Vehicle energy-saving method based on intelligent driving, vehicle and storage medium

Abstract

The application provides a vehicle energy-saving method based on intelligent driving, a vehicle and a storage medium, and relates to the technical field of intelligent driving. The method can obtain a plurality of candidate driving routes between a driving starting point and a driving ending point of this driving; for each candidate driving route, the candidate driving route is divided into a plurality of driving sections, and for each driving section, the section energy consumption of the driving section can be determined according to the preset reference energy consumption of the vehicle and the road characteristics of the driving section; the section energy consumption of each driving section of each candidate driving route is summarized to obtain the comprehensive energy consumption of each candidate driving route; finally, a candidate driving route with low comprehensive energy consumption is selected from each candidate driving route to perform an intelligent driving operation. In this way, the energy consumption of intelligent driving is low, energy saving and environmental protection, and the economic applicability is high.

Description

Technical Field

[0001] This application relates to the field of intelligent driving technology, and in particular to a vehicle energy-saving method, vehicle, and storage medium based on intelligent driving. Background Technology

[0002] Intelligent driving is a technology that uses various sensors (such as cameras, radar, and lidar) installed on vehicles to perceive the surrounding environment, and then uses computing chips and artificial intelligence algorithms to "think" and make decisions, thereby partially or completely replacing human drivers in controlling the vehicle.

[0003] Currently, drivers can plan multiple candidate routes in navigation applications, and then the vehicle will drive intelligently based on the route selected by the driver with the shortest travel time or distance. However, intelligent driving based on the above method has high energy consumption, is not energy-saving and environmentally friendly, and has low economic applicability. Summary of the Invention

[0004] This application provides a vehicle energy-saving method, vehicle, and storage medium based on intelligent driving, to solve the problem of high energy consumption in the prior art of intelligent driving.

[0005] Firstly, this application provides a vehicle energy-saving method based on intelligent driving, applied to vehicles. The method provided in this application includes: Obtain multiple candidate routes between the starting point and the destination of this trip; For each candidate driving route, the candidate driving route is divided into multiple driving segments; For each driving segment, the segment energy consumption is determined based on the preset vehicle baseline energy consumption and the road characteristics of the driving segment. For each candidate driving route, the energy consumption of each driving segment of the candidate driving route is summarized to obtain the comprehensive energy consumption of each candidate driving route. From the various candidate driving routes, select the one with the lowest overall energy consumption to perform intelligent driving operations.

[0006] In some implementations, the driving segment includes road characteristics in multiple dimensions. Based on a preset baseline energy consumption of the vehicle and the road characteristics of the driving segment, the segment energy consumption is determined, including: Find the energy consumption coefficient corresponding to each dimension of road features in the driving segment from the preset mapping relationship; The road segment energy consumption is obtained by using the preset baseline energy consumption of vehicles and the energy consumption coefficient corresponding to each dimension of road characteristics.

[0007] In some implementations, the multi-dimensional road features include at least: Road type, where the speed limit corresponding to the road type is negatively correlated with the corresponding energy consumption coefficient; Slope, where the energy consumption coefficient corresponding to the slope is greater than 0 when there is an uphill section of the driving route, and the length of the uphill road is positively correlated with the corresponding energy consumption coefficient; when there is a downhill section of the driving route, the energy consumption coefficient corresponding to the slope is less than 0, and the length of the downhill road is negatively correlated with the corresponding energy consumption coefficient. Historical average number of stops, where the historical average number of stops is positively correlated with the corresponding energy consumption coefficient; The degree of congestion is positively correlated with the corresponding energy consumption coefficient.

[0008] In some implementations, selecting a candidate driving route with low overall energy consumption from various candidate driving routes to perform intelligent driving operations includes: The shortest travel time is determined based on the travel time of each candidate route. From multiple candidate driving routes where the ratio of driving time to the shortest driving time is less than a first ratio threshold, the candidate driving route with the lowest overall energy consumption is selected to perform intelligent driving operations.

[0009] In some implementations, selecting a candidate driving route with low overall energy consumption from various candidate driving routes to perform intelligent driving operations includes: With the vehicle's intelligent driving and energy-saving mode activated, the system selects the candidate driving route with the lowest overall energy consumption to perform intelligent driving operations.

