Energy consumption control method and device of vehicle, vehicle and storage medium

Abstract

The application relates to the technical field of vehicles, in particular to a vehicle energy consumption control method and device, a vehicle and a storage medium. The method comprises the following steps: detecting an actual road condition of a vehicle, and acquiring current environment data of an environment where the vehicle is located and a current vehicle state; calculating actual energy consumption of the vehicle according to the actual road condition, the current environment data, the current vehicle state and historical energy consumption; and predicting a plurality of future energy consumptions of the vehicle along a current path planning driving according to the actual energy consumption, and generating a best driving strategy corresponding to the minimum future energy consumption. Thus, the problems that road planning information and driving information of a hybrid electric vehicle cannot be predicted, the switching of working modes cannot be reasonably arranged, the engine is frequently started and stopped without being needed, and the oil saving potential cannot be fully developed in the related art are solved, the SOC charging and discharging interval of the battery system and the energy conversion of the vehicle kinetic energy and potential energy are fully utilized, the oil saving rate of the hybrid power system of the vehicle is improved, and the recovery period of the added cost of the hybrid power system is shortened.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a method, device, vehicle, and storage medium for controlling energy consumption in a vehicle. Background Technology

[0002] In related technologies, the vehicle control strategy of plug-in hybrid electric vehicles is mostly based on logic gates. The vehicle controller switches and controls the control mode according to the current state of the engine, motor, battery, pedals and other components, and the system has good robustness.

[0003] However, because it cannot make comprehensive judgments based on factors such as the characteristics of the road ahead, the current state of the vehicle, and the environmental conditions of the vehicle, it is impossible to predict the road planning information and driving information of hybrid vehicles, and it is impossible to reasonably arrange the switching of working modes. In addition, the engine has unnecessary frequent start-stops. At the same time, in order to ensure that the power battery reserves a certain margin to meet braking and acceleration needs at all times, the power battery SOC (State of Charge) window is small, which cannot fully realize the fuel-saving potential and urgently needs to be solved. Summary of the Invention

[0004] This application provides a vehicle energy consumption control method, device, vehicle, and storage medium to solve the problems in related technologies such as the inability to predict road planning and driving information of hybrid vehicles, the inability to reasonably arrange the switching of working modes, the unnecessary frequent start-stop of the engine, and the inability to fully realize fuel-saving potential. It helps to make full use of the battery system's SOC charging and discharging range and the energy conversion of the vehicle's kinetic and potential energy, improve the overall fuel-saving rate of the hybrid vehicle, and shorten the payback period for the additional costs of the hybrid system.

[0005] The first aspect of this application provides a method for controlling the energy consumption of a vehicle, comprising the following steps:

[0006] The system detects the actual road conditions of the vehicle and obtains the current environmental data and the current vehicle status of the environment in which the vehicle is located.

[0007] The actual energy consumption of the vehicle is calculated based on the actual road conditions, the current environmental data, the current vehicle status, and historical energy consumption; and

[0008] Based on the actual energy consumption, predict multiple future energy consumptions of the vehicle along the current path and generate the optimal driving strategy corresponding to the minimum future energy consumption.

[0009] Optionally, the optimal driving strategy corresponding to generating the minimum future energy consumption includes:

[0010] Determine whether the engine's energy consumption is within its optimal efficiency range;

[0011] If the engine's energy consumption is within the optimal efficiency range, then when the vehicle is in an uphill condition, the drive motor of the vehicle is controlled to operate based on the vehicle's battery state of charge, and the vehicle speed is adjusted to a first preset speed; when the vehicle is in a downhill condition, the vehicle speed is adjusted to a second preset speed, and the drive motor of the vehicle is controlled to operate based on the vehicle's battery state of charge; when the vehicle is in other conditions besides the uphill and downhill conditions, the drive motor of the vehicle is controlled to operate based on the vehicle's battery state of charge, and the vehicle speed is adjusted to a third preset speed.

[0012] If the engine's energy consumption is not within the optimal efficiency range, then when the vehicle is in an uphill condition, the speed of the drive motor is controlled based on the downshift point, and the first average vehicle speed value of each trajectory point in the current movement trajectory is calculated using the equivalent consumption method and the reverse order algorithm, and the vehicle is controlled according to the first average vehicle speed value; when the vehicle is in a downhill condition, the speed of the drive motor is controlled based on the upshift point, and the second average vehicle speed value of each trajectory point in the current movement trajectory is calculated using the equivalent consumption method and the reverse order algorithm, and the vehicle is controlled according to the second average vehicle speed value.

[0013] Optionally, the actual road conditions include one or more of the following: basic data of navigation electronic maps, route planning data, route information data, route altitude data, road surface data, and road smoothness; the current environmental data includes one or more of the following: weather, temperature, altitude, wind speed, and wind direction; and the current vehicle status includes one or more of the following: vehicle driving habits, vehicle load, current tire pressure, vehicle basic state resistance, and vehicle driving form resistance.

