Vehicle energy consumption optimization method and system

Abstract

The present invention provides a vehicle energy consumption optimization method and system, the method comprising: detecting the vehicle driving parameters corresponding to the vehicle and the battery operating parameters corresponding to the battery pack inside the vehicle in real time; creating an original timeline in real time, and mapping the vehicle driving parameters and the battery operating parameters one by one to the original timeline in the order of time on the original timeline to generate a corresponding target timeline; integrating the corresponding vehicle driving information in real time according to the target timeline, and fusing the preset genetic algorithm and the preset particle swarm algorithm through the vehicle driving information to generate a corresponding target detection algorithm in real time; calculating the battery energy consumption corresponding to the vehicle in real time according to the battery operating parameters through the target detection algorithm, and generating the corresponding optimization strategy in real time according to the vehicle driving parameters through the target detection algorithm, so as to optimize the battery energy consumption in real time through the optimization strategy. The present invention can effectively improve the user experience.

Description

Technical Field

[0001] The present invention relates to the field of automobile technology, and in particular to a vehicle energy consumption optimization method and system. Background Art

[0002] With the advancement of science and technology and the rapid development of productivity, cars have become popular in people's daily lives and have become one of the indispensable means of transportation for people's daily travel, greatly facilitating people's lives.

[0003] Among them, the production technology of new energy electric vehicles is becoming more and more mature, and has been recognized by people, and gradually popularized in people's daily lives. Specifically, the battery pack is one of the core components of new energy electric vehicles, which is used to provide electricity to new energy electric vehicles. Therefore, controlling the energy consumption of the battery pack can directly determine the cruising range of new energy electric vehicles.

[0004] Furthermore, in the process of real-time calculation of the energy consumption of new energy battery packs, most of the existing technologies will use the existing particle swarm algorithm to perform convergence processing on the energy consumption data generated by the battery pack in real time to calculate the corresponding energy consumption value. However, this calculation method can only process a single battery energy consumption data. When it is necessary to process the battery energy consumption data of multiple targets at the same time, the problem of insufficient convergence accuracy may occur, thereby correspondingly reducing the calculation accuracy of the battery energy consumption and correspondingly reducing the user experience. Summary of the invention

[0005] Based on this, the purpose of the present invention is to provide a vehicle energy consumption optimization method and system to solve the problem that when the battery energy consumption data of multiple targets are processed simultaneously in the prior art, insufficient convergence accuracy may occur, resulting in reduced battery energy consumption calculation accuracy.

[0006] The first aspect of the embodiment of the present invention proposes:

[0007] A vehicle energy consumption optimization method, wherein the method comprises:

[0008] When it is detected in real time that the vehicle starts to move, the vehicle driving parameters corresponding to the vehicle and the battery operating parameters corresponding to the battery pack inside the vehicle are detected in real time;

[0009] Creating an original timeline in real time, and mapping the vehicle driving parameters and the battery operating parameters one by one to the original timeline in chronological order on the original timeline to generate a corresponding target timeline;

[0010] Integrate the corresponding vehicle driving information in real time according to the target time axis, and fuse the preset genetic algorithm and the preset particle swarm algorithm through the vehicle driving information to generate the corresponding target detection algorithm in real time;

[0011] The target detection algorithm is used to calculate the battery energy consumption corresponding to the vehicle in real time according to the battery operating parameters, and the target detection algorithm is used to generate a corresponding optimization strategy in real time according to the vehicle driving parameters, so as to optimize the battery energy consumption in real time through the optimization strategy.

[0012] The beneficial effect of the present invention is that by respectively acquiring the vehicle driving parameters of the vehicle and the battery operating parameters of the battery pack, the working conditions of the vehicle and the battery pack can be respectively acquired. Based on this, in order to facilitate subsequent identification and processing, the required target timeline is further generated, and the genetic algorithm and the particle swarm algorithm are further fused through the target timeline to finally obtain the required target detection algorithm, and finally the battery energy consumption is optimized through the target detection algorithm, which correspondingly improves the user experience.

[0013] Furthermore, the step of fusing the preset genetic algorithm and the preset particle swarm algorithm through the vehicle driving information to generate a corresponding target detection algorithm in real time includes:

[0014] When the vehicle driving information is acquired in real time, a target vehicle type corresponding to the vehicle is detected in real time;

[0015] Determine in real time in a preset database the fusion coefficient corresponding to the preset genetic algorithm and the preset particle swarm algorithm according to the target vehicle type;

[0016] The target detection algorithm is generated according to the fusion coefficient and the vehicle driving information.

