CN115352285A - Energy management method, system, computer device and readable storage medium - Google Patents

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

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CN115352285A
CN115352285A CN202211037289.5A CN202211037289A CN115352285A CN 115352285 A CN115352285 A CN 115352285A CN 202211037289 A CN202211037289 A CN 202211037289A CN 115352285 A CN115352285 A CN 115352285A
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vehicle
speed
energy consumption
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energy management
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洪金龙
高炳钊
刘登程
曾德全
胡一明
冯挽强
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Nanchang Intelligent New Energy Vehicle Research Institute
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L15/00—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles
    • B60L15/20—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles for control of the vehicle or its driving motor to achieve a desired performance, e.g. speed, torque, programmed variation of speed
    • B60L15/2045—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles for control of the vehicle or its driving motor to achieve a desired performance, e.g. speed, torque, programmed variation of speed for optimising the use of energy
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
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    • B—PERFORMING OPERATIONS; TRANSPORTING
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    • B60L2240/42—Drive Train control parameters related to electric machines
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/40—Drive Train control parameters
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Abstract

The invention provides an energy management method, a system, computer equipment and a readable storage medium, wherein the energy management method comprises the steps of acquiring traffic information and road information, and calculating a driving strategy of a vehicle; constructing a first constraint condition according to a driving strategy, and carrying out speed planning on the vehicle to obtain a corresponding speed track optimal curve; and carrying out speed tracking based on energy consumption optimization on the vehicle so as to enable the vehicle to track the optimal speed track curve in the prediction time domain and control the motor energy consumption of the vehicle. Through this application, through introducing external information for example traffic information, road information etc. and combine together with vehicle power transmission system bottom control for the vehicle can adapt to multiple road conditions, and can also manage the vehicle energy consumption in real time, with the energy-conserving potentiality that promotes the vehicle, has solved the non-with the optimal problem of car speed orbit in the low density traffic sight.

Description

Energy management method, system, computer device and readable storage medium
Technical Field
The invention relates to the technical field of pure electric vehicles, in particular to an energy management method, an energy management system, computer equipment and a readable storage medium.
Background
New energy automobiles, particularly pure electric automobiles, are the key development industries of China, and have been paid attention to and accepted by the national strategic level. However, as the holding capacity of the electric vehicle increases, the consumed energy of the electric vehicle is more and more, and it is estimated that the electricity consumption of the electric vehicle accounts for more than 7% of the total electricity generation amount nationwide in the year by 2031, wherein about 80% of the electricity consumption is pure electric vehicles. Therefore, the energy efficiency of the electric automobile is continuously improved, and the basis of sustainable development of the electric automobile is provided.
At present, the traditional energy management technology actually applied to mass production level automobiles mainly aims at improving the working point of a vehicle power source, optimizing gears, rationalizing the operation of each actuator and the like. However, the current research on intelligent energy management of automobiles mainly has the following disadvantages:
the method mainly focuses on the situation that specific external information is introduced in a specific scene, under the specific situation, the vehicle cannot fully exert the energy-saving potential of the whole vehicle, and the speed planning control of the vehicle is only suitable for highway working conditions with single external conditions, so that the energy-saving potential of the vehicle depends on the gradient of a road to a great extent, and the energy-saving potential of the vehicle is small for a common straight road.
Disclosure of Invention
Based on this, it is an object of the present invention to provide an energy management method, system, computer device and readable storage medium to solve the above-mentioned deficiencies in the prior art.
To achieve the above object, the present invention provides an energy management method, including:
acquiring traffic information and road information of a road, and calculating a driving strategy of a vehicle based on the traffic information;
constructing a first constraint condition according to the driving strategy of the vehicle, and carrying out speed planning on the vehicle based on a vehicle longitudinal dynamic model, the traffic information and the first constraint condition to obtain a corresponding speed track optimal curve;
and carrying out speed tracking based on energy consumption optimization on the vehicle by utilizing the vehicle longitudinal dynamics model, the motor power model, the speed track optimal curve and the road information so as to enable the vehicle to track the speed track optimal curve in a prediction time domain and control the motor energy consumption of the vehicle.
Preferably, the step of speed planning the vehicle based on the vehicle longitudinal dynamics model, the traffic information and the first constraint condition to obtain the corresponding speed trajectory optimal curve includes:
constructing a first objective function based on the energy consumption index, the expected speed index, the braking force index and the vehicle longitudinal dynamics model;
and according to the traffic information and the first constraint condition, optimizing and solving the first objective function by adopting an MPC control strategy to obtain a speed trajectory optimal curve.
Preferably, the step of performing energy consumption optimization-based speed tracking on the vehicle by using the vehicle longitudinal dynamics model, the motor power model, the speed trajectory optimal curve and the road information comprises:
constructing a second objective function according to the road information and by combining the vehicle longitudinal dynamics model and the motor power model;
and carrying out optimization solution on the second objective function based on the speed track optimal curve to obtain the expected braking torque and the expected driving torque of the vehicle at the current moment when the energy consumption is optimal.
