A vehicle speed planning method, device, electronic equipment, and medium

Abstract

This application provides a vehicle speed planning method, apparatus, electronic device, and medium, comprising: discretizing a target mileage to obtain multiple discrete distance points; discretizing the vehicle speed at each discrete distance point to obtain multiple discrete vehicle speed points; determining the vehicle speed state transition result for each discrete distance point using a vehicle speed state transition equation and constraints; calculating the energy consumption of the target vehicle under different driving modes based on the vehicle speed state transition results; selecting a vehicle speed state transition path based on the energy consumption calculation results and an objective function; and planning the vehicle speed according to the vehicle speed state transition path. This application can rationally plan the optimal vehicle speed for a target mileage, and simultaneously perform energy management based on the planned optimal vehicle speed, ensuring energy consumption optimization while reducing commuting time, improving or enhancing the user's driving experience, and achieving green wave travel.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a vehicle speed planning method and apparatus, electronic equipment and medium. Background Technology

[0002] As the automotive industry moves towards electrification and connectivity, energy management methods for plug-in hybrid electric vehicles (PHEVs) are constantly being researched. The most widely used approach is the mass-produced ECMS (Equivalent Consumption Minimization Strategy) scheme, and several derivative strategies based on it. For example, by identifying operating conditions using the current vehicle status and navigation / map information, the fuel factor or the operating state points of the engine and electric motor in the ECMS can be optimized based on specific sub-conditions. Another approach is to use global dynamic programming (DP) based on known driving cycles or vehicle speed trajectories to obtain the optimal SOC (State of Charge) trajectory, thereby achieving the optimal energy allocation strategy.

[0003] In conventional energy management strategies, the current driving conditions are used as known input information. Energy consumption optimization is performed under a given speed trajectory, limiting the potential for further optimization. Furthermore, conventional ECMS strategies execute hybrid mode switching based on the vehicle's current state, achieving only a theoretically instantaneous optimum. Common hybrid mode planning, while considering global optima, only searches for the optimal energy management strategy under a given speed trajectory, without considering whether this speed trajectory itself is the optimal speed for that road segment. Summary of the Invention

[0004] In view of the shortcomings of the prior art described above, the purpose of this application is to provide a vehicle speed planning method, apparatus, electronic device and medium to solve the technical problems existing in the prior art in vehicle speed planning.

[0005] To achieve the above and other related objectives, this application provides a vehicle speed planning method, comprising the following steps:

[0006] The target driving distance, which is determined in advance or in real time, is discretized to obtain multiple discrete distance points; and the vehicle speed at each discrete distance point is discretized to obtain multiple discrete vehicle speed points.

[0007] The vehicle speed state transition equation and pre-determined or real-time constraints are used to determine the vehicle speed state transition result at each discrete distance point, and the energy consumption of the target vehicle in different driving modes is calculated based on the vehicle speed state transition result; wherein, the vehicle speed state transition equation is obtained based on the plurality of discrete distance points and the plurality of discrete vehicle speed points.

[0008] Based on the energy consumption calculation results and the pre-set or real-time objective function, the vehicle speed state transition path is selected, and the vehicle speed is planned for the target vehicle according to the selected vehicle speed state transition path.

[0009] In one embodiment of this application, the process of obtaining the vehicle speed state transition equation based on the plurality of distance discrete points and the plurality of vehicle speed discrete points includes:

[0010] Based on the distance between the kth discrete point and the starting point of the target driving mileage, the distance between the (k+1)th discrete point and the starting point of the target driving mileage, the vehicle speed candidate states at the kth discrete point, and the vehicle speed candidate states at the (k+1)th discrete point, the vehicle speed state transition equation is established; where k is a positive integer.

[0011] In one embodiment of this application, the process of determining constraints in advance or in real time includes:

[0012] The feasible acceleration region is determined based on the preset minimum acceleration and the preset maximum acceleration, and the acceleration jerk range is determined based on the preset minimum jerk threshold and the preset maximum jerk threshold.

[0013] The feasible acceleration region and the acceleration jerk region are used as the constraints.

[0014] In one embodiment of this application, the process of calculating the energy consumption of the target vehicle under different driving modes based on the vehicle speed state transition results includes:

[0015] When the target vehicle is in parallel drive mode, the wheel-end torque demand under the vehicle speed state transition is calculated based on the vehicle longitudinal dynamics model, and the motor wheel-end torque boundary is calculated based on the wheel-end torque boundary conditions provided by the engine.

[0016] The feasible range of motor torque is determined based on the motor wheel end torque boundary, and different motor wheel end output torques are traversed within the feasible range of motor torque. Based on the front and rear motor drive rules of the target vehicle, the torque limit conditions of the front and rear drive shafts of the target vehicle, and the torque limit conditions of the front and rear motors of the target vehicle, the front and rear motor torques and engine distribution torque of the target vehicle are calculated.

[0017] The electric drive energy consumption corresponding to the front and rear motor torques and the engine energy consumption corresponding to the engine distribution torque are found from the predetermined power table. Based on the electric drive energy consumption and engine energy consumption of the target vehicle in parallel drive mode, optimization calculations are performed. The electric drive energy consumption corresponding to the optimal front and rear motor torques, the engine energy consumption corresponding to the optimal engine distribution torque, and the preset accessory energy consumption are then added together to obtain the total energy consumption of the target vehicle in parallel drive mode.

[0018] In one embodiment of this application, the process of calculating the energy consumption of the target vehicle under different driving modes based on the vehicle speed state transition results includes:

[0019] Based on the vehicle speed state transition results, when the target vehicle is in a non-parallel drive mode, the wheel-end torque demand under the vehicle speed state transition is calculated based on the vehicle longitudinal dynamics model; and...

[0020] Based on the wheel-end torque demand and different front and rear motor torque distribution ratios, we sought the optimal drive mode in pure electric mode and the corresponding electric drive energy consumption, as well as...

[0021] Based on different engine range-extending power levels, the study iterates through the range-extending speeds to find the lowest engine energy consumption corresponding to different engine range-extending power levels; and...

[0022] The total drive energy consumption corresponding to the optimal range extension mode under the range extension mode is determined based on the lowest engine energy consumption corresponding to different engine range extension power. The total drive energy consumption includes engine energy consumption and electric drive energy consumption.

[0023] Compare the electric drive energy consumption corresponding to the optimal driving mode in pure electric mode with the total driving energy consumption corresponding to the optimal range-extending mode in range-extending mode.

[0024] When the electric drive energy consumption corresponding to the optimal driving mode in pure electric mode is less than the total driving energy consumption corresponding to the optimal range-extending mode in range-extending mode, the electric drive energy consumption corresponding to the optimal driving mode in pure electric mode is added together with the preset accessory energy consumption to obtain the energy consumption of the target vehicle in pure electric driving mode.

[0025] When the electric drive energy consumption corresponding to the optimal driving mode in pure electric mode is greater than or equal to the total driving energy consumption corresponding to the optimal range-extending mode in range-extending mode, the total driving energy consumption corresponding to the optimal range-extending mode in range-extending mode is added together with the preset accessory energy consumption and used as the energy consumption of the target vehicle in range-extending driving mode.

[0026] In one embodiment of this application, the process of pre-setting or real-time setting of the objective function includes: setting the objective function based on the number of discrete distance points divided into target driving distances, engine energy consumption and electric drive energy consumption calculated based on equivalent fuel factors, vehicle travel time, dynamism value, and parking penalty term; wherein, the dynamism value is determined based on the rate of change of acceleration, and the parking penalty term is used to characterize the energy consumption fluctuation when the vehicle starts and stops. In one embodiment of this application, the method further includes: calculating the vehicle travel time based on the vehicle's normal driving time, the vehicle's waiting time at traffic lights at intersections, the vehicle speed candidate state at the kth discrete distance point, the vehicle speed candidate state at the (k+1)th discrete distance point, the distance between the kth discrete distance point and the starting point of the target driving distance, the distance between the (k+1)th discrete distance point and the starting point of the target driving distance, and the acceleration when the vehicle speed state jumps at the kth discrete distance point;

[0027] Wherein, the acceleration when the vehicle speed state jumps at the kth discrete distance point is obtained by the vehicle speed candidate state at the kth discrete distance point, the vehicle speed candidate state at the (k+1)th discrete distance point, the distance between the kth discrete distance point and the starting point of the target driving mileage, and the distance between the (k+1)th discrete distance point and the starting point of the target driving mileage; k is a positive integer.