[0010] In some implementations, performing intelligent driving operations includes: If the traffic light in front of the vehicle is green, determine the target speed required for the vehicle to pass through the traffic light without stopping, based on the remaining time of the green light and the distance between the vehicle and the traffic light. The maximum green wave speed of a vehicle is determined based on the type of road segment in which it is located. If the vehicle's current speed is less than the target speed, and the target speed is less than the maximum green wave speed, then the vehicle's current speed will be increased to the target speed.

[0011] In some implementations, performing intelligent driving operations includes: The upper limit of vehicle acceleration is determined based on the speed range in which the vehicle's speed falls. The upper limit of acceleration is negatively correlated with the lower limit of the speed range.

[0012] In some implementations, performing intelligent driving operations includes: If the speed of the vehicle in front of the vehicle is less than a second ratio threshold for a continuously set duration, and there are no other vehicles within a preset distance range of the target lane, the vehicle is allowed to change lanes. The target lane is the lane adjacent to the lane where the vehicle is currently located.

[0013] Secondly, this application also provides a vehicle including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the vehicle performs the method provided in the first aspect of this application.

[0014] Thirdly, this application also provides a storage medium storing a computer program, which, when executed by a processor, causes the computer to perform the method provided in the first aspect of this application.

[0015] Fourthly, this application also provides a vehicle energy-saving device based on intelligent driving, applied to a vehicle. The device provided by this application includes: The route planning unit is used to obtain multiple candidate routes between the starting point and the destination of this trip; The road segment division unit is used to divide each candidate driving route into multiple driving segments; The segment energy consumption determination unit is used to determine the segment energy consumption of each driving segment based on the preset vehicle baseline energy consumption and the road characteristics of the driving segment. The comprehensive energy consumption determination unit is used to summarize the segment energy consumption of each driving segment of each candidate driving route for each candidate driving route, and obtain the comprehensive energy consumption of each candidate driving route. The intelligent driving unit is used to select a candidate driving route with low overall energy consumption from various candidate driving routes and perform intelligent driving operations.

[0016] In some implementations, the driving segment includes road features in multiple dimensions. The segment energy consumption determination unit is specifically used to find the energy consumption coefficient corresponding to each dimension of the road feature from a preset mapping relationship; and to obtain the segment energy consumption of the driving segment by using a preset vehicle baseline energy consumption and the energy consumption coefficient corresponding to each dimension of the road feature.

[0017] In some implementations, the multi-dimensional road features include at least: Road type, where the speed limit corresponding to the road type is negatively correlated with the corresponding energy consumption coefficient; Slope, where the energy consumption coefficient corresponding to the slope is greater than 0 when there is an uphill section of the driving route, and the length of the uphill road is positively correlated with the corresponding energy consumption coefficient; when there is a downhill section of the driving route, the energy consumption coefficient corresponding to the slope is less than 0, and the length of the downhill road is negatively correlated with the corresponding energy consumption coefficient. Historical average number of stops, where the historical average number of stops is positively correlated with the corresponding energy consumption coefficient; The degree of congestion is positively correlated with the corresponding energy consumption coefficient.

[0018] In some implementations, the intelligent driving unit is specifically used to determine the shortest driving time based on the driving time of each candidate driving route; and to select the candidate driving route with the lowest overall energy consumption from a plurality of candidate driving routes whose driving time to the shortest driving time is less than a first ratio threshold to perform intelligent driving operations.

[0019] In some implementations, the intelligent driving unit is specifically used to select the candidate driving route with the lowest overall energy consumption and perform intelligent driving operations when the vehicle's intelligent driving energy-saving mode is activated.

[0020] In some implementations, the intelligent driving unit is specifically configured to, if the traffic light ahead of the vehicle is green, determine the target speed required for the vehicle to pass through the traffic light without stopping, based on the remaining duration of the green light and the distance between the vehicle and the traffic light; determine the maximum green wave speed for the vehicle in the traffic segment based on the type of the road segment in which the vehicle is located; and if the vehicle's current speed is less than the target speed, and the target speed is less than the maximum green wave speed, then increase the vehicle's current speed to the target speed.

