Torque control method, device and vehicle during vehicle travel

Abstract

The application provides a torque control method and device during vehicle driving and a vehicle, relates to the technical field of vehicle energy management, and obtains a road slope, a road curvature and a road speed limit, obtains front road information, and calculates a target cruise speed according to the front road information; a constraint vector within a first preset distance is constructed according to the target cruise speed, the front road information and a driving speed; a target function is constructed according to a driving cost of the vehicle within the first preset distance, at least the constraint vector is taken as a constraint condition, a driving speed sequence of the vehicle is solved by a quadratic programming solver with the minimum value of the target function as a target, an output torque of the vehicle is determined through a PID algorithm according to the driving speed sequence, and a target torque is obtained. The method solves the problem that physical information of a front road is ignored in automatic driving speed planning in the prior art, and the fuel economy is low.

Description

Technical Field

[0001] This invention relates to the field of vehicle energy management technology, and more specifically, to a torque control method, apparatus, computer-readable storage medium, and vehicle during vehicle operation. Background Technology

[0002] Traditional vehicle energy management technologies typically use network information to obtain traffic light timings and surrounding vehicle information for planning. However, they cannot effectively limit the vehicle's speed in real time by combining road condition information ahead (such as slope, curvature, and speed limits).

[0003] Furthermore, because the speed control and torque control in existing technologies cannot be combined with actual road conditions, it is difficult to establish a correlation between road conditions and the fuel economy of the whole vehicle, resulting in poor actual fuel economy. Summary of the Invention

[0004] The main objective of this application is to provide a torque control method, device, computer-readable storage medium, and vehicle during vehicle operation, so as to at least solve the problem in the prior art that the physical information of the road ahead is ignored when planning the speed of autonomous driving, resulting in low fuel economy.

[0005] To achieve the above objectives, according to one aspect of this application, a torque control method for a vehicle during operation is provided, comprising: acquiring road slope, road curvature, and road speed limit to obtain forward road information; calculating a target cruising speed based on the forward road information; constructing a constraint vector within a first preset distance based on the target cruising speed, the forward road information, and the vehicle speed, the constraint vector including one or more of a speed constraint vector, an impact constraint vector, and a position constraint vector; constructing an objective function based on the vehicle's driving cost within the first preset distance, the driving cost including energy consumption cost, impact cost, and driving distance cost, at least using the constraint vector as a constraint condition, and solving for the vehicle's driving speed sequence using a quadratic programming solver with the objective function minimizing its value; and determining the vehicle's output torque using a PID algorithm based on the driving speed sequence to obtain the target torque.

[0006] Optionally, obtaining road slope, road curvature, and road speed limit to obtain road information ahead includes: obtaining the changing trends of the road slope, road curvature, and road speed limit with distance within the first preset distance ahead of the vehicle via an external Tbox to obtain alternative road information; and determining the changing trend of the alternative road information with time based on the vehicle speed to obtain the road information ahead.

[0007] Optionally, calculating the target cruise speed based on the road information ahead and the vehicle's current state includes: determining the real-time slope value within the first preset distance based on the road information ahead to obtain the target slope value; determining the slope length corresponding to the target slope value based on the road information ahead to obtain the target slope length; if the target slope value is within a preset range, correcting the set cruise speed based on a preset speed offset to obtain the target cruise speed; if the target slope value is not within the preset range, querying a first mapping relationship based on the target slope length to obtain the target speed offset; correcting the set cruise speed based on the target speed offset to obtain the target cruise speed, wherein the first mapping relationship is a mapping relationship between slope length and speed offset.

[0008] Optionally, constructing a constraint vector within a first preset distance based on the target cruising speed, the road information ahead, and the vehicle speed includes: acquiring gravitational acceleration, air density, the vehicle's maximum driving force, rolling resistance coefficient, vehicle mass, frontal area, and drag coefficient; determining the road slope within the first preset distance based on the road information ahead; calculating the maximum vehicle speed at each time step within the first preset distance based on the gravitational acceleration, air density, maximum driving force, rolling resistance coefficient, vehicle mass, frontal area, drag coefficient, and road slope; taking the minimum value between the maximum vehicle speed and the target cruising speed at each time step as the upper limit value of the corresponding speed constraint vector; and determining the minimum permissible speed within the first preset distance as the lower limit value of the speed constraint vector to obtain the corresponding speed constraint vector.

[0009] Optionally, constructing a constraint vector within a first preset distance based on the target cruising speed, the road information ahead, and the driving speed further includes: determining the maximum permissible speed at each time step within the first preset distance based on the driving speed and the maximum permissible acceleration; determining the minimum permissible speed at each time step within the first preset distance based on the driving speed and the minimum permissible acceleration; determining the maximum position at the corresponding time step based on the maximum permissible speed at each time step; determining the minimum position at the corresponding time step based on the minimum permissible speed at each time step; and constructing the corresponding position constraint vector based on the maximum position and the minimum position at each time step.

[0010] Optionally, constructing a constraint vector within a first preset distance based on the target cruising speed, the road information ahead, and the driving speed further includes: calculating the theoretical impact at each time step within the first preset distance based on the driving speed; and taking the minimum value between the theoretical impact and the preset impact to obtain the impact constraint vector.

[0011] Optionally, constructing an objective function based on the vehicle's travel cost within the first preset distance includes: constructing an objective function based on the vehicle's travel cost within the first preset distance, wherein the objective function is... In the formula, The energy consumption cost includes fuel consumption cost and electricity consumption cost. For the impact cost, The cost is the distance traveled.

[0012] Optionally, the output torque of the vehicle is determined using a PID algorithm based on the driving speed sequence to obtain the target torque, including: using the driving speed sequence and the driving speed as input parameters, performing PID calculations based on preset proportional coefficients, preset integral coefficients, and preset derivative coefficients to obtain the wheel-end required torque; converting the wheel-end required torque into the target required torque of the transmission input shaft based on the final drive ratio and the gearbox ratio of the vehicle's transmission system; and determining the engine output torque and the electric motor output torque based on the target required torque to obtain the target torque.

[0013] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform any of the methods described.

[0014] According to another aspect of this application, a vehicle is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including methods for performing any one of the methods described.

[0015] Applying the technical solution of this application, in the torque control method during vehicle operation described above, firstly, road slope, road curvature, and road speed limits are acquired to obtain road information ahead, and the target cruising speed is calculated based on the road information ahead; then, a constraint vector within a first preset distance is constructed based on the target cruising speed, the road information ahead, and the vehicle speed, the constraint vector including one or more of a speed constraint vector, an impact constraint vector, and a position constraint vector; subsequently, an objective function is constructed based on the vehicle's driving cost within the first preset distance, the driving cost including energy consumption cost, impact cost, and driving distance cost, at least with the constraint vector as a constraint condition, and the vehicle's driving speed sequence is solved using a quadratic programming solver with the objective function minimization as the objective; finally, the vehicle's output torque is determined using a PID algorithm based on the driving speed sequence to obtain the target torque. This application acquires information about the road ahead and constructs a comprehensive objective function that includes fuel consumption, impact intensity, and driving distance. It then uses a quadratic programming solver to perform rolling optimization under the premise of satisfying constraints on vehicle speed, acceleration, impact intensity, and powertrain to plan the optimal vehicle speed curve. Based on the vehicle speed curve, it optimizes the torque output in the hybrid power system, ensuring the overall vehicle economy. This solves the problem in existing technologies where the physical information of the road ahead is ignored when planning the vehicle speed for autonomous driving, resulting in low fuel economy. Attached Figure Description

[0016] Figure 1 A hardware structure block diagram of a mobile terminal for a torque control method during vehicle operation provided in an embodiment of this application is shown.

[0017] Figure 2 A schematic flowchart of a torque control method during vehicle operation according to an embodiment of this application is shown.

