CN114954541B

CN114954541B - Cooperative method and device for power distribution and tracking speed of vehicle

Info

Publication number: CN114954541B
Application number: CN202210476312.4A
Authority: CN
Inventors: 张晗; 张擘; 刘佰博; 郭存心; 薛龙昌; 郭玉; 李明高; 张继业
Original assignee: CRRC Academy Co Ltd
Current assignee: CRRC Industry Institute Co Ltd
Priority date: 2022-04-29
Filing date: 2022-04-29
Publication date: 2023-08-18
Anticipated expiration: 2042-04-29
Also published as: CN114954541A

Abstract

The invention provides a cooperative method and a device for power distribution and tracking speed of a vehicle, wherein the method comprises the following steps: acquiring M groups of vehicle tracking speed sequences and N groups of vehicle power distribution sequences; the vehicle tracking speed sequence comprises maximum allowable speeds corresponding to every two stations, and the vehicle power distribution sequence comprises maximum allowable output powers corresponding to at least two energy sources; constructing a collaborative optimization system based on the M sets of vehicle tracking speed sequences and the N sets of vehicle power distribution sequences; and solving an optimal solution of the collaborative optimization system based on an artificial fish swarm algorithm and a multi-population firefly algorithm to obtain a target vehicle tracking speed sequence and a target vehicle power distribution sequence. The invention considers the influence of the cooperative relationship of the vehicle tracking speed sequence and the vehicle power distribution sequence on the vehicle energy supply, and can more reasonably distribute the power supply of each energy source in the hybrid power system.

Description

Cooperative method and device for power distribution and tracking speed of vehicle

Technical Field

The invention relates to the technical field of rail transit, in particular to a cooperative method and device for power distribution and tracking speed of a vehicle and electronic equipment.

Background

With the development of urban areas, urban population is increased, so that a severe test is brought to rail transit, and meanwhile, the energy supply of the rail transit is also challenged.

In the prior art, a hybrid power system with an oxyhydrogen power device as a main power source, a power battery and a super capacitor as auxiliary power sources supplies energy to a vehicle, the operation of the vehicle depends on a tracking speed curve provided by vehicle-mounted train control equipment for the vehicle, tracking speed curves obtained by calculation according to the operation conditions of the vehicle are input to a vehicle-mounted controller, and then the tracking speed information is converted into target rotating speed and target torque of a vehicle motor, and finally the target rotating speed and the target torque are used as control input of the vehicle motor controller. In order for the vehicle motor to operate at the target rotational speed and target torque, the hybrid system of the vehicle needs to provide sufficient and suitable electrical energy. And the power distribution algorithm in the control unit of the hybrid power system adjusts the power output conditions of the two energy sources in real time according to the current states of the oxyhydrogen power device and the power battery.

However, in the above prior art, the tracking speed profile of the vehicle is typically optimized separately from the power distribution algorithm.

Disclosure of Invention

Aiming at the problems existing in the prior art, the invention provides a cooperative method and device for power distribution and tracking speed of a vehicle.

The invention provides a cooperative method for power distribution and tracking speed of a vehicle, which comprises the following steps:

acquiring M groups of vehicle tracking speed sequences and N groups of vehicle power distribution sequences; the vehicle tracking speed sequence comprises maximum allowable speeds corresponding to every two stations, and the vehicle power distribution sequence comprises maximum allowable output powers corresponding to at least two energy sources;

constructing a collaborative optimization system based on the M sets of vehicle tracking speed sequences and the N sets of vehicle power distribution sequences;

and solving an optimal solution of the collaborative optimization system based on an artificial fish swarm algorithm and a multi-population firefly algorithm to obtain a target vehicle tracking speed sequence and a target vehicle power distribution sequence.

The invention provides a cooperative method of power distribution and tracking speed of a vehicle, which constructs a cooperative optimization system based on M groups of vehicle tracking speed sequences and N groups of vehicle power distribution sequences, and comprises the following steps:

constructing a vehicle tracking speed curve generating system based on the M groups of vehicle tracking speed sequences;

Constructing a vehicle power distribution system based on the N sets of vehicle power distribution sequences;

the collaborative optimization system is constructed based on the vehicle tracking speed profile generation system and the vehicle power distribution system.

The invention provides a cooperative method of power distribution and tracking speed of a vehicle, which solves the optimal solution of a cooperative optimization system based on an artificial fish swarm algorithm and a multi-group firefly algorithm to obtain a target vehicle tracking speed sequence and a target vehicle power distribution sequence, and comprises the following steps:

determining each group of the vehicle tracking speed sequences as the position of each fish, and determining the expected position of the group behavior and the expected position of the following behavior of each fish based on the position of each fish;

inputting the expected positions of the group behaviors and the expected positions of the following behaviors of each fish into the vehicle tracking speed curve generating system to obtain a first tracking speed curve corresponding to the expected positions of the group behaviors and a second tracking speed curve corresponding to the expected positions of the following behaviors;

determining a corresponding first target vehicle required power based on the first tracking speed curve, and determining a corresponding second target vehicle required power based on the second tracking speed curve;

Inputting the first target vehicle required power and the second target vehicle required power into the vehicle power distribution system, and solving the vehicle power distribution system based on the multi-population firefly algorithm to obtain a first minimum running cost corresponding to the first target vehicle required power and a second minimum running cost corresponding to the second target vehicle required power;

transmitting a first maximum charm value corresponding to the first minimum running cost and a second maximum charm value corresponding to the second minimum running cost to the vehicle tracking speed curve generating system, and solving the vehicle tracking speed curve generating system based on the artificial fish swarm algorithm to obtain the target vehicle tracking speed sequence;

the target vehicle power allocation sequence is determined based on the target vehicle tracking speed sequence.

