Method for updating vehicle gear shifting scheme, vehicle, device, storage medium and product

Abstract

The application discloses a kind of vehicle gear shift scheme updating method, vehicle, equipment, storage medium and product, belong to vehicle technical field.The method comprises: obtaining the historical data of vehicle driving in multiple fixed routes, and identifying the working condition corresponding to each route area in multiple fixed routes;Based on the working condition corresponding to each route area, generate target vehicle speed variation data corresponding to historical data;Using target vehicle speed variation data and vehicle parameters, update the gear shift scheme matched with each fixed route respectively, so that the vehicle switches to the matched gear shift scheme when driving according to different fixed routes.The application optimizes the driving economy of vehicle driving in each fixed route.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of vehicles, and particularly relates to a vehicle gear shifting scheme updating method, a vehicle, a device, a storage medium and a product. BACKGROUND

[0002] The gear shifting scheme of a vehicle has a significant influence on the driving economy or power performance of the vehicle, and for a vehicle with a fixed route, such as a public transport or logistics vehicle, since the driving route is relatively stable and predictable, adjusting the gear shifting scheme for the fixed route can improve the vehicle power or driving economy problems caused by the general scheme not being suitable for the route. SUMMARY

[0003] The present application aims to at least solve one of the technical problems in the related art. To this end, the present application proposes a vehicle gear shifting scheme updating method, a vehicle, a device, a storage medium and a product, which updates the gear shifting scheme matched with the fixed route to be targeted, so that the vehicle switches to the matched gear shifting scheme when driving along different fixed routes, thereby improving the adaptability of the vehicle to each fixed route and optimizing the driving economy of the vehicle driving along each fixed route.

[0004] In a first aspect, the present application provides a vehicle gear shifting scheme updating method, which comprises:

[0005] obtaining historical data of a vehicle driving along a plurality of fixed routes, and identifying the working conditions corresponding to each route area in the plurality of fixed routes;

[0006] generating target vehicle speed variation data corresponding to the historical data based on the working conditions corresponding to each route area;

[0007] updating the gear shifting scheme matched with each fixed route respectively using the target vehicle speed variation data and vehicle parameters, so that the vehicle switches to the matched gear shifting scheme when driving along different fixed routes.

[0008] In the above technical solution, the historical data of the vehicle driving along a plurality of fixed routes is obtained to identify the working conditions of each route area of the fixed route, and then the target vehicle speed variation data corresponding to the historical data is generated based on the working conditions of each route area. The target vehicle speed variation data can reflect the historical driving state of the vehicle on the fixed route, and the gear shifting scheme matched with the fixed route to be targeted is updated in combination with the vehicle parameters which can reflect the vehicle characteristic data, so as to solve the gear shifting scheme matched with the fixed route according to the historical data of the vehicle. After the gear shifting scheme matched with the fixed route is updated, the vehicle can shift gears according to the corresponding gear shifting scheme on the fixed route, which can reduce unnecessary energy loss, improve the adaptability of the vehicle to each fixed route, and optimize the driving economy of the vehicle driving along each fixed route.

[0009] According to some embodiments of the present application, the updating of the shift schedule matched with each fixed route respectively based on the target vehicle speed variation data and the vehicle parameters comprises:

[0010] For any fixed curve, a target vehicle speed variation data corresponding to historical data of the fixed route is taken as a working condition input, and the vehicle parameters are taken as model parameters to establish an energy consumption simulation model;

[0011] A default shift schedule executed by the vehicle on the fixed route is obtained, and a plurality of candidate shift schedules meeting the vehicle power performance demand constraints are generated;

[0012] Based on the default shift schedule and the plurality of candidate shift schedules, a target shift schedule is obtained by searching and updating the energy consumption simulation model;

[0013] The shift schedule matched with the fixed route is updated based on the target shift schedule.

[0014] In the above technical solution, the target vehicle speed variation data is taken as a working condition input, the vehicle parameters are combined to construct an energy consumption simulation model, and based on the default shift schedule, a plurality of candidate shift schedules meeting the vehicle power performance demand constraints are generated. The default shift schedule and the candidate shift schedules are sequentially input into the energy consumption simulation model, the vehicle energy consumption under each scheme is calculated, and the target shift schedule is obtained by searching and updating, which realizes the updating of the shift schedule matched with the fixed route. The updated shift schedule matched with the fixed route meets the vehicle power performance demand constraints, so that the vehicle can shift gears according to the corresponding shift schedule on the fixed route, and the driving economy of the vehicle on each fixed route can be optimized under the premise of meeting the vehicle power performance demand.

[0015] In a second aspect, the present application provides a vehicle shift schedule updating method, which comprises:

[0016] Before the vehicle drives along a fixed route, the current latitude and longitude information of the vehicle is detected;

[0017] According to the current latitude and longitude information, a matched shift schedule is searched in a shift schedule index table, and the matched shift schedule is switched to for performing corresponding shift control;

[0018] The shift schedule index table assigns corresponding index values to different fixed routes.

[0019] In the technical solution, before the vehicle travels along the fixed route, the current latitude and longitude information of the vehicle is detected, a matched gear shifting scheme is searched in a gear shifting scheme index table according to the current latitude and longitude information, and the matched gear shifting scheme is switched to, the gear shifting scheme index table contains a plurality of index values, each index value corresponds to a fixed route and a matched gear shifting scheme thereof, and based on the gear shifting scheme index table, the vehicle controller can automatically switch to the matched gear shifting scheme according to the current latitude and longitude information of the vehicle, to perform corresponding gear shifting control, so that the vehicle shifts gears according to the corresponding gear shifting scheme on the fixed route, thereby reducing unnecessary energy loss, improving the adaptability of the vehicle to the fixed route, and optimizing the energy consumption performance of the vehicle when traveling on the fixed route.

[0020] In a third aspect, the present application provides a vehicle gear shifting scheme updating device, which comprises:

[0021] a data processing unit configured to acquire historical data of the vehicle traveling on a plurality of fixed routes, and identify working conditions corresponding to each route area in the plurality of fixed routes;

[0022] a vehicle speed data generation unit configured to generate target vehicle speed change data corresponding to the historical data based on the working conditions corresponding to each route area;

[0023] a scheme updating unit configured to update gear shifting schemes matched with the plurality of fixed routes respectively by using the target vehicle speed change data and vehicle parameters, so that the vehicle switches to the matched gear shifting scheme when traveling along different fixed routes.

[0024] In a fourth aspect, the present application provides a vehicle gear shifting scheme updating device, which comprises:

[0025] a detection unit configured to detect current latitude and longitude information of the vehicle before the vehicle travels along a fixed route;

[0026] a switching and executing unit configured to search a matched gear shifting scheme in a gear shifting scheme index table according to the current latitude and longitude information, and switch to the matched gear shifting scheme to perform corresponding gear shifting control; the gear shifting scheme index table assigns corresponding index values to different fixed routes.

[0027] In a fifth aspect, the present application provides a vehicle comprising a controller configured to perform the vehicle gear shifting scheme updating method according to the second aspect.

[0028] In a sixth aspect, the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the vehicle shift scheme updating method according to the first aspect or the vehicle shift scheme updating method according to the second aspect.

[0029] In a seventh aspect, the present application provides a non-transitory computer readable storage medium, having a computer program stored thereon, wherein the computer program is executable by a processor to implement the vehicle shift scheme updating method according to the first aspect or the vehicle shift scheme updating method according to the second aspect.

[0030] In an eighth aspect, the present application provides a chip, comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is configured to execute a program or an instruction to implement the vehicle shift scheme updating method according to the first aspect or the vehicle shift scheme updating method according to the second aspect.

[0031] In a ninth aspect, the present application provides a computer program product, comprising a computer program, wherein the computer program is executable by a processor to implement the vehicle shift scheme updating method according to the first aspect or the vehicle shift scheme updating method according to the second aspect.

[0032] Additional aspects and advantages of the present application will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF DRAWINGS

[0033] The above and / or additional aspects and advantages of the present application will become apparent and be readily appreciated from the following description, including the appended drawings.

[0034] Figure 1 Fig. 1 is one of flow diagrams of the vehicle shift scheme updating method according to some embodiments of the present application;

[0035] Figure 2 Fig. 2 is a schematic diagram of updating the shift scheme according to some embodiments of the present application;

[0036] Figure 3 Fig. 3 is another of flow diagrams of the vehicle shift scheme updating method according to some embodiments of the present application;

[0037] Figure 4 Fig. 4 is still another of flow diagrams of the vehicle shift scheme updating method according to some embodiments of the present application;

[0038] Figure 5is a flowchart of an improved particle swarm algorithm provided by some embodiments of the present application;

[0039] Figure 6 is a flowchart of a fourth vehicle gear shifting scheme updating method provided by some embodiments of the present application;

[0040] Figure 7 is a schematic diagram of switching gear shifting schemes;

[0041] Figure 8 is a structural schematic diagram of a vehicle gear shifting scheme updating device provided by some embodiments of the present application;

[0042] Figure 9 is a structural schematic diagram of a vehicle gear shifting scheme updating device provided by some embodiments of the present application;

[0043] Figure 10 is a structural schematic diagram of an electronic device provided by some embodiments of the present application. DETAILED DESCRIPTION

[0044] The technical solutions in the embodiments of the present application will be clearly described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all of them. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art belong to the scope of protection of the present application.

[0045] The terms "first", "second", and the like in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are usually a class, and are not limited to the number of objects, for example, the first object can be one or more. In addition, "and / or" in the specification and claims indicates at least one of the connected objects, and the character " / ", generally indicates that the front and rear associated objects are in an "or" relationship.

[0046] The vehicle gear shifting scheme updating method, vehicle, device, storage medium and product provided by the embodiments of the present application will be described in detail below with reference to the drawings, through specific embodiments and their application scenarios.

[0047] The vehicle gear shifting scheme updating method provided in the embodiments of the present application can be executed by an electronic device or a functional module or functional entity in the electronic device capable of implementing the vehicle gear shifting scheme updating method. The electronic device mentioned in the embodiments of the present application includes but is not limited to a vehicle control unit (VCU), a vehicle electronic control unit (ECU), a cloud server, a distributed server, a remote vehicle control server, and the like. The vehicle gear shifting scheme updating method provided in the embodiments of the present application is described below by taking an electronic device as an execution subject.

[0048] Figure 1 FIG. 1 is one of flow diagrams of the vehicle gear shifting scheme updating method provided in some embodiments of the present application. As shown in FIG. 1, the method includes steps 110, 120, and 130. Figure 1

[0049] In step 110, historical data of the vehicle driving on a plurality of fixed routes is acquired, and working conditions corresponding to each passing area in the plurality of fixed routes are identified.

[0050] The fixed route can be considered as a specific route with a high driving frequency of the vehicle, for example, an operating route of a public transport vehicle or a freight route of a freight vehicle. The driving process of the vehicle on the fixed route usually shows certain regularity or repeatability.

[0051] The passing area refers to a segment divided from the fixed route, and each segment can be referred to as a passing area. The actual geographical features or driving conditions corresponding to each passing area can be different, for example, the geographical features corresponding to different passing areas of the same fixed route can be urban roads, expressways, bridges, or tunnels, and the like. In some embodiments, the passing areas in the fixed route are divided based on geographical positions, for example, can be divided based on longitude and latitude. When the vehicle drives to a certain longitude and latitude according to the fixed route, it is considered that the vehicle enters another passing area from one passing area. The historical data of the vehicle driving on a plurality of fixed routes refers to the historical driving data collected by the vehicle during driving on a plurality of fixed routes. Specifically, the historical data can include but is not limited to the vehicle speed, acceleration, accelerator pedal depth, braking frequency, and longitude and latitude of the vehicle during driving on each fixed route, which provides a data basis for determining the trajectory features of the vehicle in each passing area based on the historical data.

[0052] ​In the embodiments of the present application, the electronic device can obtain the historical data of the vehicle driving on the plurality of fixed routes from a vehicle electronic control unit (ECU) or an on-board diagnostics (OBD) device, and can further preprocess the historical data to remove outliers and noise. According to the historical data of the vehicle driving on the plurality of fixed routes, the specific way of identifying the working conditions corresponding to each passing area in the plurality of fixed routes can be various, for example, in some embodiments, a series of rules and thresholds based on driving data such as vehicle speed, acceleration, and throttle opening are defined, and different fixed routes and their passing areas are divided into different working condition types according to the parameter changes in the vehicle driving data; for example, in some embodiments, the historical data is divided into a plurality of data segments, the characteristic parameters of each data segment are calculated, and a clustering algorithm is used to cluster and analyze the extracted characteristic parameters, thereby labeling each data segment with the corresponding working condition type.

