CN112837532B - New energy bus cooperative dispatching and energy-saving driving system and control method thereof - Google Patents
New energy bus cooperative dispatching and energy-saving driving system and control method thereof Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/123—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams
- G08G1/127—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams to a central station ; Indicators in a central station
Abstract
The invention provides a new energy bus cooperative scheduling and energy-saving driving system and a control method thereof. Compared with the traditional bus dispatching, the method considers the decline of the power battery and the energy management of the new energy bus power system, utilizes the speed regulation to link the individual energy-saving driving of the bus with the operation efficiency of the whole bus system, avoids the bus bunching and uneven running distribution, reduces frequent acceleration and deceleration, improves the energy utilization efficiency of the power system, and realizes the maximization of economic benefit.
Description
Technical Field
The invention relates to a new energy bus cooperative scheduling and energy-saving driving system and a control strategy thereof, in particular to a bus system cooperatively controlled by bus scheduling, speed regulation and energy management and a control strategy thereof.
Technical Field
The influence of random factors such as traffic jam, traffic accidents, signal lamp intersection delay, weather and the like in the urban traffic environment causes the phenomenon of uneven vehicle distribution in the running process of urban buses, even leads to bus bunching to make the bus transportation capacity not fully utilized, and further influences the satisfaction degree of passengers on the buses. Bus dispatching is an important means for guaranteeing bus operation efficiency and bus service reliability, is widely applied to a bus system since being proposed in the 20 th century, and aims to enable bus operation to be recovered to a stable state through human intervention.
With the great change brought to the traffic industry by the major trend of intelligent network integration, the public traffic system gradually develops towards the cooperation and intellectualization of the bus and the real-time operation data provided by the automatic vehicle identification (AVL) and the automatic passenger counting system (APC), the micro real-time control of the bus scheduling can be realized due to the development of 5G communication and the 'bus-road-cloud' technology, and the difficulty of the strategy optimization control problem is increased when the public traffic system scheduling develops towards the networking and refinement. In addition, public transport is an important source of urban traffic energy consumption as an important path of urban traffic, and how to realize energy conservation and emission reduction while ensuring the operation efficiency of a public transport system is also an important problem in the present generation.
Disclosure of Invention
In order to solve the problems, the invention provides a bus management control system adopting bus scheduling, speed regulation and energy management cooperative control.
In order to solve the technical problems, the invention adopts the following technical solutions:
the invention provides a new energy public transport cooperative scheduling and energy-saving driving system, which comprises a public transport operation data acquisition system, a public transport scheduling subsystem, a speed regulation subsystem and an energy management subsystem; wherein
The bus running data acquisition system is used for acquiring GPS information of all buses on the same line, the state of a power battery and the number of passengers waiting at each station;
the bus dispatching subsystem is used for acquiring the state of passengers waiting for taking a bus and the bus running state and planning the time of each bus reaching the next bus stop;
the speed adjusting subsystem adjusts the bus running speed according to the traffic state and the power battery state of the current running road collected by the bus running data acquisition system and by combining the arrival time planned by the bus scheduling subsystem;
and the energy management subsystem calculates power demand according to the speed and the acceleration provided by the speed regulation subsystem and calculates energy distribution of the power source according to the power demand.
Further, the new energy bus cooperative dispatching and energy-saving driving system provided by the invention has the speed regulation mode of the speed regulation subsystem comprising: driver speed recommendation, adaptive driving assistance, and autonomous driving.
Further, the new energy bus cooperative dispatching and energy-saving driving system provided by the invention has the advantages that the energy management subsystem is suitable for the power assembly and comprises: the hybrid power vehicle comprises a super capacitor pure electric vehicle, an oil-electricity hybrid electric vehicle, a fuel cell hybrid electric vehicle and a plug-in hybrid electric vehicle.
