CN113378345B - Energy storage type tramcar charging station layout optimization method based on genetic algorithm - Google Patents
Energy storage type tramcar charging station layout optimization method based on genetic algorithm Download PDFInfo
- Publication number
- CN113378345B CN113378345B CN202010155368.0A CN202010155368A CN113378345B CN 113378345 B CN113378345 B CN 113378345B CN 202010155368 A CN202010155368 A CN 202010155368A CN 113378345 B CN113378345 B CN 113378345B
- Authority
- CN
- China
- Prior art keywords
- train
- energy storage
- charging
- station
- vehicle
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/12—Electric charging stations
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biophysics (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Theoretical Computer Science (AREA)
- Computational Linguistics (AREA)
- Molecular Biology (AREA)
- Biomedical Technology (AREA)
- Genetics & Genomics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Physiology (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
Abstract
The invention discloses an energy storage type tramcar charging station layout optimization method based on a genetic algorithm. The method comprises the following steps: firstly, establishing a dynamic model of the energy storage type tramcar according to the running characteristics of the energy storage type tramcar, and establishing a calculation model of a charging station; then establishing a train operation energy consumption model and a vehicle-mounted energy storage device energy state model according to the dynamic model of the energy storage type tramcar and the calculation model of the charging station; then, optimizing the layout of the charging station according to the constraint conditions of the layout optimization of the charging station to obtain an optimal layout optimization objective function of the charging station, wherein the optimal layout optimization objective function of the charging station can enable the vehicle to run through all the intervals, the number of the charging stations is the minimum, and the energy lowest point of the super capacitor is the maximum; and finally, carrying out genetic coding on the layout of the charging station, and optimizing the layout of the charging station of the energy storage type tramcar by adopting a genetic algorithm. The invention reduces the installation quantity of the charging devices, and reduces the project cost while ensuring the normal running of the train.
Description
Technical Field
The invention relates to the technical field of tramcar charging systems, in particular to a layout optimization method for an energy storage type tramcar charging station based on a genetic algorithm.
Background
The energy storage type tramcar is powered by the vehicle-mounted energy storage device and the contact network in a hybrid mode, namely, the tramcar is powered by the vehicle-mounted energy storage device under the condition of no network segment, the tramcar is powered by the contact network under the condition of the network segment, and the vehicle-mounted super capacitor is charged when the platform stops. For energy storage formula tram circuit, scientific design charging station overall arrangement has important meaning, but often does not have reliable data support during actual design, and charging station overall arrangement design scheme is immature yet, leads to having the problem that the charging station overall arrangement is unreasonable, resource utilization is low, project comprehensive cost is high.
Disclosure of Invention
The invention aims to provide an energy storage type tramcar charging station layout optimization method based on a genetic algorithm, which can reduce project cost while ensuring normal operation of a train.
The technical solution for realizing the purpose of the invention is as follows: an energy storage type tramcar charging station layout optimization method based on a genetic algorithm comprises the following steps:
and 4, carrying out genetic coding on the layout of the charging station, and optimizing the layout of the energy storage type tramcar charging station by adopting a genetic algorithm.
Further, according to the operation characteristics of the energy storage type tramcar, a dynamic model of the energy storage type tramcar is established, and a calculation model of the charging station is established in step 1, specifically as follows:
step 1.1, establishing a dynamic model of the energy storage type tramcar according to the running characteristics of the energy storage type tramcar, which comprises the following specific steps:
step 1.1.1, calculating the traction force of the tramcar:
the train traction force is the reaction force of the steel rail to the locomotive, which is obtained after the internal force generated by the traction motor is transmitted to the steel rail through the power transmission device, and the calculation formula is as follows:
F μ ＝G f μ (1)
in the formula, F μ For adhesive traction, G f The adhesion weight of the train, mu is the adhesion coefficient, v is the train speed, and the unit is km/h;
step 1.1.2, calculating the braking force of the tramcar:
the train braking force is divided into electric braking and mechanical braking, the electric braking is used as the main braking force of the train, the mechanical braking is used as the auxiliary braking force, and the electric braking feeds back the recovered braking energy to the super capacitor, so that the overall energy consumption is reduced;
step 1.1.3, calculating the resistance of the tramcar:
the resistance suffered by the train during the operation process comprises a basic resistance and an additional resistance:
basic resistance R r The formula is as follows:
R r (v)＝A+Bv+Cv 2 (3)
wherein A, B, C is a constant; v is the running speed of the train, and the unit is km/h;
the formula for the additional resistance is:
additional resistance w of train i By the ramp resistance w i Curve resistance w r And tunnel resistance w s The composition is shown as the following formula:
in the formula, i is a slope thousandth, R is a curve radius, and the tunnel resistance w s Is less influenced, so the tunnel resistance w is neglected s ；
Step 1.2, establishing a calculation model of the energy storage type tramcar charging station, which comprises the following specific steps:
the vehicle-mounted super capacitor is charged by contacting the pantograph with the charging rail, the charging is carried out in a constant-current voltage-limiting mode, and the charging power formula is as follows:
P sc _ charge ＝U sc I sc η c _ i η sc (5)
wherein, P sc_charge Charging power for super-capacitor, U sc Is the voltage of a super capacitor, I sc Is superCharging current of the capacitor, eta c_i For the conversion efficiency of the charging device, η sc The charging conversion efficiency of the super capacitor is improved.
