Electric heavy truck fleet battery replacement rhythm planning and vehicle predictive cruise control method

Abstract

The application relates to an electric heavy truck fleet battery replacement rhythm planning and vehicle predictive cruise control method, which comprises the following steps: sending the vehicle battery replacement state and vehicle queuing data of a battery replacement station to a target fleet; receiving the arrival time at the battery replacement station and / or the departure time sent by each vehicle in the target fleet based on the vehicle battery replacement state and the vehicle queuing data; determining the upper and lower limits of the speed of each vehicle according to the arrival time at the battery replacement station and / or the departure time; planning the speed of each vehicle by using a predictive cruise algorithm to obtain a speed sequence composed of the speed of each vehicle at each road point in the driving section of the target fleet; and performing cruise control on each vehicle according to the sequence. Thus, the problems that the electric heavy truck fleet cannot know the situation in the battery replacement station in advance and cannot reasonably plan the vehicle speed, and the energy is wasted in uphill and downhill driving of the electric heavy truck due to the driving of the vehicle based on the set speed are solved, and the foreseeability of the battery replacement rhythm decision planning is improved and the energy is saved.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a method, device, cloud server, and storage medium for electric heavy-duty truck fleet battery swapping cycle planning and vehicle predictive cruise control. Background Technology

[0002] Electric heavy-duty truck fleets need to swap batteries at battery swapping stations during their transport missions. In current production processes, when human drivers operate these electric heavy-duty trucks, the following two problems arise:

[0003] ① Human drivers cannot know the queue status of the battery swapping station or the battery swapping status of the vehicle in front in advance. At this time, the driver cannot plan and control the vehicle speed reasonably based on these conditions, which will lead to congestion in front of the battery swapping station, resulting in frequent start-stop and wasting energy, or discontinuous battery swapping, thus reducing efficiency.

[0004] ② In related technologies, cruise control systems are often used to control vehicle speed during operation to assist human drivers. However, maintaining a set speed under all road conditions causes frequent and significant increases in motor power and braking during inclines and declines, thereby increasing energy consumption and reducing motor energy recovery efficiency. This leads to a reduction in the expected driving range of electric heavy-duty trucks and energy waste. Summary of the Invention

[0005] This application provides a method, device, cloud server, and storage medium for battery swapping cycle planning and predictive cruise control of electric heavy-duty truck fleets. It addresses the problem that electric heavy-duty truck fleets cannot know in advance the queuing status of battery swapping stations and the battery swapping status of the vehicles ahead, thus failing to rationally plan and control vehicle speed based on these conditions. This leads to congestion in front of battery swapping stations causing frequent starts and stops that waste energy, or discontinuous battery swapping resulting in reduced efficiency. It also addresses the energy waste caused by vehicles traveling at set speeds while driving uphill or downhill. The application expands the scope of decision-making and planning, improves the predictability and accuracy of battery swapping cycle planning, and saves energy.

[0006] The first aspect of this application provides a method for battery swapping cycle planning and predictive cruise control of electric heavy-duty truck fleets, including the following steps:

[0007] Obtain vehicle battery swapping status and vehicle queuing data at the battery swapping station;

[0008] Send the vehicle battery swapping status and the vehicle queuing data to at least one target fleet, and receive the arrival time and / or departure time of each vehicle in the at least one target fleet based on the vehicle battery swapping status and the vehicle queuing data; and

[0009] The maximum and minimum speeds of each vehicle are determined based on the arrival time at the battery swapping station and / or the departure time. Based on the maximum and minimum speeds of each vehicle, a preset predictive cruise algorithm is used to plan the speed of each vehicle to obtain a speed sequence consisting of the speeds of each waypoint in the at least one target convoy's travel segment. Cruise control is then performed on each vehicle based on the speed sequence.

[0010] Optionally, in some embodiments, after performing cruise control on each vehicle according to the speed sequence, the method further includes:

[0011] Obtain the new vehicle battery swapping status from the battery swapping station;

[0012] The battery swapping delay duration is determined based on the new vehicle battery swapping status and the vehicle battery swapping status.

[0013] The first delayed arrival time of vehicles that have not arrived at the battery swapping station is determined based on the battery swapping delay duration, and the speed of the vehicles that have not arrived at the battery swapping station is re-planned based on the first delayed arrival time.

[0014] Optionally, in some embodiments, after performing cruise control on each vehicle according to the speed sequence, the method further includes:

[0015] Obtain new vehicle queuing data from the battery swapping station;

[0016] Based on the new vehicle queuing data and the vehicle queuing data, determine the number of vehicles to be added to the queue;

[0017] The second delayed arrival time of the vehicles that have not arrived at the battery swapping station is determined based on the number of vehicles added to the queue, and the arrival time and / or departure time of the vehicles that have not arrived at the battery swapping station are postponed based on the second delayed arrival time.

[0018] Optionally, in some embodiments, after obtaining the new vehicle queuing data from the battery swapping station, the method further includes:

[0019] Based on the new vehicle queuing data and the vehicle queuing data, the number of vehicles in the queue should be reduced.

