Control method and device of new energy heat system

Abstract

The invention provides a control method and device for a new energy thermal system, and relates to the technical field of intelligent control, and the method comprises the steps: responding to and receiving vehicle end data uploaded by a vehicle end processor; performing data fusion on the vehicle end data to generate fused data; on the basis of the fused data, generating a control strategy according to a pre-configured multi-objective optimization requirement; wherein the multi-objective optimization demand comprises at least one of the following: a minimum energy consumption demand, a maximum comfort demand and a maximum delay battery life demand; and the control strategy is issued to a vehicle-end processor, so that the vehicle-end processor controls a thermal system of the new energy vehicle based on the control strategy. According to the control method and device of the new energy thermal system, the in-vehicle environment data, the user behavior data and the external environment data can be fused, the whole control process is more intelligent, the multi-objective optimization requirement is met, the energy consumption of the new energy vehicle is reduced, the service life of a battery is prolonged, and the user experience is improved.

Description

Technical Field

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

[0002] Existing thermal management systems of new energy vehicles, such as battery cooling systems and cabin air conditioning systems, mostly use fixed vehicle-side control strategies, such as PID control. This control strategy is relatively simple and difficult to dynamically adapt to complex scenarios, such as extreme weather and long-distance travel. It not only leads to higher overall energy consumption, but also accelerates the battery life of new energy vehicles, reducing the user experience of using new energy vehicles. Summary of the Invention

[0003] In view of this, an object of the present invention is to provide a control method and device for a new energy thermal system to alleviate the above technical problems.

[0004] In the first aspect, an embodiment of the present invention provides a control method for a new energy thermal system, which is applied to a cloud-side processor, and the cloud-side processor communicates with a vehicle-side processor of a new energy vehicle. The method includes: responding to and receiving vehicle-side data uploaded by the vehicle-side processor, wherein the vehicle-side data includes in-vehicle environment data, user behavior data and external environment data; performing data fusion on the vehicle-side data to generate fused data; based on the fused data, generating a control strategy according to pre-configured multi-objective optimization requirements; wherein the multi-objective optimization requirements include at least one of the following: minimizing energy consumption requirements, maximizing comfort requirements and maximizing delayed battery life requirements; and sending the control strategy to the vehicle-side processor so that the vehicle-side processor controls the thermal system of the new energy vehicle based on the control strategy.

[0005] In combination with the first aspect, an embodiment of the present invention provides a first possible implementation method of the first aspect, wherein the above-mentioned step of generating a control strategy based on the fused data and in accordance with pre-configured multi-objective optimization requirements includes: constructing a state space vector and an action space vector of a preset deep reinforcement learning algorithm based on the fused data; and constructing a reward function of the deep reinforcement learning algorithm based on the minimization of energy consumption requirements, the maximization of comfort requirements, and the maximization of delayed battery life requirements included in the multi-objective optimization requirements; and outputting the control strategy using the deep reinforcement learning algorithm.

[0006] In combination with the first aspect, an embodiment of the present invention provides a second possible implementation method of the first aspect, wherein the above-mentioned step of generating a control strategy based on the fused data and in accordance with a pre-configured multi-objective optimization requirement includes: constructing the objective function and corresponding constraints of the multi-objective optimization requirement based on the vehicle-side data; wherein the objective function includes: minimizing the energy consumption function, maximizing the comfort function and maximizing the delayed battery life function; the constraints include at least one of the following: the battery temperature is within a pre-set safety temperature range; the interior temperature is within a pre-configured interior temperature range; based on the objective function and the constraints, a control strategy is generated according to a pre-configured multi-objective optimization algorithm.

[0007] In combination with the first possible implementation of the first aspect, an embodiment of the present invention provides a third possible implementation of the first aspect, wherein the above method also includes: obtaining the vehicle temperature control curve sent by the vehicle-side processor; extracting the in-vehicle environment data and external environment data included in the vehicle-side data; based on the in-vehicle environment data and external environment data, generating a temperature adjustment strategy according to the vehicle temperature control curve; and sending the temperature adjustment strategy to the vehicle processor so that the vehicle processor adjusts the operating mode of the air-conditioning system of the new energy vehicle based on the temperature adjustment strategy.

[0008] In combination with the first aspect, an embodiment of the present invention provides a fourth possible implementation method of the first aspect, wherein the above method also includes: receiving cache data sent by the vehicle-side processor, wherein the cache data is the vehicle key data cached by the vehicle-side processor when monitoring the new energy vehicle in a weak network environment; synchronizing the cache data to a database corresponding to the new energy vehicle.

