Electric heavy-duty truck fleet battery swapping rhythm planning and vehicle predictive cruise control method

Abstract

An electric heavy-duty truck fleet battery swapping rhythm planning and vehicle predictive cruise control method, comprising: sending a vehicle battery swapping state and vehicle queuing data in a battery swapping station to a target fleet, receiving the time of arrival at the battery swapping station and / or departure time of each vehicle in the target fleet recalculated on the basis of the vehicle battery swapping state and the vehicle queuing data, determining an upper speed limit and a lower speed limit of each vehicle on the basis of said time of arrival and / or departure time, using a predictive cruise algorithm to perform speed planning on each vehicle to obtain a speed sequence formed by the speed at each road point in a traveling road segment of the target fleet, and performing cruise control on each vehicle on the basis of the sequence. Also disclosed are an electric heavy-duty truck fleet battery swapping rhythm planning and vehicle predictive cruise control apparatus, a cloud server, and a computer readable storage medium. Therefore, the present application solves the problem that vehicle speeds cannot be reasonably planned because an electric heavy-duty truck fleet cannot know the situation in a battery swapping station in advance and the problem of energy waste caused in uphill and downhill traveling of electric heavy-duty trucks when the vehicles travel at set speeds, thereby improving the predictability of battery swapping rhythm decision-making and planning and saving the energy.

Description

Battery swapping schedule planning and vehicle predictive cruise control method for electric heavy truck fleets

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application is based on the Chinese patent application with application number 202310899452.7 and application date of July 20, 2023, and claims the priority of the Chinese patent application. The entire content of the Chinese patent application is hereby introduced into this application as a reference. Technical Field

[0003] The present application relates to the field of vehicle technology, and in particular to a method, device, cloud server and storage medium for battery replacement rhythm planning and vehicle predictive cruise control of an electric heavy-duty truck fleet. Background Art

[0004] When electric heavy trucks are completing their transport missions, they need to swap batteries at battery swap stations. In the current production process, when human drivers are driving electric heavy trucks, the following two problems may arise:

[0005] ① Human drivers cannot predict the queue situation at the battery swap station or the battery swap status of the vehicle ahead. Therefore, they cannot plan and control their speed accordingly, which can lead to congestion at the battery swap station, frequent starting and stopping, and energy waste, or discontinuous battery swapping, reducing efficiency.

[0006] ② In related technologies, cruise control systems are often used to control vehicle speed during driving to assist human drivers. However, maintaining a set speed under all road conditions results in frequent and significant increases in motor power and braking when going up and down hills, thereby increasing the vehicle's energy consumption and reducing the motor's energy recovery efficiency. This results in a reduction in the expected driving range of electric heavy-duty trucks and energy waste.

[0007] Summary of the Invention

[0008] The present application provides a method, device, cloud server and storage medium for battery swap rhythm planning and vehicle predictive cruise control of an electric heavy-duty truck fleet, so as to solve the problem that an electric heavy-duty truck fleet cannot know the queue situation of the battery swap station and the battery swap situation of the preceding vehicle in advance, and cannot reasonably plan and control the vehicle speed according to these situations, resulting in congestion in front of the battery swap station, causing frequent starting and stopping and wasting energy, or discontinuous battery swapping, which reduces efficiency, and the problem that electric heavy-duty trucks waste energy when driving uphill and downhill due to the vehicle driving based on the set speed. The application expands the scope of decision-making and planning, improves the predictability and accuracy of battery swap rhythm decision-making and planning, and saves energy.

[0009] The first embodiment of the present application provides a method for battery replacement rhythm planning and vehicle predictive cruise control for an electric heavy truck fleet, comprising the following steps:

[0010] Obtain vehicle battery swap status and vehicle queue data at the battery swap station;

[0011] Sending the vehicle battery swap status and the vehicle queuing data to at least one target fleet, and receiving the arrival time and / or departure time of each vehicle in the at least one target fleet recalculated based on the vehicle battery swap status and the vehicle queuing data; and

[0012] The maximum speed and the minimum speed of each vehicle are determined according to the arrival time at the battery swap station and / or the departure time, and based on the maximum speed and the minimum speed of each vehicle, a preset predictive cruise algorithm is used to perform speed planning for each vehicle to obtain a speed sequence consisting of the speeds of each waypoint in the driving section of the at least one target fleet, and cruise control is performed on each vehicle according to the speed sequence.

[0013] Optionally, in some embodiments, after performing cruise control on each vehicle according to the speed sequence, the method further includes:

[0014] Obtaining a new vehicle battery swap status from the battery swap station;

[0015] Determining a battery swap delay duration based on the new vehicle battery swap status and the vehicle battery swap status;

[0016] The first delayed arrival time of the vehicle that has not arrived at the battery swap station is determined based on the battery swap delay time, and the speed of the vehicle that has not arrived at the battery swap station is re-planned based on the first delayed arrival time.

[0017] Optionally, in some embodiments, after performing cruise control on each vehicle according to the speed sequence, the method further includes:

[0018] Acquire new vehicle queue data from the battery swap station;

[0019] determining the number of vehicles added to the queue based on the new vehicle queue data and the vehicle queue data;

[0020] The second delayed arrival time of the vehicles that have not arrived at the battery swap station is determined according to the increased number of vehicles in the queue, and the arrival time and / or departure time of the vehicles that have not arrived at the battery swap station are postponed according to the second delayed arrival time.