[0020] The latest arrival time of the vehicles that have not arrived at the battery swapping station is determined based on the number of vehicles reduced in the queue, and the arrival time and / or departure time of the vehicles that have not arrived at the battery swapping station are updated based on the latest arrival time.

[0021] Optionally, in some embodiments, after obtaining the new vehicle queuing data from the battery swapping station, the method further includes:

[0022] Determine whether to determine the first delayed arrival time of the vehicle that has not arrived at the battery swapping station, the second delayed arrival time of the vehicle that has not arrived at the battery swapping station, and the latest arrival time of the vehicle that has not arrived at the battery swapping station;

[0023] If the first delayed arrival time of the vehicle that has not arrived at the battery swapping station is determined, and the second delayed arrival time of the vehicle that has not arrived at the battery swapping station is determined, and the latest arrival time of the vehicle that has not arrived at the battery swapping station is determined, then the optimal speed sequence of each vehicle is determined based on the elevation map of the at least one target convoy travel segment and the map of each vehicle, and the corresponding vehicle is controlled according to the optimal speed sequence of each vehicle.

[0024] A second aspect of this application provides an electric heavy-duty truck fleet battery swapping cycle planning and vehicle predictive cruise control device, comprising:

[0025] The acquisition module is used to acquire vehicle battery swapping status and vehicle queuing data at the battery swapping station;

[0026] A sending module is configured to send the vehicle battery swapping status and the vehicle queuing data to at least one target fleet, and receive the arrival time and / or departure time of each vehicle in the at least one target fleet based on the vehicle battery swapping status and the vehicle queuing data; and

[0027] The control module is used to determine the maximum and minimum speed of each vehicle based on the arrival time at the battery swapping station and / or the departure time, and to perform speed planning for each vehicle using a preset predictive cruise algorithm based on the maximum and minimum speed of each vehicle, thereby obtaining a speed sequence consisting of the speeds of each waypoint in the at least one target convoy's travel segment, and to perform cruise control for each vehicle based on the speed sequence.

[0028] Optionally, in some embodiments, after performing cruise control on each vehicle according to the speed sequence, the control module further includes:

[0029] The first acquisition unit is used to acquire the new vehicle battery swapping status from the battery swapping station;

[0030] The first determining unit is used to determine the battery swapping delay time based on the new vehicle battery swapping status and the vehicle battery swapping status.

[0031] The first planning unit is used to determine the first delayed arrival time of vehicles that have not arrived at the battery swapping station based on the battery swapping delay duration, and to re-plan the speed of the vehicles that have not arrived at the battery swapping station based on the first delayed arrival time.

[0032] Optionally, in some embodiments, after performing cruise control on each vehicle according to the speed sequence, the control module further includes:

[0033] The second acquisition unit is used to acquire new vehicle queuing data from the battery swapping station;

[0034] The second determining unit is used to determine the number of vehicles to be added to the queue based on the new vehicle queuing data and the vehicle queuing data.

[0035] The second planning unit is used to determine the second delayed arrival time of the vehicles that have not arrived at the battery swapping station based on the number of vehicles added to the queue, and to postpone the arrival time and / or departure time of the vehicles that have not arrived at the battery swapping station based on the second delayed arrival time.

[0036] Optionally, in some embodiments, after obtaining the new vehicle queuing data from the battery swapping station, the second obtaining unit is further configured to:

[0037] Based on the new vehicle queuing data and the vehicle queuing data, the number of vehicles in the queue should be reduced.

[0038] The latest arrival time of the vehicles that have not arrived at the battery swapping station is determined based on the number of vehicles reduced in the queue, and the arrival time and / or departure time of the vehicles that have not arrived at the battery swapping station are updated based on the latest arrival time.

[0039] Optionally, in some embodiments, after obtaining the new vehicle queuing data from the battery swapping station, the second obtaining unit is further configured to:

[0040] Determine whether to determine the first delayed arrival time of the vehicle that has not arrived at the battery swapping station, the second delayed arrival time of the vehicle that has not arrived at the battery swapping station, and the latest arrival time of the vehicle that has not arrived at the battery swapping station;

[0041] If the first delayed arrival time of the vehicle that has not arrived at the battery swapping station is determined, and the second delayed arrival time of the vehicle that has not arrived at the battery swapping station is determined, and the latest arrival time of the vehicle that has not arrived at the battery swapping station is determined, then the optimal speed sequence of each vehicle is determined based on the elevation map of the at least one target convoy travel segment and the map of each vehicle, and the corresponding vehicle is controlled according to the optimal speed sequence of each vehicle.

[0042] A third aspect of this application provides a cloud server, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement the electric heavy truck fleet battery swapping cycle planning and vehicle predictive cruise control method as described in the above embodiments.

[0043] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the electric heavy truck fleet battery swapping cycle planning and vehicle predictive cruise control method as described in the above embodiments.

