Vehicle control method, device and equipment and storage medium

Abstract

The embodiment of the invention relates to the technical field of vehicles, and discloses a vehicle control method, device and equipment and a storage medium, and the method comprises the steps: receiving the operation state information, driving environment information and driving intention information of a vehicle sent by an SOA (Service Oriented Architecture) built in the vehicle; according to the running state information, the driving environment information and the driving intention information, determining a target equivalent factor and target demand power information corresponding to the vehicle during oil-electricity conversion; determining at least one candidate equivalent fuel consumption according to the target equivalent factor, the target demand power information and a preset constraint condition; according to the at least one candidate equivalent fuel consumption, target control quantity is determined and sent to an SOA in the vehicle, and the target control quantity is used for controlling power output distribution of the vehicle. By applying the technical scheme of the invention, the energy management strategy which is more accurate and better fits the actual driving condition of the user can be determined.

Description

Technical Field

[0001] The present invention relates to the field of vehicle technology, specifically to a vehicle control method, device, equipment, and storage medium. Background Technology

[0002] Hybrid electric vehicles not only possess the advantages of electric vehicles such as power, quietness, comfort, and entertainment, but also eliminate range anxiety due to the addition of an engine, making them increasingly popular among users. As users' needs for vehicle usage scenarios become more diversified, higher requirements are being placed on the energy efficiency of hybrid electric vehicles.

[0003] Current energy management strategies are usually based on experience and use fixed equivalent factors for calculation, which cannot adapt to the actual needs of various scenarios or operating conditions. Summary of the Invention

[0004] In view of the above problems, embodiments of the present invention provide a vehicle control method, device, equipment and storage medium to solve the problem that the energy management strategies of vehicles in the current technology are too simplistic and cannot meet the actual driving needs of users.

[0005] According to one aspect of the present invention, a vehicle control method is provided, the method comprising:

[0006] Receive vehicle operating status information, driving environment information, and driving intention information sent by the service-oriented architecture (SOA) built in the vehicle;

[0007] Based on the operating status information, the driving environment information, and the driving intention information, the target equivalence factor and target power demand information of the vehicle corresponding to the oil-electric conversion are determined;

[0008] Based on the target equivalence factor, the target power demand information, and preset constraints, at least one candidate equivalent fuel consumption is determined. The constraints are determined jointly based on the continuous discharge power range of the power battery in the vehicle, the operating range of the power battery, the output power range of the engine in the vehicle, and the tolerance range of the power battery.

[0009] Based on the at least one candidate equivalent fuel consumption, a target control quantity is determined and sent to the SOA in the vehicle, the target control quantity being used to control the power output distribution of the vehicle.

[0010] According to another aspect of the present invention, a vehicle control device is provided, comprising:

[0011] The acquisition module is used to receive the vehicle's operating status information, driving environment information, and driving intention information sent by the SOA built in the vehicle;

[0012] The first determining module is used to determine the target equivalence factor and target power demand information of the vehicle in the oil-electric conversion based on the operating status information, the driving environment information, and the driving intention information.

[0013] The second determining module is used to determine at least one candidate equivalent fuel consumption based on the target equivalence factor, the target demand power information, and preset constraints. The constraints are determined jointly based on the continuous discharge power range of the power battery in the vehicle, the operating range of the power battery, the output power range of the engine in the vehicle, and the tolerance range of the power battery.

[0014] The third determining module is used to determine and send a target control quantity to the SOA in the vehicle based on the at least one candidate equivalent fuel consumption, the target control quantity being used to control the power output distribution of the vehicle.

[0015] According to another aspect of the present invention, an electronic device is provided, comprising:

[0016] The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus.

[0017] The memory is used to store at least one executable instruction that causes the processor to perform the operations of the vehicle control method described above.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, the storage medium storing at least one executable instruction that causes an electronic device / vehicle control device to perform the operation of any of the above-described vehicle control methods.

[0019] According to another aspect of the present invention, a computer program product is provided, including a computer program that, when executed by a processor, causes a vehicle control device / electronic device to perform the operation of the above-described method.

[0020] This invention embodiment receives vehicle operating status information, driving environment information, and driving intention information sent by the SOA built in the vehicle; based on the operating status information, driving environment information, and driving intention information, it determines the target equivalent factor and target power demand information corresponding to the vehicle's oil-electric conversion; based on the target equivalent factor, target power demand information, and preset constraints, it determines at least one candidate equivalent fuel consumption, the constraints being jointly determined based on the continuous discharge power range of the vehicle's power battery, the power battery's operating range, the engine's output power range, and the power battery's tolerance range; based on at least one candidate equivalent fuel consumption, it determines and sends a target control quantity to the SOA in the vehicle, the target control quantity being used to control the vehicle's power output distribution. This technical solution receives vehicle operating status, driving environment, and driving intention information provided by SOA, dynamically calculates the precise target equivalent factor and required power, and then combines it with strict constraints consisting of the continuous discharge power range of the power battery, the operating range, the engine output power range, and the battery tolerance range to solve for the optimal candidate equivalent fuel consumption. Ultimately, it achieves optimal allocation and control of the power output of the engine and motor, thereby significantly improving the energy utilization efficiency of the entire vehicle and effectively reducing overall fuel consumption while ensuring vehicle power performance, battery safety, and meeting user needs.

[0021] The above description is merely an overview of the technical solutions of the embodiments of the present invention. In order to better understand the technical means of the embodiments of the present invention and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0022] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0023] Figure 1 A schematic diagram illustrating the application of the vehicle control provided by this invention is shown.

[0024] Figure 2 A flowchart of a first embodiment of the vehicle control method provided by the present invention is shown;

[0025] Figure 3 A flowchart of a second embodiment of the vehicle control method provided by the present invention is shown;

[0026] Figure 4 A flowchart of a third embodiment of the vehicle control method provided by the present invention is shown;

[0027] Figure 5A flowchart of a fourth embodiment of the vehicle control method provided by the present invention is shown;

[0028] Figure 6 The overall execution flowchart of the vehicle control method provided by the present invention is shown;

[0029] Figure 7 A schematic diagram of an embodiment of the vehicle control device provided by the present invention is shown;

[0030] Figure 8 A schematic diagram of an embodiment of the electronic device provided by the present invention is shown. Detailed Implementation

[0031] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0032] Currently, hybrid electric vehicles not only possess the advantages of electric vehicles such as power, quietness, comfort, and entertainment, but also eliminate range anxiety due to the addition of an engine, making them increasingly popular among users. As users' needs for vehicle usage scenarios become more diversified, higher requirements are being placed on the energy efficiency of hybrid electric vehicles.

[0033] Current hybrid vehicle energy management strategies are limited by fixed operating conditions and limited vehicle-side computing power. In particular, the diverse driving capabilities of vehicles with series-parallel hybrid drive capabilities cannot be fully utilized, so there is an urgent need to optimize and improve mode management methods.

