A spatial domain energy-efficient vehicle speed planning method

Abstract

The application discloses a kind of space domain energy-saving vehicle speed planning methods, comprising: obtaining road information, including elevation and speed limit of front road, current position latitude and longitude coordinates, road network and terrain and elevation, front road speed limit information;Update space domain slope information: equidistance is sampled to front road elevation, the slope corresponding to each interval is calculated;Establish energy-saving vehicle speed rolling optimization: nominal vehicle kinetic energy and transit time as state variable, acceleration as control variable, road slope as disturbance;Establish space domain state transition equation;Establish variable constraint;Design optimization goal;Construct space domain model predictive control;Energy-saving vehicle speed rolling optimization solution: balance timeliness and optimization performance, determine the prediction domain, solve energy-saving vehicle speed rolling optimization problem;Online speed control: the first acceleration signal in prediction domain is used to control speed, and rolling optimization and control are carried out forwardly.The application can guarantee good energy-saving control effect when slope, and speed planning has strong real-time performance.

Description

Technical Field

[0001] This invention belongs to the field of automotive adaptive cruise control technology and relates to a spatial domain energy-saving vehicle speed planning method. Background Technology

[0002] With the development of vehicle-road-network technology, ground vehicles can acquire road information ahead, which can be used to design predictive motion control and realize advanced driver assistance functions or even high-level autonomous driving technology. Among these, speed planning is a prerequisite for achieving high-level adaptive cruise control, and its control effect directly affects vehicle comfort and fuel economy. Therefore, speed planning methods based on road information are of great significance for achieving energy-saving and efficient assisted cruise control.

[0003] Currently, vehicle speed planning methods have been extensively studied. For example, the Chinese patent "A Vehicle Speed ​​Planning Method, Device, and Domain Controller Based on Speed ​​Limit Information" (CN202310333457.3) aims to solve the problem of variable speed limits and improve comfort. However, this method does not adequately consider terrain changes and does not incorporate energy-saving driving methods. Furthermore, existing technologies primarily operate in the time domain, representing road gradient as a polynomial function of distance over time. Such methods require continuous fitting of this polynomial function, a time-consuming process, and substituting the polynomial function into the optimization problem is not conducive to solving it. Traditional time-domain vehicle speed planning methods have high solution complexity and cannot guarantee the timeliness of speed planning. Summary of the Invention

[0004] The purpose of this invention is to address the problem that current vehicle speed planning methods cannot simultaneously consider timeliness and performance optimization, by providing a spatial domain energy-saving vehicle speed planning method that ensures good energy-saving control in gradient scenarios and effectively improves the real-time performance of vehicle speed planning.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical solution.

[0006] A spatial domain energy-saving vehicle speed planning method includes the following steps:

[0007] Step 1, Road Information Acquisition: Obtain elevation and speed limit information of the road ahead through high-precision maps; the vehicle obtains its current location's longitude and latitude coordinates by receiving satellite signals through a GPS receiver; road network, terrain, and elevation information are obtained using maps and geographic information systems; the vehicle obtains speed limit information of the road ahead through the integration of navigation system and map data.

[0008] Step 2, Spatial Domain Slope Information Update: Sample the elevation of the road ahead according to the equal spacing rule, and calculate the road slope corresponding to each spacing;

[0009] Step 3: Establishing the Energy-Saving Vehicle Speed ​​Rolling Optimization Problem: Define system variables; for the speed optimization problem, select the nominal vehicle kinetic energy E. V The system is defined as follows: travel time t is selected as the system state variable, acceleration a is selected as the system control variable, and road slope i is selected as the system disturbance. The spatial domain state transition equation is established; system variable constraints are established; optimization objectives are designed; and spatial domain model predictive control problem is constructed.