[0028] This application also provides a vehicle speed planning device, the device comprising:

[0029] The data preprocessing module is used to discretize the target driving distance determined in advance or in real time to obtain multiple distance discrete points; and to discretize the vehicle speed at each distance discrete point to obtain multiple vehicle speed discrete points.

[0030] The energy consumption calculation module is used to determine the vehicle speed state transition result at each distance discrete point through the vehicle speed state transition equation and pre-determined or real-time constraints, and to calculate the energy consumption of the target vehicle under different driving modes based on the vehicle speed state transition result; wherein, the vehicle speed state transition equation is obtained based on the plurality of distance discrete points and the plurality of vehicle speed discrete points.

[0031] The vehicle speed planning module is used to select vehicle speed state transition paths based on energy consumption calculation results and pre-set or real-time objective functions, and to plan the vehicle speed of the target vehicle according to the selected vehicle speed state transition paths.

[0032] This application also provides an electronic device, characterized in that it includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the vehicle speed planning method described in any one of the above.

[0033] This application also provides a computer-readable storage medium storing computer-readable instructions thereon, which, when executed by a computer's processor, cause the computer to perform any of the vehicle speed planning methods described above.

[0034] As described above, this application provides a vehicle speed planning method, apparatus, electronic device, and medium, which has the following beneficial effects: By discretizing the target mileage of the target vehicle, multiple discrete distance points are obtained; and at each discrete distance point, the vehicle speed is discretized to obtain multiple discrete vehicle speed points; then, through the vehicle speed state transition equation and pre-determined or real-time constraints, the vehicle speed state transition result for each discrete distance point is determined, and the energy consumption of the target vehicle under different driving modes is calculated based on the vehicle speed state transition result; wherein, the vehicle speed state transition equation is obtained based on multiple discrete distance points and multiple discrete vehicle speed points; finally, based on the energy consumption calculation result and a pre-determined or real-time objective function, a vehicle speed state transition path is selected, and vehicle speed planning is performed on the target vehicle according to the selected vehicle speed state transition path. Therefore, this application can reasonably plan the optimal vehicle speed for the target mileage, and simultaneously perform energy management based on the planned optimal vehicle speed. This not only reduces commuting time while ensuring energy consumption optimization and achieving comprehensive optimization across the entire road segment, but also improves or enhances the user's driving experience, realizing green wave travel. Attached Figure Description

[0035] Figure 1 This is a flowchart illustrating a vehicle speed planning method provided in one embodiment of this application.

[0036] Figure 2 This is a schematic diagram of the energy consumption calculation process in parallel mode provided in one embodiment of this application;

[0037] Figure 3 This is a schematic diagram of the energy consumption calculation process in non-parallel mode provided in one embodiment of this application;

[0038] Figure 4 This is a schematic diagram illustrating the principle of a dynamic programming algorithm provided in one embodiment of this application;

[0039] Figure 5 A flowchart illustrating a vehicle speed planning method provided in another embodiment of this application;

[0040] Figure 6 This is a schematic diagram of the intersection traffic light processing logic provided in one embodiment of this application;

[0041] Figure 7 A schematic diagram of the hardware structure of a vehicle speed planning device provided in an embodiment of this application;

[0042] Figure 8 This is a schematic diagram of the hardware structure of an electronic device suitable for implementing one or more embodiments of this application. Detailed Implementation

[0043] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It is understood that, without conflict, the following embodiments and features in the embodiments can be combined with each other. In addition, it is understood that the illustrations provided in the following embodiments are only schematically illustrating the basic concept of this application. Therefore, the drawings only show the components related to this application and are not drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the type, quantity and proportion of each component can be arbitrarily changed, and the component layout may also be more complex.

[0044] Figure 1 A flowchart illustrating a vehicle speed planning method is shown. Specifically, in an exemplary embodiment, as... Figure 1 As shown, this embodiment provides a vehicle speed planning method, which includes the following steps:

[0045] S110, the target driving range, determined in advance or in real time, is discretized to obtain multiple discrete distance points; and the vehicle speed is discretized at each discrete distance point to obtain multiple discrete vehicle speed points. In this embodiment or other embodiments, the target vehicle may be a vehicle containing a dual-motor plug-in hybrid structure. The driving modes of the target vehicle include, but are not limited to: pure electric drive mode (also called pure electric mode), parallel drive mode (also called parallel mode), and range-extended drive mode (also called range-extended mode). Pure electric mode means that the vehicle is entirely powered by the battery, driven by the electric motor, and the engine does not participate in driving. Parallel mode allows the engine and electric motor to provide power to the vehicle simultaneously or individually; the engine can directly drive the wheels, and the electric motor can also provide auxiliary power or become the main driving force when needed. Range-extended mode extends the driving range by adding an auxiliary engine or range extender; when the battery power is insufficient, the range extender starts and charges the battery through the generator or directly provides power to the electric motor, thereby maintaining the vehicle's driving capability. In this embodiment or other embodiments, before discretizing the predetermined or real-time target driving mileage, the Predictive Driving Range Estimation (PDRE) function developed within the target vehicle can be invoked. Based on the PDRE result, it can be estimated whether the current battery level supports completing the entire target driving mileage in pure electric mode. If the current battery level supports completing the entire target driving mileage in pure electric mode, it is then determined whether there is a charging requirement at the destination of the target driving mileage. If there is a charging requirement at the destination, the vehicle speed is planned using pure electric mode. If the current battery level does not support completing the entire target driving mileage in pure electric mode, or if there is no charging requirement at the destination, the vehicle speed is planned using hybrid mode, and the target driving mileage is then discretized to obtain multiple distance discrete points. The target driving mileage includes, but is not limited to, the navigation mileage obtained from navigation information. The hybrid mode consists of one or more of pure electric mode, parallel mode, and range extender mode. In this embodiment or other embodiments, when discretizing the target driving mileage and the vehicle speed, the discretization can be performed based on the speed limit, slope, curvature, and traffic light information of the road ahead provided by the intelligent network information.

[0046] S120 determines the vehicle speed state transition result for each discrete distance point through the vehicle speed state transition equation and pre-determined or real-time constraints, and calculates the energy consumption of the target vehicle under different driving modes based on the vehicle speed state transition result; wherein, the vehicle speed state transition equation is obtained based on multiple discrete distance points and multiple discrete vehicle speed points.

[0047] S130, based on the energy consumption calculation results and the pre-set or real-time objective function, selects the vehicle speed state transition path, and performs vehicle speed planning for the target vehicle according to the selected vehicle speed state transition path.

[0048] Therefore, this embodiment can reasonably plan the optimal vehicle speed for the target driving distance, and perform energy management based on the planned optimal vehicle speed. This not only reduces commuting time while ensuring energy consumption optimization and achieving comprehensive optimization across the entire road segment, but also improves or enhances the user's driving experience and realizes green wave travel.

[0049] In an exemplary embodiment, the process of obtaining the vehicle speed state transition equation based on multiple discrete distance points and multiple discrete vehicle speed points includes: obtaining the distance between the k-th discrete distance point and the starting point of the target driving mileage, denoted as S(k); obtaining the distance between the (k+1)-th discrete distance point and the starting point of the target driving mileage, denoted as S(k+1); obtaining the vehicle speed candidate state at the k-th discrete distance point, denoted as V(k); and obtaining the vehicle speed candidate state at the (k+1)-th discrete distance point, denoted as V(k+1); and establishing the vehicle speed state transition equation based on the distance between the k-th discrete distance point and the starting point of the target driving mileage, the distance between the (k+1)-th discrete distance point and the starting point of the target driving mileage, the vehicle speed candidate state at the k-th discrete distance point, and the vehicle speed candidate state at the (k+1)-th discrete distance point, as follows: In the formula, a(k) represents the acceleration when the vehicle's velocity state changes at the kth discrete distance point.