[0034] While some hybrid electric vehicles have adopted the equivalent fuel consumption minimization (ECMS) strategy in their energy management strategies, the current ECMS strategy is usually based on experience and uses fixed equivalent factors for calculation. It cannot dynamically adapt to actual operating conditions (such as congestion, long uphill, traffic lights, etc.), and the driver intention recognition can only be completed by local sensors, resulting in very limited prediction accuracy.

[0035] Based on the above-mentioned technical problems, the technical concept of this invention is as follows: Fixed equivalent factors cannot adapt to dynamically changing actual working conditions, and the accuracy of driving intention recognition based on local sensors is limited. To solve this core problem, SOA architecture and adaptive ECMS strategy can be deeply integrated. That is, by utilizing the powerful information integration and service calling capabilities of SOA architecture, multi-dimensional information such as vehicle status, driving environment (such as congestion, slope), and high-order driving intentions can be acquired and integrated in real time, which can be used as input for dynamically and accurately calculating the target equivalent factor and required power. Then, by constructing constraints that include multiple physical limits of the power battery and engine, the instantaneous optimal equivalent fuel consumption can be solved online, and finally, real-time adaptive optimization control of energy distribution of the hybrid power system can be achieved. This drives the control strategy to evolve from static experience-based decision-making to dynamic real-time optimization decision-making, ultimately achieving the dual goals of improving economy and smoothness.

[0036] Figure 1 The following is a schematic diagram illustrating the application of the vehicle control provided by the present invention, such as... Figure 1 As shown in the diagram, the diagram includes: a vehicle and the cloud (electronic devices).

[0037] On the vehicle side, the SOA architecture is built on in-vehicle Ethernet. Ethernet topology is the foundation of all SOA designs. Based on the product characteristics, the requirements of the whole vehicle Ethernet architecture are sorted out, and information such as Ethernet master control chip, number of channels, and bandwidth are determined. A widely used domain controller architecture is selected. Based on the service communication protocols of current SOA development applications, and taking into account performance, cost, development cycle, etc., the Scalable service-oriented middleware over IP (SOME / IP) protocol is selected as the protocol stack.

[0038] SOA architecture is based on key inputs such as use cases, functional / non-functional requirements documents, Ethernet system design, and CAN network topology and system design. Following the SOA architecture concept and service granularity, it is divided into multiple layers of independent and reusable services, including a device abstraction layer, an atomic service layer, and an application service layer. Higher-level application services can be flexibly combined and matched with lower-level application services, and services can be flexibly deployed across multiple hardware controllers as needed, achieving hardware-software decoupling. Then, a detailed SOA architecture is designed based on the vehicle's functions, defining different types of services, interfaces, and data types (i.e., defining different calling interfaces for different data services). Next, the interaction and communication behaviors between services are defined according to the service protocol, creating a service communication model and completing the overall SOA architecture design.

[0039] On the cloud side, the following can be configured:

[0040] 1. Based on factors such as cost, performance, and platformization, create cloud servers, virtual private clouds (VPCs), and storage resources to ensure that the central processing unit (CPU), memory, and network bandwidth meet the needs of the gateway and services.

[0041] 2. Configure a containerized environment: Use Kubernetes clusters or managed services to orchestrate nodes, supporting elastic scaling and resource isolation of services.

[0042] 3. Service Deployment and Gateway Configuration: By packaging the service into a Docker image and uploading it to a cloud image repository, containerized deployment is achieved through automated deployment and management of container instances using Kubernetes or docker-compose tools. Then, an application programming interface (API) cloud gateway instance is created, routing rules are configured, and load balancing strategies are set. Finally, it is connected to cloud databases (such as Relational Database Service (RDS)), message queues, and other infrastructure to ensure that the service can access necessary data and resources.

[0043] 4. Security and Network Hardening (Cloud-Vehicle Interaction): Enable identity authentication and authorization policies to restrict unauthorized access, configure VPC subnet and security group rules, open specific ports, and encrypt data transmission via Secure Sockets Layer (SSL) / Transport Layer Security (TLS). Use a Virtual Private Network (VPN) or dedicated line to connect the vehicle and the cloud to ensure data remains within the domain.

[0044] The above describes one possible hardware implementation of this solution. Based on the above, the technical solution of the present invention will be described in detail through specific embodiments. The execution subject of the present invention is an electronic device, such as the cloud (i.e., the execution process of this solution is applied in a containerized environment in the cloud).

[0045] It should be noted that the following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0046] Figure 2 A flowchart of a first embodiment of the vehicle control method provided by the present invention is shown, as follows: Figure 2 As shown, the method includes the following steps:

[0047] Step 21: Receive vehicle operating status information, driving environment information, and driving intention information sent by the SOA built in the vehicle;

[0048] In this step, an SOA architecture is built in the vehicle. Unlike the existing signal-oriented approach, such as an electronic control unit sending data (e.g., vehicle speed, RPM) at fixed times, SOA can package the capabilities (e.g., "providing vehicle speed" and "identifying traffic conditions") of multiple service providers in the vehicle (e.g., engine control module, battery management system, camera perception module, map provider application calling module) into a standard service and transmit it to the cloud through a subscription-subscription method.

[0049] Correspondingly, these service providers are the acquisition modules or perception modules corresponding to operating status information, driving environment information, and driving intention information.

[0050] Optionally, driving environment information includes: traffic data, weather data, and road condition data corresponding to the driving route; operating status information includes: vehicle speed data.

[0051] In this implementation, vehicle speed data may include vehicle speed, acceleration, etc.; road condition data may include: intersection delay information, speed limit information, road network average vehicle speed information, etc.

[0052] In addition, driving intention information includes: accelerator / brake pedal opening, gear position, driving mode, etc.

[0053] Furthermore, after collecting the aforementioned operating status information, driving environment information, and driving intention information, the vehicle can perform multi-source data fusion using Kalman filtering or particle filtering algorithms to improve the accuracy of each data point before sending it to the cloud; alternatively, after sending it to the cloud, the cloud can perform Kalman filtering or particle filtering algorithms to fuse the multi-source data to improve the accuracy of each data point.

[0054] Step 22: Based on the operating status information, driving environment information, and driving intention information, determine the target equivalent factor and target power demand information of the vehicle during the oil-electric conversion.

[0055] In this step, the factors affecting the target equivalence factor are operating status information, driving environment information, and driving intention information. Therefore, the current optimal target equivalence factor λ can be dynamically calculated by comprehensively analyzing the operating status information, driving environment information, and driving intention information.

[0056] Additionally, target power demand information refers to the total power required by the vehicle to meet the driver's operation (accelerator, brake) and maintain driving, and can also be determined based on operating status information, driving environment information, and driving intention information.

[0057] Step 23: Determine at least one candidate equivalent fuel consumption based on the target equivalence factor, target power demand information, and preset constraints.

[0058] The constraints are determined based on the continuous discharge power range of the power battery in the vehicle, the operating range of the power battery, the output power range of the engine in the vehicle, and the tolerance range of the power battery.