[0010] Step 4: Solving the energy-saving vehicle speed rolling optimization problem: Balancing the timeliness of the solution with the optimization performance, determining the size of the prediction domain, and calling the commercial solver IPOPT to solve the energy-saving vehicle speed rolling optimization problem;

[0011] Step 5, Online vehicle speed control: Use the first acceleration signal in the prediction domain to control the vehicle speed, and perform rolling forward optimization and control.

[0012] Specifically, in step 2, the road ahead is discretized with an equal interval Δs of 10m, and the elevation data at each point is obtained by linear interpolation; for road segment i, the road slope α(i) on each segment is solved by the height difference of the starting point and the distance between the starting points.

[0013] Specifically, step 3 includes:

[0014] Establishment of the spatial domain state transition equation: At the current system time k, for road segment i, let the vehicle's kinetic energy, travel time, and acceleration be respectively... t k (i) and a(i); for adjacent road segments i+1, let the vehicle's kinetic energy, travel time, and acceleration action be respectively t k (i+1) and a(i+1); Establish the spatial domain state transition equation:

[0015]

[0016] Among them, nominal kinetic energy Defined as v(i) 2 / 2;

[0017] System variable constraint equations established: vehicle kinetic energy The speed limit cannot be exceeded; to ensure comfort, the vehicle acceleration a(i) must be kept within a certain range, and the acceleration derivative impact Jerk(i) must also be kept within a certain range; to ensure vehicle traffic efficiency, a minimum road speed is set, and the vehicle's endpoint kinetic energy and travel time are limited to expected values; when the number of road segments is N, let the expected values ​​of endpoint kinetic energy and travel time be respectively... and Establish the following system variable constraint equations: v2 (i), These represent the lower and upper limits of the square of the road speed, respectively. a , These represent the lower and upper limits of road acceleration, respectively. Jerk , These represent the lower and upper limits of the impact, respectively.

[0018]

[0019] Design optimization objective: Vehicle driving energy consumption is the optimization objective; let m, g, f, C D A f ρ and ρ represent the vehicle mass, gravitational acceleration, rolling resistance coefficient, air resistance coefficient, frontal area, and air density, respectively; the vehicle's required power is obtained based on the system variables:

[0020]

[0021] Vehicle fuel consumption rate Power required by the vehicle Related, can be represented as The polynomial function is shown below.

[0022]

[0023] Among them, β2, β1, and β0 represent energy consumption characteristic parameters, which are determined by the specific vehicle model;

[0024] Within the prediction domain, the total fuel consumption of the vehicle can be expressed as:

[0025]

[0026] Constructing a spatial domain model predictive control problem: Combining the spatial domain state transition equations, system variable constraint equations, and the above formulas (1)-(5), with the goal of minimizing energy consumption, a model predictive control problem is constructed in the spatial domain. Indicates minimum energy consumption;

[0027]

[0028]

[0029] Specifically, in step 4, solving the energy-saving vehicle speed rolling optimization problem includes:

[0030] Determine the size of the prediction domain N: The selection of the prediction domain N needs to comprehensively consider the optimization performance and the timeliness of the solution; the prediction domain N for model prediction control is selected as 50;

[0031] Online solution for the energy-saving vehicle speed rolling optimization problem: The mature commercial solver IPOPT is used to solve the energy-saving vehicle speed rolling optimization problem.

[0032] Specifically, in step 5, the online vehicle speed control includes:

[0033] Acceleration signal acquisition: The first acceleration value in the prediction domain is used as the vehicle speed control signal; with reference to this control signal, the vehicle cruise system takes corresponding control measures to drive the vehicle.

[0034] Forward rolling optimization control: updating vehicle position and prediction information, repeatedly solving the energy-saving speed rolling optimization problem and online speed control.

[0035] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0036] 1. Strong Optimization Performance: Existing technologies typically maintain a constant speed during vehicle cruising, neglecting terrain changes. This leads to drastic variations in vehicle load with terrain, exacerbating energy consumption. In contrast, this invention acquires road information ahead and establishes a rolling optimization problem to plan vehicle speed with the goal of minimizing cruising energy consumption. Compared to constant-speed cruise methods and global optimal methods, this invention's spatial domain energy-saving speed planning method achieves near-optimal performance and significantly reduces energy consumption.

