Vehicle fuel saving method, device and equipment and storage medium

Abstract

The invention discloses a vehicle fuel saving method, device and equipment and a storage medium, and the method comprises the steps: carrying out the normalization and iteration processing of the obtained actual road spectrum information through a simulation calculation model GRU, so as to predict the road spectrum information at a future moment; and performing dynamic planning reverse optimization solution on the road spectrum information to obtain a control signal with optimal oil consumption on the global driving road section. According to the method, the global energy consumption of the vehicle can be optimal, the fuel economy of the vehicle is improved, and then the purposes of saving oil and reducing consumption are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of fuel saving, and in particular to a vehicle fuel saving method, device, equipment and storage medium. Background Art

[0002] Fuel economy can be significantly improved by training drivers in economical driving. However, as time goes by, the effect of reducing fuel consumption will gradually disappear after a period of time after the training. In other words, drivers need to continue to learn to maintain economical driving. This method is difficult to promote and sustain. The dynamic programming algorithm can theoretically obtain the optimal fuel economy, but the implementation of this method depends on the deterministic operating condition information of the entire driving cycle, and the calculation time of the algorithm increases exponentially with the increase of the grid discretization degree. The calculation efficiency is low, and it is difficult to apply to real-time control. The model predictive control algorithm searches for the optimal control action within a limited time domain (or distance) at each sampling moment, which can take into account the real-time and optimality of control. However, in the rolling optimization process, the actual physical constraints of the system and the artificial constraints are expressed as constraints in the same optimization problem, making it difficult to clarify the physical meaning in practical applications. At the same time, the performance of the algorithm depends on the model quality, sampling step size and prediction range length (prediction accuracy of operating condition information), and cannot guarantee the global optimal energy consumption.

[0003] Therefore, how to optimize the vehicle's global energy consumption is a technical problem that needs to be solved urgently. Summary of the invention

[0004] The main purpose of the present invention is to provide a vehicle fuel saving method, device, equipment and storage medium, which can optimize the overall energy consumption of the vehicle, improve the fuel economy of the vehicle, and thus achieve the purpose of saving fuel and reducing consumption.

[0005] In a first aspect, the present application provides a vehicle fuel saving method, wherein the method comprises the steps of:

[0006] The GRU simulation model is used to normalize and iteratively process the actual road spectrum information obtained to predict the road spectrum information at future moments.

[0007] The road spectrum information is subjected to a dynamic programming reverse optimization solution to obtain a control signal with the best fuel consumption on the global driving section.

[0008] In combination with the first aspect, as an optional implementation method, according to the predicted vehicle speed in the road spectrum information and in combination with the original vehicle speed, the optimal driving speed is planned by using dynamic programming;

[0009] Calculating the optimal speed, torque and gear position of the vehicle to achieve the optimal driving speed;

[0010] According to the optimal speed, torque and gear position signal of the vehicle, the brake, accelerator, clutch and gear position signals with the best fuel consumption on the global driving section are calculated.

[0011] In combination with the first aspect, as an optional implementation manner, actual road spectrum information is obtained, where the road spectrum information includes: vehicle speed and road slope;

[0012] The road spectrum is normalized by using the simulation calculation model GRU, and the normalized road spectrum information is iteratively calculated by using the GRU algorithm;

[0013] According to the number of iterations, the vehicle speed and road slope information at future moments are predicted.

[0014] In combination with the first aspect above, as an optional implementation, according to the formula: Vout = 3.6vv limit2 / v limit1 -v limit2 , the vehicle speed is normalized, where v limit1 is the first speed limit value set, v limit2 is the set second speed limit value, Vout∈[0,1];

[0015] According to the formula: Hout = 3.6hh limit2 / h limit1 -h limit2 , normalize the slope, where h limit1 is the set first slope limit value, h limit2 is the set second slope limit value, Hout∈[0,1].