[0059] In this step, after determining the target equivalence factor and target demand power information, and combining the predetermined constraints, multiple candidate equivalent fuel consumption values ​​can be obtained, that is, the candidate equivalent fuel consumption values ​​corresponding to multiple possible power distribution schemes are given.

[0060] Optional, in the constraints:

[0061] 1) Continuous discharge power range of the power battery: The battery cannot discharge indefinitely. This range defines the maximum power P that the motor can continuously and stably obtain from the battery. bat_max and minimum power P bat_min .

[0062] 2) Operating range of the power battery: This refers to the battery's state of charge (SOC) window. The battery should not be completely depleted (0%) or fully charged (100%). A buffer zone (e.g., 20%-80%) is typically maintained. Calculating candidate equivalent fuel consumption ensures that the selected power allocation will not cause the SOC to immediately or quickly exceed this allowable range. In other words, setting the SOC... min and ≤SOC max .

[0063] 3) Engine output power range in a vehicle: Engines also have their operating capacity limits, with a minimum idle power P. eng_min (Operating below this power will cause the engine to stall or become unstable) and maximum output power P eng_max (Limited by factors such as engine displacement and turbocharging).

[0064] 4) Battery tolerance range: Compared to continuous discharge power, the battery can provide higher peak power for a short period of time; this range defines the power range that the motor can exceed its limits for a short period of time, enabling conditions such as rapid acceleration, i.e., SOC (State of Charge). end -SOC start The value of (the difference between the end time and the start time SOC) must be between -δ and δ.

[0065] Where δ is the allowable error of SOC, which is usually required to be no more than 0.5%.

[0066] In other words, based on the above, and according to the physical characteristic parameters of the relevant components in the hybrid electric vehicle's power mode, the constraints of the series-parallel system are summarized as follows:

[0067]

[0068] Optionally, based on the constraints, all possible combinations of engine power P_eng and electric motor power P_motor are determined. For each feasible combination, a corresponding candidate equivalent fuel consumption can be calculated.

[0069] Step 24: Determine and send the target control quantity to the SOA in the vehicle based on at least one candidate equivalent fuel consumption;

[0070] The target control variable is used to control the power output distribution of the vehicle.

[0071] In this step, each candidate equivalent fuel consumption corresponds to a control quantity, namely the combination of engine power and motor power mentioned above. One of the candidate equivalent fuel consumptions is determined from at least one candidate equivalent fuel consumption, and its corresponding control quantity is used as the target control quantity and sent to the vehicle. Based on this, the vehicle coordinates and controls the actuators such as the engine, generator, and drive motor to effectively improve the vehicle's energy consumption and range.

[0072] Optionally, one possible implementation of step 24 is: determining the smallest candidate equivalent fuel consumption among at least one candidate equivalent fuel consumption as the target candidate equivalent fuel consumption; determining the first candidate control quantity corresponding to the target candidate equivalent fuel consumption as the target control quantity; and sending the target control quantity to the SOA in the vehicle.

[0073] In this implementation, the lowest cost value (the operating point with the highest energy efficiency under all physical constraints at the current moment) is selected, which is the minimum candidate equivalent fuel consumption, and is formally determined as the target candidate equivalent fuel consumption.

[0074] Furthermore, since each candidate equivalent fuel consumption corresponds to a specific set of control quantities, namely the first candidate control quantity, the first candidate control quantity corresponding to the target candidate equivalent fuel consumption is determined as the target control quantity and sent.

[0075] Optionally, the global objective function J that minimizes the equivalent fuel consumption of the entire vehicle. * Global for:

[0076]

[0077] The integral represents the total equivalent fuel consumption from the initial time 0 to the final time tf.

[0078] Furthermore, based on the above target control quantities, the power output can be determined, which may include the following: 1) Pure electric drive: battery powered, motor driven alone; 2) Pure gasoline drive: engine driven alone, and can simultaneously select whether to charge the battery; 3) Hybrid drive: engine and motor drive together (such as rapid acceleration, hill climbing); 4) Driving charging: engine outputs part of the power to drive the wheels, and the remaining power drives the motor to generate electricity to charge the battery; 5) Energy recovery: during braking, the motor converts into a generator to recover kinetic energy into electrical energy.

[0079] Furthermore, based on the target control quantity obtained by the vehicle, the power output distribution of the vehicle is controlled.

[0080] The vehicle control method provided in this invention receives vehicle operating status information, driving environment information, and driving intention information sent by the SOA built in the vehicle; based on the operating status information, driving environment information, and driving intention information, it determines the target equivalent factor and target demand power information corresponding to the vehicle's oil-electric conversion; based on the target equivalent factor, target demand power information, and preset constraints, it determines at least one candidate equivalent fuel consumption, the constraints being jointly determined based on the continuous discharge power range of the vehicle's power battery, the operating range of the power battery, the output power range of the vehicle's engine, and the tolerance range of the power battery; based on at least one candidate equivalent fuel consumption, it determines and sends a target control quantity to the SOA in the vehicle, the target control quantity being used to control the vehicle's power output distribution. This technology receives vehicle operating status, driving environment, and driving intention information provided by SOA, dynamically calculates the precise target equivalent factor and required power, and then combines it with strict constraints consisting of the continuous discharge power range of the power battery, the operating range, the engine output power range, and the battery tolerance range to solve for the optimal candidate equivalent fuel consumption. Ultimately, it achieves optimal allocation and control of the power output of the engine and motor, thereby significantly improving the energy utilization efficiency of the entire vehicle and effectively reducing overall fuel consumption while ensuring vehicle power performance, battery safety, and meeting user needs.

[0081] Based on the above embodiments, Figure 3 A flowchart of a second embodiment of the vehicle control method provided by the present invention is shown, as follows: Figure 3 As shown, the method for determining the target equivalence factor in step 22 above may include the following steps:

[0082] Step 31: Based on the operating status information, driving environment information, and driving intention information, determine the multiple influencing factors affecting the value of the equivalent factor;

[0083] In this step, the operating status information, driving environment information, and driving intention information obtained from the vehicle are quite extensive. Some factors that affect the equivalent factor value need to be filtered or further determined from the operating status information, driving environment information, and driving intention information.

[0084] Taking driving intention information as an example, during rapid acceleration, λ is reduced to prioritize the use of the electric motor response and ensure the vehicle's power; during braking, λ is reduced to maximize energy recovery and further improve energy efficiency. In other words, driving intention information is based on operating status information, and its corresponding Δλ is also set with certain preset value changes.

[0085] Taking operational status information as an example, when the vehicle is traveling at high speed, the corresponding λ needs to be increased to maintain the engine's parallel direct drive as the primary mode and the electric motor's assistance as the secondary mode, avoiding frequent engine start-stop and maximizing drive efficiency. In other words, the corresponding Δλ is also set with a certain preset value change.

[0086] Taking driving environment information as an example, based on the driving environment information, the traffic flow ahead can be determined. If congestion is detected, λ is increased in advance to maintain a higher battery level; after entering the congested section, λ is decreased to use pure electric drive as much as possible. That is, the corresponding Δλ is also set with a certain preset value change.