[0021] Optionally, in some embodiments, after acquiring the new vehicle queue data from the battery swap station, the method further includes:

[0022] determining a number of vehicles reduced in the queue based on the new vehicle queue data and the vehicle queue data;

[0023] The latest arrival time of the vehicles that have not arrived at the battery swap 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 swap station are updated based on the latest arrival time.

[0024] Optionally, in some embodiments, after acquiring the new vehicle queue data from the battery swap station, the method further includes:

[0025] Determine whether the first delayed arrival time of the vehicle that has not arrived at the battery swap station, the second delayed arrival time of the vehicle that has not arrived at the battery swap station, and the latest arrival time of the vehicle that has not arrived at the battery swap station are determined;

[0026] If the first delayed arrival time of the vehicle that has not arrived at the battery swap station is determined, the second delayed arrival time of the vehicle that has not arrived at the battery swap station is determined, and the latest arrival time of the vehicle that has not arrived at the battery swap station is determined, then the optimal speed sequence of each vehicle is determined based on the elevation map of the at least one target fleet driving section and the map map of each vehicle, and the corresponding vehicle is controlled according to the optimal speed sequence of each vehicle.

[0027] A second embodiment of the present application provides a battery replacement rhythm planning and vehicle predictive cruise control device for an electric heavy truck fleet, comprising:

[0028] The acquisition module is used to obtain the battery swap status and vehicle queue data of the battery swap station;

[0029] a planning module, configured to send the vehicle battery swap 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, which are recalculated based on the vehicle battery swap status and the vehicle queuing data; and

[0030] a control module, configured to determine the maximum speed and the minimum speed of each vehicle according to the arrival time at the battery swap station and / or the departure time, and based on the maximum speed and the minimum speed of each vehicle, perform speed planning for each vehicle using a preset predictive cruise algorithm, obtain a speed sequence consisting of the speeds of each waypoint in the travel section of the at least one target fleet, and perform cruise control on each vehicle according to the speed sequence.

[0031] Optionally, in some embodiments, after performing cruise control on each vehicle according to the speed sequence, the control module further includes:

[0032] A first acquiring unit, configured to acquire a new vehicle battery swapping status from the battery swapping station;

[0033] A first determining unit, configured to determine a battery swap delay duration based on the new vehicle battery swap status and the vehicle battery swap status;

[0034] The first planning unit is used to determine a first delayed arrival time of the vehicle that has not arrived at the battery swap station according to the battery swap delay time, and re-plan the speed of the vehicle that has not arrived at the battery swap station according to the first delayed arrival time.

[0035] Optionally, in some embodiments, after performing cruise control on each vehicle according to the speed sequence, the control module further includes:

[0036] A second acquiring unit, configured to acquire new vehicle queuing data from the battery swap station;

[0037] a second determining unit, configured to determine the number of vehicles added to the queue based on the new vehicle queue data and the vehicle queue data;

[0038] The second planning unit is used to determine the second delayed arrival time of the vehicles that have not arrived at the battery swap station according to the increased number of vehicles in the queue, and to postpone the arrival time and / or departure time of the vehicles that have not arrived at the battery swap station according to the second delayed arrival time.

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

[0040] determining a number of vehicles reduced in the queue based on the new vehicle queue data and the vehicle queue data;

[0041] The latest arrival time of the vehicles that have not arrived at the battery swap 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 swap station are updated based on the latest arrival time.

[0042] Optionally, in some embodiments, after acquiring the new vehicle queuing data from the battery swap station, the second acquiring unit is further configured to:

[0043] Determine whether the first delayed arrival time of the vehicle that has not arrived at the battery swap station, the second delayed arrival time of the vehicle that has not arrived at the battery swap station, and the latest arrival time of the vehicle that has not arrived at the battery swap station are determined;

[0044] If the first delayed arrival time of the vehicle that has not arrived at the battery swap station is determined, the second delayed arrival time of the vehicle that has not arrived at the battery swap station is determined, and the latest arrival time of the vehicle that has not arrived at the battery swap station is determined, then the optimal speed sequence of each vehicle is determined based on the elevation map of the at least one target fleet driving section and the map map of each vehicle, and the corresponding vehicle is controlled according to the optimal speed sequence of each vehicle.

[0045] The third aspect of the present application provides a cloud server, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the method for planning battery replacement rhythm and predictive cruise control of an electric heavy-duty truck fleet as described in the above embodiment.

[0046] The fourth aspect of the present application provides a computer-readable storage medium on which a computer program is stored, which is executed by a processor to implement the battery replacement rhythm planning and vehicle predictive cruise control method for an electric heavy-duty truck fleet as described in the above embodiments.