[0053] In the embodiments of the present application, the working conditions corresponding to each passing area can be urban road working conditions, highway working conditions, congestion working conditions, smooth working conditions, etc. In some embodiments, the vehicle speed-time curve of the vehicle driving in the passing area can also be used to represent the working condition of the passing area. The present application does not limit the specific working condition type, which can be set in advance according to actual needs or application scenarios.

[0054] Step 120, based on the working conditions corresponding to each passing area, generating target vehicle speed change data corresponding to the historical data.

[0055] It can be understood that the vehicle speed change data is data about vehicle speed (vehicle speed) and time, for example, it can be represented as a function curve about vehicle speed and time (i.e. a curve of vehicle speed changing with time during driving), or a record table about vehicle speed, etc., which can reflect the driving state of the vehicle, such as acceleration, deceleration, or constant speed driving, etc.

[0056] The target vehicle speed change data corresponding to the historical data refers to the vehicle speed change data generated based on the historical data of the vehicle driving on the fixed route. The target vehicle speed change data reflects the relatively typical driving state of the vehicle on the corresponding fixed route, and the driving state of the vehicle is related to the working conditions corresponding to each passing area. It can also be considered that the target vehicle speed change data reflects the working condition types of different passing areas of the fixed route.

[0057] In the embodiments of the present application, after identifying the working conditions of each route area in the plurality of fixed routes based on the historical data, the electronic device generates target vehicle speed change data corresponding to the historical data based on the working conditions of each route area. For example, key vehicle speed data points can be extracted from the historical data, and continuous vehicle speed change data can be generated using an interpolation algorithm. In some embodiments, the typical vehicle speed patterns of the vehicle under different working conditions can also be identified by analyzing the historical data, and different typical vehicle speed pattern segments can be spliced to generate target vehicle speed change data consistent with the historical data in combination with the working condition information of each route area. The generated target vehicle speed change data corresponding to the historical data reflects the historical driving state of the vehicle on the fixed route.

[0058] In step 130, the target vehicle speed change data and vehicle parameters are used to update the shift schedule matched with each fixed route respectively, so that the vehicle switches to the matched shift schedule when driving along different fixed routes.

[0059] It can be understood that the shift schedule is used to guide the vehicle (e.g., the gearshift mechanism of the vehicle, such as a transmission) to perform reasonable shifting operations under different working conditions or vehicle speeds. In some embodiments, the shift schedule defines the vehicle speed at which the vehicle should shift up or down in each gear under different accelerator pedal depths. In some embodiments, the shift schedule defines the upshift speed, the downshift speed, and the shift logic, wherein the upshift speed refers to the vehicle speed threshold at which the vehicle shifts from the current gear to a higher gear under different accelerator pedal depths; the downshift speed refers to the vehicle speed threshold at which the vehicle shifts from the current gear to a lower gear under different accelerator pedal depths; and the shift logic refers to the logic for determining the upshift or downshift based on the driving state of the vehicle (e.g., vehicle speed, acceleration, accelerator pedal depth, etc.), energy consumption demand, and other data.

[0060] The target vehicle speed change data is generated based on the historical data of the vehicle on the fixed route, and the historical data includes the actual driving data of the vehicle in different route areas and at different times, including vehicle speed, acceleration, driving time, etc. Therefore, the target vehicle speed change data can truly reflect the driving state of the vehicle on the fixed route. In addition, it can be considered that the target vehicle speed change data implicitly includes the working condition information of each route area. For example, in an urban road working condition, the vehicle speed change data will show frequent acceleration and deceleration and a low average vehicle speed; in a highway working condition, the vehicle speed change data will show relatively stable high-speed driving and less acceleration and deceleration changes.

[0061] The vehicle parameters refer to various characteristic data that can reflect the driving or power performance of the vehicle, including but not limited to vehicle mass, power and torque characteristics of the motor, transmission ratio of the transmission, rolling resistance coefficient of the tire, and air resistance coefficient. The vehicle parameters affect the power output and energy consumption performance of the vehicle at different vehicle speeds.

[0062] In the embodiments of the present application, the target vehicle speed change data and the vehicle parameters are used to update the shift schedule matched with the fixed route, which can be achieved by optimizing the original shift schedule or replacing the original shift schedule with a new shift schedule. For example, the electronic device can redefine the shift timing of different speed intervals in the shift schedule according to the target vehicle speed change data and the characteristics of the vehicle power system, and adjust the shift schedule to enable the vehicle to run at a more optimal gear in each phase such as acceleration, constant speed or deceleration by analyzing the driving state of the vehicle in different road sections in the target vehicle speed change data. In addition, the required driving force at different speeds is calculated in combination with the vehicle parameters (such as vehicle mass, click torque, etc.), and the shift point is optimized to reduce unnecessary energy consumption.

[0063] In some embodiments, the electronic device converts the updated shift schedule combined with the relevant information of the matched fixed route into a data format (such as JavaScript Object Notation (JSON) format or eXtensible Markup Language (XML) format) recognizable by the vehicle. For example, if the update method of the vehicle shift schedule is executed on the server, the shift schedule data can be sent to the control unit of the vehicle (such as Vehicle Control Unit (VCU) or Electronic Control Unit (ECU)) through the communication connection between the vehicle and the server.

[0064] The gear switching performed by the vehicle according to the shift schedule affects the operating efficiency and energy consumption of the motor of the vehicle. A reasonable shift schedule can adjust the motor speed and torque through gear shifting, so that the motor runs more in the high-efficiency interval, thereby making the motor have better energy consumption performance. For example, the vehicle switches to the matched shift schedule when driving according to different fixed routes. In the congestion working condition, the shift schedule should guide the vehicle to reduce unnecessary gear shifting to adapt to the characteristics of frequent acceleration and deceleration of the vehicle in the urban congestion working condition, thereby reducing the energy loss of the motor. For another example, in the high-speed working condition, the shift schedule should be used to guide the vehicle to maintain a suitable high gear or shift up in time to reduce the motor speed, thereby reducing the energy loss.

[0065] In the embodiments of the present application, the target vehicle speed change data is combined with the vehicle parameters to update the shift schedule matched with the fixed route, which can be considered as updating the shift schedule by combining the historical driving state of the vehicle on the fixed route and the characteristics of the current vehicle. The updated shift schedule should be able to adapt to the working conditions of the current fixed route (and the areas of each route of the fixed route). In some embodiments, the server solves the shift schedule matched with the fixed route according to the historical data of the vehicle in a simulation environment, and the shift schedule is updated and sent to the vehicle, without the need for real-time route analysis on the vehicle side, avoiding the high computing power requirement of the vehicle and improving the compatibility of the vehicle shift schedule update. In some embodiments, the electronic control unit or other devices of the vehicle can also update the shift schedule matched with each fixed route.

[0066] After updating the shift schedule matched with each fixed route, the vehicle switches to the shift schedule matched with the fixed route when driving according to different fixed routes, so that the vehicle can make reasonable shift according to the shift schedule under the current working condition, reducing unnecessary energy loss, improving the adaptability of the vehicle to multiple fixed routes, and optimizing the energy consumption performance of the vehicle driving on multiple fixed routes.

[0067] In addition, since the shift schedule matched with each fixed route is updated based on the historical data of the vehicle driving on multiple fixed routes and the vehicle parameters in the embodiments of the present application, and the vehicle switches to the matched shift schedule when driving according to different fixed routes, the update of the vehicle shift schedule is also achieved to a certain extent without relying on high-precision maps (without maps).

[0068] The vehicle shift schedule updating method provided by the embodiments of the present application acquires the historical data of the vehicle driving on multiple fixed routes to identify the working conditions of each route area of the fixed route, and then generates target vehicle speed change data corresponding to the historical data based on the working conditions of each route area. The target vehicle speed change data can reflect the historical driving state of the vehicle on the fixed route, and the shift schedule matched with the fixed route is updated by combining the vehicle parameters reflecting the characteristics of the vehicle. The shift schedule matched with the fixed route is updated, and the vehicle can shift according to the corresponding shift schedule on the fixed route, which can reduce unnecessary energy loss, improve the adaptability of the vehicle to each fixed route, and optimize the driving economy of the vehicle driving on each fixed route.

[0069] In some embodiments of the present application, the target vehicle speed change data corresponding to the historical data is generated based on the working conditions of each route area, including:

[0070] For any fixed route, obtain statistical parameters of the working conditions corresponding to each passage area in the fixed route;

[0071] Based on the statistical parameters of the working conditions corresponding to each passage area, determine a working condition transition state matrix and a working condition-vehicle speed confusion matrix matched with the fixed route;

[0072] Based on the working condition transition state matrix and the working condition-vehicle speed confusion matrix, generate target vehicle speed change data corresponding to the historical data corresponding to the fixed route.

[0073] The statistical parameters of the working conditions corresponding to each passage area refer to various data reflecting the characteristics of the working conditions corresponding to each passage area obtained by analyzing the historical data of the vehicle in each passage area, including but not limited to: the number of occurrences of each working condition, the frequency of occurrence of each working condition, the duration of each working condition, the statistical characteristics of the vehicle speed under each working condition (such as average vehicle speed, vehicle speed standard deviation, maximum vehicle speed, minimum vehicle speed, etc.), and the number of occurrences of different vehicle speeds under each working condition.

[0074] The working condition transition state matrix is used to represent the probability of mutual transition between different working conditions of the vehicle on the fixed route. For example, if the working conditions corresponding to each passage area of a fixed route include three working condition types: urban road working condition, highway working condition, and mountain road working condition, the constructed working condition transition state matrix should be a 3x3 working condition state transition matrix, and each element in the matrix represents the probability of transition from one working condition to another, such as the probability of transition from urban road working condition to highway working condition is 0.6, etc.

[0075] The working condition-vehicle speed confusion matrix is used to represent the probability of occurrence of different vehicle speed points of the vehicle under a specific working condition type. For example, for urban road working condition, the constructed vehicle speed confusion matrix may show that the probability of vehicle speed being 30 km / h is 0.4, the probability of vehicle speed being 40 km / h is 0.3, etc.

[0076] In the embodiments of the present application, the electronic device can determine the working condition transition state matrix and the working condition-vehicle speed confusion matrix matched with the fixed route in various ways, such as the Markov chain-based method, the machine learning classification algorithm-based method, etc. The present application does not make specific limitations thereto.

[0077] The working condition transition state matrix is used to represent the probability of mutual transition between different working conditions of the vehicle on the fixed route, and the working condition-vehicle speed confusion matrix is used to represent the probability of occurrence of different vehicle speed points of the vehicle under a specific working condition type. In the embodiments of the present application, the electronic device can generate target vehicle speed change data corresponding to the historical data based on the two matrices.

[0078] For example, the target vehicle speed change data can be generated based on time series iteration. Specifically, the process can be as follows: starting from the starting point of the fixed route, after the initial working condition and vehicle speed are initialized, the next time the vehicle can enter the working condition and its probability are determined according to the working condition transition state matrix, and the vehicle speed in the determined working condition is selected by referring to the working condition-vehicle speed confusion matrix, and the above steps are repeated to gradually generate the vehicle speed values at subsequent time points, and finally the complete vehicle speed change data is formed.

[0079] For another example, the target vehicle speed change data can also be generated based on Markov chain. Specifically, the process can be as follows: regarding the vehicle driving process as a Markov chain, taking the current working condition as the current state, determining the probability of transition to other working conditions in the next step by using the working condition transition state matrix, and selecting a vehicle speed value by using the working condition-vehicle speed confusion matrix after determining the next working condition state, repeating the process to simulate the driving process of the vehicle in different working conditions, and generating the vehicle speed change data.

[0080] The generated vehicle speed change data provides detailed and accurate driving state information of the vehicle on the fixed route, which helps to update the gear shifting scheme matched with the fixed route according to the vehicle speed change and working condition conversion.

[0081] The vehicle gear shifting scheme updating method provided by the embodiments of the present application is based on the working condition transition state matrix and the working condition-vehicle speed confusion matrix determined based on the working conditions of each route area, which respectively represent the transition probabilities between different working conditions on the fixed route and the probabilities of different vehicle speed points appearing in a specific working condition. Based on the working condition transition state matrix and the working condition-vehicle speed confusion matrix, the target vehicle speed change data corresponding to the historical data is generated, which can reflect the historical driving state of the vehicle on the fixed route. Subsequently, the vehicle parameters reflecting the vehicle characteristics data are combined with the vehicle speed change data to update the gear shifting scheme matched with the fixed route, so that the vehicle can shift gears according to the corresponding gear shifting scheme on the fixed route, thereby optimizing the driving economy of the vehicle on each fixed route.