[0037] 2. High computational efficiency: This invention employs spatial domain modeling, fixing the slope and speed limit information within the prediction range as functions of position, and maintaining this information throughout a single rolling optimization. This invention avoids the polynomial fitting of road slopes by traditional time-domain methods, shortening the computation time for vehicle speed planning and reducing the solution complexity of the rolling optimization problem.

[0038] 3. Experimental verification shows that this invention has the following advantages compared with the prior art:

[0039] 1) The spatial domain vehicle speed planning method of the present invention limits the longitudinal impact of the vehicle to [-1, 1] m / s. 3 This ensures a comfortable ride.

[0040] 2) The spatial domain vehicle speed planning method of the present invention achieves an energy saving of 7.58% under the condition of constant speed cruise as the baseline, and has strong optimization performance.

[0041] 3) The spatial domain vehicle speed planning method of the present invention requires only 23.5ms for each update, thus demonstrating the significant advantage of the present invention in terms of computational efficiency. Attached Figure Description

[0042] Figure 1 This is a flowchart of a spatial domain energy-saving vehicle speed planning method according to the present invention.

[0043] Figure 2 This is a schematic diagram of the altitude and road resistance in the simulation scenario.

[0044] Figure 3 This is a comparison chart of speed results between the spatial domain energy-saving vehicle speed planning method of the present invention and other methods in the prior art.

[0045] Figure 4 This is a comparison chart of acceleration results between the spatial domain energy-saving vehicle speed planning method of the present invention and other methods in the prior art.

[0046] Figure 5 This is a comparison chart of the impact results of the spatial domain energy-saving vehicle speed planning method of the present invention and other methods in the prior art. Detailed Implementation

[0047] The present invention will now be described in further detail with reference to the accompanying drawings.

[0048] Figure 1 This is a flowchart of a spatial domain energy-saving vehicle speed planning method according to the present invention. Figure 1 As shown, the method of the present invention includes the following steps:

[0049] Step 1: Road Information Acquisition: Obtain elevation and speed limit information for the road ahead using high-precision maps. The vehicle receives satellite signals via a GPS receiver to obtain its current longitude and latitude coordinates, determining its current location. Maps and geographic information systems are used to acquire road network, terrain, and elevation information. The vehicle can obtain speed limit information for the road ahead by integrating navigation system data with map data.

[0050] Step 2: Spatial Domain Slope Information Update: Sample the elevation of the road ahead according to the equal-interval rule and calculate the road slope corresponding to each interval. Discretize the road ahead with an equal interval Δs of 10m and use linear interpolation to obtain the elevation data at each point. For road segment i, solve for the road slope α(i) of each segment using the height difference of the starting point and the interval between the starting points.

[0051] Step 3: Establishing the Energy-Saving Vehicle Speed ​​Rolling Optimization Problem: Establish system variables, select nominal vehicle kinetic energy and travel time as state variables, select acceleration as control variable, take slope information as external disturbance, consider road speed limit, and design a model predictive control problem in the spatial domain with driving energy consumption and comfort as objectives, thus establishing the energy-saving vehicle speed rolling optimization problem.

[0052] Step 3, the steps for establishing the energy-saving vehicle speed rolling optimization problem, include:

[0053] Step 3.1: Establish system variables: For the energy-saving vehicle speed rolling optimization problem, select the nominal vehicle kinetic energy E. VThe travel time t is selected as the system state variable, the acceleration a is selected as the system control variable, and the road slope i is selected as the system disturbance quantity.

[0054] Step 3.2: Establishing the spatial domain state transition equation: At the current system time k, for road segment i, let the vehicle's kinetic energy, travel time, and acceleration be respectively... t k (i) and a(i). For adjacent road segments i+1, let the vehicle's kinetic energy, travel time, and acceleration action be respectively... t k (i+1) and a(i+1). Establish the following spatial domain state transition equation:

[0055]

[0056] Among them, nominal kinetic energy Defined as v(i) 2 / 2.