[0017] Furthermore, the step of generating the target detection algorithm according to the fusion coefficient and the vehicle driving information includes:

[0018] When the fusion coefficient is obtained in real time, a first weight corresponding to the preset genetic algorithm and a second weight corresponding to the preset particle swarm algorithm are matched in a preset weight table according to the fusion coefficient;

[0019] Splitting the vehicle driving information into a first data set and a second data set according to the first weight and the second weight;

[0020] The target detection algorithm is generated according to the first data set and the second data set.

[0021] Furthermore, the step of generating the target detection algorithm according to the first data set and the second data set includes:

[0022] Setting the first data set as the initialization population of the preset genetic algorithm, and creating a corresponding first detection network in real time based on the initialization population;

[0023] The second data set is set as the initialization particle swarm of the preset particle swarm algorithm, and a corresponding second detection network is created in real time based on the initialization particle swarm;

[0024] The first detection network and the second detection network are fused to generate the target detection algorithm accordingly.

[0025] Furthermore, the step of fusing the first detection network and the second detection network to generate the target detection algorithm includes:

[0026] When the first detection network and the second detection network are respectively acquired, calling out the fitness function adapted to the first detection network and the second detection network in real time;

[0027] The first detection network and the second detection network are alternately iterated through the first preset algorithm in the fitness function, and the first detection network and the second detection network are dynamically weighted adjusted through the second preset algorithm in the fitness function to correspondingly fuse into the target detection algorithm.

[0028] Furthermore, the expression of the first preset algorithm is:

[0029]

[0030] Among them, ω(t) represents the result of alternating iterative processing, ω max represents the initial value of iteration, ω min represents the final value of the iteration, T represents the maximum number of iterations, and t represents the current number of iterations.

[0031] Furthermore, the expression of the second preset algorithm is:

[0032]

[0033] in, represents the first adjustment result, represents the second adjustment result, Indicates the maximum value of the dynamic adjustment probability, Represents the minimum value of the dynamic adjustment probability, represents the maximum value of the mutation probability, represents the minimum value of the mutation probability, t represents the current number of iterations, and T represents the maximum number of iterations.

[0034] The second aspect of the embodiment of the present invention proposes:

[0035] A vehicle energy consumption optimization system, wherein the system comprises:

[0036] A detection module, for detecting in real time the vehicle driving parameters corresponding to the vehicle and the battery operating parameters corresponding to the battery pack inside the vehicle when it is detected in real time that the vehicle starts to drive;

[0037] A mapping module, used to create an original time axis in real time, and map the vehicle driving parameters and the battery operating parameters one by one to the original time axis in chronological order on the original time axis to generate a corresponding target time axis;

[0038] A fusion module, used to integrate the corresponding vehicle driving information in real time according to the target time axis, and to fuse the preset genetic algorithm and the preset particle swarm algorithm through the vehicle driving information to generate the corresponding target detection algorithm in real time;

[0039] The optimization module is used to calculate the battery energy consumption corresponding to the vehicle in real time according to the battery operating parameters through the target detection algorithm, and to generate a corresponding optimization strategy in real time according to the vehicle driving parameters through the target detection algorithm, so as to optimize the battery energy consumption in real time through the optimization strategy.

[0040] Furthermore, the fusion module is specifically used for:

[0041] When the vehicle driving information is acquired in real time, a target vehicle type corresponding to the vehicle is detected in real time;

[0042] Determine in real time in a preset database the fusion coefficient corresponding to the preset genetic algorithm and the preset particle swarm algorithm according to the target vehicle type;

[0043] The target detection algorithm is generated according to the fusion coefficient and the vehicle driving information.

[0044] Furthermore, the fusion module is specifically used for:

[0045] When the fusion coefficient is obtained in real time, a first weight corresponding to the preset genetic algorithm and a second weight corresponding to the preset particle swarm algorithm are matched in a preset weight table according to the fusion coefficient;

[0046] Splitting the vehicle driving information into a first data set and a second data set according to the first weight and the second weight;

[0047] The target detection algorithm is generated according to the first data set and the second data set.

[0048] Furthermore, the fusion module is specifically used for:

[0049] Setting the first data set as the initialization population of the preset genetic algorithm, and creating a corresponding first detection network in real time based on the initialization population;

[0050] The second data set is set as the initialization particle swarm of the preset particle swarm algorithm, and a corresponding second detection network is created in real time based on the initialization particle swarm;

[0051] The first detection network and the second detection network are fused to generate the target detection algorithm accordingly.

