A method and device for intelligent cruise control of pure electric vehicles

Abstract

The embodiment of the application discloses a kind of limit energy consumption intelligent cruise control method and device of pure electric vehicle, in the intelligent driving process of pure electric vehicle, determine the expected driving speed of vehicle, obtain the road condition information of pure electric vehicle;From multiple modes, select a target mode for intelligent driving, multiple modes include acceleration mode, uniform speed mode and deceleration mode, wherein: acceleration mode includes solving the optimal acceleration working point trajectory under current speed to expected driving speed, and distributes acceleration and uniform speed mileage according to optimal acceleration working point trajectory;Uniform speed mode includes obtaining the total mileage of uniform speed stage to intersection, and the motor acceleration working point and optimal distribution ratio are obtained by interpolation according to total mileage of uniform speed stage and expected driving speed;Deceleration mode includes determining the best deceleration time, and deceleration is realized by regenerative braking.The application effectively alleviates the problem of insufficient energy consumption when driving pure electric vehicle.

Description

Technical Field

[0001] This application relates to the field of automotive electrification technology, specifically to a method and device for intelligent cruise control of extreme energy consumption for pure electric vehicles. Background Technology

[0002] With the development of automotive technology, electric vehicles have gradually come into people's view. Compared with traditional fuel vehicles, pure electric vehicles have outstanding advantages such as zero emissions and simple driving and maintenance. However, there are still many factors restricting the development of electric vehicles, the biggest obstacle being the limited driving range of batteries, which is difficult to meet people's daily travel distance requirements.

[0003] Currently, existing battery technology is not yet mature, the construction of basic infrastructure such as charging stations still requires time, and battery status estimation is not accurate enough, making it difficult for pure electric vehicles to be charged anytime and anywhere. With limited battery energy and difficulty in timely replenishment, the battery may be depleted before reaching the destination, causing the vehicle to become stranded and unable to achieve the desired driving range. Therefore, there is an urgent need for a method to maximize the driving range of pure electric vehicles with limited energy consumption, thereby alleviating the problem of insufficient energy consumption when driving pure electric vehicles. Summary of the Invention

[0004] In view of this, this application provides a method and device for intelligent cruise control of extreme energy consumption of pure electric vehicles, which can save vehicle energy consumption during driving as much as possible and extend the driving range to the maximum extent.

[0005] The embodiments of this application disclose the following technical solutions:

[0006] In a first aspect, embodiments of this application provide a method for intelligent cruise control of extreme energy consumption for a pure electric vehicle, the method comprising:

[0007] During the intelligent driving process of the pure electric vehicle, the expected driving speed of the vehicle is determined;

[0008] Obtain road condition information for the pure electric vehicle;

[0009] Based on the road condition information, a target mode is selected from multiple modes for intelligent driving. These multiple modes include acceleration mode, constant speed mode, and deceleration mode, wherein:

[0010] The acceleration mode includes solving the optimal acceleration working point trajectory from the current speed to the expected driving speed, and allocating acceleration and constant speed mileage according to the optimal acceleration working point trajectory.

[0011] The constant speed mode includes obtaining the total mileage of the constant speed phase to the intersection, and interpolating the total mileage of the constant speed phase and the expected driving speed to obtain the motor acceleration operating point and the optimal allocation ratio.

[0012] The deceleration mode includes determining the optimal deceleration time and achieving deceleration through regenerative braking.

[0013] Optionally, the plurality of modes further includes: an interference judgment mode;

[0014] The interference judgment mode includes predicting the driving conditions of the vehicle in front and detecting the road conditions on both sides based on the road condition information, adjusting the vehicle speed to follow the vehicle in front or changing lanes; if the vehicle speed needs to be adjusted, the deceleration requirement is calculated based on the predicted driving conditions of the vehicle in front, and deceleration is achieved through regenerative braking; if the lane needs to be changed, the interference mode is exited and the mode selection is re-entered after the lane change is completed.

[0015] Optionally, selecting a target mode from multiple modes for intelligent driving based on the road condition information specifically includes:

[0016] Based on the road condition information, determine whether the road ahead is clear;

[0017] If the road ahead is blocked, select the interference judgment mode;

[0018] If the road ahead is clear, then based on the road condition information, determine whether there is a red light ahead when the distance to the intersection is preset;

[0019] If there is a red light at the intersection ahead, select the deceleration mode;

[0020] If there is no red light at the intersection ahead, select either acceleration mode or constant speed mode.

