A vehicle control method, system, vehicle controller, and cloud server

Abstract

This disclosure provides a vehicle control method, system, vehicle controller, and cloud server. The method includes: the vehicle controller acquiring relevant information about vehicle operation and determining vehicle control parameters based on the relevant information; the vehicle controller acquiring correction information for vehicle control sent by the cloud server, the correction information being determined by the cloud server based on historical data stored on the cloud server (such as geographical, traffic, or road condition information) and historical data from the cloud of at least one other vehicle (the other vehicles being those related to the vehicle); the vehicle controller adjusting the control parameters based on the correction information and controlling vehicle operation according to the adjusted control parameters. The vehicle control method, system, vehicle controller, and cloud server disclosed in this disclosure can achieve intelligent control of all functions, enable intelligent energy management across all areas, achieve low-carbon travel, and improve user experience.

Description

Technical Field

[0001] This disclosure relates to, but is not limited to, the automotive field, and particularly to a vehicle control method, system, vehicle controller, and cloud server. Background Technology

[0002] In the current environment of Intelligent Traffic Systems (ITS) and Vehicle-to-Everything (V2X) networks, vehicles can fully perceive and understand complex traffic environments and road geographic information. Against this backdrop, automotive energy-saving control technology can not only obtain the vehicle's own status, but also obtain information about the road (such as slope, curvature, speed limit, etc.) and traffic (congestion, traffic light location and timing, etc.) that the vehicle is traveling on.

[0003] Currently, most vehicle intelligent control systems rely solely on navigation information for vehicle control, such as predicting traffic congestion ahead or pre-emptively storing electrical energy. The methods are relatively simple, and the availability of functions is determined by the navigation system, which has certain limitations. Summary of the Invention

[0004] In a first aspect, embodiments of this disclosure provide a vehicle control method, including:

[0005] The vehicle controller acquires relevant information about vehicle operation and determines the control parameters of the vehicle based on the relevant information.

[0006] The vehicle controller receives vehicle control correction information sent by the cloud server. The correction information is determined by the cloud server based on cloud-based historical data of the vehicle and at least one other vehicle, which are vehicles related to the vehicle.

[0007] The vehicle controller adjusts the control parameters based on the correction information and controls the vehicle operation according to the adjusted control parameters.

[0008] Secondly, embodiments of this disclosure provide a vehicle control method, including:

[0009] The cloud server receives and stores vehicle operation-related information sent by the vehicle controller;

[0010] The cloud server determines vehicle control correction information based on cloud-based historical data of the vehicle and at least one of the other vehicles, which are vehicles related to the vehicle.

[0011] The correction information is sent to the vehicle controller, which uses the correction information to adjust the vehicle's control parameters to control the vehicle's operation.

[0012] This disclosure also provides a vehicle controller, including a memory and a processor, wherein the memory stores execution instructions; the processor invokes the execution instructions to execute the vehicle control method as described in any embodiment of the first aspect.

[0013] This disclosure also provides a cloud server, including a memory and a processor, wherein the memory is used to store execution instructions; the processor invokes the execution instructions to execute the vehicle control method as described in any embodiment of the second aspect.

[0014] This disclosure also provides a vehicle control system, including a vehicle controller as described in any embodiment and a cloud server as described in any embodiment.

[0015] The vehicle control method, system, vehicle controller, and cloud server provided in at least one embodiment of this disclosure have the following advantages compared with the prior art: they can combine vehicle information and cloud big data, adjust and update the corresponding control parameters in the vehicle controller in real time according to the correction information calculated by the cloud server, and replace the local control commands of the vehicle controller with the control parameters adjusted by the correction information to achieve intelligent control of the whole domain function. For example, it can optimize energy in a global manner, enable whole-domain intelligent energy management, achieve low-carbon travel, better driving experience, and improve user experience.

[0016] Other features and advantages of this disclosure will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the disclosure. Other advantages of this disclosure may be realized and obtained by means of the methods described in the description and the accompanying drawings. Attached Figure Description

[0017] The accompanying drawings are used to provide an understanding of the technical solution of the present disclosure and constitute a part of the specification. Together with the embodiments of the present disclosure, they are used to explain the technical solution of the present disclosure and do not constitute a limitation to the technical solution of the present disclosure.

[0018] Figure 1 This is a structural block diagram of a vehicle control system provided in an example embodiment of the present invention;

[0019] Figure 2 This is an architecture diagram of a vehicle control system provided in an example embodiment of the present invention;

[0020] Figure 3 This is a flowchart of a vehicle control method provided in an example embodiment of the present invention;

[0021] Figure 4 A flowchart of a vehicle control method provided in another exemplary embodiment of the present invention;

[0022] Figure 5This is a schematic diagram illustrating the division of vehicle routes based on navigation information, provided in an example embodiment of the present invention.

[0023] Figure 6 A flowchart of a vehicle control method provided in another exemplary embodiment of the present invention;

[0024] Figure 7 This is a structural block diagram of a vehicle controller provided in an example embodiment of the present invention;

[0025] Figure 8 This is a structural block diagram of a cloud server provided in an example embodiment of the present invention. Detailed Implementation

[0026] This disclosure describes several embodiments, but these descriptions are exemplary and not limiting, and it will be apparent to those skilled in the art that many more embodiments and implementations are possible within the scope of the embodiments described herein. Although many possible combinations of features are shown in the drawings and discussed in the detailed description, many other combinations of the disclosed features are also possible. Unless specifically limited, any feature or element of any embodiment may be used in combination with, or may replace, any feature or element of any other embodiment.

[0027] This disclosure includes and contemplates combinations of features and elements known to those skilled in the art. The embodiments, features, and elements disclosed in this disclosure may also be combined with any conventional features or elements to form a unique inventive scheme as defined by the claims. Any feature or element of any embodiment may also be combined with features or elements from other inventive schemes to form another unique inventive scheme as defined by the claims. Therefore, it should be understood that any feature shown and / or discussed in this disclosure may be implemented individually or in any suitable combination. Therefore, the embodiments are not limited except by the limitations imposed by the appended claims and their equivalents. Furthermore, various modifications and changes may be made within the scope of the appended claims.