[0050] In an exemplary embodiment, the process of determining constraints in advance or in real time includes: determining the feasible acceleration region based on a preset minimum acceleration and a preset maximum acceleration, where: k min ≤a(k)≤a max In the formula, a min Indicates the preset minimum acceleration, a max This represents the preset maximum acceleration. Furthermore, based on the preset minimum jerk threshold and the preset maximum jerk threshold, the acceleration jerk range is determined, resulting in: jerk min ≤a(k)-a(k-1)≤jerk max In the formula, a(k-1) represents the acceleration of the vehicle when its velocity state changes at the (k-1)th discrete point, jerk max This indicates the preset minimum agitation threshold, jerk. minThis represents a preset maximum abrupt change threshold. The feasible acceleration region and the abrupt change acceleration region are used as constraints. In this embodiment or other embodiments, the preset minimum acceleration, preset maximum acceleration, preset minimum abrupt change threshold, and preset maximum abrupt change threshold can be restricted or determined based on driver style, which can be determined based on the driver's historical driving data in the current vehicle. For example, the minimum acceleration determined based on driver style can be used as the preset minimum acceleration, the maximum acceleration determined based on driver style can be used as the preset maximum acceleration, the minimum abrupt change threshold restricted based on driver style can be used as the preset minimum abrupt change threshold, and the maximum abrupt change threshold restricted based on driver style can be used as the preset maximum abrupt change threshold.

[0051] In an exemplary embodiment, the process of calculating the energy consumption of a target vehicle under different driving modes based on the vehicle speed state transition results includes:

[0052] The driving mode of the target vehicle is determined based on the vehicle speed state transition results. For example, the average vehicle speed and acceleration within a single discrete distance interval in the vehicle speed state transition results can be used to determine whether the target vehicle is in parallel or non-parallel mode. Alternatively, the accelerator pedal depth indirectly obtained from the vehicle speed state transition results can also be used to determine whether the target vehicle is in parallel or non-parallel mode; the specific process will not be elaborated in this embodiment. As an example, if the average vehicle speed within a single discrete distance interval is greater than a preset vehicle speed, the driving mode of the target vehicle can be determined to be parallel mode. As another example, if the acceleration within a single discrete distance interval is greater than a preset acceleration, the driving mode of the target vehicle can be determined to be parallel mode. As yet another example, if both the average vehicle speed and the acceleration within a single discrete distance interval are greater than a preset vehicle speed, the driving mode of the target vehicle can be determined to be parallel mode.

[0053] If the target vehicle is determined to be in parallel drive mode based on the vehicle speed state transition results, the energy consumption calculation process for the target vehicle in parallel mode is as follows: Figure 2As shown. Specifically, the wheel-end torque demand under vehicle speed state transition is calculated based on the vehicle's longitudinal dynamics model, and the motor wheel-end torque boundary is calculated based on the wheel-end torque boundary conditions provided by the engine; the feasible range of motor torque is determined based on the motor wheel-end torque boundary, and different motor wheel-end output torques are traversed within the feasible range of motor torque; the front and rear motor torques and engine distribution torque of the target vehicle are calculated based on the target vehicle's front and rear motor drive rules, the target vehicle's front and rear drive shaft torque limits, and the target vehicle's front and rear motor torque limits; the electric drive energy consumption corresponding to the front and rear motor torques and the engine energy consumption corresponding to the engine distribution torque are found from a pre-determined power table, and optimization calculations are performed based on the target vehicle's electric drive energy consumption and engine energy consumption in parallel drive mode; the electric drive energy consumption corresponding to the optimal front and rear motor torques, the engine energy consumption corresponding to the optimal engine distribution torques, and the preset accessory energy consumption are superimposed to obtain the total energy consumption of the target vehicle in parallel drive mode.

[0054] If the target vehicle is determined to be in a non-parallel drive mode based on the vehicle speed state transition results, the energy consumption calculation process for the target vehicle in the non-parallel mode is as follows: Figure 3 As shown. Specifically, the wheel-end torque demand under vehicle speed state transition is calculated based on the vehicle's longitudinal dynamics model; and the optimal driving mode and corresponding electric drive energy consumption are found based on the wheel-end torque demand and different front and rear motor torque distribution ratios in pure electric mode; the minimum engine energy consumption is found by traversing the range-extending speeds based on different engine range-extending power; and the total driving energy consumption corresponding to the optimal range-extending mode is determined based on the minimum engine energy consumption corresponding to different engine range-extending power, where the total driving energy consumption includes engine energy consumption and electric drive energy consumption; the electric drive energy consumption corresponding to the optimal driving mode in pure electric mode is then calculated. The energy consumption is compared with the total driving energy consumption corresponding to the optimal range-extending mode in range-extending mode. When the electric drive energy consumption corresponding to the optimal driving mode in pure electric mode is less than the total driving energy consumption corresponding to the optimal range-extending mode in range-extending mode, the electric drive energy consumption corresponding to the optimal driving mode in pure electric mode is added together with the preset accessory energy consumption to obtain the energy consumption of the target vehicle in pure electric driving mode. When the electric drive energy consumption corresponding to the optimal driving mode in pure electric mode is greater than or equal to the total driving energy consumption corresponding to the optimal range-extending mode in range-extending mode, the total driving energy consumption corresponding to the optimal range-extending mode in range-extending mode is added together with the preset accessory energy consumption to obtain the energy consumption of the target vehicle in range-extending driving mode.

[0055] In an exemplary embodiment, the process of pre-setting or real-time setting of the objective function includes: setting the objective function based on the number of discrete distance points divided into target driving distances, engine energy consumption and electric drive energy consumption calculated based on equivalent fuel factors, vehicle travel time, dynamism value, and a parking penalty term; wherein the dynamism value is determined based on the rate of change of acceleration, and the parking penalty term is used to characterize the energy consumption fluctuations during vehicle start-stop. Specifically, In the formula, J represents the objective function value; N represents the number of discrete distance points used to divide the target mileage; J eny (k) represents the engine energy consumption and electric drive energy consumption calculated based on the equivalent fuel factor; J t (k) represents the vehicle passage time value; J jerk (k) represents the jerkiness value; J stop (k) represents the parking penalty at the intersection, accumulated based on the number of times; α and β represent weighting coefficients. The calculation process for vehicle travel time includes: J t (k)=t(k) norm +t(k) wait ; In the formula, t(k) norm Indicates normal travel time; t(k) wait V(k+1) represents the waiting time at the traffic light at the intersection; V(k+1) represents the vehicle speed candidate state at the (k+1)th discrete distance point; V(k) represents the vehicle speed candidate state at the kth discrete distance point; S(k+1) represents the distance between the (k+1)th discrete distance point and the starting point of the target driving distance; S(k) represents the distance between the kth discrete distance point and the starting point of the target driving distance; a(k) represents the acceleration when the vehicle speed state changes at the kth discrete distance point. In this embodiment or other embodiments, the objective function is calculated dimensionlessly.

[0056] In another exemplary embodiment of this application, such as Figure 5 As shown, a vehicle speed planning method is also provided, which can be applied to target vehicles with a dual-motor plug-in hybrid structure. It considers factors such as travel time on road segments, driving comfort, and the possibility of green wave travel at traffic light intersections. Based on a dynamic programming optimization algorithm, it solves for a set of optimal vehicle speed curves and corresponding hybrid energy management modes (including parallel mode, range-extended mode, and pure electric mode), range-extending power, and engine speed. This helps drivers reach their destination in a multi-objective optimal manner, improving the overall driving experience. Dynamic programming is an exhaustive optimization algorithm that discretizes the entire state space and finds the optimal trajectory under the objective function through recursive iteration of the optimal substructure. Specifically, as... Figure 4As shown, for dynamic programming of vehicle speed along the entire target mileage, the distance points of the entire road segment can be discretized on the x-axis. Then, at each discrete distance point, the vehicle speed state variables within the feasible region can be discretized on the y-axis. Finally, based on the discretized vehicle speed state variables, dynamic optimization of the optimal state transition path is performed. Here, we take the recursion from discrete distance point N-2 to discrete distance point N-1 as an example. Assume that there are only three selectable discrete vehicle speed states at both discrete distance points N-2 and N-1, and the optimal solution of the objective function from the starting point 0 of the target mileage to discrete distance point N-2 is known as J. N-2，1 J N-2，2 and J N-2，3 Then, the minimum objective function for the vehicle speed state 1 at a distance of N-1 from the discrete point can be characterized as: J N-1，1 =min((J 2-2，1 +C 1-1 ), (J N-2，2 +C 2-1 ), (J N-2，3 +C 3-1 ), where C 1-1 C 2-1 and C 3-1 Let J represent the single-step objective function values ​​for the transition from vehicle speed states 1, 2, and 3 at distance N-2 to vehicle speed state 1 at distance N-1, respectively. The path with the smallest total objective function among the three alternative state transitions is selected as the optimal trajectory to reach vehicle speed state 1 at distance N-1. Repeating the above calculation yields J. N-1，2 and J N-1，3 This allows for updating the optimal solution of the objective function and the corresponding transition path from the starting point 0 of the target driving distance to the discrete point N-1 steps away. This iterative process eventually yields the optimal vehicle speed trajectory sequence for the entire road segment. In plug-in hybrid vehicles, the energy consumption required for vehicle speed state transitions between adjacent discrete points needs further optimization based on the vehicle's ECMS strategy, including the selection of the hybrid mode, the distribution of torque between the front and rear motors under specific modes, and the selection of engine range extender power and speed. Therefore, to obtain the feasible vehicle speed domain for each road segment and achieve reasonable vehicle speed transitions, information such as speed limits, gradients, curvatures, and traffic lights provided by the network connectivity system can be used as constraints on vehicle speed states during planning. Simultaneously, transitions between vehicle speed states are also limited by the vehicle's power characteristics, hybrid topology, and the boundary parameters such as acceleration and jerkiness determined by the driver's style.