[0014] Optionally, it also includes:

[0015] Record the actual energy consumption of the current route planning;

[0016] The energy consumption prediction model is updated using the actual energy consumption, and the energy consumption prediction model is used to predict multiple future energy consumptions.

[0017] Optionally, before detecting the actual road conditions of the vehicle and obtaining the current environmental data and current vehicle status of the environment in which the vehicle is located, the method further includes:

[0018] To detect the vehicle's current time or distance traveled;

[0019] When the current time meets the update duration or the driving distance reaches the update distance, the vehicle is determined to meet the energy consumption prediction conditions.

[0020] Optionally, it also includes:

[0021] Determine whether the vehicle's remaining energy consumption is greater than the minimum future energy consumption;

[0022] If the remaining energy consumption is less than the minimum future energy consumption, an energy replenishment strategy is generated based on the difference between the remaining energy consumption and the minimum future energy consumption, and the vehicle's engine and / or drive motor are controlled to operate according to the energy replenishment strategy.

[0023] A second aspect of this application provides a vehicle energy consumption control device, comprising:

[0024] The acquisition module is used to detect the actual road conditions of the vehicle and acquire the current environmental data and the current vehicle status of the environment in which the vehicle is located.

[0025] The calculation module is used to calculate the actual energy consumption of the vehicle based on the actual road conditions, the current environmental data, the current vehicle status, and historical energy consumption; and

[0026] The control module is used to predict multiple future energy consumptions of the vehicle along the current path based on the actual energy consumption, and generate the optimal driving strategy corresponding to the minimum future energy consumption.

[0027] Optionally, the control module is specifically used for:

[0028] Determine whether the engine's energy consumption is within its optimal efficiency range;

[0029] If the engine's energy consumption is within the optimal efficiency range, then when the vehicle is in an uphill condition, the drive motor of the vehicle is controlled to operate based on the vehicle's battery state of charge, and the vehicle speed is adjusted to a first preset speed; when the vehicle is in a downhill condition, the vehicle speed is adjusted to a second preset speed, and the drive motor of the vehicle is controlled to operate based on the vehicle's battery state of charge; when the vehicle is in other conditions besides the uphill and downhill conditions, the drive motor of the vehicle is controlled to operate based on the vehicle's battery state of charge, and the vehicle speed is adjusted to a third preset speed.

[0030] If the engine's energy consumption is not within the optimal efficiency range, then when the vehicle is in an uphill condition, the speed of the drive motor is controlled based on the downshift point, and the first average vehicle speed value of each trajectory point in the current movement trajectory is calculated using the equivalent consumption method and the reverse order algorithm, and the vehicle is controlled according to the first average vehicle speed value; when the vehicle is in a downhill condition, the speed of the drive motor is controlled based on the upshift point, and the second average vehicle speed value of each trajectory point in the current movement trajectory is calculated using the equivalent consumption method and the reverse order algorithm, and the vehicle is controlled according to the second average vehicle speed value.

[0031] Optionally, the actual road conditions include one or more of the following: basic data of navigation electronic maps, route planning data, route information data, route altitude data, road surface data, and road smoothness; the current environmental data includes one or more of the following: weather, temperature, altitude, wind speed, and wind direction; and the current vehicle status includes one or more of the following: vehicle driving habits, vehicle load, current tire pressure, vehicle basic state resistance, and vehicle driving form resistance.

[0032] Optionally, it also includes:

[0033] The recording module is used to record the actual energy consumption of the current route planning.

[0034] The prediction module is used to update the energy consumption prediction model using the actual energy consumption, and to predict multiple future energy consumptions using the energy consumption prediction model.

[0035] Optionally, before detecting the actual road conditions of the vehicle and acquiring the current environmental data and current vehicle status of the environment in which the vehicle is located, the acquisition module further includes:

[0036] The detection unit is used to detect the vehicle's current time or the distance traveled.

[0037] The determination unit is used to determine that the vehicle meets the energy consumption prediction conditions when the current time meets the update duration or the driving distance reaches the update distance.

[0038] Optionally, it also includes:

[0039] The judgment module is used to determine whether the remaining energy consumption of the vehicle is greater than the minimum future energy consumption;

[0040] The generation module is configured to generate an energy replenishment strategy based on the difference between the remaining energy consumption and the minimum future energy consumption if the remaining energy consumption is less than the minimum future energy consumption, and control the engine and / or drive motor of the vehicle to work according to the energy replenishment strategy.

[0041] A third aspect of this application provides a vehicle, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the energy consumption control method for the vehicle as described in the above embodiments.

[0042] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the energy consumption control method for a vehicle as described in the above embodiments.