Further, according to the dynamic model of the energy storage type tramcar and the calculation model of the charging station, establishing a train operation energy consumption model and a vehicle-mounted energy storage device energy state model in step 2, specifically as follows:
step 2.1, establishing a train operation energy consumption model according to the kinetic model of the energy storage type tramcar, which comprises the following specific steps:
respectively establishing train operation energy consumption models under a traction working condition, a cruise working condition, an idle working condition and a braking working condition according to the operation working condition of the train:
when a train is in a traction working condition and a cruising working condition, the train operation energy consumption consists of traction energy consumption and auxiliary energy consumption, the traction energy consumption is the energy consumption of a train traction motor, the auxiliary energy consumption is the energy consumption of equipment such as vehicle-mounted lighting equipment, an air conditioner and the like, and the operation energy consumption calculation formula is as follows:
wherein J is the running energy consumption, and the unit is kWh; f k Is traction force with kN; v is the running speed, and the unit is km/h; p KF The unit is kW for auxiliary traction power; eta is the efficiency of the traction motor; delta T is the simulation step length;
when the train is in the idle working condition, the train operation energy consumption is auxiliary energy consumption, and the operation energy consumption calculation formula is as follows:
in the formula, P KF The unit is kW for auxiliary traction power;
when the tramcar is in a braking working condition, the tramcar adopts regenerative braking and mechanical braking, and when the tramcar adopts electric braking, a calculation formula of regenerative braking recovery energy is as follows:
in the formula, J Z Representing the regeneration energy in kWh; f ZB Represents the electric braking force in kN;
2.2, establishing an energy state model of the vehicle-mounted energy storage device according to the calculation model of the energy storage type tramcar charging station:
vehicle-mounted energy storage electric quantity when train starts from starting station
The formula of (1) is:
in the formula E ess Representing the rated electric quantity of the vehicle-mounted energy storage, and the unit is kWh; eta start Representing the percentage of the initial electric quantity of the vehicle-mounted energy storage device;
in the interval from any (i-1) th station to the (i-1) th station, i is more than or equal to 2 and less than or equal to n station The formula of the residual electric quantity of the vehicle-mounted energy storage device when the train arrives at the station is as follows:
in the formula (I), the compound is shown in the specification,
representing the vehicle-mounted energy storage residual capacity when the train arrives at the station i, wherein the unit is kWh;
the unit of the vehicle-mounted energy storage electric quantity is kWh when the train leaves the station from i-1; q i The unit of the electric quantity for the i-interval running of the train is kWh; g i The power supply quantity of the contact network in the i interval to the vehicle-mounted energy storage is represented, and the unit is kWh;
the formula of the minimum electric quantity of the interval vehicle-mounted energy storage is as follows;
in the formula, E i_low The lowest vehicle-mounted energy storage electric quantity in the train i interval is represented, and the unit is kWh; q i _ max The maximum value of the electric quantity of the i-interval running of the train provided by the vehicle-mounted energy storage is represented in the unit of kWh; g i_low Represents i section Q i_max The contact network at the moment supplies electric quantity to the vehicle-mounted energy storage, and the unit is kWh;
the formula of the residual electric quantity of the vehicle-mounted energy storage device when the train is started after stopping is as follows:
in the formula (I), the compound is shown in the specification,
the unit of the vehicle-mounted energy storage electric quantity is kWh when the train leaves the station i; b i The charging station of the station i supplements the vehicle-mounted energy storage with electric quantity, and the unit is kWh; e Fi And the electric quantity which represents the auxiliary energy consumption provided by the vehicle-mounted energy storage at the ith station is the kWh unit.
Further, the charging station layout is optimized according to the constraint conditions of the charging station layout optimization in step 3, so as to obtain an optimal layout optimization objective function of the charging station, where the vehicle can travel through all the intervals, the number of the charging stations is the minimum, and the energy lowest point of the super capacitor is the maximum, specifically as follows:
step 3.1, establishing a charging station layout optimization constraint condition, including the constraint of charging and discharging current of the super capacitor, the constraint of charging time of the super capacitor and the constraint of the number and installation positions of the ground charging stations, which is specifically as follows:
step 3.1.1, constraint of charging and discharging currents of the super capacitor: the maximum power when the train is pulled must be less than or equal to the maximum power that the super capacitor can provide, so the discharge current I of the super capacitor must be limited sc_out Maximum discharge current I less than or equal to super capacitor sc_out_max And the current I of the super capacitor during charging sc_in Short-time maximum charging current I which must be less than or equal to the supercapacitor sc_in_max ：
I sc_out ≤I sc_out_max (13)
I sc_in ≤I sc_in_max (14)
Step 3.1.2, constraint of charging time of the super capacitor: station charging time T of super capacitor in Scheduled time-to-charge time T max And (3) limiting:
T in ≤T max (15)
step 3.1.3, the number of the ground charging stations and the constraint of the installation positions: the number N of the ground charging stations is influenced by the total number N of the platforms total And (3) constraint:
N≤N total (16)
the optimization goal of the system is to install the minimum ground charging stations and simultaneously improve the minimum voltage of the super capacitor in the operation process as much as possible under the condition of meeting the constraint conditions;
step 3.2, analyzing the train interval operation capacity, the charging station layout and the train endurance redundancy capacity, which are specifically as follows:
step 3.2.1, analyzing the train interval operation capacity:
dividing the whole line of the operation line into intervals by taking the platform as a boundary, setting that the whole line is provided with N stations, setting the number of the intervals to be N-1, setting that the current train runs from the i-1 st station to the i-th station, and setting that i is more than or equal to 2 and is less than or equal to N station I.e., traveling in the interval i-1,
the energy storage capacity of the train starting from the i-1 station,
the energy storage electric quantity is the energy storage electric quantity when the train arrives at the station i;
setting P i For the vehicle endurance in section i, if the current sectionTo contact the network segment, then P i Is 1; if the current section is a non-contact network segment, if the train runs in the section i, the energy storage capacity at any time t
If not less than 0, the train can normally run through the interval, P i Is 1, otherwise P i Is 0;
step 3.2.2, charging station layout setting analysis:
the current line is set to have n stations, because each station can only be provided with or not provided with a charging device, the current line has 2 stations n Possibly, a binary coding mode is adopted, n bits are counted, 1 is set, 0 is not set, and the total number of charging station settings is recorded as x;
step 3.2.3, train endurance redundancy capability analysis:
the current train is set to run from the station i-1 to the station i, namely to run in the section i-1,
the energy storage capacity of the train starting from the i-1 station,
for the stored energy and electric quantity when the train arrives at station i, order
The percentage value of the lowest point of the energy of the vehicle-mounted stored energy during the full-line operation is the percentage value, and when the number of the charging stations cannot be reduced, the percentage value E is increased as much as possible extra A value of (d);
step 3.3, establishing a charging station optimal layout optimization objective function with the minimum number of charging stations and the maximum lowest energy point of the super capacitor, wherein the optimal layout optimization objective function can be realized by the vehicle in all intervals, and the optimal layout optimization objective function is as follows:
the maximum value point of the default optimization objective function is an optimal solution, and the optimization objective function is formed by three parts as follows because the optimization objective is that a train can travel through all intervals, the number of charging stations is minimum, and the energy minimum point of the super capacitor is maximum:
②-x
③E extra
the first part is used for ensuring that the train can run through all intervals of the upper and lower rows, wherein n section The number of sections is 2n since the train is continuously running in the up-down direction section ；n station For the number of charging stations to be set, P i The vehicle endurance condition of the interval i is set;
the second part is used for ensuring that as few charging stations as possible are arranged, wherein x is the number of the arranged charging stations;
the third part is used to ensure that the train has the highest possible endurance redundancy, where E extra Is the percentage of the lowest point of the super capacitor energy;
in summary, the optimization objective function is shown as follows:
when the function f (x) obtains the maximum value, namely the vehicle can run through all the intervals, the number of the charging stations is minimum, and the energy lowest point of the super capacitor is maximum, the optimal layout of the charging stations is obtained.