[0028] Furthermore, in describing representative embodiments, the specification may have presented methods and / or processes as a specific sequence of steps. However, the method or process should not be limited to the specific order of steps described herein, to the extent that the method or process does not depend on the specific order of steps described herein. As will be understood by those skilled in the art, other sequences of steps are also possible. Therefore, the specific order of steps set forth in the specification should not be construed as a limitation of the claims. Moreover, the claims relating to the method and / or process should not be limited to the steps performed in the order written, and those skilled in the art will readily understand that these orders can be varied and still remain within the spirit and scope of the embodiments disclosed herein.

[0029] Figure 1 This is a structural block diagram of a vehicle control system provided in an example embodiment of the present invention. Figure 2 This is an architecture diagram of a vehicle control system provided in an example embodiment of the present invention, as shown below. Figure 1 and Figure 2 As shown, the vehicle control system may include: a vehicle controller 11 and a cloud server 12.

[0030] By building a vehicle controller and cloud server, vehicle information and cloud big data can be combined to achieve intelligent control of all functions. For example, energy can be optimized globally, enabling intelligent energy management across the entire domain, achieving low-carbon travel and a better driving experience.

[0031] The computation and storage of onboard chips, which operate on a single-function, short-term basis, can be shifted to cloud-based big data and long-term statistical data calculations, enabling more refined and intelligent control. This transforms single-function intelligent energy management into global intelligent energy management, achieving systemic energy optimization and low-carbon intelligent travel.

[0032] In one example, such as Figure 2 As shown, the vehicle control system may further include: at least one vehicle slave controller 13, each of which is connected to the vehicle controller; the vehicle slave controller is used to collect relevant information about vehicle operation and send it to the vehicle controller, and to control vehicle operation according to the control parameters sent by the vehicle controller.

[0033] In this embodiment, the in-vehicle controller can adopt a domain-controlled layout, employing a domain-controlled structure with one vehicle controller (which can be called the master controller or domain controller) and multiple vehicle slave controllers (which can be called execution controllers). The vehicle controller is responsible for calculating the control commands of each vehicle slave controller and interacting with, managing, and controlling the output data from the cloud computing. The vehicle slave controllers are responsible for collecting vehicle information and executing the control commands (control parameters) sent by the vehicle controller.

[0034] like Figure 2As shown, a controller can be set up in the cloud server for cloud-based big data and related calculations, such as obtaining the correction information in the following embodiment through cloud-based big data calculations. The corresponding computing methods of cloud computing used in the cloud server may include artificial neural networks, transfer learning, decision trees, sequence analysis, clustering, regression, etc., and their implementation principles are the same as existing technologies. This embodiment will not limit or elaborate on them here.

[0035] like Figure 2 As shown, the vehicle controller may include at least one of the following: an entertainment-related controller 131, an electric drive controller 132, a battery controller 133, a power controller 134, and other domain controllers 135. The entertainment-related controller may include a navigation controller and a driver assistance controller, etc.

[0036] Figure 3 A flowchart of a vehicle control method provided in an example embodiment of the present invention is shown below. Figure 3 As shown, the execution subject of this embodiment of the invention is a vehicle controller, and the vehicle control method may include:

[0037] S301: The vehicle controller acquires relevant information about vehicle operation and determines the vehicle's control parameters based on that information.

[0038] The vehicle controller can transmit the acquired vehicle operation information to the cloud via the network, and determine the vehicle's control parameters based on the vehicle operation information in order to control the vehicle.

[0039] Information related to vehicle operation may include, but is not limited to: the vehicle's own status, characteristic parameters of the driver's driving behavior, road information (such as slope, curvature, speed limit, etc.) and traffic information (congestion status, traffic light location and timing, etc.). The vehicle's own status may include data related to the vehicle's powertrain system, while the road and traffic information may include route information and congestion information after navigation is activated.

[0040] In one example, in-vehicle data collection, i.e., information related to vehicle operation, may include at least one of the following:

[0041] The navigation controller provides road traffic information, such as driving routes and new route planning, distance to congestion ahead, congestion level and distance to congestion, as well as road condition information, such as slope or mountain roads.

[0042] The driver assistance controller provides information such as distance to the vehicle in front, distance to the vehicle behind, road signs, traffic lights, and fault information.

[0043] The electric drive controller provides information such as the motor's rated current, voltage, speed, angular position, zero point position, temperature, operating mode, gear information, torque, self-learning information, and fault information.

[0044] The battery controller (also known as a high-voltage battery controller) provides information such as the battery's current, voltage, status (health status, energy status, safety status, etc.), temperature, power (charging power and discharging power), and fault information.

[0045] The power controller provides information such as engine speed, torque, operating status, accelerator pedal information, brake pedal information, temperature, ignition parameters, fuel system parameters, air system parameters, and fault information.

[0046] Other necessary information provided by the domain controllers, such as high and low pressure energy conversion information, fault information, and thermal management-related information such as temperature, flow rate, and power.

[0047] S302: The vehicle controller obtains the vehicle control correction information sent by the cloud server. The correction information is determined by the cloud server based on the cloud historical data of the vehicle and at least one other vehicle, which are vehicles related to the vehicle.

[0048] Correspondingly, on the cloud server side, the cloud server receives and stores relevant information about vehicle operation sent by the vehicle controller, and determines the correction information for vehicle control based on the cloud historical data of the vehicle and at least one other vehicle; the cloud server then sends the correction information to the vehicle controller.

[0049] In one example, the cloud server can determine corrective information for vehicle control based on historical data already stored on the cloud server (such as geographic, traffic, or road condition information) and on cloud-based historical data from the vehicle and at least one of the other vehicles.

[0050] The cloud server is responsible for computing big data in the cloud, and the key functional parameters (correction information) obtained from the cloud computing are returned to the vehicle. The cloud big data computing is mainly based on long-term data transmitted from the vehicle, vehicle quantity data of the same type, and pre-stored road condition data. After statistical calculation, key correction information is obtained and sent to the vehicle controller.

[0051] The cloud server receives vehicle operation-related information, including route information after navigation is activated, congestion information, vehicle power system data, and characteristic parameters of driver behavior. Based on big data and unique algorithms, it estimates and evaluates driver driving style learning and correction information, the health status of electronic components, the correction of self-learning or adaptive function parameters of system functions, specific area identification, and specific route identification, in order to optimize specific control parameters, enable intelligent control and energy management, and achieve more refined and intelligent control to achieve the goal of low-carbon travel.

[0052] S303: The vehicle controller adjusts the control parameters based on the correction information and controls the vehicle operation according to the adjusted control parameters.

