Predictive oil-saving control method and system for high-speed driving

Abstract

The invention relates to the technical field of vehicles, and provides a high-speed driving predictive oil-saving control method and system. The method comprises the following steps: S1, determining an optimal candidate expressway path by adopting a preset road punishment strategy according to a detected expressway road condition; the expressway road condition at least comprises a construction road condition and a traffic jam road condition; s2, on the basis of the determined optimal candidate high-speed path, a vehicle target sequence algorithm is adopted, and the target vehicle speed of the target vehicle running in the candidate high-speed path is calculated; s3, based on the target vehicle speed and the sliding speed, vehicle operation parameters are calculated through a preset algorithm; wherein the vehicle operation parameters at least comprise engine torque and brake control parameters. The technical problems that in the prior art, in the using process of a predictive cruise system, the oil saving rate is low, and the user experience feeling is poor are solved.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a predictive fuel-saving control method and system for high-speed driving. Background Technology

[0002] With the development of automotive intelligent technology, high-speed predictive cruise control systems have become increasingly popular. Predictive cruise control can automatically acquire information such as road slope and curvature within a certain distance ahead on highways, and combine this information to predictively plan and control vehicle speed, reducing vehicle fuel consumption. This is especially important for users such as logistics companies that are concerned about operating costs.

[0003] Existing predictive cruise control systems mostly consider road gradient information within a 2km range ahead. Under the constraints of set vehicle speed and vertical deviation, they plan vehicle speed through predictive strategies such as coasting before going downhill and accelerating before going uphill. Then, they use a certain type of control strategy to make the vehicle travel at the planned speed (for example, the patent "Predictive Cruise Control Method, Device, Equipment and Storage Medium" published in 2024, application number: CN202411768452.4). This type of method is relatively traditional and relies on expert fuel-saving strategy design.

[0004] However, based on the product performance of predictive cruise control systems, traditional solutions have largely failed to gain user acceptance, mainly in the following aspects: Firstly, there is a significant difference between expert strategies and the inherent perceptions of some users. Users tend to believe that predictive cruise control systems are not fuel-efficient, or that they are not as fuel-efficient as some exceptionally skilled drivers. However, since the system can only operate according to the inherent expert strategies, users are unwilling to use it. Secondly, most current predictive cruise control systems only consider vehicle speed control and do not control other vehicle components that affect fuel consumption, such as windows. Thirdly, most existing predictive cruise control systems only consider road gradient for planning and control, and the factors considered are relatively singular. Therefore, there are still many areas for optimization in predictive cruise control systems.

[0005] Based on the above, there is an urgent need for a predictive fuel-saving control method and system for high-speed driving to improve the fuel-saving rate and human-machine interaction of predictive cruise systems, thereby increasing their usage rate. Summary of the Invention

[0006] The purpose of this invention is to provide a predictive fuel-saving control method and system for high-speed driving, solving the technical problems of low fuel-saving rate and poor user experience in predictive cruise control systems. The specific solution is as follows:

[0007] A method for predictive fuel-saving control during high-speed driving, the method comprising the following steps:

[0008] S1: Based on the detected highway road conditions, a preset road penalty strategy is adopted to determine the optimal candidate highway path; the highway road conditions include at least: construction road conditions and traffic jam road conditions;

[0009] S2: Based on the determined optimal candidate high-speed path, the vehicle target sequence algorithm is used to calculate the target vehicle speed within the candidate high-speed path;

[0010] S3: Based on the target vehicle speed and coasting speed, calculate the vehicle operating parameters using a preset algorithm; wherein, the vehicle operating parameters include at least: engine torque and brake control parameters.

[0011] Optionally, step S1, based on the detected highway road conditions, uses a preset road penalty strategy to determine the optimal candidate highway path, specifically including:

[0012] Several candidate highway routes are divided into segments based on highway toll stations as the dividing points;

[0013] If there is construction on a segment of a candidate highway path, a preset penalty length is applied to the length of the segment corresponding to the construction.

[0014] Calculate the traffic congestion conditions for each segment of a candidate highway route; wherein the traffic congestion conditions are determined based on the traffic flow and the number of target curves for each segment at a preset time; and the target curves are curves with curvature greater than a curvature threshold.

[0015] Based on the construction and congestion conditions of each segment of each candidate highway route, the target total length of each candidate highway route is calculated using a preset road penalty strategy.

[0016] Optionally, based on the construction and congestion conditions of each segment of each candidate highway route, a preset road penalty strategy is used to calculate the target total length of each candidate highway route, specifically including:

[0017] Based on the traffic flow of each segment path, determine the road congestion level of each segment path;

[0018] Road penalty coefficients are assigned based on road congestion levels; the higher the road congestion level, the larger the assigned road penalty coefficient.

[0019] Based on the target number of curves in each segment path, determine the road curve class of each segment path.

[0020] Curve penalty coefficients are assigned based on the road curve grade; the higher the road curve grade, the larger the assigned curve penalty coefficient.

[0021] The target length of each segment path is calculated using the first calculation formula based on the preset penalty length, road congestion level, and road curve level of each segment path.

[0022] The first calculation formula is:

[0023] Single segment target length = (actual segment length + preset penalty length) × road penalty coefficient × curve penalty coefficient;

[0024] The total target length of each candidate high-speed path is obtained by summing the single-segment target lengths of all segments of the path.