The invention provides a cooperative method of power distribution and tracking speed of a vehicle, which is characterized in that the corresponding first target vehicle required power is determined based on the first tracking speed curve, and the corresponding second target vehicle required power is determined based on the second tracking speed curve, and the cooperative method comprises the following steps:

And inputting the first tracking speed curve and the second tracking speed curve into a tracking speed and power distribution algorithm coupling model to obtain first target vehicle required power corresponding to the first tracking speed curve and second target vehicle required power corresponding to the second tracking speed curve output by the tracking speed and power distribution algorithm coupling model.

The invention provides a cooperative method of power distribution and tracking speed of a vehicle, which is characterized in that the vehicle power distribution system is solved based on the multi-group firefly algorithm to obtain a first minimum running cost corresponding to the first target vehicle required power, and the cooperative method comprises the following steps:

determining each group of vehicle power distribution sequences as the position of each firefly, and determining the first running cost corresponding to each firefly based on the first target vehicle required power;

determining a first charm value for each firefly based on a first operating cost for each firefly;

updating the position of each firefly based on the first charm value of each firefly, and circularly updating for a first preset time to obtain a first final position of each firefly;

Determining a maximum first charm value of first charm values corresponding to a first final position of each firefly, and determining a first operation cost corresponding to the maximum first charm value as a first minimum operation cost;

the solving the vehicle power distribution system based on the multi-population firefly algorithm to obtain a second minimum running cost corresponding to the first target vehicle required power comprises the following steps:

determining a second running cost corresponding to each firefly based on the second target vehicle required power;

determining a second charm value for each firefly based on a second operating cost for each firefly;

updating the position of each firefly based on the second charm value of each firefly, and circularly updating the position for the first preset time to obtain a second final position of each firefly;

and determining a maximum second charm value of second charm values corresponding to a second final position of each firefly, and determining a second operation cost corresponding to the maximum second charm value as a second minimum operation cost.

The invention provides a cooperative method of power distribution and tracking speed of a vehicle, wherein the method for transmitting a first charm value corresponding to a first minimum running cost and a second charm value corresponding to a second minimum running cost to a vehicle tracking speed curve generating system, solving the vehicle tracking speed curve generating system based on the artificial fish swarm algorithm to obtain the target vehicle tracking speed sequence comprises the following steps:

Determining the first charm value as a first food concentration value corresponding to the expected position of the group behavior of each fish, and determining the second charm value as a second food concentration value corresponding to the expected position of the following behavior of each fish;

updating the position of the corresponding fish based on the first food concentration value and the second food concentration value until the final position of each fish is obtained after updating the second preset time;

determining the maximum food concentration value in the food concentration values corresponding to the final positions of the fishes; and determining a vehicle tracking speed sequence corresponding to the fish with the maximum food concentration value as a target vehicle tracking speed sequence.

The invention provides a cooperative method of power distribution and tracking speed of a vehicle, which is characterized in that the power distribution sequence of the target vehicle is determined based on the tracking speed sequence of the target vehicle, and the cooperative method comprises the following steps:

determining a maximum food concentration value of the fish corresponding to the target vehicle tracking speed sequence, and determining a corresponding target maximum charm value based on the maximum food concentration value;

and determining a vehicle power distribution sequence corresponding to the firefly of the target maximum charm value as the target vehicle power distribution sequence.

The invention provides a cooperative method for power distribution and tracking speed of a vehicle, wherein the updating of the position of a corresponding fish based on a first food concentration value and a second food concentration value comprises the following steps:

when the first food concentration value is determined to be larger than the second food concentration value, updating the position of the corresponding fish to be the expected position of the group behavior;

and updating the position of the corresponding fish to be the expected position of the following behavior when the first food concentration value is smaller than the second food concentration value.

The invention also provides a cooperative device for power distribution and tracking speed of a vehicle, which comprises:

the acquisition unit is used for acquiring M groups of vehicle tracking speed sequences and N groups of vehicle power distribution sequences; the vehicle tracking speed sequence comprises maximum allowable speeds corresponding to every two stations, and the vehicle power distribution sequence comprises maximum allowable output powers corresponding to at least two energy sources;

the construction unit is used for constructing a collaborative optimization system based on the M groups of vehicle tracking speed sequences and the N groups of vehicle power distribution sequences;

and the solving unit is used for solving the optimal solution of the collaborative optimization system based on an artificial fish swarm algorithm and a multi-population firefly algorithm to obtain a target vehicle tracking speed sequence and a target vehicle power distribution sequence.

The invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing a synergistic method of power distribution and tracking speed of a vehicle as any one of the above when executing the program.

The present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a synergistic method of power distribution and tracking speed of a vehicle as described in any of the above.

The invention also provides a computer program product comprising a computer program which when executed by a processor implements a synergistic method of power distribution and tracking speed of a vehicle as described in any one of the above.

According to the collaborative method and device for the power distribution and the tracking speed of the vehicle, disclosed by the invention, a collaborative optimization system constructed based on the vehicle tracking speed sequence and the vehicle power distribution sequence is solved by utilizing an artificial fish swarm algorithm and a multi-group firefly algorithm, so that a target vehicle tracking speed sequence and a target vehicle power distribution sequence are obtained. It can be seen that the invention considers the influence of the cooperative relationship of the vehicle tracking speed sequence and the vehicle power distribution sequence on the vehicle energy supply, and can more reasonably distribute the power supply of each energy source in the hybrid power system.