[0053] The correction information obtained from cloud server calculations can be sent to the vehicle controller via the network to adjust and update the corresponding control parameters in the vehicle controller in real time. The control parameters adjusted by the correction information replace the local control commands of the vehicle controller, thereby optimizing the control algorithm of the vehicle controller and achieving a comprehensive improvement in system efficiency, driving quality and driving safety.

[0054] Figure 4 A flowchart of a vehicle control method provided in another exemplary embodiment of the present invention is shown below. Figure 4 As shown, it may include:

[0055] S401: The vehicle acquires data from the controller and sends the acquired signals to the vehicle controller.

[0056] S402: The vehicle controller determines the control parameters (control commands) for controlling the vehicle based on the collected signals.

[0057] S403: The vehicle controller determines whether it is an in-vehicle interaction. If yes, it sends the control parameters to the corresponding vehicle slave controller and executes S405; if no, it sends the collected signal to the cloud server and executes S404.

[0058] S404: The cloud server calculates and obtains correction information, and sends the correction information to the vehicle controller so that the vehicle controller can adjust the control parameters according to the correction information.

[0059] S405: The vehicle executes control parameters from the controller to control the vehicle's operation.

[0060] The vehicle controller is responsible for collecting in-vehicle information, transmitting this information to the cloud, and calculating local control parameters. Cloud-based big data computation and the return of key functional parameters obtained from cloud computing to the vehicle facilitate data updates for related in-vehicle controllers.

[0061] In-vehicle information collection and calculation are mainly divided into two parts: one part is the vehicle controller (execution controller), which is only responsible for signal collection and control command execution calculation tasks; the other part is the vehicle controller (control calculation controller), which is responsible for command calculation tasks of various controllers in the vehicle and interaction, management and control tasks with cloud computing output data.

[0062] Cloud-based big data computing mainly involves the controller (big data statistical computing controller) in the cloud server calculating key correction information based on long-term data transmitted from inside the vehicle, vehicle quantity data of the same type, and pre-stored road condition data, and then sending it to the vehicle controller.

[0063] The vehicle control method provided in this invention can combine vehicle information and cloud big data, and adjust and update the corresponding control parameters in the vehicle controller in real time according to the correction information calculated by the cloud server. The control parameters adjusted by the correction information replace the local control commands of the vehicle controller, thereby realizing intelligent control of all functions. For example, it can optimize energy in a global manner, enable intelligent energy management, achieve low-carbon travel, better driving experience, and improve user experience.

[0064] In one example embodiment of the present invention, the vehicle may include a hybrid vehicle or an electric vehicle; relevant information may include: road segment information where the vehicle is located; control parameters may include: energy management parameters and / or energy storage parameters, wherein the energy storage parameters are used to indicate whether electrical energy is stored in the road segment; and the energy management parameters may include at least one of the State of Charge (SOC) balance point and energy recovery level. The SOC balance point is the target value for the vehicle's battery balance, used to represent the battery state that the user (such as the vehicle owner) expects the vehicle to achieve during driving.

[0065] Among them, energy management parameters may include, but are not limited to, the SOC balance point and energy recovery level, and may also include parameters such as engine and motor efficiency and power consumption of thermal management devices.

[0066] The correction information can be the corrected energy management parameters and / or corrected energy storage parameters determined by the cloud server based on historical road segment information of the same road segment as the vehicle within a preset time period. Alternatively, the correction information can be the corrected energy management parameters and / or corrected energy storage parameters determined by the cloud server based on road segment information obtained in real time from other vehicles on the road segment where the vehicle is located.

[0067] In this embodiment, intelligent energy management is achieved by combining vehicle information (road segment information) with cloud-based big data to optimize energy globally and enable comprehensive intelligent energy management. This transforms single-function intelligent energy management into global intelligent energy management, achieving systematic energy optimization and low-carbon intelligent travel.

[0068] The vehicle controller can determine energy management parameters based on the road segment information collected from the controller, including the State of Charge (SOC) balance point and / or energy recovery level for that road segment. Furthermore, it can determine energy storage parameters based on the road segment information collected from the controller, determining whether energy storage is needed for that road segment and allowing for pre-storage of energy to maximize energy utilization in congested areas.

[0069] The cloud server can obtain traffic and road information collected from vehicles on the same road segment based on the vehicle's location, determine and correct energy management parameters, such as the corrected SOC balance point and / or corrected energy recovery level, as well as corrected energy storage parameters, and transmit them to the vehicle controller. The vehicle controller adjusts the locally calculated corrected energy management parameters and / or corrected energy storage parameters accordingly based on the received corrected energy management parameters and / or corrected energy storage parameters, and updates the relevant parameters to the vehicle controller, thereby realizing intelligent energy management and achieving optimal energy control and optimal economy.

[0070] In one example, the cloud server can obtain historical traffic and road information of the vehicle on the same road segment from the cloud, determine the corrected energy management parameters, such as the corrected SOC balance point and / or corrected energy recovery level, and the corrected energy storage parameters, and transmit them to the vehicle controller. The vehicle controller adjusts the locally calculated corrected energy management and / or corrected energy storage parameters accordingly based on the received corrected energy management and / or corrected energy storage parameters, and updates the relevant parameters to the vehicle controller, thereby realizing intelligent energy management and achieving optimal energy control and optimal economy.

[0071] In one example, the vehicle has navigation enabled, and the vehicle controller obtains relevant information about the vehicle's operation, which may include:

[0072] The vehicle controller obtains traffic congestion information for the road segment where the vehicle is located based on navigation information and uses this congestion information as the road segment information for the vehicle.

[0073] In this embodiment, the road segment information where the vehicle is located can be obtained from the navigation information obtained by activating the vehicle's navigation system. The road segment information may include, but is not limited to, traffic information obtained from the navigation system (such as road congestion distance, congestion level, etc.) and road information (such as whether the road segment has a slope and the magnitude of the slope). The vehicle controller can determine the local energy management parameters and / or energy storage parameters based on the traffic data and road information provided by the navigation controller.

[0074] Figure 5 This is a schematic diagram illustrating the division of vehicle routes based on navigation information, provided in an example embodiment of the present invention. Figure 5 As shown, after setting the navigation destination and starting navigation, the vehicle controller divides the road segments the vehicle passes through into relatively smooth urban and suburban road segments L1, smooth highway road segments L2, and congested urban road segments L3, based on the congestion distance, congestion level, and congestion speed fed back by the navigation controller.