[0021] Optionally, the acceleration mode further includes: optimizing the motor operating point based on the set lower acceleration limit and the legally prescribed upper acceleration limit as the acceleration range, with the expected driving speed as the target.

[0022] Optionally, the constant speed mode further includes: when the vehicle speed is within a preset range, splitting constant speed driving into acceleration to the high-efficiency zone and low-torque coasting.

[0023] Optionally, the road condition information is obtained based on the camera, lidar, and vehicle network big data of the pure electric vehicle.

[0024] Optionally, the method further includes:

[0025] Set the vehicle's starting point and destination, and divide the driving process into segments based on intersections or obstacles according to the starting point and destination.

[0026] Secondly, embodiments of this application provide a smart cruise control device for extreme energy consumption of a pure electric vehicle, the device comprising:

[0027] The vehicle speed preset unit is used to determine the expected vehicle speed during the intelligent driving process of the pure electric vehicle.

[0028] A road condition information acquisition unit is used to acquire road condition information of the pure electric vehicle;

[0029] The mode selection unit is used to select a target mode from multiple modes for intelligent driving based on the road condition information, wherein: the acceleration mode includes solving the optimal acceleration operating point trajectory from the current speed to the expected driving speed, and allocating acceleration and constant speed mileage according to the optimal acceleration operating point trajectory; the constant speed mode includes obtaining the total mileage of the constant speed stage to the intersection, and obtaining the motor acceleration operating point and optimal allocation ratio by interpolation of the total mileage of the constant speed stage and the expected driving speed; the deceleration mode includes determining the optimal deceleration time and achieving deceleration through regenerative braking.

[0030] Therefore, this application has the following beneficial effects:

[0031] In the method provided in this application embodiment, road condition information is used to predict and optimize the motor operating point, and braking energy recovery is used to save power consumption. By solving the local energy consumption optimal solution in different modes, the ultimate energy consumption of the vehicle's intelligent cruise is achieved, maximizing the driving range and effectively alleviating the problem of insufficient energy consumption when driving pure electric vehicles. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 A flowchart illustrating the intelligent cruise control method for extreme energy consumption of a pure electric vehicle provided in this application embodiment;

[0034] Figure 2 A flowchart illustrating the selection of the extreme energy consumption intelligent cruise mode provided in the embodiments of this application;

[0035] Figure 3 A line graph showing the intelligent cruise acceleration range provided in the embodiments of this application;

[0036] Figure 4 A simulation comparison chart of vehicle speed, power, and efficiency of different acceleration methods when the set vehicle speed is 15m / s, provided for the embodiments of this application;

[0037] Figure 5A schematic diagram showing the breakdown of the motor's constant speed operating point at a certain speed, provided in an embodiment of this application.

[0038] Figure 6 This is a schematic diagram of the entire process of the extreme energy consumption intelligent driving mode provided in the embodiments of this application;

[0039] Figure 7 This is a schematic diagram of a smart cruise control device for extreme energy consumption of a pure electric vehicle provided in an embodiment of this application. Detailed Implementation

[0040] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0041] The applicant found in their research that, given the immaturity of current battery technology and the lack of adequate charging infrastructure, battery status estimation is not accurate enough, and pure electric vehicles are difficult to charge anytime and anywhere. As a result, drivers are prone to range anxiety when the vehicle's battery is low. At the same time, intelligent driving technologies have received considerable attention, and more and more mass-produced pure electric vehicles are equipped with related hardware such as lidar and cameras. The operating condition information obtained through intelligent driving hardware can be effectively used in the energy consumption optimization and control methods of pure electric vehicles.

[0042] Therefore, this application adds an Economic Intelligent Cruise Control (EICC) function to the vehicle, based on the Intelligent Cruise Control (ICC) function. With ICC enabled, the driver can choose to activate EICC. In this state, the driver sets the expected vehicle speed, and uses hardware devices such as cameras and LiDAR, as well as vehicle network big data, to acquire road condition information. Based on this information, the system predicts and optimizes the motor's operating point, utilizes regenerative braking to save energy, and solves for local energy consumption optimization in different modes, achieving global extreme energy consumption optimization for intelligent cruise control.