[0044] Therefore, this application sends the battery swapping status and queuing data of vehicles at the battery swapping station to the target fleet. It receives the arrival time and / or departure time of each vehicle in the target fleet based on its battery swapping status and queuing data. Based on the arrival time and / or departure time, it determines the upper and lower limits of each vehicle's speed. A predictive cruise algorithm is used to plan the speed of each vehicle, resulting in a speed sequence composed of the speeds at each road point along the target fleet's route. Cruise control is then performed on each vehicle based on this sequence. This solves the problems of electric heavy-duty truck fleets not being able to know the queuing situation at the battery swapping station and the battery swapping status of the vehicle ahead, thus failing to rationally plan and control vehicle speed. This leads to congestion before the battery swapping station causing frequent starts and stops and wasting energy, or discontinuous battery swapping reducing efficiency, and energy waste when electric heavy-duty trucks travel at set speeds on inclines and declines. The application expands the scope of decision-making and planning, improves the predictability and accuracy of battery swapping cycle decision-making and planning, and saves energy.

[0045] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0046] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0047] Figure 1 This is a flowchart of the electric heavy-duty truck fleet battery swapping cycle planning and vehicle predictive cruise control method provided according to the embodiments of this application;

[0048] Figure 2 This is a schematic diagram of a cloud-based solution for combining cycle control and cruise control for electric heavy-duty truck fleets, according to an embodiment of this application.

[0049] Figure 3 This is a flowchart of the overall algorithm for truck battery swapping cycle planning and vehicle predictive cruise control within a fleet, according to an embodiment of this application.

[0050] Figure 4 This is a schematic diagram of an electric heavy-duty truck fleet battery swapping cycle planning and vehicle predictive cruise control device according to an embodiment of this application;

[0051] Figure 5This is a schematic diagram of the structure of a cloud server provided according to an embodiment of this application. Detailed Implementation

[0052] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0053] The following describes, with reference to the accompanying drawings, a method, apparatus, cloud server, and storage medium for electric heavy-duty truck fleet battery swapping cycle planning and predictive cruise control of embodiments of this application. Addressing the issues mentioned in the background art, such as the inability of electric heavy-duty truck fleets to plan vehicle speeds reasonably due to the lack of advance knowledge of the battery swapping station's conditions, and the energy waste caused by vehicles traveling at set speeds when driving uphill or downhill, this application provides a method for electric heavy-duty truck fleet battery swapping cycle planning and predictive cruise control of vehicles. In this method, the vehicle battery swapping status and vehicle queuing data at the battery swapping station are acquired and sent to at least one target fleet. The method also receives the arrival time and / or departure time of each vehicle in the at least one target fleet based on the vehicle battery swapping status and vehicle queuing data. The maximum and minimum speeds of each vehicle are determined based on the arrival and / or departure times. Based on the maximum and minimum speeds, a preset predictive cruise algorithm is used to plan the speed of each vehicle, resulting in a speed sequence composed of the speeds at each road point in the travel segment of the at least one target fleet. Cruise control of each vehicle is then performed based on the speed sequence. This solves the problems of electric heavy truck fleets not being able to know the situation inside the battery swapping station in advance and thus not being able to plan their speeds reasonably, as well as the energy waste caused by electric heavy trucks traveling on uphill and downhill slopes due to vehicles traveling at set speeds. It improves the predictability of battery swapping cycle decision-making and planning and saves energy.

[0054] Specifically, Figure 1 This is a flowchart illustrating the electric heavy-duty truck fleet battery swapping cycle planning and vehicle predictive cruise control method provided in an embodiment of this application.

[0055] like Figure 1 As shown, the method for battery swapping cycle planning and predictive cruise control of the electric heavy-duty truck fleet includes the following steps:

[0056] In step S101, the vehicle battery swapping status and vehicle queuing data of the battery swapping station are obtained.

[0057] In this embodiment of the application, the vehicle battery swapping status can be "battery swapping in progress", "battery swapping completed", "waiting for battery swapping", etc. The vehicle queuing data can include the number of vehicles currently queuing at the battery swapping station, etc., which are not specifically limited here. Those skilled in the art can set it according to the actual situation.

[0058] Understandably, because drivers of electric heavy-duty truck fleets cannot know the queuing status of battery swapping stations or the battery swapping status of the vehicles ahead in advance, they cannot rationally plan and control their vehicle speed. Therefore, the battery swapping cycle planning and predictive cruise control method for electric heavy-duty truck fleets provided in this application needs to first obtain the vehicle battery swapping status and vehicle queuing data at the battery swapping stations in order to plan the battery swapping cycle based on the vehicle battery swapping status and vehicle queuing data.

[0059] It should be noted that this application is based on a cloud control system, which is a vehicle-road-cloud integrated control system based on next-generation mobile internet technology. This system allows the target vehicle to establish connections with surrounding vehicles, roadside infrastructure, and the cloud control platform, providing the target vehicle with wide-area dynamic traffic information. This enables better prediction of upcoming events, allowing the vehicle to adjust its driving strategy more proactively. Furthermore, compared to predictive cruise control technologies in related fields that rely on vehicle-road cooperative technology, the participation of the cloud control platform can overcome the limitations of its communication distance and perception range, improve the scope and predictability of decision-making and planning, and more easily achieve real-time solution of control strategies.

[0060] Specifically, this application embodiment uses a cloud control platform to acquire queuing data of the vehicle fleet within the battery swapping station via cameras, and obtains the vehicle battery swapping status based on onboard sensors.