[0075] The vehicle controller can calculate the vehicle's target speed and State of Charge (SOC) balance point based on traffic and road information obtained from navigation, such as the vehicle's average speed, the gradient of the current road segment, and the degree and length of congestion on the road ahead. The corresponding energy recovery level is then obtained from the target speed to determine local energy management parameters. Determining the vehicle speed and SOC balance point based on traffic data and road information, as well as determining the corresponding energy recovery level based on the vehicle speed, can employ existing technologies, which are not limited or elaborated upon in this embodiment.

[0076] The vehicle controller can calculate the energy consumption of a vehicle traveling through a congested section of road based on road information such as gradient and average vehicle speed, and determine whether to store electrical energy in advance. Figure 5 For example, the local control parameters determined by the vehicle controller based on navigation information may include:

[0077] On the L1 section, switch to pure electric or hybrid mode to balance energy consumption and comfort;

[0078] In L2 sections, the vehicle switches to hybrid mode and charges the battery to ensure that the state of charge (SOC) meets the requirements for the next stage of pure electric driving. Before entering congested areas, the high-voltage battery can be charged in advance on highways to ensure that there is enough power before entering congested areas.

[0079] On L3 sections, switching to pure electric mode ensures smooth driving and saves fuel.

[0080] In practical applications, due to the inherent delay in navigation information, to avoid this delay, real-time road segment information from other vehicles on the same road segment can be obtained from cloud data to correct the control parameters obtained by the vehicle controller. The cloud server can acquire traffic and road information collected by one or more other vehicles on the same road segment to obtain corrected energy management parameters and / or corrected energy storage parameters, thereby correcting the energy management and / or energy storage parameters calculated locally by the vehicle controller.

[0081] In one example, the vehicle navigation is not activated, and the vehicle controller obtains relevant information about the vehicle's operation, which may include:

[0082] The vehicle controller obtains the location of the road segment where the vehicle is located and uses the location as the road segment information.

[0083] In this embodiment, for vehicles that have not activated navigation, the road segments that the vehicle may pass through during this driving can be predicted based on cloud data, and the energy management parameters and / or energy storage parameters can be corrected based on the predicted road segments.

[0084] Without navigation activated, the vehicle controller can obtain the vehicle's location on the road segment via position sensors, or request the vehicle's initial and / or destination location from the user via a user interaction device, and send this information to the cloud server. The cloud server can then use big data information uploaded to the cloud by one or more other vehicles in the vicinity of the vehicle, such as average speed, gradient, and the degree and length of congestion on the road ahead, combined with the destination location, or the vehicle's historical driving trajectory over a period of time obtained from the cloud, to enter a familiar route mode (i.e., internally stored road information) and predict the possible road segments that the current drive may take. Based on the calculated maximum probability driving path, and utilizing the cloud's powerful computing capabilities, it predictively and dynamically plans the target speed curve and the high-voltage battery SOC change curve to determine and correct energy management parameters, and also determines and corrects energy storage parameters based on the calculated maximum probability driving path.

[0085] In one example, based on the predicted target vehicle speed curve and the high-voltage battery SOC change curve, a hybrid energy management strategy, a drive mode switching strategy, a multi-power source torque dynamic distribution strategy, and a shifting strategy can be formulated for this trip, thereby greatly improving system efficiency, significantly reducing energy consumption, and improving drivability.

[0086] In one example, preset energy management parameters and preset energy storage parameters can be set in the vehicle controller. When the vehicle is not using navigation, the preset energy management parameters and preset energy storage parameters can be used as local energy management parameters and energy storage parameters, respectively.

[0087] In an example embodiment of the present invention, the vehicle may include a hybrid vehicle or an electric vehicle, the relevant information may include at least one of the driver's accelerator pedal information and brake pedal information, and the control parameters may include energy management parameters, which may include energy recovery level, but are not limited to including a single energy recovery level parameter.

[0088] The corrected information is the corrected energy recovery level determined by the cloud server based on real-time traffic light information and / or inter-vehicle distance information obtained from other vehicles at a distance less than or equal to a set distance from the vehicle.

[0089] In this embodiment, when the vehicle controller cannot obtain the vehicle's location information, traffic information, or road information, the energy recovery level can be determined by combining the driver's accelerator pedal information and / or brake pedal information, as well as cloud data.

[0090] When the driver releases the accelerator pedal to coast or applies the brake pedal for energy recovery, the cloud server can use information from one or more other vehicles in the vicinity, such as distance to the vehicle in front, distance to the vehicle behind, road signs (e.g., speed limits), and traffic lights, to determine whether to coast in neutral or use the regenerative braking mode. The cloud algorithm calculates the optimal braking acceleration to determine the corrected regenerative braking level. In arbitration, the cloud server prioritizes coasting in neutral. When the distance to the vehicle in front is close, or when a speed limit sign or red light is detected, the intensity of regenerative braking is dynamically adjusted to ensure safety while also considering system efficiency and improving passenger comfort.

[0091] In one example, preset energy management parameters and preset energy storage parameters can be set in the vehicle controller. For situations where the driver releases the accelerator pedal to coast or applies the brake pedal to recover energy, the preset energy management parameters and preset energy storage parameters can be used as local energy management parameters and energy storage parameters, respectively.

[0092] In an example embodiment of the present invention, the relevant information may include at least one of the driver's accelerator pedal information and brake pedal information, and the control parameters may include the driver's driving style; the correction information is the driver's corrected driving style determined by the cloud server based on the accelerator pedal history information and brake pedal history information within a preset time period of the vehicle.

[0093] The vehicle controller adjusts the control parameters based on the correction information and controls the vehicle's operation according to the adjusted control parameters, which may include:

[0094] The vehicle controller adjusts the driver's driving style to a corrected driving style and controls the vehicle's operation based on the corrected driving style.

[0095] In this embodiment, the driver's driving style can be identified based on the driver's accelerator pedal information and / or brake pedal information, combined with cloud data. Driving style refers to a person's chosen or habitual driving style. Based on the driver's choices regarding driving speed, following distance, etc., driving styles can be divided into: sporty driving style and economy driving style. In a sporty driving style, the force (actual pedal opening) of the accelerator or brake pedal is heavier, and the speed (pedal change rate) is faster, i.e., better acceleration. In an economy driving style, the force of the accelerator or brake pedal is lighter, and the speed is slower, i.e., poorer acceleration.