[0009] In the second aspect, an embodiment of the present invention also provides a control method for a new energy thermal system, which is applied to a vehicle-side processor of a new energy vehicle, and the vehicle-side processor communicates with a cloud-side processor. The method includes: collecting vehicle-side data of the current new energy vehicle, wherein the vehicle-side data includes in-vehicle environment data, user behavior data and external environment data; sending the vehicle-side data to the cloud-side processor so that the vehicle-side processor performs data fusion on the vehicle-side data to generate fused data; based on the fused data, generating a control strategy according to pre-configured multi-objective optimization requirements; wherein the multi-objective optimization requirements include at least one of the following: minimizing energy consumption requirements, maximizing comfort requirements and maximizing delayed battery life requirements; receiving the control strategy issued by the cloud-side processor, and controlling the thermal system of the new energy vehicle based on the control strategy.

[0010] In combination with the second aspect, an embodiment of the present invention provides a first possible implementation of the second aspect, wherein the above-mentioned vehicle-side processor is configured with a lightweight processing model; the method also includes: extracting temperature data included in the vehicle-side data; judging whether to trigger a pre-configured cooling program based on the temperature data through the lightweight processing model; if so, generating a control instruction, and sending the control instruction to the air-conditioning system, and adjusting the temperature data by adjusting the operating mode of the air-conditioning system.

[0011] In combination with the second aspect, an embodiment of the present invention provides a second possible implementation method of the second aspect, wherein the above method also includes: in response to the new energy vehicle being in a weak network environment, allocating cache space in a pre-configured memory; caching the vehicle key data of the new energy vehicle in the cache space; and, in response to the network recovery of the new energy vehicle, sending the vehicle key data cached in the cache space to the cloud processor, so that the cloud processor synchronizes the vehicle key data.

[0012] In the third aspect, an embodiment of the present invention also provides a control device for a new energy thermal system, which is applied to a cloud-side processor, and the cloud-side processor communicates with the vehicle-side processor of a new energy vehicle. The device includes: a receiving module for responding to and receiving vehicle-side data uploaded by the vehicle-side processor, wherein the vehicle-side data includes in-vehicle environment data, user behavior data and external environment data; a fusion module for performing data fusion on the vehicle-side data to generate fused data; an optimization module for generating a control strategy based on the fused data in accordance with pre-configured multi-objective optimization requirements; wherein the multi-objective optimization requirements include at least one of the following: minimizing energy consumption requirements, maximizing comfort requirements and maximizing delayed battery life requirements; a control module for sending the control strategy to the vehicle-side processor so that the vehicle-side processor controls the thermal system of the new energy vehicle based on the control strategy.

[0013] In a fourth aspect, an embodiment of the present invention further provides a control device for a new energy thermal system, which is applied to a vehicle-side processor of a new energy vehicle, and the vehicle-side processor communicates with a cloud-side processor. The device includes: an acquisition module for acquiring vehicle-side data of the current new energy vehicle, wherein the vehicle-side data includes in-vehicle environment data, user behavior data and external environment data; a sending module for sending the vehicle-side data to the cloud-side processor, so that the vehicle-side processor performs data fusion on the vehicle-side data to generate fused data; based on the fused data, a control strategy is generated according to pre-configured multi-objective optimization requirements; wherein the multi-objective optimization requirements include at least one of the following: minimizing energy consumption requirements, maximizing comfort requirements and maximizing delayed battery life requirements; a receiving module for receiving the control strategy issued by the cloud-side processor, and controlling the thermal system of the new energy vehicle based on the control strategy.

[0014] The embodiments of the present invention bring the following beneficial effects: The control method and device for the new energy thermal system provided by the embodiment of the present invention are capable of responding to the vehicle-side data uploaded by the vehicle-side processor on the cloud processor, performing data fusion on the vehicle-side data, and generating fused data; based on the fused data, generating a control strategy according to pre-configured multi-objective optimization requirements; wherein the multi-objective optimization requirements include at least one of the following: minimizing energy consumption requirements, maximizing comfort requirements, and maximizing delayed battery life requirements; sending the control strategy to the vehicle-side processor so that the vehicle-side processor controls the thermal system of the new energy vehicle based on the control strategy. Since the control strategy is generated on the cloud processor, the computing pressure of the vehicle-side processor can be reduced. At the same time, the in-vehicle environmental data, user behavior data, and external environmental data can be integrated to make the entire control process more intelligent and meet multi-objective optimization requirements, which not only helps to reduce the energy consumption and battery life of new energy vehicles, but also helps to improve user experience.

[0015] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or understood by practicing the present invention. The purposes and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0016] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 A flow chart of a control method for a new energy thermal system provided by an embodiment of the present invention; Figure 2 A flow chart of another method for controlling a new energy thermal system provided by an embodiment of the present invention; Figure 3 A schematic structural diagram of a control device for a new energy thermal system provided by an embodiment of the present invention; Figure 4 A schematic structural diagram of another control device for a new energy thermal system provided by an embodiment of the present invention; Figure 5 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work shall fall within the scope of protection of the present invention.