[0043] Based on the above ideas, this application provides a method for intelligent cruise control of extreme energy consumption for pure electric vehicles. During the intelligent driving process of the pure electric vehicle, the expected vehicle speed is determined, and road condition information is acquired. A target mode is selected from multiple modes for intelligent driving. These multiple modes include an acceleration mode, a constant speed mode, and a deceleration mode. Specifically: the acceleration mode involves solving for the optimal acceleration operating point trajectory from the current speed to the expected vehicle speed, and allocating acceleration and constant speed mileage based on the optimal acceleration operating point trajectory; the constant speed mode involves acquiring the total mileage during the constant speed phase to the intersection, and interpolating the total mileage during the constant speed phase and the expected vehicle speed to obtain the motor acceleration operating point and the optimal allocation ratio; the deceleration mode involves determining the optimal deceleration time and achieving deceleration through regenerative braking.

[0044] To facilitate understanding of the methods provided in the embodiments of this application, the following description will be provided in conjunction with the accompanying drawings.

[0045] See Figure 1 The figure is a flowchart illustrating the intelligent cruise control method for extreme energy consumption of a pure electric vehicle provided in an embodiment of this application. Figure 1 As shown, the method may include:

[0046] Step 101: During the intelligent driving process of the pure electric vehicle, determine the expected driving speed of the vehicle.

[0047] In this embodiment, during the intelligent driving process of a pure electric vehicle, that is, when the driver activates the EICC function, the driver can set the expected vehicle speed. It should be noted that the expected vehicle speed can also be set by the machine, or a system-recommended expected speed can be used; no limitation is made on the entity that sets the expected speed.

[0048] Step 102: Obtain the road condition information of the pure electric vehicle.

[0049] In some cases, road condition information for pure electric vehicles can include information such as the level of congestion on the road ahead and whether there is a red light at the intersection ahead.

[0050] In some possible implementations, road condition information can be obtained through hardware devices such as cameras and LiDAR, as well as vehicle-to-everything (V2X) big data.

[0051] Step 103: Based on the road condition information, select a target mode from multiple modes for intelligent driving.

[0052] Multiple modes include acceleration mode, constant speed mode, and deceleration mode. In some possible implementations, multiple modes may also include a disturbance mode.

[0053] Specifically, the target pattern can be selected using the following method:

[0054] Based on road condition information, determine whether the road ahead is clear; if the road ahead is not clear, select the interference judgment mode; if the road ahead is clear, determine whether there is a red light ahead when the distance to the intersection is preset based on road condition information; if there is a red light ahead, select the deceleration mode; if there is no red light ahead, select the acceleration mode or constant speed mode.

[0055] In practical applications, you can choose to determine the intersection status 200 meters before the intersection. Of course, you can also determine whether there is a red light at the intersection ahead at a greater or lesser distance, depending on the actual situation. Here, we do not impose any restrictions on the specific value of the preset distance to the intersection.

[0056] In some possible implementations, the driver can choose to disengage the extreme energy consumption intelligent cruise control; see [link to relevant documentation]. Figure 2 The figure is a flowchart of the extreme energy consumption intelligent cruise mode selection provided in the embodiment of this application. As shown in the figure, the operation performed after judging the road traffic conditions and the red light situation at the intersection is equivalent to the possible intelligent control methods of different modes.

[0057] The intelligent control methods for each mode are explained in detail below:

[0058] The acceleration mode includes solving the optimal acceleration working point trajectory from the current speed to the expected driving speed, and allocating acceleration and constant speed mileage based on the optimal acceleration working point trajectory.

[0059] Specifically, the acceleration mode also includes optimizing the motor operating point based on the set lower acceleration limit and the legally stipulated upper acceleration limit as the acceleration range, with the expected driving speed as the target.