[0025] The candidate high-speed path with the shortest total target length is selected as the optimal candidate high-speed path.

[0026] Optionally, step S2: Based on the determined optimal candidate highway path, the vehicle target sequence algorithm is used to calculate the target vehicle speed within the candidate highway path, specifically including:

[0027] Real-time acquisition of slope and curvature information within a first preset short distance;

[0028] Based on the slope and curvature information within a first preset short distance, a vehicle target sequence algorithm is used to calculate the target vehicle speed sequence per unit distance, and the vehicle driving is controlled based on the target vehicle speed sequence.

[0029] Optionally, based on the slope and curvature information within a first preset short distance, a vehicle target sequence algorithm is used to calculate the target vehicle speed sequence per unit distance, specifically including:

[0030] Based on the slope and curvature information within a first preset short distance, three sets of target vehicle speed sequences are calculated at unit distance intervals, and a set of target vehicle speed sequences in which the target vehicle speed at each first preset position is minimized is selected; wherein, the three sets of target vehicle speed sequences are several first preset positions set based on the same road division rule;

[0031] The three sets of target vehicle speed sequences include at least a first set of target vehicle speed sequences, a second set of target vehicle speed sequences, and a third set of target vehicle speed sequences; wherein, each set of target vehicle speed sequences has the same sequence length and includes the same number of target vehicle speed sequences; and each target vehicle speed sequence includes a target vehicle speed at a first preset position;

[0032] The vehicle is controlled based on a set of target vehicle speed sequences with a minimum target vehicle speed.

[0033] Optionally, the step of calculating three sets of target vehicle speed sequences at unit distance intervals based on the slope and curvature information within a first preset short distance, and selecting the set of target vehicle speed sequences where the target vehicle speed at each first preset position is the minimum, specifically includes:

[0034] The calculation steps for the first, second, and third groups of target vehicle speed sequences;

[0035] The calculation steps for the first set of target vehicle speed sequences specifically include:

[0036] Based on road curvature information, calculate the first speed at which each vehicle can pass through a number of first preset positions on each curve;

[0037] Based on the first vehicle speed, configure a first redundant vehicle speed for each first preset position; wherein the first redundant vehicle speed is less than the first vehicle speed;

[0038] Compare the first redundant speed with the preset cruise speed, and take the smaller value of the two as the first driving speed at the first preset position of the corresponding curve.

[0039] Based on the first driving speed at several first preset positions, generate a first set of target vehicle speed sequences at several first preset positions;

[0040] The calculation steps for the second target vehicle speed sequence include:

[0041] Calculate a series of coasting speeds at several first preset positions based on the gradient of the downhill road ahead;

[0042] If the coasting speed sequence satisfies the first constraint condition, then several coasting speed sequences at the first preset positions are used as the second set of target speed sequences; the first constraint condition is:

[0043] The minimum coasting speed of the vehicle before reaching the end of the downhill slope is greater than or equal to the set cruise speed limit.

[0044] The calculation steps for the third target vehicle speed sequence include:

[0045] Calculate a sequence of uphill vehicle speeds based on the gradient of the uphill road ahead, using the engine's economic torque at several first preset positions.

[0046] If the uphill speed sequence satisfies the second constraint, then a set of uphill speed sequences at several first preset positions will be used as the third set of target speed sequences; the second constraint is:

[0047] The maximum speed for climbing a hill is less than or equal to the set upper limit of the cruise speed, and the difference between the set lower limit of the cruise speed and the expected speed is greater than or equal to the preset difference.

[0048] By comparing the target speeds of the first set of target vehicle speed sequences, the second set of target vehicle speed sequences, and the third set of target vehicle speed sequences at each same first preset position, a set of target vehicle speed sequences is generated based on the minimum target vehicle speed at each position.

[0049] Optionally, S3, based on the target vehicle speed and coasting speed, calculates vehicle operating parameters using a preset algorithm, specifically including:

[0050] Based on the target vehicle speed and coasting speed, obtain the target acceleration and coasting acceleration respectively;

[0051] If the target acceleration is greater than or equal to the coasting acceleration, then the engine torque is calculated using the second calculation formula. ;

[0052] The second calculation formula is: , Accelerate towards the target For gliding acceleration, For vehicle quality, For the wheel radius, For the gearbox ratio, Main reducer speed ratio, For transmission efficiency;

[0053] If the target acceleration is less than the coasting acceleration, the target acceleration is input to the brake to obtain the brake control parameters.

[0054] A high-speed driving predictive fuel-saving control system, the system comprising:

[0055] The road penalty calculation module is configured to determine the optimal candidate highway path based on the detected highway road conditions and a preset road penalty strategy; the highway road conditions include at least: construction road conditions and traffic jam road conditions.

[0056] The target sequence algorithm module is configured to use a vehicle target sequence algorithm based on a determined optimal candidate high-speed path to calculate the target vehicle speed of the target vehicle traveling within the candidate high-speed path;

[0057] The operating parameter calculation module is configured to calculate vehicle operating parameters based on the target vehicle speed and coasting speed using a preset algorithm; wherein, the vehicle operating parameters include at least: engine torque and brake control parameters.