[0082] In some embodiments of the present application, the working condition transition state matrix and the working condition-vehicle speed confusion matrix matched with the fixed route are determined based on the statistical parameters of the working conditions corresponding to each route area, which includes:

[0083] According to the working condition type sequence to which each route area belongs, the working condition type transition relationship between adjacent route areas is counted to obtain the number of transitions between each working condition type.

[0084] Based on the number of transitions between each working condition type, a working condition transition state matrix is constructed.

[0085] Based on the trajectory data under each working condition type, the occurrence frequency of the corresponding vehicle speed value in different intervals is counted to obtain a working condition-vehicle speed confusion matrix matched with the fixed route, and the confusion matrix is used to reflect the joint characteristics between the working condition type and the vehicle speed distribution.

[0086] In the embodiments of the present application, the working condition type transition relationship between adjacent route areas is counted according to the working condition type sequence to which each route area belongs. For example, the working condition type combination of adjacent route areas can be traversed one by one according to the working condition type sequence to which each route area belongs, and the occurrence frequency of each combination is counted to obtain the number of transitions between each working condition type, thereby providing basic data for constructing a working condition transition state matrix.

[0087] In the embodiments of the present application, after counting the number of transitions between each working condition type, a working condition transition state matrix is constructed. For example, the number of transitions between each working condition type is normalized to obtain a transition probability, and the working condition transition state matrix is constructed according to the transition probability.

[0088] An example is given below. Assuming that the total number of working condition categories is M, an MxM working condition transition state matrix is constructed, and each element in the matrix represents the probability of transition from one state (working condition) to another state (working condition). For example, the probability of transition from working condition i to working condition j is P(i, j), and the probability is calculated as follows:

[0089]

[0090] wherein represents the number of transitions from working condition i to working condition j, represents the total number of transitions from working condition i to other working conditions.

[0091] In the embodiments of the present application, based on the trajectory data under each working condition type, the occurrence frequency of the corresponding vehicle speed value in different intervals is counted, and a working condition-vehicle speed confusion matrix is constructed accordingly. For example, from the trajectory data corresponding to each working condition type, the vehicle speed value of the vehicle is extracted, and for each working condition type, the number of times that the vehicle speed falls in each interval is counted. Specifically, the vehicle speed value in the trajectory data can be counted by traversing the vehicle speed value. The frequency obtained by counting is converted into a probability, that is, the frequency of each interval is divided by the total number of vehicle speed data points in the working condition type. The interval of the vehicle speed value can be divided according to actual needs and application scenarios. The interval is a plurality of continuous intervals, for example, 0-10 kilometers / hour, 10-20 kilometers / hour, etc.

[0092] In some embodiments, the constructed working condition-vehicle speed confusion matrix takes working condition types as rows and vehicle speed intervals as columns, and the above calculated occurrence probabilities are filled in the corresponding matrix elements, thereby forming the working condition-vehicle speed confusion matrix. For example, if the probability of occurrence of vehicle speed a under working condition category s is B(i, j), B(i, j) is filled in the matrix element, where the calculation method of B(i, j) can be the same as that of P(i, j).

[0093] The working condition transition state matrix directly reflects the probability relationship between the transitions of different working conditions of the vehicle during driving on the fixed route, and the working condition-vehicle speed confusion matrix directly reflects the occurrence probabilities of the vehicle speed values in each interval under different working condition types, that is, reflects the relationship between the working condition types and the vehicle speed distribution, thereby providing a basis for subsequently generating target vehicle speed change data corresponding to the historical data of the fixed route.

[0094] The updating method of the vehicle gear shifting scheme provided in the embodiments of the present application, according to the working condition type sequence to which each route region belongs, counts the frequency of the transition of working condition types between adjacent route regions, obtains the number of transitions between each working condition type, and constructs a working condition transition state matrix according to the number. The working condition transition state matrix directly reflects the transition probabilities of different working conditions of the vehicle during driving on the fixed route. Based on the trajectory data under each working condition type, the frequency of the occurrence of the vehicle speed in different intervals is counted, and a working condition-vehicle speed confusion matrix is constructed after the probability is converted. The working condition-vehicle speed confusion matrix directly reflects the correlation between the working condition types and the vehicle speed distribution, thereby providing data support for subsequently generating target vehicle speed change data corresponding to the historical data of the fixed route, helping to make the target vehicle speed change data accurately reflect the driving state of the vehicle, and thereby optimizing the energy consumption performance of the vehicle driving on the fixed route after the subsequent updating of the gear shifting scheme.

[0095] In some embodiments of the present application, the generation of the target vehicle speed change data corresponding to the historical data corresponding to the fixed route to be targeted based on the working condition transition state matrix and the working condition-vehicle speed confusion matrix comprises:

[0096] constructing a state transition path of a working condition sequence according to the working condition transition state matrix, generating a working condition state sequence fitting the fixed route;

[0097] generating a corresponding vehicle speed segment according to each working condition type in the working condition state sequence in combination with the corresponding vehicle speed distribution in the working condition-vehicle speed confusion matrix;

[0098] splicing a plurality of the vehicle speed segments in the order of the working condition state sequence to obtain the target vehicle speed change data corresponding to the historical data corresponding to the fixed route to be targeted.

[0099] In an embodiment of the present application, a state transition path for an operating condition sequence is constructed based on the operating condition transition state matrix. For example, starting from an initial operating condition, the operating condition types at subsequent moments are sequentially determined using a random walk algorithm or a Markov chain method, based on the transition probabilities between the various operating condition types recorded in the operating condition transition state matrix. This gradually constructs an operating condition state sequence for the vehicle throughout its travel along a fixed route (i.e., a state sequence that fits the operating condition of the fixed route). For example, within each time step, a random number generator is used to determine the next operating condition based on the current operating condition and the transition probability matrix.

[0100] In an embodiment of the present application, a corresponding speed segment is generated based on each operating condition type in the operating condition state sequence and the corresponding speed distribution in the operating condition-vehicle speed confusion matrix. For example, for each operating condition type in the operating condition state sequence, the probability of occurrence of each speed interval under the operating condition in the operating condition-vehicle speed confusion matrix is ​​referred to, and the speed value of the corresponding interval is selected by random sampling or based on the probability distribution to generate the speed data within the corresponding time segment.

[0101] After the corresponding vehicle speed segments are generated, the multiple vehicle speed segments are spliced ​​together in the order of the operating state sequence to form complete and continuous target vehicle speed change data.

[0102] It can be understood that in the above process of generating target vehicle speed change data corresponding to historical data, a working condition state sequence that matches the driving characteristics of the fixed route is first generated, so that the subsequently generated vehicle speed change data can reflect the working condition changes of the vehicle during actual driving on the fixed route. On the basis of the determined working condition sequence, a reasonable vehicle speed change is generated for each working condition segment, so that the generated vehicle speed change data also conforms to the characteristics of historical data in terms of vehicle speed.

[0103] Here is an example:

[0104] Starting from the starting point of a fixed route, initialization is performed based on the initial working conditions (e.g., urban road conditions) and vehicle speed (e.g., 30 km / h) of the fixed route;

[0105] Divide the driving time of a vehicle on a fixed route into multiple small time steps, for example, one second as a time step;

[0106] At each time step, the possible operating conditions and their probabilities at the next moment are calculated based on the current operating condition and the operating condition transition state matrix. For example, if the vehicle is currently in an urban road condition, there is a 0.6 probability of remaining in the urban road condition, a 0.3 probability of entering the highway condition, and a 0.1 probability of entering the mountainous road condition.

[0107] According to the calculated probability of transitioning to the working condition, the actual working condition at the next moment is randomly determined;

[0108] According to the determined next time condition and the vehicle speed probability distribution corresponding to the condition in the condition-vehicle speed confusion matrix, the vehicle speed at the next time is randomly selected, such as in the urban road condition, the probability of the vehicle speed being 40 kilometers per hour is 0.4, the probability of the vehicle speed being 50 kilometers per hour is 0.3, and the probability of the vehicle speed being 60 kilometers per hour is 0.2;

[0109] From the initial time, the above-mentioned condition and speed determination steps are repeatedly performed, and the vehicle speed value at each time step is gradually generated until the driving time of the entire fixed route is covered, and finally the complete target vehicle speed change data is formed.

[0110] In addition, in some embodiments, the method further comprises calculating the characteristic parameters (such as average vehicle speed, acceleration mean, etc.) of the generated vehicle speed change data, and comparing the characteristic parameters with the characteristic parameters in the historical data, and selecting the vehicle speed change data with the lowest absolute error as the final construction result, so as to improve the fitting degree of the generated vehicle speed change data and the historical data, so that the subsequent shift scheme matched with the fixed route according to the vehicle speed change data has better optimization effect, and the energy consumption performance of the vehicle driving on the fixed route is optimized. The above-mentioned absolute error can be determined according to the actual application scene, for example, in some embodiments, the absolute error is that the vehicle speed characteristic error of the vehicle speed change data and the historical data is less than a first threshold value, and the mileage error is less than a second threshold value.

[0111] The vehicle shift scheme updating method provided by the embodiments of the present application constructs the condition state sequence matched with the driving characteristics of the fixed route according to the condition transition state matrix, and generates the vehicle speed segment conforming to the actual vehicle speed distribution under each condition type according to the corresponding vehicle speed distribution in the condition-vehicle speed confusion matrix on this basis, splices the plurality of vehicle speed segments in the order of the condition state sequence to form the target vehicle speed change data corresponding to the historical data, provides a basis for the subsequent shift scheme matched with the fixed route according to the vehicle speed change data, and helps the subsequent shift scheme matched with the fixed route according to the vehicle speed change data to have better optimization effect, thereby optimizing the energy consumption performance of the vehicle driving on the fixed route.

[0112] In some embodiments of the present application, the target vehicle speed change data and the vehicle parameters are used to update the shift scheme matched with each fixed route, respectively, which comprises:

[0113] For any fixed curve, the target vehicle speed change data corresponding to the historical data of the fixed route is taken as the condition input, and the vehicle parameters are taken as the model parameters to establish an energy consumption simulation model;

[0114] The default shift scheme of the vehicle executing on the fixed route is obtained, and a plurality of candidate shift schemes conforming to the vehicle dynamic performance demand constraint are generated.

[0115] Based on the default shift scheme and a plurality of candidate shift schemes, a target shift scheme is obtained by calling an energy consumption simulation model for search updating;

[0116] Based on the target shift scheme, a shift scheme matching the fixed route is updated.

[0117] It can be understood that the energy consumption simulation model can simulate the energy consumption performance of the vehicle when executing different shift schemes on the fixed route. It simulates the energy consumption change in the vehicle running based on the input target vehicle speed change data and vehicle parameters, etc., and provides a quantitative evaluation basis for shift scheme optimization.

[0118] The default shift scheme is the shift strategy currently used by the vehicle on the fixed route, and the candidate shift scheme is a plurality of possible schemes generated based on the default scheme and meeting the vehicle power performance demand constraints. The default shift scheme and the candidate shift scheme will be used together for subsequent search updating to complete the optimization of the shift scheme matching the fixed route. The target shift scheme is the final shift scheme obtained after evaluation and search updating by the energy consumption simulation model, which can meet the power performance demand while achieving better energy consumption performance.

[0119] The vehicle power performance demand constraint is a limit condition for the power performance that the vehicle needs to meet during driving to ensure that the vehicle has sufficient performance. Specifically, the vehicle power performance demand constraint can include acceleration performance constraint, climbing ability constraint, maximum speed constraint, etc., wherein the acceleration performance constraint defines the ability of the vehicle to accelerate from a standstill to a certain speed, the climbing ability constraint defines the driving ability of the vehicle on a certain slope, and the maximum speed constraint defines the maximum speed that the vehicle can reach.

[0120] For example, in some embodiments, the vehicle power performance demand constraint includes:

[0121] The upshift speed of each gear needs to be higher than the maximum upshift speed of the previous gear, there should be a certain downshift difference between the upshift speed and the downshift speed, the motor maximum protection speed, and the motor climbing ability constraint:

[0122]

[0123]

[0124] wherein, represents the upshift speed of gear i when the accelerator pedal opening degree is Aj%, represents the downshift speed of gear i when the accelerator pedal opening degree is Aj%, represents the maximum protection speed of gear i.