[0016] In combination with the first aspect, as an optional implementation, obtaining driving data of the vehicle, the driving data including: vehicle speed, rotation speed and torque;

[0017] Searching a MAP table based on the driving data to obtain current vehicle fuel consumption;

[0018] Based on the current vehicle fuel consumption, calculate the minimum economic fuel consumption per unit distance.

[0019] In combination with the first aspect above, as an optional implementation method, the simulation calculation model GRU is verified based on the historical driving data of the vehicle to determine whether the accuracy of the simulation calculation model meets the requirements;

[0020] The historical driving data includes: vehicle braking, gear position signal, engine intake temperature, supercharger speed, engine speed, engine torque, fuel consumption, manifold pressure, throttle opening, EGR opening, exhaust flow, driving distance, vehicle speed, ambient temperature, ambient pressure, altitude and road slope.

[0021] In a second aspect, the present application provides a vehicle fuel saving device, the device comprising:

[0022] A processing module, which is used to normalize and iteratively process the acquired actual road spectrum information using the simulation calculation model GRU to predict the road spectrum information at a future moment;

[0023] The calculation module is used to perform dynamic planning and reverse optimization on the road spectrum information to obtain a control signal with the best fuel consumption on the global driving section.

[0024] In combination with the above second aspect, as an optional implementation, the calculation module is further used to plan the optimal driving speed by using dynamic programming solution based on the predicted vehicle speed in the road spectrum information and the original vehicle speed;

[0025] Calculating the optimal speed, torque and gear position of the vehicle to achieve the optimal driving speed;

[0026] According to the optimal speed, torque and gear position signal of the vehicle, the brake, accelerator, clutch and gear position signals with the best fuel consumption on the global driving section are calculated.

[0027] In a third aspect, the present application further provides an electronic device, comprising: a processor; a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the method described in any one of the first aspects is implemented.

[0028] In a fourth aspect, the present application further provides a computer-readable storage medium storing computer program instructions, which, when executed by a computer, enables the computer to execute any of the methods described in the first aspect.

[0029] The present application provides a vehicle fuel saving method, device, equipment and storage medium, wherein the method comprises the steps of: using a simulation calculation model GRU to normalize and iteratively process the actual road spectrum information obtained to predict the road spectrum information at a future moment; performing dynamic planning reverse optimization on the road spectrum information to obtain a control signal with the best fuel consumption on the global driving section. The present application can optimize the global energy consumption of the vehicle, improve the fuel economy of the vehicle, and thus achieve the purpose of saving fuel and reducing consumption.

[0030] It is to be understood that the foregoing general description and the following detailed description are exemplary only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0032] Figure 1 A flow chart of a vehicle fuel saving method provided in an embodiment of the present application;

[0033] Figure 2 A schematic diagram of a vehicle fuel saving device provided in an embodiment of the present application;

[0034] Figure 3 A schematic diagram of an electronic device provided in an embodiment of the present application;

[0035] Figure 4 A schematic diagram of a computer-readable program medium provided in an embodiment of the present application. DETAILED DESCRIPTION

[0036] Here, exemplary embodiments will be described in detail, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0037] Furthermore, the drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. Some of the blocks shown in the drawings are functional entities and do not necessarily correspond to physically or logically separate entities.

[0038] The embodiments of the present application provide a vehicle fuel saving method, device, equipment and storage medium, which can optimize the vehicle's global energy consumption, improve the vehicle's fuel economy, and thus achieve the purpose of saving fuel and reducing consumption.

[0039] In order to achieve the above technical effects, the general idea of ​​this application is as follows:

[0040] A vehicle fuel saving method, the method comprising the steps of:

[0041] S101: Use the simulation calculation model GRU to normalize and iteratively process the actual road spectrum information obtained to predict the road spectrum information at a future moment.

[0042] S102: Performing reverse optimization on the road spectrum information through dynamic programming to obtain a control signal with optimal fuel consumption on the global driving section.

[0043] The embodiments of the present application are further described in detail below in conjunction with the accompanying drawings.