[0043] Therefore, based on the detected actual road conditions, current environmental data of the vehicle's environment, current vehicle status, and historical energy consumption, the actual energy consumption of the vehicle can be calculated. Based on this actual energy consumption, multiple future energy consumptions can be predicted for the vehicle along the current planned route, and the optimal driving strategy corresponding to the minimum future energy consumption can be generated. This solves the problems in related technologies, such as the inability to predict road planning and driving information for hybrid vehicles, the inability to rationally arrange switching of operating modes, unnecessary frequent start-stop of the engine, and the inability to fully utilize fuel-saving potential. It helps to fully utilize the battery system's SOC charging and discharging range and the energy conversion of the vehicle's kinetic and potential energy, improving the overall fuel efficiency of the hybrid system and shortening the payback period for the additional costs of the hybrid system.

[0044] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0045] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0046] Figure 1 This is a flowchart of a vehicle energy consumption control method according to an embodiment of this application;

[0047] Figure 2 An example diagram illustrating the control strategy development architecture for a vehicle energy consumption control method according to an embodiment of this application;

[0048] Figure 3 This is a schematic diagram of dynamic programming optimization according to one embodiment of this application;

[0049] Figure 4 This is a flowchart of a vehicle control strategy according to an embodiment of this application;

[0050] Figure 5 This is a block diagram of a vehicle energy consumption control device according to an embodiment of this application;

[0051] Figure 6 A schematic diagram of the vehicle structure provided in the application embodiment. Detailed Implementation

[0052] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0053] The energy consumption control method, apparatus, vehicle, and storage medium of this application are described below with reference to the accompanying drawings. Addressing the problems mentioned in the background art, such as the inability to predict road planning and driving information of hybrid electric vehicles, the inability to rationally arrange switching of operating modes, unnecessary frequent start-stops of the engine, and the inability to fully utilize fuel-saving potential, this application provides a vehicle energy consumption control method. In this method, the actual energy consumption of the vehicle can be calculated based on the detected actual road conditions, current environmental data of the vehicle's environment, the current vehicle status, and historical energy consumption. Based on the actual energy consumption, multiple future energy consumptions of the vehicle along the current planned driving path are predicted, and the optimal driving strategy corresponding to the minimum future energy consumption is generated. This solves the problems in related technologies, such as the inability to predict road planning and driving information of hybrid electric vehicles, the inability to rationally arrange switching of operating modes, unnecessary frequent start-stops of the engine, and the inability to fully utilize fuel-saving potential. It helps to fully utilize the battery system's SOC charging and discharging range and the energy conversion of the vehicle's kinetic and potential energy, improving the overall fuel efficiency of the hybrid electric vehicle and shortening the payback period for the additional costs of the hybrid electric vehicle.

[0054] Specifically, Figure 1 This is a schematic flowchart illustrating a vehicle energy consumption control method provided in an embodiment of this application.

[0055] like Figure 1 As shown, the energy consumption control method for this vehicle includes the following steps:

[0056] In step S101, the actual road conditions of the vehicle are detected, and the current environmental data and the current vehicle status of the environment in which the vehicle is located are obtained.

[0057] Optionally, in some embodiments, actual road conditions include one or more of the following: basic navigation electronic map data, route planning data, route information data, route elevation data, road surface data, and road smoothness; current environmental data includes one or more of the following: weather, temperature, altitude, wind speed, and wind direction; and current vehicle status includes one or more of the following: vehicle driving habits, vehicle load, current tire pressure, vehicle basic state resistance, and vehicle driving form resistance.

[0058] Specifically, such as Figure 2As shown, the embodiments of this application can detect the actual road conditions of the vehicle to record and analyze the current basic data of the navigation electronic map, route planning data, route information data, route altitude data, road surface conditions, road smoothness, etc.; by acquiring the current environmental data of the vehicle's environment, the current environmental conditions can be recorded and analyzed, such as: weather (rain, snow, sunny, etc.), temperature, altitude, wind speed, wind direction, etc.; by acquiring the current vehicle status, the current vehicle status can be recorded and analyzed, such as: vehicle driving habits, vehicle load, current tire pressure, vehicle basic state resistance, vehicle driving form resistance, etc.

[0059] Optionally, in some embodiments, before detecting the actual road conditions of the vehicle and obtaining the current environmental data and current vehicle status of the environment in which the vehicle is located, the method further includes: detecting the current time or the distance traveled by the vehicle; and determining that the vehicle meets the energy consumption prediction conditions when the current time meets the update duration or the distance traveled reaches the update distance.

[0060] The update duration can be a user-preset duration, a duration obtained through a limited number of experiments, or a duration obtained through a limited number of computer simulations; the update distance can be a user-preset distance, a distance obtained through a limited number of experiments, or a distance obtained through a limited number of computer simulations, and is not specifically limited here.

[0061] Specifically, the update interval can be 10 minutes and the update distance can be 5km. That is to say, if the vehicle's current time meets the 10-minute interval or the vehicle's travel distance reaches 5km, then the vehicle is determined to meet the energy consumption prediction conditions and the vehicle's energy consumption can be predicted.

[0062] In step S102, the actual energy consumption of the vehicle is calculated based on the actual road conditions, current environmental data, current vehicle status, and historical energy consumption.