[0047] Therefore, the present application sends the battery swap status and vehicle queue data of the battery swap station to the target fleet, receives the arrival time and / or departure time of each vehicle in the target fleet recalculated based on the battery swap status and vehicle queue data, determines the upper and lower limits of the speed of each vehicle according to the arrival time and / or departure time, uses the predictive cruise algorithm to plan the speed of each vehicle, obtains a speed sequence composed of the speeds of each road point in the target fleet's driving section, and performs cruise control on each vehicle according to the sequence. Thus, the problem that the electric heavy truck fleet cannot know the queue situation of the battery swap station and the battery swap situation of the preceding vehicle in advance, and cannot reasonably plan and control the vehicle speed according to these situations, resulting in congestion in front of the battery swap station, causing frequent start-stop and waste of energy, or discontinuous battery swapping, which reduces efficiency, and the problem that the electric heavy truck wastes energy when driving up and downhill because the vehicle travels based on the set speed, expands the scope of decision-making and planning, improves the predictability and accuracy of battery swapping rhythm decision-making and planning, and saves energy.

[0048] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0050] FIG1 is a flow chart of a method for battery replacement rhythm planning and vehicle predictive cruise control for an electric heavy truck fleet according to an embodiment of the present application;

[0051] FIG2 is a schematic diagram of a solution for combining rhythm control and cruise control for an electric heavy truck fleet based on cloud control according to one embodiment of the present application;

[0052] FIG3 is a flowchart of an overall algorithm for battery swapping schedule planning and vehicle predictive cruise control within a fleet according to one embodiment of the present application;

[0053] FIG4 is a schematic diagram of a battery replacement rhythm planning and vehicle predictive cruise control device for an electric heavy truck fleet according to an embodiment of the present application;

[0054] FIG5 is a schematic diagram of the structure of a cloud server provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0055] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0056] The following describes the method, device, cloud server and storage medium for battery swapping rhythm planning and vehicle predictive cruise control of an electric heavy truck fleet according to an embodiment of the present application with reference to the accompanying drawings. In response to the problem mentioned in the above background technology that the electric heavy truck fleet cannot know the situation in the battery swap station in advance and cannot reasonably plan the vehicle speed, and the problem that the electric heavy truck wastes energy when driving up and downhill because the vehicle is traveling based on the set speed, the present application provides a method for battery swapping rhythm planning and vehicle predictive cruise control of an electric heavy truck fleet, in which the vehicle battery swapping status and vehicle queue data of the battery swap station are obtained, the vehicle battery swapping status and vehicle queue data are sent to at least one target fleet, and the arrival time and / or departure time of each vehicle in at least one target fleet recalculated based on the vehicle battery swapping status and vehicle queue data are received, the maximum speed and minimum speed of each vehicle are determined according to the arrival time and / or departure time, and based on the maximum speed and minimum speed of the vehicle, the speed of each vehicle is planned using a preset predictive cruise algorithm to obtain a speed sequence composed of the speeds of each waypoint in the driving section of at least one target fleet, and cruise control is performed on each vehicle according to the speed sequence. This solves the problems of electric heavy-duty truck fleets not being able to know the situation in the battery swap station in advance and thus being unable to reasonably plan their vehicle speed, and the problem of electric heavy-duty trucks wasting energy when driving uphill and downhill because the vehicles are driving based on the set speed. It improves the predictability of battery swapping rhythm decision-making and saves energy.

[0057] Specifically, Figure 1 is a flowchart of the battery replacement rhythm planning and vehicle predictive cruise control method for an electric heavy-duty truck fleet provided in an embodiment of the present application.

[0058] As shown in FIG1 , the battery replacement cycle planning and vehicle predictive cruise control method for an electric heavy truck fleet includes the following steps:

[0059] In step S101, the battery swapping status and vehicle queuing data of the vehicle at the battery swapping station are obtained.

[0060] Among them, the vehicle battery replacement status of the embodiment of the present application can be "battery replacement in progress", "battery replacement completed", "waiting for battery replacement", etc., and the vehicle queuing data may include the number of vehicles currently queuing at the battery replacement station, etc., which are not specifically limited here, and technical personnel in this field can set it according to actual conditions.

[0061] It is understandable that since the electric heavy-duty truck driver cannot know the queue status of the battery swap station and the battery swap status of the vehicle in front in advance, the driver cannot reasonably plan and control the vehicle speed. Therefore, the battery swap rhythm planning and vehicle predictive cruise control method provided by this application requires first obtaining the vehicle battery swap status and vehicle queue data at the battery swap station, so as to plan the battery swap rhythm based on the vehicle battery swap status and vehicle queue data.

[0062] It should be noted that this application is based on a cloud control system, which is a vehicle-road-cloud integrated fusion control system based on the new generation of mobile Internet technology. It allows the target vehicle to establish connections with environmental vehicles, roadside infrastructure, and the cloud control platform, providing the target vehicle with wide-area dynamic traffic information, thereby better predicting upcoming events and enabling the vehicle to adjust its driving strategy more predictively. In addition, compared to the vehicle-road collaborative technology that the predictive cruise control technology in related technologies relies on, 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 planning, and make it easier to achieve real-time solution of control strategies.

[0063] Specifically, the embodiment of the present application uses a cloud control platform to utilize cameras in a battery swap station to obtain fleet queue data in the battery swap station, and obtains vehicle battery swap status based on on-board sensors.