[0058] An electronic device includes: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus; characterized in that the memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the method.

[0059] A computer-readable storage medium storing a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the steps of the method described herein.

[0060] A simulation platform, comprising:

[0061] An electronic device for implementing the steps of the method;

[0062] A processor that runs a program, and when the program runs, it executes the steps of the method from data output by the electronic device.

[0063] A storage medium for storing a program that, when run, executes the steps of the method on data output from an electronic device.

[0064] The above solution achieves the following beneficial technical effects:

[0065] This application provides a predictive fuel-saving control method and system for high-speed driving. Based on the detected highway road conditions, a preset road penalty strategy is adopted to perform fuel-saving planning on the global driving route, determine the optimal candidate path, and then a vehicle target sequence algorithm is used to calculate the target vehicle speed at the near end distance based on the road's slope and curvature. Then, based on the comparison between the target vehicle speed and the predicted coasting speed, a preset algorithm is used to calculate vehicle operating parameters. The vehicle operating parameters include at least engine torque and brake control parameters, realizing refined control of power output and braking, and maximizing the reduction of fuel consumption during high-speed driving. Attached Figure Description

[0066] Figure 1 This is a flowchart illustrating a predictive fuel-saving control method for high-speed driving. Detailed Implementation

[0067] To make the purpose, technical solution, and advantages of this application clearer, the following will be described in conjunction with the appendix. Figure 1 This application will be described in further detail. It is obvious that the described embodiments are merely some, not all, of the embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0068] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the application. The singular forms “a,” “said,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0069] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0070] It should be understood that although the terms first, second, third, etc., may be used in the embodiments of this application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, first may also be referred to as second without departing from the scope of the embodiments of this application, and similarly, second may also be referred to as first.

[0071] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”

[0072] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that an article or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device that includes said element.

[0073] It should be noted that any symbols and / or numbers present in the specification that are not marked in the accompanying drawings are not reference numerals.

[0074] The optional embodiments of this application are described in detail below with reference to the accompanying drawings.

[0075] Figure 1 The method for predictive fuel-saving control during high-speed driving, as shown, includes the following steps:

[0076] S1: Based on the detected highway road conditions, a preset road penalty strategy is adopted to determine the optimal candidate highway path; the highway road conditions include at least: construction road conditions and traffic jam road conditions;

[0077] S2: Based on the determined optimal candidate high-speed path, the vehicle target sequence algorithm is used to calculate the target vehicle speed within the candidate high-speed path;

[0078] S3: Based on the target vehicle speed and coasting speed, calculate the vehicle operating parameters using a preset algorithm; wherein, the vehicle operating parameters include at least: engine torque and brake control parameters.

[0079] Specifically, in this embodiment, based on the detected highway road conditions, a preset road penalty strategy is first used to perform fuel-saving planning on the global driving route to determine the optimal candidate path. Then, a vehicle target sequence algorithm is used to calculate the target vehicle speed at the near end distance based on the road's slope and curvature. Then, based on the comparison between the target vehicle speed and the predicted coasting speed, a preset algorithm is used to calculate the vehicle operating parameters. The vehicle operating parameters include at least engine torque and brake control parameters, realizing refined control of power output and braking, and maximizing the reduction of fuel consumption during high-speed driving while ensuring driving efficiency and safety.

[0080] In a specific embodiment, step S1, based on the detected highway road conditions, uses a preset road penalty strategy to determine the optimal candidate highway path, specifically including:

[0081] Several candidate highway routes are divided into segments based on highway toll stations as the dividing points;

[0082] If there is construction on a segment of a candidate highway path, a preset penalty length is applied to the length of the segment corresponding to the construction.

[0083] Calculate the traffic congestion conditions for each segment of a candidate highway route; wherein the traffic congestion conditions are determined based on the traffic flow and the number of target curves for each segment at a preset time; and the target curves are curves with curvature greater than a curvature threshold.

[0084] Based on the construction and congestion conditions of each segment of each candidate highway route, the target total length of each candidate highway route is calculated using a preset road penalty strategy.

[0085] Specifically, this embodiment applies a fixed penalty length to construction road conditions and uses a two-dimensional congestion assessment based on traffic volume and the number of high-curvature curves to construct a multi-factor coupled road penalty strategy. The target total length of the candidate path can accurately reflect the actual travel cost, solving the technical problem that traditional path planning relies solely on physical length and does not fully consider the reduced traffic efficiency and increased fuel consumption caused by construction, congestion, and high curves, resulting in a mismatch between the planned path and actual driving needs.

[0086] In a specific embodiment, based on the construction and congestion conditions of each segment of each candidate highway path, a preset road penalty strategy is used to calculate the target total length of each candidate highway path, specifically including:

[0087] Based on the traffic flow of each segment path, determine the road congestion level of each segment path;

[0088] Road penalty coefficients are assigned based on road congestion levels; the higher the road congestion level, the larger the assigned road penalty coefficient.

[0089] Based on the target number of curves in each segment path, determine the road curve class of each segment path.

[0090] Curve penalty coefficients are assigned based on the road curve grade; the higher the road curve grade, the larger the assigned curve penalty coefficient.