[0063] In step S103, the vehicle's future energy consumption along the current path is predicted based on the actual energy consumption, and the optimal driving strategy corresponding to the minimum future energy consumption is generated.

[0064] Specifically, such as Figure 2As shown, this embodiment of the application can perform real-time fuel consumption analysis by combining actual road conditions, current environmental data, current vehicle status, and historical energy consumption. The analyzed fuel consumption estimate is then combined with the route planning function provided by a high-precision map. By combining the fuel consumption estimate and route planning information from both, the vehicle controller can provide fuel consumption estimates for different routes and, after selecting the appropriate route, execute drive mode selection, torque distribution, and vehicle speed planning. Therefore, this embodiment of the application integrates GPS (Global Positioning System) positioning with the navigation electronic map basic data, route planning, and map matching modules of the GIS (Geographic Information System) high-precision map system to extract current vehicle front map basic data, route planning data, route information data, route elevation data, road surface conditions, road smoothness, and other information; records and analyzes the current environmental conditions through real-time environmental analysis; and records and analyzes the current vehicle status through the vehicle controller. The system provides real-time fuel consumption analysis data, which combines road conditions, environmental conditions, and vehicle conditions with past fuel consumption records, to the vehicle controller. Then, it performs reasonable path planning based on the above information. Furthermore, by adopting a dynamic programming algorithm, the system reduces the computational load of the dynamic programming algorithm through strategies such as narrowing the path trajectory aggregation and classification, variable sampling point step size, and powertrain reachability domain. The computation time in real vehicle applications is less than 0.2 seconds, which meets the needs of practical engineering applications.

[0065] Optionally, in some embodiments, generating the optimal driving strategy corresponding to the minimum future energy consumption includes: determining whether the engine's energy consumption is within the optimal efficiency range; if the engine's energy consumption is within the optimal efficiency range, then when the vehicle is in an uphill driving condition, after controlling the vehicle's drive motor to operate based on the vehicle's battery state of charge, controlling the vehicle speed to adjust to a first preset speed; when the vehicle is in a downhill driving condition, after controlling the vehicle speed to adjust to a second preset speed, controlling the vehicle's drive motor to operate based on the vehicle's battery state of charge; when the vehicle is in other driving conditions besides uphill and downhill driving conditions, controlling the vehicle speed based on the vehicle's battery state of charge... After the vehicle's drive motor starts working, the vehicle speed is adjusted to the third preset speed. If the engine's energy consumption is not in the optimal efficiency range, when the vehicle is on an uphill slope, the speed of the drive motor is controlled based on the downshift point, and the first average speed value of each trajectory point in the current trajectory is calculated using the equivalent consumption method and the reverse sequence algorithm. The vehicle is then controlled according to the first average speed value. When the vehicle is on a downhill slope, the speed of the drive motor is controlled based on the upshift point, and the second average speed value of each trajectory point in the current trajectory is calculated using the equivalent consumption method and the reverse sequence algorithm. The vehicle is then controlled according to the second average speed value.

[0066] The first preset vehicle speed, the second preset vehicle speed, and the third preset vehicle speed can be vehicle speeds preset by the user, vehicle speeds obtained through a limited number of experiments, or vehicle speeds obtained through a limited number of computer simulations; no specific limitations are imposed here. The method for calculating the average vehicle speed value of each trajectory point in the current motion trajectory of the vehicle using the equivalent fuel consumption method and the reverse order algorithm can adopt methods from related technologies; to avoid redundancy, it will not be described in detail here.

[0067] Specifically, the embodiments of this application can divide the motion trajectory based on the dividing line, process the trajectory between the current sampling point and the first positive and negative change point of α, and continuously update it; if α is greater than 1%, it is a long uphill road; if α is less than or equal to 1% and greater than or equal to -1%, it is a horizontal road; if α is less than or equal to -1%, it is a long downhill road; different control strategies are executed in combination with different slopes and whether the current power battery and vehicle speed changes can / cannot cover the relative elevation change requirements, and the specific control strategies are shown in Table 1.

[0068] Table 1

[0069]

[0070] Among them, the ability to cover relative elevation changes can be understood as the current engine energy consumption being in the optimal efficiency range.

[0071] Optionally, in some embodiments, the above-described vehicle energy consumption control method further includes: recording the actual energy consumption of the current route planning; updating the energy consumption prediction model using the actual energy consumption; and using the energy consumption prediction model to predict multiple future energy consumptions for the next trip.

[0072] Specifically, the embodiments of this application can provide fuel consumption estimates for each driving state by fusing information on future road segments, predicting driving conditions, and predicting the current driver's driving habits. At the same time, the driving data of this time will also be recorded as a sample for the next stage of fuel consumption prediction. This process is repeated to achieve the accuracy of fuel consumption prediction and the rolling update of information.