[0087] Taking driving environment information as an example, based on the driving environment information, the road conditions ahead are determined: when going uphill, λ is increased to meet the requirements of parallel drive and enter parallel direct drive to ensure climbing power, while maintaining power generation and avoiding excessively low SOC; when going downhill, λ is decreased to maintain pure electric mode as much as possible during coasting, efficiently utilizing regenerative braking while avoiding battery overcharging. That is, the corresponding Δλ is also set with certain preset value changes.

[0088] Taking driving environment information as an example, when determining the traffic light ahead based on driving environment information: if the vehicle is predicted to be in a green wave section based on the current speed and speed limit, λ is increased. The system can determine whether to enter parallel mode or range extender mode based on vehicle speed and SOC. If it is predicted that the vehicle may stop frequently, λ is reduced in advance to maintain pure electric drive as much as possible. That is, the corresponding Δλ is also set with a certain preset value change.

[0089] Taking driving environment information as an example, based on the driving environment information to determine the specific weather: when the temperature is low, λ is appropriately increased to maintain a higher SOC and reduce battery discharge pressure; when the temperature is high, λ is appropriately increased to ensure the energy needs of the air conditioning and cooling systems; when the weather is rainy or snowy, λ is appropriately increased to fully consider the power consumption of the four-wheel drive system. That is, the corresponding Δλ is also set with a certain preset value change.

[0090] In other words, multiple influencing factors include at least those factors that may affect the equivalent factor value, such as driving intention, NVH, traffic flow, road conditions, traffic lights, and weather, which are determined based on operating status information, driving environment information, and driving intention information.

[0091] Furthermore, predictive actions can be made based on operational status information, driving environment information, and driving intention information. Specifically, map data from the driving environment information can be used to predict road congestion ahead, allowing for advance adjustment of the target battery charge to avoid frequent engine start-stop cycles in congested conditions. Predicting the vehicle's delay time at intersections and the average speed of the current road network can help determine whether to switch to parallel direct drive mode, avoiding poor NVH performance caused by repeated switching between series and parallel power modes. Vehicle sensors can predict road gradients to dynamically adjust power generation demand, preventing excessively low SOC due to prolonged steep ascents or overcharging of the battery due to prolonged descents. All of these factors also influence the equivalent factor value.

[0092] Step 32: Determine the adjustment amount of the equivalent factor based on multiple influencing factors and the preset equivalent factor adjustment strategy;

[0093] The equivalent factor adjustment strategy records the adjustment amount (i.e., Δλ above) for different influencing factors and the weight coefficients for different influencing factors.

[0094] In this step, the formula for calculating the equivalent factor adjustment can be:

[0095]

[0096] Where λ1 is the equivalent factor adjustment amount, ω i Let Δλ be the weight coefficient corresponding to the i-th influencing factor. i Let n be the adjustment amount corresponding to the i-th influencing factor, and n be the total number of influencing factors.

[0097] Step 33: Determine the target equivalence factor based on the adjustment amount of the equivalence factor and the initial equivalence factor.

[0098] In this step, the initial equivalent factor λ0 can be a benchmark value or a calibration value. It can be a fixed or segmented reference value calculated by engineers for a specific vehicle model during the vehicle development phase based on a large number of tests and global optimization algorithms (such as dynamic programming); or it can be the value of the target equivalent factor calculated in the previous step.

[0099] Therefore, the formula for calculating the target equivalence factor λ can be: λ = λ1 + λ0

[0100] The vehicle control method provided in this invention determines multiple influencing factors affecting the equivalent factor value based on operating status information, driving environment information, and driving intention information; determines the equivalent factor adjustment amount based on multiple influencing factors and a preset equivalent factor adjustment strategy; the equivalent factor adjustment strategy records the adjustment amount and weight coefficients corresponding to different influencing factors; and determines the target equivalent factor based on the equivalent factor adjustment amount and the initial equivalent factor. This technical solution, through multi-source information fusion and structured strategy design, accurately identifies multiple influencing factors by real-time analysis of operating status, driving environment, and driving intention, then calculates the equivalent factor adjustment amount based on a preset strategy (combining the weight coefficients and adjustment amounts of each factor), and finally dynamically corrects the initial equivalent factor. This allows the target equivalent factor to not only reflect the instantaneous state of the vehicle but also to predictively adapt to future operating condition changes, thereby elevating energy management from local instantaneous optimization to global trip optimization, significantly improving the overall energy efficiency and economy of the hybrid power system while ensuring the responsiveness and adaptability of the power system.

[0101] Based on the above embodiments, Figure 4 A flowchart of a third embodiment of the vehicle control method provided by the present invention is shown, as follows. Figure 4 As shown, step 23 above may include the following steps:

[0102] Step 41: Based on the constraints and target power requirements, determine at least one first candidate control variable;

[0103] Each first candidate control quantity includes: first candidate continuous discharge power and first candidate output power;

[0104] In this step, the target power requirement information can refer to the sum of the battery's continuous discharge power and the engine's output power. Based on the constraints and the target power requirement information, at least one first candidate continuous discharge power P can be determined. bat (t) and the corresponding first candidate output power P eng (t).

[0105] Step 42: For each first candidate control quantity, determine the first equivalent fuel consumption corresponding to the first candidate control quantity based on the first candidate control quantity, the target equivalent factor, the preset fuel low calorific value, and the engine working efficiency.

[0106] In this step, based on the application principles of the ECMS control strategy, the formula for calculating the candidate equivalent fuel consumption of the vehicle can be determined as follows:

[0107]

[0108] in, For any first equivalent fuel consumption, Q lhv For fuel with low calorific value, η eng (t) represents the engine efficiency, and t represents the corresponding time.

[0109] Based on the constraints and the above limitations, the first equivalent fuel consumption corresponding to the first candidate control quantity can be obtained.

[0110] Step 43: When the first state of charge data of the power battery under the first candidate control quantity meets the constraint conditions, the first equivalent fuel consumption corresponding to the first candidate control quantity is determined as the candidate equivalent fuel consumption.

[0111] In this step, after determining the first candidate control quantity, it is also necessary to perform condition judgment on the first state of charge data under the first candidate control quantity. When the constraint conditions are met (i.e. the last two items in the constraint conditions), the first equivalent fuel consumption corresponding to the first candidate control quantity is determined as the candidate equivalent fuel consumption.

[0112] Optionally, before step 43, the following steps may also be performed: determining the first state of charge data based on the first candidate control quantity; determining whether the first state of charge data meets the constraint conditions based on the first state of charge data, the operating range of the power battery, and the tolerance range of the power battery.

[0113] In this implementation, based on the first candidate control variable, the SOC within the aforementioned time period can be inferred. end SOC start The first state of charge data is compared with the operating range and tolerance range of the power battery in the above constraints to determine whether the first state of charge data meets the constraints.