Drawings

In order to more clearly illustrate the invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a coordinated method of power distribution and tracking speed for a vehicle provided by the present invention;

FIG. 2 is a schematic diagram of a collaborative optimization algorithm of an artificial fish swarm algorithm and a multi-population firefly algorithm provided by the invention;

FIG. 3 is a schematic view of the overall system model of the vehicle provided by the present invention;

FIG. 4 is a schematic diagram of the operation flow of the power allocation algorithm provided by the present invention;

FIG. 5 is a schematic diagram of a coordinated apparatus for power distribution and speed tracking of a vehicle provided by the present invention;

fig. 6 is a schematic diagram of the physical structure of the electronic device provided by the present invention.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

In the prior art, the tracking speed curve of the vehicle and the power distribution algorithm are usually optimized independently, and the influence of the cooperative relationship between the tracking speed curve of the vehicle and the power distribution algorithm on the energy supply of the vehicle is not considered.

The cooperative method of power distribution and tracking speed of the vehicle of the present invention is described below with reference to fig. 1-4.

Fig. 1 is a schematic flow chart of a method for coordinating power distribution and tracking speed of a vehicle, which is applied to an oxyhydrogen power rail vehicle, and as shown in fig. 1, the method for coordinating power distribution and tracking speed of the vehicle comprises the following steps:

step 101, acquiring M groups of vehicle tracking speed sequences and N groups of vehicle power distribution sequences; the vehicle tracking speed sequence comprises maximum allowable speeds corresponding to every two stations, and the vehicle power distribution sequence comprises maximum allowable output powers corresponding to at least two energy sources.

Each parameter in the vehicle tracking speed sequence is the maximum allowable speed of a train between two stations in the vehicle tracking speed curve, each parameter in the vehicle power distribution sequence is the maximum allowable output power of each energy source in the hybrid power system, and the maximum allowable output power of each energy source is the maximum allowable output power corresponding to each moment of each energy source.

By way of example, the invention obtains the maximum allowable speed of the train between two stations after obtaining the corrected tracking speed curve of the vehicle, and obtains the maximum allowable output power of each energy source after each energy source is determined in the train hybrid power system.

And 102, constructing a collaborative optimization system based on the M groups of vehicle tracking speed sequences and the N groups of vehicle power distribution sequences.

Illustratively, the co-optimization system is derived based on the vehicle tracking speed and the target vehicle demand power.

The relation between the vehicle tracking speed and the required power of the target vehicle is as follows:

the vehicle tracking speed is gradually converted into vehicle motor required power through intermediate models such as vehicle dynamics, vehicles, a transmission system and a motor, the vehicle motor required power is the sum of the power of three energy sources of an oxyhydrogen power device, a super capacitor and a power battery, and then the vehicle tracking speed and the target vehicle required power are shown in the following formula (1):

wherein m is the vehicle mass, g is the gravitational acceleration, v is the running speed of the train at the current moment, vt is the tracking speed of the train at the current moment, A is the resistance coefficient related to the train weight, B is the resistance coefficient related to the air momentum loss, C is the resistance coefficient related to the aerodynamics, zr is the gradient, rc is the curve radius of the line, r is the radius of the wheels, jm is the rotational inertia of the motor, z is the gear ratio, etam is the motor efficiency, and etam is the transmission efficiency of the motor efficiency gearbox. According to equation (1), a relationship of the vehicle tracking speed vt and the power distribution algorithm input Pd may be established.

And (3) obtaining target vehicle required power corresponding to the M groups of vehicle tracking speed sequences based on the formula (1), obtaining N groups of vehicle power distribution sequences based on the target vehicle required power and the maximum allowable output power corresponding to each energy source, and finally constructing a collaborative optimization system based on the M groups of vehicle tracking speed sequences and the N groups of vehicle power distribution sequences.

And step 103, solving an optimal solution of the collaborative optimization system based on an artificial fish swarm algorithm and a multi-population firefly algorithm to obtain a target vehicle tracking speed sequence and a target vehicle power distribution sequence.

Fig. 2 is a schematic diagram of a collaborative optimization algorithm of an artificial fish swarm algorithm and a multi-population firefly algorithm provided by the invention, and as shown in fig. 2, the collaborative optimization algorithm of the artificial fish swarm algorithm and the multi-population firefly algorithm comprises a system a and a system B, and the specific steps are as follows:

(1) Defining the number M of fish and the maximum iteration number Γ ₁ The perceived distance of the fish, the step size of the movement and the crowding factor. Randomly generating M fish, each fish comprising a set of sequences [ v1, v2, …, vn]。

(2) And obtaining the expected position of the group behavior of the ith fish.

Specifically, according to the following formula (2), the expected position X of the group behavior of the ith fish is obtained _isw ：

Wherein v is _l For the first dimension variable of the corresponding sequence of other fish in the field of view of the ith fish, l=1, 2,3, …, n _f ，n _f The number of other fish in the field of view of the ith fish; x is X _i The current position of the ith fish; rand is a random number obeying uniform distribution from 0 to 1;

then determine position X _isw Whether or not to crowd; if crowding, giving high punishment weight, wherein the punishment weight is used for setting the value of the food concentration, the high punishment weight corresponds to setting the value of the low food concentration, and the low punishment weight corresponds to setting the value of the high food concentration; the location is reserved if not crowded.

(3) And obtaining the expected following behavior position of the ith fish.

Specifically, the following behavior means that the ith fish follows the jth fish having the highest food concentration in its field of view. According to, e.g.The following equation (3) is given to obtain the expected position X of the following behavior _ifo ：

Wherein X is _j Is the current position of the j-th fish.