Preferably, the functional expression of the vehicle dynamics model is as follows:
Figure BDA0003817791030000021
wherein, F t (t)、F f (t)、c r The calculation formula of g is as follows:
Figure BDA0003817791030000022
Figure BDA0003817791030000023
c r g＝fcos(α(t))+sin(α(t))
wherein a (t) represents vehicle acceleration, M represents the weight of the current vehicle, F t (t) represents driving force, η represents mechanical efficiency of the power train, i f Representing the main reducer transmission ratio, T m (t) represents motor output torque, r w Representing the radius of the wheel, F f (t) represents air resistance, C D Is represented by an air resistance coefficient, A represents the frontal area of the vehicle, ρ represents the air density, v (t) represents the longitudinal running speed, g represents the gravitational acceleration, c r g represents vehicle acceleration due to rolling resistance and grade resistance, α (t) represents road grade, and f represents wheel rolling resistance coefficient; f b (t) represents braking force.
Preferably, the motor power model is a polynomial function of the motor speed and the motor torque of the current vehicle, and the polynomial function is as follows:
Figure BDA0003817791030000031
wherein P represents the motor power, b ij In order to fit the parameters of the image,
Figure BDA0003817791030000032
which represents the torque of the electric motor,
Figure BDA0003817791030000033
representing the motor speed.
Preferably, the first objective function is used for calculating the expected speed of the current vehicle at a certain time when the energy consumption is minimum, and the first objective function is as follows:
Figure BDA0003817791030000034
wherein, the calculation formula of L (x, u, t') is as follows:
Figure BDA0003817791030000035
wherein minJ represents a minimum first objective function, φ (x (T + T)) represents a terminal penalty term, and is related to vehicle safety, and w 1 、w 2 、w 3 Weighting factors, v, representing energy consumption, vehicle speed tracking and braking force, respectively r Representing desired speed, F b Representing the braking force and v representing the longitudinal travel speed.
Preferably, the second objective function is used for calculating the expected braking torque and the expected driving torque of the current vehicle at a certain time when the energy consumption is minimum, and the second objective function is as follows:
Figure BDA0003817791030000036
wherein J represents a second objective function, N represents a prediction time domain, v d Represents the desired velocity, κ 1 、κ 2 Respectively representing a weight coefficient, P delta t representing an energy consumption efficiency index, v (t) representing a longitudinal running speed, and v (N) representing a vehicle terminal time speed.
To achieve the above object, the present invention also provides an energy management system, including:
the first acquisition module is used for acquiring traffic information and road information of a road and calculating a driving strategy of a vehicle based on the traffic information;
the speed planning module is used for constructing a first constraint condition according to the driving strategy of the vehicle and planning the speed of the vehicle on the basis of a vehicle longitudinal dynamic model, the traffic information and the first constraint condition so as to obtain a corresponding speed track optimal curve;
and the speed tracking module is used for carrying out speed tracking based on energy consumption optimization on the vehicle by utilizing the vehicle longitudinal dynamics model, the motor power model, the speed track optimal curve and the road information so as to enable the vehicle to track the speed track optimal curve in a prediction time domain and control the motor energy consumption of the vehicle.
To achieve the above object, the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the energy management method when executing the computer program.
To achieve the above object, the present invention further provides a readable storage medium having a computer program stored thereon, wherein the computer program is configured to implement the energy management method described above when executed by a processor.
Compared with the related art, the energy management method, the system, the computer device and the readable storage medium provided by the application can acquire traffic information and road information by using a vehicle networking environment, calculate the driving strategy of a vehicle based on the traffic information, construct a first constraint condition according to the driving strategy of the vehicle, plan the speed of the vehicle based on a vehicle longitudinal dynamics model, the traffic information and the first constraint condition to obtain an optimal speed track curve, track the speed of the vehicle based on energy consumption optimization by using the longitudinal dynamics model, a motor power model, the optimal speed track curve and the road information, control the energy consumption of a motor of the vehicle to manage the energy of the vehicle, enable the vehicle to adapt to various road conditions by introducing external information such as the traffic information, the road information and the like, manage the energy consumption of the vehicle in real time to improve the energy saving potential of the vehicle, and solve the problem of non-following optimal speed track in a low-density traffic situation.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a flow chart of a method for energy management according to a first embodiment of the present invention;
FIG. 2 is a schematic diagram of three vehicle speed traces under a single signal light condition without a front vehicle according to a first embodiment of the invention;
FIG. 3 is a flowchart illustrating the first embodiment of the present invention;
fig. 4 is an optimization flowchart of an upper-layer velocity trajectory optimization module according to a first embodiment of the present invention;
FIG. 5 is a flow chart illustrating the optimization of the lower-layer speed planning module according to the first embodiment of the present invention;
FIG. 6 is a block diagram illustrating an energy management method according to a second embodiment of the present invention;
fig. 7 is a schematic hardware structure diagram of a computer device according to a third embodiment of the present invention.
The following detailed description will further illustrate the invention in conjunction with the above-described figures.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments provided in the present application without any inventive step are within the scope of protection of the present application.
