Energy management method, system, computer device and readable storage medium

Abstract

The present invention provides an energy management method, system, computer device, and readable storage medium. The energy management method includes obtaining traffic information and road information to calculate a vehicle's driving strategy; constructing a first constraint condition based on the driving strategy, performing speed planning on the vehicle to obtain a corresponding optimal speed trajectory curve; and performing speed tracking on the vehicle based on energy consumption optimization to enable the vehicle to track the optimal speed trajectory curve within a predicted time domain and control the vehicle's motor energy consumption. Through this application, by introducing external information such as traffic information, road information, etc. and combining it with the underlying control of the vehicle's power transmission system, the vehicle can adapt to various road conditions and can also manage the vehicle's energy consumption in real time to improve the vehicle's energy-saving potential, thereby solving the problem of optimal non-following speed trajectory in low-density traffic scenarios.

Description

Technical Field

[0001] The present invention relates to the technical field of pure electric vehicles, and in particular to an energy management method, system, computer equipment and readable storage medium. Background Art

[0002] As the number of electric vehicles increases, the energy they consume is also increasing.

[0003] Currently, traditional energy management technologies used in mass-produced vehicles primarily focus on improving the vehicle's power source operating point, optimizing gear positions, and rationalizing the operation of various actuators. However, current research on intelligent automotive energy management has the following shortcomings:

[0004] It mainly focuses on situations where specific external information is introduced in specific scenarios. In such specific situations, the vehicle cannot fully utilize the energy-saving potential of the entire vehicle. The vehicle's own speed planning and control is only applicable to highway conditions with relatively simple external conditions. Therefore, its energy-saving potential depends to a large extent on the size of the road slope. For general straight roads, its energy-saving potential is relatively small. Summary of the Invention

[0005] Based on this, the purpose of the present invention is to provide an energy management method, system, computer device and readable storage medium to solve the deficiencies in the above-mentioned prior art.

[0006] To achieve the above objectives, the present invention provides an energy management method, the method comprising:

[0007] Obtaining traffic information and road information of the road, and calculating a driving strategy of the vehicle based on the traffic information;

[0008] Constructing a first constraint condition according to the driving strategy of the vehicle, and performing speed planning for the vehicle based on a vehicle longitudinal dynamics model, the traffic information, and the first constraint condition to obtain a corresponding optimal speed trajectory curve;

[0009] The vehicle longitudinal dynamics model, the motor power model, the optimal speed trajectory curve, and the road information are used to perform speed tracking on the vehicle based on energy consumption optimization, so that the vehicle tracks the optimal speed trajectory curve within a predicted time domain and controls the motor energy consumption of the vehicle.

[0010] Preferably, the step of performing speed planning on the vehicle based on the vehicle longitudinal dynamics model, the traffic information, and the first constraint condition to obtain a corresponding optimal speed trajectory curve includes:

[0011] Constructing a first objective function based on an energy consumption index, a desired speed index, a braking force index, and a vehicle longitudinal dynamics model;

[0012] According to the traffic information and the first constraint condition, the first objective function is optimized and solved by adopting an MPC control strategy to obtain an optimal speed trajectory curve.

[0013] Preferably, the step of performing speed tracking of the vehicle based on energy consumption optimization using the vehicle longitudinal dynamics model, the motor power model, the speed trajectory optimal curve, and the road information includes:

[0014] constructing a second objective function based on the road information and in combination with the vehicle longitudinal dynamics model and the motor power model;

[0015] The second objective function is optimized and solved based on the speed trajectory optimal curve to obtain the expected braking torque and expected driving torque of the vehicle at the current moment when energy consumption is optimal.

[0016] Preferably, the functional expression of the vehicle longitudinal dynamics model is as follows:

[0017]

[0018] in, 、 、 The calculation formulas are as follows:

[0019]

[0020]

[0021]

[0022] in, represents the vehicle acceleration, Represents the current vehicle weight, Represents the driving force, represents the mechanical efficiency of the transmission system, represents the final reducer transmission ratio, Represents the motor output torque, represents the wheel radius, represents air resistance, Represents the air resistance coefficient, represents the frontal area of ​​the vehicle, represents the air density, represents the longitudinal speed, represents the acceleration due to gravity, represents the vehicle acceleration caused by rolling resistance and slope resistance, Represents the road slope, Represents the wheel rolling resistance coefficient; Represents braking force.