[0057] Specifically, Figure 5 The vehicle speed planning method shown includes the following steps:

[0058] Step 1: Use the vehicle's internal development function to predict the range estimation results and estimate whether the current battery level can support driving the entire navigation route on pure electric power.

[0059] Step 2: If the current battery level allows the entire navigation route to be completed on pure electric power, then determine whether there is a charging requirement at the destination based on the navigation information.

[0060] Step 3: If there is a charging requirement at the destination, then the global vehicle speed will be planned according to the pure electric mode.

[0061] Step 4: If the conditions in Step 1 or Step 2 are not met, switch to hybrid mode for global vehicle speed planning. The main planning logic in hybrid mode consists of two large loops: While1 is a loop through discrete road segments, and While2 is a loop through discrete alternative states of vehicle speed at the end of a specific road segment within the feasible region. For each segment's starting point and the traversal of vehicle speeds reaching the end of a single segment, the specific loops in Steps 5-10 are then executed.

[0062] Step 5: Since the existing ECMS strategy has a relatively clear definition of the entry conditions for parallel mode under hybrid mode, for specific vehicle speed state transition items, it is possible to determine whether the vehicle drive mode meets the conditions for parallel mode based on indicators such as interval average vehicle speed and acceleration.

[0063] Step 6: If the conditions for entering parallel mode are met, then according to the ECMS strategy, perform energy consumption calculation for the vehicle speed state transition in parallel mode, including specific torque distribution and electric drive energy consumption, engine energy consumption, SOC calculation, etc.

[0064] Step 7: If the conditions for entering parallel mode are not met, calculate the electric drive energy consumption required for pure electric mode and range-extended mode under the current vehicle speed state transition. Among them, pure electric mode needs to consider different front and rear motor torque distribution ratios, and range-extended mode needs to consider the total energy consumption under different range-extending power and speed. Then, compare the total drive energy consumption (electricity consumption + fuel consumption) under different modes, while taking into account the minimum SOC constraint.

[0065] Step 8: If the total driving energy consumption in pure electric mode is low, the vehicle speed state transition will be driven in pure electric mode, and the corresponding front and rear motor torque distribution ratio will be executed at the same time.

[0066] Step 9: If the total driving energy consumption of the range-extended mode is low, the vehicle speed state transition in this segment will be driven by the range-extended mode, and the corresponding range-extended power and speed will be given.

[0067] Step 10: The total driving energy consumption calculated under different modes should also include the current accessory energy consumption as the total energy consumption requirement for the current road segment. The current accessory energy consumption includes, but is not limited to: compressor energy consumption, vehicle heater PTC (Positive Temperature Coefficient) energy consumption, and DC-DC converter energy consumption.

[0068] Step 11: After calculating the energy consumption of the vehicle speed from the start point to the end point of all candidate intervals in a loop, based on the set objective function, select the optimal vehicle speed state transition path, update the hybrid mode and the corresponding SOC after the transition, and repeat Step 4 loop.

[0069] Step 12: When all loops in Step 4 are completed, output the final planned vehicle speed and corresponding drive type, and the entire planning process ends.

[0070] Therefore, this embodiment can perform multi-objective optimization under the premise of comprehensively considering intelligent network information and navigation information, reasonably plan the optimal vehicle speed curve of the entire road segment, and at the same time provide the optimal energy management strategy under the curve, thereby improving the user's driving experience.

[0071] In some exemplary embodiments, the planning strategies of the dynamic programming subject are similar in terms of travel time, ride comfort, and traffic light logic processing between pure electric and hybrid modes. Therefore, dynamic programming in pure electric mode can directly calculate the optimal allocation method and drive energy consumption according to the dual-motor drive configuration, while dynamic programming in hybrid mode can further filter and optimize specific drive modes and their corresponding torque distribution ratios and range extension power.

[0072] In some exemplary embodiments, before discretizing the target driving mileage and vehicle speed, the preprocessing process may include: after the driver sets the endpoint of the driving cycle and the navigation route, obtaining information such as the gradient, maximum and minimum speed limits, curve speed limits, and corresponding traffic light information for the entire road segment in advance through intelligent network technology associated with the vehicle, including traffic light locations, phases, remaining time, and cycles. Then, all speed limit points, intersections, and traffic light locations along the entire road segment can be used as distance discretization points. Adjacent points are then discretized at approximately equal intervals. While ensuring no loss of critical road information, the entire road segment is still discretized in an approximately equal-interval manner to guarantee the final optimization accuracy. At each distance discretization point, the vehicle speed at that point is discretized at equal intervals within the feasible region to determine the vehicle speed state variable to be optimized.

[0073] In some exemplary embodiments, within the framework of dynamic programming, discrete distance points are selected as the x-axis, and the vehicle velocity state variables within the feasible region are discretized at each discrete distance point to obtain multiple discrete vehicle velocity points; and the discrete vehicle velocity points are used as the y-axis. Assuming that the transition between any vehicle velocity states at adjacent discrete distance points is uniformly accelerated linear motion, the vehicle velocity state transition equation can be expressed as: Where S(k) represents the distance between the kth discrete point and the starting point of the target driving mileage, S(k+1) represents the distance between the (k+1)th discrete point and the starting point of the target driving mileage, V(k) represents the vehicle speed candidate state at the kth discrete point, V(k+1) represents the vehicle speed candidate state at the (k+1)th discrete point, and a(k) represents the acceleration corresponding to the vehicle speed state jump at the kth discrete point.

[0074] After traversing all vehicle velocity state transitions from the k-th discrete point to the (k+1)-th discrete point and calculating the corresponding accelerations, the validity of the current vehicle velocity state transition can be determined based on the boundary conditions constrained by the current driver style. The driver style can be determined based on the driver's historical driving data in the current vehicle. Therefore, the feasible acceleration region or acceleration constraint can be expressed as: a min ≤a(k)≤a max , where a min This indicates the preset minimum acceleration, including the minimum acceleration determined based on driver style, a max This represents the preset maximum acceleration, including the maximum acceleration determined based on driver style. Simultaneously, the rate of change of acceleration (or jerk) also needs to consider driver style constraints; therefore, state transitions are also subject to the constraint of the previous acceleration. Thus, the jerk constraint can be expressed as: jerk min ≤a(k)-a(k-1)≤jerk max Where a(k) represents the acceleration corresponding to the vehicle velocity state transition at the k-th discrete distance point, a(k-1) represents the acceleration corresponding to the vehicle velocity state transition at the (k-1)-th discrete distance point, and jerk max This indicates the preset minimum jerk threshold, including the minimum jerk threshold limited by driver style. min This indicates the preset maximum jerk threshold, including the maximum jerk threshold limited by driver style.

[0075] When a vehicle speed state transition does not meet the acceleration and jerk constraints, the vehicle speed state transition for that group of vehicles needs to be discarded.