[0061] In step S102, vehicle battery swapping status and vehicle queuing data are sent to at least one target fleet, and arrival time and / or departure time of each vehicle in at least one target fleet are received based on vehicle battery swapping status and vehicle queuing data.

[0062] It is understandable that if the target fleet cannot reasonably plan its battery swapping schedule, congestion may occur in front of the battery swapping station. Therefore, this embodiment of the application needs to send the vehicle battery swapping status and vehicle queuing data obtained in step S101 to the target fleet through a cloud control platform. Then, each vehicle in the target fleet can obtain the arrival time or departure time of the battery swapping station or the time of the battery swapping station and departure time based on the vehicle battery swapping status and vehicle queuing data.

[0063] Specifically, Figure 2 This is a schematic diagram of a cloud-based solution combining cycle control and cruise control for electric heavy-duty truck fleets, as described in an embodiment of this application. Figure 2As shown, each vehicle in the fleet calculates its appropriate arrival time or departure time to the battery swapping station based on its battery swapping status, vehicle queuing data, vehicle location, and current speed.

[0064] Therefore, this embodiment of the application controls the speed and rhythm of the heavy truck queue to plan the optimal departure time and optimal arrival time window of the vehicles in the fleet, so as to ensure that the battery swapping process is as continuous as possible and that queuing is avoided as much as possible, thereby alleviating the traffic pressure on the battery swapping station and improving the overall battery swapping efficiency of the battery swapping station.

[0065] In step S103, the maximum and minimum speeds of each vehicle are determined based on the arrival time at the battery swapping station and / or the departure time. Based on the maximum and minimum speeds of each vehicle, a preset predictive cruise algorithm is used to plan the speed of each vehicle, resulting in a speed sequence consisting of the speeds of each road point in at least one target vehicle convoy's travel segment. Cruise control is then performed on each vehicle based on the speed sequence.

[0066] Understandably, once the arrival time at the battery swapping station or the departure time is obtained, the maximum and minimum vehicle speeds in predictive cruise control can be determined.

[0067] Specifically, in this embodiment, the gradient information of the road ahead can be requested from the cloud control platform, and combined with the maximum and minimum speed of the vehicle, the Predictive Cruise Control (PCC) algorithm is used to plan the speed of the distance from the current vehicle position to the battery swapping station, thereby obtaining a speed sequence composed of the speed of each road point on the road ahead, so as to perform cruise control on each vehicle according to the speed sequence.

[0068] Therefore, this application provides an energy-saving predictive cruise algorithm based on gradient for application in electric heavy-duty trucks. The algorithm ultimately obtains an optimized speed sequence to achieve energy savings.

[0069] Furthermore, since the battery swapping status of vehicles and the queuing situation at the battery swapping station may change, this application provides a more easily implemented control strategy. This control strategy can perform real-time cruise control on vehicles based on the battery swapping status and the queuing situation at the battery swapping station. In other words, after the current vehicle obtains its speed sequence, if the battery swapping status of vehicles and the queuing situation at the battery swapping station change, the above process will be re-executed.

[0070] Optionally, in some embodiments, after performing cruise control on each vehicle according to the speed sequence, the method further includes: obtaining a new vehicle battery swapping status from the battery swapping station; determining a battery swapping delay duration based on the new vehicle battery swapping status and the vehicle battery swapping status; determining a first delayed arrival time for vehicles that have not yet arrived at the battery swapping station based on the battery swapping delay duration; and re-planning the speed of vehicles that have not yet arrived at the battery swapping station based on the first delayed arrival time.

[0071] It is understandable that the normal battery swapping time for a vehicle is 7 minutes. However, the battery swapping process for vehicles at the battery swapping station may be delayed for various reasons, which will affect vehicles queuing for battery swapping later and vehicles that have not yet arrived at the battery swapping station. Therefore, this application embodiment needs to first determine the battery swapping delay time based on the new vehicle battery swapping status and the vehicle battery swapping status in step S101, and then calculate the delayed arrival time of vehicles that have not yet arrived at the battery swapping station, which is the first delayed arrival time set in this application embodiment. Based on the first delayed arrival time, the vehicle speed and other data of vehicles that have not yet arrived at the battery swapping station are re-planned to achieve the effect of real-time cruise control of the vehicle.

[0072] For example, there is a fleet of electric heavy-duty trucks at the current battery swapping station, with a total of 6 trucks queuing for battery swapping. The first truck is currently swapping its battery, and its swapping time is delayed by 30 seconds for some reason. Therefore, the time window for all vehicles that have not yet arrived at the battery swapping station is postponed by 30 seconds, and the departure time for vehicles that have not yet departed is postponed by 30 seconds. The second truck's swapping time is 840 seconds (departing after a delay of 420 seconds).

[0073] Therefore, this application can adjust the time window of vehicles that have not yet arrived at the battery swapping station in real time by calculating the delay time when the battery swapping of vehicles is delayed, so as to ensure that the battery swapping process is as continuous as possible and that queuing is minimized.

