A predictive cruise speed gear dimensionality reduction optimization method

Abstract

This invention provides a predictive cruise speed and gear optimization method, belonging to the field of intelligent fuel-saving control technology. The method includes the following steps: segmenting the road conditions, determining the slope for each segment, calculating the speed and gear limits and requirements, and completing road network reconstruction; constructing an optimization objective function model; building a feasible state domain for speed and gear combinations for adjacent road segment transfers, calculating transfer costs, and selecting the optimal speed and gear combination as the predicted speed and gear; and using the predicted speed and gear for predictive cruise control. This invention reduces computational dimensionality by adding slope determination to optimize speed and gear limit calculations during road network reconstruction; it achieves optimal fuel consumption and economy coupling by considering the predicted gear when calculating the predicted speed and thus the cost; and it calculates the feasible state of the path transfer when calculating the path transfer cost during the predicted speed and gear calculation, reducing computational load and improving real-time performance when deployed in an embedded controller.

Description

Technical Field

[0001] This invention belongs to the field of intelligent fuel-saving control technology, specifically relating to a predictive cruise speed gear reduction optimization method. Background Technology

[0002] Currently, fuel costs account for a relatively high proportion of operating costs for commercial vehicles, which does not meet relevant fuel consumption regulations. Therefore, fuel-saving control of vehicles is necessary. Traditional commercial vehicle cruise control only considers maintaining a constant speed, ignoring the influence of road and traffic information. This can lead to driving behaviors such as rapid acceleration when going uphill and rapid deceleration when going downhill in order to maintain a constant speed when encountering uneven road conditions, resulting in high fuel consumption.

[0003] Predictive cruise control methods, by considering the slope information of the road ahead, allow the vehicle speed to fluctuate within a certain range and make full use of the vehicle's kinetic energy and the road's enabling energy to avoid high-fuel-consumption driving behaviors. However, current predictive cruise control methods have the following problems: First, predictive speed and predictive gear are separated during predictive cruise calculation, resulting in the failure to consider gear when calculating the predicted speed, inaccurate fuel cost calculation, and consequently, the coupling effect of the selected route, fuel consumption, and economy is not optimal. Second, predictive cruise calculations involving many parameters require multiple tasks to be processed simultaneously when deployed in an embedded controller, thus requiring a large amount of memory. However, actual computing resources are limited, resulting in low algorithm computation efficiency and difficulty in guaranteeing real-time performance.

[0004] This is a shortcoming of the existing technology. Therefore, it is very necessary to provide a predictive cruise speed gear reduction optimization method to address the above-mentioned defects in the existing technology. Summary of the Invention

[0005] The aforementioned predictive cruise control technology can avoid high fuel consumption driving behavior caused by maintaining a constant vehicle speed. However, existing predictive cruise has the drawbacks of separating predicted vehicle speed and gear, resulting in inaccurate calculation of fuel costs, as well as having many predictive cruise parameters and low calculation efficiency. This invention provides a predictive cruise speed and gear dimensionality reduction optimization method to solve the above technical problems.

[0006] This invention provides a predictive cruise speed gear selection optimization method, comprising the following steps:

[0007] S1. Divide the road conditions ahead of the vehicle collected from the map into segments, determine the ramp in each segment, calculate the speed, gear limit and demand value, and complete the road network reconstruction.

[0008] S2. Construct an optimization objective function model for vehicle speed and gear shifting from the current road segment to the next road segment based on vehicle dynamics equations;

[0009] S3. Construct a feasible state domain combination of vehicle speed and gear for the transfer between adjacent road segments, use an optimization objective function model to calculate the transfer cost for each road segment transfer, and select the optimal combination of vehicle speed and gear as the predicted vehicle speed and predicted gear.

[0010] S4. When the vehicle is traveling between different road segments, it uses the predicted speed and predicted gear for the corresponding adjacent road segment transfer to complete the predictive cruise control.