[0064] In step S102, the vehicle battery swap status and vehicle queuing data are sent to at least one target fleet, and the arrival time and / or departure time of each vehicle in at least one target fleet, which are recalculated based on the vehicle battery swap status and vehicle queuing data, are received.

[0065] It is understandable that if the target fleet fails to reasonably plan the battery swapping rhythm, congestion may occur in front of the battery swap station. Therefore, the embodiment of the present application needs to send the vehicle battery swapping status and vehicle queue data obtained in step S101 to the target fleet through the cloud control platform. Then, each vehicle in the target fleet can obtain the arrival time or departure time of the battery swap station or the battery swap station time and departure time based on the vehicle battery swapping status and vehicle queue data.

[0066] Specifically, Figure 2 is a schematic diagram of a cloud-based solution for combining rhythm control and cruise control for electric heavy-duty truck fleets in accordance with an embodiment of the present application. As shown in Figure 2, each vehicle in the fleet recalculates its arrival time at a battery swap station or departure time, or both, based on a time window, based on its battery swap status and queuing data, combined with its location and current speed.

[0067] Therefore, the embodiment of the present application controls the speed rhythm of the heavy truck queue and plans the optimal departure time and optimal arrival time window of the vehicles in the fleet to ensure that the battery replacement process is as continuous as possible and there is no queuing as much as possible, thereby alleviating the traffic pressure at the battery replacement station and improving the overall battery replacement efficiency of the battery replacement station.

[0068] In step S103, the maximum speed and minimum speed of each vehicle are determined according to the arrival time at the battery swap station and / or the departure time, and based on the maximum speed and minimum speed of each vehicle, the speed of each vehicle is planned using a preset predictive cruise algorithm to obtain a speed sequence consisting of the speeds of each waypoint in at least one target fleet driving section, and cruise control is performed on each vehicle according to the speed sequence.

[0069] It can be understood that after obtaining the arrival time at the battery swap station or the departure time or the battery swap station time and the departure time, the maximum speed and the minimum speed of the vehicle in the predictive cruise control can be determined.

[0070] Specifically, the embodiment of the present application can request the slope information of the road section ahead from the cloud control platform, and combine the maximum speed and minimum speed of the vehicle, and use the predictive cruise control (PCC) algorithm to plan the speed of the distance from the current vehicle position to the battery swap station, thereby obtaining a speed sequence composed of the speeds of each waypoint on the road section ahead, and performing cruise control on each vehicle according to the speed sequence.

[0071] Therefore, the present application provides an energy-saving predictive cruise algorithm for electric heavy trucks based on slope, which ultimately obtains the optimal speed sequence to achieve the purpose of energy conservation.

[0072] Furthermore, since the battery swapping status of the vehicle and the queuing situation at the battery swap station may change, the present application provides a control strategy that is easier to implement. This control strategy can perform cruise control on the vehicle in real time according to the battery swapping status of the vehicle and the queuing situation at the battery swap station. That is to say, after the current vehicle obtains the speed sequence, if the battery swapping status of the vehicle and the queuing situation at the battery swap station change, the above process is re-executed.

[0073] Optionally, in some embodiments, after cruise control is performed on each vehicle according to the speed sequence, it also includes: obtaining a new vehicle battery swap status from the battery swap station; determining the battery swap delay duration based on the new vehicle battery swap status and the vehicle battery swap status; determining a first delayed arrival time of vehicles that have not arrived at the battery swap station according to the battery swap delay duration, and re-planning the speed of vehicles that have not arrived at the battery swap station according to the first delayed arrival time.

[0074] It is understandable that the normal battery replacement time for a vehicle is 7 minutes. Vehicles that are replacing batteries at a battery replacement station may have delays in the battery replacement process due to certain reasons, which will affect the vehicles queuing for battery replacement and vehicles that have not arrived at the battery replacement station. Therefore, the embodiment of the present application needs to first determine the battery replacement delay time based on the acquired new vehicle battery replacement status and the vehicle battery replacement status in step S101, and then calculate the delayed arrival time of vehicles that have not arrived at the battery replacement station, that is, the first delayed arrival time set in the embodiment of the present application, and re-plan the speed and other data of vehicles that have not arrived at the battery replacement station based on the first delayed arrival time, so as to achieve the effect of real-time cruise control of the vehicle.

[0075] For example, there is a group of electric heavy trucks in the current battery swap station, with a total of 6 trucks queuing for battery swap. The current battery swap vehicle is the first truck, and its battery swap time is delayed by 30s for some reason. Therefore, the time window of all vehicles that have not arrived at the battery swap station is postponed by 30s, and the departure time of vehicles that have not departed is postponed by 30s. The battery swap time of the second truck is 840s (delayed by 420s to leave).

[0076] Therefore, when there is a delay in battery replacement at a battery replacement station, the present application can adjust the time window of vehicles that have not arrived at the battery replacement station in real time by calculating the delay time, so as to ensure that the battery replacement process is as continuous as possible and there is no queuing as much as possible.

[0077] Optionally, in some embodiments, after cruise control is performed on each vehicle according to the speed sequence, the method further includes: acquiring new vehicle queue data from the battery swap station; determining the number of vehicles added to the queue based on the new vehicle queue data and the vehicle queue data; determining a second delayed arrival time for vehicles that have not arrived at the battery swap station based on the number of vehicles added to the queue, and postponing the arrival time and / or departure time of the vehicles that have not arrived at the battery swap station based on the second delayed arrival time.