[0023] Preferably, the motor power model is in the form of a polynomial function of the motor speed and motor torque of the current vehicle, and the polynomial function form is as follows:

[0024]

[0025] in, Represents the motor power, is the fitting parameter, represents the motor torque, Represents the motor speed.

[0026] Preferably, the first objective function is used to calculate the expected speed of the current vehicle at a certain moment when energy consumption is minimized. The first objective function is as follows:

[0027]

[0028] in, The calculation formula is as follows:

[0029]

[0030] in, represents minimizing the first objective function, Represents the terminal penalty term, which is related to vehicle safety. 、 、 Represent the weight factors of energy consumption, vehicle speed tracking and braking force respectively, represents the expected speed, Represents braking force, Represents the longitudinal speed.

[0031] Preferably, the second objective function is used to calculate the expected braking torque and expected driving torque of the current vehicle at a certain moment when energy consumption is minimized, and the second objective function is as follows:

[0032]

[0033] in, represents the second objective function, represents the prediction time domain, represents the expected speed, 、 Represent the weight coefficients, Represents the energy efficiency index, is the longitudinal speed, Represents the vehicle terminal speed.

[0034] To achieve the above object, the present invention further provides an energy management system, comprising:

[0035] a first acquisition module, configured to acquire traffic information and road information of a road, and calculate a driving strategy of a vehicle based on the traffic information;

[0036] a speed planning module, configured to construct a first constraint condition according to the driving strategy of the vehicle, and perform speed planning for the vehicle based on a vehicle longitudinal dynamics model, the traffic information, and the first constraint condition, so as to obtain a corresponding optimal speed trajectory curve;

[0037] A speed tracking module is used to use the vehicle longitudinal dynamics model, the motor power model, the optimal speed trajectory curve, and the road information to perform speed tracking of the vehicle based on energy consumption optimization, so that the vehicle tracks the optimal speed trajectory curve within the prediction time domain and controls the motor energy consumption of the vehicle.

[0038] To achieve the above objectives, the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above energy management method when executing the computer program.

[0039] To achieve the above objectives, the present invention further provides a readable storage medium having a computer program stored thereon, wherein the program implements the above energy management method when executed by a processor.

[0040] Compared with related technologies, the energy management method, system, computer device and readable storage medium provided in this application obtain traffic information and road information by utilizing the Internet of Vehicles environment, and calculate the vehicle's driving strategy based on the traffic information, construct a first constraint condition according to the vehicle's driving strategy, and plan the vehicle's speed based on the vehicle's longitudinal dynamics model, the traffic information and the first constraint condition to obtain the optimal speed trajectory curve, and then use the longitudinal dynamics model, the motor power model, the optimal speed trajectory curve and the road information to track the vehicle's speed based on energy consumption optimization, control the vehicle's motor energy consumption, and manage the vehicle's energy. By introducing external information such as traffic information, road information, etc., the vehicle can adapt to various road conditions, and can also manage the vehicle's energy consumption in real time to improve the vehicle's energy-saving potential, thereby solving the problem of optimal non-following speed trajectory in low-density traffic scenarios.

[0041] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0043] Figure 1 A flow chart of an energy management method provided by the first embodiment of the present invention;

[0044] Figure 2 Schematic diagrams of three vehicle speed trajectories under a single traffic light condition with no preceding vehicle provided by the first embodiment of the present invention;

[0045] Figure 3 An overall flow chart of a first specific implementation method provided for the first embodiment of the present invention;

[0046] Figure 4 Optimization flow chart of the upper layer velocity trajectory optimization module provided by the first embodiment of the present invention;

[0047] Figure 5 Optimization flow chart of the lower layer speed planning module provided by the first embodiment of the present invention;

[0048] Figure 6 A structural block diagram of an energy management method provided by a second embodiment of the present invention;

[0049] Figure 7 A schematic diagram of the hardware structure of a computer device provided in the third embodiment of the present invention.

[0050] The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solutions and advantages of this application more clearly understood, the present application is described and illustrated below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely used to explain this application and are not intended to limit this application. Based on the embodiments provided in this application, all other embodiments obtained by those of ordinary skill in the art without making any creative efforts are within the scope of protection of this application.

[0052] Obviously, the drawings described below are merely examples or embodiments of the present application. Those skilled in the art can, without inventive effort, apply the present application to other similar scenarios based on these drawings. Furthermore, it is also understood that, although the effort involved in such a development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, changes in design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as an insufficiency of the content disclosed in this application.