[0011] Furthermore, the specific steps of step S1 are as follows:

[0012] S11. Obtain the road conditions ahead of the current vehicle position by collecting data from the map, and divide the driving path ahead into segments;

[0013] S12. Calculate the travel distance of each road segment and the position of each road segment on the overall path, and calculate the slope angle and gradient of each road segment;

[0014] S13. For each road segment, determine the required speed and gear before going uphill based on the uphill control status, and then calculate the maximum speed limit, minimum speed limit, maximum gear limit, and minimum gear limit based on the required speed and gear before going uphill.

[0015] Furthermore, the calculation of the travel distance of the road segment in step S12 is performed through the following steps:

[0016] Determine the original driving distance for each road segment and correct it during map reconstruction, or use the gradient to correct it.

[0017] Furthermore, the specific steps of step S12 are as follows:

[0018] S121. Calculate the travel distance for each road segment and determine the initial position of the starting road segment based on the current position on the map;

[0019] S122. Position each road segment after the starting road segment in sequence, and set the position of the positioned road segment = the position of the previous road segment + the current road segment travel distance;

[0020] S123. Convert the original slope signal into an initial slope angle by subtracting it based on the current location on the map;

[0021] S124. Position each slope angle after the initial slope angle in sequence, and then convert the slope angle at the position offset of the original slope signal by interpolating the slope at the corresponding road segment position to obtain the positioning slope angle.

[0022] Furthermore, the specific steps of step S13 are as follows:

[0023] S131. Calculate the minimum speed and required gear before going uphill for each road section based on the uphill control status.

[0024] S132. Obtain the maximum wheel-end torque, wheel-end drag torque, vehicle mass, and wheel radius, and calculate the acceleration using the following formula:

[0025] ;

[0026] S133. Obtain the vehicle speed and the distance traveled in each segment, and combine this with the calculated acceleration to calculate the maximum speed limit using the following formula:

[0027] ;

[0028] S134. Obtain the vehicle speed and distance traveled in each segment, and combine this with the calculated acceleration to calculate the minimum vehicle speed in the ideal flat state using the following formula:

[0029] ;

[0030] S135. Take the larger of the minimum speed under ideal flat conditions and the minimum speed before going uphill as the minimum vehicle limit value.

[0031] S136. Obtain the maximum gear limit by referring to the CURVE calibration table using wheel end torque and vehicle speed;

[0032] S137. Obtain the gear change threshold, and calculate the minimum gear in the ideal flat state based on the difference between the maximum gear limit and the gear change threshold;

[0033] S138. Take the smaller of the minimum gear value under ideal flat conditions and the gear value required before going uphill as the minimum gear limit.

[0034] Furthermore, the specific steps of step S131 are as follows:

[0035] S1311. The uphill control state is pre-divided into five categories. The first state corresponds to the uphill that does not require acceleration, torque increase and downshifting; the second state corresponds to the uphill that requires torque increase; the third state corresponds to the uphill that requires acceleration before going uphill; the fourth state corresponds to the uphill that requires downshifting; and the fifth state corresponds to the uphill that cannot be calculated.

[0036] S1312. Obtain the driver's set cruise speed and the maximum speed reduction threshold, and take the difference between the two as the driver's set minimum speed;

[0037] S1313. Determine the category of uphill control state based on vehicle speed and slope angle, and calculate the minimum vehicle speed before going uphill;

[0038] S1314. When it is the third state, divide it into the pre-slope stage and the uphill stage. Let the minimum speed before the uphill is determined in the pre-slope stage based on the maximum speed at the bottom of the slope when the engine reaches the top of the slope with the maximum torque, while in the uphill stage, take the minimum speed set by the driver.

[0039] S1315. When the first state, second state, fourth state, and fifth state are defined, set the minimum speed before going uphill to be equal to the minimum speed set by the driver.

[0040] S1316. Determine the uphill control state category based on vehicle speed and slope angle, and calculate the required gear before going uphill;

[0041] S1317. When in the fourth state, the gear required before going uphill is determined based on the gear at which the engine's maximum torque reaches the top of the hill during both the pre-hill and uphill stages.