It is obvious that the drawings in the following description are only examples or embodiments of the present application, and that it is also possible for a person skilled in the art to apply the present application to other similar contexts on the basis of these drawings without inventive effort. Moreover, it should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of ordinary skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms referred to herein shall have the ordinary meaning as understood by those of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar words throughout this application are not to be construed as limiting in number, and may refer to the singular or the plural. The present application is directed to the use of the terms "including," "comprising," "having," and any variations thereof, which are intended to cover non-exclusive inclusions; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to only those steps or elements but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. Reference to "connected," "coupled," and the like in this application is not intended to be limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as used herein means two or more. "and/or" describes an association relationship of associated objects, meaning that three relationships may exist, for example, "A and/or B" may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. Reference herein to the terms "first," "second," "third," and the like, are merely to distinguish similar objects and do not denote a particular ordering for the objects.
The first embodiment of the application provides an energy management method, which is based on the prediction of future road conditions of navigation, a precision map and a complex road traffic environment, comprehensively considers the traffic information, road information and the like of the future road conditions to the economical image of the current vehicle running, starts from improving driving decision and behavior, particularly operates the driving, braking and gears of the vehicle, reasonably matches the relationship between the current vehicle motion and road conditions, traffic states and vehicle performance, and achieves the purposes of energy conservation and emission reduction on the premise of meeting traveling.
Fig. 1 is a flowchart of an energy management method according to an embodiment of the present application, and as shown in fig. 1, the flowchart includes the following steps:
step S101, acquiring traffic information and road information of a road, and calculating a driving strategy of a vehicle based on the traffic information;
wherein the traffic information and the road information are acquired through V2X, and V2X means vehicle to evolution, that is, information exchange from vehicle to outside. And operates in a car networking environment that enables communication between cars, cars and base stations, and base stations. Therefore, a series of traffic information such as real-time road conditions, road information, pedestrian information and the like is obtained.
The traffic information comprises signal lamp time sequence (also called traffic light time sequence), road speed limit requirements and the like, the road information can be collected through a high-precision map in V2X, and the road information comprises road gradient, road curvature and the like.
Step S102, constructing a first constraint condition according to a driving strategy of the vehicle, and carrying out speed planning on the vehicle based on a vehicle longitudinal dynamic model, the traffic information and the first constraint condition to obtain a corresponding speed track optimal curve;
the current vehicle is a pure electric vehicle, and a power source of the current vehicle is a motor, so that a longitudinal vehicle dynamics model and a motor power model are constructed based on configuration information of the current vehicle, and it should be noted that the configuration information includes a current vehicle weight, a motor transmission mechanical efficiency of the current vehicle, a main reducer transmission ratio, a wheel radius and the like.
And S103, carrying out speed tracking based on energy consumption optimization on the vehicle by utilizing the vehicle longitudinal dynamics model, the motor power model, the speed track optimal curve and the road information, so that the vehicle tracks the speed track optimal curve in a prediction time domain, and the motor energy consumption of the vehicle is controlled.
Through the steps, traffic information and road information are obtained by using the internet of vehicles environment, the driving strategy of the vehicle is calculated based on the traffic information, a first constraint condition is established according to the driving strategy of the vehicle, the vehicle is subjected to speed planning based on a vehicle longitudinal dynamics model, the traffic information and the first constraint condition to obtain a speed track optimal curve, the vehicle is subjected to speed tracking based on energy consumption optimization by using the longitudinal dynamics model, a motor power model, the speed track optimal curve and the road information, the motor energy consumption of the vehicle is controlled to manage the energy of the vehicle, and external information such as the traffic information and the road information is introduced to enable the vehicle to adapt to various road conditions and manage the energy consumption of the vehicle in real time, so that the energy-saving potential of the vehicle is improved, and the problem of non-following speed track optimal in low-density traffic situations is solved.
In some embodiments, the step of speed planning the vehicle based on the vehicle longitudinal dynamics model, the traffic information and the first constraint condition to obtain the corresponding speed trajectory optimal curve includes:
constructing a first objective function based on the energy consumption index, the expected speed index, the braking force index and the vehicle longitudinal dynamics model;
the energy consumption index, the expected speed index and the braking force index are formulated based on actual requirements, the speed and the braking force influence the energy consumption of the vehicle, the energy saving and emission reduction purposes are required to be achieved, and therefore the current vehicle running speed and the current braking force need to be monitored.
It should be noted that the first objective function is an objective function that minimizes energy consumption.
And according to the traffic information and the first constraint condition, optimizing and solving the first objective function by adopting an MPC control strategy to obtain a speed trajectory optimal curve.
MPC is a multivariable control strategy, among others. Specifically, a 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.
In some of these embodiments, the step of performing energy consumption optimization-based speed tracking on the vehicle using the vehicle longitudinal dynamics model, the motor power model, the speed trajectory optimization curve, and the road information comprises:
constructing a second objective function according to the road information and by combining the vehicle longitudinal dynamics model and the motor power model;
and carrying out optimization solution on the second objective function based on the speed track optimal curve to obtain the expected braking torque and the expected driving torque of the vehicle at the current moment when the energy consumption is optimal.