[0053] References to "embodiments" in this application mean that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it refer to independent or alternative embodiments that are mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described in this application may be combined with other embodiments unless there is a conflict.

[0054] Unless otherwise defined, technical or scientific terms used herein shall have the ordinary meaning as understood by persons of ordinary skill in the art to which this application belongs. The terms "a," "an," "an," "the," and similar expressions used herein do not denote quantitative limitations and may refer to either the singular or the plural. The terms "comprise," "include," "have," and any variations thereof, used herein, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or modules (units) is not limited to the listed steps or units but may also include steps or units not listed, or may include other steps or units inherent to the process, method, product, or apparatus. The terms "connected," "connected," "coupled," and similar expressions used herein are not limited to physical or mechanical connections but may include electrical connections, whether direct or indirect. As used herein, "plurality" means two or more. "And / or" describes an association between associated objects, indicating that three possible relationships exist. For example, "A and / or B" may mean: A exists alone; A and B exist simultaneously; or B exists alone. The character " / " generally indicates that the objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific order for the objects.

[0055] The first embodiment of the present application provides an energy management method, which is based on the prediction of future road conditions based on navigation, precision maps and complex road traffic environments. It comprehensively considers the impact of future traffic information, road information, etc. on the current vehicle's driving economy, and starts from improving driving decisions and behaviors, especially operating vehicle drive, braking and gear, and reasonably matching the relationship between current vehicle movement and road conditions, traffic status and vehicle performance, so as to achieve the purpose of energy conservation and emission reduction while meeting travel requirements.

[0056] Figure 1 is a flow chart of the energy management method according to an embodiment of the present application. Figure 1 As shown, the flowchart includes the following steps:

[0057] Step S101, obtaining traffic information and road information of the road, and calculating the driving strategy of the vehicle based on the traffic information;

[0058] The traffic and road information is obtained through V2X (vehicle to everything), which stands for vehicle-to-everything information exchange. V2X operates within the context of the Internet of Vehicles (IoV), enabling communication between vehicles, between vehicles and base stations, and between base stations. This allows for real-time traffic information, including road conditions, pedestrian information, and more.

[0059] The traffic information includes signal light timing (also known as traffic light timing), road section speed limit requirements, etc. The road information can be collected through the high-precision map in V2X, and the road information includes road slope, road curvature and other information.

[0060] Step S102: constructing a first constraint condition according to the driving strategy of the vehicle, and performing speed planning for the vehicle based on the vehicle longitudinal dynamics model, the traffic information, and the first constraint condition to obtain a corresponding optimal speed trajectory curve;

[0061] Among them, the current vehicle is a pure electric vehicle, and its power source is an electric motor. Therefore, the vehicle longitudinal dynamics model and the motor power model are constructed based on the configuration information of the current vehicle. It should be noted that the configuration information includes the current vehicle weight, the current vehicle's motor transmission mechanical efficiency, the main reducer transmission ratio, the wheel radius, etc.

[0062] Step S103 , performing speed tracking on the vehicle based on energy consumption optimization using the vehicle longitudinal dynamics model, the motor power model, the optimal speed trajectory curve, and the road information, so that the vehicle tracks the optimal speed trajectory curve within a predicted time domain and controls the motor energy consumption of the vehicle.

[0063] Through the above steps, the Internet of Vehicles environment is used to obtain traffic information and road information, and the vehicle's driving strategy is calculated based on the traffic information. The first constraint condition is constructed according to the vehicle's driving strategy, and the vehicle's speed is planned based on the vehicle's longitudinal dynamics model, the traffic information and the first constraint condition to obtain the optimal speed trajectory curve. The longitudinal dynamics model, the motor power model, the optimal speed trajectory curve and the road information are then used to track the vehicle's speed based on energy consumption optimization, and the vehicle's motor energy consumption is controlled to manage the vehicle's energy. By introducing external information such as traffic information and road information, the vehicle can adapt to various road conditions and can also manage the vehicle's energy consumption in real time to enhance the vehicle's energy-saving potential, thereby solving the problem of optimal non-following speed trajectory in low-density traffic scenarios.

[0064] In some embodiments, the step of performing speed planning for the vehicle based on the vehicle longitudinal dynamics model, the traffic information, and the first constraint to obtain a corresponding optimal speed trajectory curve includes:

[0065] Constructing a first objective function based on an energy consumption index, a desired speed index, a braking force index, and a vehicle longitudinal dynamics model;

[0066] Among them, the energy consumption index, the expected speed index and the braking force index are formulated based on actual needs. Both speed and braking force have an impact on vehicle energy consumption. This application needs to achieve the purpose of energy conservation and emission reduction, so the current vehicle speed and braking force need to be monitored.

