working condition, further improving the battery life.

[0050] During the heating process, the battery temperature and state of charge are continuously monitored, and the heating strategy is dynamically adjusted accordingly to prevent the battery from overheating or undercharging. When the battery temperature reaches the preset threshold and the state of charge meets the conditions, the heating is terminated, and when it does not meet the standards, the heating is maintained. This mechanism can ensure battery safety while optimizing energy efficiency and extending battery life.

[0051] Figure 3 A flow chart of a method for continuously monitoring the state change of a battery pack during the heating process and adjusting the heating strategy based on the state change is shown, such as Figure 3 As shown, the following steps are included.

[0052] In step S301, the added power of the battery pack is determined, the feedback power of the battery pack is determined, and the power consumption of the battery pack is determined.

[0053] 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 executed simultaneously or in a sequential order, and this application does not make specific limitations on this.

[0054] 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.

[0055] In step S303a, if the energy comprehensive value is greater than the energy comprehensive threshold, heating is maintained.

[0056] In step S303b, if the energy comprehensive value is less than the energy comprehensive threshold, heating is terminated.

[0057] Among them, the following formula is used to determine the comprehensive energy value of the battery pack: ΔE=ΔE k +E 回TK -E 耗Tk .

[0058] Among them, ΔE represents the comprehensive energy value, ΔE k It represents the amount of electricity 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 Represents the power consumption of the battery pack in the kth time interval.

[0059] This application determines the added power, feedback power and power consumption of the battery pack, and calculates the comprehensive energy value based on these data to decide whether to continue heating. This strategy can minimize energy consumption while ensuring battery performance. The calculation of the comprehensive energy value using the formula ΔE=ΔEk+EreturnTK-EconsumptionTk helps to achieve refined energy management, thereby effectively improving the overall energy efficiency performance of electric vehicles.

[0060] In this application, the following formula is used to determine the added power of the battery pack: ΔE k =(Ah TK -Ah T（K-1） )*U 端 .

[0061] 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 capacity at temperature (TK), U 端 Represents the terminal voltage of the battery. This application uses this formula to determine the added power of the battery pack, which can quantify the actual energy increment obtained by the battery in each heating cycle. This method provides more intuitive data support, which facilitates 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.

[0062] In this application, the following formula is used to determine the power consumption of the battery pack: .

[0063] Q k = .

[0064] Among them, E 耗Tk represents the power consumption of the battery pack in the kth time interval, represents the thermoelectric conversion efficiency, Q k 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. This application uses this formula to determine the power consumption of the battery pack and based on the heat dissipation power Calculating the heat dissipation Qk can accurately assess the energy loss during the heating process. In this way, the system can better balance the gains brought by 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.

[0065] Among them, 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.

[0066] In some embodiments, the battery pack capacity can be obtained by reading a battery pack capacity table. Figure 4 A schematic diagram of a battery pack capacity table is shown.

[0067] Figure 5 A schematic diagram of SOC correction during heating is shown.

[0068] 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 in combination with the feedback SoC to achieve accurate management and optimization of the battery status. Through this method of comprehensive consideration of multiple factors, the system can dynamically adjust the heating strategy under different driving conditions and environmental changes to ensure the best performance of the battery, while improving the range and safety of electric vehicles under low temperature conditions.