[0052] Furthermore, the fusion module is specifically used for:

[0053] When the first detection network and the second detection network are respectively acquired, calling out the fitness function adapted to the first detection network and the second detection network in real time;

[0054] The first detection network and the second detection network are alternately iterated through the first preset algorithm in the fitness function, and the first detection network and the second detection network are dynamically weighted adjusted through the second preset algorithm in the fitness function to correspondingly fuse into the target detection algorithm.

[0055] Furthermore, the expression of the first preset algorithm is:

[0056]

[0057] Among them, ω(t) represents the result of alternating iterative processing, ω max represents the initial value of iteration, ω min represents the final value of the iteration, T represents the maximum number of iterations, and t represents the current number of iterations.

[0058] Furthermore, the expression of the second preset algorithm is:

[0059]

[0060] in, represents the first adjustment result, represents the second adjustment result, Indicates the maximum value of the dynamic adjustment probability, Represents the minimum value of the dynamic adjustment probability, represents the maximum value of the mutation probability, represents the minimum value of the mutation probability, t represents the current number of iterations, and T represents the maximum number of iterations.

[0061] The third aspect of the embodiment of the present invention proposes:

[0062] A computer comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the vehicle energy consumption optimization method as described above is implemented when the processor executes the computer program.

[0063] The fourth aspect of the embodiments of the present invention proposes:

[0064] A readable storage medium stores a computer program, wherein the program, when executed by a processor, implements the vehicle energy consumption optimization method as described above.

[0065] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 A flow chart of a vehicle energy consumption optimization method provided by a first embodiment of the present invention;

[0067] Figure 2 This is a structural block diagram of a vehicle energy consumption optimization system provided in the third embodiment of the present invention.

[0068] The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0069] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0070] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0071] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0072] See also Figure 1 , which shows the vehicle energy consumption optimization method provided by the first embodiment of the present invention. The vehicle energy consumption optimization method provided by this embodiment can optimize the battery energy consumption through the target detection algorithm, thereby correspondingly improving the user experience.

[0073] Specifically, this embodiment provides:

[0074] A vehicle energy consumption optimization method comprises the following steps:

[0075] Step S10, when it is detected in real time that the vehicle starts to travel, vehicle travel parameters corresponding to the vehicle and battery operating parameters corresponding to the battery pack inside the vehicle are detected in real time;

[0076] Step S20, creating an original time axis in real time, and mapping the vehicle driving parameters and the battery operating parameters one by one to the original time axis in chronological order on the original time axis to generate a corresponding target time axis;

[0077] Step S30, integrating corresponding vehicle driving information in real time according to the target time axis, and fusing the preset genetic algorithm and the preset particle swarm algorithm through the vehicle driving information to generate a corresponding target detection algorithm in real time;

[0078] Step S40, using the target detection algorithm to calculate the battery energy consumption corresponding to the vehicle in real time according to the battery operating parameters, and using the target detection algorithm to generate a corresponding optimization strategy in real time according to the vehicle driving parameters, so as to optimize the battery energy consumption in real time through the optimization strategy.

[0079] Specifically, in this embodiment, it should be noted that, in order to objectively and accurately complete the optimization of battery energy consumption, it is necessary to objectively and accurately obtain the actual driving information of the vehicle and the actual working parameters of the battery pack, and further process the two data to complete the final optimization. Based on this, the vehicle controller set inside the vehicle can monitor in real time whether the vehicle is started, wherein, when the vehicle is detected to be started and starts to drive in real time, the vehicle energy consumption optimization method will be started accordingly. Specifically, the present invention will first detect the vehicle driving parameters corresponding to the current vehicle, and correspondingly, synchronously detect the battery working parameters of the battery pack inside the current vehicle. Based on this, in order to obtain the corresponding relationship between the two parameters, so as to facilitate subsequent processing, the present invention will further create an original time axis inside the above-mentioned vehicle controller, and correspondingly, the current vehicle driving parameters and the current battery working parameters are mapped one by one to the current original time axis in real time according to the order of time arrangement on the current original time axis, and the required target time axis can be further formed.