[0021] In some implementations, the intelligent driving unit is specifically used to determine the upper limit of the vehicle's acceleration based on the speed range in which the vehicle's speed falls, wherein the upper limit of acceleration is negatively correlated with the lower limit of the speed range.

[0022] In some implementations, the intelligent driving unit is specifically configured to allow the vehicle to change lanes for a set duration when the speed of the vehicle in front of the vehicle is less than a second ratio threshold for a set duration and there are no other vehicles within a preset distance range of the target lane, wherein the target lane is the lane adjacent to the lane where the vehicle is located.

[0023] Fifthly, this application also provides a computer program product, including a computer program that, when run, causes a vehicle to perform the method provided in the first aspect of this application.

[0024] This application provides a vehicle energy-saving method, vehicle, and storage medium based on intelligent driving. The method acquires multiple candidate driving routes between the starting and ending points of a current trip. For each candidate route, it divides the route into multiple driving segments. For each segment, the energy consumption is determined based on a preset baseline energy consumption of the vehicle and the road characteristics of the segment. The energy consumption of each segment of each candidate route is aggregated to obtain the comprehensive energy consumption of all candidate routes. Finally, the candidate route with the lowest comprehensive energy consumption is selected for intelligent driving operation. This results in low energy consumption, energy conservation, environmental friendliness, and high economic applicability for intelligent driving. For example, it can reduce energy consumption per 100 kilometers by 3% to 5% in intelligent driving mode, and reduce the continuous full-load operation time of the vehicle's high-performance computing unit and sensors, which helps to reduce hardware wear and tear and extend service life. This makes users more willing to turn on the intelligent driving function in daily use, increases the usage rate of intelligent driving, and encourages users to choose green travel methods, which is in line with the national "dual carbon" strategy. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 A schematic flowchart illustrating a vehicle energy-saving method based on intelligent driving, provided as an embodiment of this application; Figure 2 A functional block diagram of a vehicle energy-saving device based on intelligent driving provided for embodiments of this application; Figure 3 This is a block diagram of a vehicle circuit module provided in an embodiment of this application. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of the embodiments of this application.

[0028] The technical solutions of this application and how they solve the aforementioned technical problems will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0029] Please see Figure 1 This application provides a vehicle energy-saving method based on intelligent driving, applicable to vehicles. The method provided in this application includes: S101: Obtain multiple candidate routes between the starting point and the destination of this trip.

[0030] For example, a user can enter the starting point and destination on the display interface of the vehicle's navigation application, and then the vehicle can determine multiple candidate routes based on the starting point and destination.

[0031] S102: For each candidate driving route, divide the candidate driving route into multiple driving segments.

[0032] For example, candidate driving routes can be divided into multiple driving segments of preset length (such as 200m, 500m, 1000m or 2000m).

[0033] S103: For each driving segment, determine the segment energy consumption based on the preset vehicle baseline energy consumption and the road characteristics of the driving segment.

[0034] Specifically, the energy consumption coefficient corresponding to each dimension of the road feature of the driving segment is found from the preset mapping relationship. Then, the road segment energy consumption is obtained by using the preset vehicle baseline energy consumption and the energy consumption coefficient corresponding to each dimension of the road feature.

[0035] For example, road features in multiple dimensions include at least: Road type, where the speed limit corresponding to the road type is negatively correlated with the corresponding energy consumption coefficient. For example, the energy consumption coefficient for urban roads is 1.0, for expressways it is 0.85, and for highways it is 0.70. Gradient, specifically, in the case of uphill sections of the road, the energy consumption coefficient corresponding to the gradient is greater than 0, and the length of the uphill road is positively correlated with the corresponding energy consumption coefficient. For example, if the uphill road is 2km long, and the energy consumption per kilometer increases by 5%, then the energy consumption coefficient corresponding to the gradient is 1 + 0.5%. =1.1; When a road segment includes a downhill section, the energy consumption coefficient corresponding to the gradient is less than 0, and the length of the downhill road is negatively correlated with the corresponding energy consumption coefficient. For example, if the downhill road is 2km long and the energy consumption per kilometer is -3%, then the energy consumption coefficient corresponding to the gradient is 1+. =0.96.