Further, the layout of the charging station in step 4 is subjected to genetic coding, and the layout of the energy storage type tramcar charging station is optimized by adopting a genetic algorithm, which specifically comprises the following steps:
step 4.1, determining the coding rule, the fitness function, the selection factor, the crossover operator and the mutation probability of the genetic algorithm, wherein t is the iteration number:
and (3) encoding rules: setting n stations in total on the current line, and counting n bits in a binary coding mode, wherein 1 is set and 0 is not set;
fitness function: selecting the optimized objective function in the step 3.3 as a fitness function, as follows:
selecting a factor: determining a selection factor by adopting a betting rotation method;
and (3) a crossover operator: adopting single-point crossing, wherein the crossing rate is 0.5-0.95;
the mutation probability: single-point variation is adopted, and the variation rate is 0.01-0.2;
4.2, optimizing the layout of the energy storage type tramcar charging station by adopting a genetic algorithm:
step 4.2.1, initialize father group P t ，t＝0；
Step 4.2.2, calculating the father group P t An individual fitness value of (a);
4.2.3, judging whether t reaches the maximum iteration times, outputting an optimization result if t reaches the maximum iteration times, and entering the next step if t does not reach the maximum iteration times;
step 4.2.4, genetic manipulation to generate a sub-population Q t ；
Step 4.2.5, calculate sub-population Q t An individual fitness value of (a);
step 4.2.6, generating P using business strategy t+1 ；
And step 4.2.7, when t is t +1, accumulating the iteration times for 1 time, and returning to step 4.2.3.
Compared with the prior art, the invention has the remarkable advantages that: (1) the installation quantity of the charging devices is reduced, and the project cost is reduced while the normal running of the train is ensured; (2) has good convergence and reliability, and has certain engineering significance.
Drawings
Fig. 1 is a flow diagram of the energy storage type tramcar charging station layout optimization method based on the genetic algorithm.
Figure 2 is a graph of the train tractive effort characteristics of an embodiment of the present invention.
Fig. 3 is a characteristic diagram of train braking force in the embodiment of the invention.
FIG. 4 is a graph of the voltage and current characteristics of a super capacitor according to an embodiment of the present invention.
FIG. 5 is a flowchart illustrating an iterative process of a genetic algorithm according to an embodiment of the present invention.
FIG. 6 is a graphical representation of the results of the genetic optimization algorithm in an embodiment of the present invention.
FIG. 7 is a comparative plot of the SOC of the super capacitor before and after the downlink optimization in the embodiment of the present invention.
FIG. 8 is a comparative plot of the SOC of the super capacitor before and after the uplink optimization in the embodiment of the present invention.
FIG. 9 is a schematic diagram illustrating a comparison of the optimized front and rear charging station layouts according to an embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the figures and the specific embodiments.
With reference to fig. 1, the invention relates to a layout optimization method for an energy storage type tramcar charging station based on a genetic algorithm, which comprises the following steps:
step 1.1, establishing a dynamic model of the energy storage type tramcar according to the running characteristics of the energy storage type tramcar, which comprises the following specific steps:
step 1.1.1, calculating the traction force of the tramcar:
the train traction force is the reaction force of the steel rail to the locomotive, which is obtained after the internal force generated by the traction motor is transmitted to the steel rail through the power transmission device, and the calculation formula is as follows:
F μ ＝G f μ (1)
in the formula, F μ For adhesive traction, G f Mu is the adhesion coefficient of the train, v is the train speed, and the unit is km/h; the magnitude of the sticking coefficient μ depends on many factors, such as the climate, the train configurationEtc., it is difficult to obtain an accurate value thereof, and a vehicle traction characteristic curve is as shown in fig. 2 below;
step 1.1.2, calculating the braking force of the tramcar:
the train braking force is divided into electric braking and mechanical braking, the electric braking is used as the main braking force of the train, the mechanical braking is used as the auxiliary braking force, and the electric braking feeds back the recovered braking energy to the super capacitor, so that the overall energy consumption is reduced; FIG. 3 is a brake characteristic curve for a train when the train speed is below V 0 Mechanical braking will be employed;
step 1.1.3, calculating the resistance of the tramcar:
the resistance suffered by the train during the operation process comprises a basic resistance and an additional resistance:
the basic formula of resistance is:
R r (v)＝A+Bv+Cv 2 (3)
wherein A, B, C is a constant (depending on the train type); v is the running speed of the train, and the unit is km/h;
the formula for the additional resistance is:
the additional resistance of the train consists of ramp resistance, curve resistance and tunnel resistance, and is as follows:
in the formula, i is a slope thousandth, R is a curve radius, and tunnel resistance is ignored because the influence of the tunnel resistance is small;
step 1.2, establishing a calculation model of the energy storage type tramcar charging station, which comprises the following specific steps:
the vehicle-mounted super capacitor is charged by contacting the pantograph with the charging rail, the charging is carried out in a constant-current voltage-limiting mode, and a charging power formula is shown as follows.