Then, the expected position X is judged _ifo Whether the place is crowded; if crowded, give the high punishment weight; the location is reserved if not crowded.

(4) The optimization process of system B begins. And sequentially inputting a sequence corresponding to the expected positions of the group behaviors of the ith fish and the expected positions of the following behaviors into the system A, and taking the obtained result as an initial condition for optimizing the system B.

(5) Initializing relevant parameters of a multi-population firefly algorithm. The method comprises the steps of insect population F, the number of fireflies in each population, the maximum iteration number, the charm coefficient, the maximum attractive force, the minimum attractive force and the moving step range.

(6) Generating initial positions of fireflies in all the insect groups.

Wherein the position of the ith firefly is represented by a sequence: [ P1, P2, P3], in addition, F sets of movement steps and minimum attractive force are generated.

(7) And (3) inputting the position sequences corresponding to all fireflies into the system B, and then inputting the whole system model for simulation to obtain a target vehicle tracking speed sequence and a target vehicle power distribution sequence.

(8) With system B, all firefly corresponding charm values were obtained.

(9) Traversing all child insect populations.

(10) All fireflies in each sub-population are traversed.

(11) The principle of updating the position of the ith firefly is to move to the firefly with the highest charm value. Firstly, randomly finding another firefly j, and calculating the distance r between the two fireflies according to the following formula (4) _ij ：

Wherein x is _i,k Is the kth variable, x in the position sequence of the ith firefly _j,k K=1, 2,3, which is the kth variable in the sequence of positions of the jth firefly.

Then, the charm values of the two fireflies were compared. Assuming that the attractive value of firefly j is greater than the attractive value of firefly i, the attractive force β of firefly j to firefly i _ji Calculated from the following equation (5):

wherein beta is ₀ For the maximum attractive force of firefly j, γ is the charm coefficient and e is the natural constant.

Finally, the position of firefly i is updated using the following equation (6):

X _i (t+1)＝X _i (t)+β _ji ·(X _i (t)-X _j (t))+α(rand-0.5) (6)

wherein X is _i (t+1) is the position of firefly i at time t+1, and α is the moving step length of firefly.

(12): after the location update, the charm values of all fireflies were calculated.

(13): and (3) performing an optimal individual migration process, namely flying fireflies with highest charm values in the jth sub-insect group into the (j+1) th sub-insect group, removing fireflies with lowest charm values in the (j+1) th sub-insect group, and the like.

(14): and (3) performing an optimal individual screening process, searching optimal fireflies in all sub-populations, and storing the optimal fireflies in an optimal population.

(15): cycling through steps (8) to (14) to obtain fireflies with the highest charm value, and transmitting the highest charm value to system a as the food concentration at the expected location of the ith fish.

(16): obtaining the food concentration Y of the expected position of the group behavior of the ith fish _isw And food concentration following the expected location of the behavior Y _ifo . Comparing the two sizes, and updating the position of the ith fish according to the comparison result.

(17): and (3) performing the steps (2) to (16) circularly until the positions of all the fishes are updated.

(18): and (3) circularly executing the steps (2) to (17) until the maximum iteration number is reached, and obtaining the optimal solution of the system A. And determining the optimal solution of the corresponding system B based on the optimal solution of the system A, and synthesizing the optimal solutions of the system A and the system B to obtain a final solution.

The invention applies a collaborative optimization algorithm based on an artificial fish swarm algorithm and a multi-population firefly algorithm to the collaborative optimization problem of power distribution and tracking speed of oxyhydrogen power vehicles. Consider system a as a vehicle tracking speed profile generation system and system B as a vehicle power distribution system. For the outer layer process in the collaborative optimization algorithm, the outer layer process is the optimization process of the system A, the inner layer process is the optimization process of the system B, the sequences [ v1, v2, …, vn ] are considered as the maximum allowable inter-station speeds in the vehicle tracking speed curve, and the sequences [ P1, P2, P3] are considered as the maximum allowable output powers of three energy sources of the oxyhydrogen power vehicle, so that the collaborative optimization of the power distribution algorithm of the oxyhydrogen power vehicle and the tracking speed curve can be completed, and the target vehicle tracking speed sequence and the target vehicle power distribution sequence can be obtained.

Further, as shown in fig. 3, after the step 103 is performed, the target vehicle power distribution sequence obtained in the step 103 may be input to a hybrid power system controller, where the hybrid power system controller distributes output power of the oxyhydrogen power device, the super capacitor and the power battery in real time according to the state of the energy source, so as to provide electric energy for the vehicle motor, and the target vehicle tracking speed sequence obtained in the step 103 is input to a speed control unit of a vehicle-mounted train controller, where the vehicle-mounted train controller outputs a tracking speed value to the vehicle motor in real time according to the current position of the vehicle.

According to the collaborative method for the power distribution and the tracking speed of the vehicle, disclosed by the invention, a collaborative optimization system constructed based on the vehicle tracking speed sequence and the vehicle power distribution sequence is solved by utilizing an artificial fish swarm algorithm and a multi-group firefly algorithm, so that a target vehicle tracking speed sequence and a target vehicle power distribution sequence are obtained. It can be seen that the invention considers the influence of the cooperative relationship of the vehicle tracking speed sequence and the vehicle power distribution sequence on the vehicle energy supply, and can more reasonably distribute the power supply of each energy source in the hybrid power system.