[0018] Figure 3 A structural block diagram of a torque control device during vehicle operation provided according to an embodiment of this application is shown. Detailed Implementation

[0019] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0020] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0021] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0022] As described in the background section, in the prior art, speed control and torque control cannot be combined with actual road conditions, making it difficult to establish a correlation between road conditions and the fuel economy of the whole vehicle, resulting in poor actual fuel economy. In order to solve the problem that the prior art ignores the physical information of the road ahead on the vehicle speed constraint when planning the speed of autonomous driving, resulting in low fuel economy, the embodiments of this application provide a torque control method, device, computer-readable storage medium and vehicle during vehicle driving.

[0023] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0024] The methods and embodiments provided in this application can be executed on a mobile terminal, computer terminal, or similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for a torque control method during vehicle operation, according to an embodiment of the present invention. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0025] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the torque control method during vehicle operation in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the aforementioned networks may include wireless networks provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0026] This embodiment provides a torque control method for a vehicle during driving, which operates on a mobile terminal, computer terminal, or similar computing device. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0027] Figure 2 This is a flowchart of a torque control method during vehicle operation according to an embodiment of this application. Figure 2 As shown, the method includes the following steps:

[0028] Step S201: Obtain road slope, road curvature and road speed limit, obtain road information ahead, and calculate the target cruising speed based on the road information ahead;

[0029] Specifically, the system acquires real-time information on road slope, road curvature, and speed limits ahead of the vehicle via an external T-box, providing a stable path data foundation for subsequent energy management. Furthermore, this application sets a cruise speed based on real-time correction of the road slope ahead to obtain the aforementioned target cruise speed, thus incorporating the aforementioned road information ahead into the scope of energy management considerations.

[0030] Step S202: Construct a constraint vector within a first preset distance based on the target cruising speed, road information ahead, and driving speed. The constraint vector includes one or more of the following: speed constraint vector, impact constraint vector, and position constraint vector.

[0031] Specifically, based on the target cruising speed, road information ahead, and current driving speed, a constraint vector within a first preset distance is constructed. This vector is used to characterize constraints in multiple dimensions such as vehicle speed, impact intensity, and position. Specifically, it includes: using the target cruising speed and the maximum permissible speed calculated based on a physical model (considering slope, maximum driving force, wind resistance, etc.) and the minimum permissible speed of the current road segment as the upper and lower limits of the vehicle speed constraint; introducing an impact intensity constraint to ensure acceleration smoothness by limiting the rate of change of acceleration between adjacent time steps; and combining vehicle dynamics to calculate the maximum and minimum acceleration, thereby deriving the maximum and minimum driving positions at each step and constructing position constraints.

[0032] Through the above operations, the planned vehicle speed curve not only meets the requirements of fuel economy, but also ensures the dynamic feasibility of the vehicle speed curve and the comfort of the driver.

[0033] Step S203: Construct an objective function based on the vehicle's driving cost within a first preset distance. The driving cost includes energy consumption cost, impact cost, and driving distance cost. At least the constraint vector is used as the constraint condition. Solve the vehicle's driving speed sequence through a quadratic programming solver with the goal of minimizing the value of the objective function.

[0034] Specifically, considering the characteristics of P2 hybrid commercial vehicles, a comprehensive energy consumption cost is defined, which includes fuel consumption cost, electricity consumption cost (converted to equivalent fuel consumption through an equivalent factor), impact cost reflecting comfort, and driving distance cost reflecting path tracking accuracy. The above objective function is obtained. Then, through the efficient convergence characteristics of the QP solver, the vehicle speed and energy distribution are optimized simultaneously within a 10-second prediction time domain (1-second step), so that the engine can operate in the high-efficiency zone as much as possible, while maintaining the battery SOC within a reasonable range.

[0035] Step S204: Determine the vehicle's output torque using a PID algorithm based on the vehicle speed sequence to obtain the target torque.

[0036] Specifically, based on the vehicle speed sequence obtained from the above planning, PID control (where the proportional coefficient P provides the basic torque response, the integral coefficient I changes with the absolute value of the speed difference and is reset online to eliminate steady-state error, and the derivative coefficient D is used to suppress overshoot) is used to convert the predicted ideal vehicle speed sequence into real-time torque commands in the actual control process, thereby obtaining the target torque mentioned above.

[0037] In this embodiment, firstly, road slope, road curvature, and road speed limits are obtained to acquire road information ahead, and the target cruising speed is calculated based on this information. Then, a constraint vector within a first preset distance is constructed based on the target cruising speed, the road information ahead, and the vehicle's current speed. This constraint vector includes one or more of the following: speed constraint vector, impact constraint vector, and position constraint vector. Next, an objective function is constructed based on the vehicle's travel costs within the first preset distance. These costs include energy consumption costs, impact costs, and travel distance costs, with at least the constraint vectors serving as constraints. A quadratic programming solver is used to solve for the vehicle's travel speed sequence, aiming to minimize the objective function value. Finally, the vehicle's output torque is determined using a PID algorithm based on the travel speed sequence, yielding the target torque. This application acquires information about the road ahead and constructs a comprehensive objective function that includes fuel consumption, impact intensity, and driving distance. It then uses a quadratic programming solver to perform rolling optimization under the premise of satisfying constraints on vehicle speed, acceleration, impact intensity, and powertrain to plan the optimal vehicle speed curve. Based on the vehicle speed curve, it optimizes the torque output in the hybrid power system, ensuring the overall vehicle economy. This solves the problem in existing technologies where the physical information of the road ahead is ignored when planning the vehicle speed for autonomous driving, resulting in low fuel economy.

[0038] In order to obtain information about the road ahead, in one optional implementation, step S201 includes:

[0039] Step S2011: Obtain the trend of road slope, road curvature and road speed limit within a first preset distance in front of the vehicle using an external Tbox to obtain alternative road information;

[0040] Specifically, an external Tbox is used as a beyond-line-of-sight communication terminal to collect real-time data on road geometry features and traffic rule constraints ahead of the vehicle's driving path. The road information includes slope and curvature, and the traffic rules include speed limits. It can be understood that the data collected by the external Tbox is obtained based on distance, resulting in alternative road information that reflects the spatial distribution characteristics of the road conditions ahead.

[0041] Step S2012: Determine the trend of the alternative road information over time based on the vehicle speed to obtain the road information ahead.

[0042] Specifically, the distance-based alternative road information, combined with the vehicle's real-time speed, is converted into time-based forward road information through kinematic relationships. In particular, the expected road characteristics and predicted speed of the vehicle at a specific future time point are calculated.

[0043] Through the above embodiments, an external Tbox is used to achieve advance perception beyond line of sight, and the perceived spatial dimension data is mapped into a time-based data sequence, enabling the control system to perform rolling optimization in units of time steps, and realizing the updating of control commands according to the real-time changes in the working conditions ahead.

[0044] In order to calculate the target cruising speed, in one optional implementation, step S201 includes:

[0045] Step S2013: Determine the real-time slope value within the first preset distance based on the road information ahead to obtain the target slope value; determine the slope length corresponding to the target slope value based on the road information ahead to obtain the target slope length.

[0046] Specifically, by extracting real-time slope values ​​and target slope lengths from the road information ahead, the specific slope magnitude and continuous length are characterized, providing a data basis for subsequent vehicle speed adjustments.

[0047] Step S2014: If the target slope value is within the preset range, set the cruise speed according to the preset speed offset correction to obtain the target cruise speed.

[0048] Specifically, when the target slope value is within a preset flat range (e.g., -0.5% to 0.5%), a fixed preset speed offset is used to correct the set cruise speed.

[0049] Step S2015: If the target slope value is not within the preset range, query the first mapping relationship based on the target slope length to obtain the target vehicle speed offset, and correct the set cruise speed based on the target vehicle speed offset to obtain the target cruise speed. The first mapping relationship is the mapping relationship between slope length and vehicle speed offset.