[0042] S1318. When the state is the first state, the second state, the third state, or the fifth state, set the gear required before going uphill to be equal to the current gear.

[0043] Furthermore, the specific steps of step S2 are as follows:

[0044] S21. The cumulative cost is the sum of the fuel consumption cost, travel time cost, and transfer comfort cost of each adjacent road segment, and the cumulative cost is used as the optimization objective.

[0045] S22. Construct an optimization objective function model based on vehicle dynamics equations to calculate the cumulative cost of vehicle speed and gear combinations during path transitions between adjacent road segments:

[0046] Where J represents cumulative cost and K represents segment. This indicates that the control variable is transferred from point i to point j in segment k-1. This represents the fuel consumption during the transition from point i to point j in segment k-1. Indicates the fuel consumption coefficient. This represents the time taken when transitioning from point i to point j in segment k-1. Indicates the travel time coefficient. This represents the comfort level when transitioning from point i to point j in segment k-1. This indicates the comfort level coefficient.

[0047] Furthermore, the specific steps of step S3 are as follows:

[0048] S31. Discretize the speed range between the maximum and minimum speed limits for each road segment to obtain M feasible state domain speed combinations.

[0049] S32. Discretize the gear interval between the maximum and minimum gear limits for each road segment to obtain N rows of feasible state domain gear combinations;

[0050] S33. Recombine the feasible state domain speeds and feasible state domain gears for each road segment to obtain M×N rows of feasible state domain speed and gear combinations;

[0051] S34. Using an optimization objective function model, select the speed and gear combination with the lowest transition product cost from the feasible state domain speed and gear combinations for each road segment as the predicted speed and predicted gear.

[0052] Furthermore, the specific steps of step S34 are as follows:

[0053] S341. Locate each road segment sequentially starting from the initial position;

[0054] S342. Obtain the feasible state domain vehicle speed gear combination of the location road segment, and locate the vehicle speed gear combination;

[0055] S343. Determine whether the engine corresponding to the vehicle speed and gear combination exceeds the external characteristics or whether the acceleration exceeds the driver's cruise comfort acceleration threshold.

[0056] If at least one condition is met, proceed to step S345;

[0057] If neither of the two conditions is true, proceed to step S344;

[0058] S344. Calculate the transfer cost for the positioning speed and gear combination using the optimization objective function model, and proceed to step S346;

[0059] S345. Set the transfer cost of the positioning speed gear combination to the maximum value, treat the positioning speed gear combination as an infeasible transfer path of the positioning road segment, and set the optimization objective function model to be uncallable;

[0060] S346. Take the speed gear combination corresponding to the minimum transfer cost of the location segment, and set the speed in the speed gear combination as the predicted speed and the gear in the speed gear combination as the target gear, and proceed to step S4.

[0061] Furthermore, the specific steps of step S4 are as follows:

[0062] S41. The vehicle will output the predicted speed to the cruise control module on each road segment to calculate the required torque for cruise control.

[0063] S42. The predicted gear is output to the transmission controller to perform the gear shift and complete the predictive cruise control.

[0064] The beneficial effects of this invention are as follows:

[0065] The predictive cruise speed and gear optimization method provided by this invention optimizes the speed and gear limits by adding slope judgment through road network reconstruction, thereby reducing the computational dimension; by predicting the speed and gear, the method considers the gear when calculating the predicted speed and then calculates the cost, achieving the best coupling effect between fuel consumption and economy; and when predicting the speed and gear, the method calculates the feasible state of path transfer when calculating the path transfer cost, reducing the amount of computation and improving real-time performance when deployed in an embedded controller.

[0066] Furthermore, the design principle of this invention is reliable, the structure is simple, and it has a very wide range of application prospects.