[0114] The vehicle control method provided in this invention determines at least one first candidate control quantity based on constraints and target power demand information. Each first candidate control quantity includes a first candidate continuous discharge power and a first candidate output power. For each first candidate control quantity, a first equivalent fuel consumption corresponding to the first candidate control quantity is determined based on the first candidate control quantity, a target equivalence factor, a preset fuel lower calorific value, and engine operating efficiency. When the first state of charge data of the power battery under the first candidate control quantity meets the constraints, the first equivalent fuel consumption corresponding to the first candidate control quantity is determined as the candidate equivalent fuel consumption. This technical solution generates all feasible candidate control quantities (ensuring that the engine and battery power output are within a safe range), accurately equivalences energy consumption to fuel consumption based on the target equivalence factor, and introduces fuel lower calorific value and operating efficiency for real-time conversion. Finally, it selects the solution whose state of charge (SOC) change also meets the battery life management requirements, thereby accurately locating the solution with the highest overall energy efficiency at every decision moment. This not only ensures the feasibility and safety of the powertrain execution, but also transforms the theoretical advantages of ECMS into practical efficiency through refined algorithms, significantly improving the vehicle's economy and adaptability.

[0115] Based on the above embodiments, Figure 5 A flowchart of a fourth embodiment of the vehicle control method provided by the present invention is shown, as follows: Figure 5 As shown, the method for determining the target power demand information in step 22 above may include the following steps:

[0116] Step 51: Based on traffic data, weather data, and road condition data, predict the usage information of electrical equipment in the vehicle;

[0117] In this step, instead of simply passively responding to driver actions in current technology, we can proactively predict vehicle energy consumption needs using traffic data, weather data, and road condition data, thereby making better global energy planning. First, we can determine the usage information of electrical equipment in the vehicle.

[0118] Optional electrical equipment may include: air conditioning system, heated seats, heated steering wheel, heated rearview mirror, headlights, fog lights, windshield wipers, defogging and defrosting devices for the windshield and rear window, infotainment system, etc.

[0119] Usage information can refer to the operating status and power consumption of these devices over a preset period of time.

[0120] Step 52: Determine the target power demand information based on usage information, vehicle speed data, and driving intention information.

[0121] In this step, the driving power of the vehicle can be determined based on the vehicle speed data; the power of the vehicle's auxiliary equipment can be determined based on the usage information of the electrical equipment; and the power required by the user to control the vehicle based on the driving intention information can be determined.

[0122] Furthermore, by combining the power of the auxiliary equipment, the intended power, and the driving power determined above, the target power requirement information can be determined.

[0123] Furthermore, the current state of charge of the battery can be obtained from the operating status information, meaning that the target power demand information can be corrected using the current state of charge. The following possibilities exist:

[0124] A lower State of Charge (SOC) may prioritize driving, moderately limiting auxiliary power consumption, or triggering the corresponding engine to charge the battery, which increases the total power demand. In Sport mode, the drive power response will be improved, resulting in a higher calculated driving power. In Eco mode, peak power may be limited to provide smooth power output. If navigation indicates a long downhill ahead, the system may allow the SOC to drop even lower, as it is expected to recharge through energy recovery, thus influencing the current power allocation strategy.

[0125] The vehicle control method provided in this invention predicts the usage information of electrical equipment in the vehicle based on traffic data, weather data, and road condition data; and determines the target power demand information based on the usage information, vehicle speed data, and driving intention information. This technical solution analyzes traffic, weather, and road condition data to predict the load demand of electrical equipment (such as air conditioners, heaters, defrosters, etc.), and then calculates a comprehensive target power demand by combining the current battery charge, real-time vehicle speed, and driving intention. This not only meets instantaneous power requests but also predicts and coordinates total energy consumption over a future journey, thus providing an optimized basis for the fuel-electric distribution of hybrid systems or energy consumption planning for pure electric vehicles, avoiding energy shortages or waste, and achieving optimal energy utilization.

[0126] Based on the above embodiments, Figure 6 The overall execution flowchart of the vehicle control method provided by the present invention is shown, as follows: Figure 6 As shown, one possible implementation example is:

[0127] S1, SOA architecture;

[0128] S2, multi-source data, such as traffic flow, weather information, road condition information, traffic lights, vehicle speed, driving intentions, etc.

[0129] S3, State estimation and prediction;

[0130] S4, Calculation and adjustment of equivalent factors;

[0131] S5, ECMS algorithm application;

[0132] S6, algorithm deployed in the cloud;

[0133] S7, power mode decision, including: pure electric mode (pure electric direct drive, pure electric recovery), parallel mode (fuel direct drive, fuel drive P1 generator, hybrid drive P1 generator, hybrid drive P1 assist, hybrid drive P1 standby, motor recovery P1 generator, motor recovery), and series mode (series drive, series recovery).

[0134] For details not described in this embodiment, please refer to the above embodiments. The following technical effects are achieved:

[0135] Technical effect 1, economy: By utilizing a service-oriented architecture and deploying some functions in the cloud, the algorithm for minimizing the equivalent fuel consumption of the vehicle can be dynamically calculated and adaptively adjusted. It can dynamically manage the vehicle's power mode based on traffic flow, road conditions, weather information fed back from the cockpit map data and driver intentions fed back from the vehicle sensors. Compared with the traditional control strategy based on local rules, it can effectively improve the vehicle's economy.

[0136] Technical effect 2, innovation: Based on SOA development, it achieves complete decoupling of software and hardware, and can comprehensively evaluate and analyze the advantages and disadvantages of the vehicle and the cloud respectively. By leveraging the computing advantages of the cloud, high computing power modules such as SOC estimation and dynamic optimization of equivalent factors are deployed in the cloud to correct the calculation of the optimal power mode on the vehicle. This not only meets the high computing power requirements of the control strategy, but also ensures normal local operation when communication is limited, thus achieving a significant improvement in vehicle energy consumption and range.

[0137] Technical effect 3, practicality: It proposes an effective solution to the pain points of high energy consumption and short range of current hybrid new energy vehicles. The implementation of this invention enables more reasonable dynamic management and switching control of vehicle power modes, so that hybrid vehicles can combine the advantages of gasoline vehicles and electric vehicles, greatly satisfying consumers' demand for long pure electric range and low high-speed fuel consumption of hybrid vehicles, and effectively improving driving range.

[0138] Technical effect 4, adaptability: Based on the current status and development direction of vehicle electronic and electrical architecture, an SOA-based development architecture is proposed. Due to its high flexibility, scalability and software reusability, this architecture is the best development architecture to adapt to the current intelligent connected vehicle's autonomous function definition, massive data transmission and agile software iteration. This architecture not only has good adaptability to current vehicles, but also has irreplaceable advantages for future vehicle-side applications of artificial intelligence algorithms, big data and big models.