[0073] Specifically, to improve the fuel efficiency of the vehicle's hybrid system, the system state variables are defined as the current vehicle speed, gear, and battery SOC; the system control variables are defined as the target vehicle speed and target gear from the current sampling point to the next sampling point. To reduce computational load, the vehicle speed change is simplified to a finite grid, with a speed interval Δv = 0.3 km / h. Figure 3 As shown.

[0074] This embodiment of the application can compress the reachability domain based on the set cruise speed and the state of the hybrid powertrain, effectively reducing the computational load and time of the DP algorithm. Under the above constraints, the calculation of segment i=1, that is, from point k to point 1, calculates the optimal solution for each sampling point, and continuously updates it to obtain the global reference gear and vehicle speed under the predictable driving mode, thereby realizing real-time predictive control of the vehicle.

[0075] Therefore, it can record and analyze fuel consumption under past driving conditions, and combine this information to analyze the vehicle's fuel consumption. A key feature is that each vehicle has its own unique fuel consumption logic, which is more consistent with and closely reflects the vehicle's actual performance. Furthermore, based on this fuel consumption logic and the currently obtained route planning information, it optimizes fuel consumption planning and control for future routes. Additionally, basic past fuel consumption information is updated in real time as the vehicle progresses, ensuring data accuracy and timeliness.

[0076] Furthermore, by knowing the road information ahead of the vehicle, the characteristics of the engine or electric motor can be fully utilized to improve efficiency; by knowing the road environment information ahead of the vehicle, the impact of external environmental conditions on the vehicle's driving can be fully assessed, improving the accuracy of the prediction; and by knowing the vehicle's overall information, the impact of the vehicle's current state on driving can be fully assessed.

[0077] Optionally, in some embodiments, the above-described vehicle energy consumption control method further includes: determining whether the vehicle's remaining energy consumption is greater than the minimum future energy consumption; if the remaining energy consumption is less than the minimum future energy consumption, generating an energy replenishment strategy based on the difference between the remaining energy consumption and the minimum future energy consumption, and controlling the vehicle's engine and / or drive motor to operate according to the energy replenishment strategy.

[0078] Specifically, in this embodiment, road information can be categorized according to traffic conditions: green sections indicate unobstructed traffic, yellow sections indicate slow-moving traffic, red sections indicate congested traffic, and dark red sections indicate extremely congested traffic. The main operating component in green sections is the engine; in yellow sections, it is both the engine and electric motor; and in red and dark red sections, it is the electric motor.

[0079] The embodiments of this application can determine the mode switching sequence based on the road conditions, driving distance, future energy consumption and time, and estimate whether the preset mode meets the requirements. If the requirements are not met, an energy replenishment strategy based on the current system mode is implemented according to the vehicle's capacity, as shown in Table 2.

[0080] Table 2

[0081]

[0082] Therefore, based on actual road conditions, current environmental data, current vehicle status, and historical energy consumption, the energy management and control strategy anticipates road and environmental information ahead. Through route planning and mode prediction, it actively charges and discharges the power battery, charging it in advance in low-speed or congested sections, prohibiting or delaying engine intervention in congested sections or before traffic lights, and discharging it in advance before charging sections. During driving, the control strategy is updated in real time based on real-time road conditions, environmental information, and current fuel consumption calculations to reduce situations where the battery is fully charged but cannot be charged or there is no need for electricity, leaving room for sufficient and reasonable mode switching and fuel consumption reduction.

[0083] To enable those skilled in the art to further understand the vehicle energy consumption control method of the embodiments of this application, the vehicle control strategy process will be described in detail below with reference to specific embodiments.

[0084] Specifically, in this embodiment, a predictable driving mode can be entered by triggering a smart switch and navigation planning. After real-time information is imported, a motion trajectory is generated based on the current location and destination information. Environmental information on the motion trajectory, including road environment (road surface information, slope information, etc.) and weather environment (temperature, wind speed, wind direction, humidity, etc.), is fused to obtain a basic data model. Subsequently, the vehicle controller collects and analyzes vehicle information (vehicle load, axle load distribution, and a model of the vehicle under basic driving conditions) to obtain a vehicle information model. After fusing the navigation information, environmental information, and vehicle information for the entire road segment, a predicted driving model for different routes is obtained. That is, the navigation driving model consists of high-precision map data such as predicted distance, vehicle speed, travel time, and traffic lights provided by the navigation software, and the integrated driving data.

[0085] In the adaptive equivalent fuel consumption minimization strategy, the fuel consumption prediction adopts a historical benchmarking method. That is, the vehicle controller integrates different road, environmental and vehicle information and combines them with different vehicle driving states and driver driving habits to generate a predicted fuel consumption analysis model. By integrating information from future road segments, predicting driving states and predicting the current driver's driving habits, a fuel consumption prediction is given for each driving state. At the same time, the driving data of this period is also recorded as a sample for the next stage of fuel consumption prediction. This process is repeated to achieve the accuracy of fuel consumption prediction and rolling updates of information.