[0044] Reference Figure 1 , Figure 1 FIG. 1 is a flow chart of a vehicle fuel saving method provided by the present invention, as shown in FIG. Figure 1 As shown, the method comprises the steps of:

[0045] Step S101: using the simulation calculation model GRU to normalize and iteratively process the acquired actual road spectrum information to predict the road spectrum information at a future moment.

[0046] Specifically, actual road spectrum information is obtained, and the road spectrum information includes: vehicle speed and road slope; the road spectrum is normalized using the simulation calculation model GRU, and the normalized road spectrum information is iteratively calculated using the GRU algorithm; based on the number of iterations, the vehicle speed and road slope information at future times are predicted.

[0047] Among them, according to the formula: The vehicle speed is normalized, where

[0048] v limit1 is the first speed limit value set, v limit2 is the set second speed limit value, Vout∈[0,1];

[0049] According to the formula: The slope is normalized, where h limit1 is the set first slope limit value, h limit2 is the set second slope limit value, Hout∈[0,1].

[0050] For easy understanding, an example is given. The input of the prediction module is the vehicle speed and the road slope. After normalization, the normalized signal is passed through the GRU algorithm to obtain the normalized output. The normalization method is based on the formula: The vehicle speed is normalized, where

[0051] v limit1 is the first speed limit value set, v limit2 is the set second speed limit value, Vout∈[0,1]; according to the formula: The slope is normalized, where h limit1 is the set first slope limit value, h limit2 is the set second slope limit value, Hout∈[0,1].

[0052] After the input signal is normalized, it is iterated multiple times by the GRU algorithm to obtain the predicted vehicle speed and road slope signal at the future moment, which depends on the number of iterations. If the number of iterations is set to 40, the GRU algorithm will be used to predict the vehicle driving information for the next 40 seconds. This is used for subsequent dynamic planning solutions.

[0053] In one embodiment, based on the existing actual road spectrum information (including vehicle speed, road slope, etc.), the actual parameters of the GRU model are simulated and calculated, and the accuracy of the model is verified through existing data, so that the GRU module can accurately estimate the vehicle speed and road slope information in the next 40 seconds. Based on actual road driving, the current time is recorded as TO, and the vehicle speed and road slope signal at time T0 are input to the model. Based on the input at time TO, the GRU module predicts the vehicle speed and road slope information at times T1-T40.

[0054] In one embodiment, driving data of the vehicle is obtained, the driving data including vehicle speed, rotation speed and torque; a MAP table is searched based on the driving data to obtain the current vehicle fuel consumption; and based on the current vehicle fuel consumption, the minimum economic consumption of fuel per unit distance is calculated.

[0055] In one embodiment, the simulation calculation model GRU is verified based on the historical driving data of the vehicle to determine whether the accuracy of the simulation calculation model meets the requirements;

[0056] The historical driving data includes: vehicle braking, gear position signal, engine intake temperature, supercharger speed, engine speed, engine torque, fuel consumption, manifold pressure, throttle opening, EGR opening, exhaust flow, driving distance, vehicle speed, ambient temperature, ambient pressure, altitude and road slope.

[0057] Step S102: Performing a reverse optimization solution on the road spectrum information through dynamic programming to obtain a control signal with the best fuel consumption on the global driving section.

[0058] Specifically, based on the predicted vehicle speed in the road spectrum information and combined with the original vehicle speed, dynamic planning is used to plan the optimal driving speed; the optimal vehicle speed, torque and gear position to achieve the optimal driving speed are calculated; based on the vehicle's optimal speed, torque and gear position signals, the brake, throttle, clutch and gear position signals with the best fuel consumption on the global driving section are calculated.

[0059] It should be explained that the core idea of ​​the dynamic programming algorithm is "reverse optimization, forward solution", which breaks down the original multi-stage decision-making problem into multiple mutually coupled sub-problems, and obtains the optimal decision for each stage by recursively solving them in sequence, thereby obtaining the optimal solution sequence for the entire multi-stage decision-making problem. For example, the dynamic programming solution process is: the start and end states are known, first solve the optimal strategy from each state of the corresponding stage C to the terminal state D, and then solve the optimal strategy from each state of the stage B to the terminal state. The optimal strategy from the corresponding state to the terminal state in the next stage can be directly substituted into the previous step result, and then recursively layer by layer until the optimal strategy from the initial state A to the terminal state D is obtained.