[0067] Therefore, it is evident that the present invention has outstanding substantive features and significant progress compared with the prior art, and the beneficial effects of its implementation are also obvious. Attached Figure Description

[0068] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0069] Figure 1 This is a flowchart illustrating an embodiment of the predictive cruise speed gear dimensionality reduction optimization method of the present invention.

[0070] Figure 2 This is a flowchart illustrating another embodiment of the predictive cruise speed gear dimensionality reduction optimization method of the present invention.

[0071] Figure 3 This is a schematic diagram of the control architecture for the predictive cruise speed and gear dimensionality reduction optimization method of the present invention. Detailed Implementation

[0072] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this invention.

[0073] Example 1:

[0074] Predictive cruise control can avoid fuel-intensive driving behaviors caused by maintaining a constant vehicle speed. However, existing predictive cruise control systems suffer from several problems, such as separating speed prediction from gear selection, leading to inaccurate fuel cost calculations, and having numerous parameters resulting in low computational efficiency. Figure 1As shown, this invention provides a predictive cruise speed gear reduction optimization method, comprising the following steps:

[0075] S1. Divide the road conditions ahead of the vehicle collected from the map into segments, determine the ramp in each segment, calculate the speed, gear limit and demand value, and complete the road network reconstruction.

[0076] S2. Construct an optimization objective function model for vehicle speed and gear shifting from the current road segment to the next road segment based on vehicle dynamics equations;

[0077] S3. Construct a feasible state domain combination of vehicle speed and gear for the transfer between adjacent road segments, use an optimization objective function model to calculate the transfer cost for each road segment transfer, and select the optimal combination of vehicle speed and gear as the predicted vehicle speed and predicted gear.

[0078] S4. When the vehicle is traveling between different road segments, it uses the predicted speed and predicted gear for the corresponding adjacent road segment transfer to complete the predictive cruise control.

[0079] This invention first reconstructs the road network and calculates the nominal speed, driving distance, maximum speed limit, minimum speed limit, slope angle, maximum gear limit, and minimum gear limit for each segment in the solution space for predicting cruise speed and gear. Then, it models the vehicle and calculates the optimization objective function for use by the dynamic programming algorithm. The dynamic programming algorithm is then used to plan the predicted speed and gear. Finally, the predicted speed and gear are output to the actuator.

[0080] Example 2:

[0081] Predictive cruise control can avoid fuel-intensive driving behaviors caused by maintaining a constant vehicle speed. However, existing predictive cruise control systems suffer from several problems, such as separating speed prediction from gear selection, leading to inaccurate fuel cost calculations, and having numerous parameters resulting in low computational efficiency. Figure 2 As shown, this invention provides a predictive cruise speed gear reduction optimization method, comprising the following steps:

[0082] S1. Divide the road conditions ahead of the vehicle collected from the map into segments, determine the slope of each segment, calculate the limits and demand values ​​for vehicle speed and gear, and complete the road network reconstruction; the specific steps of step S1 are as follows:

[0083] S11. Obtain the road conditions ahead of the current vehicle position by collecting data from the map, and divide the driving path ahead into segments;

[0084] S12. Calculate the travel distance of each road segment and the position of each road segment on the overall path, and calculate the slope angle and gradient of each road segment; the specific steps of step S12 are as follows:

[0085] S121. Calculate the travel distance for each road segment and determine the initial position of the starting road segment based on the current position on the map;

[0086] S122. Position each road segment after the starting road segment in sequence, and set the position of the positioned road segment = the position of the previous road segment + the current road segment travel distance;

[0087] S123. Convert the original slope signal into an initial slope angle by subtracting it based on the current location on the map;

[0088] S124. Position each slope angle after the initial slope angle in sequence, and then interpolate the slope at the position offset of the original slope signal through the corresponding road segment to obtain the positioning slope angle;

[0089] The calculation of the travel distance for a road segment is performed through the following steps:

[0090] Determine the original driving distance for each road segment and correct it during map reconstruction, or use the slope to correct it;