[0076] In some exemplary embodiments, energy consumption, as a crucial parameter in multi-objective vehicle speed planning, directly impacts the quality of the planning results. Optimizing driving cycle energy consumption can be approached from two aspects: firstly, by planning vehicle speed trajectories to obtain a relatively stable speed range with high energy utilization; secondly, by selecting the optimal hybrid mode for a given vehicle speed trajectory. Therefore, dynamic planning based on vehicle speed is used to calculate energy consumption under different driving modes for each segmented discrete interval of vehicle speed state transition, thereby optimizing the hybrid mode for that vehicle speed state. Specifically, according to existing ECMS strategies, the entry conditions for parallel operation are relatively clear, and the current entry into parallel mode can be identified based on vehicle state parameters such as average vehicle speed and acceleration. The energy consumption calculation process for the target vehicle in parallel mode is as follows: Figure 2 As shown, the wheel-end torque demand under vehicle speed state transition is calculated based on the vehicle's longitudinal dynamics model, and the motor wheel-end torque boundary is calculated based on the wheel-end torque boundary conditions provided by the engine. Furthermore, the feasible range of motor torque is determined based on the motor wheel-end torque boundary, and different motor wheel-end output torques are traversed within this range. Based on the target vehicle's front and rear motor drive rules, front and rear drive shaft torque constraints, and front and rear motor torque constraints, the target vehicle's front and rear motor torques and engine distribution torque are calculated. The electric drive energy consumption corresponding to the front and rear motor torques and the engine energy consumption corresponding to the engine distribution torque are retrieved from a pre-determined power table. Optimization calculations are performed based on the target vehicle's electric drive energy consumption and engine energy consumption in parallel drive mode. The electric drive energy consumption corresponding to the optimal front and rear motor torques, the engine energy consumption corresponding to the optimal engine distribution torque, and the preset accessory energy consumption are then superimposed to obtain the total energy consumption of the target vehicle in parallel drive mode. In addition, the energy consumption calculation process for the target vehicle in non-parallel mode is as follows: Figure 3As shown. Specifically, the wheel-end torque demand under vehicle speed state transition is calculated based on the vehicle's longitudinal dynamics model; and the optimal driving mode and corresponding electric drive energy consumption are found based on the wheel-end torque demand and different front and rear motor torque distribution ratios in pure electric mode; the minimum engine energy consumption is found by traversing the range-extending speeds based on different engine range-extending power; and the total driving energy consumption corresponding to the optimal range-extending mode is determined based on the minimum engine energy consumption corresponding to different engine range-extending power, where the total driving energy consumption includes engine energy consumption and electric drive energy consumption; the electric drive energy consumption corresponding to the optimal driving mode in pure electric mode is then calculated. The energy consumption is compared with the total driving energy consumption corresponding to the optimal range-extending mode in range-extending mode. When the electric drive energy consumption corresponding to the optimal driving mode in pure electric mode is less than the total driving energy consumption corresponding to the optimal range-extending mode in range-extending mode, the electric drive energy consumption corresponding to the optimal driving mode in pure electric mode is added together with the preset accessory energy consumption to obtain the energy consumption of the target vehicle in pure electric driving mode. When the electric drive energy consumption corresponding to the optimal driving mode in pure electric mode is greater than or equal to the total driving energy consumption corresponding to the optimal range-extending mode in range-extending mode, the total driving energy consumption corresponding to the optimal range-extending mode in range-extending mode is added together with the preset accessory energy consumption to obtain the energy consumption of the target vehicle in range-extending driving mode.

[0077] In some exemplary embodiments, for vehicle speed state transitions at non-intersection locations, the optimal solution can be found from the set of feasible alternative states based on constraints and objective function indices. For vehicle speed state transitions at traffic light locations, additional checks for passing the light or stopping are required, taking into account traffic light information and the time to reach the intersection. Specifically, the total time required for the planned path to reach the intersection is obtained. The travel time required within each discrete distance interval can be calculated as follows: In the formula, t(k) norm This represents the necessary travel time. Since the target time for dynamic programming optimization is the total travel time for the entire road segment, it also includes the necessary stopping and waiting time at intersections. By adding up the travel and stopping / waiting times required from the planned starting point, the total time t to reach the current planned intersection can be obtained. sum Divide by the current phase cycle period T of the intersection period That is, the red light cycle T red Green light cycle T green Yellow light cycle T yellow The sum and the remainder obtained This refers to the phase offset relative to the starting time when the traffic light reaches the intersection, where the remaining time of the phase at the initial planning time is defined as t. left .

[0078] like Figure 6 As shown, assuming the traffic light at the intersection is red at the start of the planning process, the remaining time t...left like Figure 6 As shown, if the speed of the alternative vehicle is planned to pass through the traffic lights without stopping, Should fall on Figure 6 The green area shown in the first diagram illustrates the driving section. If the alternative vehicle's speed is planned to be a uniform deceleration stop at the current intersection, then... Vehicle speed state transitions should fall within the red area indicated by the parking zone. Such transitions satisfy the intersection traffic light processing logic; other state transitions must be discarded. Considering traffic safety and backend control deviations, a time margin Δt can be introduced to compress the driving zone. Specifically, the time before the yellow-to-red transition and the time before the red-to-green transition are no longer considered reasonable driving zones. Simultaneously, the reasonable parking zone extends to the last Δt time of the yellow light. Therefore, by introducing a minimum parking time limit—that is, the first few seconds before the red-to-green transition (the time is adjustable) are no longer considered reasonable parking zones—we can consider the actual driving experience, avoid frequent short stops and starts, and improve the driving experience. Similarly, the processing logic for planning the intersection traffic lights to be green and yellow at the initial time is as described above. For necessary intersection parking scenarios, the waiting time t(k) is... wait The time remaining on the red light when you reach the intersection can be determined by the time the light turns green, so the start time of the red-to-green light can be assumed to be the start time for your vehicle to accelerate.

[0079] In some exemplary embodiments, the dynamic programming optimization objective can consider energy consumption, travel time, ride comfort, and parking penalties separately. Energy consumption can be calculated based on the vehicle's longitudinal dynamics formula and the transmission chain efficiency table, while also considering regenerative braking. The transmission chain efficiency table includes efficiency tables for the motor, battery, and engine. Since the travel time term comprises two parts—travel time on ordinary roads and waiting time at traffic lights—it can be expressed by the following formula: J t (k)=t(k) norm +t(k) wait Ride comfort can be characterized by agility, including: J jerk (k)=(a(k)-a(k-1)) 2 To achieve the goal of green wave travel, and to better represent the energy consumption fluctuations caused by start-stop conditions, a parking penalty item J at intersections can be introduced. stop (k), Parking penalty item J stop (k) can be set as a scalar quantity, accumulated over multiple iterations. Therefore, the multi-objective optimization function can be defined as follows: Where α and β are the weighting coefficients of the corresponding terms, and N is the number of discrete points divided into the entire target driving mileage. Based on this objective function, iterative optimization is performed throughout the entire road segment to obtain the optimal vehicle speed trajectory under multiple objectives, and also to obtain the corresponding hybrid management mode and decisions such as range extension power and engine speed. Where J eny (k) can be obtained by time integration of the wheel-end demand torque. The wheel-end drive torque required for the vehicle speed state transition within the discrete distance interval can be expressed as: Among them, T whl θ(k) represents the wheel-end torque required at the k-th discrete point, θ(k) is the slope within each discrete interval, m is the vehicle mass, r is the wheel radius, μ is the rolling resistance, ρ is the air density, and C u Let A be the wind resistance, δ be the frontal area, and δ be the equivalent coefficient considering rotational inertia. The wheel-end torque requirement is T. whl (k) The required torque of the motor and engine is allocated based on the hybrid mode and the current power boundary conditions. The instantaneous electric drive energy consumption and engine energy consumption are obtained by looking up their respective power characteristic tables. At the same time, the total energy consumption J under the alternative mode is obtained by time integration. eny (k).

[0080] In summary, this application provides a vehicle speed planning method. When the predicted driving range of the target vehicle meets the driving conditions for the target driving range, global vehicle speed planning is performed in pure electric mode. When the predicted driving range of the target vehicle does not meet the driving conditions for the target driving range, global vehicle speed planning is performed in hybrid mode. Specifically, the target driving range, determined in advance or in real time, is discretized based on the start and end points of the target driving range to obtain multiple distance discrete points; and the vehicle speed is discretized at each distance discrete point to obtain multiple vehicle speed discrete points. Then, the vehicle speed state transition result for each distance discrete point is determined through the vehicle speed state transition equation and the constraints determined in advance or in real time, and the energy consumption of the target vehicle under different driving modes is calculated based on the vehicle speed state transition result. The vehicle speed state transition equation is obtained based on multiple distance discrete points and multiple vehicle speed discrete points. Finally, based on the energy consumption calculation result and the objective function set in advance or in real time, a vehicle speed state transition path is selected, and vehicle speed planning is performed for the target vehicle according to the selected vehicle speed state transition path. Therefore, this method can rationally plan the optimal vehicle speed for the target driving distance. Based on this optimal speed, energy management not only reduces commuting time while ensuring energy consumption optimization and achieving overall optimization across the entire road segment, but also improves the user's driving experience, enabling green wave travel. Furthermore, this method can perform multi-objective optimization across energy consumption, driving time, driving comfort, and green wave travel. The proactively planned vehicle speed can be converted into corresponding demand torque control through the backend MPC (Model Predictive Control). Simultaneously, based on ECMS strategies and interfaces, corresponding hybrid energy management modes are provided to achieve overall optimization across the entire road segment, reducing commuting time while maximizing energy consumption optimization and comfort, thereby enabling green wave travel and improving the user's driving experience.