[0050] Specifically, when the target slope value exceeds the preset flat range (i.e. there is a substantial uphill or downhill slope), the target vehicle speed offset is determined by querying the pre-calibrated first mapping relationship (MAP map) based on the target slope length. That is, the slope length is introduced as the independent variable for querying the mapping relationship, realizing the non-linear dynamic adjustment of the vehicle speed offset with the slope length, and then the set cruise speed is corrected based on the target offset obtained by querying.

[0051] Through the above embodiments, by combining the two dimensions of slope and slope length, dynamic adjustment of vehicle speed offset is achieved, avoiding increased energy consumption and safety risks caused by excessive vehicle speed, and also preventing a decrease in traffic efficiency caused by excessive vehicle speed.

[0052] In an optional implementation, to construct the constraint vector, step S202 above includes:

[0053] Step S2021: Obtain gravitational acceleration, air density, maximum driving force of the vehicle, rolling resistance coefficient, total vehicle mass, frontal area and drag coefficient, and determine the road slope within the first preset distance based on the road information ahead;

[0054] Step S2022: Calculate the maximum driving speed of the vehicle at each time step within the first preset distance based on the gravitational acceleration, air density, maximum driving force, rolling resistance coefficient, vehicle mass, frontal area, drag coefficient and road slope.

[0055] Specifically, this application obtains multi-dimensional physical parameters such as gravitational acceleration, air density, maximum vehicle driving force, rolling resistance coefficient, vehicle mass, frontal area, and drag coefficient, and combines them with real-time road gradient information from the road ahead to calculate the maximum vehicle speed at each time step using dynamic formulas.

[0056] Understandably, the above settings take into account factors such as the increase in the gravitational component when going uphill, the decrease in the gravitational component when going downhill, and the change in air resistance with speed, thereby achieving accurate quantification of the feasible maximum vehicle speed under specific operating conditions.

[0057] Step S2023: Take the minimum of the maximum driving speed and the target cruising speed at each time step as the upper limit of the corresponding speed constraint vector, and determine the minimum allowable speed within the first preset distance as the lower limit of the speed constraint vector to obtain the corresponding speed constraint vector.

[0058] Specifically, by taking the minimum value between the physical maximum driving speed calculated at each time step and the target cruising speed, the upper limit of the speed constraint vector is established, and the preset minimum allowable speed is used as the lower limit. In this way, the speed constraint boundary of vehicle physical performance and traffic rules can be satisfied while satisfying the driver's intention, thus obtaining the above-mentioned speed constraint vector.

[0059] Through the above embodiments, the vehicle speed constraint during the vehicle driving process is modeled based on the vehicle dynamics characteristics. By introducing physical parameters and road slope for coupled calculation, the vehicle speed constraint is realized to follow the dynamic changes of the working conditions.

[0060] In an optional implementation, to construct the constraint vector, step S202 above further includes:

[0061] Step S2024: Determine the maximum permissible vehicle speed at each time step within the first preset distance based on the vehicle speed and the maximum permissible acceleration; determine the minimum permissible vehicle speed at each time step within the first preset distance based on the vehicle speed and the minimum permissible acceleration.

[0062] Specifically, dynamic acceleration boundary constraints are introduced to calculate the upper and lower limits of velocity at each time step, ensuring that the planned velocity sequence is always within the physically achievable range under the current operating conditions of the vehicle, thus avoiding the problem of meaningless planning results or control failure caused by exceeding the vehicle's physical baseline.

[0063] Step S2025: Determine the maximum position of the corresponding time step based on the maximum permissible vehicle speed of each time step, and determine the minimum position of the corresponding time step based on the minimum permissible vehicle speed of each time step.

[0064] Specifically, based on the maximum and minimum permissible vehicle speeds at each time step, the maximum and minimum positions that the vehicle can reach at the corresponding time step are further calculated. By transforming speed constraints into position constraints, spatial envelope control of the vehicle's future trajectory is achieved.

[0065] Step S2026: Construct the corresponding position constraint vector based on the maximum and minimum positions at each time step.

[0066] Specifically, by constructing a comprehensive constraint vector that includes positional constraints, the quadratic programming solver can satisfy the vehicle dynamics limits.

[0067] In an optional implementation, to construct the constraint vector, step S202 above further includes:

[0068] Step S2027: Calculate the theoretical impact at each time step within the first preset distance based on the vehicle speed;

[0069] Specifically, using the vehicle speed at the current moment and the next time step, combined with the time step length (e.g., 1 second), the rate of change of acceleration, i.e., the impact, is calculated. The larger the value, the less smooth the power transmission. The specific calculation formula is j=Δa / Δt, where acceleration a=Δv / Δt, Δv is the change in vehicle speed within the corresponding time step, and Δt is the time step length mentioned above.

[0070] Through the above operations, the dynamic response characteristics of the vehicle powertrain system are transformed into quantifiable numerical indicators, providing key data reflecting the vehicle's ride comfort for subsequent optimization.

[0071] Step S2028: Take the minimum value between the theoretical impact degree and the preset impact degree to obtain the impact degree constraint vector.

[0072] Specifically, the actual power response capability of the vehicle is combined with a preset comfort standard to obtain the impact constraint on the planned vehicle speed. In one embodiment, the preset impact standard (e.g., 0.8 m / s³) represents the upper limit of the driver's acceptable comfort level.

[0073] Through the above embodiments, an impact constraint vector is introduced to ensure that during the optimization algorithm's solution process, the planned vehicle speed change at each time step will not cause the actual impact to exceed the comfort threshold. Specifically, if the theoretically calculated impact exceeds a preset value, the allowable acceleration change is forcibly reduced, thereby limiting the magnitude of the vehicle speed change; if it does not exceed the limit, the original constraint is maintained.

[0074] In an optional implementation, to construct the objective function, step S203 above includes:

[0075] Step S2031: Construct an objective function based on the vehicle's travel cost within a first preset distance. The objective function is: In the formula, Energy costs, which include fuel consumption costs and electricity consumption costs, For impact cost, Cost based on travel distance.

[0076] It is understandable that:

[0077] Energy consumption cost is the combined cost of fuel consumption and electricity consumption, which can be expressed by formula J. f =J eng +η×J ele In the formula, J eng For fuel consumption costs, J ele η is the electricity cost, reflecting the electrical energy consumed by the drive motor. η is the equivalence factor used to convert the electricity cost into an equivalent fuel cost, so as to optimize fuel and electricity in a unified manner under the same dimension.

[0078] The impact can be quantified by methods such as squaring the impact or weighted summation. Squaring is used to penalize drastic changes in acceleration, making the planned speed curve smoother and avoiding sudden acceleration or braking, thereby improving comfort. Weighted summation can be used to combine user feedback to determine the impact weight of sudden acceleration or braking.

[0079] In the Model Predictive Control (MPC) framework, the cost of travel distance is usually related to the deviation between the vehicle's actual travel position and the reference path (or the road position based on time mapping). Specifically, its calculation can be based on the deviation between the predicted position of the vehicle at each step in the prediction time domain and the ideal position (or target position). The greater the deviation between the vehicle and the planning result, the larger the value of this item.

[0080] Through the above settings, the comprehensive energy distribution based on fuel consumption and electricity consumption, the impact of acceleration changes based on impact on comfort, and the deviation between driving state and preset trajectory constitute a multi-dimensional cost function, enabling the optimization algorithm to automatically find the balance point with the lowest fuel consumption, the most comfortable ride, and the most accurate driving path while meeting physical constraints.

[0081] In order to plan the target torque for the vehicle's operation, in one optional implementation, step S204 includes:

[0082] Step S2041: Using the vehicle speed sequence and vehicle speed as input parameters, perform PID calculation based on preset proportional coefficient, preset integral coefficient and preset derivative coefficient to obtain the wheel end required torque;

[0083] Specifically, by introducing an integral term to eliminate steady-state error, a proportional term to accelerate response speed, and a derivative term to suppress overshoot, the vehicle can be ensured to track the optimal vehicle speed sequence generated by the quadratic programming solver in real time and smoothly.