[0091] S13. For each road segment, determine the uphill acceleration and gear requirements before the uphill ascent based on the uphill control status. Then, calculate the maximum speed limit, minimum speed limit, maximum gear limit, and minimum gear limit based on the required uphill speed and gear. The specific steps of S13 are as follows:

[0092] S131. Calculate the minimum speed and required gear before going uphill for each road segment based on the uphill control status; the specific steps of step S131 are as follows:

[0093] S1311. The uphill control state is pre-divided into five categories. The first state corresponds to the uphill that does not require acceleration, torque increase and downshifting; the second state corresponds to the uphill that requires torque increase; the third state corresponds to the uphill that requires acceleration before going uphill; the fourth state corresponds to the uphill that requires downshifting; and the fifth state corresponds to the uphill that cannot be calculated.

[0094] S1312. Obtain the driver's set cruise speed and the maximum speed reduction threshold, and take the difference between the two as the driver's set minimum speed;

[0095] S1313. Determine the category of uphill control state based on vehicle speed and slope angle, and calculate the minimum vehicle speed before going uphill;

[0096] S1314. When it is the third state, divide it into the pre-slope stage and the uphill stage. Let the minimum speed before the uphill is determined in the pre-slope stage based on the maximum speed at the bottom of the slope when the engine reaches the top of the slope with the maximum torque, while in the uphill stage, take the minimum speed set by the driver.

[0097] S1315. When the first state, second state, fourth state, and fifth state are defined, set the minimum speed before going uphill to be equal to the minimum speed set by the driver.

[0098] S1316. Determine the uphill control state category based on vehicle speed and slope angle, and calculate the required gear before going uphill;

[0099] S1317. When in the fourth state, the gear required before going uphill is determined based on the gear at which the engine's maximum torque reaches the top of the hill during both the pre-hill and uphill stages.

[0100] S1318. When the state is the first state, the second state, the third state, or the fifth state, set the gear required before going uphill to be equal to the current gear.

[0101] S132. Obtain the maximum wheel-end torque, wheel-end drag torque, vehicle mass, and wheel radius, and calculate the acceleration using the following formula:

[0102] ;

[0103] S133. Obtain the vehicle speed and the distance traveled in each segment, and combine this with the calculated acceleration to calculate the maximum speed limit using the following formula:

[0104] ;

[0105] S134. Obtain the vehicle speed and distance traveled in each segment, and combine this with the calculated acceleration to calculate the minimum vehicle speed in the ideal flat state using the following formula:

[0106] ;

[0107] S135. Take the larger of the minimum speed under ideal flat conditions and the minimum speed before going uphill as the minimum vehicle limit value.

[0108] S136. Obtain the maximum gear limit by referring to the CURVE calibration table using wheel end torque and vehicle speed;

[0109] S137. Obtain the gear change threshold, and calculate the minimum gear in the ideal flat state based on the difference between the maximum gear limit and the gear change threshold;

[0110] S138. Take the smaller of the minimum gear value under ideal flat conditions and the median value of the gear value required before going uphill as the minimum gear limit;

[0111] S2. Construct an optimization objective function model for vehicle speed and gear shifting in the current road segment and the next road segment based on the vehicle dynamics equations; the specific steps of step S2 are as follows:

[0112] S21. The cumulative cost is the sum of the fuel consumption cost, travel time cost, and transfer comfort cost of each adjacent road segment, and the cumulative cost is used as the optimization objective.

[0113] S22. Construct an optimization objective function model based on vehicle dynamics equations to calculate the cumulative cost of vehicle speed and gear combinations during path transitions between adjacent road segments:

[0114] Where J represents cumulative cost and K represents segment. This indicates that the control variable is transferred from point i to point j in segment k-1. This represents the fuel consumption during the transition from point i to point j in segment k-1. Indicates the fuel consumption coefficient. This represents the time taken when transitioning from point i to point j in segment k-1. Indicates the travel time coefficient. This represents the comfort level when transitioning from point i to point j in segment k-1. Indicates the comfort level coefficient;

[0115] S3. Construct feasible state domain speed and gear combinations for adjacent road segment transfers, calculate the transfer cost for each road segment transfer using an optimization objective function model, and select the optimal speed and gear combination as the predicted speed and predicted gear; the specific steps of step S3 are as follows:

[0116] S31. Discretize the speed range between the maximum and minimum speed limits for each road segment to obtain M feasible state domain speed combinations.