[0081] In another exemplary embodiment of this application, such as Figure 7 As shown, this embodiment also provides a vehicle speed planning device, including:

[0082] The data preprocessing module 710 is used to discretize the target driving mileage determined in advance or in real time to obtain multiple discrete distance points; and to discretize the vehicle speed at each discrete distance point to obtain multiple discrete vehicle speed points. In this embodiment or other embodiments, the target vehicle may be a vehicle containing a dual-motor plug-in hybrid structure. The driving modes of the target vehicle include, but are not limited to: pure electric drive mode (also known as pure electric mode), parallel drive mode (also known as parallel mode), and range-extended drive mode (also known as range-extended mode). Pure electric mode means that the vehicle is entirely powered by the battery, driven by the electric motor, and the engine does not participate in driving. Parallel mode allows the engine and electric motor to provide power to the vehicle simultaneously or individually; the engine can directly drive the wheels, and the electric motor can also provide auxiliary power or become the main driving force when needed. Range-extended mode extends the driving range by adding an auxiliary engine or range extender; when the battery power is insufficient, the range extender starts and charges the battery through the generator or directly provides power to the electric motor, thereby maintaining the vehicle's driving capability. In this embodiment or other embodiments, before discretizing the predetermined or real-time target driving mileage, the Predictive Driving Range Estimation (PDRE) function developed within the target vehicle can be invoked. Based on the PDRE result, it can be estimated whether the current battery level supports completing the entire target driving mileage in pure electric mode. If the current battery level supports completing the entire target driving mileage in pure electric mode, it is then determined whether there is a charging requirement at the destination of the target driving mileage. If there is a charging requirement at the destination, the vehicle speed is planned using pure electric mode. If the current battery level does not support completing the entire target driving mileage in pure electric mode, or if there is no charging requirement at the destination, the vehicle speed is planned using hybrid mode, and the target driving mileage is then discretized to obtain multiple distance discrete points. The target driving mileage includes, but is not limited to, the navigation mileage obtained from navigation information. The hybrid mode consists of one or more of pure electric mode, parallel mode, and range extender mode. In this embodiment or other embodiments, when discretizing the target driving mileage and the vehicle speed, the discretization can be performed based on the speed limit, slope, curvature, and traffic light information of the road ahead provided by the intelligent network information.

[0083] The energy consumption calculation module 720 is used to determine the vehicle speed state transition result at each distance discrete point through the vehicle speed state transition equation and pre-determined or real-time constraints, and to calculate the energy consumption of the target vehicle under different driving modes based on the vehicle speed state transition result; wherein, the vehicle speed state transition equation is obtained based on multiple distance discrete points and multiple vehicle speed discrete points.

[0084] The vehicle speed planning module 730 is used to select vehicle speed state transition paths based on energy consumption calculation results and pre-set or real-time objective functions, and to perform vehicle speed planning for the target vehicle according to the selected vehicle speed state transition paths.

[0085] Therefore, this embodiment can reasonably plan the optimal vehicle speed for the target driving distance, and perform energy management based on the planned optimal vehicle speed. This not only reduces commuting time while ensuring energy consumption optimization and achieving comprehensive optimization across the entire road segment, but also improves or enhances the user's driving experience and realizes green wave travel.

[0086] In an exemplary embodiment, the process of obtaining the vehicle speed state transition equation based on multiple discrete distance points and multiple discrete vehicle speed points includes: obtaining the distance between the k-th discrete distance point and the starting point of the target driving mileage, denoted as S(k); obtaining the distance between the (k+1)-th discrete distance point and the starting point of the target driving mileage, denoted as S(k+1); obtaining the vehicle speed candidate state at the k-th discrete distance point, denoted as V(k); and obtaining the vehicle speed candidate state at the (k+1)-th discrete distance point, denoted as V(k+1); and establishing the vehicle speed state transition equation based on the distance between the k-th discrete distance point and the starting point of the target driving mileage, the distance between the (k+1)-th discrete distance point and the starting point of the target driving mileage, the vehicle speed candidate state at the k-th discrete distance point, and the vehicle speed candidate state at the (k+1)-th discrete distance point, as follows: In the formula, a(k) represents the acceleration when the vehicle's velocity state changes at the kth discrete distance point.

[0087] In an exemplary embodiment, the process of determining constraints in advance or in real time includes: determining the feasible acceleration region based on a preset minimum acceleration and a preset maximum acceleration, wherein: a min ≤a(k)≤a max In the formula, a min Indicates the preset minimum acceleration, a max This represents the preset maximum acceleration. Furthermore, based on the preset minimum jerk threshold and the preset maximum jerk threshold, the acceleration jerk range is determined, resulting in: jerk min ≤a(k)-a(k-1)≤jerk max In the formula, a(k-1) represents the acceleration of the vehicle when its velocity state changes at the (k-1)th discrete point, jerk max This indicates the preset minimum agitation threshold, jerk. minThis represents a preset maximum abrupt change threshold. The feasible acceleration region and the abrupt change acceleration region are used as constraints. In this embodiment or other embodiments, the preset minimum acceleration, preset maximum acceleration, preset minimum abrupt change threshold, and preset maximum abrupt change threshold can be restricted or determined based on driver style, which can be determined based on the driver's historical driving data in the current vehicle. For example, the minimum acceleration determined based on driver style can be used as the preset minimum acceleration, the maximum acceleration determined based on driver style can be used as the preset maximum acceleration, the minimum abrupt change threshold restricted based on driver style can be used as the preset minimum abrupt change threshold, and the maximum abrupt change threshold restricted based on driver style can be used as the preset maximum abrupt change threshold.

[0088] In an exemplary embodiment, the process of calculating the energy consumption of a target vehicle under different driving modes based on the vehicle speed state transition results includes:

[0089] The driving mode of the target vehicle is determined based on the vehicle speed state transition results. For example, the average vehicle speed and acceleration within a single discrete distance interval in the vehicle speed state transition results can be used to determine whether the target vehicle is in parallel or non-parallel mode. Alternatively, the accelerator pedal depth indirectly obtained from the vehicle speed state transition results can also be used to determine whether the target vehicle is in parallel or non-parallel mode; the specific process will not be elaborated in this embodiment. As an example, if the average vehicle speed within a single discrete distance interval is greater than a preset vehicle speed, the driving mode of the target vehicle can be determined to be parallel mode. As another example, if the acceleration within a single discrete distance interval is greater than a preset acceleration, the driving mode of the target vehicle can be determined to be parallel mode. As yet another example, if both the average vehicle speed and the acceleration within a single discrete distance interval are greater than a preset vehicle speed, the driving mode of the target vehicle can be determined to be parallel mode.

[0090] If the target vehicle is determined to be in parallel drive mode based on the vehicle speed state transition results, the energy consumption calculation process for the target vehicle in parallel mode is as follows: Figure 2As shown. Specifically, the wheel-end torque demand under vehicle speed state transition is calculated based on the vehicle's longitudinal dynamics model, and the motor wheel-end torque boundary is calculated based on the wheel-end torque boundary conditions provided by the engine; the feasible range of motor torque is determined based on the motor wheel-end torque boundary, and different motor wheel-end output torques are traversed within the feasible range of motor torque; the front and rear motor torques and engine distribution torque of the target vehicle are calculated based on the target vehicle's front and rear motor drive rules, the target vehicle's front and rear drive shaft torque limits, and the target vehicle's front and rear motor torque limits; the electric drive energy consumption corresponding to the front and rear motor torques and the engine energy consumption corresponding to the engine distribution torque are found from a pre-determined power table, and optimization calculations are performed based on the target vehicle's electric drive energy consumption and engine energy consumption in parallel drive mode; the electric drive energy consumption corresponding to the optimal front and rear motor torques, the engine energy consumption corresponding to the optimal engine distribution torques, and the preset accessory energy consumption are superimposed to obtain the total energy consumption of the target vehicle in parallel drive mode.