[0074] Optionally, in some embodiments, after cruise control of each vehicle according to the speed sequence, the method further includes: obtaining new vehicle queuing data from the battery swapping station; determining the number of vehicles to be added to the queue based on the new vehicle queuing data and the vehicle queuing data; determining a second delayed arrival time for vehicles that have not yet arrived at the battery swapping station based on the number of vehicles to be added to the queue; and postponing the arrival time and / or departure time of the vehicles that have not yet arrived at the battery swapping station based on the second delayed arrival time.

[0075] It is understandable that if the number of vehicles queuing at the battery swapping station changes, such as the addition of social vehicles and other vehicles in the fleet to the queue, it will also affect the cruise control of the vehicles. Therefore, this application obtains new vehicle queuing data from the battery swapping station through the cloud control platform, and determines the number of vehicles to be added to the queue based on the new vehicle queuing data and the vehicle queuing data in step S101. Then, based on the number of vehicles added, the delayed arrival time of vehicles that have not yet arrived at the battery swapping station is determined, that is, the second delayed arrival time of this application embodiment, so as to re-plan the arrival time or departure time of vehicles that have not yet arrived at the battery swapping station or the time of the battery swapping station and the departure time based on the second delayed arrival time.

[0076] For example, there is a fleet of electric heavy trucks in the current battery swapping station, with a total of 6 trucks queuing for battery swapping. When the number of vehicles in the queue increases by n, the time window for all vehicles that have not yet arrived at the battery swapping station is postponed by 7*n (min). If a vehicle in the queue appears at the battery swapping station 420 seconds after the first vehicle departs, the time window for all vehicles that have not yet arrived at the battery swapping station is postponed by 7 minutes.

[0077] Optionally, in some embodiments, after obtaining new vehicle queuing data from the battery swapping station, the method further includes: determining the number of vehicles whose queues have been reduced based on the new vehicle queuing data and the vehicle queuing data; determining the latest arrival time of vehicles that have not yet arrived at the battery swapping station based on the number of vehicles whose queues have been reduced; and updating the arrival time and / or departure time of vehicles that have not yet arrived at the battery swapping station based on the latest arrival time.

[0078] It is understandable that if the number of vehicles queuing at the battery swapping station changes, such as the number of vehicles queuing decreases, it will also affect the vehicle's cruise control. Therefore, this application embodiment determines the number of vehicles in the queue to be reduced based on the new vehicle queuing data and the vehicle queuing data, and plans the latest arrival time of vehicles that have not yet arrived at the battery swapping station based on the reduced number of vehicles.

[0079] For example, there is a fleet of electric heavy-duty trucks in the current battery swapping station, with a total of 6 trucks queuing to swap batteries. The first truck's battery swapping time is 300 seconds. The vehicle's sensors upload this battery swapping time to the cloud control system. The trucks that have not yet arrived receive the message that the first truck left 120 seconds in advance, and then update their arrival time at the battery swapping station, departure time, or the time of the battery swapping station and departure time.

[0080] Therefore, this application can use a predictive cruise algorithm to plan the speed of each vehicle when the number of vehicles queuing at a battery swapping station changes, and update the arrival time and / or departure time of vehicles that have not yet arrived at the battery swapping station in real time, thereby alleviating traffic pressure at the battery swapping station and improving the overall battery swapping efficiency of the battery swapping station.

[0081] In some cases, the three typical operating conditions mentioned above may occur simultaneously, namely, changes in the number of vehicles queuing at the battery swapping station, delays in the battery swapping process, and vehicles leaving the battery swapping station. The cloud control system in this embodiment receives the number of vehicles queuing at the battery swapping station and the current battery swapping time of each vehicle in real time, and calculates the vehicle's arrival and departure times based on the vehicle's status and queuing data. Therefore, this embodiment can perform real-time speed simulation and route planning for vehicles that have not yet arrived at the battery swapping station, thereby improving vehicle operating efficiency.

[0082] Optionally, in some embodiments, after obtaining new vehicle queuing data from the battery swapping station, the method further includes: determining whether a first delayed arrival time, a second delayed arrival time, and a latest arrival time of vehicles that have not yet arrived at the battery swapping station are determined; if the first delayed arrival time, the second delayed arrival time, and the latest arrival time of vehicles that have not yet arrived at the battery swapping station are determined, then the optimal speed sequence of each vehicle is determined based on the elevation map of at least one target convoy travel segment and the map of each vehicle, and the corresponding vehicle is controlled according to the optimal speed sequence of each vehicle.

[0083] Specifically, such as Figure 2 As shown, after determining the first delayed arrival time of vehicles that have not yet arrived at the battery swapping station, the second delayed arrival time of vehicles that have not yet arrived at the battery swapping station, and the latest arrival time of vehicles that have not yet arrived at the battery swapping station, this embodiment of the application, based on the cloud control system, determines the optimal speed sequence of each vehicle according to the elevation map of the target fleet's driving route and the map of each vehicle, so as to control the corresponding vehicles through the optimal speed sequence.

[0084] To enable those skilled in the art to further understand the electric heavy-duty truck fleet battery swapping cycle planning and vehicle predictive cruise control method of this application, the following embodiments, in conjunction with the accompanying drawings, illustrate the overall algorithm flow of the truck fleet battery swapping cycle planning and vehicle predictive cruise control provided by this application.