[0139] Technical benefit 5, Scalability: By adopting a service-oriented architecture and a strategy of deploying some functions in the cloud, the architecture can be flexibly adjusted and adapted when new functional requirements arise, which greatly improves the vehicle's ability to expand its functions. Functions can be deployed on mass-produced models through over-the-air (OTA) updates, which is very conducive to the large-scale promotion and application of this invention.

[0140] Figure 7 A schematic diagram of an embodiment of the vehicle control device provided by the present invention is shown. Figure 7 As shown, the device includes:

[0141] The acquisition module 71 is used to receive vehicle operating status information, driving environment information, and driving intention information sent by the service-oriented architecture (SOA) built in the vehicle.

[0142] The first determining module 72 is used to determine the target equivalence factor and target power demand information of the vehicle corresponding to the oil-electric conversion based on the operating status information, driving environment information, and driving intention information.

[0143] The second determining module 73 is used to determine at least one candidate equivalent fuel consumption based on the target equivalence factor, target demand power information, and preset constraints. The constraints are determined jointly based on the continuous discharge power range of the power battery in the vehicle, the operating range of the power battery, the output power range of the engine in the vehicle, and the tolerance range of the power battery.

[0144] The third determining module 74 is used to determine and send a target control quantity to the SOA in the vehicle based on at least one candidate equivalent fuel consumption, the target control quantity being used to control the power output distribution of the vehicle.

[0145] In one or more embodiments, the first determining module 72 determines the target equivalence factor corresponding to the vehicle's oil-electric conversion based on the operating status information, driving environment information, and driving intention information, specifically for:

[0146] Based on operational status information, driving environment information, and driving intention information, multiple influencing factors affecting the value of the equivalent factor are determined;

[0147] Based on multiple influencing factors and a pre-set equivalent factor adjustment strategy, the equivalent factor adjustment amount is determined. The equivalent factor adjustment strategy records the adjustment amount corresponding to different influencing factors and the weight coefficient corresponding to different influencing factors.

[0148] The target equivalence factor is determined based on the adjustment amount of the equivalence factor and the initial equivalence factor.

[0149] In one or more embodiments, the second determining module 73 determines at least one candidate equivalent fuel consumption based on the target equivalence factor, target power demand information, and preset constraints, specifically for:

[0150] Based on the constraints and target power requirements, at least one first candidate control quantity is determined. Each first candidate control quantity includes: first candidate continuous discharge power and first candidate output power.

[0151] For each first candidate control quantity, the first equivalent fuel consumption corresponding to the first candidate control quantity is determined based on the first candidate control quantity, the target equivalent factor, the preset lower calorific value of fuel, and the working efficiency of the engine.

[0152] When the first state-of-charge data of the power battery under the first candidate control quantity meets the constraint conditions, the first equivalent fuel consumption corresponding to the first candidate control quantity is determined as the candidate equivalent fuel consumption.

[0153] In one or more embodiments, the third determining module 74 is specifically used for:

[0154] The smallest candidate equivalent fuel consumption among at least one candidate equivalent fuel consumption is determined as the target candidate equivalent fuel consumption.

[0155] The first candidate control quantity corresponding to the target candidate equivalent fuel consumption is determined as the target control quantity, and the target control quantity is sent to the SOA in the vehicle.

[0156] In one or more embodiments, before determining the first equivalent fuel consumption corresponding to the first candidate control quantity as the candidate equivalent fuel consumption quantity when the first state of charge data of the power battery under the first candidate control quantity meets the constraint conditions, the second determining module 73 is further configured to:

[0157] The first state of charge data is determined based on the first candidate control variable;

[0158] Based on the first state of charge data, the operating range of the power battery, and the tolerance range of the power battery, determine whether the first state of charge data meets the constraints.

[0159] In one or more embodiments, the driving environment information includes: traffic data, weather data, and road condition data corresponding to the driving route;

[0160] Operational status information includes: vehicle speed data.

[0161] In one or more embodiments, the first determining module 72 determines the target power demand information based on the operating status information, driving environment information, and driving intention information, specifically for:

[0162] Based on traffic data, weather data, and road condition data, predict the usage information of electrical equipment in vehicles;

[0163] Based on usage information, vehicle speed data, and driving intention information, the target power demand information is determined.

[0164] It should be noted that the division of the various modules in the above device is only a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical element, or they can be physically separated. Furthermore, these modules can be implemented entirely in software through processing element calls, or entirely in hardware. Alternatively, some modules can be implemented through processing element calls in software, while others can be implemented in hardware. Moreover, these modules can be fully or partially integrated together, or implemented independently. The processing element here can be an integrated circuit with signal processing capabilities. During implementation, each step of the above method or each of the above modules can be completed through the integrated logic circuits in the hardware of the processor element or through software instructions.

[0165] As can be seen from the above, the vehicle control device provided in this embodiment of the invention receives vehicle operating status, driving environment and driving intention information provided by SOA, dynamically calculates the accurate target equivalent factor and required power, and then combines the strict constraints composed of the continuous discharge power range of the power battery, the working range, the engine output power range and the battery tolerance range to solve for the optimal candidate equivalent fuel consumption. Finally, it achieves the optimal allocation control of the power output of the engine and motor, thereby significantly improving the energy utilization efficiency of the whole vehicle and effectively reducing the overall fuel consumption while ensuring vehicle power performance, battery safety and meeting user needs.

[0166] Figure 8 A schematic diagram of an embodiment of the electronic device provided by the present invention is shown, such as... Figure 8 As shown, the electronic device may include: a processor 82, a communications interface 84, a memory 86, and a communications bus 88.

[0167] The processor 82, communication interface 84, and memory 86 communicate with each other via communication bus 88. Communication interface 84 is used to communicate with other network elements such as clients or other servers. The processor 82 executes program 80, specifically performing the relevant steps in the above method embodiments.

[0168] Specifically, program 80 may include program code, which includes computer-executable instructions.

[0169] Processor 82 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The vehicle may include one or more processors of the same type, such as one or more CPUs, or processors of different types, such as one or more CPUs and one or more ASICs.

[0170] Memory 86 is used to store program 80. Memory 86 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0171] Program 80 can be called by processor 82 to cause the vehicle to perform the following operations:

[0172] Receive vehicle operating status information, driving environment information, and driving intention information sent by the service-oriented architecture (SOA) built in the vehicle;

[0173] Based on the operating status information, driving environment information, and driving intention information, determine the target equivalent factor and target power demand information of the vehicle during the oil-electric conversion.

[0174] Based on the target equivalence factor, target power demand information, and preset constraints, at least one candidate equivalent fuel consumption is determined. The constraints are determined jointly based on the continuous discharge power range of the power battery in the vehicle, the operating range of the power battery, the output power range of the engine in the vehicle, and the tolerance range of the power battery.

[0175] Based on at least one candidate equivalent fuel consumption, a target control quantity is determined and sent to the SOA in the vehicle. The target control quantity is used to control the power output distribution of the vehicle.