[0020] Traditional thermal management strategies for new energy vehicles mostly adopt a single model, and it is difficult to coordinate multi-source data, resulting in lower thermal management control and reduced user experience.

[0021] Based on this, the control method and device of a new energy thermal system provided by the embodiment of the present invention can globally optimize the thermal management strategy of new energy vehicles, thereby improving the energy efficiency of the thermal management strategy of new energy vehicles.

[0022] To facilitate understanding of this embodiment, a control method for a new energy thermal system disclosed in an embodiment of the present invention is first introduced in detail.

[0023] In one possible implementation, an embodiment of the present invention provides a control method for a new energy thermal system. Specifically, the method is applied to a cloud processor, and the cloud processor communicates with a vehicle-side processor of a new energy vehicle. Specifically, the cloud processor and the vehicle-side processor in the embodiment of the present invention communicate through 5G network technology, such as the 5G-V2X (5G Vehicle-to-Everything) communication method, which can utilize the low latency (<10ms) characteristics to achieve millisecond-level synchronization of instructions issued by the cloud processor and those executed by the vehicle-side processor.

[0024] Furthermore, the vehicle-side controller in the embodiment of the present invention is primarily used to control the battery thermal management control system, the electric drive electronic thermal management control system, and the air conditioning thermal management control system of the new energy vehicle. For example, the battery thermal management control system controls the battery's heating mode, cooling mode, charging mode, and energy-saving mode, etc.; the electric drive electronic thermal management control system controls the electric drive's cooling mode and waste heat recovery mode; and the air conditioning thermal management control system controls the passenger compartment cooling mode, passenger compartment heating mode, heat pump mode, energy-saving mode, as well as the electric compressor, expansion valve, water pump, PTC electric heater, and multi-way valve, etc.

[0025] Specifically, if Figure 1 The flowchart of a control method of a new energy thermal system shown in FIG. 1 includes the following steps: Step S102, responding to and receiving vehicle-side data uploaded by the vehicle-side processor; The vehicle-side data in the embodiment of the present invention includes in-vehicle environment data, user behavior data and external environment data; Step S104: performing data fusion on the vehicle-side data to generate fused data; In actual use, the control method of the new energy thermal system provided by the embodiment of the present invention is to combine the cloud processor and the vehicle-side processor to perform thermal control on the new energy vehicle during the entire driving process of the new energy vehicle. In addition, in addition to communicating with the vehicle-side processor of the new energy vehicle, the cloud processor can also communicate with the user terminal. At the same time, the user terminal can also establish communication with the vehicle processor. An application APP corresponding to the new energy vehicle can be installed on the user terminal, so that a user interaction layer can be realized. That is, the user can interact with the new energy vehicle or the server through the graphical user interface provided by the user terminal. For example, the user can preset the temperature, select the energy-saving mode, view the energy efficiency data of the new energy vehicle, and fault alarm information, etc.

[0026] In the above-mentioned step S102 in the embodiment of the present invention, the user can input travel information through the user terminal and send it to the vehicle-side processor. At the same time, the vehicle-side processor can respond, collect vehicle-side data and upload it to the cloud processor, or the vehicle-side processor can directly send a data acquisition command to the cloud processor to obtain vehicle-side data, or the vehicle-side processor automatically collects and uploads vehicle-side data after detecting that the current new energy vehicle is in use. The specific process of uploading vehicle-side data is not limited by the embodiment of the present invention.

[0027] Furthermore, the in-vehicle environment data included in the above-mentioned vehicle-side data generally refers to the in-vehicle environment data collected by on-board sensors: such as real-time data such as in-vehicle temperature, humidity, battery temperature, and motor temperature; user behavior data refers to the user's temperature setting preferences, air conditioning usage frequency, seat heating / cooling habits, etc., which can be obtained based on the user's historical vehicle usage; external environment data refers to weather forecasts (temperature, humidity, wind speed, etc.), charging pile distribution information, traffic conditions, etc. In an embodiment of the present invention, after fusing these data, fused data can be obtained, and the following steps can be executed.

[0028] Step S106: Based on the fused data, a control strategy is generated according to pre-configured multi-objective optimization requirements; The multi-objective optimization requirements in the embodiment of the present invention include at least one of the following: minimizing energy consumption requirements, maximizing comfort requirements, and maximizing delayed battery life requirements; Step S108: Send the control strategy to the vehicle-side processor so that the vehicle-side processor controls the thermal system of the new energy vehicle based on the control strategy.

[0029] In actual use, the control strategy in the embodiment of the present invention is to balance the need to minimize energy consumption, maximize comfort and maximize delayed battery life. That is, the control strategy generated based on the current vehicle data can achieve the multi-objective optimization needs of minimizing energy consumption, maximizing comfort and maximizing delayed battery life.