Alternatively, step 102 may be specifically implemented by:

Illustratively, the process of constructing a vehicle tracking speed profile generation system based on M sets of vehicle tracking speed sequences is as follows:

for the route between the i-th station and the i+1th station, the corresponding tracking speed curve can be expressed by the following formula (7):

{v _t (x)|x _s,i ≤x≤x _s,i+1 ,0≤v _t (x)≤v _lim (x)} (7)

wherein v is _t (x) For tracking speed of vehicle at position x, x _s,i For the position of the ith station, x _s,i+1 V is the position of the (i+1) th station _lim (x) Is the speed limit of the vehicle at x.

First, assume that the maximum travel speed of the vehicle between two stations is v _maxi Then the vehicle is from x _s,i Starting to accelerate to v with infinite acceleration _maxi Then uniformly driving to x _s,i+1 Finally braking with infinite deceleration, which is an ideal tracking speed profile for the vehicle. The line speed limit constraints are then taken into account for the vehicle ideal tracking speed profile. The vehicle tracking speed profile limit speed is calculated according to the following formula (8):

wherein S is _rh Is the distance between the center lines of the rail heads of the two steel rails. H _r Setting super high value for vehicle running track, H _d A maximum underelevation value is allowed for the vehicle running track. g is gravitational acceleration. r is (r) _cu Is the curve radius of the running track of the vehicle. According to v _lim (x) The ideal tracking speed profile can be modified. The acceleration performance constraints are then taken into account for the vehicle tracking speed profile. The vehicle acceleration capacity is limited by the motor maximum traction, the electrical device constraints, and the maximum output power of the hybrid powertrain system, calculated according to equation (9) below:

a _ra ＝min(a _d ，a _equ ，a _pow ) (9)

wherein a is _ra Is the actual maximum acceleration of the vehicle. a, a _d 、a _equ And a _pow Maximum acceleration taking into account motor, electrical equipment and hybrid powertrain constraints, respectively. Using a _d The velocity profile of equation (7) is modified. Finally, the vehicle tracking speed profile is considered with respect to constraints on braking performance. The solution of the braking capability limit is similar to the acceleration case. And (3) after the actual maximum braking acceleration of the vehicle is obtained, correcting the speed curve obtained by the formula (7) by using a reverse pushing method.

The process of constructing a vehicle power distribution system based on N sets of vehicle power distribution sequences is as follows:

the power distribution algorithm decides the output power of each energy source in real time according to the current state of the vehicle. The power distribution algorithm makes the power distribution according to the bus demand power, the state of charge of the super capacitor and the power battery, and refers to a decision rule made in advance to distribute the output power of each energy source. The flow of operation of the power allocation algorithm is shown in fig. 4.

Wherein Pd is the vehicle required power. Pfma, psdm, pbdm are the maximum allowable output power of the oxyhydrogen power plant, super capacitor and power battery, respectively. Pscm, pbcm are the maximum allowable input power of the oxyhydrogen power device, the super capacitor and the power battery respectively. SOCs and SOCb are the states of charge of the super capacitor and the power battery, respectively. SOCsh and SOCbh are the upper limit values of the charge states of the super capacitor and the power battery respectively. Pfmi is the minimum allowable output power of the oxyhydrogen electrochemical reaction device. According to the illustration of fig. 4, the vehicle power distribution algorithm divides the state in 11. The power balance equations for the oxyhydrogen power plant output power Pf, the super capacitor output power Ps, and the power battery output power Pb in each state are as follows:

state 1: pd=pf.

State 2: pd=pf+ps.

State 3: pd=pf+ps+pb.

State 4: pd=pf- |ps|.

State 5: pd=pf- |pb|.

State 6: pd=pf- |ps|.

State 7: pd=pf- |pb|.

State 8: pd+pf= |+|ps|+|pb|+|pm|.

State 9: pd+pf= |+|ps|+|pb|.

State 10: pd+pf= |ps|.

State 11: pd=pf+ps+pb.

Then, based on the formula (1), a collaborative optimization system is constructed based on the M sets of vehicle tracking speed sequences and the N sets of vehicle power distribution sequences.

According to the collaborative method for the power distribution and the tracking speed of the vehicle, the actual limiting factors are considered for the tracking speed sequence of the vehicle, the real tracking speed sequence can be obtained, the real-time power distribution algorithm of the vehicle can be obtained based on the vehicle power distribution sequence and the vehicle target required power, and the collaborative optimization system constructed based on the vehicle tracking speed sequence and the vehicle power distribution sequence can be used for obtaining the real-time power distribution algorithm of the vehicle based on the real tracking speed sequence of the vehicle, so that the power supply of each energy source in the hybrid power system can be distributed more reasonably.

Optionally, the solving the optimal solution of the collaborative optimization system based on the artificial fish swarm algorithm and the multi-population firefly algorithm to obtain a target vehicle tracking speed sequence and a target vehicle power distribution sequence includes:

Wherein, the expected position of the group behavior of each fish is determined based on the above formula (2), and the expected position of the following behavior of each fish is determined based on the above formula (3).

And then inputting the expected positions of the group behaviors of each fish into a vehicle tracking speed curve generating system, namely a formula (7), so as to obtain a first tracking speed curve corresponding to the expected positions of the group behaviors, and inputting the expected positions of the following behaviors of each fish into the vehicle tracking speed curve generating system, namely the formula (7), so as to obtain a second tracking speed curve corresponding to the expected positions of the following behaviors.

Then, the first tracking speed curve is input into the formula (1), the corresponding first target vehicle required power is determined, the second tracking speed curve is input into the formula (1), and the corresponding second target vehicle required power is determined.