[0078] It is understandable that if the number of vehicles queuing in the battery swap station changes, such as social vehicles and other vehicles in the fleet are added to the queue, in this case, the cruise control of the vehicle will also be affected. Therefore, the present application obtains new vehicle queue data from the battery swap station through the cloud control platform, and determines the number of additional vehicles in the queue based on the new vehicle queue data and the vehicle queue data in step S101, and then determines the delayed arrival time of vehicles that have not arrived at the battery swap station based on the increased number of vehicles, that is, the second delayed arrival time of the embodiment of the present application, so as to re-plan the arrival time or departure time of the vehicles that have not arrived at the battery swap station or the battery swap station time and departure time according to the second delayed arrival time.

[0079] For example, there is a group of electric heavy trucks in the current battery swap station, with a total of 6 trucks queuing for battery swapping. When n vehicles are added to the current queue, the time window of all vehicles that have not arrived at the battery swap station will be postponed by 7*n (min). If a queued vehicle appears at the battery swap station 420s after the first vehicle departs, the time window of all vehicles that have not arrived at the battery swap station will be postponed by 7 minutes.

[0080] Optionally, in some embodiments, after obtaining new vehicle queue data from the battery swap station, it also includes: determining the number of vehicles reduced in the queue based on the new vehicle queue data and the vehicle queue data; determining the latest arrival time of vehicles that have not arrived at the battery swap station based on the number of vehicles reduced in the queue, and updating the arrival time and / or departure time of vehicles that have not arrived at the battery swap station based on the latest arrival time.

[0081] It is understandable that if the number of vehicles queuing in the battery swap station changes, such as a decrease in the number of vehicles queuing, the cruise control of the vehicle will also be affected. Therefore, the embodiment of the present application determines the number of vehicles in the queue that has been reduced based on the new vehicle queue data and the vehicle queue data, and plans the latest arrival time of vehicles that have not arrived at the battery swap station based on the reduced number of vehicles.

[0082] For example, there is a group of electric heavy trucks in the current battery swap station, with a total of 6 trucks queuing for battery swap. The battery swap time for the first vehicle is 300s. The vehicle sensor will upload the battery swap time to the cloud control system. The vehicles that have not arrived will receive the message that the first vehicle left 120s in advance, and then update the arrival time at the battery swap station or the departure time or the battery swap station time and the departure time.

[0083] Therefore, when the number of vehicles queuing at the battery swap station changes, the present application can use the predictive cruise algorithm to plan the speed of each vehicle, and update the arrival time and / or departure time of vehicles that have not arrived at the battery swap station in real time, so as to alleviate the traffic pressure at the battery swap station and improve the overall battery swap efficiency of the battery swap station.

[0084] In some cases, the three typical operating conditions mentioned above may occur simultaneously, namely, the number of vehicles queuing at the battery swap station changes, the battery swap process is delayed, and a vehicle leaves the battery swap station. The cloud control system of the embodiment of the present application receives the number of vehicles queuing at the battery swap station and the battery swap time of the current battery swap vehicle in real time, and calculates data such as the vehicle's arrival time and departure time based on the battery swap vehicle status and vehicle queue data. Therefore, the embodiment of the present application can perform real-time speed simulation and route planning for vehicles that have not arrived at the battery swap station according to actual conditions, thereby improving vehicle driving efficiency.

[0085] Optionally, in some embodiments, after obtaining new vehicle queuing data from the battery swap station, it also includes: judging whether the first delayed arrival time of the vehicle that has not arrived at the battery swap station, the second delayed arrival time of the vehicle that has not arrived at the battery swap station, and the latest arrival time of the vehicle that has not arrived at the battery swap station are determined; if the first delayed arrival time of the vehicle that has not arrived at the battery swap station is determined, and the second delayed arrival time of the vehicle that has not arrived at the battery swap station is determined, and the latest arrival time of the vehicle that has not arrived at the battery swap station is determined, then the optimal speed sequence of each vehicle is determined based on the elevation map of at least one target fleet driving section and the Map map of each vehicle, and the corresponding vehicle is controlled according to the optimal speed sequence of each vehicle.

[0086] Specifically, as shown in Figure 2, after determining the first delayed arrival time of the vehicle that has not arrived at the battery swap station, determining the second delayed arrival time of the vehicle that has not arrived at the battery swap station, and determining the latest arrival time of the vehicle that has not arrived at the battery swap station, the embodiment of the present application is based on the cloud control system to determine the optimal speed sequence of each vehicle according to the elevation map of the target fleet's driving section and the Map map of each vehicle, so as to control the corresponding vehicle through the optimal speed sequence.

[0087] In order to enable those skilled in the art to further understand the electric heavy-duty truck fleet battery replacement rhythm planning and vehicle predictive cruise control method of the present application, the following examples are listed in combination with the accompanying drawings to schematically illustrate the overall algorithm flow of the truck battery replacement rhythm planning and vehicle predictive cruise control within the fleet provided by the present application.