[0030] Therefore, the control method of the new energy thermal system provided by the embodiment of the present invention is that the cloud processor can respond to the vehicle-side data uploaded by the vehicle-side processor, perform data fusion on the vehicle-side data, and generate fused data; based on the fused data, a control strategy is generated according to the pre-configured multi-objective optimization requirements; wherein the multi-objective optimization requirements include at least one of the following: minimizing energy consumption requirements, maximizing comfort requirements, and maximizing delayed battery life requirements; the control strategy is sent to the vehicle-side processor so that the vehicle-side processor controls the thermal system of the new energy vehicle based on the control strategy. Since the control strategy is generated by the cloud processor, the computing pressure of the vehicle-side processor can be reduced. At the same time, the in-vehicle environmental data, user behavior data and external environmental data can be integrated to make the entire control process more intelligent and meet multi-objective optimization requirements, which not only helps to reduce the energy consumption and battery life of new energy vehicles, but also helps to improve user experience.

[0031] In specific implementation, in order to achieve the above-mentioned multi-objective optimization requirements, in an embodiment of the present invention, a deep reinforcement learning algorithm can be used.

[0032] Specifically, in the above step S106, when generating the control strategy, the state space vector and action space vector of the preset deep reinforcement learning algorithm can be constructed based on the fusion data; and the reward function of the deep reinforcement learning algorithm can be constructed based on the multi-objective optimization requirements including minimizing energy consumption requirements, maximizing comfort requirements, and maximizing delayed battery life requirements; and the deep reinforcement learning algorithm is used to output the control strategy.

[0033] Specifically, when constructing the state space vector, the environmental data included in the vehicle-side data can be extracted, such as representing the parameter states such as the temperature, humidity, battery temperature, and user preferences in the vehicle as a vector, as the state space vector of an embodiment of the present invention; when constructing the action space vector, the user behavior data such as the user adjusting the air-conditioning compressor power, turning on / off the seat heating, and adjusting the air supply mode are defined as executable actions; when constructing the reward function, the effectiveness of the current new energy vehicle behavior can be evaluated based on multi-objective optimization requirements such as minimizing energy consumption requirements, maximizing comfort requirements, and maximizing delayed battery life requirements. For example, while reducing energy consumption, it is also necessary to ensure the stability of user comfort and battery life.

[0034] Furthermore, in addition to the above-mentioned deep reinforcement learning algorithm, in the present invention, when generating a control strategy according to a pre-configured multi-objective optimization requirement, a multi-objective optimization function calculation may also be used. Specifically, in the above-mentioned step S106, when generating the control strategy, the following steps are also included: Based on the vehicle-side data, the objective function and corresponding constraints of the multi-objective optimization requirements are constructed; wherein the objective function includes: minimizing the energy consumption function, maximizing the comfort function and maximizing the delayed battery life function; the constraints include at least one of the following: the battery temperature is within a pre-set safety temperature range; the vehicle interior temperature is within a pre-configured vehicle interior temperature range; then, based on the objective function and the constraints, a control strategy is generated according to the pre-configured multi-objective optimization algorithm.

[0035] In actual use, the above-mentioned energy consumption minimization function is usually somewhat related to the power consumption of new energy vehicles, such as reducing compressor power, reducing power consumption, etc.; therefore, the above-mentioned energy consumption minimization function can be the superposition of these power consumption structures, so as to facilitate the adjustment of these power consumption structures and thus reduce energy consumption.

[0036] Furthermore, the above-mentioned maximization comfort function is to ensure that the temperature and humidity in the vehicle are within the user's preset range to avoid overcooling or overheating. Therefore, the above-mentioned maximization comfort function is usually a function that represents comfort, such as temperature and humidity; Furthermore, considering that battery life is related to temperature, the above-mentioned maximizing delayed battery life function can reduce battery aging and performance degradation by controlling the battery temperature within a safe range (20°C-40°C). The specific setting range can be set according to actual usage, and the embodiment of the present invention does not limit this.

[0037] Furthermore, the above-mentioned multi-objective optimization algorithm can refer to the NSGA-II multi-objective genetic algorithm to balance objectives such as energy consumption, comfort, and battery life; or, adopt the DDPG deep deterministic policy gradient algorithm to adjust the control strategy in real time to adapt to environmental changes. The specific implementation shall be based on actual usage and is not limited in this embodiment of the present invention.