[0060] Specifically, based on the predicted vehicle speed and the original vehicle speed, the optimal driving speed for the road section can be planned, and the optimal speed, torque and gear signal to achieve the speed can be calculated; based on the vehicle's optimal speed, torque and gear signal, the required throttle signal, brake signal, clutch and gear signal can be calculated.

[0061] For ease of understanding, an example is given. Dynamic programming is used to solve the engine torque, speed, gear and other signals required to reach the vehicle speed and vehicle position at time T40 from T39. The engine torque, speed and gear signals required to reach the engine speed at time T40 from T39 are the actual driving operation signals required: brake, accelerator, clutch and gear signals.

[0062] In one embodiment, based on the obtained brake, accelerator, and clutch gear signals, the engine state is unique, and based on the calibration data in the development process and the actual collected map (such as the exhaust temperature map, the corresponding exhaust temperature information can be found when the vehicle speed, speed, and torque are known), the fuel consumption of the engine at this time is determined.

[0063] In one embodiment, all driving steps T38-T39, T37-T38…T0-T1 are iteratively calculated, and the path output with the best fuel consumption on the road section is obtained by the idea of ​​reverse optimization through dynamic programming. It can be understood that based on the data (vehicle speed and road slope) obtained in the previous simulation, the vehicle speed and road slope are used as input to obtain the vehicle speed and road slope of T38-T39, and the speed, torque and gear signal output at the current moment are obtained through the dynamic programming algorithm. Then, based on the current speed, torque and gear, the brake, throttle, clutch and gear with the best fuel consumption on the driving section are calculated. The same is true for T37-T38…T0-T1.

[0064] It can be understood that in order to find the most economical driving method within a given distance, the fuel consumption of the current working condition is set to A (L / 100km). Among them, A changes with the operating conditions. The vehicle is assumed to be running between M and N. The distance is divided into multiple sections, each section is recorded as B1, B2,...Bn, etc. The fuel consumption of each distance section is recorded as A1, A2...An. The total consumption of MN is (A1B1+A2B2+…+AnBn), recorded as C. During driving, fuel consumption is coupled with driving operation and vehicle power system. During vehicle driving, each accelerator pedal position corresponds to an engine speed characteristic curve, and the engine speed and torque will change along the curve. The throttle, gear, clutch and brake actions will affect the engine speed and torque and their rate of change, thereby affecting fuel consumption. Through the driving demand model predicted by GRU for a period of time in the future, combined with the idea of ​​dynamic programming, the optimal path with the minimum fuel consumption between MN is obtained, so as to provide the most economical and reasonable driving advice at each moment, and obtain the most fuel-saving driving plan from M to N.

[0065] It is understandable that the idea of ​​multi-stage global optimization of dynamic programming is followed, the prediction characteristics of future working condition changes are introduced, and the control decision that makes the global energy consumption expectation optimal is found. The optimal fuel consumption control strategy on the global driving section is realized through the changes in future working conditions predicted by GRU and the dynamic programming algorithm, so as to achieve the purpose of saving fuel and reducing consumption. The method to achieve this goal is: the original road information (vehicle speed, road slope, etc.) is used as input to GRU for model prediction, and the information such as vehicle speed and road slope is output. The input is solved by the dynamic programming method, and the throttle, gear, brake, clutch and other signals are output.

[0066] Reference Figure 2 , Figure 2 FIG. 1 is a schematic diagram of a vehicle fuel saving device provided by the present invention, such as Figure 2 As shown, the device comprises:

[0067] Processing module 201: It is used to use the simulation calculation model GRU to normalize and iteratively process the actual road spectrum information obtained to predict the road spectrum information at a future moment.

[0068] The calculation module 202 is used to perform reverse optimization on the road spectrum information through dynamic planning to obtain a control signal with the best fuel consumption on the global driving section.

