CN106682759B - Battery supply system for electric taxi and network optimization method - Google Patents
Battery supply system for electric taxi and network optimization method Download PDFInfo
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- CN106682759B CN106682759B CN201610765305.0A CN201610765305A CN106682759B CN 106682759 B CN106682759 B CN 106682759B CN 201610765305 A CN201610765305 A CN 201610765305A CN 106682759 B CN106682759 B CN 106682759B
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
Abstract
The invention discloses a battery supply system and a network optimization method for an electric taxi, which are specially designed for the electric taxi by applying a big data technology and meet the requirement of the electric taxi in a city by adopting an economic strategy of centralized charging. The existing gas stations can meet the refueling requirements of all vehicles in a city and can also meet the requirements of taxis. The way of cooperating with the already existing gasoline stations (with the already existing gasoline stations as alternative points) can not only avoid the selected area being infeasible (geographical, legal, etc. reasons) but also save a large investment cost. When the taxi selects the battery replacement station, various factors such as road congestion degree, battery replacement station service level, queuing time, driver driving habits and the like are taken into consideration through the logit model, and the taxi battery replacement station more accords with the actual situation.
Description
Technical Field
The invention relates to a planning method for scrapped automobile recycling, which is particularly based on balanced recycling enterprise pricing and recycling cost. Relating to the calculation of a patent classification number G06; calculating; count G06Q is particularly applicable to a data processing system or method for administrative, commercial, financial, administrative, supervisory or forecasting purposes; treatment systems or methods G06Q50/00, not otherwise provided for, specifically adapted for administrative, commercial, financial, management, supervisory or forecasting purposes, are specifically adapted for systems or methods of a particular department of operation, such as utility or tourism G06Q50/06 electricity, gas or water supply.
Background
The rapid development of economy brings about improvement of living standard of people on one hand and obtains a severe test on energy problems on the other hand. The electric automobile is a new energy vehicle, has the characteristics of low energy consumption, low use cost, less pollutants and the like, can effectively solve the problem of urban traffic pollution, is one of strategic emerging industries which are mainly supported by China nowadays, and is a future development trend.
However, without a complete service supporting facility, the electric vehicle cannot be widely popularized and used. The strengthening of the infrastructure of the electric automobile charging facilities and the guiding of the consumption demands of the electric automobiles with advanced matching service levels are important measures for promoting low-carbon economy, building resource-saving and environment-friendly society.
The charging facility comprises a charging pile and a battery replacement station, and the battery replacement technology has absolute advantages in time as shown in the following table. Although the idea of changing battery stations was proposed and acted upon as early as 2007, failures were declared because of the inability to afford significant infrastructure investment and the attitude of most vehicle enterprises not being actively engaged. Indeed, the battery of the electric vehicle belongs to the core technology like the engine of the existing vehicle, and the sharing of the battery of the large vehicle enterprises is difficult to realize, so that the establishment of the battery replacement station may be hindered for private vehicles. Public transport means such as taxis are quite different from private cars, the models of the taxis and buses are uniform easily, and the core technology sharing problem does not exist. Moreover, the driving mileage of a private car is relatively short after all, and charging for several hours after driving for a period of time may be tolerated, but the taxi does not run.
Table 1 electric power supplementary type table
Disclosure of Invention
The invention provides a battery supply system and a network optimization method for an electric taxi aiming at the problems, which comprises the following steps:
obtaining the geographic position distribution of the taxies in the charging period by analyzing and recording the GPS historical data of the taxies;
establishing a selection scheme model based on a plurality of Logit models to determine a selection scheme of a power station for a taxi;
-determining the cost of the taxi to reach the target change point station according to said selection scheme; establishing a power station site selection model containing the cost; selecting an optimal power conversion station site selection scheme meeting various constraints from a plurality of alternative power conversion station site selection schemes through the site selection model;
establishing a charging center position model based on a Thiessen polygon according to the obtained optimal power conversion station selection scheme and the taxi to power conversion station selection scheme; inputting an alternative charging center layout scheme into the model to obtain a cluster formed by the charging center and the affiliated power exchanging stations;
and traversing the cost of each cluster, and outputting a charging center position model represented by the cluster with the minimum cost to complete the optimization of the system network.
As a preferred embodiment, the selection scheme model based on the multiple Logit models at least comprises a tendency of taxis to a busy area, a distance between the geographic position distribution and a gas station, a service level of a power swapping station and a queuing penalty function of the power swapping station; the weight coefficient of each influencing factor is theta1、θ2、θ3And theta4。
Further, the penalty function of the power swapping station is as follows:
the function is a relation function of the queue time of the power station and the customer satisfaction degree.