[0086] Finally, based on the driving mode suggestions given by the estimated driving conditions, an adaptive equivalent fuel consumption minimization strategy is used to estimate the mode fuel consumption. Since the current vehicle status and road condition information are known, the next working mode and working duration can be determined through dynamic programming algorithm, and continuously updated. The process is as follows:

[0087] a. Mode Activation: The instrument panel is equipped with an energy management enhancement switch, which can be used for route planning and driving mode setting via on-board or external devices. In combination with battery power, the mode selection is usually kept as separate as possible.

[0088] b. Motion trajectory classification: After initialization and activation, mode planning is carried out based on real-time road conditions and real-time fuel consumption analysis. Taking into account the assembly capacity and calculation cycle requirements, the driving mode is updated in real time according to real-time road condition feedback. The fuel consumption analysis interval is usually 5km or 10min. The latest fuel consumption analysis is provided based on the previous analysis interval. The useless analysis data of the car when it is just started and the part that has been stationary since the start is discarded.

[0089] Considering the accuracy of software measurements and the complexity of road conditions, when the expected driving conditions and the actual driving conditions do not match, the route and fuel consumption planning should be updated simultaneously. However, when the route does not match for a long time, or the route cannot be queried or other uncontrollable situations occur, the current working mode is immediately canceled and switched to the basic logic gate working mode.

[0090] c. Implementation of control strategies, and

[0091] d. Real-time traffic updates and real-time fuel consumption updates.

[0092] Among them, the vehicle control strategy is as follows Figure 4 As shown, the control strategy includes the following steps:

[0093] S401, trigger the predictable driving function switch.

[0094] S402, the controller initial curvature is less than a certain threshold.

[0095] S403 aggregates 60 sampling points based on the positive and negative changes of α. After i changes, the points are aggregated into i+1 times.

[0096] In this embodiment, the motion trajectory can be divided based on the dividing line, and the trajectory between the current sampling point and the first positive and negative change point of α can be processed and continuously updated.

[0097] S404: If α is greater than 1%, it is a long uphill road; if α is less than or equal to 1% and greater than or equal to -1%, it is a level road; if α is less than or equal to -1%, it is a long downhill road.

[0098] The S405 employs different control strategies based on varying road slopes and whether changes in the current power battery and vehicle speed can or cannot cover changes in relative elevation.

[0099] The vehicle energy consumption control method proposed in this application can calculate the vehicle's actual energy consumption based on the detected actual road conditions, current environmental data of the vehicle's environment, current vehicle status, and historical energy consumption. It then predicts multiple future energy consumptions for the vehicle along the current path based on the actual energy consumption and generates the optimal driving strategy corresponding to the minimum future energy consumption. This solves the problems in related technologies, such as the inability to predict road planning and driving information for hybrid vehicles, the inability to rationally arrange switching of operating modes, unnecessary frequent start-stop operations of the engine, and the inability to fully utilize fuel-saving potential. It helps to fully utilize the battery system's SOC charging and discharging range and the conversion of the vehicle's kinetic and potential energy, improving the overall fuel efficiency of the hybrid system and shortening the payback period for the added costs of the hybrid system.

[0100] Next, with reference to the accompanying drawings, an energy consumption control device for a vehicle according to an embodiment of this application is described.

[0101] Figure 5 This is a block diagram of a vehicle energy consumption control device according to an embodiment of this application.

[0102] like Figure 5 As shown, the energy consumption control device 10 of the vehicle includes: an acquisition module 100, a calculation module 200, and a control module 300.

[0103] The acquisition module 100 is used to detect the actual road conditions of the vehicle and acquire the current environmental data and the current vehicle status of the environment in which the vehicle is located.

[0104] The calculation module 200 is used to calculate the vehicle's actual energy consumption based on actual road conditions, current environmental data, current vehicle status, and historical energy consumption; and

[0105] The control module 300 is used to predict multiple future energy consumptions of the vehicle along the current path based on the actual energy consumption, and generate the optimal driving strategy corresponding to the minimum future energy consumption.

[0106] Optionally, the control module 300 is specifically used for:

[0107] Determine whether the engine's energy consumption is within its optimal efficiency range;

[0108] If the engine's energy consumption is within the optimal efficiency range, then when the vehicle is going uphill, the drive motor is activated based on the vehicle's battery state of charge, and the vehicle speed is adjusted to a first preset speed; when the vehicle is going downhill, the vehicle speed is adjusted to a second preset speed, and the drive motor is activated based on the vehicle's battery state of charge; when the vehicle is in any other condition besides uphill or downhill, the drive motor is activated based on the vehicle's battery state of charge, and the vehicle speed is adjusted to a third preset speed.

[0109] If the engine's energy consumption is not within the optimal efficiency range, when the vehicle is on an uphill slope, the speed of the drive motor is controlled based on the downshift point, and the first average vehicle speed value of each trajectory point in the current movement trajectory is calculated using the equivalent consumption method and the reverse sequence algorithm, and the vehicle is controlled according to the first average vehicle speed value; when the vehicle is on a downhill slope, the speed of the drive motor is controlled based on the upshift point, and the second average vehicle speed value of each trajectory point in the current movement trajectory is calculated using the equivalent consumption method and the reverse sequence algorithm, and the vehicle is controlled according to the second average vehicle speed value.