[0125] In the embodiments of the present application, the target vehicle speed change data is used as the working condition input, and a vehicle parameter is combined to construct an energy consumption simulation model. For example, an energy consumption simulation model can be built in an equal simulation platform, the vehicle speed change data and parameters are input to simulate the vehicle running and calculate the energy consumption, and the construction of the energy consumption simulation model can involve constructing a vehicle dynamics module to simulate the longitudinal and lateral dynamic behavior of the vehicle, and constructing a vehicle powertrain system module to simulate the output characteristics of the vehicle motor, including the speed-torque curve, efficiency map, etc., so that the energy consumption simulation model can more comprehensively simulate the energy consumption performance of the vehicle when executing different shift schemes on a fixed route. After the energy consumption simulation model is established, based on the current default shift scheme, a plurality of candidate shift schemes that meet the vehicle power performance requirement constraints are generated.

[0126] For example, the electronic device can use a particle swarm algorithm or a genetic algorithm to generate a plurality of candidate shift schemes based on the default shift scheme while meeting the vehicle power performance requirements, input the default shift scheme and the candidate shift schemes into the energy consumption simulation model in sequence after generating the candidate shift schemes, calculate the vehicle energy consumption under each scheme, and perform search updating to obtain the target shift scheme. For example, if the candidate scheme is generated by the particle swarm algorithm, the search mechanism of the particle swarm algorithm can be used to iteratively update the shift scheme based on the energy consumption result, and gradually approach the lowest energy consumption target. For another example, if the genetic algorithm is used to generate the candidate scheme, the scheme is iteratively updated through the crossover and mutation operations related to the genetic algorithm until a set iteration number or fitness threshold is reached, thereby obtaining the target shift scheme. After the target shift scheme is determined, the default shift scheme is replaced by the target shift scheme, and the update of the shift scheme matched with the fixed route is completed.

[0127] Figure 2 Fig. 1 is a schematic diagram of updating a shift scheme provided by some embodiments of the present application. As shown in Fig. 1, in some embodiments, the target vehicle speed change data and the vehicle parameters are used to update the shift scheme matched with each fixed route, including: Figure 2

[0128] Import vehicle parameters and fixed route working condition characteristics (target vehicle speed change data);

[0129] Use the original shift curve of the vehicle on the fixed route as the default scheme;

[0130] Call the energy consumption simulation model to simulate the torque execution and shift action of the vehicle under the fixed route working condition, and calculate the energy consumption;

[0131] Call the particle swarm algorithm to dynamically adjust the shift scheme and the corresponding constraints, and compare the energy consumption;

[0132] ​Gradient updating is performed on the shift curve, and a jump is made to calling an energy consumption simulation model to simulate torque execution and shift actions of the vehicle in a fixed route working condition, and the step of calculating energy consumption is repeatedly executed.

[0133] The shift curve with the optimal 100km energy consumption is output.

[0134] The updating method of the vehicle shift scheme provided in the embodiments of the application takes the target vehicle speed change data as working condition input, combines vehicle parameters to construct an energy consumption simulation model, and generates a plurality of candidate shift schemes meeting vehicle dynamic performance demand constraints based on a default shift scheme. The default shift scheme and the candidate shift schemes are sequentially input into the energy consumption simulation model, the energy consumption of the vehicle under each scheme is calculated, and the target shift scheme is obtained through search updating, which realizes updating of the shift scheme matched with the fixed route. The updated shift scheme matched with the fixed route meets the vehicle dynamic performance demand constraints, so that the vehicle shifts gears according to the corresponding shift scheme in the fixed route, and the driving economy of the vehicle in each fixed route can be optimized under the premise of meeting the vehicle dynamic performance demand.

[0135] In some embodiments of the application, the target shift scheme is obtained through search updating by calling the energy consumption simulation model based on the default shift scheme and the plurality of candidate shift schemes, and the search updating includes:

[0136] Each shift scheme in the default shift scheme and the plurality of candidate shift schemes is represented as a particle to obtain an initial particle group containing a plurality of particles;

[0137] The initial particle group is initialized to preliminarily determine the speed and position of each particle;

[0138] In each iteration, the energy consumption of the shift scheme corresponding to each particle is calculated by calling the pre-established energy consumption simulation model to serve as the fitness value of each particle in the iteration;

[0139] Under the constraint of the particle boundary condition, the speed and position of each particle are updated based on the fitness value of each particle in the iteration;

[0140] The step of calculating the energy consumption of the shift scheme corresponding to each particle by calling the pre-established energy consumption simulation model is returned to and continuously executed until the iteration termination condition is reached to stop and obtain the target shift scheme.

[0141] In the embodiments of the present application, the particle swarm optimization algorithm is improved to realize the default shift scheme and multiple candidate shift schemes, and the energy consumption simulation model is called to search and update to obtain the target shift scheme. Specifically, each shift scheme is represented as a particle. In the initialization, the particle swarm is composed of the default shift scheme and multiple randomly generated candidate schemes. The position of each particle represents the specific parameters of the shift scheme, for example, the vehicle speed threshold at a specific accelerator pedal depth for the vehicle to shift from the current gear to a higher gear, and the vehicle speed threshold for the vehicle to shift from the current gear to a lower gear. The speed of the particle represents the step size and direction of the shift scheme parameter update, and the size and direction of the speed affect the speed and path of the particle convergence. If the speed is too large, the particle may pass the optimal solution, and if the speed is too small, the convergence speed is slow. The initial position and speed of the particle can be randomly generated or set within a range based on experience.

[0142] Each particle (shift scheme) is input into the energy consumption simulation model to simulate the energy consumption performance of the vehicle on a fixed route, and the energy consumption performance is used as the fitness value of the particle. Based on the fitness value, the velocity and position of the particle are adjusted using the update formula of the particle swarm optimization algorithm. At the same time, the boundary conditions of the particles are set to ensure that the adjusted shift scheme parameters remain within a reasonable range, avoiding unrealistic schemes, such as the upshift speed being lower than the minimum speed of the current gear or the downshift speed being higher than the maximum speed of the current gear. Through multiple iterations, the particle swarm is continuously updated, gradually approaching the global optimal solution. The iteration termination condition can be that the maximum number of iterations is reached or the fitness value change is less than the convergence threshold. The final target shift scheme can significantly reduce energy consumption, for example, the energy consumption per 100 kilometers of the shift scheme is optimized from 15 kWh to 12 kWh, effectively improving the energy consumption performance of the vehicle on each fixed route.

[0143] The updating method for the vehicle shift scheme provided in the embodiments of the present application, each particle represents a shift scheme, the position and speed of the particle represent the shift scheme parameters and their update directions, after the initialization of the particle swarm is completed, the particles are input into the energy consumption simulation model to calculate the energy consumption as the fitness, the particle position and speed are updated according to the fitness, and the boundary is constrained to ensure that the scheme is reasonable, the iteration is performed until the termination condition is met, and the target shift scheme with the lowest energy consumption is obtained. The target shift scheme matches the fixed route, and the vehicle shifts according to the corresponding shift scheme on the fixed route, which can reduce unnecessary energy consumption, improve the adaptability of the vehicle to each fixed route, and optimize the driving economy of the vehicle on each fixed route.

[0144] In some embodiments of the present application, under the constraint of the particle boundary condition, the velocity and position of each particle are updated based on the fitness value of each particle in the current iteration, including:

[0145] In the current iteration, if the current fitness value of the particle is higher than the historical individual optimal fitness value of the particle itself, the historical individual optimal fitness value and the corresponding historical individual optimal position of the particle are updated;

[0146] Based on the particle with the highest historical individual optimal fitness value among all particles, the population optimal fitness value and the corresponding population optimal position are updated;

[0147] According to the speed and position of the particle in the current iteration, the historical individual optimal position and the population optimal position, the speed of the particle is updated;

[0148] Based on the updated speed, the position of the particle in the next iteration is calculated, and the position is corrected when the particle boundary condition is exceeded.

[0149] In the embodiments of the present application, in the process of updating the speed and position of each particle, it can be considered that the high and low of the fitness value directly reflects the pros and cons of the shift scheme represented by the particle. The lower the energy consumption, the higher the fitness of the shift scheme. For example, the energy consumption value calculated by the energy consumption simulation model of a certain particle is 12 kWh per 100 kilometers, which is lower than the energy consumption of other particles of 15 kWh per 100 kilometers. The fitness value of the particle is higher, indicating that the shift scheme represented by the particle performs better in terms of energy consumption.

[0150] The historical individual optimal fitness value records the best energy consumption performance of the particle in the historical iteration, and the individual optimal position records the shift parameters when the particle achieves the best energy consumption. For example, a particle finds in the iteration process that setting the upshift speed to 65 kilometers per hour at a certain accelerator pedal depth can achieve lower energy consumption, and this setting will be recorded as the individual optimal position of the particle.

[0151] The population optimal fitness value and the population optimal position reflect the optimal performance of the entire particle group. In each iteration, the individual optimal fitness values of all particles are traversed to find the best value and the corresponding shift parameters, which are taken as the population optimal fitness value and position. For example, if the shift scheme corresponding to the population optimal fitness value can achieve an energy consumption performance of 12 kWh per 100 kilometers in simulation, this scheme will be used as a guide to attract other particles to it.

[0152] The particle velocity reflects the moving direction and step length of the particle in the solution space. By combining the current velocity of the particle, the individual optimal position and the group optimal position, the velocity of the next iteration of the particle can be calculated using the velocity update formula. This process not only considers the optimal historical position of the particle itself, but also considers the optimal position of the entire group, so that the particle can find a balance between individual exploration and group cooperation. For example, if the current velocity of a particle moves it towards a potentially better solution, while the group optimal position shows a better solution in another direction, the velocity update formula of the particle will integrate these two factors to adjust the moving direction and step length of the particle in order to find the global optimal solution.

[0153] The setting of the particle boundary condition ensures that the position and velocity of the particle remain within a reasonable range, avoiding the particle deviating from the actual feasible solution space during the search process. For example, the upshift speed cannot be lower than the minimum speed of the current gear, and the downshift speed cannot be higher than the maximum speed of the current gear. After calculating the new position of the particle based on the updated velocity in each iteration, it is checked whether the position exceeds the particle boundary condition. If it does, the position of the particle is corrected to within the boundary range to ensure the feasibility of the shift schedule.

[0154] Under the constraint of the particle boundary condition, the velocity and position of the particle are updated based on the fitness value, and the particle swarm can continuously approach a better shift schedule. As the number of iterations increases, the particle swarm gradually approaches the global optimal solution, and the final shift schedule can meet the vehicle power performance requirements while reducing energy consumption. For example, after multiple iterations of optimization, the energy consumption per 100 kilometers of the shift schedule is reduced from 15 kWh to 12 kWh, effectively reducing energy consumption and improving the driving economy of the vehicle.

[0155] The updating method for the vehicle shift schedule provided by the embodiments of the present application compares the current fitness value of the particle with the historical individual optimal fitness value of the particle itself in each iteration to update the individual optimal fitness value and position, and in each iteration, the individual optimal fitness values of all particles are traversed to find the highest value and the corresponding individual optimal position, and the group optimal fitness value and the group optimal position are updated accordingly. According to the velocity update formula of the particle swarm algorithm, the velocity of the particle in the next iteration is calculated by combining the current velocity of the particle, the individual optimal position and the group optimal position. The new position of the particle in the next iteration is calculated based on the updated velocity. If the new position exceeds the particle boundary condition, the position of the particle is corrected back to the boundary range. Through the above steps, the velocity and position of the particle are updated based on the fitness value under the constraint of the particle boundary condition. This process can guide the particle swarm to continuously approach a better shift schedule, gradually approaching the global optimal solution, and then determine the target shift schedule with the lowest energy consumption and meeting the vehicle power performance requirement constraints after the subsequent iteration ends.

[0156] In some embodiments of the present application, the pre-established energy consumption simulation model is called to calculate the energy consumption of each particle corresponding to the shift scheme, including:

[0157] For each particle, according to the shift scheme corresponding to each particle, the vehicle simulation is executed to perform the shift scheme corresponding to the particle under the constraint of the shift speed limit condition, to perform the upshift or downshift operation;

[0158] The upshift times and downshift times in the simulation process are recorded, and the upshift energy consumption corresponding to the upshift operation and the downshift energy consumption corresponding to the downshift operation are determined respectively;

[0159] The motor efficiency corresponding to different gears is obtained based on the motor parameter lookup table, and the driving energy consumption in the simulation process is calculated based on the motor efficiency;

[0160] Based on the driving energy consumption, the upshift energy consumption and the downshift energy consumption, the energy consumption of the shift scheme corresponding to the particle is determined.