The invention also provides a control method of the new energy bus cooperative scheduling and energy-saving driving system, which comprises the following specific steps:
s01), collecting and acquiring GPS information, power battery states and the number of passengers waiting at each station of all buses on the same line by a bus operation data acquisition system;
s02), carrying out feature extraction, data analysis and information fusion on the collected data by using a data analysis model based on artificial intelligence;
s03), the bus dispatching subsystem receives the required information from the vehicle big data platform, and plans the time of each bus arriving at the next bus stop according to the state of the passenger waiting for taking the bus and the bus running state;
s04), orderly sending the data into the selected application module through the vehicle-mounted communication system, wherein the data sending method comprises the following steps: a power source energy management system, a brake recovery energy management system and an electric accessory energy management system; the method comprises the steps that a power system and an energy system are subjected to balanced adjustment through an energy-saving driving model based on artificial intelligence, the optimal solution of system balance under multiple targets is solved, and upper-layer control quantity is output;
s05), the speed regulation subsystem receives the control quantity, transmits a control instruction to a corresponding control object through the CAN bus, and completes regulation of the bus running speed by combining the current traffic state of the running road and the power battery state;
s06), the energy management subsystem receives the control quantity, transmits a control instruction to a corresponding control object through the CAN bus, calculates the power demand according to the required speed and acceleration, and calculates the energy distribution of the power source according to the kilometer demand;
s07), acquiring a new state generated by the vehicle after the corresponding control quantity is applied, and storing the new state data into a vehicle-mounted database to form closed-loop control.
Further, in S04), the energy-saving driving model based on artificial intelligence performs balanced adjustment on the power system and the energy system, solves the optimal system balance solution under multiple objectives, and outputs upper-layer control quantity, specifically describes the operation mechanism of the public transportation system based on a multi-agent discrete time model, and constructs a public transportation scheduling mathematical model by adjusting the relative space and speed with noise; the method specifically comprises the following steps:
(1) calibrating the state of the power battery, acquiring the initial value of the power battery of each vehicle in the bus queue, and estimating the influence of a bus dispatching strategy on the service life of the battery according to a battery cycle life empirical model; the battery decline of the new energy public transport power battery under different working states is described through the lithium ion battery power battery cycle life empirical model:
in the formula, BexpIs a cofactor, with CrateIn an inverse relationship, R is the gas constant, TbattIs the average temperature of the battery, AhIs the accumulated charge-discharge ampere hours of the power battery;
(2) the public traffic system regulation problem is described by a relative space and speed differential equation system with noise:
xn(t+T)＝an,1xn(t)+εn,1(t)
wherein, T is response time, xn(t)，
Is a state variable, an,1,an,2Being a static model parameter, epsilonn,1(t)，εn,2(t) is white noise.
Furthermore, when describing the adjustment problem of the public transportation system, the control method provided by the invention adjusts the speed of the subsequent public transportation at the cost of the minimum battery loss of the system, so that the actual space headway between the adjacent public transportation is kept at the expected pre-specified target value, and an objective function is established:
wherein q is1,q2,r>0 is a weight coefficient, xdFor the expected headway, u (k) represents the control variable as power cell loss.
Further, when the control method provided by the invention is used for describing the adjustment problem of the public transport system, a multi-vehicle energy management strategy collaborative training algorithm framework based on asynchronous deep reinforcement learning establishes a multi-target optimization problem by using the multi-target coupling relation among the new energy public transport vehicle scheduling time compliance, the whole vehicle dynamic property, the energy consumption economy and the power battery service life:
wherein X ═ X is given by a maximization vector f (X) which represents a weighted scalar that optimizes pareto or maximizes all sub-targets1,x2,…xN]T∈RNIs a variable to be optimized, function gi(X),i＝1,…,mgConstraint representing the problem, fi(X)，i＝1,2,…,mfIs an optimization function.
Compared with the prior art, the invention adopts the technical means, and has the advantages that:
compared with the traditional bus scheduling, the method considers the decline of the power battery of the new energy bus power system and the optimization of energy management, combines speed regulation to link the individual energy-saving driving of the bus with the operation efficiency of the whole bus system, reasonably utilizes the speed regulation to avoid bus crossing and uneven running distribution, reduces frequent acceleration and deceleration, improves the energy utilization efficiency of the power system, and realizes the maximization of economic benefit.
Drawings
Fig. 1 is a new energy bus cooperative dispatching and energy-saving driving system architecture provided by the invention.