[0091] The target length of each segment path is calculated using the first calculation formula based on the preset penalty length, road congestion level, and road curve level of each segment path.

[0092] The first calculation formula is:

[0093] Single segment target length = (actual segment length + preset penalty length) × road penalty coefficient × curve penalty coefficient;

[0094] The total target length of each candidate high-speed path is obtained by summing the single-segment target lengths of all segments of the path.

[0095] The candidate high-speed path with the shortest total target length is selected as the optimal candidate high-speed path.

[0096] Specifically, this embodiment constructs a multi-level cost model that includes fixed construction penalties, congestion ratio penalties, and curve ratio penalties. This model considers road traffic obstacles and, based on traffic efficiency and driving difficulty or safety, makes the path cost more closely reflect the actual driving experience. Furthermore, the penalty rules employ a tiered coefficient configuration, allowing for flexible weight adjustments to suit different road conditions and high compatibility with the cost calculation logic of the road penalty strategy. This ensures the algorithm's optimization efficiency while significantly improving the applicability of the recommended routes. It effectively avoids construction sections, highly congested sections, and dangerous sections with multiple curves, ensuring that the planned optimal path simultaneously satisfies both traffic efficiency and fuel economy.

[0097] For example, for road construction, if construction increases the length of the route by 100km, and for road congestion, the penalty length is increased according to the level of road congestion.

[0098] (1) Divide the expressway into sections with toll stations as the dividing points;

[0099] (2) If there is construction on a certain road section, a pre-set penalty length of 50km will be added to the length of that road section;

[0100] (3) Count the traffic flow and the number of curves with curvature exceeding the threshold for each section of the highway in the past hour;

[0101] (4) The road congestion level is divided into three levels according to the traffic flow, from low to high: 1, 2, and 3.

[0102] (5) Increase the road length used in A* calculation according to the congestion level. For level 1, the A* calculation length is the actual road length * 1. For level 2, the A* calculation length is the actual road length * 1.3. For level 3, the A* calculation length is the actual road length * 1.5.

[0103] (6) Based on the number of curves in (3), the number of curves in the road section is divided into three levels, from low to high: 1, 2, and 3.

[0104] (7) The road length used in A* calculation is increased according to the number of curves. For level 1, the A* calculation length is (5) calculation length * 1; for level 2, the A* calculation length is (5) calculation length * 1.1; for level 3, the A* calculation length is (5) calculation length * 1.2.

[0105] (8) Summing up the final calculated lengths of all segments of each candidate high-speed path to obtain the target total length of each candidate high-speed path, thereby selecting the optimal candidate high-speed path.

[0106] In a specific embodiment, step S2: Based on the determined optimal candidate high-speed path, a vehicle target sequence algorithm is used to calculate the target vehicle speed of the target vehicle traveling within the candidate high-speed path, specifically including:

[0107] Real-time acquisition of slope and curvature information within a first preset short distance;

[0108] Based on the slope and curvature information within a first preset short distance, a vehicle target sequence algorithm is used to calculate the target vehicle speed sequence per unit distance, and the vehicle driving is controlled based on the target vehicle speed sequence.

[0109] In a specific embodiment, based on the slope and curvature information within a first preset short distance, a vehicle target sequence algorithm is used to calculate the target vehicle speed sequence per unit distance, specifically including:

[0110] Based on the slope and curvature information within a first preset short distance, three sets of target vehicle speed sequences are calculated at unit distance intervals, and a set of target vehicle speed sequences in which the target vehicle speed at each first preset position is minimized is selected; wherein, the three sets of target vehicle speed sequences are several first preset positions set based on the same road division rule;

[0111] The three sets of target vehicle speed sequences include at least a first set of target vehicle speed sequences, a second set of target vehicle speed sequences, and a third set of target vehicle speed sequences; wherein, each set of target vehicle speed sequences has the same sequence length and includes the same number of target vehicle speed sequences; and each target vehicle speed sequence includes a target vehicle speed at a first preset position;

[0112] The vehicle is controlled based on a set of target vehicle speed sequences with a minimum target vehicle speed.

[0113] Specifically, in this embodiment, slope and curvature information within a first preset short distance is collected in real time. A vehicle target sequence algorithm is used to generate three sets of target vehicle speed sequences based on the same road segmentation rule at unit distance intervals. The vehicle's movement is controlled by selecting the sequence with the minimum target speed. The advantages of this design are that it achieves precise adaptation and dynamic response to short-distance road conditions. By using a multi-sequence optimization (minimum speed) method, driving safety is ensured. This allows for the early avoidance of uneconomical driving conditions caused by changes in slope and curvature, making speed control more closely match actual road characteristics. While ensuring smooth and safe driving in scenarios such as cornering and uphill / downhill driving, it effectively reduces fuel consumption and improves fuel efficiency and driving comfort at high speeds.

[0114] In a specific embodiment, the step of calculating three sets of target vehicle speed sequences at unit distance intervals based on the slope and curvature information within a first preset short distance, and selecting a set of target vehicle speed sequences where the target vehicle speed at each first preset position is the minimum, specifically includes:

[0115] The calculation steps for the first, second, and third groups of target vehicle speed sequences;

[0116] The calculation steps for the first set of target vehicle speed sequences specifically include:

[0117] Based on road curvature information, calculate the first speed at which each vehicle can pass through a number of first preset positions on each curve;

[0118] Based on the first vehicle speed, configure a first redundant vehicle speed for each first preset position; wherein the first redundant vehicle speed is less than the first vehicle speed;

[0119] Compare the first redundant speed with the preset cruise speed, and take the smaller value of the two as the first driving speed at the first preset position of the corresponding curve.