[0096] The vehicle controller can determine the driver's selection of driving speed and following distance based on accelerator pedal information or brake pedal information provided by the power controller. This allows it to identify the driver's driving style and adjust relevant parameters such as accelerator pedal position (MAP), energy recovery torque, and gear shift point to improve vehicle acceleration. Accelerator pedal information can include the accelerator pedal change rate and the actual accelerator pedal position. Brake pedal information can include the brake pedal change rate and the actual brake pedal position.

[0097] In practical applications, the user experience is poor due to delays or ineffectiveness in achieving driving expectations caused by delays in accelerator pedal or brake pedal information. In this embodiment, the locally determined driving style can be corrected based on cloud data. The cloud server can identify the driver's corrected driving style based on historical accelerator pedal change rate, brake pedal change rate statistics, accelerator pedal opening, and brake pedal opening statistics obtained over a long period (1 day, 1 week, or 1 month, etc.), and output it to the vehicle controller. The controller compares relevant parameters (accelerator pedal opening (MAP), energy recovery torque, gear shift point, etc.) and updates relevant parameters to the vehicle controller to accurately identify the driving style and achieve driving expectations.

[0098] In this embodiment, the driver's driving style can be learned based on the vehicle's accelerator pedal information or brake pedal information, and the driving mode can be intelligently corrected by combining the historical accelerator pedal information or brake pedal information in the cloud, so as to find the best driving mode and driving style.

[0099] In one example embodiment of the present invention, the modified driving style can be represented by a modification coefficient. Different values ​​or ranges of the modification coefficient correspond to different modified driving styles.

[0100] In this embodiment, the cloud server can calculate a corrected driving style based on historical data from the cloud, which serves as the current driver's driving style. It can also output a driving style coefficient to the vehicle controller as correction information to adjust the calculated driving style. For example, if the driving style is determined to be an economy driving style, the output coefficient is less than 1; if the driving style is determined to be a sporty driving style, the output coefficient is greater than 1.

[0101] The vehicle controller can update the corresponding parameters based on the received correction coefficients. For example, if the cloud server determines the driving style to be economy driving style, i.e., the correction coefficient is less than 1, the vehicle controller will multiply the relevant parameters corresponding to the driving style it determined, such as the accelerator pedal curve and shift points, by the correction coefficient to obtain the corrected driving style coefficient, thereby obtaining the corresponding corrected driving style (or driving mode). The implementation principle of the correction coefficients for other parameters is similar, and will not be described in detail in this embodiment.

[0102] In an exemplary embodiment of the present invention, controlling vehicle operation based on a modified driving style may include:

[0103] Based on the modified driving style, at least one of the vehicle's accelerator pedal MAP, energy recovery torque, and gear shift point is determined to control vehicle operation.

[0104] In this embodiment, parameters such as accelerator pedal opening (MAP), energy recovery torque, and gear shift point can be adjusted according to the modified driving style to improve vehicle acceleration.

[0105] In one exemplary embodiment of the present invention, the vehicle control method may further include:

[0106] The vehicle controller acquires electrical parameters of the electrical equipment on the vehicle; the vehicle controller acquires fault prediction information sent by the cloud server, which is determined by the cloud server based on historical fault information of electrical parameters of other vehicles with the same type of electrical equipment after running for a fixed period of time; and provides fault warnings for the electrical equipment based on the fault prediction information.

[0107] In this embodiment, the health status of vehicle components (such as electrical equipment) can be estimated and assessed based on cloud data. The vehicle controller can acquire the electrical parameters of a certain electrical device on the vehicle and send the electrical parameters and the model (or identifier) ​​of the electrical device to the cloud server. The cloud server searches the cloud data for historical data of other vehicles (referred to as family vehicles) with the same model of electrical equipment. It obtains the electrical parameters of the family vehicles after running for a fixed period of time (such as 1 year, 5 years, or 10 years) or after a fixed mileage (such as 10,000 kilometers, 30,000 kilometers, etc.). It determines whether there is a fault in the electrical parameters after the fixed period or fixed mileage and the cause of the fault, so as to assess the health status of the current vehicle's electrical equipment and realize the early warning of faults in the current vehicle's electrical equipment.

[0108] Electrical equipment may include at least one of the following: battery (high-voltage battery), engine, motor, transmission, in-vehicle sensors, etc.

[0109] In one example, the electrical equipment may include a battery, and the health status of the vehicle battery can be assessed based on cloud data. The vehicle controller can acquire information such as the vehicle battery current, voltage, status (charging or discharging), temperature, or power, and send it to the cloud server. The cloud server receives the battery data uploaded in real time by the vehicle controller and combines it with historical data from other vehicles in the same family that have operated for a fixed period or mileage to estimate the battery's SOX (Solution to Exhaust) value. For example, if the cloud data shows that a battery in a vehicle of the same family has experienced a high-voltage thermal runaway fault after 5 years or 10,000 kilometers of operation, the cloud server can provide a fault warning for the current vehicle's battery based on this fault. For instance, the warning may indicate that the battery is at risk of high-voltage thermal runaway. The cloud server can determine the potential battery failure and the probability of a serious failure before the current battery failure (e.g., high-voltage thermal runaway) occurs. Depending on the severity of the failure, the server can proactively prompt the driver to perform vehicle repairs, trigger the vehicle's fault protection strategy in advance, or warn the driver to evacuate the vehicle. This significantly reduces the probability of serious high-voltage battery failure and protects the personal and property safety of passengers.

[0110] A battery's SOX can include: State of Charge (SOC), State of Health (SOH), State of Power (SOP), or State of Function (SOF). SOC can be understood as the percentage of remaining battery charge; SOH as the percentage of the battery's current capacity relative to its factory capacity; SOP as the maximum discharge and charge power the battery can provide during a sustained high current flow, such as 2, 10, or 30 seconds; and SOF as a parameter in the control function strategy.

[0111] In one example, the cloud server can dynamically adjust the high-voltage battery control strategy based on battery status information and a battery aging analysis algorithm, so as to correct the battery energy and capacity estimation after aging and achieve precise control of battery power output.

[0112] In this embodiment, the cloud server can derive the SOX aging correction coefficient related to mileage and running time based on battery status information and cloud-based battery fault prediction.

[0113] In one example, the cloud server can derive a temperature-related fault diagnosis and protection early warning coefficient based on battery status information and cloud-based battery fault prediction.