According to the cooperative method for the power distribution and the tracking speed of the vehicle, an artificial fish swarm algorithm and a multi-group firefly algorithm are applied to a vehicle tracking speed curve generating system and a vehicle power distribution system, a tracking speed curve, target vehicle required power, vehicle running cost and vehicle power distribution can be related, and a target vehicle tracking speed sequence and a target vehicle power distribution sequence can be obtained through cooperative solution.

Optionally, the determining the corresponding first target vehicle required power based on the first tracking speed curve and the determining the corresponding second target vehicle required power based on the second tracking speed curve includes:

Wherein the tracking speed and power allocation algorithm coupling model is constructed based on formula (1). And then, inputting the first tracking speed curve into a formula (1), determining the corresponding first target vehicle required power, inputting the second tracking speed curve into the formula (1), and determining the corresponding second target vehicle required power.

The method for combining the power distribution and the tracking speed of the vehicle can convert the tracking speed curve into the required power of the target vehicle through the coupling model of the tracking speed and the power distribution algorithm.

Optionally, the solving the vehicle power distribution system based on the multi-population firefly algorithm to obtain a first minimum running cost corresponding to the first target vehicle required power includes:

Illustratively, the total vehicle operation cost Cf is calculated according to formulas (10) to (22):

Cf＝FCf+FCc+FCr+FCm (10)

FCf＝mf·Cfu (11)

FCc＝(Pdf·Cd+Caf·Cfc)·CRF (12)

wherein FCf is hydrogen fuel cost, FCc is device acquisition cost, FCr is device replacement cost, and FCm is device maintenance cost. mf is the fuel consumption, cfu is the hydrogen fuel consumption unit price. Pdf is the device's corresponding power converter capacity, CD is the power converter capacity price, caf is the device's power, and Cfc is the device's price per unit of power. I is interest rate. T is the expected maximum usage time of the system. Nrf is the number of devices that need to be replaced. Lf is the expected lifetime of the device. CRF is the capital recovery factor. k is the accumulated variable of the computation cost.

The cost of the super capacitor is shown in the formulas (15) to (18):

Cs＝SCc+SCr+SCm+SCch (15)

SCc＝(Pds·Cd+Cas·Csc)·CRF (16)

SCch＝Es·Cg (18)

wherein Cs is the super capacitor cost, SCc is the super capacitor acquisition cost, SCr is the super capacitor replacement cost, SCm is the super capacitor maintenance cost, and SCch is the super capacitor charging cost. Pds is the supercapacitor corresponds to the power converter capacity, cas is the supercapacitor power, and Csc is the price per unit power of the supercapacitor. Nrs is the number of supercapacitors that need to be replaced. Ls is the expected lifetime of the supercapacitor. Es is the charging energy of the super capacitor, and Cg is the electricity price.

The power battery cost is shown in the formulas (19) to (22):

Cb＝BATc+BATr+BATm+BATch (19)

BATc＝(Pdb·Cd+Cab·Cba)·CRF (20)

BATch＝Eb·Cg (22)

in the above formula, cb is the cost of the power battery, BATc is the acquisition cost of the power battery, BATr is the replacement cost of the power battery, BATm is the maintenance cost of the power battery, and BATch is the charging cost of the power battery. Pdb is the power battery corresponding power converter capacity, cab is the power battery power, and Cba is the price per unit power of the power battery. Nrb is the number of power cells that need to be replaced. Lb is the life expectancy of the power cell. Eb is the charging energy of the power battery.

The lower the running cost, the larger the corresponding charm value, for example, the running cost is 100 yuan, the corresponding charm value thereof may be set to 10, the running cost is 200 yuan, and the corresponding charm value thereof may be set to 5.

Optionally, the transferring the first charm value corresponding to the first minimum running cost and the second charm value corresponding to the second minimum running cost to the vehicle tracking speed curve generating system, solving the vehicle tracking speed curve generating system based on the artificial fish swarm algorithm to obtain the target vehicle tracking speed sequence includes:

For example, if the first charm value corresponding to the expected position of the group behavior of a certain fish is calculated to be 10, the first food concentration value corresponding to the certain fish is determined to be 10, and if the second charm value corresponding to the expected position of the following behavior of the certain fish is calculated to be 5, the second food concentration value corresponding to the certain fish is determined to be 5.

Optionally, the determining the target vehicle power allocation sequence based on the target vehicle tracking speed sequence includes:

For example, if the target vehicle tracking speed sequence corresponds to a fish corresponding to a maximum food concentration value, the corresponding maximum food concentration value may be determined based on the target vehicle tracking speed sequence, and since the food concentration value of the fish is a charm value of fireflies, the corresponding target maximum charm value may be determined based on the maximum food concentration value.

And then determining a vehicle power distribution sequence corresponding to the firefly of the target maximum charm value as the target vehicle power distribution sequence.

Optionally, the updating the location of the corresponding fish based on the first food concentration value and the second food concentration value includes:

For example, when the first food concentration value is determined to be larger than the second food concentration value, the food concentration value at the expected position of the group behavior is larger, and the fish moves to the position with the larger food concentration value, so that the position where the corresponding fish is located is updated to be the expected position of the group behavior; when the second food concentration value is determined to be larger than the first food concentration value, the food concentration value at the expected position of the following behavior is larger, and the fish moves to the position with the larger food concentration value, so that the position where the corresponding fish is located is updated to be the expected position of the following behavior.