[0176] In one or more embodiments, the target equivalence factor corresponding to the vehicle's oil-electric conversion is determined based on operating status information, driving environment information, and driving intention information, including:

[0177] Based on operational status information, driving environment information, and driving intention information, multiple influencing factors affecting the value of the equivalent factor are determined;

[0178] Based on multiple influencing factors and a pre-set equivalent factor adjustment strategy, the equivalent factor adjustment amount is determined. The equivalent factor adjustment strategy records the adjustment amount corresponding to different influencing factors and the weight coefficient corresponding to different influencing factors.

[0179] The target equivalence factor is determined based on the adjustment amount of the equivalence factor and the initial equivalence factor.

[0180] In one or more embodiments, determining at least one candidate equivalent fuel consumption based on a target equivalence factor, target power demand information, and preset constraints includes:

[0181] Based on the constraints and target power requirements, at least one first candidate control quantity is determined. Each first candidate control quantity includes: first candidate continuous discharge power and first candidate output power.

[0182] For each first candidate control quantity, the first equivalent fuel consumption corresponding to the first candidate control quantity is determined based on the first candidate control quantity, the target equivalent factor, the preset lower calorific value of fuel, and the working efficiency of the engine.

[0183] When the first state-of-charge data of the power battery under the first candidate control quantity meets the constraint conditions, the first equivalent fuel consumption corresponding to the first candidate control quantity is determined as the candidate equivalent fuel consumption.

[0184] In one or more embodiments, determining and sending a target control quantity to the SOA in the vehicle based on at least one candidate equivalent fuel consumption includes:

[0185] The smallest candidate equivalent fuel consumption among at least one candidate equivalent fuel consumption is determined as the target candidate equivalent fuel consumption.

[0186] The first candidate control quantity corresponding to the target candidate equivalent fuel consumption is determined as the target control quantity, and the target control quantity is sent to the SOA in the vehicle.

[0187] In one or more embodiments, before determining the first equivalent fuel consumption corresponding to the first candidate control quantity as the candidate equivalent fuel consumption quantity when the first state of charge data of the power battery under the first candidate control quantity meets the constraint conditions, the following is also performed:

[0188] The first state of charge data is determined based on the first candidate control variable;

[0189] Based on the first state of charge data, the operating range of the power battery, and the tolerance range of the power battery, determine whether the first state of charge data meets the constraints.

[0190] In one or more embodiments, the driving environment information includes: traffic data, weather data, and road condition data corresponding to the driving route;

[0191] Operational status information includes: vehicle speed data.

[0192] In one or more embodiments, the target power demand information is determined based on operating status information, driving environment information, and driving intention information, including:

[0193] Based on traffic data, weather data, and road condition data, predict the usage information of electrical equipment in vehicles;

[0194] Based on usage information, vehicle speed data, and driving intention information, the target power demand information is determined.

[0195] As can be seen from the above, the electronic device provided in this embodiment of the invention receives vehicle operating status, driving environment and driving intention information provided by SOA, dynamically calculates the accurate target equivalent factor and required power, and then combines the strict constraints composed of the continuous discharge power range of the power battery, the working range, the engine output power range and the battery tolerance range to solve for the optimal candidate equivalent fuel consumption. Finally, it achieves the optimal allocation control of the power output of the engine and motor, thereby significantly improving the energy utilization efficiency of the whole vehicle and effectively reducing the overall fuel consumption while ensuring vehicle power performance, battery safety and meeting user needs.

[0196] This invention provides a computer-readable storage medium storing at least one executable instruction that, when executed on a vehicle control device / electronic device, causes the vehicle control device / electronic device to perform the vehicle control method in any of the above-described method embodiments.

[0197] Specifically, the executable instructions can be used to cause the vehicle control unit / electronic equipment to perform the following operations:

[0198] Receive vehicle operating status information, driving environment information, and driving intention information sent by the service-oriented architecture (SOA) built in the vehicle;

[0199] Based on the operating status information, driving environment information, and driving intention information, determine the target equivalent factor and target power demand information of the vehicle during the oil-electric conversion.

[0200] Based on the target equivalence factor, target power demand information, and preset constraints, at least one candidate equivalent fuel consumption is determined. The constraints are determined jointly based on the continuous discharge power range of the power battery in the vehicle, the operating range of the power battery, the output power range of the engine in the vehicle, and the tolerance range of the power battery.

[0201] Based on at least one candidate equivalent fuel consumption, a target control quantity is determined and sent to the SOA in the vehicle. The target control quantity is used to control the power output distribution of the vehicle.

[0202] In one or more embodiments, the target equivalence factor corresponding to the vehicle's oil-electric conversion is determined based on operating status information, driving environment information, and driving intention information, including:

[0203] Based on operational status information, driving environment information, and driving intention information, multiple influencing factors affecting the value of the equivalent factor are determined;

[0204] Based on multiple influencing factors and a pre-set equivalent factor adjustment strategy, the equivalent factor adjustment amount is determined. The equivalent factor adjustment strategy records the adjustment amount corresponding to different influencing factors and the weight coefficient corresponding to different influencing factors.

[0205] The target equivalence factor is determined based on the adjustment amount of the equivalence factor and the initial equivalence factor.

[0206] In one or more embodiments, determining at least one candidate equivalent fuel consumption based on a target equivalence factor, target power demand information, and preset constraints includes:

[0207] Based on the constraints and target power requirements, at least one first candidate control quantity is determined. Each first candidate control quantity includes: first candidate continuous discharge power and first candidate output power.

[0208] For each first candidate control quantity, the first equivalent fuel consumption corresponding to the first candidate control quantity is determined based on the first candidate control quantity, the target equivalent factor, the preset lower calorific value of fuel, and the working efficiency of the engine.

[0209] When the first state-of-charge data of the power battery under the first candidate control quantity meets the constraint conditions, the first equivalent fuel consumption corresponding to the first candidate control quantity is determined as the candidate equivalent fuel consumption.

[0210] In one or more embodiments, determining and sending a target control quantity to the SOA in the vehicle based on at least one candidate equivalent fuel consumption includes:

[0211] The smallest candidate equivalent fuel consumption among at least one candidate equivalent fuel consumption is determined as the target candidate equivalent fuel consumption.

[0212] The first candidate control quantity corresponding to the target candidate equivalent fuel consumption is determined as the target control quantity, and the target control quantity is sent to the SOA in the vehicle.

[0213] In one or more embodiments, before determining the first equivalent fuel consumption corresponding to the first candidate control quantity as the candidate equivalent fuel consumption quantity when the first state of charge data of the power battery under the first candidate control quantity meets the constraint conditions, the following is also performed:

[0214] The first state of charge data is determined based on the first candidate control variable;

[0215] Based on the first state of charge data, the operating range of the power battery, and the tolerance range of the power battery, determine whether the first state of charge data meets the constraints.

[0216] In one or more embodiments, the driving environment information includes: traffic data, weather data, and road condition data corresponding to the driving route;

[0217] Operational status information includes: vehicle speed data.