[0067] It should be noted that the first objective function is an objective function for minimizing energy consumption.

[0068] According to the traffic information and the first constraint condition, the first objective function is optimized and solved by adopting an MPC control strategy to obtain an optimal speed trajectory curve.

[0069] MPC is a multivariable control strategy. Specifically, the first constraint condition is used as a prediction interval, and the first objective function is optimized to calculate and output the optimal speed trajectory curve.

[0070] In some embodiments, the step of performing energy-optimized speed tracking on the vehicle using the vehicle longitudinal dynamics model, the motor power model, the speed trajectory optimal curve, and the road information includes:

[0071] constructing a second objective function based on the road information and in combination with the vehicle longitudinal dynamics model and the motor power model;

[0072] The second objective function is optimized and solved based on the speed trajectory optimal curve to obtain the expected braking torque and expected driving torque of the vehicle at the current moment when energy consumption is optimal.

[0073] Among them, the second objective function is defined as an objective function for minimizing energy consumption. The second objective function is combined with actual road conditions and the underlying control of the vehicle transmission system to output effective driving torque and braking torque to the current vehicle, so that the current vehicle can achieve the purpose of optimal energy saving and emission reduction.

[0074] Compared with the traditional rule-based control strategy in the prior art, which does not take external intelligent information into consideration, in this embodiment, by combining the speed optimization strategy with the underlying control of the vehicle powertrain system, the vehicle energy management system can reduce the energy consumption of the entire vehicle by 15% to 20%.

[0075] It should be noted that the above steps consider the problem of passing a single signal light without a preceding vehicle, and the corresponding time-space relationship is as follows: Figure 2 The schematic diagram of three vehicle speed trajectories under the working condition of a single signal light without a preceding vehicle is shown in Figure 2 In the figure, we assume that the speed trajectory is divided into three phases: acceleration, cruising, and deceleration. The figure shows three different vehicle speed trajectories, all starting from the same starting point and with the same initial speed. When a vehicle is traveling at a traffic light, if maintaining its current speed prevents it from passing the intersection within the green light phase, it is recommended to accelerate or decelerate in advance (as shown in speed trajectories 2 and 3).

[0076] It is understandable that during the vehicle's driving process, the maximum permissible speed of the vehicle is defined. Expected speed, Represents the average speed in the predicted time domain, and the remaining green light time is , the distance from the vehicle to the next intersection is d, and the longitudinal speed is .

[0077] Please refer to speed trajectory 2 in the above diagram. If the vehicle can pass the intersection within the green light period while maintaining the current maximum permitted speed, the vehicle will maintain or increase its average speed to pass the intersection within the green light period, but the speed must be within the maximum permitted speed range: Less than or equal to ;

[0078] Please refer to the speed trajectory 3 in the above figure. If the vehicle maintains the current maximum allowed speed and cannot pass through the intersection within the green waiting time period, that is, If it is less than or equal to d, the vehicle needs to slow down in advance to pass the intersection during the next green light phase.

[0079] It can be understood that the optimal speed trajectory curve can refer to the above illustration.

[0080] In some embodiments, the first constraint condition includes a terminal constraint, a road constraint, and a signal light timing constraint, and the first constraint condition is used to constrain the first objective function;

[0081] The road constraint is the speed limit condition in the current time domain, that is, the minimum and maximum speeds required for driving in the current time domain. The signal light timing constraint is the time when the signal light at the next intersection changes. The terminal constraint is the terminal value of the designed time, position, and speed. It is expressed as a constraint control problem with fixed terminal time and state constraints. Specifically, the first constraint is expressed in the following form:

[0082]

[0083]

[0084]

[0085] ,

[0086] in, and Respectively represent the minimum form speed and maximum form speed in the current time domain, represents the terminal penalty term, represents the vehicle speed at the terminal moment, represents the target vehicle speed at the terminal moment, represents the terminal moment displacement, represents the target displacement at the terminal moment, represents the vehicle speed at the initial moment, Represents the velocity trajectory in the predicted time domain, represents the displacement trajectory in the predicted time domain, Indicates the minimum driving force that the vehicle can output. Represents the motor driving force at the current moment, Indicates the maximum driving force that the vehicle can output. Represents the motor driving force at the current moment, Indicates the maximum braking force the vehicle can output.