[0161] The shift speed limit condition refers to the speed range limit that must be followed by the vehicle when performing the upshift or downshift operation during driving. Specifically, the electronic device simulates the vehicle driving process by calling the energy consumption simulation model, controls the vehicle to perform the upshift or downshift operation in the simulation environment according to the shift scheme represented by the particle. And in the simulation process, each upshift and downshift is recorded to count the upshift times and downshift times, and the energy consumed by each upshift and downshift operation is calculated by analyzing the working state of the vehicle power system. For example, the electronic device can determine the energy consumption of the upshift and downshift operation by monitoring the torque change and speed change of the vehicle motor, and combining the efficiency curve of the motor. According to the running state of the vehicle at different gears (such as motor speed, torque, etc.), the corresponding motor efficiency is obtained from the motor performance table. Then, combined with the driving force demand and driving distance of the vehicle, the driving energy consumption in the simulation process is calculated. After considering the driving energy consumption, the upshift energy consumption and the downshift energy consumption, the total energy consumption of the vehicle under the specific shift scheme is obtained.

[0162] The following proposes a specific example of calling a pre-established energy consumption simulation model to calculate the energy consumption of each particle corresponding to the shift scheme:

[0163] The energy consumption simulation model is built on the Simulink simulation platform based on the principle of the vehicle longitudinal model. The input of the model includes the working condition, vehicle model parameter and motor characteristic, and the output is the energy consumption data of the whole vehicle. The model includes the following key modules: working condition import module, driving force calculation module, shift action simulation module, motor working efficiency query module and energy consumption calculation module:

[0164] The working condition import module is used to receive the input vehicle model parameter, vehicle speed curve (target vehicle speed change data) and motor Map parameter;

[0165] a driving force calculation module configured to calculate a vehicle driving force according to a vehicle longitudinal model and a motor torque :

[0166]

[0167]

[0168] wherein, represents a vehicle mass, represents a gravity acceleration, represents a rolling resistance coefficient, represents a current road slope, represents an air resistance coefficient, is a windward area, represents a current vehicle speed, represents an inertia coefficient, represents a current acceleration, represents the motor torque, represents a current gear ratio, represents a rolling radius, represents a vehicle mechanical efficiency, represents a motor efficiency. The vehicle driving force is calculated based on the vehicle mass, the gravity acceleration, the rolling resistance coefficient, the current road slope, the air resistance coefficient, the windward area, the current vehicle speed, the inertia coefficient and the current acceleration; and the motor torque is calculated based on the vehicle driving force, the current gear ratio, the rolling radius and the vehicle mechanical efficiency. The above formula is used to calculate the vehicle driving force based on the vehicle running state, and the motor output torque is back calculated in combination with the transmission system parameters.

[0169] a shift action simulation module configured to judge a current vehicle speed , an accelerator pedal depth , a current gear rank, query a current upshift vehicle speed and a downshift vehicle speed according to a gear shifting scheme, if the current vehicle speed exceeds the upshift vehicle speed, upshift, if the current gear is lower than the downshift vehicle speed, downshift, otherwise, maintain the current gear;

[0170] a motor working efficiency query module configured to obtain a corresponding motor working efficiency e according to a current motor speed n and the motor torque by table look-up, and correct the driving force:

[0171]

[0172] It is easy to understand that during the operation of the vehicle, the driving force Ft required by the whole vehicle is provided by the motor, but there is an efficiency loss in the process of the motor converting electric energy into mechanical energy. Therefore, in order to meet the demand of the vehicle for the driving force Ft, the motor actually has to output a larger driving force Ft', the size of which is equal to the driving force required by the vehicle divided by the efficiency of the motor. Therefore, the above formula is used to correct the driving force of the whole vehicle according to the efficiency of the motor, so as to convert the ideal driving force required by the vehicle during actual operation into the actual driving force required to be provided by the motor.

[0173] The energy consumption calculation module is used to introduce a shift energy consumption penalty factor on the basis of driving energy consumption, and calculate the driving energy consumption per 100 kilometers of the whole vehicle:

[0174]

[0175] In the formula, represents the driving force of the whole vehicle, represents the current vehicle speed, represents the upshift energy consumption, represents the number of upshifts, represents the downshift energy consumption, represents the number of downshifts. It can be understood that the driving energy consumption per 100 kilometers of the whole vehicle is calculated on the basis of the driving force of the whole vehicle, the current vehicle speed, the mechanical efficiency of the whole vehicle and the energy consumption of the shift operation (including the upshift and downshift energy consumption). Specifically, the driving energy consumption part is obtained by integrating the change of the product of the driving force of the whole vehicle and the vehicle speed with time; at the same time, the energy consumption of the upshift and downshift operations is considered, and the total shift energy consumption part is obtained by multiplying the energy consumption of the upshift and downshift operations by the corresponding number of shifts respectively. The driving energy consumption and the shift energy consumption are added and divided by the driving distance (obtained by integrating the ratio of the vehicle speed and time), to obtain the driving energy consumption per 100 kilometers of the whole vehicle.

[0176] The above formula is used to calculate the energy consumption per unit distance of the whole vehicle on the basis of the driving force, the efficiency of the motor and the number of shifts, and can be used to measure the energy consumption performance of a certain shift scheme under a given vehicle speed curve, and comprehensively considers the power output and the shift influence, and is the basis for evaluating the advantages and disadvantages of each group of candidate shift schemes in the simulation process.

[0177] The updating method of the vehicle shift scheme provided by the embodiments of the present application simulates the execution of the corresponding shift operation under the constraint of the shift speed limit condition according to the shift scheme corresponding to each particle, and records the number of upshifts and downshifts in the simulation process, so as to evaluate the influence of the shift operation on the energy consumption, and comprehensively consider the driving energy consumption, the upshift energy consumption and the downshift energy consumption, and calculate the total energy consumption of the shift scheme corresponding to the particle, thereby providing a quantitative index for evaluating different shift schemes. The energy consumption is used as the fitness value of each particle in the current iteration, which is helpful to determine the target shift scheme with the best energy consumption performance.

[0178] In combination with the above embodiment, a specific example of obtaining a target shifting plan by searching and updating the energy consumption simulation model is proposed below:

[0179] (1) Parameter settings

[0180] Set the particle swarm size to n, the maximum number of iterations to G, the current number of iterations to g, and the inertia weight parameter to , , the acceleration constant is , , , , convergence accuracy .

[0181] (2) Population initialization

[0182] Generate a random initial population of n particles. Each particle represents a shifting scheme that satisfies the shifting capacity constraint, i.e., the accelerator pedal opening-vehicle speed matrix RxA, where R represents the number of upshift lines and downshift lines, and A represents the preset accelerator pedal opening node.

[0183] The accelerator pedal opening-vehicle speed matrix can be referred to Table 1.

[0184] Table 1 Accelerator pedal opening-vehicle speed matrix

[0185]

[0186] in, Indicates the upshift vehicle speed when the accelerator pedal opening is Aj% in gear position i, It represents the downshift vehicle speed when the accelerator pedal opening is Aj% in gear i. For example, v_u11 represents the upshift vehicle speed when the accelerator pedal opening is A1% in gear 1, and v_d31 represents the downshift vehicle speed when the accelerator pedal opening is A1% in gear 3.

[0187] (3) Particle individual fitness evaluation

[0188] Import the shifting scheme, typical working conditions and vehicle model parameters into the energy consumption simulation model, calculate the energy consumption per 100 kilometers under typical working conditions using the shifting scheme, use this result as the fitness value of the shifting scheme, and compare it with the current scheme The fitness value of The best solution in its historical search process Fitness value ,like , then Adjust to the current shifting scheme, otherwise remain unchanged. When the number of iterations g=1, = .

[0189] (4) Fitness evaluation of particle population

[0190] Compare the optimal fitness of current solution with the optimal fitness of particle population If the current fitness is better, set as the current position of the particle, otherwise keep it unchanged;

[0191] (5) Update of particle population state

[0192] Update the position and velocity vector of the particle according to the current inertia weight and acceleration constant, and set the iteration number as g+1:

[0193]

[0194]

[0195]

[0196]

[0197]

[0198] where, , denote the velocity before and after the update of particle state; , denote the position before and after the update of particle state; denotes the inertia weight, i.e. the degree of inheritance of the moving speed of the particle in the iteration process; , denote the acceleration constant, which determines the inheritance weight of the particle to its own optimal position and the global optimal position, respectively; , are uniform random numbers in the range of [0, 1]; , are the upper and lower limit values of the inertia weight, usually set as = 0.4, = 0.9; , , , are the upper and lower limit values of the acceleration constant, usually set as = = 2.5, = = 0.5; is the iteration number; is the maximum iteration number; is the historical optimal position of the particle ; and is the global optimal position. The natural base is taken.

[0199] (6) Particle boundary processing

[0200] When the particle searches for the optimal position near the boundary, the particle may cross the boundary due to the influence of the random parameter, resulting in an infeasible solution. To constrain the particle in the feasible solution space, when the particle position exceeds the boundary, the position and velocity vector of the particle need to be adjusted according to the following boundary-crossing processing strategy:

[0201]

[0202]

[0203] wherein, , is the upper and lower limit value of the particle search speed, , is the upper and lower limit value of the gear shifting scheme, is the particle speed at the th iteration, indicates the inertia weight, is the particle position at the th iteration.

[0204] (7) Termination condition judgment

[0205] When the iteration number g G, the algorithm is terminated, otherwise go to (3) particle individual fitness evaluation, and perform fitness evaluation. When the fitness value change is less than the preset convergence precision , the calculation is terminated, otherwise go to (2) population initialization.

[0206] In some embodiments of the present application, the identification of the working conditions corresponding to each route area in the plurality of fixed routes comprises:

[0207] For any fixed route, feature extraction is performed on the corresponding historical data to obtain trajectory features of the vehicle in each route area of the fixed route.

[0208] The trajectory features are analyzed by clustering to identify the working conditions corresponding to each route area.

[0209] The trajectory feature is a characteristic value determined based on analysis and calculation of historical data of the vehicle driving, and can reflect the driving state of the vehicle in the route area of the fixed route. It can be considered that the trajectory feature quantitatively describes the driving state of the vehicle in each route area, and provides a data basis for subsequent clustering analysis to identify the working conditions of the route area.

[0210] Clustering analysis is generally used to divide data points in a dataset into different clusters, such that data points within the same cluster have higher similarity, while data points between different clusters have greater difference. In the embodiments of the present application, through clustering, the route areas with similar trajectory features can be classified into the same working condition type, providing data support for subsequent updating and fixing the shift scheme matched with the route, so that the shift scheme can better adapt to different route areas of each fixed route, thereby improving the driving energy consumption or driving efficiency of the vehicle.

[0211] In the embodiments of the present application, the trajectory features can be analyzed by the electronic device based on the fuzzy c-means clustering (Fuzzy C-Means, FCM) algorithm to identify the working conditions corresponding to each route area:

[0212] (1) Initialization: initial membership matrix U, randomly select initial cluster centers;

[0213] (2) Assignment step: according to the Euclidean distance, each data point is assigned to the cluster represented by the nearest centroid;

[0214] (3) Update step: update the membership matrix and the cluster center V;

[0215] (4) Iteration: repeat the assignment and update steps until the centroid no longer changes significantly or the preset number of iterations is reached.

[0216] (5) Result output: output the working conditions corresponding to each route area and the associated latitude and longitude.

[0217] The above gives an example of clustering analysis of trajectory features based on FCM to identify the working conditions corresponding to each route area. In addition to the above FCM, the specific way of clustering analysis of trajectory features to identify the working conditions corresponding to each route area can also be K-means clustering algorithm, K-medoids clustering algorithm, etc., which is not limited in the present application.

[0218] The updating method of the vehicle shift scheme provided by the embodiments of the present application extracts the trajectory features of each route area of the vehicle in the fixed route by extracting the features of the corresponding historical data of the fixed route, and performs clustering analysis on the trajectory features to identify the working conditions corresponding to each route area of the fixed route. The working conditions corresponding to each route area can be further used to generate target vehicle speed change data corresponding to the historical data. Subsequently, combined with the vehicle speed change data and the vehicle parameters reflecting the vehicle characteristics data, the shift scheme matched with the fixed route can be updated, so that the vehicle can shift gears according to the corresponding shift scheme in the fixed route, thereby optimizing the driving economy of the vehicle in each fixed route.