P sc_charge ＝U sc I sc η c_i η sc (5)
Wherein, P sc_charge Charging power for super-capacitor, U sc Is the voltage of a super capacitor, I sc Is superCharging current of capacitor, eta c_i For the conversion efficiency of the charging device, η sc The charging conversion efficiency of the super capacitor is improved; the characteristic curves of the voltage and the current of the super capacitor in the constant-current voltage-limiting charging mode are shown in fig. 4.
step 2.1, establishing a train operation energy consumption model according to the kinetic model of the energy storage type tramcar, which comprises the following specific steps:
respectively establishing train operation energy consumption models under a traction working condition, a cruising working condition, an idling working condition and a braking working condition according to the operation working conditions of the train:
when a train is in a traction working condition and a cruising working condition, the train operation energy consumption consists of traction energy consumption and auxiliary energy consumption, the traction energy consumption is the energy consumption of a train traction motor, the auxiliary energy consumption is the energy consumption of equipment such as vehicle-mounted lighting equipment, an air conditioner and the like, and the operation energy consumption calculation formula is as follows:
wherein J is the running energy consumption, and the unit is kWh; f k Is traction force with kN; v is the running speed, and the unit is km/h; p KF The unit is kW for auxiliary traction power; eta is the efficiency of the traction motor; delta T is the simulation step length;
when the train is in the coasting working condition, the train operation energy consumption is auxiliary energy consumption, and the operation energy consumption calculation formula is as follows:
in the formula, P KF The unit is kW for auxiliary traction power;
when the tramcar is in the braking operating mode, the tramcar adopts regenerative braking and mechanical braking, and when adopting electric braking, the formula for calculating the regenerative braking recovered energy is as follows:
in the formula, J Z Representing the regeneration energy in kWh; f ZB Represents the electric braking force in kN;
2.2, establishing an energy state model of the vehicle-mounted energy storage device according to the calculation model of the energy storage type tramcar charging station:
for guaranteeing tram safe and reliable's operation, must satisfy contact net, on-vehicle energy memory and provide the electric quantity and be greater than the required electric quantity of train operation, on-vehicle energy memory electric quantity state divide into following several kinds of condition:
when the train runs in a network segment, the vehicle-mounted energy storage electric quantity is increased, and if the electric quantity reaches the allowable charging upper limit, the charging is stopped;
when the train runs to the non-contact network segment, the electric quantity of the vehicle-mounted energy storage device is consumed under the working conditions of train traction, cruising and coasting, and energy is fed back to the vehicle-mounted energy storage device under the working condition of electric braking;
when the train stops at a platform provided with a charging station, the vehicle-mounted energy storage device is charged, and the charging is stopped when the train is fully charged;
when the train stops at a platform without a charging station, the vehicle-mounted energy storage device provides auxiliary energy consumption for the train, and the energy storage electric quantity is reduced;
the formula of the vehicle-mounted energy storage electric quantity when the train starts from the starting station is as follows:
in the formula E ess Representing the rated electric quantity of the vehicle-mounted energy storage, and the unit is kWh; eta start Representing the percentage of the initial electric quantity of the vehicle-mounted energy storage device;
in the i-1 interval from any station i-1 to station i, i is more than or equal to 2 and less than or equal to n station The formula of the residual electric quantity of the vehicle-mounted energy storage device when the train arrives at the station is as follows:
in the formula (I), the compound is shown in the specification,
representing the vehicle-mounted energy storage residual capacity when the train arrives at the station i, wherein the unit is kWh;
the unit of the vehicle-mounted energy storage electric quantity is kWh when the train leaves the station from i-1; q i The unit of the electric quantity for the i-interval running of the train is kWh; g i The method comprises the following steps of (1) representing that the contact network in the i section supplements electric quantity to vehicle-mounted energy storage, wherein the unit is kWh;
the formula of the minimum electric quantity of the interval vehicle-mounted energy storage is as follows;
in the formula, E i_low The lowest vehicle-mounted energy storage electric quantity in the train i interval is represented, and the unit is kWh; q i_max The maximum value of the electric quantity of the i-interval running of the train provided by the vehicle-mounted energy storage is represented in the unit of kWh; g i_low Represents i section Q i_max The contact network at the moment supplies electric quantity to the vehicle-mounted energy storage, and the unit is kWh;
the formula of the residual electric quantity of the vehicle-mounted energy storage device when the train is started after stopping is as follows:
in the formula, B i The charging station represents that the charging station of the station i supplements the vehicle-mounted energy storage with electric quantity, and the unit is kWh; e Fi And the electric quantity which represents the auxiliary energy consumption provided by the vehicle-mounted energy storage at the ith station is the kWh unit.