[0087] The method further comprises:

[0088] Acquire the status information of the current vehicle according to the vehicle CAN bus;

[0089] The status information includes vehicle longitudinal information, vehicle position information, motor speed information, motor torque information and braking torque information.

[0090] A second constraint condition is constructed based on the state information, where the second constraint condition includes a motor speed constraint and a torque constraint, and the second constraint condition is used to constrain the second objective function.

[0091] The second constraint is expressed in the following form:

[0092]

[0093]

[0094]

[0095] in, and Respectively represent the minimum and maximum values ​​of the vehicle speed allowed. and Respectively represent the minimum and maximum values ​​of the motor speed, and Represent the minimum and maximum output torque of the motor respectively.

[0096] In some embodiments, the functional expression of the vehicle longitudinal dynamics model is as follows:

[0097]

[0098] in, 、 、 The calculation formulas are as follows:

[0099]

[0100]

[0101]

[0102] in, represents the vehicle acceleration, Represents the current vehicle weight, Represents the driving force, represents the mechanical efficiency of the transmission system, represents the final reducer transmission ratio, Represents the motor output torque, represents the wheel radius, represents air resistance, Represents the air resistance coefficient, represents the frontal area of ​​the vehicle, represents the air density, represents the longitudinal speed, represents the acceleration due to gravity, represents the vehicle acceleration caused by rolling resistance and slope resistance, Represents the road slope, Represents the wheel rolling resistance coefficient; Represents braking force.

[0103] In some embodiments, the motor power model is in the form of a polynomial function of the motor speed and motor torque of the current vehicle, and the polynomial function is as follows:

[0104]

[0105] in, Represents the motor power, is the fitting parameter, represents the motor torque, Represents the motor speed.

[0106] In some embodiments, the first objective function is used to calculate the expected speed of the current vehicle at a certain moment when energy consumption is minimized. The first objective function is as follows:

[0107]

[0108] in, The calculation formula is as follows:

[0109]

[0110] in, represents minimizing the first objective function, Represents the terminal penalty term, which is related to vehicle safety. 、 、 Represent the weight factors of energy consumption, vehicle speed tracking and braking force respectively, represents the expected speed, Represents braking force, Represents the longitudinal speed.

[0111] In some embodiments, the second objective function is used to calculate the expected braking torque and the expected driving torque of the current vehicle at a certain moment when energy consumption is minimized. The second objective function is as follows:

[0112]

[0113] in, represents the second objective function, represents the prediction time domain, represents the expected speed, 、 Represent the weight coefficients, Represents the energy efficiency index, is the longitudinal speed, Represents the vehicle speed at the terminal moment.

[0114] In some embodiments, such as Figure 3 As shown, this is a specific implementation of the first embodiment of the present application. The summary is that, by utilizing traffic information such as signal light timing and speed limit and road information such as slope and curvature in the vehicle network environment, and considering factors such as the pure electric vehicle motor power model, driving strategy, and vehicle speed tracking performance, a hierarchical predictive energy management method is proposed to perform pure electric vehicle energy management control from two levels: the upper reference speed trajectory optimization module and the lower speed tracking module.

[0115] It should be noted that traffic information such as traffic light timing and speed limits are collected through V2X, and road information such as slope and curvature are collected through high-precision maps, and both traffic information and road information are transmitted to the vehicle.

[0116] It should be noted that the upper-level speed trajectory optimization module considers the non-following vehicle condition in the low-density traffic scene. The goal is to find the optimal speed trajectory curve to reduce energy consumption, and output it as the reference speed to the lower-level speed tracking module. In the upper-level speed trajectory optimization module, traffic information such as traffic light timing and speed limit are used to make decisions on driving strategies, and the speed trajectory optimization problem composed of dynamic constraints, driving strategies, energy consumption minimum objective function and other conditions is solved to obtain the speed trajectory, which is output to the lower-level speed tracking module. For details, please refer to Figure 4 Shown is the optimization flow chart of the upper-level speed trajectory optimization module;

[0117] The lower-level speed tracking module considers the non-following vehicle condition in low-density traffic scenarios. The goal is to minimize the energy consumption of the pure electric vehicle motor in the prediction time domain and track the reference speed trajectory well. In the lower-level speed tracking module, the road information such as slope and curvature is used to solve the speed tracking rolling optimization problem, so that the pure electric vehicle motor energy consumption in the prediction time domain is minimized and the reference speed trajectory is tracked well. For details, please refer to Figure 5 Shown is the optimization flow chart of the lower-level speed planning module.