[0069] The present application exemplifies a complete process of heating a vehicle battery pack under low temperature conditions, including the following steps 1 to 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. 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 temperature of the battery T0, and the initial SoC (SoC0); 2. Heat the battery; 3. When the battery temperature T is lower than T2 and the SoC is lower than SoC2, T=TK; 4. Continue heating T=TK+1; 5. Obtain / input the battery's T-SoC-OCV table and battery pack capacity table. Based on the battery's T-SoC-OCV table, obtain the actual SoC at the current temperature TK(t) , at temperature Tk, the remaining capacity of the battery is Ah TK =SoC TK C TK +ΔAh TK -T(K-1); 6. Obtain / input the battery pack heat dissipation table and calculate the heat dissipation power P of the battery pack based on the current vehicle speed and ambient temperature. TK(t) , the heat dissipation of the battery pack Qk= ,power consumption ; 7. Calculate the increase in power ΔEk when the temperature is heated from T (K-1) to TK = (Ah TK -Ah T（K-1） )*U端 ; 8. Get / input the battery pack feedback power P=f(SoC,TK), calculate the feedback power E back to TK= ; 9. Calculate ΔE = ΔE k +E 回TK -E 耗Tk , ΔE>0, return to step 3, and repeat steps 3 to 9; 10. ΔE k <0 or does not meet step 3, end heating; 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 ; 12. Discharge ends; 13. Record the discharge duration t.

[0070] in, , ΔAh TK-T(K-1) The capacity loss is calculated as follows: ΔAh TK-T(K-1) =DisAh TK -ChaAh T(K-1) ; 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 discharge capacity loss is ΔAh TK-T(K-1) At temperature TK, the remaining capacity of the battery is Ah TK =SOC TK C T(K-1) .

[0071] ΔQ K =CM*1,K=1,…,N is the Coulomb efficiency, is the thermoelectric conversion efficiency.

[0072] The present application also provides an apparatus embodiment that is consistent with the above embodiment, which is used to implement the method steps of the above embodiment. The explanation based on the same name meaning is the same as the above embodiment, and has the same technical effect as the above embodiment, which will not be repeated here.

[0073] like Figure 6 As shown, the present application provides a new energy vehicle low temperature endurance intelligent optimization device 600, comprising: The battery pack information acquisition module 601 is used to collect relevant information of the battery pack in real time, and the relevant information includes at least the battery temperature, the remaining battery power and the battery health. The vehicle operating condition information acquisition module 602 is used to collect the vehicle operating condition information in real time, and the operating condition information includes at least the vehicle speed, mileage and ambient temperature. The model calculation module 603 is used to 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 vehicle operating condition information. The heating control module 604 is used to control the heater to heat the battery pack according to the optimal heating strategy. The state monitoring and adjustment module 605 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.

[0074] Regarding the device 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.

[0075] Figure 7 It is a block diagram of an electronic device 700 for intelligent optimization of low-temperature endurance of a new energy vehicle according to an exemplary embodiment.

[0076] like Figure 7 As shown, one embodiment of the present application provides an electronic device 700. Among them, the electronic device 700 includes a memory 701, a processor 702, and an input / output (I / O) interface 703. Among them, the memory 701 is used to store instructions. The processor 702 is used to call the instructions stored in the memory 701 to execute the new energy vehicle low-temperature endurance intelligent optimization method of the embodiment of the present application. Among them, the processor 702 is connected to the memory 701 and the I / O interface 703 respectively, for example, it can be connected through a bus system and / or other forms of connection mechanisms (not shown). The memory 701 can be used to store programs and data, including the program of the new energy vehicle low-temperature endurance intelligent optimization method involved in the embodiment of the present application. The processor 702 executes various functional applications and data processing of the electronic device 700 by running the program stored in the memory 701.

[0077] In the embodiment of the present application, the processor 702 can be implemented in at least one hardware form of a digital signal processor (Digital Signal Processing, DSP), a field programmable gate array (Field Programmable Gate Array, FPGA), and a programmable logic array (Programmable Logic Array, PLA). The processor 702 can be a central processing unit (Central Processing Unit, CPU) or one or a combination of other forms of processing units with data processing capabilities and / or instruction execution capabilities.

[0078] The memory 701 in the embodiment of the present application may include one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include, for example, read-only memory (ROM), flash memory, hard disk drive (HDD), or solid state drive (SSD), etc.