[0080] Furthermore, at this time, the vehicle driving information corresponding to the current vehicle can be immediately obtained in full according to the current target timeline. Based on this, it is only necessary to fuse the genetic algorithm and particle swarm algorithm pre-set in the above-mentioned vehicle controller in real time through the current vehicle driving information to further obtain the required target detection algorithm. Preferably, the target detection algorithm contains the required optimization algorithm and calculation method. Based on this, the present invention can further calculate the battery energy consumption corresponding to the current vehicle in real time through the target detection algorithm. At the same time, the corresponding optimization strategy is generated in real time through the target detection algorithm, and finally the optimization processing of the current battery energy consumption is completed through the optimization strategy, thereby effectively reducing the energy consumption of the battery pack and correspondingly improving the user experience.

[0081] Second embodiment

[0082] Furthermore, the step of fusing the preset genetic algorithm and the preset particle swarm algorithm through the vehicle driving information to generate a corresponding target detection algorithm in real time includes:

[0083] When the vehicle driving information is acquired in real time, a target vehicle type corresponding to the vehicle is detected in real time;

[0084] Determine in real time in a preset database the fusion coefficient corresponding to the preset genetic algorithm and the preset particle swarm algorithm according to the target vehicle type;

[0085] The target detection algorithm is generated according to the fusion coefficient and the vehicle driving information.

[0086] Furthermore, the step of generating the target detection algorithm according to the fusion coefficient and the vehicle driving information includes:

[0087] When the fusion coefficient is obtained in real time, a first weight corresponding to the preset genetic algorithm and a second weight corresponding to the preset particle swarm algorithm are matched in a preset weight table according to the fusion coefficient;

[0088] Splitting the vehicle driving information into a first data set and a second data set according to the first weight and the second weight;

[0089] The target detection algorithm is generated according to the first data set and the second data set.

[0090] Furthermore, the step of generating the target detection algorithm according to the first data set and the second data set includes:

[0091] Setting the first data set as the initialization population of the preset genetic algorithm, and creating a corresponding first detection network in real time based on the initialization population;

[0092] The second data set is set as the initialization particle swarm of the preset particle swarm algorithm, and a corresponding second detection network is created in real time based on the initialization particle swarm;

[0093] The first detection network and the second detection network are fused to generate the target detection algorithm accordingly.

[0094] Furthermore, the step of fusing the first detection network and the second detection network to generate the target detection algorithm includes:

[0095] When the first detection network and the second detection network are respectively acquired, calling out the fitness function adapted to the first detection network and the second detection network in real time;

[0096] The first detection network and the second detection network are alternately iterated through the first preset algorithm in the fitness function, and the first detection network and the second detection network are dynamically weighted adjusted through the second preset algorithm in the fitness function to correspondingly fuse into the target detection algorithm.

[0097] Furthermore, the expression of the first preset algorithm is:

[0098]

[0099] Among them, ω(t) represents the result of alternating iterative processing, ω maxrepresents the initial value of iteration, ω min represents the final value of the iteration, T represents the maximum number of iterations, and t represents the current number of iterations.

[0100] Furthermore, the expression of the second preset algorithm is:

[0101]

[0102] in, represents the first adjustment result, represents the second adjustment result, Indicates the maximum value of the dynamic adjustment probability, Represents the minimum value of the dynamic adjustment probability, represents the maximum value of the mutation probability, represents the minimum value of the mutation probability, t represents the current number of iterations, and T represents the maximum number of iterations.

[0103] In addition, in this embodiment, it is also necessary to explain that after the required vehicle driving information and the corresponding genetic algorithm and particle swarm algorithm are obtained through the above steps, in order to objectively and effectively complete the fusion processing of the current two algorithms, it is preferably necessary to first determine the connection between the current two algorithms. Based on this, it is necessary to first detect the target vehicle model corresponding to the current vehicle, and further determine the fusion coefficient adapted to the above two algorithms in real time in the preset database according to the current target vehicle model. Based on this, based on the current fusion coefficient, the first weight corresponding to the above preset genetic algorithm and the second weight corresponding to the above preset particle swarm algorithm are further matched in the preset weight table, wherein the sum of the first weight and the second weight is 1. Based on this, the above vehicle driving information can be further split into the required first data set and second data set.

[0104] Furthermore, in the actual fusion process, preferably, the present invention will directly set the content contained in the current first data set as the initialization population of the above-mentioned preset genetic algorithm, and correspondingly, further train the required first detection network according to the current initialization population. Similarly, the present invention will directly set the content contained in the current second data set as the initialization particle group of the above-mentioned preset particle swarm algorithm, and correspondingly, further train the required second detection network according to the current initialization particle group. Based on this, the first preset algorithm and the second preset algorithm respectively contained in the above-mentioned target detection algorithm are called out. On this basis, the alternating iteration and dynamic weight adjustment processing between the above-mentioned first detection network and the above-mentioned second detection network can be finally completed respectively, and the required target detection algorithm can be finally generated to further complete the subsequent optimization processing, thereby correspondingly improving the user experience.