[0118] It should be noted that the steps shown in the above process or the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0119] The second embodiment of the present application also provides an energy management system, which is used to implement the above-mentioned first embodiment and preferred implementation mode, and will not be repeated here. As used below, the terms "module", "unit", "sub-unit", etc. can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0120] Figure 6 is a structural block diagram of the energy management system according to the second embodiment of the present application, such as Figure 3 As shown, the system includes:

[0121] A first acquisition module 10 is used to acquire traffic information and road information of the road, and calculate the driving strategy of the vehicle based on the traffic information;

[0122] a speed planning module 20 for constructing a first constraint condition according to the driving strategy of the vehicle, and performing speed planning for the vehicle based on a vehicle longitudinal dynamics model, the traffic information, and the first constraint condition to obtain a corresponding optimal speed trajectory curve;

[0123] The speed tracking module 30 is used to use the vehicle longitudinal dynamics model, the motor power model, the optimal speed trajectory curve and the road information to perform speed tracking of the vehicle based on energy consumption optimization, so that the vehicle tracks the optimal speed trajectory curve within the prediction time domain and controls the motor energy consumption of the vehicle.

[0124] Through the above steps, the Internet of Vehicles environment is used to obtain traffic information and road information, and the vehicle's driving strategy is calculated based on the traffic information. The first constraint condition is constructed according to the vehicle's driving strategy, and the vehicle's speed is planned based on the vehicle's longitudinal dynamics model, the traffic information and the first constraint condition to obtain the optimal speed trajectory curve. The longitudinal dynamics model, the motor power model, the optimal speed trajectory curve and the road information are then used to track the vehicle's speed based on energy consumption optimization, and the vehicle's motor energy consumption is controlled to manage the vehicle's energy. By introducing external information such as traffic information and road information, the vehicle can adapt to various road conditions and can also manage the vehicle's energy consumption in real time to enhance the vehicle's energy-saving potential, thereby solving the problem of optimal non-following speed trajectory in low-density traffic scenarios.

[0125] In some embodiments, the speed planning module 20 includes:

[0126] A first construction unit is configured to construct a first objective function based on an energy consumption index, a desired speed index, a braking force index, and a vehicle longitudinal dynamics model;

[0127] The first optimization unit is used to optimize and solve the first objective function according to the traffic information and the first constraint condition by adopting an MPC control strategy to obtain an optimal speed trajectory curve.

[0128] In some embodiments, the speed tracking module 30 includes:

[0129] A second construction unit is configured to construct a second objective function based on the road information and in combination with the vehicle longitudinal dynamics model and the motor power model;

[0130] The second optimization unit is used to optimize and solve the second objective function based on the speed trajectory optimal curve to obtain the expected braking torque and expected driving torque of the vehicle at the current moment when energy consumption is optimal.

[0131] In some embodiments, the first constraint condition includes a terminal constraint, a road constraint, and a signal light timing constraint, and the first constraint condition is used to constrain the first objective function;

[0132] The system further comprises:

[0133] The second acquisition module is used to obtain the status information of the current vehicle according to the vehicle CAN bus, and construct a second constraint condition based on the status information. The second constraint condition includes a motor speed constraint and a torque constraint. The second constraint condition is used to constrain the second objective function.

[0134] In some embodiments, the functional expression of the vehicle longitudinal dynamics model is as follows:

[0135]

[0136] in, 、 、 The calculation formulas are as follows:

[0137]

[0138]

[0139]

[0140] in, represents the vehicle acceleration, Represents the current vehicle weight, Represents the driving force, represents the mechanical efficiency of the transmission system, represents the final reducer transmission ratio, Represents the motor output torque, represents the wheel radius, represents air resistance, Represents the air resistance coefficient, represents the frontal area of ​​the vehicle, represents the air density, represents the longitudinal speed, represents the acceleration due to gravity, represents the vehicle acceleration caused by rolling resistance and slope resistance, Represents the road slope, Represents the wheel rolling resistance coefficient; Represents braking force.

[0141] In some embodiments, the motor power model is in the form of a polynomial function of the motor speed and motor torque of the current vehicle, and the polynomial function is as follows:

[0142]

[0143] in, Represents the motor power, is the fitting parameter, represents the motor torque, Represents the motor speed.