[0038] Furthermore, during the use of the vehicle, the vehicle-side processor can generate a vehicle temperature control curve when collecting the in-vehicle environmental data through the on-board sensors, such as generating a vehicle temperature control curve for the user within 24 hours. The vehicle temperature control curve can be sent to the cloud processor through the vehicle-side processor. Therefore, the cloud processor can also obtain the vehicle temperature control curve sent by the vehicle-side processor; extract the in-vehicle environmental data and external environmental data included in the vehicle-side data; generate a temperature adjustment strategy according to the vehicle temperature control curve based on the in-vehicle environmental data and external environmental data; and send the temperature adjustment strategy to the vehicle processor so that the vehicle processor adjusts the operating mode of the air-conditioning system of the new energy vehicle based on the temperature adjustment strategy.

[0039] The above-mentioned temperature control curve can realize dynamic adjustment of the temperature and humidity in the car. For example, before the user uses the car, a dynamic temperature control command is sent to the cloud processor. At this time, the cloud processor can dynamically adjust the air-conditioning operation mode according to the user's vehicle temperature control curve within 24 hours to ensure that the temperature / humidity in the car reaches the preset value when the user gets on and off the car; further, seasonal adjustments can also be realized. For example, in winter, battery heating and in-car heating can be given priority to extend the driving range; in summer, the cooling mode will be optimized to reduce energy consumption.

[0040] Furthermore, in an embodiment of the present invention, the cloud server can also implement the function of scene adaptive learning. For example, in extremely cold areas in the north, the cloud server can give priority to battery heating and in-car heating to ensure the normal operation of new energy vehicles and user comfort. In the high temperature and high humidity scenario in the south, transfer learning can be used to quickly adjust the strategy to adapt to the high temperature and high humidity environment in the south. In addition, a cloud virtual mirror can be constructed with the help of digital twin simulation technology, that is, through three-dimensional modeling and physical simulation technology, a virtual mirror of the thermal system of the new energy vehicle is constructed, including key components such as the air-conditioning system and the battery system, and then a high-precision thermal system model is constructed based on the actual vehicle data. In this way, extreme working conditions such as extreme cold start-up, high temperature operation, etc. can be simulated in a virtual environment to evaluate the performance and reliability of the air-conditioning system.

[0041] Furthermore, to prevent privacy leaks during vehicle-side data transmission, a pre-set encryption algorithm can be used to encrypt the transmitted vehicle-side data, preventing malicious attacks or tampering. Furthermore, model updates can be synchronized across different devices (such as user terminals and vehicle-side processors) to ensure consistent models across all devices.

[0042] In addition, considering that new energy vehicles may encounter a weak network environment during operation, the cloud processor in the embodiment of the present invention can also receive cached data sent by the vehicle-side processor, where the cached data is the vehicle's key data cached by the vehicle-side processor when it monitors the new energy vehicle in a weak network environment; the cached data is then synchronized to the database corresponding to the new energy vehicle to achieve data synchronization between the cloud processor and the vehicle-side processor.

[0043] Furthermore, corresponding to the above-mentioned cloud processor, an embodiment of the present invention also provides another control method for a new energy thermal system, which is applied to a vehicle-side processor of a new energy vehicle, and the vehicle-side processor communicates with the cloud processor, specifically, as follows Figure 2 The flowchart of another control method of a new energy thermal system shown includes the following steps: Step S202, collecting vehicle-side data of the current new energy vehicle; Among them, vehicle-side data includes in-vehicle environment data, user behavior data and external environment data; Step S204: Send the vehicle-side data to the cloud processor, so that the vehicle-side processor performs data fusion on the vehicle-side data to generate fused data; based on the fused data, generate a control strategy according to the pre-configured multi-objective optimization requirements; The multi-objective optimization requirements in the embodiment of the present invention include at least one of the following: minimizing energy consumption requirements, maximizing comfort requirements, and maximizing delayed battery life requirements; Step S206: Receive the control strategy sent by the cloud processor, and control the thermal system of the new energy vehicle based on the control strategy.

[0044] In actual use, the vehicle-side processor is configured with a lightweight processing model, such as the TensorFlow Lite model, to implement the edge computing layer of vehicle-side processing.

[0045] Furthermore, the process of collecting vehicle-side data in the above step S202 can be achieved through vehicle-mounted sensors, such as vehicle-mounted sensors collecting real-time data such as temperature, humidity, battery temperature, and motor temperature in the vehicle.

[0046] Furthermore, in addition to sending the vehicle-side data to the cloud processor, the vehicle-side processor also performs certain model reasoning and decision-making locally. Specifically, it includes extracting the temperature data included in the vehicle-side data; judging whether to trigger the pre-configured cooling program based on the temperature data through a lightweight processing model; if so, generating a control instruction and sending the control instruction to the air-conditioning system, and adjusting the temperature data by adjusting the operating mode of the air-conditioning system.