And the second objective function is defined as an objective function with minimized energy consumption, and the second objective function is controlled by combining actual road conditions and the bottom layer of a vehicle transmission system so as to output effective driving torque and braking torque to the current vehicle, so that the current vehicle achieves the purposes of optimal energy conservation and emission reduction.
Compared with the traditional rule-based control strategy in the prior art, the control category is not considered by external intelligent information, and in the embodiment, the speed optimization strategy is combined with the bottom layer control of the vehicle power transmission system, so that the energy consumption of the whole vehicle can be reduced by 15-20%.
It should be noted that, in the above steps, the problem of single signal light passing without a front vehicle is considered, and the corresponding space-time relationship is as shown in fig. 2, which is a schematic diagram of three vehicle speed trajectories under the working condition of a single signal light without a front vehicle, in fig. 2, it is assumed that the speed trajectory is divided into three stages of acceleration, cruising and deceleration, three different vehicle speed trajectories are shown in the diagram, all starting from the same starting point, and the initial speeds are the same. When the vehicle is traveling in front of the signal lights, it is preferable to accelerate or decelerate in advance (as shown by speed trajectory 2 and speed trajectory 3) if the current speed is kept unable to pass the intersection in the green light phase.
It will be appreciated that during the travel of the vehicle, a maximum allowable vehicle speed v of the vehicle is defined p (t)＝min{v r ，v f，avg (t) }. Wherein v is r Representing the desired speed, v f，avg (t) represents the average speed in the prediction time domain, and the remaining green time is set as t r The distance from the vehicle to the next intersection is d, and the longitudinal traveling speed is v (t).
Wherein, referring to the speed trajectory 2 in the above illustration, if the vehicle keeps the current maximum allowable speed to pass through the intersection in the green light time period, the vehicle will keep or increase its average speed, and pass through the intersection in the green light time period, but the vehicle speed must be within the range of the maximum allowable vehicle speed: v (t) is less than or equal to v p (t)；
Referring to the speed trajectory 3 in the above illustration, if the vehicle keeps the current maximum allowable speed, it cannot pass through the intersection within a green time period or the like, i.e., v p t r If d is less than or equal to d, the vehicle needs to be decelerated in advance to pass through the intersection in the next green light stage.
It will be appreciated that the velocity trajectory optimisation curve can be referred to the above legend.
In some of these embodiments, the first constraint includes a dead-end constraint, a road constraint, and a signal light timing constraint, the first constraint being used to constrain the first objective function;
the road constraint is a speed limit condition in the current time domain, namely a minimum vehicle speed and a maximum vehicle speed which are required to be driven in the current time domain, the signal lamp time sequence constraint is the lamp changing time of a signal lamp of a next intersection, the terminal constraint is a terminal value of design time, position and speed, and is expressed as a constraint control problem of fixed terminal time and state constraint, and specifically, the first constraint condition is expressed in the following form:
Figure BDA0003817791030000091
v(t)＝v 0 ，s(t)＝s 0
v min (t)≤v(t)≤v max (t)
u b ≤F t (t)≤u a ，0≤F b (t)≤u c
wherein v is min (t) and v max (t) represents the minimum and maximum formal velocities in the current time domain, respectively, phi (x (t) f ) Represents a terminal penalty term, v (t) f ) Representing the terminal time speed, v f Representing the target vehicle speed at the terminal time, s (t) f ) Representing the terminal time shift, s f Representing the target displacement at the terminal moment, v 0 Representing the vehicle speed at the initial moment, v (t) representing the predicted speed track in the time domain, s (t) representing the predicted displacement track in the time domain, u b Representing that the vehicle can output a minimum driving force, F t (t) represents a motor driving force at the present time, u a Representing the maximum driving force that the vehicle can output, F b (t) represents the motor braking force at the present time, u c Representing that the vehicle is capable of outputting the maximum braking force.
The method further comprises the following steps:
acquiring the state information of the current vehicle according to a vehicle CAN bus;
the state information comprises vehicle longitudinal information, vehicle position information, motor rotating speed information, motor torque information and braking torque information.
And constructing a second constraint condition based on the state information, wherein the second constraint condition comprises a motor rotating speed constraint and a torque constraint, and the second constraint condition is used for constraining the second objective function.
Wherein the second constraint is expressed by the following form:
v min (t)≤v(t)≤v max (t)
n m，min (t)≤n m (t)≤n m，max (t)
T m，min (n m (t))≤T m (n m (t))≤T m，max (n m (t))
wherein v is min (t) and v max (t) represents the minimum and maximum allowable vehicle speed values, n m，min (t) and n m，max (T) represents the minimum and maximum values of the motor rotation speed, respectively, T m，min (n m (T)) and T m，max (n m (t)) represent the minimum and maximum values of the motor output torque, respectively.