[0144] In some embodiments, the first objective function is used to calculate the expected speed of the current vehicle at a certain moment when energy consumption is minimized. The first objective function is as follows:

[0145]

[0146] in, The calculation formula is as follows:

[0147]

[0148] in, represents minimizing the first objective function, Represents the terminal penalty term, which is related to vehicle safety. 、 、 Represent the weight factors of energy consumption, vehicle speed tracking and braking force respectively, represents the expected speed, Represents braking force, Represents the longitudinal speed.

[0149] In some embodiments, the second objective function is used to calculate the expected braking torque and the expected driving torque of the current vehicle at a certain moment when energy consumption is minimized. The second objective function is as follows:

[0150]

[0151] in, represents the second objective function, represents the prediction time domain, represents the expected speed, 、 Represent the weight coefficients, Represents the energy efficiency index, is the longitudinal speed, Represents the vehicle speed at the terminal moment.

[0152] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0153] In addition, combined Figure 1 The energy management method described in the embodiment of the present application can be implemented by a computer device. Figure 7 Schematic diagram of the hardware structure of a computer device according to the third embodiment of the present application.

[0154] The computer device may include a processor 32 and a memory 33 storing computer program instructions.

[0155] Specifically, the processor 32 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0156] Memory 33 may include a large-capacity memory for data or instructions. By way of example, and not limitation, memory 33 may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 33 may include removable or non-removable (or fixed) media. Where appropriate, memory 33 may be internal or external to the data processing device. In certain embodiments, memory 33 is non-volatile memory. In certain embodiments, memory 33 includes read-only memory (ROM) and random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM) or a flash memory (FLASH), or a combination of two or more of these. Under appropriate circumstances, the RAM can be a static random access memory (SRAM) or a dynamic random access memory (DRAM), where the DRAM can be a fast page mode dynamic random access memory (FPMDRAM), an extended data out dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0157] The memory 33 may be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor 32 .

[0158] The processor 32 implements any one of the energy management methods in the above embodiments by reading and executing computer program instructions stored in the memory 33 .

[0159] In some embodiments, the computer device may further include a communication interface 34 and a bus 31. Figure 7 As shown, the processor 32 , the memory 33 , and the communication interface 34 are connected via a bus 31 and communicate with each other.

[0160] The communication interface 34 is used to implement communication between the various modules, devices, units, and / or devices in the embodiments of the present application. The communication interface 34 can also implement data communication with other components such as external devices, image / data acquisition devices, databases, external storage, and image / data processing workstations.

[0161] Bus 31 includes hardware, software, or both, and couples components of a computer device to each other. Bus 31 includes, but is not limited to, at least one of the following: a data bus, an address bus, a control bus, an expansion bus, and a local bus. By way of example and not limitation, bus 31 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 31 may include one or more buses. Although embodiments herein describe and illustrate a particular bus, this application contemplates any suitable bus or interconnect.

[0162] The computer device can execute the energy management method in the embodiment of the present application based on the acquired computer program, thereby realizing the combination Figure 1 Describe the energy management approach.

[0163] In addition, in conjunction with the energy management method in the above embodiments, the present application can provide a readable medium for implementation. The readable medium stores computer program instructions; when the computer program instructions are executed by a processor, any one of the energy management methods in the above embodiments is implemented.