[0047] Among them, this process can trigger an emergency cooling program on the vehicle-side processor. For example, the vehicle-side processor detects that the battery temperature of the current vehicle-side suddenly increases. At this time, the lightweight processing model determines that emergency cooling is required and immediately sends a control instruction to the air-conditioning system to adjust the operating mode and increase the cooling capacity to ensure that the battery temperature returns to a safe range as soon as possible.

[0048] Furthermore, the vehicle-side controller can also monitor the network connection status with the cloud server, and in response to the new energy vehicle being in a weak network environment, allocate cache space in a pre-configured memory; cache the vehicle's key data of the new energy vehicle in the cache space; and, in response to the new energy vehicle network recovery, send the vehicle's key data cached in the cache space to the cloud processor, so that the cloud processor can synchronize the vehicle's key data.

[0049] In practice, vehicle-side processors typically include data caching capabilities to ensure the stability and reliability of new energy vehicles in weak network environments such as tunnels, underground garages, and remote areas. Furthermore, key vehicle data, including battery status and air conditioning operating parameters, can be cached in weak network environments. Furthermore, the vehicle-side processor can rationally allocate cache space to ensure efficient operation of the entire control process even with large data volumes.

[0050] Furthermore, after the vehicle-side processor detects that the network has been restored, the cached data needs to be uploaded to the cloud processor in a timely manner and synchronized with the data of the cloud processor to ensure the integrity and consistency of the data.

[0051] In addition, certain strategies can be preset on the vehicle-side processor. For example, a series of decision-making rules can be preset based on typical vehicle operation scenarios and historical data for reference by the edge computing layer in a weak network environment. In addition, the operating parameters of the air-conditioning system can be dynamically adjusted based on cached data and real-time sensor information to ensure a comfortable in-vehicle environment in a weak network environment. In a weak network environment, the edge computing layer can perform basic fault diagnosis and handle it according to preset strategies, such as adjusting the operating mode, reducing power consumption, etc.

[0052] In summary, the control method of the new energy thermal system provided by the embodiment of the present invention can realize the coordinated control of the cloud processor and the vehicle-side processor. It can not only integrate multi-source data, but also effectively improve the energy consumption and battery life of new energy vehicles, thereby improving the user experience.

[0053] Furthermore, an embodiment of the present invention also provides a control device for a new energy thermal system, which is applied to a cloud processor, and the cloud processor communicates with a vehicle-side processor of a new energy vehicle, such as Figure 3 The schematic diagram of the structure of a control device for a new energy thermal system is shown, and the device includes: a receiving module 30, configured to respond to and receive vehicle-side data uploaded by the vehicle-side processor, wherein the vehicle-side data includes in-vehicle environment data, user behavior data, and external environment data; A fusion module 32 is used to perform data fusion on the vehicle-side data to generate fused data; an optimization module 34 for generating a control strategy based on the fused data and in accordance with pre-configured multi-objective optimization requirements; wherein the multi-objective optimization requirements include at least one of the following: minimizing energy consumption requirements, maximizing comfort requirements, and maximizing delayed battery life requirements; The control module 36 is used to send the control strategy to the vehicle-side processor so that the vehicle-side processor controls the thermal system of the new energy vehicle based on the control strategy.

[0054] Furthermore, the above-mentioned step of generating a control strategy based on the fused data in accordance with pre-configured multi-objective optimization requirements includes: constructing a state space vector and an action space vector of a preset deep reinforcement learning algorithm based on the fused data; and constructing a reward function of the deep reinforcement learning algorithm based on the minimization of energy consumption requirements, the maximization of comfort requirements, and the maximization of delayed battery life requirements included in the multi-objective optimization requirements; and outputting the control strategy using the deep reinforcement learning algorithm.

[0055] Furthermore, the above-mentioned step of generating a control strategy based on the fused data and in accordance with pre-configured multi-objective optimization requirements includes: constructing the objective function and corresponding constraints of the multi-objective optimization requirements based on the vehicle-side data; wherein the objective function includes: minimizing the energy consumption function, maximizing the comfort function and maximizing the delayed battery life function; the constraints include at least one of the following: the battery temperature is within a pre-set safety temperature range; the vehicle interior temperature is within a pre-configured vehicle interior temperature range; based on the objective function and the constraints, generating a control strategy according to a pre-configured multi-objective optimization algorithm.

[0056] Furthermore, the above-mentioned device is also used to: obtain the vehicle temperature control curve sent by the vehicle-side processor; extract the in-vehicle environment data and external environment data included in the vehicle-side data; generate a temperature adjustment strategy according to the vehicle temperature control curve based on the in-vehicle environment data and external environment data; and send the temperature adjustment strategy to the vehicle processor so that the vehicle processor adjusts the operating mode of the air-conditioning system of the new energy vehicle based on the temperature adjustment strategy.