As a preferred embodiment, the selection scheme model based on the multiple Logit models is as follows:
the endurance is restrained, and the power change station, namely Bg, in the range of the endurance of each taxi is determined;
the utility function is used for determining the utility value of each taxi to each battery replacement station within the endurance capacity of the taxi;
the probability function is used for determining the probability of each taxi to each battery replacement station within the endurance capacity of the taxi;
wherein gamma isgbrThe r variable which influences the taxi g to select the u power change station; thetarA weight coefficient representing an r-th variable; vgbRepresenting a utility function of the electric taxi g for selecting the battery replacement station u; pgbRepresenting the probability of selecting the battery replacement station u by the electric taxi g; e0Representing the initial residual capacity of the electric taxi; e represents the average power consumption of the electric taxi per unit time; b denotes a set B ═ { u | w | of the selected power change stationsu＝1}；BgAnd the set G which represents the battery replacement stations within the range of the cruising ability of the taxi G represents the set of the taxies.
Furthermore, the site selection model of the power conversion station is as follows:
Subject to:
the fund is restricted, so that the total fund invested into the system is less than a certain limit;
battery capacity constraint, which means that the battery demand from every day to the u-th power change station does not exceed the battery capacity of the u-th power change station;
service rate constraint, ensuring that more than 90% of taxi battery replacement behaviors are met;
a variable value constraint;
wherein a represents the average cost of the electric taxi in unit time; h isuRepresenting the fixed cost of u for establishing the power conversion station; qb represents the battery capacity of the battery replacement station u; and U represents a set of power swapping stations.
As a preferred embodiment, the process of selecting the optimal site selection scheme for the power conversion station, which satisfies various constraints, from the multiple alternative site selection schemes for the power conversion station through the site selection model is specifically as follows:
-generating a set of swapping station layout solutions M from the candidate swapping stations1,M2,,,,,,MnIn which M is1Is an alternative power changing station 1, M2Is an alternative power change station 2, by analogy, MnIs an alternative power changing station n; total number of alternative power station combinations
-taking a solution from said set of solutions;
-checking said battery number constraint, capital constraint, service rate constraint, respectively; calculating the cost of the charging center;
the taxi in the scheme is summed up with the cost of arriving at the battery replacement station, the construction cost of the battery replacement station and the cost related to the charging center;
and traversing each scheme to obtain the scheme with the lowest cost as the optimal site selection scheme of the power conversion station.
In a preferred embodiment, the charging center position model is as follows:
Subject to：
BPR function:
ensure no distribution paths between charging centers:
ensuring that a distribution path is inevitably formed between the charging center and the battery replacement station:
calculating the number of distribution vehicles used in the system:
0/1 variable value, WvIndicates whether the charging center v is established, xijkIndicating whether the batteries are distributed by the distribution vehicle k between (i, j);
wherein A represents the distribution cost of the battery distribution vehicle in unit time; m represents the number of battery delivery vehicles; m represents the cost of the delivery vehicle; alpha represents the undetermined parameter of the BPR function, and the suggested value is 0.15; beta represents a BPR function undetermined parameter, and the suggested value is 4; t is tijRepresenting the actual passing time from the charging center i to the battery replacement station j; t is tijRepresenting the free running time from the charging center i to the battery replacement station j; hvRepresents a fixed fee for building the charging center v; qijRepresenting the actual traffic volume from the charging center i to the charging station j; cijThe traffic capacity from the charging center i to the battery replacement station j is represented; k represents a collection of battery delivery vehicles; v represents a set of charging centers; o denotes a set of selected charge centers O ═ { v | W ═ Wv＝1}。
The main contribution of the invention is that a battery supply system is designed for the electric taxi by applying a big data technology, and the economic strategy of centralized charging is adopted to meet the requirement of the electric taxi in a city. The existing gas stations can meet the refueling requirements of all vehicles in a city and can also meet the requirements of taxis. The way of cooperating with the already existing gasoline stations (with the already existing gasoline stations as alternative points) can not only avoid the selected area being infeasible (geographical, legal, etc. reasons) but also save a large investment cost. When the taxi selects the battery replacement station, various factors such as road congestion degree, battery replacement station service level, queuing time, driver driving habits and the like are taken into consideration through the logit model, and the taxi battery replacement station more accords with the actual situation. In conclusion, the invention carries out overall design and optimization on the whole urban battery supply network from the perspective of taxi companies (or governments) and finds a better location selection scheme for the charging center and the battery replacement station. For the above reasons, the present invention can be widely applied to the field of electric taxis.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a system operation diagram of the present invention
FIG. 2 is a schematic flow chart of the present invention
FIG. 3 is a cluster map in the computation process of the present invention
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the following describes the technical solutions of the embodiments of the present invention clearly and completely with reference to the accompanying drawings in the embodiments of the present invention:
as shown in fig. 1-3:
data information of a charging demand point needs to be collected firstly, then a selection scheme of a taxi is obtained, secondly, the position of a battery replacement station is optimized, and finally, the position of a charging center is optimized. The position optimization of the charging center is a problem which needs a single decision and is a sub-problem which influences the position of the power exchange station, and the invention is further explained by combining the drawings and the specific embodiment:
1) the point of charge demand, i.e. the location of the taxi, is determined.