[0085] Specifically, Figure 3 This is a flowchart illustrating the overall algorithm for truck battery swapping cycle planning and vehicle predictive cruise control within a fleet, as described in an embodiment of this application. Figure 3 As shown, the algorithm process includes the following steps:

[0086] Step S301: Initialize the time window, departure time, and speed sequence;

[0087] In step S302, the cloud control platform obtains the situation inside the battery swapping station through cameras and vehicle sensors, and sends the situation inside the battery swapping station to the fleet of vehicles waiting to swap batteries.

[0088] Step S303: Based on the information of the battery swapping fleet and the current queuing situation at the battery swapping station, determine whether it is necessary to change the time window and departure time. If it is necessary to change the departure time, proceed to step S304; otherwise, proceed to step S306.

[0089] In step S304, each vehicle in the convoy calculates the time window (earliest arrival time and latest arrival time) and departure time to reach the battery swapping station based on this information, combined with the vehicle's position and current speed, thereby determining the maximum and minimum speed of the vehicle in predictive cruise control.

[0090] Step S305: Request the slope information of the road ahead from the cloud control platform, combine the maximum and minimum speeds, use the predictive cruise algorithm (PPC) to plan the speed of the route from the current location to the battery swapping station, and update the speed sequence.

[0091] Step S306: Determine whether the vehicle to be swapped has arrived at the battery swapping station. If it has arrived at the battery swapping station, end the process; otherwise, proceed to step S307.

[0092] In step S307, the vehicles waiting to be swapped that have not yet arrived at the battery swapping station are controlled according to the speed sequence planned in step S305, and then step S303 is repeated to continuously detect and plan the time window and departure time.

[0093] Therefore, this application, based on a predictive cruise algorithm, calculates the upper and lower limits of the speed in the state space for each vehicle in the fleet based on the obtained time window and the vehicle's current speed. The vehicle's current speed is then uploaded to the cloud as the initial speed. In the cloud, a dynamic programming algorithm, combined with the elevation map of the road ahead and the electric vehicle's map, searches for the most energy-efficient speed sequence and sends this sequence to the corresponding vehicle. This application is based on a cloud control system. Compared to vehicle-road cooperative technology or single-vehicle intelligence technology, the cloud control platform overcomes the limitations of communication distance and perception range, expanding the scope of decision-making and planning. This allows vehicles to more accurately predict traffic conditions and adjust driving strategies, thereby improving driving efficiency. Furthermore, the system reduces the vehicle's computational burden, achieving efficient utilization of computing resources.

[0094] Furthermore, this application has a wide range of applications, such as highway toll stations where queuing may occur. In these situations, it can improve throughput, reduce queuing time, and save energy.

[0095] The electric heavy-duty truck fleet battery swapping cycle planning and predictive cruise control method proposed in this application sends the vehicle battery swapping status and vehicle queuing data from the battery swapping station to the target fleet. It receives the arrival time and / or departure time of each vehicle in the target fleet based on the battery swapping status and queuing data. Based on the arrival time and / or departure time, it determines the upper and lower limits of each vehicle's speed. A predictive cruise algorithm is used to plan the speed of each vehicle, resulting in a speed sequence composed of the speeds at each road point along the target fleet's route. Cruise control is then performed on each vehicle based on this sequence. This solves the problems of electric heavy-duty truck fleets not being able to know the queuing situation at the battery swapping station and the battery swapping status of the vehicle ahead in advance, thus failing to rationally plan and control vehicle speed. This leads to congestion before the battery swapping station causing frequent starts and stops and wasting energy, or discontinuous battery swapping reducing efficiency. It also addresses the problem of energy waste when electric heavy-duty trucks travel on inclines and declines due to vehicles traveling at set speeds. The method expands the scope of decision-making and planning, improves the predictability and accuracy of battery swapping cycle decision-making and planning, and saves energy.

[0096] Next, referring to the accompanying drawings, a battery swapping cycle planning and vehicle predictive cruise control device for electric heavy truck fleets according to embodiments of this application is described.

[0097] Figure 4 This is a block diagram of an electric heavy-duty truck fleet battery swapping cycle planning and vehicle predictive cruise control device according to an embodiment of this application.

[0098] like Figure 4 As shown, the electric heavy truck fleet battery swapping cycle planning and vehicle predictive cruise control device 10 includes: an acquisition module 100, a transmission module 200, and a control module 300.

[0099] The system includes an acquisition module 100 for acquiring vehicle battery swapping status and vehicle queuing data at the battery swapping station; a transmission module 200 for transmitting vehicle battery swapping status and vehicle queuing data to at least one target fleet, and receiving arrival time and / or departure time of each vehicle in the target fleet based on its battery swapping status and vehicle queuing data; and a control module 300 for determining the maximum and minimum speed of each vehicle based on its arrival time and / or departure time, and for performing speed planning for each vehicle using a preset predictive cruise algorithm based on its maximum and minimum speeds to obtain a speed sequence consisting of the speeds of each road point in the route of the target fleet, and for performing cruise control on each vehicle based on the speed sequence.