[0036] The historical average number of parking times is positively correlated with the corresponding energy consumption factor. For example, the historical average number of parking times can be obtained by multiplying the historical average number of parking times by a preset parking energy consumption factor. For instance, if the historical average number of parking times is 3 and the preset parking energy consumption factor is 0.04 kWh, then the historical average number of parking times is 0.12.

[0037] The degree of congestion is positively correlated with the corresponding energy consumption coefficient. For example, the energy consumption coefficient for unobstructed roads is 1.0, for slow-moving roads it is 1.2, and for congested roads it is 1.5.

[0038] Furthermore, the formula P1 = P2 × R × T × S + L × M can be used, where P1 is the energy consumption of the driving segment, P2 is the preset baseline energy consumption of the vehicle, R is the energy consumption coefficient of the road type, T is the energy consumption coefficient of the congestion level, S is the energy consumption coefficient of the slope, M is the historical average number of stops, and L is the preset parking energy consumption factor. For example, the vehicle's baseline energy consumption can be equal to the vehicle's energy consumption per 100 kilometers divided by 500, and then divided by the length of the driving segment. If the vehicle's energy consumption per 100 kilometers is 15 kWh / 100 km and the length of the driving segment is 200 meters, then the vehicle's baseline energy consumption is 0.03 kWh / 200 m.

[0039] S104: For each candidate driving route, summarize the energy consumption of each driving segment of the candidate driving route to obtain the comprehensive energy consumption of each candidate driving route.

[0040] For example, the candidate driving routes include candidate driving route A, candidate driving route B, candidate driving route C, and candidate driving route D. The comprehensive energy consumption of candidate driving route A is 2.12 kWh, the comprehensive energy consumption of candidate driving route B is 1.76 kWh, the comprehensive energy consumption of candidate driving route C is 2.03 kWh, and the comprehensive energy consumption of candidate driving route D is 2.21 kWh.

[0041] S105: Select a candidate driving route with low overall energy consumption from various candidate driving routes and execute intelligent driving operations.

[0042] For example, the specific implementation of S104 includes, but is not limited to, the following two methods: The first method involves determining the shortest driving time based on the driving time of each candidate route. From multiple candidate routes where the ratio of driving time to the shortest driving time is less than a first ratio threshold (e.g., 1.1, 1.15, or 1.20), the route with the lowest overall energy consumption is selected for intelligent driving operation. For example, if candidate route A has a driving time of 50 minutes and an overall energy consumption of 2.12 kWh, candidate route B has a driving time of 60 minutes and an overall energy consumption of 1.176 kWh, and candidate route C has a driving time of 80 minutes and an overall energy consumption of 1.1 kWh, then candidate route B is selected for intelligent driving operation.

[0043] The second method: When the vehicle's intelligent driving energy-saving mode is on, select the candidate driving route with the lowest overall energy consumption and execute intelligent driving operations.

[0044] For example, if candidate route A has a driving time of 50 minutes and a combined energy consumption of 2.12 kWh, candidate route B has a driving time of 60 minutes and a combined energy consumption of 1.176 kWh, and candidate route C has a driving time of 80 minutes and a combined energy consumption of 1.1 kWh, then candidate route C will be selected for intelligent driving operation. Furthermore, when the vehicle's intelligent driving energy-saving mode is activated, the suspension height can be automatically lowered to a "low posture" to reduce air resistance and lower high-speed energy consumption.

[0045] In summary, the vehicle energy-saving method based on intelligent driving provided in this application embodiment can obtain multiple candidate driving routes between the starting point and the ending point of the current trip; for each candidate driving route, the route is divided into multiple driving segments; for each driving segment, the segment energy consumption is determined based on the preset vehicle baseline energy consumption and the road characteristics of the driving segment; the segment energy consumption of each driving segment of each candidate driving route is summarized to obtain the comprehensive energy consumption of each candidate driving route; finally, the candidate driving route with the lowest comprehensive energy consumption is selected from all candidate driving routes to perform intelligent driving operations. This results in low energy consumption, energy saving and environmental protection (reduced carbon emissions), and high economic applicability for intelligent driving. For example, it can reduce energy consumption per 100 kilometers by 3% to 5% in intelligent driving mode, and reduce the continuous full-load operation time of the vehicle's high-performance computing unit and sensors, which helps to reduce hardware wear and tear and extend service life. This makes users more willing to turn on the intelligent driving function in daily use, increases the usage rate of intelligent driving, and encourages users to choose green travel methods, which is in line with the national "dual carbon" strategy.