[0120] Based on the first driving speed at several first preset positions, generate a first set of target vehicle speed sequences at several first preset positions;

[0121] The calculation steps for the second target vehicle speed sequence include:

[0122] Calculate a series of coasting speeds at several first preset positions based on the gradient of the downhill road ahead;

[0123] If the coasting speed sequence satisfies the first constraint condition, then several coasting speed sequences at the first preset positions are used as the second set of target speed sequences; the first constraint condition is:

[0124] The minimum coasting speed of the vehicle before reaching the end of the downhill slope is greater than or equal to the set cruise speed limit.

[0125] The calculation steps for the third target vehicle speed sequence include:

[0126] Calculate a sequence of uphill vehicle speeds based on the gradient of the uphill road ahead, using the engine's economic torque at several first preset positions.

[0127] If the uphill speed sequence satisfies the second constraint, then a set of uphill speed sequences at several first preset positions will be used as the third set of target speed sequences; the second constraint is:

[0128] The maximum speed for climbing a hill is less than or equal to the set upper limit of the cruise speed, and the difference between the set lower limit of the cruise speed and the expected speed is greater than or equal to the preset difference.

[0129] By comparing the target speeds of the first set of target vehicle speed sequences, the second set of target vehicle speed sequences, and the third set of target vehicle speed sequences at each same first preset position, a set of target vehicle speed sequences is generated based on the minimum target vehicle speed at each position.

[0130] Specifically, in this embodiment, based on the slope and curvature information within a first preset short distance, three sets of target vehicle speed sequences are calculated (including: a first set of target vehicle speed sequences based on curve safety redundancy, a second set of target vehicle speed sequences based on downhill coasting energy saving, and a third set of target vehicle speed sequences based on uphill economic ramping). The first preset position and sequence length of each set of target vehicle speed sequences are consistent. By taking the minimum target vehicle speed at each first preset position to generate the target vehicle speed sequence, refined vehicle speed planning for different working conditions such as curves and uphill / downhill sections is achieved. This can accurately adapt to changes in road conditions at close range, and avoid problems such as speeding when cornering, excessive downhill coasting, and wasted power when going uphill. While ensuring safety and controllability in each driving scenario, it maximizes the fuel-saving potential under different working conditions, making vehicle speed control more stable and economical, and significantly improving fuel-saving performance and driving safety at high speeds.

[0131] For example, vehicle speed planning is based on slope and curvature information at close range on the map (e.g., 2km ahead). For curvature information, a first set of target speed sequences is generated, whose value is the minimum of the allowable curve-passing speed minus a 1km / h safety redundancy speed and the set cruise speed. For slope information, the main considerations are pre-downhill coasting and pre-uphill ramping strategies. For the pre-downhill coasting strategy, to fully utilize the speed increase brought by the downhill slope, the vehicle will coast and reduce speed in advance before entering the downhill section. The coasting speed sequence is calculated based on the slope of the road ahead, and it needs to meet the following condition: when the minimum coasting speed before reaching the end of the downhill section is not lower than the set cruise speed lower limit, the calculated coasting speed sequence from this point onwards is used as the second set of target speed sequences. For the pre-uphill ramping scenario... To reduce fuel consumption increases caused by excessive engine throttle operation in uneconomical zones during steep uphill climbs, the vehicle accelerates ahead of the climb. Based on the road gradient ahead, the current uphill speed sequence using the engine's economical torque is calculated. When the maximum uphill speed does not exceed the set cruise speed limit and the vehicle speed is expected to be 20 km / h lower than the set cruise speed limit during the uphill climb (which can be calibrated according to the actual vehicle model), the breakthrough speed sequence from this point onwards is used as the third target speed sequence. Finally, the smaller value of the target speed at each location in the first, second, and third target speed sequences is taken as the short-distance predictive speed planning target speed sequence. The target speed sequence is generated and calculated in real time at 50m intervals.

[0132] Furthermore, in this embodiment, the opening and closing of the windows and air conditioning can also be controlled based on the ambient temperature.

[0133] For example, when the temperature is between 23-28℃, if the vehicle speed is above 80km / h, the cloud will issue a command to close the windows and turn on the air conditioning. If the vehicle speed is below 80km / h, the cloud will issue a command to open the windows and turn off the air conditioning. When the temperature is above 28℃, the cloud will issue a command to close the windows and turn on the air conditioning. When the temperature is below 23℃, the cloud will not take any control and will follow the driver's commands. When the cloud issues a command to open the windows, the degree of window opening is preset by the driver in the vehicle's infotainment system according to their preferences. When the cloud issues a command to turn on the air conditioning, the default air conditioning temperature is 24℃, and the fan speed is medium. The driver can also adjust the temperature manually on the dashboard.

[0134] Furthermore, this application also includes fuel-efficient cruise optimization steps:

[0135] Collect driver feedback on fuel-saving cruise control; the feedback includes at least the following key feedback: acceleration, deceleration, closing windows and turning on air conditioning, and opening windows and turning off air conditioning.