[0110] Optionally, in some embodiments, actual road conditions include one or more of the following: basic navigation electronic map data, route planning data, route information data, route elevation data, road surface data, and road smoothness; current environmental data includes one or more of the following: weather, temperature, altitude, wind speed, and wind direction; and current vehicle status includes one or more of the following: vehicle driving habits, vehicle load, current tire pressure, vehicle basic state resistance, and vehicle driving form resistance.

[0111] Optionally, in some embodiments, the above-described vehicle energy consumption control device 10 further includes:

[0112] The recording module is used to record the actual energy consumption of the current route planning.

[0113] The forecasting module is used to update the energy consumption forecasting model with actual energy consumption, and to forecast multiple future energy consumptions using the energy consumption forecasting model.

[0114] Optionally, in some embodiments, before detecting the actual road conditions of the vehicle and acquiring the current environmental data and current vehicle status, the acquisition module 100 further includes:

[0115] The detection unit is used to detect the vehicle's current time or the distance traveled.

[0116] The determination unit is used to determine whether a vehicle meets the energy consumption prediction conditions when the update duration is met or the driving distance reaches the update distance at the current time.

[0117] Optionally, in some embodiments, the above-described vehicle energy consumption control device 10 further includes:

[0118] The judgment module is used to determine whether the vehicle's remaining energy consumption is greater than the minimum future energy consumption;

[0119] The generation module is used to generate an energy replenishment strategy based on the difference between the remaining energy consumption and the minimum future energy consumption if the remaining energy consumption is less than the minimum future energy consumption, and to control the vehicle's engine and / or drive motor to work according to the energy replenishment strategy.

[0120] It should be noted that the foregoing explanation of the vehicle energy consumption control method embodiment also applies to the vehicle energy consumption control device of this embodiment, and will not be repeated here.

[0121] The vehicle energy consumption control device proposed in this application can calculate the vehicle's actual energy consumption based on the detected actual road conditions, current environmental data of the vehicle's environment, current vehicle status, and historical energy consumption. It then predicts multiple future energy consumptions for the vehicle along the current path based on the actual energy consumption and generates the optimal driving strategy corresponding to the minimum future energy consumption. This solves the problems in related technologies, such as the inability to predict road planning and driving information for hybrid vehicles, the inability to rationally arrange switching of operating modes, unnecessary frequent start-stop of the engine, and the inability to fully utilize fuel-saving potential. It helps to fully utilize the battery system's SOC charging and discharging range and the conversion of the vehicle's kinetic and potential energy, improving the overall fuel efficiency of the hybrid system and shortening the payback period for the added costs of the hybrid system.

[0122] Figure 6 This is a schematic diagram of the structure of a vehicle provided in an embodiment of this application. The electronic device may include:

[0123] The memory 601, the processor 602, and the computer program stored on the memory 601 and capable of running on the processor 602.

[0124] When the processor 602 executes the program, it implements the vehicle energy consumption control method provided in the above embodiments.

[0125] Furthermore, the vehicle also includes:

[0126] Communication interface 603 is used for communication between memory 601 and processor 602.

[0127] The memory 601 is used to store computer programs that can run on the processor 602.

[0128] The memory 601 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0129] If the memory 601, processor 602, and communication interface 603 are implemented independently, then the communication interface 603, memory 601, and processor 602 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0130] Optionally, in a specific implementation, if the memory 601, processor 602, and communication interface 603 are integrated on a single chip, then the memory 601, processor 602, and communication interface 603 can communicate with each other through an internal interface.

[0131] The processor 602 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0132] This embodiment also provides a computer-readable storage medium storing a computer program thereon, characterized in that the program, when executed by a processor, implements the above-described vehicle energy consumption control method.

[0133] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0134] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0135] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0136] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0137] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

Claims

1. A method for controlling energy consumption in a vehicle, characterized in that, Includes the following steps: The system detects the actual road conditions of the vehicle and obtains the current environmental data and the current vehicle status of the environment in which the vehicle is located. The actual energy consumption of the vehicle is calculated based on the actual road conditions, the current environmental data, the current vehicle status, and historical energy consumption. as well as Based on the actual energy consumption, predict multiple future energy consumptions of the vehicle along the current path and generate the optimal driving strategy corresponding to the minimum future energy consumption. It also includes: determining whether the vehicle's remaining energy consumption is greater than the minimum future energy consumption; if the remaining energy consumption is less than the minimum future energy consumption, generating an energy replenishment strategy based on the difference between the remaining energy consumption and the minimum future energy consumption, and controlling the vehicle's engine and / or drive motor to operate according to the energy replenishment strategy, wherein the energy replenishment strategy includes a mapping relationship between the actual road conditions and the vehicle's engine and / or drive motor; the mapping relationship between the actual road conditions and the vehicle's engine and / or drive motor includes: in a smooth road condition, the engine maintains its current operating state and charges or discharges the battery according to the next operating condition; when approaching an intersection, the SOC / engine can cover the changes in the current operating condition; after a long period of deceleration near a traffic light intersection, the engine stops and the drive motor operates; after passing through, the engine starts operating; before detecting the vehicle's actual road conditions and obtaining the current environmental data and current vehicle status, it also includes: To detect the vehicle's current time or distance traveled; When the current time meets the update duration or the driving distance reaches the update distance, the vehicle is determined to meet the energy consumption prediction conditions.