[0117] S32. Discretize the gear interval between the maximum and minimum gear limits for each road segment to obtain N rows of feasible state domain gear combinations;

[0118] S33. Recombine the feasible state domain speeds and feasible state domain gears for each road segment to obtain M×N rows of feasible state domain speed and gear combinations;

[0119] S34. Using an optimization objective function model, select the speed-gear combination with the lowest transition product cost from the feasible state domain speed-gear combinations for each road segment as the predicted speed and predicted gear; the specific steps of step S34 are as follows:

[0120] S341. Locate each road segment sequentially starting from the initial position;

[0121] S342. Obtain the feasible state domain vehicle speed gear combination of the location road segment, and locate the vehicle speed gear combination;

[0122] S343. Determine whether the engine corresponding to the vehicle speed and gear combination exceeds the external characteristics or whether the acceleration exceeds the driver's cruise comfort acceleration threshold.

[0123] If at least one condition is met, proceed to step S345;

[0124] If neither of the two conditions is true, proceed to step S344;

[0125] S344. Calculate the transfer cost for the positioning speed and gear combination using the optimization objective function model, and proceed to step S346;

[0126] S345. Set the transfer cost of the positioning speed gear combination to the maximum value, treat the positioning speed gear combination as an infeasible transfer path of the positioning road segment, and set the optimization objective function model to be uncallable;

[0127] S346. Take the speed gear combination corresponding to the minimum transfer cost of the location segment, and let the speed in the speed gear combination be the predicted speed and the gear in the speed gear combination be the target gear, and proceed to step S4.

[0128] Poll the last feasible state domain combination of vehicle speed and gear in turn;

[0129] For each feasible speed-gear combination in the last segment, let segment k = R-1. Iterate through each segment, calculating the cumulative cost according to the objective function formula, until k = 1. When calculating the transfer cost for each segment transitioning to the next, first determine if the engine exceeds its external characteristics or if the acceleration exceeds the driver's cruise comfort acceleration threshold. If so, set the transfer cost to the maximum value and set the selected speed-gear combination transfer path from the current segment to the next segment as unreliable; the objective function is no longer called. Otherwise, call the objective function to calculate the transfer cost; R is the total number of segments.

[0130] For each feasible speed gear in the final segment, a comparison is made to obtain the minimum. And the index for the last feasible state domain vehicle speed and gear combination;

[0131] Determine whether the vehicle speed and gear combination polling has been completed in the last feasible state domain. If yes, proceed to the segment polling step; otherwise, proceed to the last segment to continue polling the vehicle speed and gear combination.

[0132] Let segment k=1, and iterate through each segment in turn. Calculate the cumulative cost and path transition confidence state according to the optimization objective function formula, and compare them to obtain the minimum. And the selected speed and gear combination for each segment, until k=N;

[0133] S4. When the vehicle travels between different road segments, it uses the predicted speed and predicted gear for the corresponding adjacent road segment transition to complete predictive cruise control; the specific steps of step S4 are as follows:

[0134] S41. The vehicle will output the predicted speed to the cruise control module on each road segment to calculate the required torque for cruise control.

[0135] S42. The predicted gear is output to the transmission controller to perform the gear shift and complete the predictive cruise control.

[0136] Although the present invention has been described in detail with reference to the accompanying drawings and preferred embodiments, the invention is not limited thereto. Various equivalent modifications or substitutions can be made to the embodiments of the invention by those skilled in the art without departing from the spirit and essence of the invention, and such modifications or substitutions should all be within the scope of the invention. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the invention should also be covered within the protection scope of the invention. Therefore, the protection scope of the invention should be determined by the scope of the claims.