[0100] Optionally, in some embodiments, after performing cruise control on each vehicle according to the speed sequence, the control module 300 further includes: a first acquisition unit, a first determination unit, and a first planning unit, wherein the first acquisition unit is used to acquire a new vehicle battery swapping status from the battery swapping station; the first determination unit is used to determine a battery swapping delay duration based on the new vehicle battery swapping status and the vehicle battery swapping status; and the first planning unit is used to determine a first delayed arrival time for vehicles that have not yet arrived at the battery swapping station based on the battery swapping delay duration, and to re-plan the speed of vehicles that have not yet arrived at the battery swapping station based on the first delayed arrival time.

[0101] Optionally, in some embodiments, after cruise control of each vehicle according to the speed sequence, the control module 300 further includes: a second acquisition unit, a second determination unit, and a second planning unit, wherein the second acquisition unit is used to acquire new vehicle queuing data from the battery swapping station; the second determination unit is used to determine the number of vehicles to be added to the queue based on the new vehicle queuing data and the vehicle queuing data; and the second planning unit is used to determine a second delayed arrival time for vehicles that have not yet arrived at the battery swapping station based on the number of vehicles to be added to the queue, and to postpone the arrival time and / or departure time of the vehicles that have not yet arrived at the battery swapping station based on the second delayed arrival time.

[0102] Optionally, in some embodiments, after obtaining new vehicle queuing data from the battery swapping station, the second acquisition unit is further configured to: determine the number of vehicles with reduced queue based on the new vehicle queuing data and the vehicle queuing data; determine the latest arrival time of vehicles that have not yet arrived at the battery swapping station based on the number of vehicles with reduced queue, and update the arrival time and / or departure time of vehicles that have not yet arrived at the battery swapping station based on the latest arrival time.

[0103] Optionally, in some embodiments, after obtaining new vehicle queuing data from the battery swapping station, the second acquisition unit is further configured to: determine whether the first delayed arrival time, the second delayed arrival time, and the latest arrival time of vehicles that have not yet arrived at the battery swapping station are determined; if the first delayed arrival time, the second delayed arrival time, and the latest arrival time of vehicles that have not yet arrived at the battery swapping station are determined, then the optimal speed sequence of each vehicle is determined based on the elevation map of at least one target vehicle convoy travel segment and the map of each vehicle, and the corresponding vehicle is controlled according to the optimal speed sequence of each vehicle.

[0104] It should be noted that the foregoing explanation of the embodiment of the electric heavy-duty truck fleet battery swapping cycle planning and vehicle predictive cruise control method also applies to the electric heavy-duty truck fleet battery swapping cycle planning and vehicle predictive cruise control device of this embodiment, and will not be repeated here.

[0105] The electric heavy-duty truck fleet battery swapping cycle planning and predictive cruise control device proposed in this application sends the vehicle battery swapping status and vehicle queuing data from the battery swapping station to the target fleet. It receives the arrival time and / or departure time of each vehicle in the target fleet based on the battery swapping status and queuing data. Based on the arrival time and / or departure time, it determines the upper and lower limits of each vehicle's speed. A predictive cruise algorithm is used to plan the speed of each vehicle, resulting in a speed sequence composed of the speeds at each road point along the target fleet's route. Cruise control is then performed on each vehicle based on this sequence. This solves the problems of electric heavy-duty truck fleets not being able to know the queuing situation at the battery swapping station and the battery swapping status of the vehicle ahead, thus failing to rationally plan and control vehicle speed. This leads to congestion before the battery swapping station causing frequent starts and stops and wasting energy, or discontinuous battery swapping reducing efficiency. It also addresses the problem of energy waste when electric heavy-duty trucks travel on inclines and declines due to vehicles traveling at set speeds. The device expands the scope of decision-making and planning, improves the predictability and accuracy of battery swapping cycle decision-making and planning, and saves energy.

[0106] Figure 5 A schematic diagram of the structure of a cloud server provided in an embodiment of this application. The cloud server may include:

[0107] The memory 501, the processor 502, and the computer program stored on the memory 501 and capable of running on the processor 502.

[0108] When processor 502 executes the program, it implements the electric heavy truck fleet battery swapping cycle planning and cloud server predictive cruise control method provided in the above embodiments.

[0109] Furthermore, cloud servers also include:

[0110] Communication interface 503 is used for communication between memory 501 and processor 502.

[0111] The memory 501 is used to store computer programs that can run on the processor 502.

[0112] The memory 501 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0113] If the memory 501, processor 502, and communication interface 503 are implemented independently, then the communication interface 503, memory 501, and processor 502 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0114] Optionally, in a specific implementation, if the memory 501, processor 502, and communication interface 503 are integrated on a single chip, then the memory 501, processor 502, and communication interface 503 can communicate with each other through an internal interface.

[0115] The processor 502 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of this application.

[0116] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for electric heavy-duty truck fleet battery swapping cycle planning and vehicle predictive cruise control.

[0117] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0118] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0119] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0120] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (FPGAs), field-programmable gate arrays (FPGAs), etc.