[0084] In one specific embodiment, the proportional coefficient P is a calibrated value, initially set to 8000. The integral coefficient I changes with the absolute value of the speed difference (the difference between the actual vehicle speed and the desired vehicle speed). When the speed difference between the previous scheduling time and the current scheduling time is negative, the integral output torque is reset. The derivative coefficient D is a calibrated value, defaulting to 0.

[0085] Step S2042: Based on the final drive ratio of the vehicle's transmission system and the gearbox ratio, convert the wheel-end required torque into the target required torque of the gearbox input shaft.

[0086] Specifically, by converting physical transmission parameters, the required torque at the wheel end is converted into the target required torque of the gearbox input shaft.

[0087] Step S2043: Determine the engine output torque and electric motor output torque based on the target torque requirement to obtain the target torque.

[0088] Specifically, by combining the specific architecture of the hybrid system (such as P2 hybrid), the output torque of the engine and the output torque of the electric motor are determined. Based on the previously planned speed and torque requirements, the torque contribution ratio of the engine and the electric motor is dynamically adjusted to ensure that the engine operates in the fuel-efficient range as much as possible. At the same time, the electric motor is used for torque compensation or energy recovery, which solves the technical problems of rigid energy management strategies and poor fuel economy of hybrid vehicles under complex road conditions.

[0089] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0090] This application also provides a torque control device for vehicle operation. It should be noted that the torque control device for vehicle operation in this application can be used to execute the torque control method for vehicle operation provided in this application. This device is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0091] The following describes the torque control device for vehicle operation provided in the embodiments of this application.

[0092] Figure 3 This is a structural block diagram of a torque control device during vehicle operation according to an embodiment of this application. Figure 3 As shown, the device includes:

[0093] The acquisition unit 10 is used to acquire road slope, road curvature and road speed limit, obtain road information ahead, and calculate the target cruising speed based on the road information ahead.

[0094] Specifically, the system acquires real-time information on road slope, road curvature, and speed limits ahead of the vehicle via an external T-box, providing a stable path data foundation for subsequent energy management. Furthermore, this application sets a cruise speed based on real-time correction of the road slope ahead to obtain the aforementioned target cruise speed, thus incorporating the aforementioned road information ahead into the scope of energy management considerations.

[0095] The first construction unit 20 is used to construct a constraint vector within a first preset distance based on the target cruising speed, road information ahead and driving speed. The constraint vector includes one or more of the vehicle speed constraint vector, impact constraint vector and position constraint vector.

[0096] Specifically, based on the target cruising speed, road information ahead, and current driving speed, a constraint vector within a first preset distance is constructed. This vector is used to characterize constraints in multiple dimensions such as vehicle speed, impact intensity, and position. Specifically, it includes: using the target cruising speed and the maximum permissible speed calculated based on a physical model (considering slope, maximum driving force, wind resistance, etc.) and the minimum permissible speed of the current road segment as the upper and lower limits of the vehicle speed constraint; introducing an impact intensity constraint to ensure acceleration smoothness by limiting the rate of change of acceleration between adjacent time steps; and combining vehicle dynamics to calculate the maximum and minimum acceleration, thereby deriving the maximum and minimum driving positions at each step and constructing position constraints.

[0097] Through the above operations, the planned vehicle speed curve not only meets the requirements of fuel economy, but also ensures the dynamic feasibility of the vehicle speed curve and the comfort of the driver.

[0098] The second construction unit 30 is used to construct an objective function based on the vehicle's driving cost within a first preset distance. The driving cost includes energy consumption cost, impact cost, and driving distance cost. At least the constraint vector is used as the constraint condition. The vehicle's driving speed sequence is solved by a quadratic programming solver with the goal of minimizing the value of the objective function.

[0099] Specifically, considering the characteristics of P2 hybrid commercial vehicles, a comprehensive energy consumption cost is defined, which includes fuel consumption cost, electricity consumption cost (converted to equivalent fuel consumption through an equivalent factor), impact cost reflecting comfort, and driving distance cost reflecting path tracking accuracy. The above objective function is obtained. Then, through the efficient convergence characteristics of the QP solver, the vehicle speed and energy distribution are optimized simultaneously within a 10-second prediction time domain (1-second step), so that the engine can operate in the high-efficiency zone as much as possible, while maintaining the battery SOC within a reasonable range.

[0100] The first determining unit 40 is used to determine the output torque of the vehicle based on the driving speed sequence using a PID algorithm, and obtain the target torque.

[0101] Specifically, based on the vehicle speed sequence obtained from the above planning, PID control (where the proportional coefficient P provides the basic torque response, the integral coefficient I changes with the absolute value of the speed difference and is reset online to eliminate steady-state error, and the derivative coefficient D is used to suppress overshoot) is used to convert the predicted ideal vehicle speed sequence into real-time torque commands in the actual control process, thereby obtaining the target torque mentioned above.

[0102] In this embodiment, the acquisition unit acquires road slope, road curvature, and road speed limits to obtain road information ahead, and calculates the target cruising speed based on the road information ahead. The first construction unit constructs a constraint vector within a first preset distance based on the target cruising speed, the road information ahead, and the driving speed. The constraint vector includes one or more of the following: speed constraint vector, impact constraint vector, and position constraint vector. The second construction unit constructs an objective function based on the vehicle's driving cost within the first preset distance. The driving cost includes energy consumption cost, impact cost, and driving distance cost. At least the constraint vector is used as a constraint condition. A quadratic programming solver is used to solve for the vehicle's driving speed sequence with the objective function minimization as the objective. The first determination unit determines the vehicle's output torque based on the driving speed sequence using a PID algorithm to obtain the target torque. This application acquires information about the road ahead and constructs a comprehensive objective function that includes fuel consumption, impact intensity, and driving distance. It then uses a quadratic programming solver to perform rolling optimization under the premise of satisfying constraints on vehicle speed, acceleration, impact intensity, and powertrain to plan the optimal vehicle speed curve. Based on the vehicle speed curve, it optimizes the torque output in the hybrid power system, ensuring the overall vehicle economy. This solves the problem in existing technologies where the physical information of the road ahead is ignored when planning the vehicle speed for autonomous driving, resulting in low fuel economy.

[0103] In order to obtain information about the road ahead, in one optional implementation, the acquisition unit includes:

[0104] The first acquisition module is used to acquire the road slope, road curvature and road speed limit change trends with distance within a first preset distance in front of the vehicle through an external Tbox, and obtain candidate road information.

[0105] Specifically, an external Tbox is used as a beyond-line-of-sight communication terminal to collect real-time data on road geometry features and traffic rule constraints ahead of the vehicle's driving path. The road information includes slope and curvature, and the traffic rules include speed limits. It can be understood that the data collected by the external Tbox is obtained based on distance, resulting in alternative road information that reflects the spatial distribution characteristics of the road conditions ahead.

[0106] The first processing module is used to determine the changing trend of alternative road information over time based on the vehicle speed, and to obtain the road information ahead.

[0107] Specifically, the distance-based alternative road information, combined with the vehicle's real-time speed, is converted into time-based forward road information through kinematic relationships. In particular, the expected road characteristics and predicted speed of the vehicle at a specific future time point are calculated.

[0108] Through the above embodiments, an external Tbox is used to achieve advance perception beyond line of sight, and the perceived spatial dimension data is mapped into a time-based data sequence, enabling the control system to perform rolling optimization in units of time steps, and realizing the updating of control commands according to the real-time changes in the working conditions ahead.

[0109] In an optional implementation, to calculate the target cruising speed, the acquisition unit includes:

[0110] The first determining module is used to determine the real-time slope value within a first preset distance based on the road information ahead, to obtain the target slope value, and to determine the slope length corresponding to the target slope value based on the road information ahead, to obtain the target slope length.