And 3, optimizing the layout of the charging station according to the constraint conditions of the layout optimization of the charging station to obtain an optimal layout optimization objective function of the charging station, wherein the optimal layout optimization objective function of the charging station has the minimum number of the charging stations and the maximum energy lowest point of the super capacitor, and the optimal layout optimization objective function of the charging station is as follows:
step 3.1, establishing a charging station layout optimization constraint condition, including the constraint of charging and discharging current of the super capacitor, the constraint of charging time of the super capacitor and the constraint of the number and installation positions of the ground charging stations, which is specifically as follows:
step 3.1.1, constraint of charging and discharging currents of the super capacitor: the maximum power when the train is pulled must be less than or equal to the maximum power that the super capacitor can provide, so the discharge current I of the super capacitor must be limited sc_out Maximum discharge current I less than or equal to super capacitor sc_out_max And current I of super capacitor in charging sc_in Short-time maximum charging current I which must be less than or equal to the supercapacitor sc_in_max ：
I sc _ out ≤I sc_out_max (13)
I sc_in ≤I sc_in_max (14)
Step 3.1.2, constraint of charging time of the super capacitor: station charging time T of super capacitor in Scheduled charging time T max And (3) limiting:
T in ≤T max (15)
step 3.1.3, constraint of the number and the installation positions of the ground charging stations: the number N of the ground charging stations is influenced by the total number N of the platforms total And (3) constraint:
N≤N total (16)
the optimization goal of the system is to install the minimum ground charging stations under the condition of meeting the constraint conditions, and simultaneously improve the minimum voltage of the super capacitor in the operation process as much as possible so as to ensure higher endurance redundancy;
step 3.2, analyzing the train interval running capacity, the charging station layout and the train endurance redundancy capacity, and specifically comprising the following steps:
step 3.2.1, analyzing the train interval operation capacity:
dividing the whole line of the operation line into sections by taking the platform as a boundary, and setting the whole line to be commonly setIf N stations exist, the number of the sections is N-1, the current train is set to run from the i-1 th station to the i-th station, and i is more than or equal to 2 and less than or equal to N station I.e., traveling in the interval i-1,
the energy storage capacity of the train starting from the i-1 station,
the energy storage electric quantity is the energy storage electric quantity when the train arrives at the station i;
setting P i For the vehicle endurance condition of the interval i, if the current segment is the contact network segment, P i Is 1; if the current section is a non-contact network segment, if the train runs in the section i, the energy storage capacity at any time t
If not less than 0, the train can normally run through the interval, P i Is 1, otherwise P i Is 0;
step 3.2.2, charging station layout setting analysis:
the current line is set to have n stations, and because each station can only be provided with or not provided with a charging device, the current line has 2 stations n The method comprises the following steps that (1) a binary coding mode is adopted, n bits are counted, 1 is set, 0 is not set, and the total number of charging station settings is recorded as x;
step 3.2.3, train endurance redundancy capability analysis:
the current train is set to run from the station i-1 to the station i, namely to run in the section i-1,
the energy storage capacity of the train starting from the i-1 station,
for the stored energy and electric quantity when the train arrives at station i, order
I.e. vehicle-mounted during full-line operationThe percentage value of the lowest point of the energy stored is increased as much as possible when the number of charging stations cannot be reduced extra A value of (d);
step 3.3, establishing a charging station optimal layout optimization objective function with the minimum number of charging stations and the maximum lowest energy point of the super capacitor, wherein the optimal layout optimization objective function can be realized by the vehicle in all intervals, and the optimal layout optimization objective function is as follows:
the maximum value point of the default optimization objective function is an optimal solution, and the optimization objective function is formed by three parts as follows because the optimization objective is that a train can travel through all intervals, the number of charging stations is minimum, and the energy minimum point of the super capacitor is maximum:
②-x
③E extra
the first part is used for ensuring that the train can run through all sections of the upper and lower rows, wherein n section The number of sections is 2n since the train is continuously running in the up-down direction section ；n station For the number of charging stations to be set, P i The vehicle endurance condition of the interval i is set;
the second part is used for ensuring that fewer charging stations are arranged, wherein x is the number of the arranged charging stations;
the third part is used for ensuring that the train has high endurance redundancy, wherein E extra Is the percentage of the lowest point of energy of the supercapacitor, if the lowest point is 20%, then E extra Is 0.2;
in summary, the optimization objective function is shown as follows:
when the function f (x) obtains the maximum value, namely the vehicle can run through all the intervals, the number of the charging stations is minimum, and the energy lowest point of the super capacitor is maximum, the optimal layout of the charging stations is obtained.
And 4, carrying out genetic coding on the layout of the charging station, and optimizing the layout of the energy storage type tramcar charging station by adopting a genetic algorithm, wherein the genetic coding specifically comprises the following steps:
step 4.1, determining the coding rule, the fitness function, the selection factor, the crossover operator and the mutation probability of the genetic algorithm, wherein t is the iteration number:
and (3) encoding rules: setting n stations in total on the current line, and counting n bits in a binary coding mode, wherein 1 is set and 0 is not set;
fitness function: selecting the optimized objective function in the step 3.3 as a fitness function, as follows:
selecting a factor: determining a selection factor by adopting a betting rotation method;
and (3) a crossover operator: adopting single-point crossing, wherein the crossing rate is 0.5-0.95;
mutation probability: single-point variation is adopted, and the variation rate is 0.01-0.2;
step 4.2, optimizing the layout of the energy storage type tramcar charging station by adopting a genetic algorithm, and combining with the graph 5, specifically, the method comprises the following steps:
step 4.2.1, initializing parent group P t (t＝0)；
Step 4.2.2, calculating the father group P t An individual fitness value of (a);
4.2.3, judging whether t reaches the maximum iteration times, outputting an optimization result if t reaches the maximum iteration times, and entering the next step if t does not reach the maximum iteration times;
step 4.2.4 genetic manipulation to generate a sub-population Q t ；
Step 4.2.5, calculate sub-population Q t An individual fitness value of (a);
step 4.2.6, generating P using business strategy t+1 ；
And step 4.2.7, when t is t +1, accumulating the iteration times for 1 time, and returning to step 4.2.3.
Example 1
In this embodiment, the energy storage type tramcar charging station layout optimization method based on the genetic algorithm provided by the invention is adopted to perform optimization simulation on the charging station layout of a certain tramcar running line. The whole line of the line is provided with 10 platforms, the characteristic parameters of the running train and the characteristic parameters of the super capacitor are shown in the following tables 1 and 2, and the layout of the original charging station is shown in the following table 3. All the platforms are provided with charging stations during engineering design, and the vehicle-mounted energy storage device is charged when the train stops.
TABLE 1 train characteristic parameters
TABLE 2 supercapacitors characteristic parameters
In the optimization simulation, a genetic algebra is set to be 50 generations, each generation of population has 50 individuals, the crossing rate is 0.7, the variation rate is 0.1, the requirement of safe running of a train is considered, the residual effective energy of the super capacitor is specified to be not less than 35%, the simulation result is shown in the following figure 6, the abscissa is the genetic algebra, and the ordinate is the fitness value.