[0164] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0165] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. An energy management method, characterized in that: The method comprises: Obtaining traffic information and road information of the road, and calculating a driving strategy of the vehicle based on the traffic information; Constructing a first constraint condition according to the driving strategy of the vehicle, and performing speed planning for the vehicle based on a vehicle longitudinal dynamics model, the traffic information, and the first constraint condition to obtain a corresponding optimal speed trajectory curve; performing speed tracking on the vehicle based on energy consumption optimization using the vehicle longitudinal dynamics model, the motor power model, the optimal speed trajectory curve, and the road information, so that the vehicle tracks the optimal speed trajectory curve within a prediction time domain and controls motor energy consumption of the vehicle; The step of performing speed planning on the vehicle based on the vehicle longitudinal dynamics model, the traffic information, and the first constraint condition to obtain a corresponding optimal speed trajectory curve includes: Constructing a first objective function based on an energy consumption index, an expected speed index, a braking force index, and a vehicle longitudinal dynamics model, wherein the first objective function is used to calculate an expected speed of the vehicle at a certain moment when energy consumption is minimized; According to the traffic information and the first constraint condition, the first objective function is optimized and solved by using an MPC control strategy to obtain an optimal speed trajectory curve; The step of performing energy consumption optimization-based speed tracking on the vehicle by utilizing the vehicle longitudinal dynamics model, the motor power model, the speed trajectory optimal curve, and the road information comprises: constructing a second objective function based on the road information and in combination with the vehicle longitudinal dynamics model and the motor power model; The second objective function is optimized and solved based on the optimal speed trajectory curve to obtain the expected braking torque and expected driving torque of the vehicle at the current moment when energy consumption is optimal. The second objective function is used to calculate the expected braking torque and expected driving torque of the vehicle at a certain moment when energy consumption is minimized. The second objective function is as follows: in, represents the second objective function, represents the prediction time domain, represents the expected speed, 、 Represent the weight coefficients, Represents the energy efficiency index, is the longitudinal speed, Represents the vehicle terminal speed; The motor power model is a polynomial function of the motor speed and motor torque of the vehicle, and the polynomial function is as follows: in, Represents the motor power, is the fitting parameter, represents the motor torque, Represents the motor speed.

2. The energy management method according to claim 1, characterized in that: The functional expression of the vehicle longitudinal dynamics model is as follows: in, 、 、 The calculation formulas are as follows: in, represents the vehicle acceleration, Represents the current vehicle weight, Represents the driving force, represents the mechanical efficiency of the transmission system, represents the final reducer transmission ratio, Represents the motor output torque, represents the wheel radius, represents air resistance, Represents the air resistance coefficient, represents the frontal area of ​​the vehicle, represents the air density, represents the longitudinal speed, represents the acceleration due to gravity, represents the vehicle acceleration caused by rolling resistance and slope resistance, Represents the road slope, Represents the wheel rolling resistance coefficient; Represents braking force.

3. The energy management method according to claim 1, characterized in that: The first objective function is as follows: in, The calculation formula is as follows: in, represents minimizing the first objective function, Represents the terminal penalty term, which is related to vehicle safety. 、 、 Represent the weight factors of energy consumption, vehicle speed tracking and braking force respectively, represents the expected speed, Represents braking force, Represents the longitudinal speed.

4. An energy management system, characterized in that: include: a first acquisition module, configured to acquire traffic information and road information of a road, and calculate a driving strategy of a vehicle based on the traffic information; a speed planning module, configured to construct a first constraint condition according to the driving strategy of the vehicle, and perform speed planning for the vehicle based on a vehicle longitudinal dynamics model, the traffic information, and the first constraint condition, so as to obtain a corresponding optimal speed trajectory curve; a speed tracking module, configured to perform speed tracking of the vehicle based on energy consumption optimization using the vehicle longitudinal dynamics model, the motor power model, the optimal speed trajectory curve, and the road information, so that the vehicle tracks the optimal speed trajectory curve within a prediction time domain and controls the motor energy consumption of the vehicle; The speed planning module includes: a first constructing unit, configured to construct a first objective function based on an energy consumption index, an expected speed index, a braking force index, and a vehicle longitudinal dynamics model, wherein the first objective function is configured to calculate an expected speed of the vehicle at a certain moment when energy consumption is minimized; a first optimization unit, configured to optimize and solve the first objective function according to the traffic information and the first constraint condition by adopting an MPC control strategy to obtain an optimal speed trajectory curve; The speed tracking module includes: A second construction unit is configured to construct a second objective function based on the road information and in combination with the vehicle longitudinal dynamics model and the motor power model; a second optimization unit, configured to optimize and solve the second objective function based on the speed trajectory optimal curve to obtain an expected braking torque and an expected driving torque of the vehicle at a current moment when energy consumption is optimal; The second objective function is used to calculate the expected braking torque and expected driving torque of the vehicle at a certain moment when energy consumption is minimized. The second objective function is as follows: in, represents the second objective function, represents the prediction time domain, represents the expected speed, 、 Represent the weight coefficients, Represents the energy efficiency index, is the longitudinal speed, Represents the vehicle terminal speed; The motor power model is a polynomial function of the motor speed and motor torque of the vehicle, and the polynomial function is as follows: in, Represents the motor power, is the fitting parameter, represents the motor torque, Represents the motor speed.

5. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the energy management method according to any one of claims 1 to 3 is implemented.

6. A readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the energy management method according to any one of claims 1 to 3 is implemented.

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