[0057] Step 3.3: Establishing system variable constraint equations: vehicle kinetic energy The speed limit cannot be exceeded. To ensure comfort, the vehicle acceleration a(i) must be kept within a certain range, and the acceleration derivative impact Jerk(i) must also be kept within a certain range. To ensure vehicle traffic efficiency, a minimum road speed is set, and the vehicle's endpoint kinetic energy and travel time are limited to expected values. With N road segments, let the expected values ​​of endpoint kinetic energy and travel time be respectively... and Establish the following system variable constraint equations: v 2 (i), These represent the lower and upper limits of the square of the road speed, respectively. a , These represent the lower and upper limits of road acceleration, respectively. Jerk , These represent the lower and upper limits of the impact, respectively.

[0058]

[0059] Step 3.4: Design Optimization Objective: Use vehicle driving energy consumption as the optimization objective. Let m, g, f, and C... D A f Let ρ be the vehicle mass, gravitational acceleration, rolling resistance coefficient, air resistance coefficient, frontal area, and air density, respectively. The vehicle's required power is obtained from the system variables:

[0060]

[0061] Vehicle fuel consumption rate Power required by the vehicle Related, can be represented as The polynomial function is shown below.

[0062]

[0063] Among them, β2, β1, and β0 represent energy consumption characteristic parameters, which are determined by the specific vehicle model.

[0064] Within the prediction domain, the total fuel consumption of the vehicle can be expressed as:

[0065]

[0066] Step 3.5: Combining formulas (1)-(5), construct the spatial domain model predictive control problem: With the goal of minimizing energy consumption, construct the model predictive control problem in the spatial domain. This indicates the minimum energy consumption.

[0067]

[0068]

[0069] Step 4: Solving the energy-saving vehicle speed rolling optimization problem: Balancing the timeliness of the solution with the optimization performance, determining the size of the prediction domain, and calling the commercial solver IPOPT to solve the energy-saving vehicle speed rolling optimization problem.

[0070] Step 4, which solves the energy-saving vehicle speed rolling optimization problem, includes the following steps:

[0071] Step 4.1: Determine the size of the prediction domain N: The selection of the prediction domain N needs to comprehensively consider both optimization performance and solution timeliness. The prediction domain N for model prediction control is selected as 50.

[0072] Step 4.2: Online solution of the energy-saving vehicle speed rolling optimization problem: The mature commercial solver IPOPT is used to solve the energy-saving vehicle speed rolling optimization problem.

[0073] Step 5: Online control: Use the first acceleration signal in the prediction domain to control the vehicle speed, and perform rolling forward optimization and control.

[0074] Step 5, the online vehicle speed control step, includes:

[0075] Step 5.1: Acceleration Signal Acquisition: The first acceleration value within the prediction domain is used as the vehicle speed control signal. Referring to this control signal, the vehicle cruise system takes corresponding control measures to drive the vehicle.

[0076] Step 5.2: Forward rolling optimization control: Update vehicle position and prediction information, repeat the energy-saving speed rolling optimization problem and online speed control.

[0077] Figure 2 This is a schematic diagram of the altitude and road resistance in a simulation scenario according to one embodiment of the present invention.

[0078] Construct a 1km long virtual road with elevation changes as follows: Figure 2 As shown in the shaded area. In traditional time-domain planning methods, slope information needs to be continuously updated based on the vehicle's position. Therefore, a suitable function is needed to perform slope estimation and thus update the driving resistance. To more intuitively illustrate the impact of slope on driving resistance, road resistance F is defined. road as follows:

[0079] F road = mg(sinα+fcosα) (7)

[0080] In this embodiment, the original road resistance data is as follows: Figure 2 As shown in the middle circle. The original data is fitted with trigonometric functions using a nonlinear least squares method, and the fitted curve is shown below. Figure 2 As shown by the solid line, the road resistance F can be obtained. road The fitting model with respect to distance s is as follows:

[0081]

[0082] Among them, b j c j d j This represents the fitted parameters for road resistance related to slope.

[0083] The spatial domain energy-saving vehicle speed planning method of this invention avoids the above-mentioned fitting process, thereby reducing the computational complexity in principle.

[0084] To further verify the advantages of the present invention, this embodiment also compares the results of the cruise control method, the traditional time-domain vehicle speed planning method, and the dynamic programming global optimal method with the spatial domain energy-saving vehicle speed planning of the present invention.

[0085] Figure 3 A speed result comparison chart is shown between the spatial domain energy-saving vehicle speed planning method of the present invention and other methods in the prior art.

[0086] Figure 4 A comparison chart of acceleration results is shown between the spatial domain energy-saving vehicle speed planning method of the present invention and other methods in the prior art.

[0087] Figure 5 A comparison chart showing the impact results of the spatial domain energy-saving vehicle speed planning method of the present invention with other methods in the prior art is presented.

[0088] Figures 3 to 5In the diagram, the solid line represents the spatial domain energy-saving vehicle speed planning method of this invention, the dotted line represents the traditional time domain vehicle speed planning method, the dashed line represents the cruise control method, and the dashed line represents the dynamic programming global optimal method. Figure 3 As shown, the spatial domain energy-saving vehicle speed planning method of this invention follows the speed limit requirements, and its results are close to those of traditional time domain vehicle speed planning methods and dynamic programming global optimal methods. Figure 4 As shown, the spatial domain energy-saving vehicle speed planning method of this invention effectively limits acceleration. Figure 5 As shown, the spatial domain energy-saving vehicle speed planning method of the present invention limits the longitudinal impact of the vehicle to [-1, 1] m / s. 3 This ensures a comfortable ride.

[0089] Table 1 compares the performance of the method of this invention with three existing vehicle speed planning methods:

[0090] Table 1

[0091]

[0092] The average speeds of different speed planning methods are nearly identical. Regarding fuel consumption, the globally optimal method achieved the lowest objective function value of 22.03. Using cruise control as a baseline, its energy-saving performance improvement reached 8.24%. Furthermore, the time-domain speed planning method and the spatial-domain energy-saving speed planning method of this invention achieved energy savings of 7.77% and 7.58%, respectively. It can be seen that the optimization performance of the method of this invention is quite close to that of the globally optimal method.

[0093] The computation time of vehicle speed planning methods is crucial for the online implementation of optimized control. As shown in the last row of Table 1, the computation time of the globally optimal method is close to 2 seconds, which is unsuitable for real-time control. The spatial domain energy-saving vehicle speed planning method of this invention has a computation time of 23.5 ms, while the time domain vehicle speed planning method increases this by 281.7%, reaching 89.7 ms. Considering that the performance difference between the two is very small, the spatial domain energy-saving vehicle speed planning method has a significant advantage in computational efficiency. In summary, the spatial domain energy-saving vehicle speed planning method of this invention has the best overall performance.

Claims

1. A spatial domain energy-saving vehicle speed planning method, characterized in that, Includes the following steps: Step 1, Road Information Acquisition: Obtain the elevation and speed limit information of the road ahead using a high-precision map; The vehicle receives satellite signals through a GPS receiver to obtain its current location's longitude and latitude coordinates, thus determining its current position; it uses maps and geographic information systems to obtain road network, terrain, and altitude information; and it obtains speed limit information for the road ahead by integrating the navigation system with map data. Step 2, Spatial Domain Slope Information Update: Sample the elevation of the road ahead according to the equal spacing rule, and calculate the road slope corresponding to each spacing; Step 3: Establishing the Energy-Saving Vehicle Speed ​​Rolling Optimization Problem: Determining System Variables; To address the speed optimization problem, the nominal vehicle kinetic energy is selected. Travel time Acceleration was selected as the system state variable. Road slope was selected as the system control variable. As a system disturbance, establish the spatial domain state transition equation; Establish system variable constraints; design optimization objectives; construct a spatial domain model for predictive control problems; Step 4: Solving the energy-saving vehicle speed rolling optimization problem: Balancing the timeliness of the solution with the optimization performance, determining the size of the prediction domain, and calling the commercial solver IPOPT to solve the energy-saving vehicle speed rolling optimization problem; Step 5, Online vehicle speed control: Use the first acceleration signal in the prediction domain to control the vehicle speed, and perform rolling forward optimization and control; In step 2, at equal intervals For a 10m discretized road ahead, linear interpolation is used to obtain elevation data at each point; for road segments... The road slope on each segment is solved using the height difference and the distance between the starting points. ; In step 3, establishing the spatial domain state transition equation and establishing system variable constraints specifically includes: Establishment of the spatial domain state transition equation: at the current system time... Below, for road segments Let the vehicle's kinetic energy, travel time, and acceleration be respectively... , and For adjacent road segments Let the vehicle's kinetic energy, travel time, and acceleration be respectively... , and Establish the state transition equation in the spatial domain: （1） Among them, nominal kinetic energy Defined as ; System variable constraint equations established; vehicle kinetic energy The speed limit must not be exceeded; to ensure comfort, vehicle acceleration... It needs to be kept within a certain range, while the acceleration derivative impact degree It also needs to be kept within a certain range; to ensure vehicle traffic efficiency, a minimum road speed is set, and the vehicle's endpoint kinetic energy and travel time are limited to expected values; the number of road segments is... Under the condition that the expected values ​​of the endpoint kinetic energy and travel time are respectively and Establish the following system variable constraint equations: These represent the lower and upper limits of the square of the road speed, respectively. These represent the lower and upper limits of road acceleration, respectively. These represent the lower and upper limits of the impact, respectively. （2）。 2. The spatial domain energy-saving vehicle speed planning method according to claim 1, characterized in that, In step 3, the design optimization objective and the construction of the spatial domain model predictive control problem specifically include: Design optimization objective: Vehicle driving energy consumption is the optimization objective; Let... and These are the vehicle mass, gravitational acceleration, rolling drag coefficient, air resistance coefficient, frontal area, and air density; the vehicle's power requirement is obtained based on these system variables. （3） Vehicle fuel consumption rate Power required by the vehicle Related, can be represented as The polynomial function is shown below. （4） in, The characteristic parameters representing energy consumption are determined by the specific vehicle model; Within the prediction domain, the total fuel consumption of the vehicle can be expressed as: （5） Constructing a spatial domain model predictive control problem: Combining the spatial domain state transition equations, system variable constraint equations, and the above formulas (1)-(5), with the goal of minimizing energy consumption, a model predictive control problem is constructed in the spatial domain. Indicates minimum energy consumption; （6）。 3. The spatial domain energy-saving vehicle speed planning method according to claim 1, characterized in that, In step 4, solving the energy-saving vehicle speed rolling optimization problem specifically includes: Determine the prediction domain Size: Prediction Domain The selection of the model should comprehensively consider both optimization performance and solution timeliness; the model predicts the control prediction domain. Select 50; Online solution for the energy-saving vehicle speed rolling optimization problem: The mature commercial solver IPOPT is used to solve the energy-saving vehicle speed rolling optimization problem.

4. The spatial domain energy-saving vehicle speed planning method according to claim 1, characterized in that, In step 5, the online vehicle speed control specifically includes: Acceleration signal acquisition: The first acceleration value in the prediction domain is used as the vehicle speed control signal; with reference to this control signal, the vehicle cruise system takes corresponding control measures to drive the vehicle. Forward rolling optimization control: updating vehicle position and prediction information, repeatedly solving the energy-saving speed rolling optimization problem and online speed control.

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