The first step in the site selection optimization is to determine the location of the customer site, i.e., the location of the taxi driver. As a vehicle that is constantly moving, taxis are generally considered trip requirements. It is considered that although the position of the taxi is dynamically changed, dynamic balance is achieved to some extent within a certain period of time. In other words, the location of a particular taxi does change constantly, but the number of taxis in a certain area is constant over a period of time. The position distribution condition of the taxi charging time period can be obtained by carrying out GPS analysis through a big data technology.
2) And obtaining a selection scheme of the taxi to the power station.
The method is characterized in that a plurality of battery replacement stations are arranged in the range of the cruising ability of a taxi, the taxi is generally considered to select the battery replacement station closest to the taxi to replace the battery, but the service level of the battery replacement stations, the queue leader, the driving habit of a driver and the like in actual life all affect the selection result, the influence is expressed by a selection scheme model based on a plurality of Loit models, and the model is specifically shown as follows:
table 2 table of influence factors for selecting power change station
γgbrThe r variable which influences the taxi g to select the u power change station; thetarA weight coefficient representing an r-th variable; vgbRepresenting a utility function of the electric taxi g for selecting the battery replacement station u; pgbRepresenting the probability of selecting the battery replacement station u by the electric taxi g; e0Representing the initial residual capacity of the electric taxi; e represents the average power consumption of the electric taxi per unit time; b denotes a set B ═ { u | w | of the selected power change stationsu＝1}；BgThe set G of the battery replacement stations in the range of the G endurance capacity of the taxi represents the set of the taxi
The calculation steps of the selection process of the taxi power station replacement by adopting the logit model are as follows:
and Step1, finding all the battery replacement stations within the range of the cruising ability of each taxi.
And Step2, calculating the utility value of the battery swap station to the taxi according to three indexes of distance, service level and tendency to the busy region, and determining the initial scheme of selecting the battery swap station for each taxi according to a probability function.
And Step3, measuring the crowdedness of the fourth index by using the obtained initial scheme, and finally measuring the electric quantity supplement plan of the taxi by using the four indexes.
3) Site selection model of power station
The final site selection is as follows, and the aim of the model is to minimize the total cost of the logistics system, including the cost of a taxi arriving at a battery replacement station, the construction cost of the battery replacement station and the cost related to a charging center. The service rate is guaranteed by hard constraints so that low cost and high service rate can be guaranteed at the same time.
Subject to:
a represents the average cost of the electric taxi in unit time; h isuRepresenting the fixed cost of u for establishing the power conversion station; q. q.sbRepresenting the battery capacity of the battery replacement station u; and U represents a set of power swapping stations.
Equation (5) is a capital constraint such that the total capital invested into the system is less than a certain limit.
Equation (6) is a battery level constraint that indicates that the battery demand from the nth power change station per day does not exceed the battery level of the nth power change station.
Formula (7) is a service rate constraint, which ensures that more than 90% of taxi battery replacement behaviors are satisfied.
Equation (8) is a variable value constraint.
The site selection optimization process of the power conversion station comprises the following calculation steps:
step1, generating a series of power station replacement layout schemes { M ] from candidate power station replacement1,M2,,,,,,MnIn which M is1Is an alternative power changing station 1, M2Is an alternative power change station 2, by analogy, MnIs an alternative power changing station n; alternative battery replacement station setSum of sum
And Step3, checking the battery quantity constraint (6)) and obtaining a final power supplement scheme. And if n taxis exceed the service limit of a certain swapping station, checking whether the n taxis closest to the swapping station can reach other swapping stations. If the cruising power permits and other power exchange stations have batteries left, the power exchange stations are arranged to new power exchange stations, otherwise the power exchange stations are not served, namely the final power supply scheme.
Step4: checking for capital constraints (constraint (5)). If the cost of this solution is in the capital range, Step5 is performed. Otherwise, this scheme is discarded and Step8 is performed.
Step 5: check service rate constraints (constraint (7)). If the service rate of the scheme is greater than 90%, step8 is performed. Otherwise, this scheme is discarded and Step8 is performed.
Step6: calculate costs for the charging center (detailed in the next section).