[0136] The steps for iteratively optimizing the speed plan based on feedback include:

[0137] If a driver provides the same opinion more than five times within a week, it will be considered that the driver strongly agrees with that opinion, and the strategy will be adjusted.

[0138] (1) Regarding the suggestion of “acceleration”, the parameters of the vehicle-side near-distance predictive speed planning module will be adjusted, and the upper and lower limits of the cruise speed will be increased by 2km / h.

[0139] (2) Regarding the suggestion to "reduce speed", the parameters of the vehicle-side near-distance predictive speed planning module will be adjusted, and the upper and lower limits of the cruise speed will be lowered by 2km / h.

[0140] (3) Regarding the suggestion of "closing windows and turning on air conditioning", the activation speed will be reduced by 3 km / h and the temperature range will be reduced by 1℃;

[0141] (4) Regarding the suggestion of “opening windows and turning off the air conditioner”, the activation speed will be increased by 2 km / h and the temperature range will be increased by 1℃.

[0142] After the strategy is adjusted, the generated voice will be played through the vehicle's speakers to inform the driver that the driver's suggestion has been successfully recognized and executed. The voice synthesis used also adopts the above-mentioned large voice processing model, and the voice broadcast includes "Your acceleration suggestion has been received", "Your deceleration suggestion has been received", "Your window closing and air conditioning turning on suggestion has been received", and "Your window opening and air conditioning turning off suggestion has been received".

[0143] In a specific embodiment, step S3, based on the target vehicle speed and coasting speed, calculates vehicle operating parameters using a preset algorithm, specifically including:

[0144] Based on the target vehicle speed and coasting speed, obtain the target acceleration and coasting acceleration respectively;

[0145] If the target acceleration is greater than or equal to the coasting acceleration, then the engine torque is calculated using the second calculation formula. ;

[0146] The second calculation formula is: , Accelerate towards the target For gliding acceleration, For vehicle quality, For the wheel radius, For the gearbox ratio, Main reducer speed ratio, For transmission efficiency;

[0147] If the target acceleration is less than the coasting acceleration, the target acceleration is input to the brake to obtain the brake control parameters.

[0148] It is understandable that if the target acceleration is less than the coasting acceleration, it means that the vehicle will be faster than the planned speed by coasting alone, and braking is required to decelerate. Therefore, the difference between the target acceleration and the coasting acceleration (i.e. the required braking acceleration) is calculated first. Then, the braking acceleration is used as input, and the control parameters of the brake (such as braking pressure and braking torque) are obtained according to the characteristics of the vehicle braking system (such as braking performance curve and braking force distribution strategy). The brake is then controlled to make the vehicle acceleration match the target acceleration.

[0149] It should be noted that in this embodiment, the vehicle speed control module is based on a PID controller, enabling the vehicle to travel at the planned speed. The PID controller takes the speed error as input and obtains the target acceleration based on the speed error and the set parameters kp (proportional coefficient), ki (integral coefficient), and kd (derivative coefficient). kp, ki, and kd are obtained through actual vehicle calibration. Each parameter needs to be calibrated separately at different vehicle speeds and loads. Five discrete typical values ​​can be selected for parameter calibration within the design speed range and load range. After calibration, the kp, ki, and kd parameters at other vehicle speeds and loads are obtained through linear interpolation. After obtaining the target acceleration, the engine and brake control quantities are further calculated based on the relationship between the target acceleration and the coasting acceleration.

[0150] Then, the system status information and cloud platform status information are sent to the instrument display system through the control signal sending module, the engine torque control quantity is sent to the engine, the brake deceleration control quantity is sent to the brake, the window and air conditioning control quantity is sent to the body controller, and the cloud platform recognized voice results are sent to the speaker.

[0151] It should be noted that the predictive fuel-saving control device applied to the predictive fuel-saving control method for high-speed driving in this application includes:

[0152] The vehicle-side components include the TBOX, controller, instrument display system, speaker, microphone, and cloud platform. The predictive fuel-saving control method mainly includes a vehicle-side short-distance predictive speed planning module, a cloud-based long-distance route planning module, a cloud-based window / air conditioning control calculation module, a driver voice opinion recognition module, an iterative optimization module, a vehicle-side speed control module, and a control signal transmission module.

[0153] The TBOX is used for communication between the vehicle and the cloud platform. On the one hand, the TBOX sends ADAS map information such as road slope and curvature within a certain distance in front of the vehicle, as well as cloud control commands, from the cloud to the vehicle controller. On the other hand, the TBOX can also send the driver's voice information from the vehicle to the cloud for recognition. In addition, the TBOX also integrates a GPS positioning module, which can obtain the real-time GPS positioning location of the vehicle. Its positioning antenna needs to be installed in a location with low electromagnetic interference and good signal.

[0154] The controller is used to implement a high-speed driving predictive fuel-saving control method. It receives various information from the TBOX and vehicle chassis system, plans the desired fuel-saving speed, and then obtains the vehicle engine torque percentage and braking deceleration control quantity from the planned speed, and sends the control quantity to the vehicle chassis.