[0114] In one example, the electrical equipment may include a motor, and the health status of the vehicle motor can be assessed based on cloud data. The vehicle controller can acquire motor parameters such as motor temperature, torque, current, zero-point self-learning value (zero-point preset value), gear information, or running time in the electric drive and send them to the cloud server.

[0115] The cloud server receives motor parameters uploaded in real time from the vehicle controller. Combined with historical data from other vehicles in the same family that the motors have run for a fixed period or mileage, the server corrects the motor's zero position based on this historical data, improving torque control accuracy and electronic control efficiency. Alternatively, it can correct transmission oil pressure control, clutch contact points, and efficiency curves to further improve control accuracy and shift quality. This ensures precise control of motor torque output and shift quality even after the transmission has aged.

[0116] In one example, the cloud server can derive the motor zero-point learning aging coefficient associated with mileage and running time based on historical data of the motors of vehicles of the same family running for a fixed period of time or a fixed mileage in the cloud data, and derive the aging coefficients of the transmission contact point, torque conversion coefficient, oil filling correction, etc., associated with mileage.

[0117] In one example, the electrical equipment may include an engine, whose health status can be assessed based on cloud data. The vehicle controller can acquire power information such as vehicle speed, torque, temperature, air circuit information, fuel circuit information, ignition information, or running time, and send it to the cloud server. The cloud server receives the power information uploaded in real time by the vehicle controller, combines it with historical data from the cloud database of engines of similar vehicles running for a fixed period or mileage, and corrects the self-learning values ​​of the fuel and air circuits to accurately control power and torque output after aging.

[0118] In one example, the cloud server can derive a correction factor for the flow of fuel lines, air lines, or engine exhaust gas return (EGR) associated with mileage and running time, based on historical data of the engines of vehicles of the same family running for a fixed period of time or a fixed mileage in the cloud data. This correction factor is greater than 1.

[0119] In one example, the vehicle controller can acquire information such as the vehicle's transmission or engine self-learning values ​​(preset fixed values) and send them to the cloud server. Based on the transmission self-learning values, engine self-learning values, and running time information of multiple vehicles in the same region on the cloud, the cloud server can predict relevant estimated values, correct the self-learning correction values ​​for each aging period, achieve the purpose of feedforward control, and realize precise control of each controlled object.

[0120] In one example, the cloud server can derive a feedforward aging correction coefficient related to mileage and running time based on the self-learning values ​​of transmissions, engines, and running time of multiple vehicles in the same region in the cloud. This coefficient is greater than 1, such as in a turbocharger feedforward controller.

[0121] The embodiments of this invention mainly embody a comprehensive and systematic approach, realizing intelligent energy management that integrates multiple aspects and functions, including battery, power supply, inefficiency caused by component aging, driving style, energy recovery, route planning, or familiar road mode.

[0122] Figure 6 A flowchart of a vehicle control method provided in another exemplary embodiment of the present invention is shown below. Figure 6 As shown, the execution entity in this embodiment is a cloud server, and the vehicle control method may include:

[0123] S601: The cloud server receives and stores relevant information about vehicle operation sent by the vehicle controller.

[0124] S602: The cloud server determines the correction information for vehicle control based on the cloud-based historical data of the vehicle and at least one of the other vehicles, which are vehicles related to the vehicle.

[0125] S603: The cloud server sends the correction information to the vehicle controller. The correction information is used by the vehicle controller to adjust the vehicle's control parameters in order to control the vehicle's operation.

[0126] The execution subject of this invention is a cloud server. The technical solution of the method embodiment executed by the cloud server corresponds to the technical solution of the method embodiment executed by the vehicle controller shown in any embodiment. Their implementation principles and effects are similar. For the specific execution principle, please refer to the description of the cloud server in any of the above embodiments. This embodiment will not repeat it.

[0127] In one example embodiment of the present invention, the vehicle may include: a hybrid vehicle or an electric vehicle; the relevant information may include: information about the road segment where the vehicle is located;

[0128] The cloud server determines corrective information for vehicle control based on historical cloud data from the vehicle and at least one of the other vehicles, which may include:

[0129] The cloud server determines and / or corrects energy management parameters and / or energy storage parameters based on historical road segment information that is the same as the road segment where the vehicle is located within a preset time period.

[0130] And / or,

[0131] The cloud server determines and corrects energy management parameters and / or energy storage parameters based on real-time road information obtained from other vehicles on the same road segment.

[0132] In one example embodiment of the present invention, the vehicle may include a hybrid vehicle or an electric vehicle, and the relevant information may include at least one of the driver's accelerator pedal information and brake pedal information.

[0133] The cloud server determines corrective information for vehicle control based on historical cloud data from the vehicle and at least one of the other vehicles, which may include:

[0134] The cloud server determines the corrected energy recovery level based on real-time traffic light information and / or inter-vehicle distance information obtained from other vehicles that are less than or equal to a set distance from the vehicle.

[0135] In one example embodiment of the present invention, the relevant information may include at least one of the driver's accelerator pedal information and brake pedal information;

[0136] The cloud server determines corrective information for vehicle control based on historical cloud data from the vehicle and at least one of the other vehicles, which may include:

[0137] The cloud server determines the driver's correct driving style based on the accelerator pedal history information and brake pedal history information within a preset time period of the vehicle.

[0138] In one exemplary embodiment of the present invention, the vehicle control method may further include:

[0139] The system acquires electrical parameters of the electrical equipment on the vehicle from the vehicle controller; determines fault prediction information based on historical fault information of electrical parameters of other vehicles with the same type of electrical equipment after running for a fixed period of time; and sends the fault prediction information to the vehicle controller, which uses the fault prediction information to provide early warning of faults to the electrical equipment.

[0140] Figure 7 This is a structural block diagram of a vehicle controller provided in an example embodiment of the present invention, as shown below. Figure 7 As shown, the vehicle controller may include a memory 71 and a processor 72.

[0141] The memory stores execution instructions. The processor can be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits that implement embodiments of the present invention. When the vehicle controller is running, the processor communicates with the memory, and the processor invokes execution instructions to perform the following operations:

[0142] Obtain relevant information about vehicle operation, and determine the control parameters of the vehicle based on the relevant information;

[0143] Obtain vehicle control correction information sent by the cloud server. The correction information is determined by the cloud server based on cloud-based historical data of the vehicle and at least one other vehicle, wherein the other vehicles are vehicles related to the vehicle.

[0144] The control parameters are adjusted according to the correction information, and the vehicle is controlled to operate according to the adjusted control parameters.