[0088] Specifically, FIG3 is a flowchart of the overall algorithm for battery replacement rhythm planning and vehicle predictive cruise control within a fleet of trucks according to an embodiment of the present application. As shown in FIG3 , the algorithm flow includes the following steps:

[0089] Step S301, initializing the time window, departure time and speed sequence;

[0090] Step S302: The cloud control platform obtains the situation inside the battery swap station through the camera inside the battery swap station and the on-board sensor, and sends the situation inside the battery swap station to the fleet waiting for battery swap;

[0091] Step S303: Based on the information of the fleet to be swapped and the current queue situation at the battery swap station, determine whether the time window and departure time need to be changed. If the departure time needs to be changed, execute step S304; otherwise, execute step S306.

[0092] In step S304, each vehicle in the fleet calculates the time window (earliest arrival time and latest arrival time) and departure time for arriving at the battery swap station based on this information, combined with the vehicle's location and current speed, to determine the maximum and minimum speeds for the vehicle in predictive cruise control.

[0093] Step S305: Request the cloud control platform for the slope information of the road ahead, combine the maximum speed and minimum speed, use the predictive cruise control (PPC) algorithm to plan the speed of the journey from the current location to the battery swap station, and update the speed sequence;

[0094] Step S306: Determine whether the vehicle to be replaced has arrived at the battery replacement station. If it has arrived at the battery replacement station, the process ends; otherwise, execute step S307;

[0095] In step S307, the vehicles to be replaced that have not arrived at the battery replacement 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.

[0096] Therefore, based on the predictive cruise algorithm, this application obtains the upper and lower limits of the speed in the state space for each vehicle in the fleet based on the obtained time window combined with the current speed of the vehicle, and uploads the current speed of the vehicle as the initial speed to the cloud. In the cloud, a dynamic planning algorithm is used to combine the elevation map of the road ahead and the map of the electric vehicle to search for a set of the most energy-efficient speed sequences, and send this set of speed sequences to the corresponding vehicles. This application is based on a cloud control system. Compared with vehicle-road collaborative technology or single-vehicle intelligent technology, the cloud control platform can overcome the limitations of communication distance and perception range and expand the scope of decision-making and planning. This enables vehicles to more accurately predict traffic conditions and adjust driving strategies, thereby improving driving efficiency. In addition, the system can reduce the computing burden of vehicles and achieve efficient use of computing resources.

[0097] In addition, the present invention has a wide range of application scenarios, such as highway toll booths and other scenarios where queues may occur. In these situations, it can improve the passing efficiency, reduce the queue time, and save energy.

[0098] According to the method for battery swapping rhythm planning and vehicle predictive cruise control of an electric heavy truck fleet proposed in the embodiment of the present application, the battery swapping status and vehicle queue data of the vehicle at the battery swapping station are sent to the target fleet, the arrival time and / or departure time of each vehicle in the target fleet recalculated based on the vehicle battery swapping status and vehicle queue data are received, the upper and lower limits of the speed of each vehicle are determined according to the arrival time and / or departure time of the battery swapping station, and the speed of each vehicle is planned using a predictive cruise algorithm to obtain a speed sequence composed of the speeds of each waypoint in the target fleet's driving section, and cruise control is performed on each vehicle according to the sequence. Thus, the problem that the electric heavy truck fleet cannot know the queue situation of the battery swapping station and the battery swapping situation of the preceding vehicle in advance, and cannot reasonably plan and control the vehicle speed according to these situations, resulting in congestion in front of the battery swapping station, causing frequent start-stop and waste of energy, or discontinuous battery swapping, which reduces efficiency, and the problem that the electric heavy truck wastes energy when driving up and downhill due to the vehicle driving based on the set speed is expanded. The scope of decision-making and planning is expanded, the predictability and accuracy of battery swapping rhythm decision-making and planning are improved, and energy is saved.

[0099] Next, the battery replacement rhythm planning and vehicle predictive cruise control device for an electric heavy truck fleet proposed in accordance with an embodiment of the present application will be described with reference to the accompanying drawings.

[0100] FIG4 is a block diagram of a battery replacement rhythm planning and vehicle predictive cruise control device for an electric heavy truck fleet according to an embodiment of the present application.

[0101] As shown in FIG4 , the battery replacement rhythm planning and vehicle predictive cruise control device 10 for an electric heavy truck fleet includes: an acquisition module 100 , a planning module 200 and a control module 300 .

[0102] Among them, the acquisition module 100 is used to obtain the vehicle battery swapping status and vehicle queuing data at the battery swapping station; the planning module 200 is used to send the vehicle battery swapping status and vehicle queuing data to at least one target fleet, and receive the arrival time and / or departure time of each vehicle in at least one target fleet recalculated based on the vehicle battery swapping status and vehicle queuing data; and the control module 300 is used to determine the maximum speed and minimum speed of each vehicle according to the arrival time and / or departure time of the battery swapping station, and based on the maximum speed and minimum speed of each vehicle, use a preset predictive cruise algorithm to perform speed planning for each vehicle, obtain a speed sequence composed of the speed of each waypoint in the driving section of at least one target fleet, and perform cruise control on each vehicle according to the speed sequence.