Fig. 2 is a control flow of the new energy bus cooperative scheduling and energy-saving driving system provided by the invention.
Detailed Description
The invention will be described in further detail with reference to the following detailed description and accompanying drawings:
it will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The new energy bus cooperative dispatching and energy-saving driving system provided by the invention is shown in figure 1. In the operation process of the bus system, GPS information of all buses, states of power batteries and the number of passengers waiting at each station in the same line are acquired, the time of each bus arriving at the next bus station is planned according to the states of the passengers waiting for taking the bus and the bus running state, the bus regulates the bus running speed by combining the current traffic state of the running road and the states of the power batteries, then the bus energy management system calculates power requirements according to the required speed acceleration, and calculates energy distribution of power sources according to the power requirements.
The specific steps of the invention are as follows:
s01), the big data information collecting and processing system collects and obtains all bus GPS information, power battery state and the number of passengers waiting at each station on the same line;
s02), performing operations such as feature extraction, data analysis and information fusion on the collected data by using the artificial intelligence-based data analysis model.
S03), the bus module receives needed information from the vehicle big data platform, and plans the time of each bus arriving at the next bus stop according to the state of passengers waiting for taking the bus and the bus driving state.
S04), orderly sending data into selected application modules through the vehicle-mounted communication system, wherein the application modules comprise but are not limited to a power source energy management system, a brake recovery energy management system and an electric accessory energy management system; and (3) carrying out balance adjustment on the power system and the energy system by using an energy-saving driving model based on artificial intelligence in the system, solving a system balance optimal solution under multiple targets, and outputting upper-layer control quantity.
S05), the lower-layer longitudinal power control system receives the control quantity, and transmits the control quantity to a corresponding control object through a CAN bus, so that the adjustment of the bus running speed is completed by combining the traffic state of the current running road and the state of the power battery.
S06), the lower energy management control system receives the control quantity, transmits the control quantity to a corresponding control object through the CAN bus, calculates the power demand according to the required speed and acceleration, and calculates the energy distribution of the power source according to the kilometer demand.
S07), the vehicle is applied with corresponding control quantity to generate new state, and the new state data is stored in the vehicle-mounted database to form closed-loop control.
The following illustrates embodiments of the invention with reference to specific models:
1) considering battery degradation of the new energy public transport power battery in different working states, researching the change rule of battery capacity along with charging and discharging current, and establishing a cycle life empirical model of the lithium ion battery power battery, wherein the model formula is as follows:
in the formula, BexpIs a cofactor, with CrateIn an inverse relationship, R is the gas constant, TbattIs the average temperature of the battery, AhIs the accumulated charge-discharge ampere hours of the power battery. And calibrating the state of the power battery through the battery model, acquiring the initial value of the power battery of each vehicle in the bus queue, and estimating the influence of the bus dispatching strategy on the battery life according to the battery life empirical model.
2) Establishing a multi-agent discrete time model of a bus system, and regarding a bus crossing problem as a noisy relative space and speed regulation problem, wherein a dynamic system of each bus can be expressed as follows:
wherein T is a response time, xn(t)，
Is a state variable, an,1,an,2Being a static model parameter, epsilonn,1(t)，εn,2(t) is white noise. Therefore, the discrete-time state transition equation of the public transportation system is as follows:
x(k+1)＝Ax(k)+B v(k)+γ(k) (11)
the dispatching control aims at adjusting the following bus speeds by using the cost of the minimum battery loss of the system, so that the actual space headway between adjacent buses is kept at an expected pre-specified target value, the bus distribution is prevented from being uneven and even the buses are connected in series, and an objective function is established as follows:
wherein q is1,q2,r>0 is a weight coefficient, xdFor the expected headway, u (k) represents the control variable as power cell loss. Obtaining time-invariant feedback of specific time domain according to iteration iLQR control theoryThe resulting control equation is:
3) a multi-vehicle energy management strategy collaborative training algorithm framework based on asynchronous deep reinforcement learning is established, and a simulation environment facing multi-thread operation is established so as to simulate multi-vehicle collaborative strategy training realized by means of Internet of vehicles communication, and improve strategy training efficiency and optimization effect. In the algorithm training process, each vehicle accumulates the weight gradient according to the self-experience working condition, as shown in the following formula; each time the sharing count T reaches a certain number NtargetA target weight update is performed: thetaT μ←θE μ，θT Q←θE Q(ii) a Every time a certain vehicle private count t reaches a certain number of times NAsyncUpdateAccording to the cumulative gradient d theta of the vehicleQAnd d θμUpdating the primary sharing weight thetaE μAnd thetaE QAnd clearing the vehicle accumulated gradient.