Claims

1. A predictive cruise speed gear reduction optimization method, characterized in that, Includes the following steps: S1. Divide the road conditions ahead of the vehicle collected from the map into segments, determine the uphill slope for each segment, calculate the maximum speed limit, minimum speed limit, maximum gear limit, minimum gear limit, required speed before uphill, and required gear before uphill, and complete the road network reconstruction. S2. Construct an optimization objective function model for vehicle speed and gear shifting from the current road segment to the next road segment based on vehicle dynamics equations; The specific steps of step S2 are as follows: S21. The cumulative cost is the sum of the fuel consumption cost, travel time cost, and transfer comfort cost of each adjacent road segment, and the cumulative cost is used as the optimization objective. S22. Construct an optimization objective function model based on vehicle dynamics equations to calculate the cumulative cost of vehicle speed and gear combinations during path transitions between adjacent road segments: Where J represents cumulative cost and K represents segment. This indicates that the control variable is transferred from point i to point j in segment k-1. This represents the fuel consumption during the transition from point i to point j in segment k-1. Indicates the fuel consumption coefficient. This represents the time taken when transitioning from point i to point j in segment k-1. Indicates the travel time coefficient. This represents the comfort level when transitioning from point i to point j in segment k-1. Indicates the comfort level coefficient; S3. Construct a feasible state domain combination of vehicle speed and gear for the transfer between adjacent road segments, use an optimization objective function model to calculate the transfer cost for each road segment transfer, and select the optimal combination of vehicle speed and gear as the predicted vehicle speed and predicted gear. The specific steps of step S3 are as follows: S31. Discretize the speed range between the maximum and minimum speed limits for each road segment to obtain M feasible state domain speed combinations. S32. Discretize the gear interval between the maximum and minimum gear limits for each road segment to obtain N rows of feasible state domain gear combinations; S33. Recombine the feasible state domain speeds and feasible state domain gears for each road segment to obtain M×N rows of feasible state domain speed and gear combinations; S34. Using an optimization objective function model, select the speed and gear combination with the lowest transition product cost from the feasible state domain speed and gear combinations for each road segment as the predicted speed and predicted gear. S4. When the vehicle is traveling between different road segments, it uses the predicted speed and predicted gear for the corresponding adjacent road segment transfer to complete the predictive cruise control.

2. The predictive cruise speed gear reduction optimization method as described in claim 1, characterized in that, The specific steps of step S1 are as follows: S11. Obtain the road conditions ahead of the current vehicle position by collecting data from the map, and divide the driving path ahead into segments; S12. Calculate the travel distance of each road segment and the position of each road segment on the overall path, and calculate the slope angle and gradient of each road segment; S13. For each road segment, determine the required speed and gear before going uphill based on the uphill control status, and then calculate the maximum speed limit, minimum speed limit, maximum gear limit, and minimum gear limit based on the required speed and gear before going uphill.

3. The predictive cruise speed gear reduction optimization method as described in claim 2, characterized in that, The calculation of the travel distance for the road segment in step S12 is performed through the following steps: Determine the original driving distance for each road segment and correct it during map reconstruction, or use the gradient to correct it.

4. The predictive cruise speed gear reduction optimization method as described in claim 2, characterized in that, The specific steps of step S12 are as follows: S121. Calculate the travel distance for each road segment and determine the initial position of the starting road segment based on the current position on the map; S122. Position each road segment after the starting road segment in sequence, and set the position of the positioned road segment = the position of the previous road segment + the current road segment travel distance; S123. Convert the original slope signal into an initial slope angle by subtracting it based on the current location on the map; S124. Position each slope angle after the initial slope angle in sequence, and then convert the slope angle at the position offset of the original slope signal by interpolating the slope at the corresponding road segment position to obtain the positioning slope angle.