[0155] The instrument display system is generally a vehicle dashboard, but it can also be a large in-vehicle screen when used in conjunction with advanced autonomous driving functions. The instrument display system can display the status of the fuel-saving control system, the cloud platform status, etc. The fuel-saving control system status includes four states: initialization, waiting, activation, and fault. Different states are displayed by icons of different colors / graphics. The cloud platform status includes five states: initialization, waiting, activation, driver command reception, and fault.

[0156] The microphone is used to collect the driver's voice and transmit the voice information to the cloud platform via TBOX;

[0157] The speaker is used to inform the driver of the current system status and the result of the driver's suggestion. When the fuel-saving control function changes from off to on, the speaker will beep once. When the fuel-saving control function changes from on to off, the speaker will beep twice. When the driver's intention is successfully recognized by the cloud platform, the speaker will also announce the result through voice, such as "Your acceleration suggestion has been received" or "Your deceleration suggestion has been received".

[0158] The cloud platform analyzes the input voice to obtain the driver's intention. Based on the driver's intention, it activates / deactivates the fuel-saving control function or iteratively optimizes the function. For activating / deactivating the fuel-saving control function, when the driver says "activate fuel-saving control," the cloud platform converts this request into an activation signal and sends it to the vehicle controller via the TBOX, which then activates the function. When the driver says "deactivate fuel-saving control," the cloud platform converts this request into a deactivation signal and sends it to the vehicle controller via the TBOX, which then deactivates the function. For iterative optimization, when the driver makes suggestions for correcting vehicle control, such as "I should accelerate uphill here" or "Why didn't I ease off the accelerator?", the cloud platform abstracts the driver's words into vehicle control command suggestions, such as "accelerate" or "easing off the accelerator," and sends these suggestions to the vehicle controller via the TBOX. The controller adjusts according to the suggestions from the cloud platform, resulting in the final output control command for the vehicle. The cloud platform and the vehicle communicate using the MQTT protocol.

[0159] On the other hand, this application provides a high-speed driving predictive fuel-saving control system, the system comprising:

[0160] The road penalty calculation module is configured to determine the optimal candidate highway path based on the detected highway road conditions and a preset road penalty strategy; the highway road conditions include at least: construction road conditions and traffic jam road conditions.

[0161] The target sequence algorithm module is configured to use a vehicle target sequence algorithm based on a determined optimal candidate high-speed path to calculate the target vehicle speed of the target vehicle traveling within the candidate high-speed path;

[0162] The operating parameter calculation module is configured to calculate vehicle operating parameters based on the target vehicle speed and coasting speed using a preset algorithm; wherein, the vehicle operating parameters include at least: engine torque and brake control parameters.

[0163] On the other hand, this application provides an electronic device, including: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0164] The memory stores a computer program that, when executed by a processor, causes the processor to perform the steps of the method.

[0165] On the other hand, this application provides a computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implement the steps of the method.

[0166] A simulation platform, comprising:

[0167] An electronic device for implementing the steps of the method;

[0168] A processor that runs a program, which, when running, executes the steps of the method claimed in the electronic device from data output by the program.

[0169] A storage medium for storing a program that, when run, executes the steps of the method on data output from an electronic device.

[0170] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0171] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A predictive fuel-saving control method for high-speed driving, characterized in that, The method includes the following steps: S1: Based on the detected highway road conditions, a preset road penalty strategy is adopted to determine the optimal candidate highway path; the highway road conditions include at least: construction road conditions and traffic jam road conditions; S2: Based on the determined optimal candidate high-speed path, the vehicle target sequence algorithm is used to calculate the target vehicle speed within the candidate high-speed path; S3: Based on the target vehicle speed and coasting speed, calculate the vehicle operating parameters using a preset algorithm; wherein, the vehicle operating parameters include at least: engine torque and brake control parameters.

2. The method according to claim 1, characterized in that, Step S1, based on the detected highway road conditions, employs a preset road penalty strategy to determine the optimal candidate highway path, specifically including: Several candidate highway routes are divided into segments based on highway toll stations as the dividing points; If there is construction on a segment of a candidate highway path, a preset penalty length is applied to the length of the segment corresponding to the construction. Calculate the traffic congestion conditions for each segment of a candidate highway route; wherein the traffic congestion conditions are determined based on the traffic flow and the number of target curves for each segment at a preset time; and the target curves are curves with curvature greater than a curvature threshold. Based on the construction and congestion conditions of each segment of each candidate highway route, the target total length of each candidate highway route is calculated using a preset road penalty strategy.

3. The method according to claim 2, characterized in that, Based on the construction and congestion conditions of each segment of each candidate highway route, a preset road penalty strategy is used to calculate the target total length of each candidate highway route, specifically including: Based on the traffic flow of each segment path, determine the road congestion level of each segment path; Road penalty coefficients are assigned based on road congestion levels; the higher the road congestion level, the larger the assigned road penalty coefficient. Based on the target number of curves in each segment path, determine the road curve class of each segment path. Curve penalty coefficients are assigned based on the road curve grade; the higher the road curve grade, the larger the assigned curve penalty coefficient. The target length of each segment path is calculated using the first calculation formula based on the preset penalty length, road congestion level, and road curve level of each segment path. The first calculation formula is: Single segment target length = (actual segment length + preset penalty length) × road penalty coefficient × curve penalty coefficient; The total target length of each candidate high-speed path is obtained by summing the single-segment target lengths of all segments of the path. The candidate high-speed path with the shortest total target length is selected as the optimal candidate high-speed path.