[0219] In some embodiments of the present application, for any fixed route, feature extraction is performed on the corresponding historical data to obtain trajectory features of the vehicle in each route area of the fixed route, including:

[0220] For any fixed route, original data of the vehicle driving according to the fixed route is obtained; the original data includes time, vehicle speed, and latitude and longitude information;

[0221] The original data is fragmented according to a vehicle speed threshold to obtain a plurality of data segments;

[0222] For each data segment, a multi-dimensional trajectory feature value is calculated to describe the behavior of the vehicle, and dimension reduction processing is performed on the multi-dimensional trajectory feature value to obtain a plurality of trajectory features;

[0223] According to the latitude and longitude information of each data segment, the route area to which it belongs is determined, and the corresponding trajectory feature is classified into the route area to which it belongs, so as to form the trajectory features of the vehicle in each route area.

[0224] In some embodiments, the original data of the vehicle is Telematics BOX (T-BOX) data in ASC format (ASC format is a text file format based on American Standard Code for Information Interchange (ASCII) character set). In the case where the server is the main body of the vehicle gear shifting scheme updating method, the server can obtain the original data stored in the vehicle locally by issuing data collection instructions to the vehicle and the like.

[0225] It can be considered that the original data of the vehicle is not all valid. For example, during the driving of the vehicle on the fixed route, the vehicle may stop due to abnormal conditions, and the original data under such abnormal conditions cannot effectively represent the actual driving behavior of the vehicle. The trajectory features determined based on the abnormal data cannot reflect the normal driving state of the vehicle in the route area of the fixed route.

[0226] Therefore, in the embodiments of the present application, a vehicle speed threshold (for example, set to zero) is set, and a data segment with a vehicle speed greater than the vehicle speed threshold and continuous is extracted, so as to realize the segmentation and screening of the original data. For each data segment, a multi-dimensional trajectory feature value for describing the vehicle behavior is calculated. For example, the average value of all vehicle speed values in the data segment can be calculated, that is, the total distance divided by the total time, and the average value of the acceleration calculated by the change of the vehicle speed at adjacent time points can be calculated. The multi-dimensional trajectory feature value has multiple dimensions and is obtained based on the analysis and calculation of the historical data of the vehicle running, including but not limited to the running time, the idling time, the acceleration time, the braking time, the constant speed time, the average vehicle speed, the average running speed, the maximum vehicle speed, the vehicle speed standard deviation, the average acceleration, the maximum acceleration, the acceleration standard deviation, the average acceleration pedal depth, the acceleration pedal depth standard deviation, the average deceleration, the maximum deceleration, the deceleration standard deviation, the average brake depth, and the brake depth standard deviation. In some embodiments, the trajectory feature (type) of the vehicle is set based on the relevant working condition classification standard, for example, China Automotive Test Cycle (CATC).

[0227] By dimension reduction processing on the multi-dimensional trajectory feature value, the multi-dimensional data is converted into a few principal components, which can simplify the data structure, the trajectory feature after dimension reduction is easier to process and analyze, and the redundancy and noise in the multi-dimensional trajectory feature value are removed, which helps to improve the efficiency and accuracy of subsequent clustering analysis.

[0228] After obtaining the trajectory features of each data segment, the data segments need to be classified into the original corresponding route areas. It can be understood that each data segment actually corresponds to a driving distance of the vehicle driving on a fixed route. According to the longitude and latitude corresponding to each driving distance, the longitude and latitude range of each route area divided in advance is compared to determine the specific route area to which each data segment belongs. The trajectory features of each data segment are classified into the corresponding route area to form the trajectory features of the vehicle in each route area, which provides a data basis for subsequent clustering analysis of the trajectory features to identify the working conditions corresponding to each route area.

[0229] In some embodiments, after obtaining the original data of the vehicle driving on the fixed route, the method further includes data cleaning of the original data.

[0230] For example, in some embodiments, the original data is divided by time, and the original data corresponding to one timestamp is an information unit. If there is a missing feature value such as vehicle speed, time, longitude and latitude in the information unit, or any feature value does not match the actual performance parameter interval of the vehicle model (such as out of range), the information unit is excluded.

[0231] In addition, since the original data usually includes multiple features. In the data processing of the original data, there may be some features whose data points are not evenly distributed in time distribution, or some data is missing at some time points. For example, the vehicle speed data may be recorded once per second, while the acceleration data may be recorded once every two seconds, or the acceleration pedal depth data at some time points may be missing. Therefore, in some embodiments, the time points of the vehicle speed data are taken as the basis (vehicle speed time base point) to interpolate the data of other features. Interpolation is a mathematical method for estimating the value of unknown data points based on known data points. Common interpolation methods include linear interpolation, polynomial interpolation, spline interpolation, etc. Through interpolation, the estimated values of other features can be supplemented at each time point of the vehicle speed data, so that the data of all features are aligned on the time axis.

[0232] The updating method of the vehicle gear shifting scheme provided by the embodiments of the present application acquires original data of a vehicle driving according to a fixed route, performs fragmentation processing on the original data by setting a vehicle speed threshold, and screens out data segments with continuous vehicle speeds greater than the threshold, so as to extract effective data segments representing the actual driving behavior of the vehicle. For each data segment, multi-dimensional trajectory feature values are calculated to describe the behavior of the vehicle, and dimension reduction processing is performed on the multi-dimensional trajectory feature values to obtain multiple trajectory features. The dimension of the multi-dimensional trajectory feature values is reduced, and the redundancy and noise in the multi-dimensional trajectory feature values are removed. According to the longitude and latitude information of each data segment, the corresponding trajectory features are classified into the route area to which they belong, and the trajectory features of the vehicle in each route area are constructed, providing a data basis for subsequent cluster analysis to obtain working conditions and update the gear shifting scheme.

[0233] In some embodiments of the present application, the dimension reduction processing on the multi-dimensional trajectory feature values to obtain multiple trajectory features includes:

[0234] The multi-dimensional trajectory feature values are standardized to eliminate differences in dimensions and numerical ranges of the multi-dimensional trajectory feature values;

[0235] The multi-dimensional trajectory feature values are linearly transformed by a principal component analysis method to extract multiple principal components covering main data variation information;

[0236] The multiple principal components are taken as trajectory features to complete the dimension reduction processing on the multi-dimensional trajectory feature values.

[0237] It can be understood that the multi-dimensional trajectory feature values usually include different types of data, such as vehicle speed, acceleration, acceleration pedal depth, etc., which may have different dimensions and numerical ranges. For example, the vehicle speed is in units of kilometers per hour, the acceleration is in units of meters per second, and the numerical ranges are quite different.

[0238] The purpose of standardizing the multi-dimensional trajectory feature values is to eliminate the differences in the above dimensions and numerical ranges, for example, each of the multi-dimensional trajectory feature values can be linearly transformed to the same scale range (such as [0, 1] or [-1, 1]). In the embodiments of the present application, the electronic device can standardize the multi-dimensional trajectory feature values by maximum-minimum standardization, Z-score standardization and the like. The specific manner of standardization is not limited in the present application.

[0239] Principal Component Analysis (PCA) is a statistical analysis method, which can be used to convert a plurality of related variables into a few uncorrelated comprehensive variables (i.e., principal components), and the principal components can retain most of the information of the original data while reducing the data dimension.

[0240] It can be understood that linear transformation is an important step in principal component analysis. For example, the linear transformation of the multi-dimensional trajectory feature values by the principal component analysis method can be to convert the original multi-dimensional trajectory feature values into a new coordinate system by a specific mathematical method (such as matrix multiplication), to eliminate the correlation between the original variables (multi-dimensional trajectory feature values), and to make the new variables (principal components) independent of each other.

[0241] The principal components are linear combinations of the original multi-dimensional trajectory feature values, and the principal components are sorted according to the degree of explanation of the data variation. The first principal component explains the most variation, the second principal component explains the most remaining variation under the condition of being orthogonal to the first principal component, and so on. By extracting a plurality of principal components covering the main data variation information and taking the principal components as the trajectory features, the dimension of the multi-dimensional trajectory feature values can be reduced while retaining the key features of the multi-dimensional trajectory feature values, and the redundancy and noise in the multi-dimensional trajectory feature values can be removed, which helps to improve the efficiency and accuracy of subsequent clustering analysis.

[0242] In the embodiments of the present application, the electronic device can perform principal component analysis on the multi-dimensional trajectory feature values by the following methods:

[0243] (1) Calculate the covariance matrix R of the standardized sample values:

[0244]

[0245] wherein, and are the values of the sample on the first and the second characteristics, and are the mean values of the first and the second characteristics, is the number of samples.

[0246] (2) Calculate the eigenvalues of the covariance matrix R and the eigenvector:

[0247]

[0248] where, is the eigenvalue of the covariance matrix, is the identity matrix.

[0249] The eigenvalue corresponding eigenvector is:

[0250]

[0251] where, .. is the component of the eigenvector .

[0252] (3) Calculate the principal component contribution rate and cumulative contribution rate:

[0253] Contribution rate:

[0254]

[0255] where, denotes the th eigenvalue, denotes the sum of all eigenvalues.

[0256] Cumulative contribution rate:

[0257] (i=1,2,...,p)

[0258] where p is the number of original characteristics, denotes the sum of the first eigenvalues, denotes the sum of all eigenvalues.

[0259] (4) Principal component explanation: generally take the first m principal components whose cumulative contribution rate exceeds 80%, the i th principal component calculation method is:

[0260]

[0261] where, is the i th principal component, is the component of the i th eigenvector, is the characteristic variable of the original data.

[0262] (5) Data dimension reduction: form the characteristic matrix of n*m (original data matrix is n*P)

[0263]

[0264] wherein, are the first m principal components extracted.

[0265] The vehicle shift scheme updating method provided by the embodiments of the present application eliminates the differences in dimensions and numerical ranges of the feature values, which helps to improve the consistency and comparability between the multi-dimensional trajectory feature values, and the principal component analysis method is used to linearly transform the standardized data, extract multiple principal components covering the main data variation information, and the principal components are linear combinations of the original feature values. These principal components are used as trajectory features to complete dimension reduction processing, which reduces the dimension of the multi-dimensional trajectory feature values while retaining the key features of the multi-dimensional trajectory feature values, removes the redundancy and noise in the multi-dimensional trajectory feature values, and helps to improve the efficiency and accuracy of subsequent clustering analysis. The working conditions obtained by clustering are used to transfer the state matrix and the working condition-vehicle speed confusion matrix, and finally the shift scheme is updated, which helps to improve the effectiveness of the updated shift scheme.

[0266] In some embodiments of the present application, the method further comprises:

[0267] Based on the updated shift scheme, a shift scheme index table for matching different fixed routes is constructed, so that the vehicle continuously obtains the corresponding index value from the shift scheme index table according to the current latitude and longitude information during driving, and executes the matched shift scheme according to the obtained index value.

[0268] The shift scheme index table contains multiple index values, and each index value corresponds to a fixed route and its matched shift scheme.

[0269] It can be understood that for each fixed route, the matched shift scheme can be updated by the method provided in the above embodiments. In the embodiments of the present application, when there are multiple fixed routes, the electronic device creates a shift scheme index table and generates a unique index value for each fixed route and its corresponding optimized shift scheme. The index value, fixed route data and corresponding shift scheme are stored in the shift scheme index table. For example, the index value "001" in the shift scheme index table corresponds to the fixed route A and its shift scheme X, and the index value "002" corresponds to the fixed route B and its shift scheme Y.

[0270] If the shift scheme index table for matching different fixed routes is constructed on the server, the server can issue the shift scheme index table as an instruction to the vehicle. For example, the shift scheme index table can be converted into a data format that can be received by the vehicle, such as a JavaScript Object Notation (JSON) format or an eXtensible Markup Language (XML) format, and sent to a vehicle control unit (VCU) of the vehicle. After the vehicle receives the index table, the vehicle stores the index table so as to call the index table during driving. If the shift scheme index table is constructed locally on the vehicle, the issuing operation is not required.

[0271] During driving, the vehicle can obtain the longitude and latitude information of the vehicle in real time by using a positioning device such as a Beidou positioning system or a Global Positioning System (GPS), compare the obtained longitude and latitude information with the pre-stored fixed route data, determine the fixed route currently driven, find the corresponding index value in the index table according to the determined fixed route, and then obtain the shift scheme matched with the route.