[0218] In one or more embodiments, the target power demand information is determined based on operating status information, driving environment information, and driving intention information, including:

[0219] Based on traffic data, weather data, and road condition data, predict the usage information of electrical equipment in vehicles;

[0220] Based on usage information, vehicle speed data, and driving intention information, the target power demand information is determined.

[0221] As can be seen from the above, the electronic device / vehicle control device provided in this embodiment of the invention receives vehicle operating status, driving environment and driving intention information provided by SOA, dynamically calculates the accurate target equivalent factor and required power, and then combines the strict constraints composed of the continuous discharge power range of the power battery, the working range, the engine output power range and the battery tolerance range to solve for the optimal candidate equivalent fuel consumption. Finally, it achieves the optimal allocation control of the power output of the engine and motor, thereby significantly improving the energy utilization efficiency of the whole vehicle and effectively reducing the overall fuel consumption while ensuring vehicle power performance, battery safety and meeting user needs.

[0222] This invention provides a computer program product, including a computer program that, when executed by a processor, implements the operation of the vehicle control method described above.

[0223] Its implementation principle and technical effects are as disclosed above.

[0224] The description of the various embodiments above tends to emphasize the differences between the various embodiments. The similarities or similarities between them can be referred to, and for the sake of brevity, they will not be repeated here.

[0225] The methods disclosed in the various method embodiments provided by this invention can be arbitrarily combined without conflict to obtain new method embodiments.

[0226] The features disclosed in the various product embodiments provided by this invention can be arbitrarily combined without conflict to obtain new product embodiments.

[0227] The features disclosed in the various method or device embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method or device embodiments.

[0228] It should be noted that the aforementioned computer-readable storage media can be ROM, Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Ferromagnetic Random Access Memory (FRAM), Flash Memory, Magnetic Surface Memory, Optical Disc, or Compact Disc Read-Only Memory (CD-ROM), etc. It can also be various vehicles that include one or any combination of the above-mentioned storage media.

[0229] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0230] The sequence numbers of the above embodiments of the present invention are for description only and do not represent the superiority or inferiority of the embodiments.

[0231] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware nodes. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, vehicle terminal, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0232] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatuses, devices, and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0233] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0234] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The algorithms or displays provided herein for the functions specified in the boxes or boxes are not inherently related to any particular computer, virtual system, or other device. Furthermore, the embodiments of this invention are not directed to any particular programming language.

[0235] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

Claims

1. A vehicle control method, characterized in that, The method includes: Receive vehicle operating status information, driving environment information, and driving intention information sent by the service-oriented architecture (SOA) built in the vehicle; Based on the operating status information, the driving environment information, and the driving intention information, the target equivalence factor and target power demand information of the vehicle corresponding to the oil-electric conversion are determined; Based on the target equivalence factor, the target power demand information, and preset constraints, at least one candidate equivalent fuel consumption is determined. The constraints are determined jointly based on the continuous discharge power range of the power battery in the vehicle, the operating range of the power battery, the output power range of the engine in the vehicle, and the tolerance range of the power battery. Based on the at least one candidate equivalent fuel consumption, a target control quantity is determined and sent to the SOA in the vehicle, the target control quantity being used to control the power output distribution of the vehicle.

2. The method according to claim 1, characterized in that, The step of determining the target equivalence factor of the vehicle during the oil-electric conversion based on the operating status information, the driving environment information, and the driving intention information includes: Based on the operating status information, the driving environment information, and the driving intention information, multiple influencing factors affecting the value of the equivalent factor are determined; Based on the multiple influencing factors and the preset equivalent factor adjustment strategy, the equivalent factor adjustment amount is determined. The equivalent factor adjustment strategy records the adjustment amount corresponding to different influencing factors and the weight coefficient corresponding to different influencing factors. The target equivalence factor is determined based on the adjustment amount of the equivalence factor and the initial equivalence factor.

3. The method according to claim 1 or 2, characterized in that, The step of determining at least one candidate equivalent fuel consumption based on the target equivalence factor, the target power demand information, and preset constraints includes: Based on the constraints and the target power demand information, at least one first candidate control quantity is determined, and each first candidate control quantity includes: a first candidate continuous discharge power and a first candidate output power; For each first candidate control quantity, a first equivalent fuel consumption corresponding to the first candidate control quantity is determined based on the first candidate control quantity, the target equivalent factor, the preset fuel lower calorific value, and the engine working efficiency. When the first state of charge data of the power battery under the first candidate control quantity meets the constraint conditions, the first equivalent fuel consumption corresponding to the first candidate control quantity is determined as the candidate equivalent fuel consumption.

4. The method according to claim 3, characterized in that, The step of determining and sending a target control quantity to the SOA in the vehicle based on the at least one candidate equivalent fuel consumption includes: The smallest candidate equivalent fuel consumption among the at least one candidate equivalent fuel consumption is determined as the target candidate equivalent fuel consumption. The first candidate control quantity corresponding to the target candidate equivalent fuel consumption is determined as the target control quantity, and the target control quantity is sent to the SOA in the vehicle.

5. The method according to claim 3, characterized in that, Before determining the first equivalent fuel consumption corresponding to the first candidate control quantity as the candidate equivalent fuel consumption when the first state of charge data of the power battery under the first candidate control quantity meets the constraint condition, the method further includes: The first state of charge data is determined based on the first candidate control quantity; Based on the first state of charge data, the operating range of the power battery, and the tolerance range of the power battery, determine whether the first state of charge data meets the constraints.

6. The method according to claim 1 or 2, characterized in that, The driving environment information includes: traffic data, weather data, and road condition data corresponding to the driving route; The operational status information includes: vehicle speed data.

7. The method according to claim 6, characterized in that, Based on the operating status information, the driving environment information, and the driving intention information, the target power demand information is determined, including: Based on the traffic data, the weather data, and the road condition data, predict the usage information of the electrical equipment in the vehicle; The target power demand information is determined based on the usage information, the vehicle speed data, and the driving intention information.

8. A vehicle control device, characterized in that, The device includes: The acquisition module is used to receive the vehicle's operating status information, driving environment information, and driving intention information sent by the service-oriented architecture (SOA) built in the vehicle. The first determining module is used to determine the target equivalence factor and target power demand information of the vehicle in the oil-electric conversion based on the operating status information, the driving environment information, and the driving intention information. The second determining module is used to determine at least one candidate equivalent fuel consumption based on the target equivalence factor, the target demand power information, and preset constraints. The constraints are determined jointly based on the continuous discharge power range of the power battery in the vehicle, the operating range of the power battery, the output power range of the engine in the vehicle, and the tolerance range of the power battery. The third determining module is used to determine and send a target control quantity to the SOA in the vehicle based on the at least one candidate equivalent fuel consumption, the target control quantity being used to control the power output distribution of the vehicle.

9. An electronic device, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction that causes the processor to perform the operation of the vehicle control method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The storage medium stores at least one executable instruction, which, when executed on a vehicle control device / electronic device, causes the vehicle control device / electronic device to perform the operation of the vehicle control method as described in any one of claims 1-7.

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