[0111] Specifically, by extracting real-time slope values ​​and target slope lengths from the road information ahead, the specific slope magnitude and continuous length are characterized, providing a data basis for subsequent vehicle speed adjustments.

[0112] The second processing module is used to set the cruise speed according to the preset speed deviation correction when the target slope value is within the preset range, so as to obtain the target cruise speed.

[0113] Specifically, when the target slope value is within a preset flat range (e.g., -0.5% to 0.5%), a fixed preset speed offset is used to correct the set cruise speed.

[0114] The third processing module is used to query the first mapping relationship based on the target slope length to obtain the target vehicle speed offset when the target slope value is not within the preset range, and to adjust the cruise speed setting based on the target vehicle speed offset to obtain the target cruise speed. The first mapping relationship is the mapping relationship between slope length and vehicle speed offset.

[0115] Specifically, when the target slope value exceeds the preset flat range (i.e. there is a substantial uphill or downhill slope), the target vehicle speed offset is determined by querying the pre-calibrated first mapping relationship (MAP map) based on the target slope length. That is, the slope length is introduced as the independent variable for querying the mapping relationship, realizing the non-linear dynamic adjustment of the vehicle speed offset with the slope length, and then the set cruise speed is corrected based on the target offset obtained by querying.

[0116] Through the above embodiments, by combining the two dimensions of slope and slope length, dynamic adjustment of vehicle speed offset is achieved, avoiding increased energy consumption and safety risks caused by excessive vehicle speed, and also preventing a decrease in traffic efficiency caused by excessive vehicle speed.

[0117] To construct the constraint vector, in one alternative implementation, the first constructing unit described above includes:

[0118] The second acquisition module is used to acquire gravitational acceleration, air density, maximum driving force of the vehicle, rolling resistance coefficient, total vehicle mass, frontal area and drag coefficient, and determine the road slope within the first preset distance based on the road information ahead;

[0119] The first calculation module is used to calculate the maximum driving speed of the vehicle at each time step within a first preset distance based on gravitational acceleration, air density, maximum driving force, rolling resistance coefficient, vehicle mass, frontal area, wind resistance coefficient and road slope.

[0120] Specifically, this application obtains multi-dimensional physical parameters such as gravitational acceleration, air density, maximum vehicle driving force, rolling resistance coefficient, vehicle mass, frontal area, and drag coefficient, and combines them with real-time road gradient information from the road ahead to calculate the maximum vehicle speed at each time step using dynamic formulas.

[0121] Understandably, the above settings take into account factors such as the increase in the gravitational component when going uphill, the decrease in the gravitational component when going downhill, and the change in air resistance with speed, thereby achieving accurate quantification of the feasible maximum vehicle speed under specific operating conditions.

[0122] The second determining module is used to take the minimum value between the maximum driving speed and the target cruising speed at each time step as the upper limit value of the corresponding speed constraint vector, and determine the minimum allowable speed within the first preset distance as the lower limit value of the speed constraint vector to obtain the corresponding speed constraint vector.

[0123] Specifically, by taking the minimum value between the physical maximum driving speed calculated at each time step and the target cruising speed, the upper limit of the speed constraint vector is established, and the preset minimum allowable speed is used as the lower limit. In this way, the speed constraint boundary of vehicle physical performance and traffic rules can be satisfied while satisfying the driver's intention, thus obtaining the above-mentioned speed constraint vector.

[0124] Through the above embodiments, the vehicle speed constraint during the vehicle driving process is modeled based on the vehicle dynamics characteristics. By introducing physical parameters and road slope for coupled calculation, the vehicle speed constraint is realized to follow the dynamic changes of the working conditions.

[0125] To construct the constraint vector, in one optional implementation, the first constructing unit further includes:

[0126] The third determining module is used to determine the maximum permissible vehicle speed at each time step within the first preset distance based on the vehicle speed and the maximum permissible acceleration, and to determine the minimum permissible vehicle speed at each time step within the first preset distance based on the vehicle speed and the minimum permissible acceleration.

[0127] Specifically, dynamic acceleration boundary constraints are introduced to calculate the upper and lower limits of velocity at each time step, ensuring that the planned velocity sequence is always within the physically achievable range under the current operating conditions of the vehicle, thus avoiding the problem of meaningless planning results or control failure caused by exceeding the vehicle's physical baseline.

[0128] The fourth determining module is used to determine the maximum position of the corresponding time step based on the maximum allowable vehicle speed of each time step, and to determine the minimum position of the corresponding time step based on the minimum allowable vehicle speed of each time step.

[0129] Specifically, based on the maximum and minimum permissible vehicle speeds at each time step, the maximum and minimum positions that the vehicle can reach at the corresponding time step are further calculated. By transforming speed constraints into position constraints, spatial envelope control of the vehicle's future trajectory is achieved.

[0130] The first construction module is used to construct the corresponding position constraint vector based on the maximum and minimum positions at each time step.

[0131] Specifically, by constructing a comprehensive constraint vector that includes positional constraints, the quadratic programming solver can satisfy the vehicle dynamics limits.

[0132] To construct the constraint vector, in one optional implementation, the first constructing unit further includes:

[0133] The second calculation module is used to calculate the theoretical impact at each time step within the first preset distance based on the vehicle speed.

[0134] Specifically, using the vehicle speed at the current moment and the next time step, combined with the time step length (e.g., 1 second), the rate of change of acceleration, i.e., the impact, is calculated. The larger the value, the less smooth the power transmission. The specific calculation formula is j=Δa / Δt, where acceleration a=Δv / Δt, Δv is the change in vehicle speed within the corresponding time step, and Δt is the time step length mentioned above.

[0135] Through the above operations, the dynamic response characteristics of the vehicle powertrain system are transformed into quantifiable numerical indicators, providing key data reflecting the vehicle's ride comfort for subsequent optimization.

[0136] The fourth processing module is used to take the minimum value between the theoretical impact degree and the preset impact degree to obtain the impact degree constraint vector.

[0137] Specifically, the actual power response capability of the vehicle is combined with a preset comfort standard to obtain the impact constraint on the planned vehicle speed. In one embodiment, the preset impact standard (e.g., 0.8 m / s³) represents the upper limit of the driver's acceptable comfort level.

[0138] Through the above embodiments, an impact constraint vector is introduced to ensure that during the optimization algorithm's solution process, the planned vehicle speed change at each time step will not cause the actual impact to exceed the comfort threshold. Specifically, if the theoretically calculated impact exceeds a preset value, the allowable acceleration change is forcibly reduced, thereby limiting the magnitude of the vehicle speed change; if it does not exceed the limit, the original constraint is maintained.

[0139] In an optional implementation, to construct the objective function, the second construction unit described above includes:

[0140] The second construction module is used to construct an objective function based on the vehicle's travel cost within a first preset distance. The objective function is: In the formula, Energy costs, which include fuel consumption costs and electricity consumption costs, For impact cost, Cost based on travel distance.

[0141] It is understandable that:

[0142] Energy consumption cost is the combined cost of fuel consumption and electricity consumption, which can be expressed by formula J. f =J eng +η×J ele Calculate, where J eng For fuel consumption costs, J ele η is the electricity cost, reflecting the electrical energy consumed by the drive motor. η is the equivalence factor used to convert the electricity cost into an equivalent fuel cost, so as to optimize fuel and electricity in a unified manner under the same dimension.

[0143] Furthermore, the aforementioned fuel consumption cost is calculated using the formula... Calculation. Among them, Engine speed, This refers to the engine torque. , , , , , , , , These are all coefficients for the fuel consumption polynomial fitting. The above-mentioned electricity consumption cost is calculated using the formula... Calculation. Among them, This refers to the power consumed by the motor in a single step. The duration of a single step is 1 second in this case.