[0060] See Figure 3 The figure is a line graph of the intelligent cruise acceleration range provided in this application embodiment. As shown in the figure, the maximum acceleration curve reflects the relationship between the maximum acceleration during cruise and the vehicle speed under the ISO22179-2019 standard. Considering the driving experience during acceleration, and to avoid slow acceleration affecting traffic, this application sets a lower limit for acceleration in each driving mode. The specific acceleration range is as follows: Figure 3 The shaded area is shown. Using the expected vehicle speed as the target and this acceleration range as a constraint, the motor operating point is optimized to consistently maintain the lowest overall motor power consumption during acceleration.

[0061] The constant speed mode includes obtaining the total mileage of the constant speed phase to the intersection, and interpolating the total mileage of the constant speed phase and the expected driving speed to obtain the motor acceleration operating point and the optimal allocation ratio.

[0062] In some possible implementations, the constant speed mode also includes, when the vehicle speed is within a preset range, splitting constant speed driving into acceleration to the high-efficiency zone and low-torque coasting.

[0063] In this embodiment, under constant speed mode, within the expected driving speed range of ±5km / h, the motor operating point is optimized to make reasonable use of the motor's efficient operating range. Simultaneously, intersection information is obtained through vehicle-to-everything (V2X) big data to ensure the lowest possible overall power consumption of the motor during the journey to the next intersection. Furthermore, the maximum speed change rate during this constant speed optimization process is set to 0.06g to avoid passenger dizziness caused by frequent acceleration and deceleration during the optimization process.

[0064] The deceleration mode includes determining the optimal deceleration time and achieving deceleration through regenerative braking.

[0065] In this embodiment, if the vehicle needs to stop at a red light at the intersection ahead, it enters deceleration mode. With the goal of minimizing energy consumption during the entire deceleration process, the regenerative braking intensity is optimized within a deceleration range of 0.1g to 0.4g (the regenerative braking deceleration of a vehicle equipped with Ibooster is approximately 0.1g, and the maximum regenerative braking deceleration is 0.4g). At the same time, the distance distribution for constant speed, coasting, and deceleration conditions is rationally planned to ensure that the vehicle comes to a complete stop at the intersection.

[0066] The interference mode includes predicting the driving conditions of the vehicle in front and detecting the road conditions on both sides based on the road condition information, adjusting the vehicle speed to follow the vehicle in front or changing lanes; if the vehicle speed needs to be adjusted, the deceleration requirement is calculated based on the predicted driving conditions of the vehicle in front, and deceleration is achieved through regenerative braking; if the lane needs to be changed, the interference mode is exited and the mode selection is re-entered after the lane change is completed.

[0067] The principles and specific implementation methods of energy consumption optimization under each mode in the embodiments of this application are described in detail below. It should be noted that the following description is only one possible implementation method of this application.

[0068] In the process of optimizing the motor operating point, the overall efficiency is first defined as the product of the motor efficiency and the gearbox transmission efficiency; secondly, the optimization objective is defined as minimizing the overall power consumption of the motor. Based on the constraints mentioned above, the vehicle dynamics model expression is established as follows:

[0069]

[0070]

[0071] Among them, Road a,b,c Here, is the vehicle's road resistance function, i is the transmission ratio, r is the wheel radius, θ is the overall motor efficiency, and E is the overall motor power consumption. The specific implementation methods for energy consumption optimization in each mode of the extreme energy consumption intelligent cruise control method are as follows:

[0072] Optimization of motor operating point in acceleration mode: The vehicle dynamics model shows that if acceleration is performed at the motor's highest efficiency operating point, the acceleration increases, and the distance traveled to the expected speed is shortened. Although energy consumption during acceleration decreases, a period of constant speed travel is still required afterward, leading to an increase in the average speed during the acceleration phase. A specific case study is provided: Assuming the expected speed is 15 m / s, the total distance during acceleration is S1 = 120 m. Three acceleration processes are simulated for comparison (the first is accelerating to the target speed with a lower acceleration limit of 0.1g; the second is accelerating to the target speed with 0.2g and then traveling at a constant speed for 120 m; the third is accelerating to the target speed with 0.25g and then traveling at a constant speed for 120 m). Figure 4 As shown in the figure, this is a simulation comparison of vehicle speed, power, and efficiency for different acceleration methods at a set vehicle speed of 15 m / s, according to an embodiment of this application. The figure shows that the first method has the longest acceleration time, resulting in the lowest average vehicle speed but also the latest arrival at the motor's efficient operating zone. The third method reaches the motor's efficient operating zone earlier, but involves a period of inefficient operation during the initial phase. The second method avoids this inefficient zone while reaching the motor's efficient operating point earlier. The results show that the first method consumes 0.0756 kWh of energy during acceleration, the third method consumes 0.0749 kWh, and the second method consumes 0.0747 kWh, representing energy savings of 1.2% and 0.27% respectively compared to the first two methods. This demonstrates that there is room for optimization in the motor's operating point during acceleration. Therefore, by combining the vehicle motor's overall efficiency and dynamic parameters, the optimal acceleration operating point trajectory of the motor at each target vehicle speed can be solved offline within the set acceleration range. This allows for the reasonable allocation of acceleration and constant speed mileage during the acceleration process, ensuring the lowest energy consumption throughout the entire acceleration process.