[0069] Furthermore, in a possible implementation manner, the calculation module is further used to plan the optimal driving speed by using dynamic programming solution according to the predicted vehicle speed in the road spectrum information and in combination with the original vehicle speed;

[0070] Calculating the optimal speed, torque and gear position of the vehicle to achieve the optimal driving speed;

[0071] According to the optimal speed, torque and gear position signal of the vehicle, the brake, accelerator, clutch and gear position signals with the best fuel consumption on the global driving section are calculated.

[0072] Furthermore, in a possible implementation manner, the processing module is further used to obtain actual road spectrum information, where the road spectrum information includes: vehicle speed and road slope;

[0073] The road spectrum is normalized by using the simulation calculation model GRU, and the normalized road spectrum information is iteratively calculated by using the GRU algorithm;

[0074] According to the number of iterations, the vehicle speed and road slope information at future moments are predicted.

[0075] Furthermore, in a possible implementation manner, the processing module is further configured to: Vout = 3.6vv limit2 / v limit1 -v limit2 , the vehicle speed is normalized, where vlimit1 is the first speed limit value set, v limit2 is the set second speed limit value, Vout∈[0,1];

[0076] According to the formula: Hout = 3.6hh limit2 / h limit1 -h limit2 , normalize the slope, where h limit1 is the set first slope limit value, h limit2 is the set second slope limit value, Hout∈[0,1].

[0077] Furthermore, in a possible implementation manner, the calculation module is further used to obtain driving data of the vehicle, wherein the driving data includes: vehicle speed, rotation speed and torque;

[0078] Searching a MAP table based on the driving data to obtain current vehicle fuel consumption;

[0079] Based on the current vehicle fuel consumption, calculate the minimum economic fuel consumption per unit distance.

[0080] Furthermore, in a possible implementation manner, the processing module is further used to verify the simulation calculation model GRU based on the historical driving data of the vehicle to determine whether the accuracy of the simulation calculation model meets the requirements;

[0081] The historical driving data includes: vehicle braking, gear position signal, engine intake temperature, supercharger speed, engine speed, engine torque, fuel consumption, manifold pressure, throttle opening, EGR opening, exhaust flow, driving distance, vehicle speed, ambient temperature, ambient pressure, altitude and road slope.

[0082] Refer to the following Figure 3 The electronic device 300 according to this embodiment of the present invention will be described. Figure 3 The electronic device 300 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0083] like Figure 3 As shown, the electronic device 300 is in the form of a general computing device. The components of the electronic device 300 may include but are not limited to: at least one processing unit 310, at least one storage unit 320, and a bus 330 connecting different system components (including the storage unit 320 and the processing unit 310).

[0084] The storage unit stores program codes, which can be executed by the processing unit 310, so that the processing unit 310 executes the steps according to various exemplary embodiments of the present invention described in the above “Embodiment Method” section of this specification.

[0085] The storage unit 320 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 321 and / or a cache memory unit 322 , and may further include a read-only memory unit (ROM) 323 .

[0086] The storage unit 320 may also include a program / utility 324 having a set (at least one) of program modules 325, such program modules 325 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0087] Bus 330 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0088] The electronic device 300 may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 300, and / or communicate with any device that enables the electronic device 300 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 350. Furthermore, the electronic device 300 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 360. As shown, the network adapter 360 communicates with other modules of the electronic device 300 via a bus 330. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 300, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0089] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0090] According to the solution of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible implementations, various aspects of the present invention can also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary implementations of the present invention described in the above "Exemplary Method" section of the present specification.

[0091] refer to Figure 4 As shown, a program product 400 for implementing the above method according to an embodiment of the present invention is described, which can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus or a device.

[0092] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0093] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0094] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0095] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0096] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0097] In summary, the present application provides a vehicle fuel saving method, device, equipment and storage medium, wherein the method comprises the steps of: using the simulation calculation model GRU to normalize and iteratively process the actual road spectrum information obtained to predict the road spectrum information at the future moment; performing dynamic planning reverse optimization on the road spectrum information to obtain the control signal with the best fuel consumption on the global driving section. The present application can optimize the global energy consumption of the vehicle, improve the fuel economy of the vehicle, and thus achieve the purpose of saving fuel and reducing consumption.