In some of these embodiments, the functional expression of the vehicle dynamics model is as follows:
Figure BDA0003817791030000101
wherein, F t (t)、F f (t)、c r The calculation formula of g is as follows:
Figure BDA0003817791030000102
Figure BDA0003817791030000103
c r g＝fcos(α(t))+sin(α(t))
wherein a (t) represents vehicle acceleration, M represents the weight of the current vehicle, F t (t) represents driving force, η represents mechanical efficiency of the power train, i f Representing the main reducer transmission ratio, T m (t) represents motor output torque, r w Representing the radius of the wheel, F f (t) represents air resistance, C D Represented by an air resistance coefficient, A represents the frontal area of the vehicle, ρ represents the air density, v (t) represents the longitudinal running speed, g represents the gravitational acceleration, c represents the longitudinal running speed r g represents vehicle acceleration due to rolling resistance and grade resistance, α (t) represents road grade, and f represents wheel rolling resistance coefficient; f b (t) represents braking force.
In some of these embodiments, the motor power model is a polynomial function of motor speed and motor torque of the current vehicle, the polynomial function being as follows:
Figure BDA0003817791030000111
wherein P represents the motor power, b ij In order to fit the parameters to the image,
Figure BDA0003817791030000112
which is representative of the torque of the electric motor,
Figure BDA0003817791030000113
representing the motor speed.
In some embodiments, the first objective function is used to calculate the expected speed of the current vehicle at a certain time when the energy consumption is minimum, and the first objective function is as follows:
Figure BDA0003817791030000114
wherein, the calculation formula of L (x, u, t') is as follows:
Figure BDA0003817791030000115
wherein minJ represents a minimized first objective function, and phi (x (T + T)) represents a terminal penalty term, and is related to vehicle safety，w 1 、w 2 、w 3 Weight factors, v, representing energy consumption, vehicle speed tracking and braking force, respectively r Representing the desired speed, F b Representing the braking force and v representing the longitudinal travel speed.
In some of these 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 time when the energy consumption is minimum, and the second objective function is as follows:
Figure BDA0003817791030000116
wherein J represents a second objective function, N represents a prediction time domain, v d Represents the desired speed, κ 1 、κ 2 Respectively represent weight coefficients, P multiplied by delta t represents an energy consumption efficiency index, v (t) is longitudinal running speed, and v (N) represents terminal time vehicle speed.
In some embodiments, as shown in fig. 3, which is a specific implementation manner of the first embodiment of the present application, an overview is to provide a hierarchical predictive energy management method by using traffic information such as signal lamp timing sequence and speed limit, and road information such as gradient and curvature in an internet of vehicles environment, and taking into consideration factors such as a motor power model of a pure electric vehicle, a driving strategy, and vehicle speed tracking performance, and perform pure electric vehicle energy management control from two levels of an upper reference speed trajectory optimization module and a lower speed tracking module.
It should be noted that traffic information such as traffic light timing sequence and speed limit is acquired through V2X, road information such as gradient and curvature is acquired through a high-precision map, and both the traffic information and the road information are transmitted to the vehicle.
It should be noted that the upper-layer speed trajectory optimization module considers non-following vehicle conditions in a low-density traffic scene, aims to find an optimal speed trajectory curve to reduce energy consumption, and outputs the optimal speed trajectory curve to the lower-layer speed tracking module as a reference vehicle speed, and in the upper-layer speed trajectory optimization module, traffic information such as traffic light timing sequence, speed limit and the like is used to make a decision on a driving strategy, and a speed trajectory optimization problem composed of conditions such as dynamic constraint, driving strategy, energy consumption minimum objective function and the like is solved to obtain a speed trajectory, which is output to the lower-layer speed tracking module, and specifically, an optimization flow chart of the upper-layer speed trajectory optimization module can be referred to fig. 4;
the method includes that a non-following vehicle condition in a low-density traffic scene is considered in a lower-layer speed tracking module, the aim is to minimize energy consumption of a pure electric vehicle motor in a prediction time domain and well track a reference speed track, in the lower-layer speed tracking module, a speed tracking rolling optimization problem is solved by using road information such as gradient and curvature, so that energy consumption of the pure electric vehicle motor in the prediction time domain is minimized, and the reference speed track is well tracked, and specifically, an optimization flow chart of a lower-layer speed planning module can be shown in fig. 5.
It should be noted that the steps illustrated in the above-described flow diagrams or in the flow diagrams of the figures may be performed in a computer system, such as a set of computer-executable instructions, and that, although a logical order is illustrated in the flow diagrams, in some cases, the steps illustrated or described may be performed in an order different than presented herein.
The second embodiment of the present application further provides an energy management system, which is used to implement the first embodiment and the preferred embodiment, and the description of the system that has been already made is omitted. As used hereinafter, the terms "module," "unit," "subunit," and the like may implement a combination of software and/or hardware for a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware or a combination of software and hardware is also possible and contemplated.