[0121] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it includes one or a combination of the steps of the method embodiments.

[0122] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for battery swapping cycle planning and predictive cruise control of electric heavy-duty truck fleets, characterized in that, Includes the following steps: Obtain vehicle battery swapping status and vehicle queuing data at the battery swapping station; Send the vehicle battery swapping status and the vehicle queuing data to at least one target fleet, and receive the arrival time and / or departure time of each vehicle in the at least one target fleet based on the vehicle battery swapping status and the vehicle queuing data; as well as The maximum and minimum speeds of each vehicle are determined based on the arrival time at the battery swapping station and / or the departure time. Based on the maximum and minimum speeds of each vehicle, a preset predictive cruise algorithm is used to plan the speed of each vehicle to obtain a speed sequence consisting of the speeds of each road point in the at least one target convoy's travel segment. Cruise control is then performed on each vehicle based on the speed sequence. The method further includes, after performing cruise control on each vehicle according to the speed sequence: Obtain the new vehicle battery swapping status from the battery swapping station; The battery swapping delay duration is determined based on the new vehicle battery swapping status and the vehicle battery swapping status. The first delayed arrival time of vehicles that have not arrived at the battery swapping station is determined based on the battery swapping delay duration, and the speed of vehicles that have not arrived at the battery swapping station is re-planned based on the first delayed arrival time. After performing cruise control on each vehicle according to the speed sequence, the method further includes: Obtain new vehicle queuing data from the battery swapping station; Based on the new vehicle queuing data and the vehicle queuing data, determine the number of vehicles to be added to the queue; The second delayed arrival time of the vehicles that have not arrived at the battery swapping station is determined based on the number of vehicles added to the queue, and the arrival time and / or departure time of the vehicles that have not arrived at the battery swapping station are postponed based on the second delayed arrival time.

2. The method according to claim 1, characterized in that, After obtaining the new vehicle queuing data from the battery swapping station, the process also includes: Based on the new vehicle queuing data and the vehicle queuing data, the number of vehicles in the queue should be reduced. The latest arrival time of the vehicles that have not arrived at the battery swapping station is determined based on the number of vehicles reduced in the queue, and the arrival time and / or departure time of the vehicles that have not arrived at the battery swapping station are updated based on the latest arrival time.

3. The method according to claim 2, characterized in that, Also includes: Determine whether to determine the first delayed arrival time of the vehicle that has not arrived at the battery swapping station, the second delayed arrival time of the vehicle that has not arrived at the battery swapping station, and the latest arrival time of the vehicle that has not arrived at the battery swapping station; If the first delayed arrival time of the vehicle that has not arrived at the battery swapping station is determined, and the second delayed arrival time of the vehicle that has not arrived at the battery swapping station is determined, and the latest arrival time of the vehicle that has not arrived at the battery swapping station is determined, then the optimal speed sequence of each vehicle is determined based on the elevation map of the at least one target convoy travel segment and the map of each vehicle, and the corresponding vehicle is controlled according to the optimal speed sequence of each vehicle.

4. A battery swapping cycle planning and predictive cruise control device for electric heavy-duty truck fleets, characterized in that, include: The acquisition module is used to acquire vehicle battery swapping status and vehicle queuing data at the battery swapping station; The sending module is used to send the vehicle battery swapping status and the vehicle queuing data to at least one target fleet, and to receive the arrival time and / or departure time of each vehicle in the at least one target fleet based on the vehicle battery swapping status and the vehicle queuing data. as well as The control module is used to determine the maximum speed and minimum speed of each vehicle based on the arrival time at the battery swapping station and / or the departure time, and to perform speed planning for each vehicle using a preset predictive cruise algorithm based on the maximum speed and minimum speed of each vehicle, thereby obtaining a speed sequence composed of the speeds of each road point in the at least one target convoy's travel segment, and to perform cruise control for each vehicle based on the speed sequence. After performing cruise control on each vehicle according to the speed sequence, the control module further includes: The first acquisition unit is used to acquire the new vehicle battery swapping status from the battery swapping station; The first determining unit is used to determine the battery swapping delay time based on the new vehicle battery swapping status and the vehicle battery swapping status. The first planning unit is used to determine the first delayed arrival time of vehicles that have not arrived at the battery swapping station based on the battery swapping delay time, and to re-plan the speed of the vehicles that have not arrived at the battery swapping station based on the first delayed arrival time. The second acquisition unit is used to acquire new vehicle queuing data from the battery swapping station; The second determining unit is used to determine the number of vehicles to be added to the queue based on the new vehicle queuing data and the vehicle queuing data. The second planning unit is used to determine the second delayed arrival time of the vehicles that have not arrived at the battery swapping station based on the number of vehicles added to the queue, and to postpone the arrival time and / or departure time of the vehicles that have not arrived at the battery swapping station based on the second delayed arrival time.

5. A cloud server, characterized in that, include: The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the electric heavy-duty truck fleet battery swapping cycle planning and vehicle predictive cruise control method as described in any one of claims 1-3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the electric heavy truck fleet battery swapping cycle planning and vehicle predictive cruise control method as described in any one of claims 1-3.

Images