[0105] See also Figure 2 , the third embodiment of the present invention provides:

[0106] A vehicle energy consumption optimization system, wherein the system comprises:

[0107] A detection module, for detecting in real time the vehicle driving parameters corresponding to the vehicle and the battery operating parameters corresponding to the battery pack inside the vehicle when it is detected in real time that the vehicle starts to drive;

[0108] A mapping module, used to create an original time axis in real time, and map the vehicle driving parameters and the battery operating parameters one by one to the original time axis in chronological order on the original time axis to generate a corresponding target time axis;

[0109] A fusion module, used to integrate the corresponding vehicle driving information in real time according to the target time axis, and to fuse the preset genetic algorithm and the preset particle swarm algorithm through the vehicle driving information to generate the corresponding target detection algorithm in real time;

[0110] The optimization module is used to calculate the battery energy consumption corresponding to the vehicle in real time according to the battery operating parameters through the target detection algorithm, and to generate a corresponding optimization strategy in real time according to the vehicle driving parameters through the target detection algorithm, so as to optimize the battery energy consumption in real time through the optimization strategy.

[0111] Furthermore, the fusion module is specifically used for:

[0112] When the vehicle driving information is acquired in real time, a target vehicle type corresponding to the vehicle is detected in real time;

[0113] Determine in real time in a preset database the fusion coefficient corresponding to the preset genetic algorithm and the preset particle swarm algorithm according to the target vehicle type;

[0114] The target detection algorithm is generated according to the fusion coefficient and the vehicle driving information.

[0115] Furthermore, the fusion module is specifically used for:

[0116] When the fusion coefficient is obtained in real time, a first weight corresponding to the preset genetic algorithm and a second weight corresponding to the preset particle swarm algorithm are matched in a preset weight table according to the fusion coefficient;

[0117] Splitting the vehicle driving information into a first data set and a second data set according to the first weight and the second weight;

[0118] The target detection algorithm is generated according to the first data set and the second data set.

[0119] Furthermore, the fusion module is specifically used for:

[0120] Setting the first data set as the initialization population of the preset genetic algorithm, and creating a corresponding first detection network in real time based on the initialization population;

[0121] The second data set is set as the initialization particle swarm of the preset particle swarm algorithm, and a corresponding second detection network is created in real time based on the initialization particle swarm;

[0122] The first detection network and the second detection network are fused to generate the target detection algorithm accordingly.

[0123] Furthermore, the fusion module is specifically used for:

[0124] When the first detection network and the second detection network are respectively acquired, calling out the fitness function adapted to the first detection network and the second detection network in real time;

[0125] The first detection network and the second detection network are alternately iterated through the first preset algorithm in the fitness function, and the first detection network and the second detection network are dynamically weighted adjusted through the second preset algorithm in the fitness function to correspondingly fuse into the target detection algorithm.

[0126] Furthermore, the expression of the first preset algorithm is:

[0127]

[0128] Among them, ω(t) represents the result of alternating iterative processing, ω max represents the initial value of iteration, ω min represents the final value of the iteration, T represents the maximum number of iterations, and t represents the current number of iterations.

[0129] Furthermore, the expression of the second preset algorithm is:

[0130]

[0131] in, represents the first adjustment result, represents the second adjustment result, Indicates the maximum value of the dynamic adjustment probability, Represents the minimum value of the dynamic adjustment probability, represents the maximum value of the mutation probability, represents the minimum value of the mutation probability, t represents the current number of iterations, and T represents the maximum number of iterations.

[0132] A fourth embodiment of the present invention provides a computer, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the vehicle energy consumption optimization method as described above when executing the computer program.

[0133] A fifth embodiment of the present invention provides a readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the vehicle energy consumption optimization method as described above.

[0134] In summary, the vehicle energy consumption optimization method and system provided in the above embodiments of the present invention can optimize battery energy consumption through a target detection algorithm, thereby correspondingly improving the user experience.

[0135] It should be noted that the above modules can be functional modules or program modules, and can be implemented by software or hardware. For modules implemented by hardware, the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0136] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0137] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0138] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0139] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0140] The above-described embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the attached claims.