5. The predictive cruise speed gear reduction optimization method as described in claim 2, characterized in that, The specific steps of step S13 are as follows: S131. Calculate the minimum speed and required gear before going uphill for each road section based on the uphill control status. S132. Obtain the maximum wheel-end torque, wheel-end drag torque, vehicle mass, and wheel radius, and calculate the acceleration using the following formula: ； S133. Obtain the vehicle speed and the distance traveled in each segment, and combine this with the calculated acceleration to calculate the maximum speed limit using the following formula: ； S134. Obtain the vehicle speed and distance traveled in each segment, and combine this with the calculated acceleration to calculate the minimum vehicle speed in the ideal flat state using the following formula: ； S135. Take the larger of the minimum speed under ideal flat conditions and the minimum speed before going uphill as the minimum vehicle limit value. S136. Obtain the maximum gear limit by referring to the CURVE calibration table using wheel end torque and vehicle speed; S137. Obtain the gear change threshold, and calculate the minimum gear in the ideal flat state based on the difference between the maximum gear limit and the gear change threshold; S138. Take the smaller of the minimum gear value under ideal flat conditions and the gear value required before going uphill as the minimum gear limit.

6. The predictive cruise speed gear reduction optimization method as described in claim 5, characterized in that, The specific steps of step S131 are as follows: S1311. The uphill control state is pre-divided into five categories. The first state corresponds to the uphill that does not require acceleration, torque increase and downshifting; the second state corresponds to the uphill that requires torque increase; the third state corresponds to the uphill that requires acceleration before going uphill; the fourth state corresponds to the uphill that requires downshifting; and the fifth state corresponds to the uphill that cannot be calculated. S1312. Obtain the driver's set cruise speed and the maximum speed reduction threshold, and take the difference between the two as the driver's set minimum speed; S1313. Determine the category of uphill control state based on vehicle speed and slope angle, and calculate the minimum vehicle speed before going uphill; S1314. When it is the third state, divide it into the pre-slope stage and the uphill stage. Let the minimum speed before the uphill is determined in the pre-slope stage based on the maximum speed at the bottom of the slope when the engine reaches the top of the slope with the maximum torque, while in the uphill stage, take the minimum speed set by the driver. S1315. When the first state, second state, fourth state, and fifth state are defined, set the minimum speed before going uphill to be equal to the minimum speed set by the driver. S1316. Determine the uphill control state category based on vehicle speed and slope angle, and calculate the required gear before going uphill; S1317. When in the fourth state, the gear required before going uphill is determined based on the gear at which the engine's maximum torque reaches the top of the hill during both the pre-hill and uphill stages. S1318. When the state is the first state, the second state, the third state, or the fifth state, set the gear required before going uphill to be equal to the current gear.

7. The predictive cruise speed gear reduction optimization method as described in claim 2, characterized in that, The specific steps of step S34 are as follows: S341. Locate each road segment sequentially starting from the initial position; S342. Obtain the feasible state domain vehicle speed gear combination of the location road segment, and locate the vehicle speed gear combination; S343. Determine whether the engine corresponding to the vehicle speed and gear combination exceeds the external characteristics or whether the acceleration exceeds the driver's cruise comfort acceleration threshold. If at least one condition is met, proceed to step S345; If neither of the two conditions is true, proceed to step S344; S344. Calculate the transfer cost for the positioning speed and gear combination using the optimization objective function model, and proceed to step S346; S345. Set the transfer cost of the positioning speed gear combination to the maximum value, treat the positioning speed gear combination as an infeasible transfer path of the positioning road segment, and set the optimization objective function model to be uncallable; S346. Take the speed gear combination corresponding to the minimum transfer cost of the location segment, and set the speed in the speed gear combination as the predicted speed and the gear in the speed gear combination as the target gear, and proceed to step S4.

8. The predictive cruise speed gear reduction optimization method as described in claim 1, characterized in that, The specific steps of step S4 are as follows: S41. The vehicle will output the predicted speed to the cruise control module on each road segment to calculate the required torque for cruise control. S42. The predicted gear is output to the transmission controller to perform the gear shift and complete the predictive cruise control.

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