[0079] In the embodiment of the present application, the I / O interface 703 may be used to receive input instructions (such as digital or character information, and key signal input related to user settings and function control of the electronic device 700), and may also output various information to the outside (such as images or sounds). In the embodiment of the present application, the I / O interface 703 may include one or more of a physical keyboard, a function key (such as a volume control key, a switch key, etc.), a mouse, a joystick, a trackball, a microphone, a speaker, and a touch panel.

[0080] In some embodiments, the present application provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed by a processor, any of the methods described above is performed.

[0081] 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, any of the methods described above is performed.

[0082] 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 achieve desired results. In certain circumstances, multitasking and parallel processing may be advantageous.

[0083] The methods and devices of the present application can be implemented using standard programming techniques, using rule-based logic or other logic to implement various method steps. It should also be noted that the words "device" and "module" 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.

[0084] 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 module is implemented using a computer program product including a computer-readable medium containing computer program code, which can be executed by a computer processor to perform any or all of the described steps, operations or procedures.

[0085] The foregoing description of the implementation of the present application has been given for the purpose of illustration and description. The foregoing description is not exhaustive nor is it intended to limit the present application to the exact form disclosed, and various variations and modifications may exist according to the above teachings or may be obtained from the practice of the present application. These embodiments are selected and described in order to illustrate the principles of the present application and its practical application, so that those skilled in the art can utilize the present application in various embodiments and various modifications suitable for the specific purpose contemplated.

[0086] Regarding the device 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.

[0087] It can be further understood that, unless otherwise specified, “connection” includes a direct connection without other components between the two, and also includes an indirect connection with other components between the two.

[0088] It is further understood that, although the operations are described in a specific order in the drawings in the embodiments of the present application, it should not be understood as requiring the operations to be performed in the specific order or serial order shown, or requiring the execution of all the operations shown to obtain the desired results. In certain environments, multitasking and parallel processing may be advantageous.

[0089] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to encompass any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the field of the present application that are not disclosed in the present application. The specification and embodiments are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following scope of rights.

[0090] 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.

[0091] 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 at least including vehicle speed, mileage and ambient temperature; Calculate the best heating strategy under the current state according to the pre-established model and in combination with the battery pack information and the vehicle operating condition information; According to the optimal heating strategy, controlling the heater to heat the battery pack; 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 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 is sufficient to enable the vehicle to have a maximum vehicle range.

3. The method according to claim 1 or 2, characterized in that: The method further comprises: 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 are acquired.

4. The method according to claim 3, 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 include: 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 the 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.

5. The method according to claim 1, 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 include: Determining the increased power of the battery pack; Determining the amount of power returned by 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, heating is maintained; If the energy comprehensive value is less than the energy comprehensive threshold, the heating is terminated; The comprehensive energy value of the battery pack is determined by the following formula: ΔE=ΔE k +E 回TK -E 耗Tk ; Among them, ΔE represents the comprehensive energy value, ΔE k It represents the amount of electricity 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 Represents the power consumption of the battery pack in the kth time interval.

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

7. The method according to claim 5, characterized in that The power consumption of the battery pack is determined using the following formula: ； Q k = ； Among them, E 耗Tk represents the power consumption of the battery pack in the kth time interval, represents the thermoelectric conversion efficiency, Q k 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.

8. A new energy vehicle low temperature endurance intelligent optimization device, characterized in that: include: A battery pack information acquisition module, used to collect relevant information of the battery pack in real time, the relevant information at least including battery temperature, battery remaining power and battery health; The vehicle operating condition information acquisition module is used to collect the vehicle operating condition information in real time, and the operating condition information at least includes the vehicle driving speed, driving mileage and ambient temperature; A model calculation module, used to calculate the best heating strategy under the current state according to a pre-established model and in combination with the battery pack information and the vehicle operating condition information; a heating control module, configured to control a heater to heat the battery pack according to the optimal heating strategy; The state monitoring and adjustment module is used to continuously monitor the state change of the battery pack during the heating process and adjust the heating strategy based on the state change.

9. 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 7.

10. 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 7 is implemented.

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