4. The method according to claim 3, characterized in that, Step S2: Based on the determined optimal candidate highway path, the vehicle target sequence algorithm is used to calculate the target vehicle speed within the candidate highway path, specifically including: Real-time acquisition of slope and curvature information within a first preset short distance; Based on the slope and curvature information within a first preset short distance, a vehicle target sequence algorithm is used to calculate the target vehicle speed sequence per unit distance, and the vehicle driving is controlled based on the target vehicle speed sequence.

5. The method according to claim 4, characterized in that, Based on the slope and curvature information within a first preset short distance, a vehicle target sequence algorithm is used to calculate the target vehicle speed sequence per unit distance, specifically including: Based on the slope and curvature information within a first preset short distance, three sets of target vehicle speed sequences are calculated at unit distance intervals, and a set of target vehicle speed sequences in which the target vehicle speed at each first preset position is minimized is selected; wherein, the three sets of target vehicle speed sequences are several first preset positions set based on the same road division rule; The three sets of target vehicle speed sequences include at least a first set of target vehicle speed sequences, a second set of target vehicle speed sequences, and a third set of target vehicle speed sequences; wherein, each set of target vehicle speed sequences has the same sequence length and includes the same number of target vehicle speed sequences; and each target vehicle speed sequence includes a target vehicle speed at a first preset position; The vehicle is controlled based on a set of target vehicle speed sequences with a minimum target vehicle speed.

6. The method according to claim 5, characterized in that, Based on the slope and curvature information within a first preset short distance, three sets of target vehicle speed sequences are calculated at unit distance intervals. A set of target vehicle speed sequences where the target vehicle speed at each first preset position is the minimum value is selected. Specifically, this includes: The calculation steps for the first, second, and third groups of target vehicle speed sequences; The calculation steps for the first set of target vehicle speed sequences specifically include: Based on road curvature information, calculate the first speed at which each vehicle can pass through a number of first preset positions on each curve; Based on the first vehicle speed, configure a first redundant vehicle speed for each first preset position; wherein the first redundant vehicle speed is less than the first vehicle speed; Compare the first redundant speed with the preset cruise speed, and take the smaller value of the two as the first driving speed at the first preset position of the corresponding curve. Based on the first driving speed at several first preset positions, generate a first set of target vehicle speed sequences at several first preset positions; The calculation steps for the second target vehicle speed sequence include: Calculate a series of coasting speeds at several first preset positions based on the gradient of the downhill road ahead; If the coasting speed sequence satisfies the first constraint condition, then several coasting speed sequences at the first preset positions are used as the second set of target speed sequences; the first constraint condition is: The minimum coasting speed of the vehicle before reaching the end of the downhill slope is greater than or equal to the set cruise speed limit. The calculation steps for the third target vehicle speed sequence include: Calculate a sequence of uphill vehicle speeds based on the gradient of the uphill road ahead, using the engine's economic torque at several first preset positions. If the uphill speed sequence satisfies the second constraint, then a set of uphill speed sequences at several first preset positions will be used as the third set of target speed sequences; the second constraint is: The maximum speed for climbing a hill is less than or equal to the set upper limit of the cruise speed, and the difference between the set lower limit of the cruise speed and the expected speed is greater than or equal to the preset difference. By comparing the target speeds of the first set of target vehicle speed sequences, the second set of target vehicle speed sequences, and the third set of target vehicle speed sequences at each same first preset position, a set of target vehicle speed sequences is generated based on the minimum target vehicle speed at each position.

7. The method according to claim 6, characterized in that, S3, based on the target vehicle speed and coasting speed, uses a preset algorithm to calculate vehicle operating parameters, specifically including: Based on the target vehicle speed and coasting speed, obtain the target acceleration and coasting acceleration respectively; If the target acceleration is greater than or equal to the coasting acceleration, then the engine torque is calculated using the second calculation formula. ; The second calculation formula is: , Accelerate towards the target For gliding acceleration, For vehicle quality, For the wheel radius, For the gearbox ratio, Main reducer speed ratio, For transmission efficiency; If the target acceleration is less than the coasting acceleration, the target acceleration is input to the brake to obtain the brake control parameters.

8. A high-speed driving predictive fuel-saving control system, characterized in that, The system includes: The road penalty calculation module is configured to determine the optimal candidate highway path based on the detected highway road conditions and a preset road penalty strategy; the highway road conditions include at least: construction road conditions and traffic jam road conditions. The target sequence algorithm module is configured to use a vehicle target sequence algorithm based on a determined optimal candidate high-speed path to calculate the target vehicle speed of the target vehicle traveling within the candidate high-speed path; The operating parameter calculation module is configured to calculate vehicle operating parameters based on the target vehicle speed and coasting speed using a preset algorithm; wherein, the vehicle operating parameters include at least: engine torque and brake control parameters.

9. An electronic device, comprising: The system comprises a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus; characterized in that the memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the method described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The device stores a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the steps of the method as described in any one of claims 1 to 7.

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