[0103] Optionally, in some embodiments, after cruise control is performed 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 obtain a new vehicle battery swap status from the battery swap station; the first determination unit is used to determine the battery swap delay duration based on the new vehicle battery swap status and the vehicle battery swap status; the first planning unit is used to determine a first delayed arrival time of vehicles that have not arrived at the battery swap station according to the battery swap delay duration, and re-plan the speed of vehicles that have not arrived at the battery swap station according to the first delayed arrival time.

[0104] Optionally, in some embodiments, after cruise control is performed on 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 queue data from the battery swap station; the second determination unit is used to determine the number of vehicles added to the queue based on the new vehicle queue data and the vehicle queue data; the second planning unit is used to determine the second delayed arrival time of vehicles that have not arrived at the battery swap station according to the number of vehicles added to the queue, and postpone the arrival time and / or departure time of the vehicles that have not arrived at the battery swap station according to the second delayed arrival time.

[0105] Optionally, in some embodiments, after acquiring new vehicle queue data from the battery swap station, the second acquisition unit is further used to: determine the number of vehicles reduced in the queue based on the new vehicle queue data and the vehicle queue data; determine the latest arrival time of vehicles that have not arrived at the battery swap station based on the number of vehicles reduced in the queue, and update the arrival time and / or departure time of vehicles that have not arrived at the battery swap station based on the latest arrival time.

[0106] Optionally, in some embodiments, after acquiring new vehicle queuing data from the battery swap station, the second acquisition unit is further used to: determine whether the first delayed arrival time of the vehicle that has not arrived at the battery swap station, the second delayed arrival time of the vehicle that has not arrived at the battery swap station, and the latest arrival time of the vehicle that has not arrived at the battery swap station are determined; if the first delayed arrival time of the vehicle that has not arrived at the battery swap station is determined, and the second delayed arrival time of the vehicle that has not arrived at the battery swap station is determined, and the latest arrival time of the vehicle that has not arrived at the battery swap station is determined, then determine the optimal speed sequence of each vehicle based on the elevation map of at least one target fleet driving section and the Map map of each vehicle, and control the corresponding vehicle according to the optimal speed sequence of each vehicle.

[0107] It should be noted that the above explanation of the embodiment of the method for planning the battery replacement rhythm of an electric heavy-duty truck fleet and predictive cruise control for vehicles is also applicable to the device for planning the battery replacement rhythm of an electric heavy-duty truck fleet and predictive cruise control for vehicles of this embodiment, and will not be repeated here.

[0108] According to the battery swap rhythm planning and vehicle predictive cruise control device for electric heavy truck fleets proposed in the embodiment of the present application, by sending the battery swap status and vehicle queue data of the vehicle at the battery swap station to the target fleet, receiving the arrival time and / or departure time of each vehicle in the target fleet recalculated based on the vehicle battery swap status and vehicle queue data, determining the upper and lower limits of the speed of each vehicle according to the arrival time and / or departure time, using the predictive cruise algorithm to plan the speed of each vehicle, obtaining a speed sequence composed of the speeds of each waypoint in the target fleet's driving section, and performing cruise control on each vehicle according to the sequence. Thus, the problem that the electric heavy truck fleet cannot know the queue situation of the battery swap station and the battery swap situation of the preceding vehicle in advance, and cannot reasonably plan and control the vehicle speed according to these situations, resulting in congestion in front of the battery swap station, causing frequent start-stop and waste of energy, or discontinuous battery swapping, which reduces efficiency, and the problem that the electric heavy truck wastes energy when driving up and downhill due to the vehicle driving based on the set speed is expanded. The scope of decision-making and planning is expanded, the predictability and accuracy of battery swap rhythm decision-making and planning are improved, and energy is saved.

[0109] FIG5 is a schematic diagram of the structure of a cloud server provided in an embodiment of the present application. The cloud server may include:

[0110] Memory 501 , processor 502 , and computer programs stored in the memory 501 and executable on the processor 502 .

[0111] When the processor 502 executes the program, the electric heavy truck fleet battery replacement rhythm planning and cloud server predictive cruise control method provided in the above embodiment is implemented.

[0112] Furthermore, the cloud server also includes:

[0113] The communication interface 503 is used for communication between the memory 501 and the processor 502 .

[0114] The memory 501 is used to store computer programs that can be run on the processor 502 .

[0115] The memory 501 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0116] If the memory 501, processor 502, and communication interface 503 are implemented independently, the communication interface 503, memory 501, and processor 502 can be interconnected via a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus. Buses can be divided into address buses, data buses, control buses, etc. For ease of illustration, FIG5 shows only one thick line, but this does not mean that there is only one bus or only one type of bus.

[0117] Optionally, in a specific implementation, if the memory 501, the processor 502 and the communication interface 503 are integrated on a chip, the memory 501, the processor 502 and the communication interface 503 can communicate with each other through an internal interface.

[0118] The processor 502 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0119] An embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, it implements the above-mentioned method for planning the battery replacement rhythm and predictive cruise control of an electric heavy-duty truck fleet.

[0120] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0121] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of this application, "N" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0122] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing a custom logical function or process step, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed in a different order than shown or discussed, including performing functions in a substantially simultaneous manner or in a reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application pertain.

[0123] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiment, the N steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array, a field programmable gate array, etc.