[0046] Furthermore, during intelligent driving operations, if the traffic light ahead of the vehicle is green, the target speed required for the vehicle to pass through the traffic light without stopping is determined based on the remaining duration of the green light and the distance between the vehicle and the traffic light. For example, if the remaining duration of the green light is 10 seconds and the distance between the vehicle and the traffic light is 150 meters, then the target speed required for the vehicle to pass through the traffic light without stopping is determined to be 54 km / h.

[0047] Furthermore, based on the type of road segment the vehicle is on, the maximum green wave speed for that road segment is determined. For example, the maximum green wave speed on urban roads is 60 km / h, on expressways it is no more than 80 km / h, and on highways it is 110 km / h. It should be noted that the maximum green wave speeds corresponding to the above road segment types are preset empirical values. When a vehicle passes through a corresponding type of road segment at a speed less than or equal to the maximum green wave speed, the required energy consumption is lower.

[0048] Understandably, since waiting at traffic lights wastes energy, and rapid acceleration also requires significant energy, if the vehicle's current speed is less than the target speed (such as 54 km / h as mentioned above), and the target speed is less than the maximum green wave speed, then the vehicle's current speed should be increased to the target speed. This further minimizes the vehicle's energy consumption.

[0049] In addition, during the execution of intelligent driving operations, the upper limit of vehicle acceleration can be determined based on the speed range in which the vehicle speed is located. The upper limit of acceleration is negatively correlated with the lower limit of the speed range.

[0050] For example, if the vehicle speed is in the range of 0km / h-30km / h, it means that the vehicle is in a starting scenario or a low-speed following scenario. Therefore, the vehicle's acceleration is limited to ≤ 1.2 m / s² (i.e., the upper limit of acceleration). This can avoid the energy waste caused by the instantaneous increase of motor torque.

[0051] For example, if the vehicle speed is between 30km / h and 100km / h, it indicates a medium-to-high speed scenario. Therefore, the vehicle's acceleration is limited to ≤ 0.8 m / s² (i.e., the upper limit of acceleration). This avoids energy waste caused by a sudden increase in motor torque. It should be noted that the acceleration requirements in starting or low-speed following scenarios are greater than those in medium-to-high speed scenarios. Therefore, the upper limit of acceleration in starting or low-speed following scenarios is higher than that in medium-to-high speed scenarios, which significantly improves the passenger experience and reduces motion sickness.

[0052] In some implementations, during the execution of intelligent driving operations, if the speed of the vehicle in front is less than a second ratio threshold (e.g., 70%, 80%, or 85%) for a continuously set duration (e.g., 10s, 15s, or 20s), and there are no other vehicles within a preset distance of the target lane, then the vehicle is allowed to change lanes. The target lane is the lane adjacent to the vehicle's current lane. This significantly reduces the number of invalid lane changes, thereby reducing unnecessary increases in travel distance and wind resistance losses, further reducing energy consumption.

[0053] Additionally, please see Figure 2 This application also provides a vehicle energy-saving device based on intelligent driving, applied to a vehicle. It should be noted that the basic principle and technical effects of the vehicle energy-saving device based on intelligent driving provided in this application are the same as those in the above embodiments. For the sake of brevity, any parts not mentioned in this application can be referred to the corresponding content in the above embodiments. The device provided in this application includes a route planning unit, a road segment division unit, a road segment energy consumption determination unit, a comprehensive energy consumption determination unit, and an intelligent driving unit, wherein... The route planning unit is used to obtain multiple candidate routes between the starting point and the destination of this trip; The road segment division unit is used to divide each candidate driving route into multiple driving segments; The segment energy consumption determination unit is used to determine the segment energy consumption of each driving segment based on the preset vehicle baseline energy consumption and the road characteristics of the driving segment. The comprehensive energy consumption determination unit is used to summarize the segment energy consumption of each driving segment of each candidate driving route for each candidate driving route, and obtain the comprehensive energy consumption of each candidate driving route. The intelligent driving unit is used to select a candidate driving route with low overall energy consumption from various candidate driving routes and perform intelligent driving operations.