From the optimization simulation results, the binary code of the optimal arrangement is 1101001001, and the charging station layouts before and after optimization are compared, as shown in table 3 below.
TABLE 3 comparison of charging station layouts before and after optimization
6.2 simulation result verification
In order to verify the optimal arrangement obtained by the optimization simulation and keep the characteristic data of the vehicle and the data such as the line parameters and the like unchanged, the uplink simulation and the downlink simulation are respectively performed according to the optimal charging station arrangement in table 3, and the running state comparison of the vehicle-mounted super capacitor is shown in tables 4 and 5.
TABLE 4 comparison of super capacitor operating states before and after downlink optimization
TABLE 5 comparison of operating states of super capacitors before and after uplink optimization
Taking the platform as the abscissa and the SOC of the super capacitor as the ordinate, the SOC comparison graph of the super capacitor before and after the uplink and downlink optimization is obtained, as shown in fig. 7 and 8. Similarly, the platform is used as the abscissa, the charging station setting is used as the ordinate, and a comparison graph of the charging station layout before and after optimization is obtained from table 3, as shown in fig. 9, when the value in the graph is 1, the charging station is set, and when the value is 0, the charging station is not set. As can be seen from tables 4 and 5 and fig. 7 and 8, after optimization, the lowest voltage points of the downlink super capacitor and the uplink super capacitor are 467.082v and 465.005v respectively, the energy at the moment is 38.19% and 36.71% respectively, the requirement that the lowest effective energy point is not lower than 35% is met, 10 charging devices need to be arranged before optimization, only 5 charging devices need to be arranged after optimization, the project cost is greatly reduced while the normal operation of the train is ensured, and the method for optimizing the layout of the energy storage type tramcar charging station based on the genetic algorithm is proved to have certain engineering guidance significance.
Claims (3)
1. An energy storage type tramcar charging station layout optimization method based on a genetic algorithm is characterized by comprising the following steps:
step 1, establishing a dynamic model of the energy storage type tramcar according to the running characteristics of the energy storage type tramcar, and establishing a calculation model of a charging station;
step 2, establishing a train operation energy consumption model and a vehicle-mounted energy storage device energy state model according to the dynamic model of the energy storage type tramcar and the calculation model of the charging station;
and 3, optimizing the layout of the charging station according to the constraint conditions of the layout optimization of the charging station to obtain an optimal layout optimization objective function of the charging station, wherein the optimal layout optimization objective function of the charging station has the minimum number of the charging stations and the maximum energy lowest point of the super capacitor, and the optimal layout optimization objective function of the charging station is as follows:
step 3.1, establishing a charging station layout optimization constraint condition, including the constraint of charging and discharging current of the super capacitor, the constraint of charging time of the super capacitor and the constraint of the number and installation positions of the ground charging stations, which is specifically as follows:
step 3.1.1, constraint of charging and discharging currents of the super capacitor: the maximum power when the train is pulled must be less than or equal to the maximum power that the super capacitor can provide, so the discharge current I of the super capacitor must be limited sc_out Maximum discharge current I less than or equal to super capacitor sc_out_max And the current I of the super capacitor during charging sc_in Short-time maximum charging current I which must be less than or equal to the supercapacitor sc_in_max ：
I sc_out ≤I sc_out_max (13)
I sc_in ≤I sc_in_max (14)
Step 3.1.2, constraint of charging time of the super capacitor: station charging time T of super capacitor in Scheduled time-to-charge time T max And (3) limiting:
T in ≤T max (15)
step 3.1.3, constraint of the number and the installation positions of the ground charging stations: the number N of the ground charging stations is influenced by the total number N of the platforms total And (3) constraint:
N≤N total (16)
the optimization goal of the system is to install the minimum ground charging stations and improve the minimum voltage of the super capacitor in the operation process under the condition of meeting the constraint conditions;
step 3.2, analyzing the train interval operation capacity, the charging station layout and the train endurance redundancy capacity, which are specifically as follows:
step 3.2.1, analyzing the train interval operation capacity:
dividing the whole line of the operation line into intervals by taking the platform as a boundary, setting that the whole line is provided with N stations, setting the number of the intervals to be N-1, setting that the current train runs from the i-1 th station to the i-th station, and setting that i is more than or equal to 2 and less than or equal to N station I.e., traveling in the interval i-1,
the energy storage capacity of the train starting from the i-1 station,
the energy storage electric quantity is the energy storage electric quantity when the train arrives at the station i;
setting P i For the vehicle endurance condition of the interval i, if the current segment is the contact network segment, P i Is 1; if the current section is a non-contact network segment, if the train runs in the section i, the energy storage capacity at any time t
If not less than 0, the train can normally run through the interval, P i Is 1, otherwise P i Is 0;
step 3.2.2, analyzing the layout of the charging station:
the current line is set to have n stations, because each station can only be provided with or not provided with a charging device, the current line has 2 stations n Possibly, a binary coding mode is adopted, n bits are counted, 1 is set, 0 is not set, and the total number of charging station settings is recorded as x;
step 3.2.3, train endurance redundancy capability analysis:
the current train is set to run from the station i-1 to the station i, namely to run in the interval i-1,