Fig. 6 is a block diagram of an energy management system according to a second embodiment of the present application, as shown in fig. 3, the system including:
the first acquisition module 10 is used for acquiring traffic information and road information of a road and calculating a driving strategy of a vehicle based on the traffic information;
the speed planning module 20 is configured to construct a first constraint condition according to a driving strategy of the vehicle, and perform speed planning on the vehicle based on a vehicle longitudinal dynamics model, the traffic information, and the first constraint condition to obtain a corresponding speed trajectory optimal curve;
and the speed tracking module 30 is configured to perform speed tracking based on energy consumption optimization on the vehicle by using the vehicle longitudinal dynamics model, the motor power model, the speed trajectory optimal curve and the road information, so that the vehicle tracks the speed trajectory optimal curve in a prediction time domain, and controls motor energy consumption of the vehicle.
Through the steps, traffic information and road information are obtained by using the internet of vehicles environment, the driving strategy of the vehicle is calculated based on the traffic information, a first constraint condition is established according to the driving strategy of the vehicle, the vehicle is subjected to speed planning based on a vehicle longitudinal dynamics model, the traffic information and the first constraint condition to obtain a speed track optimal curve, the vehicle is subjected to speed tracking based on energy consumption optimization by using the longitudinal dynamics model, a motor power model, the speed track optimal curve and the road information, the motor energy consumption of the vehicle is controlled to manage the energy of the vehicle, and external information such as the traffic information and the road information is introduced to enable the vehicle to adapt to various road conditions and manage the energy consumption of the vehicle in real time, so that the energy-saving potential of the vehicle is improved, and the problem of non-following speed track optimal in low-density traffic situations is solved.
In some of these embodiments, the speed planning module 20 includes:
the first construction unit is used for constructing a first objective function based on the energy consumption index, the expected speed index, the braking force index and the vehicle longitudinal dynamics model;
and the first optimization unit is used for optimizing and solving the first objective function by adopting an MPC control strategy according to the traffic information and the first constraint condition to obtain a speed track optimal curve.
In some of these embodiments, the velocity tracking module 30 includes:
the second construction unit is used for constructing a second objective function according to the road information and by combining the vehicle longitudinal dynamics model and the motor power model;
and the second optimization unit is used for carrying out optimization solution on the second objective function based on the speed track optimal curve to obtain the expected braking torque and the expected driving torque of the vehicle at the current moment when the energy consumption is optimal.
In some of these embodiments, the first constraint includes a dead-end constraint, a road constraint, and a signal light timing constraint, the first constraint being used to constrain the first objective function;
the system further comprises:
and the second acquisition module is used for acquiring the state information of the current vehicle according to a vehicle CAN bus and constructing a second constraint condition based on the state information, wherein the second constraint condition comprises motor rotating speed constraint and torque constraint, and the second constraint condition is used for constraining the second objective function.
In some of these embodiments, the functional expression of the vehicle dynamics model is as follows:
Figure BDA0003817791030000141
wherein, F t (t)、F f (t)、c r The calculation formula of g is as follows:
Figure BDA0003817791030000142
Figure BDA0003817791030000143
c r g3fcos(α(t))+sin(α(t))
wherein a (t) represents vehicle acceleration, M represents the weight of the current vehicle, F t (t) represents driving force, η represents mechanical efficiency of the power train, i f Representing the main reducer transmission ratio，T m (t) represents motor output torque, r w Represents the wheel radius, F f (t) represents air resistance, C D Represented by an air resistance coefficient, A represents the frontal area of the vehicle, ρ represents the air density, v (t) represents the longitudinal running speed, g represents the gravitational acceleration, c represents the longitudinal running speed r g represents vehicle acceleration due to rolling resistance and grade resistance, α (t) represents road grade, and f represents wheel rolling resistance coefficient; f b (t) represents braking force.
In some of these embodiments, the motor power model is a polynomial function of motor speed and motor torque of the current vehicle, the polynomial function being as follows:
Figure BDA0003817791030000144
wherein P represents the motor power, b ij In order to fit the parameters of the image,
Figure BDA0003817791030000145
which represents the torque of the electric motor,
Figure BDA0003817791030000146
representing the motor speed.
In some of these embodiments, the first objective function is used to calculate the expected speed of the current vehicle at a certain time when the energy consumption is minimum, and the first objective function is as follows:
Figure BDA0003817791030000147
wherein, the calculation formula of L (x, u, t') is as follows:
Figure BDA0003817791030000151
wherein minJ represents a minimized first objective function, φ (x (T + T)) represents a terminal penalty term, and the vehicleSafety-related, w 1 、w 2 、w 3 Weight factors, v, representing energy consumption, vehicle speed tracking and braking force, respectively r Representing desired speed, F b Representing the braking force and v representing the longitudinal travel speed.
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 time when the energy consumption is minimum, and the second objective function is as follows:
Figure BDA0003817791030000152
wherein J represents a second objective function, N represents a prediction time domain, v d Represents the desired speed, κ 1 、κ 2 Respectively represent weight coefficients, P multiplied by delta t represents an energy consumption efficiency index, v (t) is longitudinal running speed, and v (N) represents terminal time vehicle speed.
The above modules may be functional modules or program modules, and may be implemented by software or hardware. For a module implemented by hardware, the modules may be located in the same processor; or the modules may be located in different processors in any combination.