[0144] The impact can be quantified by methods such as squaring the impact or weighted summation. Squaring is used to penalize drastic changes in acceleration, making the planned speed curve smoother and avoiding sudden acceleration or braking, thereby improving comfort. Weighted summation can be used to combine user feedback to determine the impact weight of sudden acceleration or braking.

[0145] In the Model Predictive Control (MPC) framework, the cost of travel distance is usually related to the deviation between the vehicle's actual travel position and the reference path (or the road position based on time mapping). Specifically, its calculation can be based on the deviation between the predicted position of the vehicle at each step in the prediction time domain and the ideal position (or target position). The greater the deviation between the vehicle and the planning result, the larger the value of this item.

[0146] Through the above settings, the comprehensive energy distribution based on fuel consumption and electricity consumption, the impact of acceleration changes based on impact on comfort, and the deviation between driving state and preset trajectory constitute a multi-dimensional cost function, enabling the optimization algorithm to automatically find the balance point with the lowest fuel consumption, the most comfortable ride, and the most accurate driving path while meeting physical constraints.

[0147] In order to plan the target torque for the vehicle's operation, in one optional implementation, the first determining unit includes:

[0148] The third calculation module is used to perform PID calculations based on preset proportional coefficients, preset integral coefficients, and preset derivative coefficients, taking the vehicle speed sequence and vehicle speed as input parameters, to obtain the wheel end torque requirement.

[0149] Specifically, by introducing an integral term to eliminate steady-state error, a proportional term to accelerate response speed, and a derivative term to suppress overshoot, the vehicle can be ensured to track the optimal vehicle speed sequence generated by the quadratic programming solver in real time and smoothly.

[0150] In one specific embodiment, the proportional coefficient P is a calibrated value, initially set to 8000. The integral coefficient I changes with the absolute value of the speed difference (the difference between the actual vehicle speed and the desired vehicle speed). When the speed difference between the previous scheduling time and the current scheduling time is negative, the integral output torque is reset. The derivative coefficient D is a calibrated value, defaulting to 0.

[0151] The fourth calculation module is used to convert the wheel-end torque requirement into the target torque requirement of the gearbox input shaft based on the final drive ratio and gearbox ratio of the vehicle's transmission system.

[0152] Specifically, by converting physical transmission parameters, the required torque at the wheel end is converted into the target required torque of the gearbox input shaft.

[0153] The fifth calculation module is used to determine the engine output torque and the electric motor output torque based on the target torque requirement, thereby obtaining the target torque.

[0154] Specifically, by combining the specific architecture of the hybrid system (such as P2 hybrid), the output torque of the engine and the output torque of the electric motor are determined. Based on the previously planned speed and torque requirements, the torque contribution ratio of the engine and the electric motor is dynamically adjusted to ensure that the engine operates in the fuel-efficient range as much as possible. At the same time, the electric motor is used for torque compensation or energy recovery, which solves the technical problems of rigid energy management strategies and poor fuel economy of hybrid vehicles under complex road conditions.

[0155] The torque control device during vehicle operation includes a processor and a memory. The acquisition unit, first construction unit, second construction unit, and first determination unit are all stored as program units in the memory. The processor executes the program units stored in the memory to achieve the corresponding functions. All of the above modules reside in the same processor; alternatively, the modules may be located in different processors in any combination.

[0156] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and adjusting kernel parameters can improve fuel economy during vehicle operation.

[0157] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0158] This invention provides a computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the torque control method during vehicle operation.

[0159] Specifically, torque control methods during vehicle operation include:

[0160] Step S201: Obtain road slope, road curvature and road speed limit, obtain road information ahead, and calculate the target cruising speed based on the road information ahead;

[0161] Specifically, the system acquires real-time information on road slope, road curvature, and speed limits ahead of the vehicle via an external T-box, providing a stable path data foundation for subsequent energy management. Furthermore, this application sets a cruise speed based on real-time correction of the road slope ahead to obtain the aforementioned target cruise speed, thus incorporating the aforementioned road information ahead into the scope of energy management considerations.

[0162] Step S202: Construct a constraint vector within a first preset distance based on the target cruising speed, road information ahead, and driving speed. The constraint vector includes one or more of the following: speed constraint vector, impact constraint vector, and position constraint vector.

[0163] Specifically, based on the target cruising speed, road information ahead, and current driving speed, a constraint vector within a first preset distance is constructed. This vector is used to characterize constraints in multiple dimensions such as vehicle speed, impact intensity, and position. Specifically, it includes: using the target cruising speed and the maximum permissible speed calculated based on a physical model (considering slope, maximum driving force, wind resistance, etc.) and the minimum permissible speed of the current road segment as the upper and lower limits of the vehicle speed constraint; introducing an impact intensity constraint to ensure acceleration smoothness by limiting the rate of change of acceleration between adjacent time steps; and combining vehicle dynamics to calculate the maximum and minimum acceleration, thereby deriving the maximum and minimum driving positions at each step and constructing position constraints.

[0164] Through the above operations, the planned vehicle speed curve not only meets the requirements of fuel economy, but also ensures the dynamic feasibility of the vehicle speed curve and the comfort of the driver.

[0165] Step S203: Construct an objective function based on the vehicle's driving cost within a first preset distance. The driving cost includes energy consumption cost, impact cost, and driving distance cost. At least the constraint vector is used as the constraint condition. Solve the vehicle's driving speed sequence through a quadratic programming solver with the goal of minimizing the value of the objective function.

[0166] Specifically, considering the characteristics of P2 hybrid commercial vehicles, a comprehensive energy consumption cost is defined, which includes fuel consumption cost, electricity consumption cost (converted to equivalent fuel consumption through an equivalent factor), impact cost reflecting comfort, and driving distance cost reflecting path tracking accuracy. The above objective function is obtained. Then, through the efficient convergence characteristics of the QP solver, the vehicle speed and energy distribution are optimized simultaneously within a 10-second prediction time domain (1-second step), so that the engine can operate in the high-efficiency zone as much as possible, while maintaining the battery SOC within a reasonable range.

[0167] Step S204: Determine the vehicle's output torque using a PID algorithm based on the vehicle speed sequence to obtain the target torque.

[0168] Specifically, based on the vehicle speed sequence obtained from the above planning, PID control (where the proportional coefficient P provides the basic torque response, the integral coefficient I changes with the absolute value of the speed difference and is reset online to eliminate steady-state error, and the derivative coefficient D is used to suppress overshoot) is used to convert the predicted ideal vehicle speed sequence into real-time torque commands in the actual control process, thereby obtaining the target torque mentioned above.

[0169] This invention provides a processor for running a program, wherein the program executes the torque control method during vehicle operation.

[0170] Specifically, torque control methods during vehicle operation include:

[0171] Step S201: Obtain road slope, road curvature and road speed limit, obtain road information ahead, and calculate the target cruising speed based on the road information ahead;

[0172] Specifically, the system acquires real-time information on road slope, road curvature, and speed limits ahead of the vehicle via an external T-box, providing a stable path data foundation for subsequent energy management. Furthermore, this application sets a cruise speed based on real-time correction of the road slope ahead to obtain the aforementioned target cruise speed, thus incorporating the aforementioned road information ahead into the scope of energy management considerations.

[0173] Step S202: Construct a constraint vector within a first preset distance based on the target cruising speed, road information ahead, and driving speed. The constraint vector includes one or more of the following: speed constraint vector, impact constraint vector, and position constraint vector.

[0174] Specifically, based on the target cruising speed, road information ahead, and current driving speed, a constraint vector within a first preset distance is constructed. This vector is used to characterize constraints in multiple dimensions such as vehicle speed, impact intensity, and position. Specifically, it includes: using the target cruising speed and the maximum permissible speed calculated based on a physical model (considering slope, maximum driving force, wind resistance, etc.) and the minimum permissible speed of the current road segment as the upper and lower limits of the vehicle speed constraint; introducing an impact intensity constraint to ensure acceleration smoothness by limiting the rate of change of acceleration between adjacent time steps; and combining vehicle dynamics to calculate the maximum and minimum acceleration, thereby deriving the maximum and minimum driving positions at each step and constructing position constraints.