[0145] In one example embodiment of the present invention, the vehicle may include: a hybrid vehicle or an electric vehicle;

[0146] The relevant information may include: information about the road segment where the vehicle is located; the control parameters may include: energy management parameters and / or energy storage parameters; the energy storage parameters are used to indicate whether to store electrical energy in the road segment; the energy management parameters include at least one of SOC balance point and energy recovery level.

[0147] The correction information is either the corrected energy management parameters and / or corrected energy storage parameters determined by the cloud server based on historical road segment information of the same road segment as the vehicle within a preset time period, or the correction information is the corrected energy management parameters and / or corrected energy storage parameters determined by the cloud server based on road segment information obtained in real time from other vehicles on the road segment where the vehicle is located.

[0148] In an exemplary embodiment of the present invention, the vehicle has activated navigation, and the processor obtains relevant information about the vehicle's operation, which may include:

[0149] Obtain congestion information of the road segment where the vehicle is located based on navigation information, and use the congestion information as the road segment information where the vehicle is located;

[0150] or,

[0151] The vehicle is not using navigation. The processor obtains relevant information about the vehicle's operation, which may include:

[0152] Obtain the location of the road segment where the vehicle is located, and use the location as the road segment information where the vehicle is located.

[0153] In one example embodiment of the present invention, the vehicle may include: a hybrid vehicle or an electric vehicle;

[0154] The relevant information may include at least one of the driver's accelerator pedal information and brake pedal information, and the control parameters may include energy management parameters, including energy recovery level;

[0155] The correction information is a corrected energy recovery level determined by the cloud server based on real-time traffic light information and / or vehicle-to-vehicle distance information obtained from other vehicles at a distance less than or equal to a set distance from the vehicle.

[0156] In an example embodiment of the present invention, the relevant information may include at least one of the driver's accelerator pedal information and brake pedal information, and the control parameters may include the driver's driving style.

[0157] The correction information is the driver's corrected driving style determined by the cloud server based on the accelerator pedal history information and brake pedal history information within a preset time period of the vehicle.

[0158] The processor adjusts the control parameters based on the correction information, and controls the vehicle operation according to the adjusted control parameters, which may include:

[0159] The driver's driving style is adjusted to the modified driving style, and the vehicle operation is controlled according to the modified driving style.

[0160] In one example embodiment of the present invention, the modified driving style can be represented by a correction coefficient. Different values ​​or ranges of the correction coefficient correspond to different modified driving styles.

[0161] In an exemplary embodiment of the present invention, the processor controlling vehicle operation according to the modified driving style may include:

[0162] Based on the modified driving style, at least one of the vehicle's accelerator pedal MAP, energy recovery torque, and gear shift point is determined to control vehicle operation.

[0163] In one exemplary embodiment of the present invention, the processor is further configured to:

[0164] Obtain electrical parameters of the electrical equipment on the vehicle;

[0165] Obtain fault prediction information sent by the cloud server. The fault prediction information is determined by the cloud server based on historical fault information of electrical parameters existing in other vehicles with the same type of electrical equipment after running for a fixed period of time.

[0166] The electrical equipment is given a fault warning based on the fault prediction information.

[0167] Figure 8 This is a structural block diagram of a cloud server provided in an example embodiment of the present invention, as shown below. Figure 8 As shown, the vehicle controller may include a memory 81 and a processor 82.

[0168] The memory stores execution instructions. The processor can be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits that implement embodiments of this invention. When the cloud server is running, the processor communicates with the memory, and the processor invokes execution instructions to perform the following operations:

[0169] Receive and store vehicle operation-related information sent by the vehicle controller;

[0170] Correction information for vehicle control is determined based on cloud-based historical data of the vehicle and at least one of the other vehicles, which are vehicles associated with the vehicle.

[0171] The correction information is sent to the vehicle controller, which uses the correction information to adjust the vehicle's control parameters to control the vehicle's operation.

[0172] In one example embodiment of the present invention, the vehicle may include: a hybrid vehicle or an electric vehicle; the relevant information may include: road segment information where the vehicle is located;

[0173] The processor determines corrective information for vehicle control based on cloud-based historical data from the vehicle and at least one of the other vehicles, which may include:

[0174] Based on the historical road segment information of the same road segment as the vehicle within a preset time period, determine the corrected energy management parameters and / or corrected energy storage parameters.

[0175] And / or,

[0176] The energy management parameters and / or energy storage parameters are adjusted based on real-time road segment information obtained from other vehicles on the same road segment as the vehicle.

[0177] In an example embodiment of the present invention, the vehicle may include a hybrid vehicle or an electric vehicle, and the relevant information may include at least one of the driver's accelerator pedal information and brake pedal information.

[0178] The processor determines corrective information for vehicle control based on cloud-based historical data from the vehicle and at least one of the other vehicles, which may include:

[0179] The corrected energy recovery level is determined based on real-time signal light information and / or inter-vehicle distance information obtained from other vehicles at a distance less than or equal to a set distance from the vehicle in question.

[0180] In one example embodiment of the present invention, the relevant information may include at least one of the driver's accelerator pedal information and brake pedal information;

[0181] The processor determines corrective information for vehicle control based on cloud-based historical data from the vehicle and at least one of the other vehicles, which may include:

[0182] The driver's correct driving style is determined based on the accelerator pedal history and brake pedal history within a preset time period of the vehicle.

[0183] In one example embodiment of the present invention, the modified driving style can be represented by a correction coefficient. Different values ​​or ranges of the correction coefficient correspond to different modified driving styles.

[0184] In one exemplary embodiment of the present invention, the processor is further configured to:

[0185] Obtain the electrical parameters of the electrical equipment on the vehicle sent by the vehicle controller;

[0186] Fault prediction information is determined based on historical fault information of electrical parameters of other vehicles equipped with the same type of electrical equipment after running for a fixed period of time.

[0187] The fault prediction information is sent to the vehicle controller, and the fault prediction information is used by the vehicle controller to provide fault warnings for the electrical equipment.