In addition, the energy management method described in conjunction with fig. 1 in the embodiment of the present application may be implemented by a computer device. Fig. 7 is a hardware configuration diagram of a computer device according to a third embodiment of the present application.
The computer device may include a processor 32 and a memory 33 in which computer program instructions are stored.
Specifically, the processor 32 may include a Central Processing Unit (CPU), or A Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.
Memory 33 may include, among other things, mass storage for data or instructions. By way of example, and not limitation, memory 33 may include a Hard Disk Drive (Hard Disk Drive, abbreviated to HDD), a floppy Disk Drive, a Solid State Drive (SSD), flash memory, an optical Disk, a magneto-optical Disk, magnetic tape, or a Universal Serial Bus (USB) Drive or a combination of two or more of these. Memory 33 may include removable or non-removable (or fixed) media, where appropriate. The memory 33 may be internal or external to the data processing apparatus, where appropriate. In a particular embodiment, the memory 33 is a Non-Volatile (Non-Volatile) memory. In certain embodiments, memory 33 includes Read-Only Memory (ROM) and Random Access Memory (RAM). Where appropriate, the ROM may be mask-programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically Erasable PROM (EEPROM), electrically Alterable ROM (EAROM), or FLASH Memory (FLASH), or a combination of two or more of these. The RAM may be a Static Random-Access Memory (SRAM) or a Dynamic Random-Access Memory (DRAM), where the DRAM may be a Fast Page Mode Dynamic Random-Access Memory (FPMDRAM), an Extended data output Dynamic Random-Access Memory (EDODRAM), a Synchronous Dynamic Random-Access Memory (SDRAM), and the like.
Memory 33 may be used to store or cache various data files for processing and/or communication use, as well as possibly computer program instructions for execution by processor 32.
The processor 32 implements any of the energy management methods in the above embodiments by reading and executing computer program instructions stored in the memory 33.
In some of these embodiments, the computer device may also include a communication interface 34 and a bus 31. As shown in fig. 7, the processor 32, the memory 33, and the communication interface 34 are connected via a bus 31 to complete mutual communication.
The communication interface 34 is used for implementing communication between various modules, devices, units and/or apparatuses in the embodiments of the present application. The communication interface 34 may also enable communication with other components such as: the data communication is carried out among external equipment, image/data acquisition equipment, a database, external storage, an image/data processing workstation and the like.
The bus 31 comprises hardware, software, or both coupling the components of the computer device to each other. Bus 31 includes, but is not limited to, at least one of the following: data Bus (Data Bus), address Bus (Address Bus), control Bus (Control Bus), expansion Bus (Expansion Bus), and Local Bus (Local Bus). By way of example and not limitation, bus 31 may include an Accelerated Graphics Port (AGP) or other Graphics Bus, an Enhanced Industry Standard Architecture (EISA) Bus, a Front-Side Bus (FSB), a Hyper Transport (HT) Interconnect, an ISA (ISA) Bus, an InfiniBand (InfiniBand) Interconnect, a Low Pin Count (LPC) Bus, a memory Bus, a microchannel Architecture (MCA) Bus, a PCI (Peripheral Component Interconnect) Bus, a PCI-Express (PCI-X) Bus, a Serial Advanced Technology Attachment (SATA) Bus, a vlslave Bus, a Video Bus, or a combination of two or more of these suitable electronic buses. Bus 31 may include one or more buses, where appropriate. Although specific buses are described and shown in the embodiments of the application, any suitable buses or interconnects are contemplated by the application.
The computer device may execute the energy management method in the embodiment of the present application based on the acquired computer program, thereby implementing the energy management method described in conjunction with fig. 1.
In addition, in combination with the energy management method in the foregoing embodiment, the embodiment of the present application may provide a readable medium to implement. The readable medium having stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement any of the energy management methods of the above embodiments.
All possible combinations of the technical features of the above embodiments may not be described for the sake of brevity, but should be considered as within the scope of the present disclosure as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent application shall be subject to the appended claims.

Claims (10)

1. A method of energy management, the method comprising:
acquiring traffic information and road information of a road, and calculating a driving strategy of a vehicle based on the traffic information;
constructing a first constraint condition according to the driving strategy of the vehicle, and carrying out speed planning on the vehicle based on a vehicle longitudinal dynamic model, the traffic information and the first constraint condition to obtain a corresponding speed track optimal curve;
and carrying out speed tracking based on energy consumption optimization on the vehicle by utilizing the vehicle longitudinal dynamics model, the motor power model, the speed track optimal curve and the road information so as to enable the vehicle to track the speed track optimal curve in a prediction time domain and control the motor energy consumption of the vehicle.
2. The energy management method according to claim 1, wherein the step of speed planning the vehicle based on the vehicle longitudinal dynamics model, the traffic information and the first constraint condition to obtain the corresponding speed trajectory optimal curve comprises:
constructing a first objective function based on the energy consumption index, the expected speed index, the braking force index and the vehicle longitudinal dynamics model;
and according to the traffic information and the first constraint condition, optimizing and solving the first objective function by adopting an MPC control strategy to obtain a speed trajectory optimal curve.