the energy storage capacity of the train starting from the i-1 station,
for the energy storage electric quantity when the train arrives at station iLet us order
The percentage value of the lowest point of the energy of the vehicle-mounted energy storage during the full-line operation is the percentage value, and the E is increased when the number of the charging stations cannot be reduced extra A value of (d);
step 3.3, establishing a charging station optimal layout optimization objective function with the vehicle capable of driving through all intervals, the minimum number of charging stations and the maximum lowest energy point of the super capacitor, wherein the optimal layout optimization objective function specifically comprises the following steps:
the maximum value point of the default optimization objective function is an optimal solution, and the optimization objective function is formed by three parts as follows because the optimization objective is that a train can travel through all intervals, the number of charging stations is minimum, and the energy minimum point of the super capacitor is maximum:
①
②-x
③E extra
the first part is used for ensuring that the train can run through all intervals of the upper and lower rows, wherein n section The number of sections is 2n since the train is continuously running in the up-down direction section ；n station For the number of charging stations to be set, P i The vehicle endurance condition of the interval i is set;
the second part is used for setting the number of the charging stations, wherein x is the number of the set charging stations;
the third part is used to make the train have high endurance redundancy, wherein E extra Is the percentage of the lowest energy point of the super capacitor;
in summary, the optimization objective function is shown as follows:
when the function f (x) obtains the maximum value, namely the vehicle can run through all the intervals, the number of the charging stations is minimum, and the lowest energy point of the super capacitor is maximum, namely the optimal layout of the charging stations is obtained;
and 4, carrying out genetic coding on the layout of the charging station, and optimizing the layout of the charging station of the energy storage type tramcar by adopting a genetic algorithm, wherein the genetic coding comprises the following specific steps:
step 4.1, determining the coding rule, the fitness function, the selection factor, the crossover operator and the mutation probability of the genetic algorithm, wherein t is the iteration number:
and (3) encoding rules: setting n stations in total on the current line, and counting n bits in a binary coding mode, wherein 1 is set and 0 is not set;
fitness function: selecting the optimized objective function in the step 3.3 as a fitness function, as follows:
selecting a factor: determining a selection factor by adopting a betting rotation method;
and (3) a crossover operator: adopting single-point crossing, wherein the crossing rate is 0.5-0.95;
the mutation probability: single-point variation is adopted, and the variation rate is 0.01-0.2;
4.2, optimizing the layout of the energy storage type tramcar charging station by adopting a genetic algorithm:
step 4.2.1, initialize father group P t ，t＝0；
Step 4.2.2, calculating the father group P t An individual fitness value of (a);
4.2.3, judging whether t reaches the maximum iteration times, outputting an optimization result if t reaches the maximum iteration times, and entering the next step if t does not reach the maximum iteration times;
step 4.2.4 genetic manipulation to generate a sub-population Q t ；
Step 4.2.5, calculating the sub-population Q t An individual fitness value of (a);
step 4.2.6, generating P using business strategy t+1 ；
And step 4.2.7, when t is t +1, accumulating the iteration times for 1 time, and returning to step 4.2.3.
2. The method for optimizing the layout of the charging station of the energy storage type tramcar based on the genetic algorithm as claimed in claim 1, wherein the step 1 is to establish a dynamic model of the energy storage type tramcar and a calculation model of the charging station according to the operation characteristics of the energy storage type tramcar, and specifically comprises the following steps:
step 1.1, establishing a dynamic model of the energy storage type tramcar according to the running characteristics of the energy storage type tramcar, which comprises the following specific steps:
step 1.1.1, calculating the traction force of the tramcar:
the train traction force is the reaction force of the steel rail to the locomotive, which is obtained after the internal force generated by the traction motor is transmitted to the steel rail through the power transmission device, and the calculation formula is as follows:
F μ ＝G f μ (1)
in the formula, F μ For adhesive traction, G f Mu is the adhesion coefficient of the train, v is the train speed, and the unit is km/h;
step 1.1.2, calculating the braking force of the tramcar:
the train braking force is divided into electric braking and mechanical braking, the electric braking is used as the main braking force of the train, the mechanical braking is used as the auxiliary braking force, and the electric braking feeds back the recovered braking energy to the super capacitor, so that the overall energy consumption is reduced;
step 1.1.3, calculating the resistance of the tramcar:
the resistance suffered by the train during the operation process comprises a basic resistance and an additional resistance:
basic resistance R r The formula is as follows:
R r (v)＝A+Bv+Cv 2 (3)
wherein A, B, C is a constant; v is the running speed of the train, and the unit is km/h;
the formula for the additional drag is:
additional resistance w of train j By the ramp resistance w i Curve resistance w r And tunnel resistance w s The composition is shown as the following formula:
in the formula, i is a slope thousandth, R is a curve radius, and the tunnel resistance w s Has little influence, so the tunnel resistance w is ignored s ；
Step 1.2, establishing a calculation model of the energy storage type tramcar charging station, which comprises the following specific steps:
the vehicle-mounted super capacitor is charged by contacting the pantograph with the charging rail, the charging is carried out in a constant-current voltage-limiting mode, and the charging power formula is as follows:
P sc_charge ＝U sc I sc η c_i η sc (5)
wherein, P sc_charge Charging power for super capacitor, U sc Is the voltage of a super capacitor, I sc Charging current, η, for the supercapacitor c_i For the conversion efficiency of the charging device, eta sc The charging conversion efficiency of the super capacitor is improved.