2. The method according to claim 1, characterized in that, The optimal driving strategy corresponding to generating the minimum future energy consumption includes: Determine whether the engine's energy consumption is within its optimal efficiency range; If the engine's energy consumption is within the optimal efficiency range, then when the vehicle is in an uphill condition, the drive motor of the vehicle is controlled to operate based on the vehicle's battery state of charge, and the vehicle speed is adjusted to a first preset speed; when the vehicle is in a downhill condition, the vehicle speed is adjusted to a second preset speed, and the drive motor of the vehicle is controlled to operate based on the vehicle's battery state of charge; when the vehicle is in other conditions besides the uphill and downhill conditions, the drive motor of the vehicle is controlled to operate based on the vehicle's battery state of charge, and the vehicle speed is adjusted to a third preset speed. If the engine's energy consumption is not within the optimal efficiency range, then when the vehicle is in an uphill condition, the speed of the drive motor is controlled based on the downshift point, and the first average vehicle speed value of each trajectory point in the current movement trajectory is calculated using the equivalent consumption method and the reverse order algorithm, and the vehicle is controlled according to the first average vehicle speed value; when the vehicle is in a downhill condition, the speed of the drive motor is controlled based on the upshift point, and the second average vehicle speed value of each trajectory point in the current movement trajectory is calculated using the equivalent consumption method and the reverse order algorithm, and the vehicle is controlled according to the second average vehicle speed value.

3. The method according to claim 1, characterized in that, The actual road conditions include one or more of the following: basic data of navigation electronic map, route planning data, route information data, route altitude data, road surface data, and road smoothness. The current environmental data includes one or more of the following: weather, temperature, altitude, wind speed, and wind direction. The current vehicle status includes one or more of the following: vehicle driving habits, vehicle load, current tire pressure, vehicle basic state resistance, and vehicle driving form resistance.

4. The method according to claim 1, characterized in that, Also includes: Record the actual energy consumption of the current route planning; The energy consumption prediction model is updated using the actual energy consumption, and the energy consumption prediction model is used to predict multiple future energy consumptions.

5. A vehicle energy consumption control device, characterized in that, include: The acquisition module is used to detect the actual road conditions of the vehicle and acquire the current environmental data and the current vehicle status of the environment in which the vehicle is located. The calculation module is used to calculate the actual energy consumption of the vehicle based on the actual road conditions, the current environmental data, the current vehicle status, and historical energy consumption. as well as The control module is used to predict multiple future energy consumptions of the vehicle along the current path based on the actual energy consumption, and generate the optimal driving strategy corresponding to the minimum future energy consumption. The judgment module determines whether the vehicle's remaining energy consumption is greater than the minimum future energy consumption; A generation module is used to generate an energy replenishment strategy based on the difference between the remaining energy consumption and the minimum future energy consumption if the remaining energy consumption is less than the minimum future energy consumption, and to control the vehicle's engine and / or drive motor to operate according to the energy replenishment strategy. The energy replenishment strategy includes a mapping relationship between the actual road conditions and the vehicle's engine and / or drive motor. This mapping relationship includes: in a smooth traffic flow state, the engine maintains its current operating state and charges or discharges the battery according to the next operating condition; when approaching an intersection, the SOC / engine can cover changes in the current operating condition; after a prolonged deceleration near a traffic light intersection, the engine stops and the drive motor operates; after passing through, the engine resumes operation. Before detecting the vehicle's actual road conditions and acquiring the current environmental data and the current vehicle status, the module further includes: detecting the vehicle's current time or travel distance; and determining that the vehicle meets the energy consumption prediction conditions when the current time meets the update duration or the travel distance reaches the update distance.

6. The apparatus according to claim 5, characterized in that, The actual road conditions include one or more of the following: basic data of navigation electronic map, route planning data, route information data, route altitude data, road surface data, and road smoothness. The current environmental data includes one or more of the following: weather, temperature, altitude, wind speed, and wind direction. The current vehicle status includes one or more of the following: vehicle driving habits, vehicle load, current tire pressure, vehicle basic state resistance, and vehicle driving form resistance.

7. A vehicle, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the energy consumption control method for a vehicle as described in any one of claims 1-3.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the energy consumption control method for the vehicle as described in any one of claims 1-3.

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