In the formula, stAnd st+1The working conditions of time t and t +1, atIs the amount of motion at time t, rtQ and mu are respectively an action value mapping network and a strategy network of a single target for feedback reward at the time t.
Under an asynchronous deep reinforcement learning framework, a trained RMSProp random gradient descent optimization algorithm is adopted to train a strategy network and an action value network:
where θ is the shared network weight, Δ θiFor each vehicle, a private cumulative gradient, g being the moving average of the square of the gradient, in each caseThe vehicles are shared, so that the learning efficiency of the whole algorithm is improved.
4) The method comprises the following steps of considering the multi-target coupling relation among the new energy public transport vehicle scheduling time compliance, the whole vehicle dynamic property, the energy consumption economy and the service life of a power battery in public transport energy-saving driving, and establishing the multi-target optimization problem mathematical expression as follows:
by maximizing the vector f (X), which represents the pareto optimal or weighted scalar that maximizes all sub-targets, X ═ X1,x2,…xN]T∈RNIs a variable to be optimized, function gi(X),i＝1,…,mgConstraint representing the problem, fi(X)(i＝1,2,…,mf) Is an optimization function.
For multi-objective reinforcement learning, an agent needs to optimize two or more objective tasks simultaneously. The research of the project needs to realize the collaborative optimization of multiple targets such as the longitudinal speed planning of the whole vehicle, the energy management and the service life of the power battery. Each target i obtains a corresponding state action function according to a fixed strategy pi
The final state action function is then expressed as:
the final state action function is obtained by simplifying a Bellman equation, and the final optimization goal of the multi-objective intelligent agent is to solve the maximum value of the state action function:
the optimal strategy can be obtained by the following formula:
and the state action function Q of each targetiThe update rule of (s, a) may be expressed as:
reward function r for ith target taskiA feedback signal representing the environment given to the agent, a is the learning rate, and s ', a' represent the state and action of the agent at the next moment, respectively. In the present study, the environmental conditions include the vehicle's own state such as the power system rotational speed and torque, the battery SOC and SOH, the vehicle speed and acceleration; and external traffic environment states such as the current passenger number, vehicle position, station position, traffic flow, headway, arrival time and the like.
5) The importance of the optimization tasks in the multiobjective optimization problem is differentiated by introducing an attention mechanism, wherein the importance of the velocity planning and the energy allocation is relatively higher. And the learning efficiency and stability of the whole algorithm are improved through an asynchronous deep reinforcement learning collaborative training framework. By means of the multi-objective optimization of the part of research contents, the operation efficiency and the energy consumption economy of the new energy bus can be improved, the macro scheduling in the research contents is coordinated with the macro scheduling in the research contents, and the macro/micro level combined energy-saving optimization of the networked new energy bus system is achieved.