[0175] Through the above operations, the planned vehicle speed curve not only meets the requirements of fuel economy, but also ensures the dynamic feasibility of the vehicle speed curve and the comfort of the driver.

[0176] Step S203: Construct an objective function based on the vehicle's driving cost within a first preset distance. The driving cost includes energy consumption cost, impact cost, and driving distance cost. At least the constraint vector is used as the constraint condition. Solve the vehicle's driving speed sequence through a quadratic programming solver with the goal of minimizing the value of the objective function.

[0177] Specifically, considering the characteristics of P2 hybrid commercial vehicles, a comprehensive energy consumption cost is defined, which includes fuel consumption cost, electricity consumption cost (converted to equivalent fuel consumption through an equivalent factor), impact cost reflecting comfort, and driving distance cost reflecting path tracking accuracy. The above objective function is obtained. Then, through the efficient convergence characteristics of the QP solver, the vehicle speed and energy distribution are optimized simultaneously within a 10-second prediction time domain (1-second step), so that the engine can operate in the high-efficiency zone as much as possible, while maintaining the battery SOC within a reasonable range.

[0178] Step S204: Determine the vehicle's output torque using a PID algorithm based on the vehicle speed sequence to obtain the target torque.

[0179] Specifically, based on the vehicle speed sequence obtained from the above planning, PID control (where the proportional coefficient P provides the basic torque response, the integral coefficient I changes with the absolute value of the speed difference and is reset online to eliminate steady-state error, and the derivative coefficient D is used to suppress overshoot) is used to convert the predicted ideal vehicle speed sequence into real-time torque commands in the actual control process, thereby obtaining the target torque mentioned above.

[0180] This invention provides a vehicle, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs at least the following steps:

[0181] Step S201: Obtain road slope, road curvature and road speed limit, obtain road information ahead, and calculate the target cruising speed based on the road information ahead;

[0182] Specifically, the system acquires real-time information on road slope, road curvature, and speed limits ahead of the vehicle via an external T-box, providing a stable path data foundation for subsequent energy management. Furthermore, this application sets a cruise speed based on real-time correction of the road slope ahead to obtain the aforementioned target cruise speed, thus incorporating the aforementioned road information ahead into the scope of energy management considerations.

[0183] Step S202: Construct a constraint vector within a first preset distance based on the target cruising speed, road information ahead, and driving speed. The constraint vector includes one or more of the following: speed constraint vector, impact constraint vector, and position constraint vector.

[0184] Specifically, based on the target cruising speed, road information ahead, and current driving speed, a constraint vector within a first preset distance is constructed. This vector is used to characterize constraints in multiple dimensions such as vehicle speed, impact intensity, and position. Specifically, it includes: using the target cruising speed and the maximum permissible speed calculated based on a physical model (considering slope, maximum driving force, wind resistance, etc.) and the minimum permissible speed of the current road segment as the upper and lower limits of the vehicle speed constraint; introducing an impact intensity constraint to ensure acceleration smoothness by limiting the rate of change of acceleration between adjacent time steps; and combining vehicle dynamics to calculate the maximum and minimum acceleration, thereby deriving the maximum and minimum driving positions at each step and constructing position constraints.

[0185] Through the above operations, the planned vehicle speed curve not only meets the requirements of fuel economy, but also ensures the dynamic feasibility of the vehicle speed curve and the comfort of the driver.

[0186] Step S203: Construct an objective function based on the vehicle's driving cost within a first preset distance. The driving cost includes energy consumption cost, impact cost, and driving distance cost. At least the constraint vector is used as the constraint condition. Solve the vehicle's driving speed sequence through a quadratic programming solver with the goal of minimizing the value of the objective function.

[0187] Specifically, considering the characteristics of P2 hybrid commercial vehicles, a comprehensive energy consumption cost is defined, which includes fuel consumption cost, electricity consumption cost (converted to equivalent fuel consumption through an equivalent factor), impact cost reflecting comfort, and driving distance cost reflecting path tracking accuracy. The above objective function is obtained. Then, through the efficient convergence characteristics of the QP solver, the vehicle speed and energy distribution are optimized simultaneously within a 10-second prediction time domain (1-second step), so that the engine can operate in the high-efficiency zone as much as possible, while maintaining the battery SOC within a reasonable range.

[0188] Step S204: Determine the vehicle's output torque using a PID algorithm based on the vehicle speed sequence to obtain the target torque.

[0189] Specifically, based on the vehicle speed sequence obtained from the above planning, PID control (where the proportional coefficient P provides the basic torque response, the integral coefficient I changes with the absolute value of the speed difference and is reset online to eliminate steady-state error, and the derivative coefficient D is used to suppress overshoot) is used to convert the predicted ideal vehicle speed sequence into real-time torque commands in the actual control process, thereby obtaining the target torque mentioned above.

[0190] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program having at least the following method steps:

[0191] Step S201: Obtain road slope, road curvature and road speed limit, obtain road information ahead, and calculate the target cruising speed based on the road information ahead;

[0192] Specifically, the system acquires real-time information on road slope, road curvature, and speed limits ahead of the vehicle via an external T-box, providing a stable path data foundation for subsequent energy management. Furthermore, this application sets a cruise speed based on real-time correction of the road slope ahead to obtain the aforementioned target cruise speed, thus incorporating the aforementioned road information ahead into the scope of energy management considerations.

[0193] Step S202: Construct a constraint vector within a first preset distance based on the target cruising speed, road information ahead, and driving speed. The constraint vector includes one or more of the following: speed constraint vector, impact constraint vector, and position constraint vector.

[0194] Specifically, based on the target cruising speed, road information ahead, and current driving speed, a constraint vector within a first preset distance is constructed. This vector is used to characterize constraints in multiple dimensions such as vehicle speed, impact intensity, and position. Specifically, it includes: using the target cruising speed and the maximum permissible speed calculated based on a physical model (considering slope, maximum driving force, wind resistance, etc.) and the minimum permissible speed of the current road segment as the upper and lower limits of the vehicle speed constraint; introducing an impact intensity constraint to ensure acceleration smoothness by limiting the rate of change of acceleration between adjacent time steps; and combining vehicle dynamics to calculate the maximum and minimum acceleration, thereby deriving the maximum and minimum driving positions at each step and constructing position constraints.

[0195] Through the above operations, the planned vehicle speed curve not only meets the requirements of fuel economy, but also ensures the dynamic feasibility of the vehicle speed curve and the comfort of the driver.

[0196] Step S203: Construct an objective function based on the vehicle's driving cost within a first preset distance. The driving cost includes energy consumption cost, impact cost, and driving distance cost. At least the constraint vector is used as the constraint condition. Solve the vehicle's driving speed sequence through a quadratic programming solver with the goal of minimizing the value of the objective function.

[0197] Specifically, considering the characteristics of P2 hybrid commercial vehicles, a comprehensive energy consumption cost is defined, which includes fuel consumption cost, electricity consumption cost (converted to equivalent fuel consumption through an equivalent factor), impact cost reflecting comfort, and driving distance cost reflecting path tracking accuracy. The above objective function is obtained. Then, through the efficient convergence characteristics of the QP solver, the vehicle speed and energy distribution are optimized simultaneously within a 10-second prediction time domain (1-second step), so that the engine can operate in the high-efficiency zone as much as possible, while maintaining the battery SOC within a reasonable range.

[0198] Step S204: Determine the vehicle's output torque using a PID algorithm based on the vehicle speed sequence to obtain the target torque.