[0272] The vehicle shift scheme updating method provided in the embodiments of the present application can continuously obtain the corresponding index value according to the current longitude and latitude information during driving of the vehicle, and execute the matched shift scheme according to the obtained index value, so that the vehicle can shift gears according to the corresponding shift scheme on each fixed route, unnecessary energy loss can be reduced, the adaptability of the vehicle to each fixed route can be improved, and the driving economy of the vehicle on each fixed route can be optimized.

[0273] Figure 3 FIG. 2 is a flowchart of a vehicle shift scheme updating method provided in some embodiments of the present application. As shown in FIG. 2, the vehicle shift scheme updating method comprises the following steps. Figure 2

[0274] Step 1.1, condition classification based on short travel and FCM method, comprising: calculating the value of a specified characteristic parameter for each motion segment, performing dimensionality reduction on the characteristic parameter by principal component analysis, and selecting the principal component with a cumulative contribution rate reaching a standard as a classification standard; setting a clustering threshold, classifying the motion segments by using a FCM algorithm, and constructing a working condition segment library.

[0275] ​Step 1.2, fixed route working condition based on Markov chain, including: fixed route motion segment driving state division: combined with Kneser-Ney smoothing method to construct state transition matrix; determine the starting state and the ending state of the working condition, and use the maximum probability transition principle to construct the intermediate transition process; calculate the working condition characteristic parameters, duration, and distance, and form a candidate working condition library.

[0276] Step 1.3, working condition evaluation based on characteristic parameters, including: comparing the characteristic parameters of the original data segment and the design working condition, calculating the relative average error, and outputting the design working condition that meets the error threshold.

[0277] Step 2.1, shift curve optimization based on particle swarm algorithm, including: establishing an energy consumption simulation model to simulate the driving force output, shift action judgment, motor efficiency query and energy consumption accumulation process during vehicle operation; taking the selected design working condition as the background, taking the lowest energy consumption per 100 kilometers as the objective function, and taking the vehicle dynamic demand as the constraint condition, calling the improved multi-dimensional particle swarm algorithm with constraints to optimize the shift curve under the typical working condition.

[0278] Step 2.2, fixed route shift curve switching, including: converting the design working condition coordinates, converting the time coordinate axis to the mileage coordinate axis, and recording the latitude and longitude information corresponding to each shift curve; design a fixed route recognition scheme, the VCU receives the current vehicle's latitude and longitude information in real time, and judges whether the vehicle enters the preset fixed route area combined with the heading angle; after entering the fixed route area and lasting for a corresponding time threshold, switch to the shift scheme corresponding to the current position; continuously monitor the vehicle's operation process, extract and update the fixed route data, and repeat the process of data setting, shift scheme optimization, and VCU scheme integration.

[0279] Figure 4 is a flowchart of a vehicle shift scheme updating method provided by some embodiments of the present application. As shown in Figure 4 , the vehicle shift scheme updating method further includes:

[0280] Batch reading of ASC messages; dividing the messages into kinematic segments according to the idle start and end points; segment characteristic value calculation to form a characteristic matrix of n*F, n being the number of segments and P being the number of characteristics; principal component dimension reduction of characteristic variables; FCM clustering analysis; calculating the characteristic values of each typical working condition, merging the typical working conditions by category; calculating the state transition matrix according to the working condition type, and calculating the confusion matrix according to the vehicle speed points in each working condition, and entering the Markov algorithm; forming a working condition curve that meets the state transition probability, vehicle speed characteristic error <Y%, and mileage error <X m.

[0281] Among them, the principal component dimension reduction of characteristic variables includes:

[0282] Feature data standardization; calculate the Pearson correlation coefficient between indicators, eliminate indicators with correlation exceeding A; call the pca principal component analysis function to calculate the contribution rate of each principal component and the indicator coefficient; extract the top n principal components with a contribution rate of P; process the feature matrix according to the principal component coefficient after dimension reduction.

[0283] The FCM clustering analysis includes:

[0284] Set the expected number of categories N, the distance calculation method is Euclidean square, and the maximum number of iterations G; initialize the membership matrix U, calculate the initial clustering center of each category, iteratively update the membership matrix and the clustering center, until the distance between the clustering sample and the clustering center meets the convergence condition; store the kinematic segment by type.

[0285] Figure 5 is a flowchart of the improved particle swarm optimization algorithm provided by some embodiments of the present application. As shown in Figure 5 The updating method of the vehicle gear shifting scheme further includes:

[0286] Particle swarm initialization: generate an initial population of g particles (g gear shifting curves), the maximum number of iterations M, the inertia weight w, and the self-learning factor and group learning factor c1 / c2;

[0287] Initial population particle fitness value calculation: call the simulation model to calculate the full-condition 100 km energy consumption obtained from the gear shifting curve formed according to the particle, and use it as the fitness value of the particle, and record the position information of the particle. If the particle corresponds to the historical particle position information library, set the fitness value of the repeated particle to the maximum value to avoid repeated calculation;

[0288] Optimal scheme updating: record the particle information corresponding to the minimum fitness value, i.e. the gear shifting curve scheme with the lowest full-condition 100 km energy consumption of the whole vehicle;

[0289] Particle population iteration: enter the particle swarm iteration process. When the number of iterations is less than M and the calculation result has not converged, the optimal particle information is mutated according to the updating rule;

[0290] Boundary processing: perform boundary processing and determine whether it exists in the historical particle information library. If all conditions are met, calculate and update the optimal particle fitness value and the corresponding position information;

[0291] Calculation result output: stop iteration when the number of iterations reaches the maximum preset iteration number or the calculation result converges for A consecutive times, and output the optimal gear shifting scheme.

[0292] The vehicle gear shifting scheme updating method provided in the embodiments of the present application is applied to a controller of a vehicle, that is, the execution subject of the method can be the controller of the vehicle or a functional module or functional entity in the controller of the vehicle that can implement the vehicle gear shifting scheme updating method. The controller of the vehicle mentioned in the embodiments of the present application includes but is not limited to a vehicle control unit (VCU), a vehicle electronic control unit (ECU), etc. The vehicle gear shifting scheme updating method provided in the embodiments of the present application is described below by taking the controller of the vehicle as the execution subject.

[0293] Figure 6 FIG. 4 is a fourth flowchart of a vehicle gear shifting scheme updating method provided in some embodiments of the present application. As shown in FIG. 4, the vehicle gear shifting scheme updating method is applied to a controller of a vehicle, and the method includes steps 610 and 620. Figure 6

[0294] In step 610, the current latitude and longitude information of the vehicle is detected before the vehicle travels along a fixed route.

[0295] In step 620, a matched gear shifting scheme is searched for in a gear shifting scheme index table according to the current latitude and longitude information, and the matched gear shifting scheme is switched to for performing corresponding gear shifting control.

[0296] The gear shifting scheme index table assigns corresponding index values to different fixed routes.

[0297] The gear shifting scheme index table can be generated locally in the vehicle, for example, in a vehicle control unit or a vehicle electronic control unit, or can be generated in a server, for example, by the server according to original data transmitted back by the vehicle when traveling along a fixed route. If the gear shifting scheme index table is generated in the server, the gear shifting schemes matched with the fixed routes can be determined in advance by the server and delivered to the controller of the vehicle in the form of the gear shifting scheme index table, without real-time analysis in the vehicle, thereby avoiding high computing power requirements for the vehicle and improving the compatibility of the vehicle gear shifting scheme updating.

[0298] It can be understood that the index table includes a plurality of index values, each index value corresponding to a fixed route and a gear shifting scheme matched with the fixed route. The controller of the vehicle can search for a matched gear shifting scheme in the gear shifting scheme index table according to the current latitude and longitude information.

[0299] ​For example, the controller of the vehicle can receive the latitude and longitude data sent by the telematics box (T-BOX), so as to realize the monitoring of the current latitude and longitude information of the vehicle during driving, and the T-BOX can be a positioning device such as a Beidou positioning system or a global positioning system (GPS) to obtain the latitude and longitude information of itself in real time.

[0300] After obtaining the latitude and longitude information, the vehicle can compare the current latitude and longitude information of the vehicle with the pre-stored fixed route data, determine the fixed route currently driven, find the corresponding index value in the index table according to the determined fixed route, and then obtain the shift scheme matched with the route. The corresponding shift control is executed, that is, the controller of the vehicle controls the shift mechanism (such as a gearbox) of the vehicle according to the shift scheme to perform the shift.

[0301] Figure 7 FIG. 1 is a schematic diagram of switching shift schemes provided by some embodiments of the present application. As shown in FIG. 1, the vehicle continuously detects the shift scheme index value (Indx) corresponding to the current latitude and longitude during driving, and dynamically switches the shift scheme according to the value of Indx: Figure 7

[0302] If Indx = 0, it indicates that the current latitude and longitude of the vehicle is not in the shift scheme index table or the current shift scheme is the shift scheme matched with the fixed route corresponding to the current latitude and longitude in the shift scheme index table, at this time the shift scheme remains unchanged;

[0303] If Indx = 1, it indicates that the vehicle is currently driving on the fixed route corresponding to Indx = 1, and the vehicle switches to shift scheme 1;

[0304] If Indx = n, it indicates that the vehicle is currently driving on the fixed route corresponding to Indx = n, and the vehicle switches to shift scheme n.

[0305] The updating method of the vehicle shift scheme provided by the embodiments of the present application detects the current latitude and longitude information of the vehicle before the vehicle drives along the fixed route, finds the matched shift scheme in the shift scheme index table according to the current latitude and longitude information, and switches to the matched shift scheme. The shift scheme index table contains a plurality of index values, each index value corresponds to a fixed route and its matched shift scheme. Based on the shift scheme index table, the vehicle controller can automatically switch to the matched shift scheme according to the current latitude and longitude information of the vehicle, to perform the corresponding shift control, so that the vehicle shifts gears according to the corresponding shift scheme on the fixed route, to reduce unnecessary energy loss, improve the adaptability of the vehicle to the fixed route, and optimize the energy consumption performance of the vehicle driving on the fixed route.

[0306] ​The vehicle gear shifting scheme updating method provided in the embodiments of the present application can be executed by a vehicle gear shifting scheme updating device. The vehicle gear shifting scheme updating device provided in the embodiments of the present application is described by taking the vehicle gear shifting scheme updating device executing the vehicle gear shifting scheme updating method as an example.

[0307] Figure 8 is one of the structural diagrams of the vehicle gear shifting scheme updating device provided in some embodiments of the present application. As shown in Figure 8 the vehicle gear shifting scheme updating device 800 comprises:

[0308] a data processing unit 801 configured to acquire historical data of a vehicle driving on a plurality of fixed routes, and identify working conditions corresponding to each passing area in the plurality of fixed routes;

[0309] a vehicle speed data generating unit 802 configured to generate target vehicle speed change data corresponding to the historical data based on the working conditions corresponding to each passing area;

[0310] a scheme updating unit 803 configured to update a gear shifting scheme matched with each fixed route respectively by using the target vehicle speed change data and vehicle parameters, so that the vehicle switches to the matched gear shifting scheme when driving on different fixed routes.

[0311] The vehicle gear shifting scheme updating device provided in the embodiments of the present application can realize each process of the vehicle gear shifting scheme updating method provided in the embodiments of the present application, and thus the details are not described herein again.

[0312] The vehicle gear shifting scheme updating method applied to the controller of the vehicle provided in the embodiments of the present application can be executed by a vehicle gear shifting scheme updating device. The vehicle gear shifting scheme updating device provided in the embodiments of the present application is described by taking the vehicle gear shifting scheme updating device executing the vehicle gear shifting scheme updating method applied to the controller of the vehicle as an example.

[0313] Figure 9 is another structural diagram of the vehicle gear shifting scheme updating device provided in some embodiments of the present application. As shown in Figure 9 the vehicle gear shifting scheme updating device 900 comprises:

[0314] a detection unit 901 configured to detect current latitude and longitude information of the vehicle before the vehicle drives along a fixed route;

[0315] a switching execution unit 902 configured to find a matched gear shifting scheme in a gear shifting scheme index table according to the current latitude and longitude information, and switch to the matched gear shifting scheme to execute corresponding gear shifting control; the gear shifting scheme index table assigns corresponding index values to different fixed routes.

[0316] The vehicle gear shifting scheme updating apparatus provided by the embodiments of the present application can realize each process of the vehicle gear shifting scheme updating method applied to the vehicle controller, and thus repeated description is omitted here.