3. The energy management method of claim 1, wherein the step of performing energy consumption optimization-based speed tracking on the vehicle using the vehicle longitudinal dynamics model, the motor power model, the speed trajectory optimization curve, and the road information comprises:
constructing a second objective function according to the road information and by combining the vehicle longitudinal dynamics model and the motor power model;
and carrying out optimization solution on the second objective function based on the speed track optimal curve to obtain the expected braking torque and the expected driving torque of the vehicle at the current moment when the energy consumption is optimal.
4. The energy management method of claim 1, wherein the functional expression of the vehicle dynamics model is as follows:
Figure FDA0003817791020000011
wherein, F t (t)、F f (t)、c r The calculation formula of g is as follows:
Figure FDA0003817791020000021
Figure FDA0003817791020000022
c r g＝fcos(α(t))+sin(α(t))
wherein a (t) represents the acceleration of the vehicle, M represents the weight of the current vehicle, F t (t) represents driving force, η represents mechanical efficiency of the power train, i f Representing the main reducer transmission ratio, T m (t) represents motor output torque, r w Representing the radius of the wheel, F f (t) represents air resistance, C D Is represented by an air resistance coefficient, A represents the frontal area of the vehicle, ρ represents the air density, v (t) represents the longitudinal running speed, g represents the gravitational acceleration, c r g represents vehicle acceleration due to rolling resistance and grade resistance, α (t) represents road grade, and f represents wheel rolling resistance coefficient; f b (t) represents braking force.
5. The energy management method of claim 1, wherein the motor power model is a polynomial function of motor speed and motor torque of the current vehicle, the polynomial function being as follows:
Figure FDA0003817791020000023
wherein P represents the motor power, b ij In order to fit the parameters to the image,
Figure FDA0003817791020000024
which represents the torque of the electric motor,
Figure FDA0003817791020000025
representing the motor speed.
6. The energy management method of claim 1, wherein the first objective function is used to calculate a desired speed of the current vehicle at a certain time when energy consumption is minimum, and the first objective function is as follows:
Figure FDA0003817791020000026
wherein, the calculation formula of L (x, u, t') is as follows:
Figure FDA0003817791020000027
wherein minJ represents a minimum first objective function, φ (x (T + T)) represents a terminal penalty term, and is related to vehicle safety, and w 1 、w 2 、w 3 Weight factors, v, representing energy consumption, vehicle speed tracking and braking force, respectively r Representing desired speed, F b Representing the braking force and v representing the longitudinal running speed.
7. The energy management method according to claim 1, wherein the second objective function is used for calculating the expected braking torque and the expected driving torque of the current vehicle at a certain moment when the energy consumption is minimum, and the second objective function is as follows:
Figure FDA0003817791020000031
wherein J represents a second objective function, N represents a prediction time domain, v d Represents the desired speed, κ 1 、κ 2 Respectively represent weight coefficients, P multiplied by delta t represents an energy consumption efficiency index, v (t) is a longitudinal running speed, and v (N) represents a vehicle terminal time speed.
8. An energy management system, comprising:
the first acquisition module is used for acquiring traffic information and road information of a road and calculating a driving strategy of a vehicle based on the traffic information;
the speed planning module is used for constructing a first constraint condition according to the driving strategy of the vehicle, and carrying out speed planning on the vehicle on the basis of a vehicle longitudinal dynamic model, the traffic information and the first constraint condition so as to obtain a corresponding speed track optimal curve;
and the speed tracking module is used for carrying out speed tracking based on energy consumption optimization on the vehicle by utilizing the vehicle longitudinal dynamics model, the motor power model, the speed track optimal curve and the road information so as to enable the vehicle to track the speed track optimal curve in a prediction time domain and control the motor energy consumption of the vehicle.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the energy management method according to any of claims 1 to 7 when executing the computer program.
10. A readable storage medium on which a computer program is stored which, when being executed by a processor, carries out the energy management method according to any one of claims 1 to 7.
CN202211037289.5A 2022-08-26 2022-08-26 Energy management method, system, computer device and readable storage medium Pending CN115352285A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116612648A (en) * 2023-05-15 2023-08-18 深圳市显科科技有限公司 Road staged dynamic traffic jam-dredging prompting method and device based on information board
CN116729106A (en) * 2023-07-12 2023-09-12 吉林大学 Intelligent energy management method for pure electric automobile

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116612648A (en) * 2023-05-15 2023-08-18 深圳市显科科技有限公司 Road staged dynamic traffic jam-dredging prompting method and device based on information board
CN116612648B (en) * 2023-05-15 2024-03-26 深圳市显科科技有限公司 Road staged dynamic traffic jam-dredging prompting method and device based on information board
CN116729106A (en) * 2023-07-12 2023-09-12 吉林大学 Intelligent energy management method for pure electric automobile
CN116729106B (en) * 2023-07-12 2023-11-21 吉林大学 Intelligent energy management method for pure electric automobile

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