[0054] In some implementations, the driving segment includes road features in multiple dimensions. The segment energy consumption determination unit is specifically used to find the energy consumption coefficient corresponding to each dimension of the road feature from a preset mapping relationship; and to obtain the segment energy consumption of the driving segment by using a preset vehicle baseline energy consumption and the energy consumption coefficient corresponding to each dimension of the road feature.

[0055] In some implementations, the multi-dimensional road features include at least: Road type, where the speed limit corresponding to the road type is negatively correlated with the corresponding energy consumption coefficient; Slope, where the energy consumption coefficient corresponding to the slope is greater than 0 when there is an uphill section of the driving route, and the length of the uphill road is positively correlated with the corresponding energy consumption coefficient; when there is a downhill section of the driving route, the energy consumption coefficient corresponding to the slope is less than 0, and the length of the downhill road is negatively correlated with the corresponding energy consumption coefficient. Historical average number of stops, where the historical average number of stops is positively correlated with the corresponding energy consumption coefficient; The degree of congestion is positively correlated with the corresponding energy consumption coefficient.

[0056] In some implementations, the intelligent driving unit is specifically used to determine the shortest driving time based on the driving time of each candidate driving route; and to select the candidate driving route with the lowest overall energy consumption from a plurality of candidate driving routes whose driving time to the shortest driving time is less than a first ratio threshold to perform intelligent driving operations.

[0057] In some implementations, the intelligent driving unit is specifically used to select the candidate driving route with the lowest overall energy consumption and perform intelligent driving operations when the vehicle's intelligent driving energy-saving mode is activated.

[0058] In some implementations, the intelligent driving unit is specifically configured to, if the traffic light ahead of the vehicle is green, determine the target speed required for the vehicle to pass through the traffic light without stopping, based on the remaining duration of the green light and the distance between the vehicle and the traffic light; determine the maximum green wave speed for the vehicle in the traffic segment based on the type of the road segment in which the vehicle is located; and if the vehicle's current speed is less than the target speed, and the target speed is less than the maximum green wave speed, then increase the vehicle's current speed to the target speed.

[0059] In some implementations, the intelligent driving unit is specifically used to determine the upper limit of the vehicle's acceleration based on the speed range in which the vehicle's speed falls, wherein the upper limit of acceleration is negatively correlated with the lower limit of the speed range.

[0060] In some implementations, the intelligent driving unit is specifically configured to allow the vehicle to change lanes for a set duration during which the speed of the vehicle in front of the vehicle is less than a second ratio threshold, provided that there are no other vehicles within a preset distance range of the target lane, wherein the target lane is the lane adjacent to the lane in which the vehicle is located.

[0061] Figure 3 This is a schematic diagram of the structure of a vehicle according to one embodiment of this application. Please refer to it. Figure 3At the hardware level, the vehicle includes a processor, and optionally an internal bus, network interface, and memory. The memory may include RAM, such as high-speed random-access memory (RAM), or non-volatile memory, such as at least one disk drive. Of course, the vehicle may also include other hardware required for other business operations.

[0062] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0063] Memory is used to store programs. Specifically, programs may include program code, which includes computer operation instructions. Memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0064] The processor reads the corresponding computer program from non-volatile memory into main memory and then runs it, forming a vehicle energy-saving device based on intelligent driving at the logical level. The processor executes the program stored in memory and performs the methods provided in the above embodiments of this application.

[0065] The methods described in the embodiments of this application can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0066] The vehicle can also perform Figure 1 The method, and the implementation of vehicle energy-saving devices based on intelligent driving. Figure 1 The functions of the embodiments shown are not described in detail here.

[0067] Of course, in addition to the software implementation, the vehicle in this application embodiment does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc. That is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0068] In addition, embodiments of this application also propose a storage medium that stores one or more programs, the one or more programs including instructions that, when executed by a vehicle including multiple applications, enable the vehicle to perform the methods provided in the above embodiments of this application.