The following describes in detail a cooperative method of power distribution and tracking speed of a vehicle with reference to a cooperative optimization algorithm of an artificial fish swarm algorithm and a multi-population firefly algorithm shown in fig. 2, and a cooperative optimization system corresponding to the cooperative method of power distribution and tracking speed of a vehicle includes a vehicle tracking speed curve generating system (outer layer system) and a vehicle power distribution system (inner layer system):

firstly, setting parameters of a collaborative optimization system: the number of fish in the fish shoal is M, and the maximum iteration number gamma ₁ For 50, the perceived distance of the fish, the step size of the movement and the crowding factor are set. The population number of fireflies is G, the number of individuals contained in each firefly population is u, and the maximum iteration number gamma ₂ For 50, the charm coefficient, the maximum charm value, the minimum charm value, and the movement step range are set at the same time. Then starting a collaborative optimization process, wherein the steps of the collaborative optimization process are as follows:

1) An initial fish school is generated. According to the constraints, M sets of vehicle tracking speed sequences [ vmax1, vmax2, …, vmaxn ] are randomly generated, each set representing the location of a fish. M fish together constitute the initial fish school. And obtaining M vehicle tracking speed curves according to the speed sequences corresponding to the M fishes.

2) The outer layer optimization process is started. Starting from the 1 st fish, the expected position of the group behavior of the 1 st fish is obtained according to the formula (2), and the expected position of the following behavior of the 1 st fish is obtained according to the formula (3). And respectively inputting the expected positions of the group behaviors and the expected positions of the following behaviors into a vehicle tracking speed curve generating system to obtain two vehicle tracking speed curves. Substituting the two vehicle tracking speed curves into a tracking speed and power distribution algorithm coupling model serving as initial conditions, wherein the tracking speed and power distribution algorithm coupling model is constructed based on a formula (1). And converting a vehicle tracking speed curve into target vehicle required power through a tracking speed and power distribution algorithm coupling model, and synchronously performing an inner layer optimization process on the expected positions of the group behaviors and the expected positions of the following behaviors.

3) The inner layer optimization process is started. According to the constraints, G times u groups (G times u equals N) of vehicle power distribution sequences [ Pfm, lm, pbm ], each group of vehicle power distribution sequences representing the location of each firefly, are randomly generated. Thus, G populations were generated, each population u together constituting the initial multiple population. Depending on the location of the firefly and what is described in fig. 4, all firefly-corresponding power distribution algorithms can be obtained. The power distribution algorithm is substituted into the complete vehicle model simultaneously with the corresponding tracking speed curve. The complete vehicle model comprises a tracking speed curve generating system, a vehicle power distribution system and an energy source model besides the formula (1). Route simulation is then performed based on the complete vehicle model. And obtaining output power change curves of the oxyhydrogen power device, the power battery and the super capacitor and charge state change curves of the power battery and the super capacitor from simulation results, and calculating the total running cost Cf of the vehicle according to formulas (10) to (23).

4) And (5) inner layer iterative optimization. And converting the operation cost corresponding to all fireflies into charm values of the fireflies, and based on the charm values, carrying out position updating on the fireflies by combining the formulas (4) to (6).

5) Circularly executing the step 4) common gamma ₂ And respectively obtaining expected positions of the group behaviors and positions of fireflies with highest charm values after the cycle corresponding to the expected positions of the following behaviors is finished, namely the current optimal solution of the vehicle power distribution system. And transmitting the first maximum charm value corresponding to the expected position of the group action and the second maximum charm value corresponding to the expected position of the following action to an outer layer optimization process, and simultaneously converting the first maximum charm value and the second maximum charm value into the values of the food concentration of the expected position of the group action and the expected position of the following action of the 1 st fish respectively. Comparing the expected positions of the group behaviors and the food concentrations of the expected positions of the following behaviors, and moving the 1 st fish to the position corresponding to the higher physical concentration value.

6) And updating the fish school position. Steps 2) to 5) are performed for each fish cycle to obtain the food concentration values for all fish locations. Then, the positions of all the fish in the fish school are updated.

7) A final solution is obtained. Executing Γ for each fish cycle ₁ And 2) to 6), obtaining the final positions of all the fishes, determining the maximum food concentration value according to the food concentration values of the final positions of all the fishes, and taking the fishes corresponding to the maximum food concentration value as optimal fishes. The vehicle tracking speed sequence corresponding to the optimal fish is the target vehicle tracking speed sequence of the oxyhydrogen power rail vehicle, and the target vehicle power distribution sequence is determined based on the target vehicle tracking speed sequence.

8) And (5) optimal solution conversion application. And converting the target vehicle tracking speed sequence into a vehicle tracking speed curve and inputting the vehicle tracking speed curve into a vehicle-mounted train controller. The vehicle-mounted train controller can acquire the tracking speed of the vehicle from the curve in real time according to the current position of the vehicle, so as to guide the vehicle to run. The target vehicle power distribution sequence is converted into a power distribution algorithm of the hybrid power system and is input to a hybrid power system controller. The hybrid power system controller can distribute the output power of three energy sources in real time according to the real-time required power of the vehicle and the states of the oxyhydrogen power device, the power battery and the super capacitor.

The following describes a cooperative apparatus for power distribution and tracking speed of a vehicle, and the cooperative apparatus for power distribution and tracking speed of a vehicle described below and the cooperative method for power distribution and tracking speed of a vehicle described above may be referred to correspondingly to each other.