[0091] If the target vehicle is determined to be in a non-parallel drive mode based on the vehicle speed state transition results, the energy consumption calculation process for the target vehicle in the non-parallel mode is as follows: Figure 3 As shown. Specifically, the wheel-end torque demand under vehicle speed state transition is calculated based on the vehicle's longitudinal dynamics model; and the optimal driving mode and corresponding electric drive energy consumption are found based on the wheel-end torque demand and different front and rear motor torque distribution ratios in pure electric mode; the minimum engine energy consumption is found by traversing the range-extending speeds based on different engine range-extending power; and the total driving energy consumption corresponding to the optimal range-extending mode is determined based on the minimum engine energy consumption corresponding to different engine range-extending power, where the total driving energy consumption includes engine energy consumption and electric drive energy consumption; the electric drive energy consumption corresponding to the optimal driving mode in pure electric mode is then calculated. The energy consumption is compared with the total driving energy consumption corresponding to the optimal range-extending mode in range-extending mode. When the electric drive energy consumption corresponding to the optimal driving mode in pure electric mode is less than the total driving energy consumption corresponding to the optimal range-extending mode in range-extending mode, the electric drive energy consumption corresponding to the optimal driving mode in pure electric mode is added together with the preset accessory energy consumption to obtain the energy consumption of the target vehicle in pure electric driving mode. When the electric drive energy consumption corresponding to the optimal driving mode in pure electric mode is greater than or equal to the total driving energy consumption corresponding to the optimal range-extending mode in range-extending mode, the total driving energy consumption corresponding to the optimal range-extending mode in range-extending mode is added together with the preset accessory energy consumption to obtain the energy consumption of the target vehicle in range-extending driving mode.

[0092] In an exemplary embodiment, the process of pre-setting or real-time setting of the objective function includes: setting the objective function based on the number of discrete distance points divided into target driving distances, engine energy consumption and electric drive energy consumption calculated based on equivalent fuel factors, vehicle travel time, dynamism value, and a parking penalty term; wherein the dynamism value is determined based on the rate of change of acceleration, and the parking penalty term is used to characterize the energy consumption fluctuations during vehicle start-stop. Specifically, In the formula, J represents the objective function value; N represents the number of discrete distance points used to divide the target mileage; J eny (k) represents the engine energy consumption and electric drive energy consumption calculated based on the equivalent fuel factor; J t (k) represents the vehicle passage time value; J jerk (k) represents the jerkiness value; J stop (k) represents the parking penalty at the intersection, accumulated based on the number of times; α and β represent weighting coefficients. The calculation process for vehicle travel time includes: J t (k)=t(k) norm +t(k) wait ; In the formula, t(k) norm Indicates normal travel time; t(k) wait V(k+1) represents the waiting time at the traffic light at the intersection; V(k+1) represents the vehicle speed candidate state at the (k+1)th discrete distance point; V(k) represents the vehicle speed candidate state at the kth discrete distance point; S(k+1) represents the distance between the (k+1)th discrete distance point and the starting point of the target driving distance; S(k) represents the distance between the kth discrete distance point and the starting point of the target driving distance; a(k) represents the acceleration when the vehicle speed state changes at the kth discrete distance point. In this embodiment or other embodiments, the objective function is calculated dimensionlessly.

[0093] It is understood that the vehicle speed planning device and the vehicle speed planning method provided in the above embodiments belong to the same concept. The specific way in which the vehicle speed planning method is executed has been described in detail in the above embodiments and will not be repeated here. In practical applications, the vehicle speed planning device provided in the above embodiments can be assigned to different functional modules as needed. That is, the internal structure of the vehicle speed planning device can be divided into different functional modules, and then all or part of the functions of the corresponding functional modules can be implemented by the vehicle speed planning method described in the above embodiments. No specific limitations are imposed here.

[0094] In summary, this application provides a vehicle speed planning device. When the predicted driving range of the target vehicle meets the driving conditions for the target driving range, global vehicle speed planning is performed in pure electric mode. When the predicted driving range of the target vehicle does not meet the driving conditions for the target driving range, global vehicle speed planning is performed in hybrid mode. Specifically, the target driving range, determined in advance or in real time, is discretized based on the start and end points of the target driving range to obtain multiple distance discrete points; and the vehicle speed is discretized at each distance discrete point to obtain multiple vehicle speed discrete points. Then, the vehicle speed state transition result for each distance discrete point is determined through the vehicle speed state transition equation and the constraints determined in advance or in real time, and the energy consumption of the target vehicle in different driving modes is calculated based on the vehicle speed state transition result. The vehicle speed state transition equation is obtained based on multiple distance discrete points and multiple vehicle speed discrete points. Finally, based on the energy consumption calculation result and the objective function set in advance or in real time, a vehicle speed state transition path is selected, and vehicle speed planning is performed for the target vehicle according to the selected vehicle speed state transition path. Therefore, this device can rationally plan the optimal vehicle speed for the target mileage and perform energy management based on the planned optimal vehicle speed. This not only reduces commuting time while ensuring energy consumption optimization and achieving comprehensive optimization across the entire road segment, but also improves or enhances the user's driving experience, enabling green wave travel. Furthermore, this device can perform multi-objective optimization in terms of energy consumption, travel time, driving comfort, and green wave travel. The actively planned vehicle speed can be converted into corresponding demand torque control through backend model predictive control (MPC). Simultaneously, based on ECMS strategies and interfaces, it provides corresponding hybrid energy management modes to achieve comprehensive optimization across the entire road segment, reducing commuting time while maximizing energy consumption optimization and comfort, thereby enabling green wave travel and improving the user's driving experience.

[0095] This application also provides an electronic device, which may include a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to cause the electronic device to perform... Figure 1 or Figure 5 The steps of the vehicle speed planning method described above. Figure 8 A schematic diagram of the structure of an electronic device 1000 is shown. (See attached diagram.) Figure 8 As shown, the electronic device 1000 includes: a processor 1010, a memory 1020, a power supply 1030, a display unit 1040, and an input unit 1060.

[0096] The processor 1010 is the control center of the electronic device 1000. It connects to various components via various interfaces and lines, and executes various functions of the electronic device 1000 by running or executing computer programs / instructions stored in the memory 1020, thereby providing overall monitoring of the electronic device 1000. In this embodiment, when the processor 1010 calls the computer program stored in the memory 1020, it executes... Figure 1 or Figure 5 The steps of the vehicle speed planning method are described above. Optionally, the processor 1010 may include one or more processing units; preferably, the processor 1010 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. In some embodiments, the processor and memory can be implemented on a single chip; in some embodiments, they can also be implemented separately on independent chips.

[0097] The memory 1020 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, various applications, etc.; the data storage area may store instruction data created based on the use of the electronic device 1000, etc. In addition, the memory 1020 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device, etc.

[0098] The electronic device 1000 also includes a power supply 1030 (such as a battery) that supplies power to various components. The power supply can be logically connected to the processor 1010 through a power management system, thereby enabling the management of functions such as charging, discharging, and power consumption through the power management system.

[0099] The display unit 1040 can be used to display information input by the user or information provided to the user, as well as various menus of the electronic device 1000. In this embodiment, it is mainly used to display the display interfaces of various applications in the electronic device 1000, as well as text, images, and other objects displayed on the display interfaces. The display unit 1040 may include a display panel 1050. The display panel 1050 may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), or the like.

[0100] The input unit 1060 can be used to receive information such as numbers or characters input by the user. The input unit 1060 may include a touch panel 1070 and other input devices 1080. The touch panel 1070, also known as a touch screen, can collect touch operations on or near the touch panel 1070 by the user (such as operations performed by the user using a finger, stylus, or any suitable object or accessory on or near the touch panel 1070).

[0101] Specifically, the touch panel 1070 can detect user touch operations and the signals generated by these operations, convert them into touch point coordinates, send them to the processor 1010, and receive and execute commands from the processor 1010. Furthermore, the touch panel 1070 can be implemented using various types of touch technologies, including resistive, capacitive, infrared, and surface acoustic wave. Other input devices 1080 can include, but are not limited to, one or more of the following: physical keyboard, function keys (such as volume control buttons, power buttons, etc.), trackball, mouse, joystick, etc.

[0102] Of course, the touch panel 1070 can cover the display panel 1050. When the touch panel 1070 detects a touch operation on or near it, it transmits the information to the processor 1010 to determine the type of touch event. Subsequently, the processor 1010 provides corresponding visual output on the display panel 1050 based on the type of touch event. Although in Figure 8 In this embodiment, the touch panel 1070 and the display panel 1050 are two separate components to realize the input and output functions of the electronic device 1000. However, in some embodiments, the touch panel 1070 and the display panel 1050 can be integrated to realize the input and output functions of the electronic device 1000.