[0073] Optimization of motor operating point in constant speed mode: Based on the motor efficiency map, at commonly used vehicle speeds, the operating point used for constant speed driving is often not the most efficient. Therefore, within a low speed range, the original constant speed driving can be divided into two operating conditions: acceleration to the high-efficiency zone and low-torque coasting. See also Figure 5 This figure is a schematic diagram showing the breakdown of the motor's constant-speed operating point at a certain rotational speed according to an embodiment of this application. Figure 5 As can be seen, by reasonably allocating the ratio α between the two operating conditions mentioned above, the average efficiency can be improved. Therefore, based on the vehicle dynamics model, at the expected driving speed V... setWithin a range of ±5km / h and a speed change rate not exceeding 0.06g, the efficient operating point of motor acceleration and the optimal allocation ratio α are calculated offline for each set vehicle speed and constant speed mileage to ensure the lowest energy consumption in constant speed mode. First, the total mileage S of the constant speed segment to the intersection is obtained through camera and vehicle network data. Then, based on S and V... set Interpolation yields the motor's acceleration operating point and optimal allocation ratio α. By first accelerating to the high-efficiency zone and then coasting with low torque, the constant-speed driving process is completed.

[0074] Optimization in deceleration mode: Similar to acceleration mode, the braking process can optimize energy consumption during braking by rationally allocating constant speed, coasting, and braking distance. Therefore, based on the driver's expected vehicle speed V... set The optimal braking distance can be calculated offline. When the camera and vehicle network data recognize a red light at the intersection ahead, the vehicle enters the intersection braking stage. The optimal braking point is determined by interpolating the current set vehicle speed. By optimizing regenerative braking, the vehicle is brought to a stop at the intersection with minimal energy loss.

[0075] Optimization under interference mode: When the lidar and camera detect a vehicle ahead, the vehicle enters a lane-changing / braking state. The Markov chain-Monte Carlo method is used to predict the operating conditions of the vehicle ahead, and the vehicle speed is adjusted to the following speed through regenerative braking, or the vehicle changes lanes if permitted by the adjacent lanes.

[0076] In some possible implementations, the driver can also exit the EICC function at any time by pressing the brake pedal or turning the steering wheel and regain control of the vehicle.

[0077] Furthermore, in some possible implementations, the method also includes:

[0078] Set the vehicle's starting point and destination, and divide the driving process into segments based on intersections or obstacles according to the starting point and destination.

[0079] In this embodiment, the global optimization problem of minimizing energy consumption throughout the entire cruise process is computationally intensive and difficult to implement effectively from an engineering perspective. Therefore, this embodiment comprehensively considers constraints such as drivability and the driver's desired vehicle speed, setting limits on acceleration and speed. Furthermore, the entire cruise process is divided into sub-conditions based on intersections, and local energy consumption optimization solutions are found for acceleration, constant speed, and deceleration modes within each sub-condition to achieve a global optimization. Moreover, each sub-condition is further divided into three stages based on mileage for extreme energy consumption optimization:

[0080] Acceleration phase: For the corresponding acceleration mode, if the distance traveled to the set speed with minimum acceleration is S1, then the first S1 mileage of this segment is defined as the acceleration phase.

[0081] Deceleration phase: For the corresponding deceleration mode, if the distance traveled from the current speed to a stop at the minimum deceleration is S2, then the last S2 mileage of this segment is defined as the deceleration phase.