Fig. 5 is a schematic structural diagram of a cooperative apparatus for power distribution and tracking speed of a vehicle, provided by the present invention, and as shown in fig. 5, the cooperative apparatus for power distribution and tracking speed of a vehicle includes an acquisition unit 501, a construction unit 502, and a solving unit 503; wherein:

An acquisition unit 501 for acquiring M sets of vehicle tracking speed sequences and N sets of vehicle power distribution sequences; the vehicle tracking speed sequence comprises maximum allowable speeds corresponding to every two stations, and the vehicle power distribution sequence comprises maximum allowable output powers corresponding to at least two energy sources;

a construction unit 502 for constructing a collaborative optimization system based on the M sets of vehicle tracking speed sequences and the N sets of vehicle power allocation sequences;

and the solving unit 503 is configured to solve an optimal solution of the collaborative optimization system based on an artificial fish swarm algorithm and a multi-population firefly algorithm, so as to obtain a target vehicle tracking speed sequence and a target vehicle power distribution sequence.

According to the cooperative device for the power distribution and the tracking speed of the vehicle, disclosed by the invention, a cooperative optimization system constructed based on the vehicle tracking speed sequence and the vehicle power distribution sequence is solved by utilizing an artificial fish swarm algorithm and a multi-group firefly algorithm, so that a target vehicle tracking speed sequence and a target vehicle power distribution sequence are obtained. It can be seen that the invention considers the influence of the cooperative relationship of the vehicle tracking speed sequence and the vehicle power distribution sequence on the vehicle energy supply, and can more reasonably distribute the power supply of each energy source in the hybrid power system.

Based on any of the above embodiments, the construction unit 502 is specifically configured to:

Based on any of the above embodiments, the solving unit 503 is specifically configured to:

Fig. 6 illustrates a physical schematic diagram of an electronic device, as shown in fig. 6, which may include: processor 610, communication interface (Communications Interface) 620, memory 630, and communication bus 640, wherein processor 610, communication interface 620, and memory 630 communicate with each other via communication bus 640. The processor 610 may invoke logic instructions in the memory 630 to perform a coordinated method of power distribution and tracking speed of a vehicle, the method comprising: acquiring M groups of vehicle tracking speed sequences and N groups of vehicle power distribution sequences; the vehicle tracking speed sequence comprises maximum allowable speeds corresponding to every two stations, and the vehicle power distribution sequence comprises maximum allowable output powers corresponding to at least two energy sources;

Further, the logic instructions in the memory 630 may be implemented in the form of software functional units and stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

In another aspect, the present invention also provides a computer program product comprising a computer program storable on a non-transitory computer readable storage medium, the computer program, when executed by a processor, being capable of performing the synergistic method of power distribution and tracking speed of a vehicle provided by the methods described above, the method comprising: acquiring M groups of vehicle tracking speed sequences and N groups of vehicle power distribution sequences; the vehicle tracking speed sequence comprises maximum allowable speeds corresponding to every two stations, and the vehicle power distribution sequence comprises maximum allowable output powers corresponding to at least two energy sources;

In yet another aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which when executed by a processor is implemented to perform a synergistic method of power distribution and tracking speed of a vehicle provided by the methods described above, the method comprising: acquiring M groups of vehicle tracking speed sequences and N groups of vehicle power distribution sequences; the vehicle tracking speed sequence comprises maximum allowable speeds corresponding to every two stations, and the vehicle power distribution sequence comprises maximum allowable output powers corresponding to at least two energy sources;

The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.

From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.

Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method of coordinating power distribution and tracking speed of a vehicle, comprising:

solving an optimal solution of the collaborative optimization system based on an artificial fish swarm algorithm and a multi-population firefly algorithm to obtain a target vehicle tracking speed sequence and a target vehicle power distribution sequence;

Wherein said constructing a collaborative optimization system based on said M sets of vehicle tracking speed sequences and said N sets of vehicle power distribution sequences comprises:

constructing the collaborative optimization system based on the vehicle tracking speed profile generation system and the vehicle power distribution system;

the method for solving the optimal solution of the collaborative optimization system based on the artificial fish swarm algorithm and the multi-population firefly algorithm to obtain a target vehicle tracking speed sequence and a target vehicle power distribution sequence comprises the following steps:

2. The cooperative method of power distribution and tracking speed of a vehicle of claim 1, wherein the determining a corresponding first target vehicle demand power based on the first tracking speed profile and determining a corresponding second target vehicle demand power based on the second tracking speed profile comprises:

3. The method for collaborative power distribution and speed tracking for a vehicle according to claim 1, wherein solving the vehicle power distribution system based on the multi-population firefly algorithm to obtain a first minimum operating cost corresponding to the first target vehicle demand power comprises:

4. The method for collaborative power distribution and tracking of a vehicle according to claim 1, wherein the transferring the first charm value corresponding to the first minimum operating cost and the second charm value corresponding to the second minimum operating cost to the vehicle tracking speed profile generation system, solving the vehicle tracking speed profile generation system based on the artificial fish swarm algorithm to obtain the target vehicle tracking speed sequence, comprises:

5. The cooperative method of power distribution and tracking speed of a vehicle of claim 4, wherein the determining the target vehicle power distribution sequence based on the target vehicle tracking speed sequence comprises:

6. The method of claim 4, wherein updating the location of the corresponding fish based on the first food concentration value and the second food concentration value comprises:

7. A cooperative apparatus for power distribution and tracking speed of a vehicle, comprising:

the solving unit is used for solving the optimal solution of the collaborative optimization system based on an artificial fish swarm algorithm and a multi-population firefly algorithm to obtain a target vehicle tracking speed sequence and a target vehicle power distribution sequence;

The construction unit is specifically used for:

the solving unit is specifically configured to:

8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements a coordinated method of power distribution and tracking speed of a vehicle as claimed in any one of claims 1 to 6 when the program is executed by the processor.

9. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements a synergistic method of power distribution and tracking speed of a vehicle as claimed in any one of claims 1 to 6.