[0098] The above description is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest range consistent with the principles and novel features applied for herein.

[0099] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the process in the flowchart. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

Claims

1. A vehicle fuel saving method, characterized in that: include: The GRU simulation model is used to normalize and iteratively process the actual road spectrum information obtained to predict the road spectrum information at future moments. The road spectrum information is subjected to a dynamic programming reverse optimization solution to obtain a control signal with the best fuel consumption on the global driving section.

2. The method according to claim 1, characterized in that The method of performing reverse optimization on the road spectrum information through dynamic programming to obtain a control signal with the best fuel consumption on the global driving section includes: According to the predicted vehicle speed in the road spectrum information and in combination with the original vehicle speed, the optimal driving speed is planned by using dynamic programming; Calculating the optimal speed, torque and gear position of the vehicle to achieve the optimal driving speed; According to the optimal speed, torque and gear position signal of the vehicle, the brake, accelerator, clutch and gear position signals with the best fuel consumption on the global driving section are calculated.

3. The method according to claim 1, characterized in that The method of using the simulation calculation model GRU to normalize and iteratively process the acquired actual road spectrum information to predict the road spectrum information at a future moment includes: Acquire actual road profile information, the road profile information including: vehicle speed and road slope; The road spectrum is normalized by using the simulation calculation model GRU, and the normalized road spectrum information is iteratively calculated by using the GRU algorithm; According to the number of iterations, the vehicle speed and road slope information at future moments are predicted.

4. The method according to claim 3, characterized in that include: According to the formula: Vout = 3.6vv limit2 / v limit1 -v limit2 , the vehicle speed is normalized, where v limit1 is the first speed limit value set, v limit2 is the set second speed limit value, Vout∈[0,1]; According to the formula: Hout = 3.6hh limit2 / h limit1 -h limit2 , the slope is normalized, where h limit1 is the set first slope limit value, h limit2 is the set second slope limit value, Hout∈[0,1].

5. The method according to claim 1, characterized in that Also includes: Acquiring driving data of the vehicle, the driving data including: vehicle speed, rotation speed and torque; Searching a MAP table based on the driving data to obtain current vehicle fuel consumption; Based on the current vehicle fuel consumption, calculate the minimum economic fuel consumption per unit distance.

6. The method according to claim 1, characterized in that Also includes: Verify the simulation calculation model GRU based on the historical vehicle driving data to determine whether the accuracy of the simulation calculation model meets the requirements; The historical driving data includes: vehicle braking, gear position signal, engine intake temperature, supercharger speed, engine speed, engine torque, fuel consumption, manifold pressure, throttle opening, EGR opening, exhaust flow, driving distance, vehicle speed, ambient temperature, ambient pressure, altitude and road slope.

7. A vehicle fuel saving device, characterized in that: include: A processing module, which is used to normalize and iteratively process the acquired actual road spectrum information using the simulation calculation model GRU to predict the road spectrum information at a future moment; The calculation module is used to perform dynamic planning and reverse optimization on the road spectrum information to obtain a control signal with the best fuel consumption on the global driving section.

8. The device according to claim 7, characterized in that: The calculation module is further used to plan the optimal driving speed by using dynamic programming solution according to the predicted vehicle speed in the road spectrum information and in combination with the original vehicle speed; Calculating the optimal speed, torque and gear position of the vehicle to achieve the optimal driving speed; According to the optimal speed, torque and gear position signal of the vehicle, the brake, accelerator, clutch and gear position signals with the best fuel consumption on the global driving section are calculated.

9. An electronic device, characterized in that: The electronic device comprises: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium, characterized in that: The computer program instructions are stored therein, and when the computer program instructions are executed by a computer, the computer is caused to execute the method according to any one of claims 1 to 6.

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