3. The energy storage type tramcar charging station layout optimization method based on genetic algorithm as claimed in claim 1, wherein the train operation energy consumption model and the vehicle-mounted energy storage device energy state model are established according to the kinetic model of the energy storage type tramcar and the calculation model of the charging station in step 2, and specifically as follows:
step 2.1, establishing a train operation energy consumption model according to the kinetic model of the energy storage type tramcar, which comprises the following specific steps:
respectively establishing train operation energy consumption models under a traction working condition, a cruising working condition, an idling working condition and a braking working condition according to the operation working conditions of the train:
when the train is in a traction working condition and a cruising working condition, the train operation energy consumption consists of traction energy consumption and auxiliary energy consumption, the traction energy consumption is the energy consumption of a train traction motor, the auxiliary energy consumption is the energy consumption of vehicle-mounted lighting and an air conditioner, and the operation energy consumption calculation formula is as follows:
wherein J is the operation energy consumption, and the unit is kWh; f k Is traction force with kN; v is the running speed, and the unit is km/h; p KF The unit is kW for auxiliary traction power; eta is the efficiency of the traction motor; delta T is the simulation step length;
when the train is in the coasting working condition, the train operation energy consumption is auxiliary energy consumption, and the operation energy consumption calculation formula is as follows:
in the formula, P KF Is traction auxiliary power with the unit of kW;
when the tramcar is in the braking operating mode, the tramcar adopts regenerative braking and mechanical braking, and when adopting electric braking, the formula for calculating the regenerative braking recovered energy is as follows:
in the formula, J Z Representing the regeneration energy in kWh; f ZB Represents the electric braking force in kN;
2.2, establishing an energy state model of the vehicle-mounted energy storage device according to the calculation model of the energy storage type tramcar charging station:
vehicle-mounted energy storage electric quantity of train starting from starting station
The formula of (1) is:
in the formula E ess The unit of the vehicle-mounted energy storage rated electric quantity is kWh; eta start Representing the percentage of the initial electric quantity of the vehicle-mounted energy storage device;
in the interval from any (i-1) th station to the (i-1) th station, i is more than or equal to 2 and less than or equal to n station The formula of the residual electric quantity of the vehicle-mounted energy storage device when the train arrives at the station is as follows:
in the formula (I), the compound is shown in the specification,
representing the vehicle-mounted energy storage residual capacity when the train arrives at the station i, wherein the unit is kWh;
the unit of the vehicle-mounted energy storage electric quantity is kWh when the train leaves the station from i-1; q i The unit of the electric quantity for the i-interval running of the train is kWh; g i The power supply quantity of the contact network in the i interval to the vehicle-mounted energy storage is represented, and the unit is kWh;
the formula of the minimum electric quantity of the interval vehicle-mounted energy storage is as follows;
in the formula, E i_low The lowest vehicle-mounted energy storage electric quantity in the section i of the train is represented in the unit of kWh; q i_max The maximum value of the electric quantity of the i-interval running of the train provided by the vehicle-mounted energy storage is represented in the unit of kWh; g i_low Represents i section Q i_max The contact network at the moment supplies electric quantity to the vehicle-mounted energy storage, and the unit is kWh;
the formula of the residual electric quantity of the vehicle-mounted energy storage device when the train is started after the train stops is as follows:
in the formula (I), the compound is shown in the specification,
the unit of the vehicle-mounted energy storage electric quantity when the train leaves the station i is kWh; b is i The charging station represents that the charging station of the station i supplements the vehicle-mounted energy storage with electric quantity, and the unit is kWh; e Fi And the electric quantity which represents the auxiliary energy consumption provided by the vehicle-mounted energy storage at the ith station is the kWh unit.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010155368.0A CN113378345B (en) | 2020-03-09 | 2020-03-09 | Energy storage type tramcar charging station layout optimization method based on genetic algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010155368.0A CN113378345B (en) | 2020-03-09 | 2020-03-09 | Energy storage type tramcar charging station layout optimization method based on genetic algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113378345A CN113378345A (en) | 2021-09-10 |
| CN113378345B true CN113378345B (en) | 2022-09-06 |
Family
ID=77569294
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010155368.0A Active CN113378345B (en) | 2020-03-09 | 2020-03-09 | Energy storage type tramcar charging station layout optimization method based on genetic algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113378345B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117688968A (en) * | 2024-02-04 | 2024-03-12 | 中国铁建电气化局集团有限公司 | Tramcar layout method based on particle swarm optimization |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107368920A (en) * | 2017-07-01 | 2017-11-21 | 南京理工大学 | A kind of off-peak period multi-train movement energy conservation optimizing method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170298890A1 (en) * | 2016-04-19 | 2017-10-19 | The Wise Labs, Inc. | Systems and methods for uninterruptable power supply |
-
2020
- 2020-03-09 CN CN202010155368.0A patent/CN113378345B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107368920A (en) * | 2017-07-01 | 2017-11-21 | 南京理工大学 | A kind of off-peak period multi-train movement energy conservation optimizing method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113378345A (en) | 2021-09-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105416077B (en) | The EMS and management method of a kind of electric automobile | |
| CN110155057B (en) | Vehicle energy management system and management method | |
| CN108674195B (en) | Method for recovering braking energy of urban rail vehicle without power supply of contact network | |
| CN110795834B (en) | Urban rail train energy consumption optimization method with intermittent power supply | |
| JP4417949B2 (en) | Railway vehicle drive system | |
| CN108832611B (en) | Rail transit emergency power supply system and control method | |
| CN108376190B (en) | Method for determining parameters of vehicle-mounted super capacitor bank of urban rail train | |
| CN108454449B (en) | Virtual rail transit train distributed power system and matching control method | |
| CN107719132A (en) | A kind of evaluation method of braking energy of electric automobiles organic efficiency | |
| CN113378345B (en) | Energy storage type tramcar charging station layout optimization method based on genetic algorithm | |
| CN112693464A (en) | Braking energy feedback control method suitable for new energy commercial vehicle | |
| CN114714924A (en) | Dual-source power supply system of pure electric mining truck, mining truck and application method | |
| CN111598434B (en) | Method for determining configuration scheme of super capacitor energy storage device of mountain urban rail transit | |
| CN109829259B (en) | Hybrid power train operation simulation calculation method | |
| Shah et al. | An energy management system for a battery ultracapacitor hybrid electric vehicle | |
| CN113688541B (en) | Traction power supply simulation system and simulation method for energy storage type vehicle of urban rail transit | |
| CN113486541B (en) | Simulation method of urban rail direct-current power supply system based on train safety distance and voltage | |
| Khanra et al. | Improvement of regenerative braking energy of fully battery electric vehicle through optimal driving | |
| CN107264303B (en) | Method and system for converting vehicle running condition into power battery running condition | |
| Sumpavakup et al. | Peak demand cutting strategy with an on-board energy storage system in mass rapid transit | |
| CN111806241B (en) | Method for determining regenerative electric energy recovery space of rail transit train | |
| CN115648955A (en) | Electric braking energy recovery control method and device for vehicle | |
| Kashani et al. | Applying neural network and genetic algorithm for optimal placement of ultra-capacitors in metro systems | |
| CN112124372A (en) | Energy-saving method for optimizing regenerative braking control by using train stopping time | |
| CN111976509A (en) | Range extender system control method and device and automobile |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |