Heavy vehicle eco-driving control method for multi-red-green-light intersection considering preceding vehicle factors

Abstract

The application provides a heavy-load vehicle ecological driving control method for multiple-red-green-light intersections considering the influence of preceding vehicles, comprising the following steps: S1. obtaining the start point and end point position information of the current heavy-load vehicle and the traffic information between the start point and the end point of the current heavy-load vehicle, and determining the feasible path between the start point and the end point; S2. determining the global optimal path from the feasible path, and determining the remaining paths as suboptimal passing paths; S3. establishing a control strategy, including a first control strategy, a second control strategy and a third control strategy; S4. determining the maximum passing time and the minimum passing time of the current heavy-load vehicle passing through the kth red-green-light intersection between the start point and the end point, and predicting the green light interval of the front intersection in the feasible path of the heavy-load vehicle based on the maximum passing time and the minimum passing time; S5. determining control strategy determination parameters, including the real-time distance between vehicles, the safety distance between vehicles, the distance between the vehicle and the red-light intersection in the kth road section, the distance between the end point of the kth road section and the red-light intersection, and the average speed of the heavy-load vehicle on the distance already traveled in the kth road section; and S6. determining the control strategy of the current heavy-load vehicle according to the control strategy determination parameters.

Description

Technical Field

[0001] This invention relates to a vehicle driving control method, and more particularly to an eco-driving control method for heavy-duty vehicles at multi-traffic light intersections that takes into account the influence of factors from the vehicle in front. Background Technology

[0002] Frequent stops by heavy-duty vehicles at traffic light intersections lead to additional energy consumption, affect driving comfort, and reduce intersection traffic efficiency.

[0003] In the context of intelligent transportation systems, vehicles can obtain traffic light information along their travel path in advance and plan their speed to avoid frequent starts and stops, thereby improving overall vehicle fuel economy and driving comfort. Speed ​​planning for roads with continuous traffic lights is gradually becoming a research hotspot.

[0004] Compared to highways and mountain roads, intelligent control of heavy-duty vehicles in urban outer ring areas faces greater challenges due to traffic light restrictions, multiple speed limits, and variable traffic conditions, resulting in more constraints and uncertainties. Eco-driving is widely used for vehicle entry and exit at traffic light intersections. Existing eco-driving strategies primarily focus on the characteristics and usage scenarios of small passenger vehicles, offering limited functionality and failing to directly provide eco-driving guidance for heavy-duty vehicles.

[0005] Therefore, in order to solve the above-mentioned technical problems, it is urgent to propose a new technical approach. Summary of the Invention

[0006] In view of this, the purpose of this invention is to provide an ecological driving control method for heavy-duty vehicles at multi-traffic light intersections that takes into account the influence of preceding vehicles. This method can control the driving of heavy-duty vehicles in the long term when they are subject to multiple speed restrictions and interference from preceding vehicles, effectively avoiding the problems of frequent starts and stops, slow traffic speed, and low traffic efficiency of heavy-duty vehicles during driving.

[0007] This invention provides an eco-driving control method for heavy-duty vehicles at multi-traffic light intersections that considers the influence of preceding vehicle factors, comprising the following steps:

[0008] S1. Obtain the starting and ending point location information of the current heavy-load vehicle and the traffic information between the starting and ending points of the current heavy-load vehicle, and determine the feasible path between the starting and ending points.

[0009] S2. Determine the globally optimal path from the feasible paths, and identify the remaining paths as suboptimal paths;

[0010] S3. Establish control strategies, including a first control strategy, a second control strategy, and a third control strategy;

[0011] S4. Determine the maximum and minimum passage times for the current heavy-load vehicle to pass through the kth traffic light from the starting point to the end point, and predict the green light intervals of the next intersection in the feasible path of the heavy-load vehicle based on the maximum and minimum passage times;

[0012] S5. Determine the control strategy decision parameters, including the real-time distance between vehicles, the safe distance between vehicles, the distance between the vehicle and the traffic light intersection in the kth road segment, the distance between the end of the kth road segment and the traffic light intersection, and the average speed of the heavy-load vehicle over the distance already traveled in the kth road segment.

[0013] S6. Determine the control strategy for the current heavy-load vehicle based on the control strategy determination parameters.

[0014] Furthermore, in step S2, the A* algorithm is used to determine the globally optimal path.

[0015] Furthermore, in step S4, the maximum and minimum passage times for the k-th traffic light are determined as follows:

[0016] The feasible path between the starting point and the destination of the current heavy-load vehicle is divided into several independent distance units using traffic lights as nodes. The formulas for calculating the maximum and minimum travel times are as follows:

[0017]

[0018]

[0019] Where: t max,k For the maximum passage time, t min,k For the minimum travel time, v avg,k Let v be the average velocity of the k-th distance unit. min,k Let a be the minimum velocity of the k-th distance unit. s For comfortable acceleration when driving heavy-duty vehicles, a b To improve driving comfort when driving heavy-duty vehicles.

[0020] Furthermore, based on the maximum and minimum travel times, the green light intervals at the next intersection in the predicted feasible path for heavy-duty vehicles specifically include:

[0021] Construct a green light prediction model for traffic light intersections:

[0022] When the initial light is red:

[0023]

[0024] When the initial light is green:

[0025]

[0026] flag k (t) represents the state of the k-th traffic light at time t, when flag k When (t) = 0, it indicates that the k-th traffic light is red. When flag k When (t) = 1, it means that the kth traffic light is green;

[0027] in:

[0028]

[0029]

[0030] Wherein: T 0,k R is defined as the transition period after the green light ends at the first traffic light intersection before the starting point of the heavy-duty vehicle. k t represents the cycle number of the k-th signal at the current time; [·] represents the rounding up sign; t represents the current time.

[0031] T l,k T is the complete cycle time of the kth traffic light. l,k =T g,k +T r,k ;T s,k T is the transition time between each traffic light at the vehicle's starting point and the next traffic light; g,k T represents the green light time of the kth traffic light; r,k This represents the red light time of the kth traffic light.

[0032] Furthermore, a control strategy is established using the following methods:

[0033] Constructing a longitudinal dynamics model for heavy-duty vehicles:

[0034]

[0035] The longitudinal dynamic model is discretized using the forward Euler method:

[0036]

[0037]

[0038] Where: Δt is the step size of the time step during discretization, j is the j-th time step; η is the mechanical efficiency of the transmission system, i o The main reducer transmission ratio, i g For the gear ratio of the transmission, T tq For engine torque, r eWhere is the wheel radius; m is the total mass of the car; g is the acceleration due to gravity; f is the rolling resistance coefficient; β is the road gradient; C D ρ is the air resistance coefficient, A is the frontal area of ​​the car, ρ is the air density, v is the car speed, and β is the slope of the vehicle.

[0039] The first control strategy is:

[0040]

[0041] subject to

[0042]

[0043]

[0044] u min,k ≤u k (j)≤u max,k

[0045] a min,k ≤a k (j)≤a max,k

[0046] v min,k ≤v k (j)≤v real,k ;

[0047] The second control strategy is:

[0048]

[0049] subject to

[0050]

[0051]

[0052] u min,k ≤u(j)≤u max,k

[0053] a min,k ≤a(j)≤a max,k

[0054] v min,k ≤v(j)≤v real,k

[0055]

[0056] The third control strategy is:

[0057]

[0058] subject to

[0059]

[0060]

[0061] u min,k ≤u(j)≤u max,k

[0062] a min,k ≤a(j)≤a max,k

[0063] v min,k ≤v(j)≤v real,k

[0064]

[0065] Furthermore, the following method is used to determine the following following speed v of the current heavy-duty vehicle. real,k：

[0066] v real,k =max[0,|v s,k -θ×b×rand()|];

[0067] Where: θ is the driver proficiency coefficient, rand() is a random value during vehicle operation, and v s,k It is the vehicle's desired speed;

[0068] v s,k =min[v t,k +bΔt,v max,k ,v safe,k ];

[0069] b is the maximum acceleration of the heavy-duty vehicle, v max,k To limit the maximum speed of vehicles on the road, v safe,k For the safe speed of heavy-duty vehicles,

[0070]

[0071] s v_fl,k This refers to the real-time distance between vehicles; v p,k v is the speed of the vehicle in front; k τ represents the current speed of the heavily loaded vehicle; 'a' represents the maximum deceleration of the heavily loaded vehicle; τ represents the current speed of the heavily loaded vehicle. k This refers to the driver's reaction time.

[0072] Furthermore, the fuel consumption rate Q of heavy-duty vehicles s Determined using the following method:

[0073]

[0074] Where: t i,j ω is the fitting coefficient. e T is the engine speed. e This refers to the engine torque.

[0075] Furthermore, determining the control strategy for the current heavy-duty vehicle based on the control strategy determination parameters specifically includes:

[0076] Definition: s v_fl,k s represents the real-time distance between vehicles. Limit d is the set safe distance between vehicles. t_k Let d be the distance at k distance units. avg_k The median distance of the entire road segment in distance unit k

[0077] S61. When a vehicle is detected in front of a heavily loaded vehicle, if s is satisfied... v_f l _k Limit When this happens, the vehicle is controlled to follow the preceding vehicle according to the first control strategy, if s is satisfied simultaneously. v_fl_k Limit and d t_k <d avg_k Then proceed to step S62;

[0078] S62. If the average speed of the heavy-load vehicle that has traveled the distance in the current distance unit meets the average speed range of the optimal green light passage in the current location, then control the vehicle to pass through the current traffic light intersection at the optimal traffic light window. If the average speed of the heavy-load vehicle that has traveled the distance in the current distance unit does not meet the average speed range of the optimal green light passage in the current location, then proceed to step S63.

[0079] S63. Determine whether there is a suboptimal green light window. If so, proceed to step S64; otherwise, proceed to step S65.

[0080] S64. Determine whether the current average speed meets the average speed for passing through the suboptimal green light. If it does, and the vehicle flow condition is also met, then control the vehicle to pass through the current intersection at the speed determined by the second control strategy in the suboptimal green light window. If it does not meet, then execute step S65.

[0081] S65. Determine whether there is a feasible path at the intersection ahead. If there is, calculate and determine the optimal or suboptimal green light window passage on the feasible route. When the route switching is satisfied, control the vehicle to pass through the traffic light intersection according to the calculated green light window and the third control strategy. Repeat the determination steps S61-S65 during the driving process. When the route switching is not satisfied, execute step S66.

[0082] ​​S66. Determine if there is a feasible route at the intersection ahead. If not, continue to follow the vehicle in front according to the first control strategy. When stopping at the traffic light intersection, start global path planning and green light window planning again from the current stopping point.

[0083] Furthermore, the optimal or suboptimal green light window is determined using the following method:

[0084] t max,k For maximum passage time and t min,k The minimum travel time is divided into several equally divided intervals:

[0085]

[0086]

[0087] This represents the starting green light time of the τth interval within the maximum and minimum time intervals of the kth traffic light. Let t represent the starting green light time of the τth interval within the maximum and minimum time intervals of the kth traffic light. g_r,k This indicates the remaining time of the green light cycle in the τth interval within the maximum and minimum time intervals of the kth traffic light. This indicates the proportion of the remaining time of the τth interval of the complete cycle in the maximum and minimum time intervals of the kth traffic light;

[0088] Extract the green light passage intervals from each maximum and minimum time interval:

[0089]

[0090] The green light passage interval is the smallest and largest interval among all intersections;

[0091] The A* algorithm is used to determine the optimal green light window, and the remaining green light windows are determined as the suboptimal green light windows.

[0092] The beneficial effects of this invention are as follows: This invention enables long-term control of heavy-duty vehicles when they are subject to multiple speed limits and interference from vehicles in front, effectively avoiding the problems of frequent starts and stops, slow speeds, and low traffic efficiency of heavy-duty vehicles during operation. It also improves the traffic efficiency of traffic light intersections, reduces fuel consumption and emissions, and is beneficial to environmental protection. Attached Figure Description

[0093] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0094] Figure 1 This is a flowchart of the present invention.

[0095] Figure 2 This is a schematic diagram of the intersection path and traffic signal cycle.

[0096] Figure 3 The optimal green light window is obtained by using the A* algorithm to find the feasible route. Detailed Implementation

[0097] The present invention will be further described in detail below:

[0098] This invention provides an eco-driving control method for heavy-duty vehicles at multi-traffic light intersections that considers the influence of preceding vehicle factors, comprising the following steps:

[0099] S1. Obtain the starting and ending point location information of the current heavy-load vehicle and the traffic information between the starting and ending points of the current heavy-load vehicle, and determine the feasible path between the starting and ending points.

[0100] S2. Determine the globally optimal path from the feasible paths, and identify the remaining paths as suboptimal paths;

[0101] S3. Establish control strategies, including a first control strategy, a second control strategy, and a third control strategy;

[0102] S4. Determine the maximum and minimum passage times for the current heavy-load vehicle to pass through the kth traffic light from the starting point to the end point, and predict the green light intervals of the next intersection in the feasible path of the heavy-load vehicle based on the maximum and minimum passage times;

[0103] S5. Determine the control strategy decision parameters, including the real-time distance between vehicles, the safe distance between vehicles, the distance between the vehicle and the traffic light intersection in the kth road segment, the distance between the end of the kth road segment and the traffic light intersection, and the average speed of the heavy-load vehicle over the distance already traveled in the kth road segment.

[0104] S6. Determine the control strategy for the current heavy-load vehicle based on the control strategy determination parameters. Using the above method, long-term control of heavy-load vehicles can be achieved when subjected to multiple speed limits and interference from vehicles ahead, effectively avoiding problems such as frequent starts and stops, slow traffic speed, and low traffic efficiency during operation.

[0105] In this embodiment, step S2 uses the A* algorithm to determine the globally optimal path. Specifically:

[0106] The evaluation function of the A* algorithm is shown in the following equation.

[0107] Φ(i) = p(i) + h(i);

[0108] Φ(i) is the evaluation function, i = 1, 2, ..., m1; p(i) is the actual cost from the starting point to node i; h(i) is the heuristic function, which represents the estimated cost of the cost function from node i to the endpoint;

[0109] The actual cost p(i) in the A* algorithm evaluation function is the sum of the optimal green light time, the sum of the shortest distance, and the sum of the minimum number of traffic lights for feasible paths from the starting point of the heavily loaded vehicle to the current traffic light intersection.

[0110]

[0111] g t i d t i and d t i These represent the sum of green light times, the sum of distances from the starting point to the current position, and the sum of the number of traffic lights, respectively.

[0112] h(i) is the sum of the optimal green light time, the sum of the shortest distance, and the sum of the minimum number of traffic lights from the current node to the destination, as shown in the following formula:

[0113]

[0114] In this embodiment, in step S4, the maximum and minimum passage times of the kth traffic light are determined as follows:

[0115] The feasible path between the starting point and the ending point of the current heavy-load vehicle is divided into several independent distance units using traffic lights as nodes. Specifically, taking the k-th traffic light as an example...

[0116]

[0117] For traffic light-related Spatial and location information, S k T represents the distance between the k-th traffic light and the (k-1)-th traffic light; s,k T is the transition time between each traffic light at the vehicle's starting point and the next traffic light; g,k T represents the green light time of the kth traffic light; r,k R represents the red light duration of the k-th traffic light; k P represents the number of cycles for the k-th traffic light; k This represents the initial signal of each traffic light when the vehicle is at the starting point; P k =1 indicates a green light signal; P k =0 indicates a red light signal; T l,k This represents the complete cycle time of the kth traffic light.

[0118] The formulas for calculating the maximum and minimum passage times are as follows:

[0119]

[0120]

[0121] Where: t max,k For the maximum passage time, t min,k For the minimum travel time, v avg,k Let v be the average velocity of the k-th distance unit. min,k Let a be the minimum velocity of the k-th distance unit. s For comfortable acceleration when driving heavy-duty vehicles, a b To improve driving comfort when driving heavy-duty vehicles.

[0122] In this embodiment, the green light interval at the next intersection in the feasible path of a heavy-load vehicle, based on the maximum and minimum travel times, specifically includes:

[0123] Construct a green light prediction model for traffic light intersections:

[0124] When the initial light is red:

[0125]

[0126] When the initial light is green:

[0127]

[0128] flag k (t) represents the state of the k-th traffic light at time t, when flag k When (t) = 0, it indicates that the k-th traffic light is red. When flag k When (t) = 1, it means that the kth traffic light is green;

[0129] in:

[0130]

[0131]

[0132] Wherein: T 0,k R is defined as the transition period after the green light ends at the first traffic light intersection before the starting point of the heavy-duty vehicle. k t represents the cycle number of the k-th signal at the current time; [·] represents the rounding up sign; t represents the current time.

[0133] T l,k T is the complete cycle time of the kth traffic light.l,k =T g,k +T r,k ;T s,k T is the transition time between each traffic light at the vehicle's starting point and the next traffic light; g,k T represents the green light time of the kth traffic light; r,k This represents the red light time of the kth traffic light.

[0134] When a heavily loaded vehicle is interfered with by another vehicle ahead, its speed will be limited compared to the ideal situation where there is no interference. Therefore, a traffic light interval evaluation and prediction model considering the interference from the vehicle ahead is designed. When the predicted interval is the optimal suboptimal green light interval, different controllers are selected for planning based on different predicted intervals. When the predicted interval is the red light interval, it searches for other globally suboptimal paths at the intersection ahead. If so, it searches for whether other globally suboptimal paths can proceed with a green light. If so, it selects the suboptimal global path and assumes that lane changes during the heavy-load vehicle's journey are normal lane changes.

[0135] In this embodiment, the control strategy is established using the following method:

[0136] Constructing a longitudinal dynamics model for heavy-duty vehicles:

[0137]

[0138] The longitudinal dynamic model is discretized using the forward Euler method:

[0139]

[0140]

[0141] Where: Δt is the step size of the time step during discretization, j is the j-th time step; η is the mechanical efficiency of the transmission system, i o The main reducer transmission ratio, i g For the gear ratio of the transmission, T tq For engine torque, r e Where is the wheel radius; m is the total mass of the car; g is the acceleration due to gravity; f is the rolling resistance coefficient; β is the road gradient; C D ρ is the air resistance coefficient, A is the frontal area of ​​the car, ρ is the air density, v is the car speed, and β is the slope of the vehicle.

[0142] The first control strategy is:

[0143]

[0144] subject to

[0145]

[0146]

[0147] u min,k ≤u k (j)≤u max,k

[0148] a min,k ≤a k (j)≤a max,k

[0149] v min,k ≤v k (j)≤v real,k ;

[0150] The second control strategy is:

[0151]

[0152] subject to

[0153]

[0154]

[0155] u min,k ≤u(j)≤u max,k

[0156] a min,k ≤a(j)≤a max,k

[0157] v min,k ≤v(j)≤v real,k

[0158]

[0159] The third control strategy is:

[0160]

[0161] subject to

[0162]

[0163]

[0164] u min,k ≤u(j)≤u max,k

[0165] a min,k ≤a(j)≤a max,k

[0166] v min,k ≤v(j)≤v real,k

[0167] The control strategy described above determines the optimal speed of the heavy-load vehicle at time step j+1, while satisfying the objective function, and controls the vehicle's movement accordingly. The objective function is solved using an existing sequential quadratic programming algorithm, where:

[0168]

[0169]

[0170]

[0171]

[0172] The following method is used to determine the following following speed v of the current heavy-duty vehicle. real,k :

[0173] v real,k =max[0,|v s,k -θ×b×rand()|];

[0174] Where: θ is the driver proficiency coefficient, rand() is a random value during vehicle operation, and v s,k It is the vehicle's desired speed;

[0175] v s,k =min[v t,k +bΔt,v max,k ,v safe,k ];

[0176] b is the maximum acceleration of the heavy-duty vehicle, v max,k To limit the maximum speed of vehicles on the road, v safe,k For the safe speed of heavy-duty vehicles,

[0177]

[0178] s v_fl,k This refers to the real-time distance between vehicles; v p,k v is the speed of the vehicle in front; k τ represents the current speed of the heavily loaded vehicle; 'a' represents the maximum deceleration of the heavily loaded vehicle; τ represents the current speed of the heavily loaded vehicle. k This refers to the driver's reaction time.

[0179] Fuel consumption rate Q of heavy-duty vehicles s Determined using the following method:

[0180]

[0181] Where: t i,j ω is the fitting coefficient. e T is the engine speed. e This refers to the engine torque.

[0182] In this embodiment, determining the control strategy for the current heavy-load vehicle based on the control strategy determination parameters specifically includes:

[0183] Definition: s v_fl,k s represents the real-time distance between vehicles. Limit d is the set safe distance between vehicles. t_k Let d be the distance at k distance units. avg_k The median distance of the entire road segment in distance unit k

[0184] S61. When a vehicle is detected in front of a heavily loaded vehicle, if s is satisfied... v_fl_k Limit When this happens, the vehicle is controlled to follow the preceding vehicle according to the first control strategy, if s is satisfied simultaneously. v_fl_k Limit and d t_k <d avg_k Then proceed to step S62;

[0185] S62. If the average speed of the heavy-load vehicle after traveling the distance in the current distance unit satisfies the average speed range of the optimal green light passage at the current location, that is: satisfying... Then control the vehicle to pass through the current traffic light intersection at the optimal traffic light window. If the average speed of the heavy vehicle that has traveled the distance in the current distance unit does not meet the average speed range of the optimal green light passage at the current location, then proceed to step S63.

[0186] S63. Determine whether there is a suboptimal green light window, i.e., whether it satisfies the condition. If so, proceed to step S64; otherwise, proceed to step S65.

[0187] S64. Determine if the current average speed meets the suboptimal average speed for green light passage, i.e., it does. If it does, and it also meets the vehicle flow condition, the vehicle flow condition is: v min,k =v avg,k α, which is the relationship between the speed limit of the kth road segment and the average speed, is a scaling factor used to characterize the maximum fluctuation of vehicle speed in terms of road smoothness. If the current average speed is lower than the speed limit, the smoothness of vehicle traffic is not met. In this case, the vehicle is controlled to pass through the current intersection at the suboptimal green light window according to the speed determined by the second control strategy. If the condition is not met, step S65 is executed.

[0188] ​​S65. Determine whether there is a feasible path at the intersection ahead. If there is, calculate and determine the optimal or suboptimal green light window passage on the feasible route. When the route switching is satisfied, control the vehicle to pass through the traffic light intersection according to the calculated green light window and the third control strategy. Repeat the determination steps S61-S65 during the driving process. When the route switching is not satisfied, execute step S66.

[0189] S66. Determine if there is a feasible route at the intersection ahead. If not, continue to follow the vehicle in front according to the first control strategy. When stopping at the traffic light intersection, start global path planning and green light window planning again from the current stopping point.

[0190] The above judgment process can be simply understood as follows: when the predicted interval is the optimal or suboptimal green light interval, different controllers are selected for planning based on different predicted intervals. When the predicted interval is the red light interval, it is searched to see if there are other global suboptimal paths at the intersection ahead of the current road segment. If there are, it is searched to see if other global suboptimal paths can pass through with a green light. If they can, the suboptimal global path is selected for driving.

[0191] Furthermore, the optimal or suboptimal green light window is determined using the following method:

[0192] t max,k For maximum passage time and t min,k The minimum travel time is divided into several equally divided intervals:

[0193]

[0194]

[0195] This represents the starting green light time of the τth interval within the maximum and minimum time intervals of the kth traffic light. Let t represent the starting green light time of the τth interval within the maximum and minimum time intervals of the kth traffic light. g_r,k This indicates the remaining time of the green light cycle in the τth interval within the maximum and minimum time intervals of the kth traffic light. This indicates the proportion of the remaining time of the τth interval of the complete cycle in the maximum and minimum time intervals of the kth traffic light;

[0196] Extract the green light passage intervals from each maximum and minimum time interval:

[0197]

[0198] The green light passage interval is the smallest and largest interval among all intersections;

[0199] The A* algorithm is used to determine the optimal green light window, and the remaining green light windows are designated as suboptimal. Specifically, the A* algorithm and a cost function are used to find the optimal green light passage interval. The evaluation function of the A* algorithm is:

[0200] Φ * (q)=p * (q)+h * (q)

[0201] In the above formula, Φ * (q) is the evaluation function for the green light window; p(i) is the actual cost from the starting point to node q; h(q) is the heuristic function, representing the estimated cost of the cost function from node q to the destination. The specific expression is shown in the following equation:

[0202]

[0203]

[0204] In the above formula To indicate whether a green light interval is a complete interval, incomplete intervals will be given a larger weight to increase the cost of passing through that node; The k-th traffic light represents the feasible green light passage interval; the optimal green light window is obtained when the cost function reaches its minimum.

[0205] Using this method for speed planning and control, the overall number of stops for all test vehicles was optimized by 10.48%, and the overall fuel consumption was optimized by 4.12%, resulting in a fuel saving of 1.06 liters per 100 kilometers, which translates to an economic cost of approximately 7.5 yuan. Analyzing a single vehicle, the average fuel consumption optimization per 100 kilometers was 2.06 liters, equivalent to an economic cost of 14.6 yuan; analyzing a single route, the average fuel consumption optimization per 100 kilometers was 1.59 liters, equivalent to an economic cost of 11.3 yuan.

[0206] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for eco-driving control of heavy-duty vehicles at multi-traffic light intersections considering the influence of preceding vehicle factors, characterized in that: Includes the following steps: S1. Obtain the starting and ending point location information of the current heavy-load vehicle and the traffic information between the starting and ending points of the current heavy-load vehicle, and determine the feasible path between the starting and ending points. S2. Determine the globally optimal path from the feasible paths, and identify the remaining paths as suboptimal paths; S3. Establish control strategies, including a first control strategy, a second control strategy, and a third control strategy; S4. Determine the maximum and minimum passage times for the current heavy-load vehicle to pass through the kth traffic light from the starting point to the end point, and predict the green light intervals of the next intersection in the feasible path of the heavy-load vehicle based on the maximum and minimum passage times; S5. Determine the control strategy decision parameters, including the real-time distance between vehicles, the safe distance between vehicles, the distance between the vehicle and the traffic light intersection in the kth road segment, the distance between the end of the kth road segment and the traffic light intersection, and the average speed of the heavy-load vehicle over the distance already traveled in the kth road segment. S6. Determine the control strategy for the current heavy-load vehicle based on the control strategy determination parameters; Establish a control strategy using the following method: Constructing a longitudinal dynamics model for heavy-duty vehicles: ； The longitudinal dynamic model is discretized using the forward Euler method: ； ； in: This refers to the time step size during discretization. For the j-th time step; For the mechanical efficiency of the transmission system, Main reducer transmission ratio, This refers to the gear ratio of the transmission. For engine torque, The radius of the wheel; Here, g is the total mass of the car, f is the acceleration due to gravity, and f is the rolling resistance coefficient of the wheels. Road slope; It is the air resistance coefficient, and A is the frontal area of ​​the car. It is air density. It is the speed of the car; It is the slope on which the vehicle travels; The first control strategy is: ； The second control strategy is: ； The third control strategy is: ； Among them: Q s (j) represents the vehicle's fuel consumption rate.

2. The method for ecological driving control of heavy-duty vehicles at multi-traffic light intersections considering the influence of preceding vehicles, as described in claim 1, is characterized in that: In step S2, the A* algorithm is used to determine the globally optimal path.

3. The method for ecological driving control of heavy-duty vehicles at multi-traffic light intersections considering the influence of preceding vehicles, as described in claim 1, is characterized in that: In step S4, the maximum and minimum passage times for the k-th traffic light are determined as follows: The feasible path between the starting point and the destination of the current heavy-load vehicle is divided into several independent distance units using traffic lights as nodes. The formulas for calculating the maximum and minimum travel times are as follows: ； ； in: For the maximum passage time, Minimum passage time, Let be the average velocity of the k-th distance unit. The minimum velocity of the k-th distance unit. For comfortable acceleration when driving heavy-duty vehicles, To improve driving comfort when driving heavy-duty vehicles.

4. The method for ecological driving control of heavy-duty vehicles at multi-traffic light intersections considering the influence of preceding vehicles, as described in claim 3, is characterized in that: The green light intervals at the next intersection in the predicted feasible path for heavily loaded vehicles based on maximum and minimum travel times specifically include: Construct a green light prediction model for traffic light intersections: When the initial light is red: ； When the initial light is green: ； This represents the state of the k-th traffic light at time t. When, it means that the k-th traffic light is red. When, it means that the k-th traffic light is green; in: ; ； ； in: Defined as the transition period after the green light ends at the first traffic light intersection before the starting point of heavy-duty vehicles. This represents the cycle number of the k-th signal at the current moment; The rounding up symbol; For the current moment, The complete cycle time of the kth traffic light. ; The transition time between each traffic light at the starting point of a vehicle and the next traffic light. This represents the green light time for the kth traffic light. P represents the red light time of the kth traffic light; k =1 indicates a green light signal; P k =0 indicates a red light signal.