[0188] It will be understood by those skilled in the art that all or some of the steps, systems, or apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all components may be implemented as software executed by a processor, such as a digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software may be distributed on a computer-readable medium, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

Claims

1. A vehicle control method, characterized in that, include: The vehicle controller acquires relevant information about vehicle operation and determines the control parameters of the vehicle based on the relevant information. The vehicle controller obtains vehicle control correction information sent by the cloud server. The correction information is determined by the cloud server based on cloud historical data of the vehicle and at least one other vehicle, which are vehicles related to the vehicle. The vehicle controller adjusts the control parameters according to the correction information, and controls the vehicle operation according to the adjusted control parameters. The method further includes: The vehicle controller acquires electrical parameters of the electrical equipment on the vehicle; The vehicle controller obtains fault prediction information sent by the cloud server. The fault prediction information is determined by the cloud server based on historical fault information of electrical parameters existing in other vehicles with the same type of electrical equipment after running for a fixed period of time. The electrical equipment is given a fault warning based on the fault prediction information.

2. The method according to claim 1, characterized in that, The vehicles include: hybrid vehicles or electric vehicles; The relevant information includes: information on the road segment where the vehicle is located; the control parameters include: energy management parameters and / or energy storage parameters; the energy storage parameters are used to indicate whether to store electrical energy in the road segment; the energy management parameters include at least one of SOC balance point and energy recovery level. The correction information is either the corrected energy management parameters and / or corrected energy storage parameters determined by the cloud server based on historical road segment information of the same road segment as the vehicle within a preset time period, or the correction information is the corrected energy management parameters and / or corrected energy storage parameters determined by the cloud server based on road segment information obtained in real time from other vehicles on the road segment where the vehicle is located.

3. The method according to claim 2, characterized in that, The vehicle has activated navigation, and the vehicle controller obtains relevant information about the vehicle's operation, including: The vehicle controller obtains the congestion information of the road segment where the vehicle is located based on the navigation information, and uses the congestion information as the road segment information where the vehicle is located. or, The vehicle navigation is not activated. The vehicle controller obtains relevant information about the vehicle's operation, including: The vehicle controller obtains the location of the road segment where the vehicle is located and uses the location as the road segment information of the vehicle.

4. The method according to claim 1, characterized in that, The vehicles include: hybrid vehicles or electric vehicles; The relevant information includes at least one of the driver's accelerator pedal information and brake pedal information, and the control parameters include energy management parameters, which include energy recovery level; The correction information is a corrected energy recovery level determined by the cloud server based on real-time traffic light information and / or vehicle-to-vehicle distance information obtained from other vehicles at a distance less than or equal to a set distance from the vehicle.

5. The method according to claim 1, characterized in that, The relevant information includes at least one of the driver's accelerator pedal information and brake pedal information, and the control parameters include the driver's driving style. The correction information is the driver's corrected driving style determined by the cloud server based on the accelerator pedal history information and brake pedal history information within a preset time period of the vehicle. The vehicle controller adjusts the control parameters according to the correction information, and controls the vehicle operation according to the adjusted control parameters, including: The vehicle controller adjusts the driver's driving style to the corrected driving style and controls the vehicle's operation according to the corrected driving style.

6. The method according to claim 5, characterized in that, The modified driving style is represented by a modification coefficient. Different values ​​or ranges of the modification coefficient correspond to different modified driving styles.

7. The method according to claim 5, characterized in that, The method of controlling vehicle operation according to the modified driving style includes: Based on the modified driving style, at least one of the vehicle's accelerator pedal opening, energy recovery torque, and gear shift point is determined to control vehicle operation.

8. A vehicle control method, characterized in that, include: The cloud server receives and stores vehicle operation-related information sent by the vehicle controller; The cloud server determines vehicle control correction information based on cloud-based historical data of the vehicle and at least one of the other vehicles, which are vehicles related to the vehicle. The cloud server sends the correction information to the vehicle controller, and the correction information is used by the vehicle controller to adjust the vehicle's control parameters in order to control the vehicle's operation. The method further includes: Obtain the electrical parameters of the electrical equipment on the vehicle sent by the vehicle controller; Fault prediction information is determined based on historical fault information of electrical parameters of other vehicles equipped with the same type of electrical equipment after running for a fixed period of time. The fault prediction information is sent to the vehicle controller, and the fault prediction information is used by the vehicle controller to provide fault warnings for the electrical equipment.

9. The method according to claim 8, characterized in that, The vehicles include hybrid vehicles or electric vehicles; the relevant information includes: information about the road segment where the vehicle is located; The cloud server determines the corrective information for vehicle control based on historical cloud data from the vehicle and at least one of the other vehicles, including: The cloud server determines and / or corrects energy management parameters and / or energy storage parameters based on historical road segment information that is the same as the road segment where the vehicle is located within a preset time period. And / or, The cloud server determines and corrects energy management parameters and / or energy storage parameters based on real-time road segment information obtained from other vehicles on the same road segment as the vehicle.

10. The method according to claim 8, characterized in that, The vehicle includes a hybrid vehicle or an electric vehicle, and the relevant information includes at least one of the driver's accelerator pedal information and brake pedal information. The cloud server determines the corrective information for vehicle control based on historical cloud data from the vehicle and at least one of the other vehicles, including: The cloud server determines the corrected energy recovery level based on real-time traffic light information and / or inter-vehicle distance information obtained from other vehicles that are less than or equal to a set distance from the vehicle.

11. The method according to claim 8, characterized in that, The relevant information includes at least one of the driver's accelerator pedal information and brake pedal information; The cloud server determines the corrective information for vehicle control based on historical cloud data from the vehicle and at least one of the other vehicles, including: The cloud server determines the driver's correct driving style based on the accelerator pedal history information and brake pedal history information within a preset time period of the vehicle.

12. The method according to claim 11, characterized in that, The modified driving style is represented by a modification coefficient. Different values ​​or ranges of the modification coefficient correspond to different modified driving styles.

13. A vehicle controller, characterized in that, It includes a memory and a processor, wherein the memory is used to store execution instructions; the processor invokes the execution instructions to execute the vehicle control method as described in any one of claims 1-7.

14. A cloud server, characterized in that, It includes a memory and a processor, wherein the memory is used to store execution instructions; the processor invokes the execution instructions to execute the vehicle control method as described in any one of claims 8-12.

15. A vehicle control system, characterized in that, This includes the vehicle controller as described in claim 13 and the cloud server as described in claim 14.

16. The system according to claim 15, characterized in that, The system further includes: at least one vehicle slave controller, each vehicle slave controller being connected to the vehicle controller; The vehicle controller is used to collect relevant information about vehicle operation and send it to the vehicle controller, and to control vehicle operation according to the control parameters sent by the vehicle controller.

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