Claims

1. A vehicle energy consumption optimization method, characterized in that: The method comprises: When it is detected in real time that the vehicle starts to move, the vehicle driving parameters corresponding to the vehicle and the battery operating parameters corresponding to the battery pack inside the vehicle are detected in real time; Creating an original timeline in real time, and mapping the vehicle driving parameters and the battery operating parameters one by one to the original timeline in chronological order on the original timeline to generate a corresponding target timeline; Integrate the corresponding vehicle driving information in real time according to the target time axis, and fuse the preset genetic algorithm and the preset particle swarm algorithm through the vehicle driving information to generate the corresponding target detection algorithm in real time; The target detection algorithm is used to calculate the battery energy consumption corresponding to the vehicle in real time according to the battery operating parameters, and the target detection algorithm is used to generate a corresponding optimization strategy in real time according to the vehicle driving parameters, so as to optimize the battery energy consumption in real time according to the optimization strategy; The step of fusing the preset genetic algorithm and the preset particle swarm algorithm through the vehicle driving information to generate a corresponding target detection algorithm in real time includes: When the vehicle driving information is acquired in real time, a target vehicle type corresponding to the vehicle is detected in real time; Determine in real time in a preset database the fusion coefficient corresponding to the preset genetic algorithm and the preset particle swarm algorithm according to the target vehicle type; Generate the target detection algorithm according to the fusion coefficient and the vehicle driving information; The step of generating the target detection algorithm according to the fusion coefficient and the vehicle driving information includes: When the fusion coefficient is obtained in real time, a first weight corresponding to the preset genetic algorithm and a second weight corresponding to the preset particle swarm algorithm are matched in a preset weight table according to the fusion coefficient; Splitting the vehicle driving information into a first data set and a second data set according to the first weight and the second weight; Generate the target detection algorithm according to the first data set and the second data set; The step of generating the target detection algorithm according to the first data set and the second data set includes: Setting the first data set as the initialization population of the preset genetic algorithm, and creating a corresponding first detection network in real time based on the initialization population; The second data set is set as the initialization particle swarm of the preset particle swarm algorithm, and a corresponding second detection network is created in real time based on the initialization particle swarm; Performing a fusion process on the first detection network and the second detection network to generate the target detection algorithm accordingly; The step of fusing the first detection network and the second detection network to generate the target detection algorithm accordingly includes: When the first detection network and the second detection network are respectively acquired, calling out the fitness function adapted to the first detection network and the second detection network in real time; Performing alternating iterative processing on the first detection network and the second detection network through a first preset algorithm in the fitness function, and performing dynamic weight adjustment processing on the first detection network and the second detection network through a second preset algorithm in the fitness function, so as to correspondingly fuse them into the target detection algorithm; The expression of the first preset algorithm is: Among them, ω(t) represents the result of alternating iterative processing, ω max represents the initial value of iteration, ω min represents the final value of the iteration, T represents the maximum number of iterations, and t represents the current number of iterations.

2. The vehicle energy consumption optimization method according to claim 1, characterized in that: The expression of the second preset algorithm is: in, represents the first adjustment result, represents the second adjustment result, Indicates the maximum value of the dynamic adjustment probability, represents the minimum value of the dynamic adjustment probability, represents the maximum value of the mutation probability, represents the minimum value of the mutation probability, t represents the current number of iterations, and T represents the maximum number of iterations.

3. A vehicle energy consumption optimization system, characterized in that: For implementing the vehicle energy consumption optimization method according to any one of claims 1 to 2, the system comprises: A detection module, for detecting in real time the vehicle driving parameters corresponding to the vehicle and the battery operating parameters corresponding to the battery pack inside the vehicle when it is detected in real time that the vehicle starts to drive; A mapping module, used to create an original time axis in real time, and map the vehicle driving parameters and the battery operating parameters one by one to the original time axis in chronological order on the original time axis to generate a corresponding target time axis; A fusion module, used to integrate the corresponding vehicle driving information in real time according to the target time axis, and to fuse the preset genetic algorithm and the preset particle swarm algorithm through the vehicle driving information to generate the corresponding target detection algorithm in real time; The optimization module is used to calculate the battery energy consumption corresponding to the vehicle in real time according to the battery operating parameters through the target detection algorithm, and to generate a corresponding optimization strategy in real time according to the vehicle driving parameters through the target detection algorithm, so as to optimize the battery energy consumption in real time through the optimization strategy.

4. A computer 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, the vehicle energy consumption optimization method as described in any one of claims 1 to 2 is implemented.

5. A readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the vehicle energy consumption optimization method as described in any one of claims 1 to 2 is implemented.

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