[0082] Constant speed phase: The constant speed phase is defined as the intermediate mileage remaining after subtracting the acceleration and deceleration phases from the total mileage of the segment.

[0083] The Vehicle Control Unit (VCU) optimizes the motor operating point during vehicle acceleration and constant speed driving, improving overall efficiency (motor efficiency and transmission efficiency) and reducing drive energy consumption. During vehicle deceleration, it determines the optimal deceleration moment and achieves deceleration only through regenerative braking, avoiding mechanical braking losses and improving energy recovery. In addition, the extreme energy consumption intelligent driving cruise mode is executed throughout the entire process, and the vehicle speed will also be protected by regulations.

[0084] See Figure 6 The diagram shown in this application embodiment illustrates the full-stage process of the extreme energy consumption intelligent driving mode. The starting stage corresponds to the acceleration stage in this embodiment, i.e., the control process under acceleration mode; the constant speed stage corresponds to the control process under constant speed mode; the stage with vehicles or obstacles ahead corresponds to the control process under interference mode; and the stopping stage corresponds to the control process under deceleration mode. In some possible implementations, each sub-condition can be divided into stages corresponding to different modes before driving, and local energy consumption optimization can be performed within the limited mileage of each stage to achieve a globally optimal solution.

[0085] Based on the above method embodiments, this application provides a smart cruise control device for extreme energy consumption of pure electric vehicles. See [link to relevant documentation]. Figure 7 This figure is a schematic diagram of an intelligent cruise control system for extreme energy consumption of a pure electric vehicle provided in an embodiment of this application. Figure 7 As shown, the device may include:

[0086] The vehicle speed preset unit 201 is used to determine the expected vehicle speed during the intelligent driving process of the pure electric vehicle.

[0087] The road condition information acquisition unit 202 is used to acquire the road condition information of the pure electric vehicle;

[0088] The mode selection unit 203 is used to select a target mode for intelligent driving from multiple modes based on the road condition information. The multiple modes include an acceleration mode, a constant speed mode, and a deceleration mode. The acceleration mode includes solving the optimal acceleration operating point trajectory from the current speed to the expected driving speed, and allocating acceleration and constant speed mileage according to the optimal acceleration operating point trajectory. The constant speed mode includes obtaining the total mileage of the constant speed phase to the intersection, and obtaining the motor acceleration operating point and optimal allocation ratio by interpolation of the total mileage of the constant speed phase and the expected driving speed. The deceleration mode includes determining the optimal deceleration time and achieving deceleration through regenerative braking.

[0089] It should be noted that the implementation of each unit in this embodiment can be found in the above method embodiment, and will not be repeated here.

[0090] In addition, this application embodiment also provides a device, the device including: a processor and a memory; the memory is used to store instructions; the processor is used to execute the instructions in the memory to execute the extreme energy consumption intelligent cruise control method for pure electric vehicles.

[0091] This application provides a computer-readable storage medium storing program code or instructions that, when run on a computer, cause the computer to execute the above-described intelligent cruise control method for extreme energy consumption of pure electric vehicles.

[0092] As can be seen, the embodiments of this application achieve the ultimate energy consumption of the vehicle's intelligent cruise by solving the local energy consumption optimal solution in different modes, thereby maximizing the driving range and effectively alleviating the problem of insufficient energy consumption when driving pure electric vehicles.

[0093] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems or apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple, and relevant parts can be referred to the method section.

[0094] It should also be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0095] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0096] The above description is merely a preferred embodiment of this application and is not intended to limit the application in any way. Although this application has disclosed preferred embodiments above, it is not intended to limit the application. Any person skilled in the art can make many possible variations and modifications to the technical solutions of this application using the methods and techniques disclosed above, or modify them into equivalent embodiments with equivalent changes, without departing from the scope of the technical solutions of this application. Therefore, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of this application without departing from the content of the technical solutions of this application shall still fall within the protection scope of the technical solutions of this application.