[0199] Specifically, based on the vehicle speed sequence obtained from the above planning, PID control (where the proportional coefficient P provides the basic torque response, the integral coefficient I changes with the absolute value of the speed difference and is reset online to eliminate steady-state error, and the derivative coefficient D is used to suppress overshoot) is used to convert the predicted ideal vehicle speed sequence into real-time torque commands in the actual control process, thereby obtaining the target torque mentioned above.

[0200] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0201] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can 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.

[0202] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided 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... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0203] These 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 1The function specified in one or more boxes.

[0204] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment 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.

[0205] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0206] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0207] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0208] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0209] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0210] As can be seen from the above description, the embodiments of this application achieve the following technical effects:

[0211] 1) The torque control method for vehicle driving process of this application firstly obtains road slope, road curvature, and road speed limit to obtain road information ahead, and calculates the target cruising speed based on the road information ahead; then, constructs a constraint vector within a first preset distance based on the target cruising speed, road information ahead, and driving speed, the constraint vector including one or more of speed constraint vector, impact constraint vector, and position constraint vector; then, constructs an objective function based on the driving cost of the vehicle within the first preset distance, the driving cost including energy consumption cost, impact cost, and driving distance cost, at least with the constraint vector as a constraint condition, and solves the vehicle driving speed sequence through a quadratic programming solver with the objective function minimization as the objective; finally, determines the vehicle output torque based on the driving speed sequence using a PID algorithm to obtain the target torque. This application acquires information about the road ahead and constructs a comprehensive objective function that includes fuel consumption, impact intensity, and driving distance. It then uses a quadratic programming solver to perform rolling optimization under the premise of satisfying constraints on vehicle speed, acceleration, impact intensity, and powertrain to plan the optimal vehicle speed curve. Based on the vehicle speed curve, it optimizes the torque output in the hybrid power system, ensuring the overall vehicle economy. This solves the problem in existing technologies where the physical information of the road ahead is ignored when planning the vehicle speed for autonomous driving, resulting in low fuel economy.

[0212] 2) The torque control device for vehicle operation in this application includes: an acquisition unit that acquires road slope, road curvature, and road speed limits to obtain road information ahead, and calculates the target cruising speed based on the road information ahead; a first construction unit that constructs a constraint vector within a first preset distance based on the target cruising speed, road information ahead, and vehicle speed, the constraint vector including one or more of speed constraint vectors, impact constraint vectors, and position constraint vectors; a second construction unit that constructs an objective function based on the vehicle's driving cost within the first preset distance, the driving cost including energy consumption cost, impact cost, and driving distance cost, at least using the constraint vectors as constraints, and solves the vehicle's driving speed sequence through a quadratic programming solver with the objective function minimizing its value; and a first determination unit that determines the vehicle's output torque based on the driving speed sequence using a PID algorithm to obtain the target torque. This application acquires information about the road ahead and constructs a comprehensive objective function that includes fuel consumption, impact intensity, and driving distance. It then uses a quadratic programming solver to perform rolling optimization under the premise of satisfying constraints on vehicle speed, acceleration, impact intensity, and powertrain to plan the optimal vehicle speed curve. Based on the vehicle speed curve, it optimizes the torque output in the hybrid power system, ensuring the overall vehicle economy. This solves the problem in existing technologies where the physical information of the road ahead is ignored when planning the vehicle speed for autonomous driving, resulting in low fuel economy.

[0213] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A torque control method during vehicle operation, characterized in that, include: The system obtains road gradient, road curvature, and road speed limits to acquire road information ahead, and calculates the target cruising speed based on the road information ahead. Based on the target cruising speed, the road information ahead, and the driving speed, a constraint vector is constructed within a first preset distance. The constraint vector includes one or more of the following: a speed constraint vector, an impact constraint vector, and a position constraint vector. The objective function is constructed based on the vehicle's driving cost within the first preset distance. The driving cost includes energy consumption cost, impact cost, and driving distance cost. The vehicle's driving speed sequence is solved by a quadratic programming solver with the goal of minimizing the value of the objective function, at least with the constraint vector as the constraint condition. The output torque of the vehicle is determined by a PID algorithm based on the driving speed sequence, thus obtaining the target torque.

2. The method according to claim 1, characterized in that, Obtain road gradient, road curvature, and road speed limits to get information about the road ahead, including: The road gradient, road curvature, and road speed limit within the first preset distance in front of the vehicle are obtained using an external Tbox to obtain alternative road information. Based on the vehicle speed, the trend of the alternative road information changing over time is determined to obtain the road information ahead.

3. The method according to claim 1, characterized in that, Calculate the target cruising speed based on the road information ahead and the vehicle's current status, including: Based on the road information ahead, the real-time slope value within the first preset distance is determined to obtain the target slope value. Based on the road information ahead, the slope length corresponding to the target slope value is determined to obtain the target slope length. If the target slope value is within a preset range, the cruise speed is set according to the preset speed offset correction to obtain the target cruise speed; If the target slope value is not within the preset range, the target vehicle speed offset is obtained by querying the first mapping relationship based on the target slope length. The set cruise speed is then corrected based on the target vehicle speed offset to obtain the target cruise speed. The first mapping relationship is the mapping relationship between slope length and vehicle speed offset.

4. The method according to claim 1, characterized in that, Based on the target cruising speed, the road information ahead, and the vehicle speed, a constraint vector within a first preset distance is constructed, including: The vehicle acquires gravitational acceleration, air density, maximum driving force, rolling resistance coefficient, vehicle mass, frontal area, and drag coefficient, and determines the road slope within the first preset distance based on the road information ahead. The maximum vehicle speed at each time step within the first preset distance is calculated based on the gravitational acceleration, air density, maximum driving force, rolling resistance coefficient, vehicle mass, frontal area, drag coefficient, and road slope. The minimum value between the maximum driving speed and the target cruising speed at each time step is taken as the upper limit value of the corresponding speed constraint vector, and the minimum allowable speed within the first preset distance is determined as the lower limit value of the speed constraint vector, thus obtaining the corresponding speed constraint vector.

5. The method according to claim 1, characterized in that, Constructing a constraint vector within a first preset distance based on the target cruising speed, the road information ahead, and the vehicle speed also includes: The maximum permissible speed at each time step within the first preset distance is determined based on the vehicle speed and the maximum permissible acceleration, and the minimum permissible speed at each time step within the first preset distance is determined based on the vehicle speed and the minimum permissible acceleration. The maximum position of the corresponding time step is determined based on the maximum permissible vehicle speed of each time step, and the minimum position of the corresponding time step is determined based on the minimum permissible vehicle speed of each time step. The corresponding position constraint vector is constructed based on the maximum and minimum positions at each time step.

6. The method according to claim 1, characterized in that, Constructing a constraint vector within a first preset distance based on the target cruising speed, the road information ahead, and the vehicle speed also includes: The theoretical impact at each time step within the first preset distance is calculated based on the vehicle speed. The minimum value between the theoretical impact degree and the preset impact degree is taken to obtain the impact degree constraint vector.

7. The method according to claim 1, characterized in that, The objective function is constructed based on the vehicle's travel cost within the first preset distance, including: The objective function is constructed based on the vehicle's travel cost within the first preset distance. The objective function is: In the formula, The energy consumption cost includes fuel consumption cost and electricity consumption cost. For the impact cost, The cost is the distance traveled.

8. The method according to claim 1, characterized in that, The output torque of the vehicle is determined using a PID algorithm based on the vehicle speed sequence to obtain the target torque, including: Using the vehicle speed sequence and the vehicle speed as input parameters, PID calculation is performed based on preset proportional coefficient, preset integral coefficient and preset derivative coefficient to obtain the wheel end torque requirement. Based on the final drive ratio and gearbox ratio of the vehicle's transmission system, the wheel-end torque requirement is converted into the target torque requirement of the gearbox input shaft. The engine output torque and the electric motor output torque are determined based on the target torque requirement to obtain the target torque.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method according to any one of claims 1 to 7.

10. A vehicle, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising methods for performing any one of claims 1 to 7.

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