[0057] Furthermore, the above-mentioned device is also used to: receive cached data sent by the vehicle-side processor, wherein the cached data is the vehicle key data cached by the vehicle-side processor when it monitors the new energy vehicle in a weak network environment; and synchronize the cached data to the database corresponding to the new energy vehicle.

[0058] Furthermore, the embodiment of the present invention also provides another control device for a new energy thermal system, which is applied to a vehicle-side processor of a new energy vehicle, wherein the vehicle-side processor communicates with a cloud processor, such as Figure 4 The structure diagram of another new energy thermal system control device shown is as follows, the device includes: The acquisition module 40 is used to collect vehicle-side data of the current new energy vehicle, wherein the vehicle-side data includes in-vehicle environment data, user behavior data and external environment data; a sending module 42 configured to send the vehicle-side data to the cloud processor so that the vehicle-side processor performs data fusion on the vehicle-side data to generate fused data; and generate a control strategy based on the fused data in accordance with pre-configured multi-objective optimization requirements; wherein the multi-objective optimization requirements include at least one of the following: minimizing energy consumption, maximizing comfort, and maximizing delayed battery life; The receiving module 44 is used to receive the control strategy sent by the cloud processor and control the thermal system of the new energy vehicle based on the control strategy.

[0059] Furthermore, the vehicle-side processor is configured with a lightweight processing model; the above-mentioned device is also used to: extract the temperature data included in the vehicle-side data; determine whether to trigger a pre-configured cooling program based on the temperature data through the lightweight processing model; if so, generate a control instruction, and send the control instruction to the air-conditioning system, and adjust the temperature data by adjusting the operating mode of the air-conditioning system.

[0060] Furthermore, the above-mentioned device is also used to: in response to the new energy vehicle being in a weak network environment, allocate cache space in a pre-configured memory; cache the vehicle key data of the new energy vehicle in the cache space; and, in response to the network recovery of the new energy vehicle, send the vehicle key data cached in the cache space to the cloud processor, so that the cloud processor synchronizes the vehicle key data.

[0061] The control device of the new energy thermal system provided in the embodiment of the present invention has the same technical features as the control method of the new energy thermal system provided in the above embodiment, so it can also solve the same technical problems and achieve the same technical effects.

[0062] Furthermore, an embodiment of the present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0063] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are executed.

[0064] Furthermore, an embodiment of the present invention also provides a structural diagram of an electronic device, such as Figure 5 As shown, it is a structural diagram of the electronic device, wherein the electronic device includes a processor 51 and a memory 50, the memory 50 stores computer executable instructions that can be executed by the processor 51, and the processor 51 executes the computer executable instructions to implement the above method.

[0065] exist Figure 5 In the illustrated embodiment, the electronic device further includes a bus 52 and a communication interface 53 , wherein the processor 51 , the communication interface 53 and the memory 50 are connected via the bus 52 .

[0066] Among them, the memory 50 may include high-speed random access memory (RAM), and may also include non-volatile memory (non-volatile memory), such as at least one disk storage. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 53 (which can be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. can be used. The bus 52 can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus 52 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0067] The processor 51 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method may be performed by hardware integrated logic circuits or software instructions in the processor 51. The processor 51 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of the present invention may be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory, and the processor 51 reads the information in the memory and completes the above method in combination with its hardware.

[0068] The computer program product of the control method and device of the new energy thermal system provided in the embodiment of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the method described in the previous method embodiment. The specific implementation can be referred to the method embodiment and will not be repeated here.

[0069] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0070] In addition, in the description of the embodiments of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; they may refer to mechanical connections or electrical connections; they may refer to direct connections or indirect connections through an intermediate medium; and they may refer to internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0071] If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0072] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0073] Finally, it should be noted that the above embodiments are only specific implementation methods of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. Such modifications, changes or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A control method for a new energy thermal system, characterized in that: Applied to a cloud processor that communicates with a vehicle-side processor of a new energy vehicle, the method includes: Responding to receiving vehicle-side data uploaded by the vehicle-side processor, wherein the vehicle-side data includes in-vehicle environment data, user behavior data, and external environment data; Performing data fusion on the vehicle-side data to generate fused data; Based on the fused data, a control strategy is generated according to pre-configured multi-objective optimization requirements; wherein the multi-objective optimization requirements include at least one of the following: minimizing energy consumption requirements, maximizing comfort requirements, and maximizing delayed battery life requirements; The control strategy is sent to the vehicle-side processor so that the vehicle-side processor controls the thermal system of the new energy vehicle based on the control strategy.

2. The method according to claim 1, characterized in that The steps of generating a control strategy based on the fused data and in accordance with pre-configured multi-objective optimization requirements include: Constructing a state space vector and an action space vector of a preset deep reinforcement learning algorithm based on the fused data; and Constructing a reward function of the deep reinforcement learning algorithm based on the multi-objective optimization requirements including the minimization of energy consumption requirement, the maximization of comfort requirement, and the maximization of delayed battery life requirement; The control strategy is output using the deep reinforcement learning algorithm.