[0069] Specifically, storage media, including permanent and non-permanent, removable and non-removable media, can be used to store information by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0070] In addition, this application also provides a computer program product, including a computer program that, when run, causes a vehicle to perform the method provided in the above embodiments of this application.

[0071] It should also be noted that 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. Without further limitation, 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 said element.

[0072] In summary, the above descriptions are merely preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A vehicle energy-saving method based on intelligent driving, characterized in that, Applied to vehicles, the method includes: Obtain multiple candidate routes between the starting point and the destination of this trip; For each of the candidate driving routes, the candidate driving route is divided into multiple driving segments; For each of the driving segments, the segment energy consumption is determined based on the preset baseline energy consumption of the vehicle and the road characteristics of the driving segment. For each candidate driving route, the energy consumption of each driving segment of the candidate driving route is summarized to obtain the comprehensive energy consumption of each candidate driving route. From the candidate driving routes, select the one with the lowest overall energy consumption to perform intelligent driving operations.

2. The method according to claim 1, characterized in that, The driving segment includes road features in multiple dimensions. Determining the segment energy consumption based on the preset baseline energy consumption of the vehicle and the road features of the driving segment includes: From the preset mapping relationship, find the energy consumption coefficient corresponding to each dimension of the road features of the driving segment; The road segment energy consumption is obtained by using the preset baseline energy consumption of the vehicle and the energy consumption coefficient corresponding to the road features of each dimension.

3. The method according to claim 2, characterized in that, The road features in the multiple dimensions include at least: Road type, wherein the speed limit corresponding to the road type is negatively correlated with the corresponding energy consumption coefficient; Slope, wherein, when there is an uphill road in the driving segment, the energy consumption coefficient corresponding to the slope is greater than 0, and the length of the uphill road is positively correlated with the corresponding energy consumption coefficient; when there is a downhill road in the driving segment, the energy consumption coefficient corresponding to the slope is less than 0, and the length of the downhill road is negatively correlated with the corresponding energy consumption coefficient. Historical average number of stops, wherein the historical average number of stops is positively correlated with the corresponding energy consumption coefficient; The degree of congestion is positively correlated with the corresponding energy consumption coefficient.

4. The method according to claim 1, characterized in that, The step of selecting a candidate driving route with low overall energy consumption from the candidate driving routes to perform intelligent driving operations includes: The shortest travel time is determined based on the travel time of each of the candidate routes. From multiple candidate driving routes where the ratio of driving time to the shortest driving time is less than a first ratio threshold, the candidate driving route with the lowest overall energy consumption is selected to perform intelligent driving operations.

5. The method according to claim 1, characterized in that, The step of selecting a candidate driving route with low overall energy consumption from the candidate driving routes to perform intelligent driving operations includes: When the vehicle's intelligent driving energy-saving mode is enabled, the candidate driving route with the lowest overall energy consumption is selected to perform intelligent driving operations.

6. The method according to claim 1, characterized in that, The execution of intelligent driving operations includes: If the traffic light in front of the vehicle is green, the target speed required for the vehicle to pass through the traffic light without stopping is determined based on the remaining duration of the green light and the distance between the vehicle and the traffic light. The maximum green wave speed of the vehicle in the traffic segment is determined according to the type of the traffic segment in which the vehicle is located. If the current speed of the vehicle is less than the target speed, and the target speed is less than the maximum green wave speed, then the current speed of the vehicle will be increased to the target speed.

7. The method according to claim 1, characterized in that, The execution of intelligent driving operations includes: The upper limit of the vehicle's acceleration is determined based on the speed range in which the vehicle's speed falls, wherein the upper limit of the acceleration is negatively correlated with the lower limit of the speed range.

8. The method according to claim 1, characterized in that, The execution of intelligent driving operations includes: If the speed of the vehicle in front of the vehicle is less than a second ratio threshold for a continuously set duration, and there are no other vehicles within a preset distance range of the target lane, the vehicle is allowed to change lanes, wherein the target lane is the lane adjacent to the lane where the vehicle is located.

9. A vehicle comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it causes the vehicle to perform the method as described in any one of claims 1 to 8.

10. A storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it causes the computer to perform the method as described in any one of claims 1 to 8.

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