5. The method for ecological driving control of heavy-duty vehicles at multi-traffic light intersections considering the influence of preceding vehicles, as described in claim 1, is characterized in that: The following method is used to determine the following following speed of the current heavy-duty vehicle. : ； in: Here, `rand()` represents the driver's proficiency coefficient, and `rand()` represents a random value generated during vehicle operation. It is the vehicle's desired speed; ； b is the maximum acceleration of the heavy-duty vehicle. To limit the maximum speed of vehicles on the road, For the safe speed of heavy-duty vehicles, ； This refers to the real-time spacing between vehicles. The speed of the vehicle in front; The current speed of the heavily loaded vehicle; This represents the maximum deceleration of a heavy-duty vehicle. This refers to the driver's reaction time.

6. The method for ecological driving control of heavy-duty vehicles at multi-traffic light intersections considering the influence of preceding vehicles, as described in claim 1, is characterized in that: Fuel consumption rate of heavy-duty vehicles Determined using the following method: ； in: These are the fitting coefficients. Engine speed, This refers to the engine torque.

7. The method for ecological driving control of heavy-duty vehicles at multi-traffic light intersections considering the influence of preceding vehicles, as described in claim 1, is characterized in that: Determining the control strategy for the current heavy-load vehicle based on the control strategy determination parameters specifically includes: definition: This represents the real-time distance between vehicles. The set safe distance between vehicles, Let k be the distance in k-distance units. The median distance of the entire road segment in distance unit k S61. When a vehicle is detected in front of a heavily loaded vehicle, if the following conditions are met... When this occurs, the vehicle is controlled to follow the preceding vehicle according to the first control strategy, if the following conditions are met simultaneously: and Then proceed to step S62; S62. If the average speed of the heavy-load vehicle over the distance already traveled in the current distance unit meets the average speed range of the optimal green light passage at the current location, then control the vehicle to pass through the current traffic light intersection at the optimal traffic light window; if the average speed of the heavy-load vehicle over the distance already traveled in the current distance unit does not meet the average speed range of the optimal green light passage at the current location, then proceed to step S63. S63. Determine if a suboptimal green light window exists. If so, proceed to step S64; otherwise, proceed to step S65. S64. Determine whether the current average speed meets the average speed for passing through the suboptimal green light. If it does, and the vehicle flow condition is also met, control the vehicle to pass through the current intersection at the speed determined by the second control strategy within the suboptimal green light window. If it does not meet, proceed to step S65. S65. Determine whether there is a feasible path at the intersection ahead. If there is, calculate and determine the optimal or suboptimal green light window passage on the feasible route. When the route switching is satisfied, control the vehicle to pass through the traffic light intersection according to the calculated green light window and the third control strategy. Repeat the determination steps S61-S65 during the driving process. When the route switching is not satisfied, execute step S66. S66. Determine if there is a feasible route at the intersection ahead. If not, continue to follow the vehicle in front according to the first control strategy. When stopping at the traffic light intersection, start global path planning and green light window planning again from the current stopping point.

8. The method for ecological driving control of heavy-duty vehicles at multi-traffic light intersections considering the influence of preceding vehicle factors, as described in claim 4, is characterized by: The optimal or suboptimal green light window can be determined using the following method: Will For maximum passage time The minimum travel time is divided into several equally divided intervals: ； This represents the maximum and minimum time intervals of the k-th traffic light. The start time of the green light in the section. This represents the maximum and minimum time intervals of the k-th traffic light. The start time of the green light in the section. This indicates the maximum and minimum time intervals of the k-th traffic light. Remaining time of the green light cycle in the section. This indicates the maximum and minimum time intervals during the k-th traffic light. The proportion of the remaining time of the first interval period to the total time of the complete period; Extract the green light passage intervals from each maximum and minimum time interval: The green light passage interval is the smallest and largest interval among all intersections; The A* algorithm is used to determine the optimal green light window, and the remaining green light windows are determined as the suboptimal green light windows.

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