3. The method according to claim 1, characterized in that The steps of generating a control strategy based on the fused data and in accordance with pre-configured multi-objective optimization requirements include: Constructing an objective function and corresponding constraints for the multi-objective optimization requirement based on the vehicle-side data; wherein the objective function includes minimizing an energy consumption function, maximizing a comfort function, and maximizing a delayed battery life function; and the constraints include at least one of the following: the battery temperature is within a preset safe temperature range; the vehicle interior temperature is within a pre-configured vehicle interior temperature range; Based on the objective function and the constraints, a control strategy is generated according to a pre-configured multi-objective optimization algorithm.

4. The method according to claim 2, characterized in that The method further comprises: Obtaining a vehicle temperature control curve sent by the vehicle-side processor; Extracting in-vehicle environment data and external environment data included in the vehicle-side data; generating a temperature adjustment strategy according to the vehicle temperature control curve based on the in-vehicle environment data and the external environment data; The temperature adjustment strategy is sent to the vehicle processor, so that the vehicle processor adjusts the operating mode of the air-conditioning system of the new energy vehicle based on the temperature adjustment strategy.

5. The method according to claim 1, wherein The method further comprises: Receiving cached data sent by the vehicle-side processor, wherein the cached data is key vehicle data cached by the vehicle-side processor when monitoring the new energy vehicle in a weak network environment; The cached data is synchronized to a database corresponding to the new energy vehicle.

6. A control method for a new energy thermal system, characterized in that: A vehicle-side processor for a new energy vehicle, wherein the vehicle-side processor communicates with a cloud processor, and the method includes: Collecting vehicle-side data of the current new energy vehicle, wherein the vehicle-side data includes in-vehicle environment data, user behavior data, and external environment data; The vehicle-side data is transmitted to the cloud processor, so that the vehicle-side processor performs data fusion on the vehicle-side data to generate fused data; based on the fused data, a control strategy is generated according to pre-configured multi-objective optimization requirements; wherein the multi-objective optimization requirements include at least one of the following: minimizing energy consumption requirements, maximizing comfort requirements, and maximizing delayed battery life requirements; Receive the control strategy sent by the cloud processor, and control the thermal system of the new energy vehicle based on the control strategy.

7. The method according to claim 6, characterized in that The vehicle-side processor is configured with a lightweight processing model; the method further includes: Extracting temperature data included in the vehicle-side data; determining, by the lightweight processing model and based on the temperature data, whether to trigger a pre-configured cooling program; If yes, a control instruction is generated and sent to the air-conditioning system, so as to adjust the temperature data by adjusting the operating mode of the air-conditioning system.

8. The method according to claim 6, characterized in that The method further comprises: In response to the new energy vehicle being in a weak network environment, allocating cache space in a pre-configured memory; caching the key vehicle data of the new energy vehicle in the cache space; and, In response to the new energy vehicle network being restored, the vehicle key data cached in the cache space is sent to the cloud processor, so that the cloud processor synchronizes the vehicle key data.

9. A control device for a new energy thermal system, characterized in that: Applied to a cloud processor that communicates with a vehicle-side processor of a new energy vehicle, the device includes: a receiving module, configured to respond to and receive vehicle-side data uploaded by the vehicle-side processor, wherein the vehicle-side data includes in-vehicle environment data, user behavior data, and external environment data; A fusion module, configured to fuse the vehicle-side data to generate fused data; an optimization module, configured to generate a control strategy based on the fused data and in accordance with pre-configured multi-objective optimization requirements; wherein the multi-objective optimization requirements include at least one of the following: minimizing energy consumption requirements, maximizing comfort requirements, and maximizing delayed battery life requirements; A control module is used to send the control strategy to the vehicle-side processor so that the vehicle-side processor controls the thermal system of the new energy vehicle based on the control strategy.

10. A control device for a new energy thermal system, characterized in that: A vehicle-side processor for a new energy vehicle, the vehicle-side processor communicating with a cloud processor, includes: A collection module, configured to collect vehicle-side data of the current new energy vehicle, wherein the vehicle-side data includes in-vehicle environment data, user behavior data, and external environment data; a sending module configured to send the vehicle-side data to the cloud processor so that the vehicle-side processor performs data fusion on the vehicle-side data to generate fused data; and generate a control strategy based on the fused data in accordance with pre-configured multi-objective optimization requirements, wherein the multi-objective optimization requirements include at least one of the following: minimizing energy consumption requirements, maximizing comfort requirements, and maximizing delayed battery life requirements; A receiving module is used to receive the control strategy sent by the cloud processor and control the thermal system of the new energy vehicle based on the control strategy.

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