The foregoing is only a partial embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (4)
1. A new energy public transport cooperative dispatching and energy-saving driving system is characterized by comprising a public transport operation data acquisition system, a public transport dispatching subsystem, a speed regulation subsystem and an energy management subsystem; wherein
The bus running data acquisition system is used for acquiring GPS information of all buses on the same line, the state of a power battery and the number of passengers waiting at each station;
the bus dispatching subsystem is used for acquiring the state of passengers waiting for taking a bus and the bus running state and planning the time of each bus reaching the next bus stop;
the speed adjusting subsystem adjusts the bus running speed according to the traffic state and the power battery state of the current running road collected by the bus running data acquisition system and by combining the arrival time planned by the bus scheduling subsystem;
the energy management subsystem calculates power demand according to the speed and the acceleration provided by the speed regulation subsystem and calculates energy distribution of the power source according to the power demand;
the method comprises the following steps that a public transportation system operation mechanism is described based on a multi-agent discrete time model, and a public transportation scheduling mathematical model is established through relative space and speed regulation with noise; the method specifically comprises the following steps:
(1) calibrating the state of the power battery, acquiring the initial value of the power battery of each vehicle in the bus queue, and estimating the influence of a bus dispatching strategy on the service life of the battery according to a battery cycle life empirical model; the battery decline of the new energy public transport power battery under different working states is described through the lithium ion battery power battery cycle life empirical model:
in the formula, BexpIs a cofactor, with CrateIn an inverse relationship, R is the gas constant, TbattIs the average temperature of the battery, AhIs the accumulated charge-discharge ampere hours of the power battery;
(2) the public traffic system regulation problem is described by a relative space and speed differential equation system with noise:
xn(t+T)＝an，1xn(t)+εn，1(t)
wherein T is a response time, xn(t)，
Is a state variable, an，1，an，2Being a static model parameter, epsilonn，1(t)，εn，2(t) is white noise;
when the public transportation system adjustment problem is described, the speed of the subsequent public transportation is adjusted at the cost of the minimum battery loss of the system, so that the actual space headway between adjacent public transportation is kept at a desired pre-specified target value, and an objective function is established:
wherein q is1，q2R > 0 is a weight coefficient, xdFor the expected headway, u (k) represents the control variable as power battery loss;
when the adjustment problem of the public transport system is described, a multi-vehicle energy management strategy collaborative training algorithm framework based on asynchronous deep reinforcement learning establishes a multi-target optimization problem according to the multi-target coupling relation among the new energy public transport vehicle scheduling time compliance, the whole vehicle dynamic property, the energy consumption economy and the power battery service life:
wherein X ═ X is given by a maximization vector f (X) which represents a weighted scalar that optimizes pareto or maximizes all sub-targets1，x2，...xN]T∈RNIs a variable to be optimized, function gi(X)，i＝1，…，mgConstraints representing the problemCondition (f)i(X)，i＝1，2，...，mfIs an optimization function.
2. The system for collaborative scheduling and energy-saving driving of new energy buses as claimed in claim 1, wherein the speed regulation manner of the speed regulation subsystem comprises: driver speed recommendation, adaptive driving assistance, and autonomous driving.
3. The system for collaborative scheduling and energy-saving driving of new energy buses as claimed in claim 1, wherein the energy management subsystem is adapted to the powertrain and comprises: the hybrid power vehicle comprises a super capacitor pure electric vehicle, an oil-electricity hybrid electric vehicle, a fuel cell hybrid electric vehicle and a plug-in hybrid electric vehicle.
4. The control method of the new energy bus collaborative scheduling and energy-saving driving system based on any one of claims 1 to 3 is characterized by comprising the following specific steps:
s01), collecting and acquiring GPS information, power battery states and the number of passengers waiting at each station of all buses on the same line by a bus operation data acquisition system;
s02), carrying out feature extraction, data analysis and information fusion on the collected data by using a data analysis model based on artificial intelligence;
s03), the bus dispatching subsystem receives the required information from the vehicle big data platform, and plans the time of each bus arriving at the next bus stop according to the state of the passenger waiting for taking the bus and the bus running state;
s04), orderly sending the data into the selected application module through the vehicle-mounted communication system, wherein the data sending method comprises the following steps: a power source energy management system, a brake recovery energy management system and an electric accessory energy management system; the method comprises the steps that a power system and an energy system are subjected to balanced adjustment through an energy-saving driving model based on artificial intelligence, the optimal solution of system balance under multiple targets is solved, and upper-layer control quantity is output;
s05), the speed regulation subsystem receives the control quantity, transmits a control instruction to a corresponding control object through the CAN bus, and completes regulation of the bus running speed by combining the current traffic state of the running road and the power battery state;
s06), the energy management subsystem receives the control quantity, transmits a control instruction to a corresponding control object through the CAN bus, calculates the power demand according to the required speed and acceleration, and calculates the energy distribution of the power source according to the kilometer demand;
s07), acquiring a new state generated by the vehicle after the corresponding control quantity is applied, and storing the new state data into a vehicle-mounted database to form closed-loop control.
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