[0124] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0125] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for battery replacement rhythm planning and vehicle predictive cruise control for an electric heavy truck fleet, characterized in that: The following steps are involved: Obtain vehicle battery swap status and vehicle queue data at the battery swap station; Sending the vehicle battery swap status and the vehicle queue data to at least one target fleet, and receiving the arrival time and / or departure time of each vehicle in the at least one target fleet recalculated based on the vehicle battery swap status and the vehicle queue data; as well as The maximum speed and the minimum speed of each vehicle are determined according to the arrival time at the battery swap station and / or the departure time, and based on the maximum speed and the minimum speed of each vehicle, a preset predictive cruise algorithm is used to perform speed planning for each vehicle, so as to obtain a speed sequence composed of the speeds of each waypoint in the driving section of the at least one target fleet, and cruise control is performed on each vehicle according to the speed sequence.

2. The method according to claim 1, characterized in that After performing cruise control on each of the vehicles according to the speed sequence, the method further includes: Obtaining a new vehicle battery replacement status from the battery replacement station; Determine the battery replacement delay duration based on the new vehicle battery replacement state and the vehicle battery replacement state; A first delayed arrival time of the vehicle that has not arrived at the battery swap station is determined based on the battery swap delay time, and the speed of the vehicle that has not arrived at the battery swap station is re-planned based on the first delayed arrival time.

3. The method according to claim 2, characterized in that After performing cruise control on each of the vehicles according to the speed sequence, the method further includes: Acquire new vehicle queue data from the battery swap station; Determine the number of vehicles added to the queue based on the new vehicle queue data and the vehicle queue data; The second delayed arrival time of the vehicles that have not arrived at the battery swap station is determined according to the increased number of vehicles in the queue, and the arrival time and / or departure time of the vehicles that have not arrived at the battery swap station are postponed according to the second delayed arrival time.

4. The method according to claim 3, characterized in that After acquiring the new vehicle queue data from the battery swap station, the method further includes: determining a reduction in the number of vehicles in the queue based on the new vehicle queue data and the vehicle queue data; The latest arrival time of the vehicles that have not arrived at the battery swap station is determined according to 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 swap station are updated according to the latest arrival time.

5. The method according to claim 4, characterized in that Also includes: Determine whether to determine the first delayed arrival time of the vehicle that has not arrived at the battery swap station, the second delayed arrival time of the vehicle that has not arrived at the battery swap station, and the latest arrival time of the vehicle that has not arrived at the battery swap station; If the first delayed arrival time of the vehicle that has not arrived at the battery swap station is determined, the second delayed arrival time of the vehicle that has not arrived at the battery swap station is determined, and the latest arrival time of the vehicle that has not arrived at the battery swap station is determined, then the optimal speed sequence of each vehicle is determined based on the elevation map of the at least one target fleet driving section and the Map map of each vehicle, and the corresponding vehicle is controlled according to the optimal speed sequence of each vehicle.

6. A battery replacement rhythm planning and vehicle predictive cruise control device for an electric heavy truck fleet, characterized in that: include: An acquisition module is used to obtain the battery swap status and vehicle queue data of the battery swap station; A planning module, used for sending the vehicle battery swap status and the vehicle queuing data to at least one target fleet, and receiving the arrival time and / or departure time of each vehicle in the at least one target fleet recalculated based on the vehicle battery swap status and the vehicle queuing data; as well as A control module is used to determine the maximum speed and the minimum speed of each vehicle according to the arrival time at the battery swap station and / or the departure time, and based on the maximum speed and the minimum speed of each vehicle, use a preset predictive cruise algorithm to perform speed planning for each vehicle, obtain a speed sequence composed of the speeds of each waypoint in the driving section of the at least one target fleet, and perform cruise control on each vehicle according to the speed sequence.

7. The device according to claim 6, characterized in that After performing cruise control on each of the vehicles according to the speed sequence, the control module further includes: A first acquisition unit, used to acquire a new vehicle battery replacement status from the battery replacement station; A first determining unit, configured to determine a battery swap delay duration based on the new vehicle battery swap state and the vehicle battery swap state; The first planning unit is used to determine a first delayed arrival time of the vehicle that has not arrived at the battery swap station according to the battery swap delay time, and re-plan the speed of the vehicle that has not arrived at the battery swap station according to the first delayed arrival time.

8. The device according to claim 7, characterized in that After performing cruise control on each of the vehicles according to the speed sequence, the control module further includes: A second acquisition unit, used to acquire new vehicle queue data from the battery swap station; A second determining unit, configured to determine the number of vehicles added to the queue based on the new vehicle queue data and the vehicle queue data; The second planning unit is used to determine the second delayed arrival time of the vehicles that have not arrived at the battery swap station according to the increased number of vehicles in the queue, and postpone the arrival time and / or departure time of the vehicles that have not arrived at the battery swap station according to the second delayed arrival time.

9. A cloud server, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for battery replacement rhythm planning and vehicle predictive cruise control for an electric heavy truck fleet as described in any one of claims 1 to 5.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the method for battery replacement rhythm planning and vehicle predictive cruise control for an electric heavy truck fleet as described in any one of claims 1-5.

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