Claims

1. A method for intelligent cruise control of extreme energy consumption for pure electric vehicles, characterized in that, The method includes: During the intelligent driving process of the pure electric vehicle, the expected driving speed of the vehicle is determined; Obtain road condition information for the pure electric vehicle; Based on the road condition information, a target mode is selected from multiple modes for intelligent driving. These multiple modes include acceleration mode, constant speed mode, deceleration mode, and interference detection mode, wherein: The acceleration mode includes solving the optimal acceleration working point trajectory from the current speed to the expected driving speed, and allocating acceleration and constant speed mileage according to the optimal acceleration working point trajectory. The constant speed mode includes obtaining the total mileage of the constant speed phase to the intersection, and interpolating the total mileage of the constant speed phase and the expected driving speed to obtain the motor acceleration operating point and the optimal allocation ratio. The deceleration mode includes determining the optimal deceleration time and achieving deceleration through regenerative braking; The interference judgment mode includes predicting the driving conditions of the vehicle in front and detecting the road conditions on both sides based on the road condition information, adjusting the vehicle speed to follow the vehicle in front or changing lanes; if the vehicle speed needs to be adjusted, the deceleration requirement is calculated based on the predicted driving conditions of the vehicle in front, and deceleration is achieved through regenerative braking; if the lane needs to be changed, the interference judgment mode is exited and the mode selection is re-entered after the lane change is completed. The step of selecting a target mode from multiple modes for intelligent driving based on the road condition information specifically includes: Based on the road condition information, determine whether the road ahead is clear; If the road ahead is blocked, select the interference judgment mode; If the road ahead is clear, then based on the road condition information, determine whether there is a red light ahead when the distance to the intersection is preset; If there is a red light at the intersection ahead, select the deceleration mode; If there is no red light at the intersection ahead, select either acceleration mode or constant speed mode.

2. The method according to claim 1, characterized in that, The acceleration mode also includes: optimizing the motor operating point based on the set lower acceleration limit and the upper acceleration limit stipulated by regulations as the acceleration range, with the expected driving speed as the target.

3. The method according to claim 1, characterized in that, The constant speed mode also includes: when the vehicle speed is within a preset range, constant speed driving is divided into acceleration to the high-efficiency zone and low-torque coasting.

4. The method according to claim 1, characterized in that, The road condition information is obtained based on the camera, lidar, and vehicle network big data of the pure electric vehicle.

5. The method according to claim 1, characterized in that, The method further includes: Set the vehicle's starting point and destination, and divide the driving process into segments based on intersections or obstacles according to the starting point and destination.

6. A smart cruise control device for extreme energy consumption of a pure electric vehicle, characterized in that, The device includes: The vehicle speed preset unit is used to determine the expected vehicle speed during the intelligent driving process of the pure electric vehicle. A road condition information acquisition unit is used to acquire road condition information of the pure electric vehicle; The mode selection unit is used to select a target mode for intelligent driving from multiple modes based on the road condition information. These multiple modes include an acceleration mode, a constant speed mode, a deceleration mode, and an interference judgment mode. Specifically: the acceleration mode involves calculating the optimal acceleration operating point trajectory from the current speed to the expected driving speed, and allocating acceleration and constant speed mileage based on the optimal acceleration operating point trajectory; the constant speed mode involves obtaining the total mileage of the constant speed phase to the intersection, and interpolating the total mileage of the constant speed phase and the expected driving speed to obtain the motor acceleration operating point and the optimal allocation ratio; the deceleration mode involves determining the optimal deceleration time and achieving deceleration through regenerative braking; the interference judgment mode involves predicting the driving conditions of the vehicle in front and detecting the road conditions on both sides based on the road condition information, adjusting the vehicle speed to follow the vehicle in front or changing lanes; if speed adjustment is required, the deceleration requirement is calculated based on the predicted driving conditions of the vehicle in front and decelerated through regenerative braking; if lane changing is required, the interference judgment mode is exited and the mode selection mode is re-entered after the lane change is completed. The mode selection unit is specifically used to determine whether the road ahead is clear based on the road condition information; if the road ahead is not clear, an interference judgment mode is selected; if the road ahead is clear, a red light is determined based on the road condition information at a preset distance from the intersection; if there is a red light at the intersection ahead, a deceleration mode is selected; if there is no red light at the intersection ahead, an acceleration mode or a constant speed mode is selected.

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