[0103] The electronic device 1000 may also include one or more sensors, such as a pressure sensor, a gravity acceleration sensor, a proximity sensor, etc. Of course, depending on the specific application requirements, the electronic device 1000 may also include other components such as a camera.

[0104] This application also provides a computer-readable storage medium storing a computer program / instructions. When executed by a processor, the computer program / instructions enable the aforementioned device to perform the functions described in this application. Figure 1 or Figure 5 The steps of the vehicle speed planning method described above.

[0105] It will be understood by those skilled in the art that Figure 8This is merely an example of an electronic device and does not constitute a limitation on the device. The device may include more or fewer components than illustrated, or a combination of certain components, or different components. For ease of description, the above sections are divided into modules (or units) according to their functions and described separately. Of course, in implementing this application, the functions of each module (or unit) can be implemented in one or more software or hardware components.

[0106] Those skilled in the art will understand that this application may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of this application, and should be understood to be achievable by computer program instructions for each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams. These computer program instructions may be applied to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0107] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A vehicle speed planning method characterized by, The method includes the following steps: The target driving distance, which is determined in advance or in real time, is discretized to obtain multiple discrete distance points; and the vehicle speed at each discrete distance point is discretized to obtain multiple discrete vehicle speed points. The vehicle speed state transition equation and pre-determined or real-time constraints are used to determine the vehicle speed state transition result at each discrete distance point, and the energy consumption of the target vehicle in different driving modes is calculated based on the vehicle speed state transition result; wherein, the vehicle speed state transition equation is obtained based on the plurality of discrete distance points and the plurality of discrete vehicle speed points. Based on the energy consumption calculation results and the pre-set or real-time objective function, the vehicle speed state transition path is selected, and the vehicle speed is planned for the target vehicle according to the selected vehicle speed state transition path.

2. The vehicle speed planning method according to claim 1, characterized by, The process of obtaining the vehicle speed state transition equation based on the multiple discrete distance points and the multiple discrete vehicle speed points includes: Based on the distance between the kth discrete point and the starting point of the target driving mileage, the distance between the (k+1)th discrete point and the starting point of the target driving mileage, the vehicle speed candidate states at the kth discrete point, and the vehicle speed candidate states at the (k+1)th discrete point, the vehicle speed state transition equation is established; where k is a positive integer.

3. The vehicle speed planning method according to claim 2, characterized by, The process of determining constraints in advance or in real time includes: The feasible acceleration region is determined based on the preset minimum acceleration and the preset maximum acceleration, and the acceleration jerk range is determined based on the preset minimum jerk threshold and the preset maximum jerk threshold. The feasible acceleration region and the acceleration jerk region are used as the constraints.

4. The vehicle speed planning method according to any one of claims 1 to 3, characterized by, The process of calculating the energy consumption of the target vehicle under different driving modes based on the vehicle speed state transition results includes: When the target vehicle is in parallel drive mode, the wheel-end torque demand under the vehicle speed state transition is calculated based on the vehicle longitudinal dynamics model, and the motor wheel-end torque boundary is calculated based on the wheel-end torque boundary conditions provided by the engine. The feasible range of motor torque is determined based on the motor wheel end torque boundary, and different motor wheel end output torques are traversed within the feasible range of motor torque. Based on the front and rear motor drive rules of the target vehicle, the torque limit conditions of the front and rear drive shafts of the target vehicle, and the torque limit conditions of the front and rear motors of the target vehicle, the front and rear motor torques and engine distribution torque of the target vehicle are calculated. The electric drive energy consumption corresponding to the front and rear motor torques and the engine energy consumption corresponding to the engine distribution torque are found from the predetermined power table. Based on the electric drive energy consumption and engine energy consumption of the target vehicle in parallel drive mode, optimization calculations are performed. The electric drive energy consumption corresponding to the optimal front and rear motor torques, the engine energy consumption corresponding to the optimal engine distribution torque, and the preset accessory energy consumption are then added together to obtain the total energy consumption of the target vehicle in parallel drive mode.

5. The vehicle speed planning method according to any one of claims 1 to 3, characterized by, The process of calculating the energy consumption of the target vehicle under different driving modes based on the vehicle speed state transition results includes: Based on the vehicle speed state transition results, when the target vehicle is in a non-parallel drive mode, the wheel-end torque demand under the vehicle speed state transition is calculated based on the vehicle longitudinal dynamics model; and... Based on the required wheel-end torque and different front and rear motor torque distribution ratios, the optimal driving mode in pure electric mode and the corresponding electric drive energy consumption are determined; and... Based on different engine range-extending power levels, the study iterates through the range-extending speeds to find the lowest engine energy consumption corresponding to different engine range-extending power levels; and... The total drive energy consumption corresponding to the optimal range extension mode under the range extension mode is determined based on the lowest engine energy consumption corresponding to different engine range extension power. The total drive energy consumption includes engine energy consumption and electric drive energy consumption. Compare the electric drive energy consumption corresponding to the optimal driving mode in pure electric mode with the total driving energy consumption corresponding to the optimal range-extending mode in range-extending mode. When the electric drive energy consumption corresponding to the optimal driving mode in pure electric mode is less than the total driving energy consumption corresponding to the optimal range-extending mode in range-extending mode, the electric drive energy consumption corresponding to the optimal driving mode in pure electric mode is added together with the preset accessory energy consumption to obtain the energy consumption of the target vehicle in pure electric driving mode. When the electric drive energy consumption corresponding to the optimal driving mode in pure electric mode is greater than or equal to the total driving energy consumption corresponding to the optimal range-extending mode in range-extending mode, the total driving energy consumption corresponding to the optimal range-extending mode in range-extending mode is added together with the preset accessory energy consumption and used as the energy consumption of the target vehicle in range-extending driving mode.

6. The vehicle speed planning method according to any one of claims 1 to 3, characterized by, The process of setting the objective function in advance or in real time includes: setting the objective function based on the number of discrete points of the target driving mileage, the engine energy consumption and electric drive energy consumption calculated based on the equivalent fuel factor, the vehicle travel time value, the jerk value, and the parking penalty term; wherein, the jerk value is determined based on the rate of change of acceleration, and the parking penalty term is used to characterize the energy consumption fluctuation when the vehicle starts and stops.

7. The vehicle speed planning method according to claim 6, characterized by, The method further includes: calculating the vehicle travel time value based on the vehicle's normal travel time, the vehicle's waiting time at the traffic lights at the intersection, the vehicle speed candidate state at the kth distance discrete point, the vehicle speed candidate state at the (k+1)th distance discrete point, the distance between the kth distance discrete point and the starting point of the target travel distance, the distance between the (k+1)th distance discrete point and the starting point of the target travel distance, and the acceleration when the vehicle speed state jumps at the kth distance discrete point; Wherein, the acceleration when the vehicle speed state jumps at the kth discrete distance point is obtained by the vehicle speed candidate state at the kth discrete distance point, the vehicle speed candidate state at the (k+1)th discrete distance point, the distance between the kth discrete distance point and the starting point of the target driving mileage, and the distance between the (k+1)th discrete distance point and the starting point of the target driving mileage; k is a positive integer.

8. A vehicle speed planning device characterized by comprising: The device includes: The data preprocessing module is used to discretize the target driving mileage determined in advance or in real time to obtain multiple discrete distance points; Furthermore, the vehicle speed is discretized at each discrete distance point to obtain multiple discrete vehicle speed points; The energy consumption calculation module is used to determine the vehicle speed state transition result at each distance discrete point through the vehicle speed state transition equation and pre-determined or real-time constraints, and to calculate the energy consumption of the target vehicle under different driving modes based on the vehicle speed state transition result; wherein, the vehicle speed state transition equation is obtained based on the plurality of distance discrete points and the plurality of vehicle speed discrete points. The vehicle speed planning module is used to select vehicle speed state transition paths based on energy consumption calculation results and pre-set or real-time objective functions, and to plan the vehicle speed of the target vehicle according to the selected vehicle speed state transition paths.

9. An electronic device, comprising: The system includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the vehicle speed planning method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores computer-readable instructions that, when executed by a computer's processor, cause the computer to perform the vehicle speed planning method as described in any one of claims 1 to 7.

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