[0317] The vehicle gear shifting scheme updating apparatus in the embodiments of the present application can be an electronic device or a component in an electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal or other device than a terminal. For example, the electronic device can be a mobile phone, a tablet computer, a notebook computer, a palm computer, a vehicle-mounted electronic device, a Mobile Internet Device (MID), an augmented reality (AR) / virtual reality (VR) device, a robot, a wearable device, an ultra-mobile personal computer (UMPC), a netbook, or a personal digital assistant (PDA), and can also be a server, a Network Attached Storage (NAS), a personal computer (PC), a television (TV), a teller machine, or a self-service machine, and the like. The embodiments of the present application are not limited in this regard.

[0318] The vehicle gear shifting scheme updating apparatus in the embodiments of the present application can be a device with an operating system. The operating system can be a Windows operating system, an Android operating system, an IOS operating system, or other possible operating system, and the embodiments of the present application are not limited in this regard.

[0319] The embodiments of the present application also provide a vehicle including a controller configured to perform each process of the vehicle gear shifting scheme updating method applied to the vehicle controller.

[0320] Figure 10 FIG. 1 is a structural schematic diagram of an electronic device provided by some embodiments of the present application. In some embodiments, as shown in FIG. 1, the electronic device 1000 includes a processor 1001, a memory 1002, and a computer program stored in the memory 1002 and capable of running on the processor 1001. Figure 10 The embodiments of the present application also provide an electronic device 1000 including a processor 1001, a memory 1002, and a computer program stored in the memory 1002 and capable of running on the processor 1001. When the computer program is executed by the processor 1001, each process of the vehicle gear shifting scheme updating method is implemented, and the same technical effects are achieved. Thus, repeated description is omitted here.

[0321] It should be noted that the electronic device in the embodiments of the present application includes the mobile electronic device and the non-mobile electronic device described above.

[0322] The embodiments of the present application also provide a non-transitory computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement each process of the embodiments of the vehicle gear shift scheme updating method and achieve the same technical effects. To avoid repetition, details are not described herein.

[0323] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes a computer readable storage medium, such as a computer readable only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0324] The embodiments of the present application also provide a computer program product, which includes a computer program. The computer program is executed by a processor to implement the vehicle gear shift scheme updating method.

[0325] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes a computer readable storage medium, such as a computer readable only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0326] The embodiments of the present application also provide a chip. The chip includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is configured to execute a program or an instruction to implement each process of the embodiments of the vehicle gear shift scheme updating method and achieve the same technical effects. To avoid repetition, details are not described herein.

[0327] It should be understood that the chip mentioned in the embodiments of the present application can also be referred to as a system level chip, a system chip, a chip system or a system on chip, etc.

[0328] It should be noted that, in the present document, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises a", "comprising", or "comprises" does not, without more constraints, preclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element. Additionally, it should be noted that the terms "one embodiment", "some embodiments", "certain embodiments", "certain examples", or "some examples" as used in the present document are intended to refer to one or more embodiments or examples that do not necessarily have to cover all embodiments or examples of the present application. In other words, use of the above terms does not necessarily refer to the same embodiment or example. Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0329] Those skilled in the art can clearly understand that the above-mentioned embodiment method can be realized by means of software and necessary general hardware platform, of course, it can also be realized by hardware, but in many cases, the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a plurality of instructions for making a terminal (which can be a mobile phone, computer, server, or network equipment, etc.) execute the method described in each embodiment of the present application.

[0330] The embodiments of the present application are described above in conjunction with the drawings, but the present application is not limited to the above-mentioned specific embodiments, and the above-mentioned specific embodiments are only illustrative, not restrictive, and those skilled in the art can make many forms without departing from the purpose of the present application and the scope protected by the claims under the inspiration of the present application, which all belong to the protection of the present application.

[0331] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "exemplary embodiment", "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 application. In the present specification, the exemplary description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the described specific features, structures, materials or characteristics can be combined in any suitable manner in any one or more embodiments or examples.

[0332] While the embodiments of the application have been shown and described, it is to be understood that the embodiments can be varied, modified, substituted and changed by those skilled in the art without departing from the principles and spirit of the application, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for updating a vehicle shifting plan, characterized in that: The method comprises: Acquiring historical data of a vehicle traveling on a plurality of fixed routes, and identifying operating conditions corresponding to respective areas along the plurality of fixed routes; generating target vehicle speed change data corresponding to the historical data based on the operating conditions corresponding to each route area; For any fixed route, an energy consumption simulation model is established using target vehicle speed change data corresponding to historical data corresponding to the fixed route as operating condition input and vehicle parameters as model parameters; Obtaining a default shift plan executed by the vehicle on a targeted fixed route, and generating a plurality of candidate shift plans that meet the vehicle's power performance requirement constraints; Representing the default gear shifting scheme and each of the multiple candidate gear shifting schemes as a particle to obtain an initial particle group including multiple particles; Initializing the initial particle group to preliminarily determine the speed and position of each particle; In each iteration, the pre-established energy consumption simulation model is called to calculate the energy consumption of the shifting scheme corresponding to each particle, which is used as the fitness value of each particle in the current iteration; Under the constraints of particle boundary conditions, the speed and position of each particle are updated based on the fitness value of each particle in the current iteration; Returning to the step of calling the pre-established energy consumption simulation model to calculate the energy consumption of the gear shifting scheme corresponding to each particle, the step continues until the iteration termination condition is reached, and the target gear shifting scheme is obtained; The shifting scheme that matches the targeted fixed route is updated based on the target shifting scheme, so that the vehicle switches to a matching shifting scheme when traveling along different fixed routes.

2. The method for updating a vehicle shifting plan according to claim 1, characterized in that: The generating of target vehicle speed change data corresponding to the historical data based on the operating conditions corresponding to each route area includes: For any fixed route, obtain statistical parameters of the working conditions corresponding to each area passed through in the fixed route; Based on the statistical parameters of the working conditions corresponding to each route area, a working condition transfer state matrix and a working condition-vehicle speed confusion matrix matching the fixed route are determined; Based on the operating condition transfer state matrix and the operating condition-vehicle speed confusion matrix, target vehicle speed change data corresponding to the historical data corresponding to the fixed route is generated.

3. The method for updating a vehicle shifting plan according to claim 2, characterized in that: The method of determining a working condition transfer state matrix and a working condition-vehicle speed confusion matrix that match the fixed route based on the statistical parameters of the working conditions corresponding to each route area includes: According to the sequence of working condition types of each path area, the working condition type transfer relationship between adjacent path areas is counted to obtain the number of transfers between each working condition type; Based on the number of transfers between the various operating condition types, a working condition transfer state matrix is ​​constructed; Based on the trajectory data under each operating condition type, the frequency of occurrence of the corresponding vehicle speed values ​​in different intervals is counted to obtain an operating condition-vehicle speed confusion matrix that matches the targeted fixed route. The confusion matrix is ​​used to reflect the joint characteristics between the operating condition type and the vehicle speed distribution.

4. The method for updating a vehicle shifting plan according to claim 2 or 3, characterized in that: The generating target vehicle speed change data corresponding to the historical data corresponding to the fixed route based on the operating condition transfer state matrix and the operating condition-vehicle speed confusion matrix includes: Constructing a state transition path of an operating condition sequence according to the operating condition transfer state matrix to generate an operating condition state sequence fitting the fixed route; According to each operating condition type in the operating condition state sequence, combined with the corresponding vehicle speed distribution in the operating condition-vehicle speed confusion matrix, a corresponding vehicle speed segment is generated; The plurality of vehicle speed segments are spliced ​​together in the order of the operating state sequence to obtain target vehicle speed change data corresponding to the historical data corresponding to the fixed route.

5. The method for updating a vehicle shifting plan according to claim 1, characterized in that: The updating of the velocity and position of each particle based on the fitness value of each particle in the current iteration under the constraint of the particle boundary conditions includes: In the current iteration, if the current fitness value of the targeted particle is higher than its own historical individual optimal fitness value, the historical individual optimal fitness value of the targeted particle and the corresponding historical individual optimal position are updated; Based on the particle with the highest historical individual optimal fitness value among all particles, update the group optimal fitness value and the corresponding group optimal position; Update the speed of the targeted particle according to its speed and position in the current iteration, the historical individual optimal position and the group optimal position; The position of the targeted particle in the next iteration is calculated based on the updated velocity, and the position is corrected when the particle boundary conditions are exceeded.

6. The method for updating a vehicle shifting plan according to claim 1 or 5, characterized in that: The calling of a pre-established energy consumption simulation model to calculate the energy consumption of the shifting scheme corresponding to each particle includes: For each particle, according to the gear shift plan corresponding to each particle, under the constraints of the gear shift speed limit condition, the vehicle is controlled to simulate the execution of the gear shift plan corresponding to the particle to perform an upshift or downshift operation; Recording the number of upshifts and downshifts during the simulation process, and determining the upshift energy consumption corresponding to the upshift operation and the downshift energy consumption corresponding to the downshift operation; Obtain corresponding motor efficiency based on the motor parameter table under different gears, and calculate the driving energy consumption during the simulation process based on the motor efficiency; Based on the driving energy consumption, the upshift energy consumption, and the downshift energy consumption, the energy consumption of the gear shift scheme corresponding to the targeted particles is determined.

7. The method for updating a vehicle shifting plan according to claim 1, characterized in that: The identifying the operating conditions corresponding to the respective passage areas in the plurality of fixed routes includes: For any fixed route, feature extraction is performed on the corresponding historical data to obtain the trajectory features of the vehicle in each area of ​​the fixed route; Cluster analysis is performed on the trajectory features to identify the working conditions corresponding to each path area.

8. The method for updating a vehicle shifting plan according to claim 7, characterized in that: For any fixed route, feature extraction is performed on the corresponding historical data to obtain the trajectory features of the vehicle in each area passed by the fixed route, including: For any fixed route, obtain the original data of the vehicle traveling along the fixed route; the original data includes time, speed, and latitude and longitude information; Segmenting the original data according to a vehicle speed threshold to obtain a plurality of data segments; For each data segment, calculating a multidimensional trajectory feature value used to describe vehicle behavior, and performing dimensionality reduction processing on the multidimensional trajectory feature value to obtain multiple trajectory features; According to the latitude and longitude information of each data segment, the path area to which it belongs is determined, and the corresponding trajectory features are classified into the path area to which it belongs, so as to form the trajectory features of the vehicle in each path area.

9. The method for updating a vehicle shifting plan according to claim 8, characterized in that: The dimensionality reduction processing is performed on the multi-dimensional trajectory feature value to obtain a plurality of trajectory features, including: Standardizing the multi-dimensional trajectory characteristic values ​​to eliminate differences in dimension and value range among the multi-dimensional trajectory characteristic values; Using principal component analysis to perform linear transformation on the multidimensional trajectory eigenvalues, and extracting multiple principal components covering the main data variation information; The plurality of principal components are used as trajectory features to complete dimensionality reduction processing of the multi-dimensional trajectory feature values.

10. The method for updating a vehicle shifting plan according to claim 1, characterized in that: The method further comprises: Based on the updated shifting scheme, a shifting scheme index table is constructed for matching different fixed routes, so that the vehicle continuously obtains corresponding index values ​​from the shifting scheme index table according to the current latitude and longitude information during driving, and executes the matching shifting scheme according to the obtained index values; The shift scheme index table includes a plurality of index values, each index value corresponding to a fixed route and its matching shift scheme.

11. A method for updating a vehicle shifting plan, characterized in that: The method for updating a vehicle shifting scheme according to claim 1 is applied to a vehicle controller, the method comprising: Before the vehicle travels along a fixed route, the vehicle's current latitude and longitude information is detected; Searching for a matching shift scheme in a shift scheme index table according to the current latitude and longitude information, and switching to the matching shift scheme to execute corresponding shift control; The shifting scheme index table assigns corresponding index values ​​to different fixed routes.

12. A vehicle, characterized in that: A controller is included, wherein the controller is used to execute the vehicle shift plan updating method according to claim 11.

13. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, it implements the vehicle shifting scheme updating method according to any one of claims 1 to 10, or executes the vehicle shifting scheme updating method according to claim 11.

14. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements the method for updating the vehicle shifting scheme according to any one of claims 1 to 10, or implements the method for updating the vehicle shifting scheme according to claim 11.

15. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, it implements the method for updating a vehicle shifting scheme according to any one of claims 1 to 10, or executes the method for updating a vehicle shifting scheme according to claim 11.

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