And Step7, summing the cost of each part of the scheme (the cost of the taxi to the battery replacement station, the construction cost of the battery replacement station and the cost of the charging center). If the total cost is less than the last record, the record proceeds to step 8. Otherwise, step8 is not recorded directly.
Step8 if all the protocols have been tested, Step9 is performed. Otherwise, the next solution is selected from the set of step1 and returned to step 3.
And Step9, ending and outputting a final result.
4) Charging center position optimization model
The number of charging centers is balanced, and if only one charging center is selected, the construction cost is saved, but the transportation cost is increased. If a plurality of charging centers are selected, in turn, the transportation cost is reduced and the construction cost is increased. Where the charging center is constructed, it is required to determine where several charging centers are constructed.
We use the thiessen polygon for distance-based clustering (fig. 3), so that a multi-center solution is decomposed into multiple single-center VRP problems.
The model for the battery distribution problem is as follows, with the objective function aiming to minimize the cost of three components, respectively:
vehicle delivery costs;
the construction cost of the charging center;
vehicle use cost;
the vehicle distribution cost is the part of the cost related to the travel distance;
the cost of vehicle usage is that portion of the cost associated with the number of vehicles.
Because the load capacity of the battery distribution vehicle is limited, a point-to-point distribution mode is adopted according to the current battery weight, and the distribution cost can be saved by adopting a closed-loop transportation mode if the technology is changed later. The vehicle delivers the battery τ times per day, so the battery delivery cost is multiplied by τ. τ is a self-defined value, and increases as taxi charging behavior increases.
Subject to：
A represents the distribution cost of the battery distribution vehicle in unit time; m represents the number of battery delivery vehicles; m represents the cost of the delivery vehicle; alpha represents the undetermined parameter of the BPR function, and the suggested value is 0.15; beta represents a BPR function undetermined parameter, and the suggested value is 4; t is tijRepresenting the actual passing time from the charging center i to the battery replacement station j; t is tijRepresenting the free running time from the charging center i to the battery replacement station j; hvRepresents a fixed fee for building the charging center v; qijRepresenting the actual traffic volume from the charging center i to the charging station j; cijThe traffic capacity from the charging center i to the battery replacement station j is represented; k represents a collection of battery delivery vehicles; v represents a set of charging centers; o denotes a set of selected charge centers O ═ { v | W ═ Wv＝1}。
Equation (10) is the BPR function.
Equation (11) ensures that there is no distribution path between charging centers.
The formula (12) ensures that a distribution path is necessarily arranged between the charging center and the power changing station.
Equation (13) is the number of delivery vehicles used in the computing system
Equation (14) is the value of the 0/1 variable, WvIndicates whether the charging center v is established, xijkIndicating whether the battery is distributed by the distribution vehicle k between (i, j).
The site selection optimization process of the power conversion station comprises the following calculation steps:
and Step1, generating a series of charging center layout schemes from the candidate charging centers.
Step2 if all the solutions have been tested, Step7 is switched to. Otherwise, take a solution from the set in step1 and turn to step 3.
Step3, finding the nearest charging center for each charging station, and then each charging center and the 'its charging station' form a class. Each scheme consists of several clusters, i.e. clusters based on thiessen polygons.
Step4 the total cost for each class is calculated. (vehicle distribution cost, charge center construction cost, vehicle use cost).
Step 5: the costs of all classes in the scheme are summed. If the total cost is less than the last record, record and go to step6 otherwise, no record goes directly to step 6.
Step6 take the next charge center solution from the collection of Step1 and return to Step 2.
Step7 ends and outputs the final result.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (6)
1. A battery supply system and a network optimization method for an electric taxi are suitable for a battery supply system comprising a battery replacement station and a charging center, wherein the charging center in the system is responsible for intensively charging batteries, and a battery distribution vehicle sends full batteries to each battery replacement station and brings back empty batteries, and is characterized by comprising the following steps:
obtaining the geographic position distribution of the taxies in the charging period by analyzing and recording the GPS historical data of the taxies;
establishing a selection scheme model based on a plurality of Logit models, and determining a selection scheme of a taxi to a power exchange station;
determining the cost of the taxi to reach the target power change station according to the selection scheme; establishing a power station site selection model containing the cost; selecting an optimal power conversion station site selection scheme meeting various constraints from a plurality of alternative power conversion station site selection schemes through the site selection model;
establishing a charging center position model based on a Thiessen polygon according to the obtained optimal power conversion station selection scheme and the taxi to power conversion station selection scheme; inputting an alternative charging center layout scheme into the model to obtain a cluster formed by the charging center and the affiliated power exchanging stations;
traversing the cost of each cluster, and outputting a charging center position model represented by the cluster with the minimum cost to complete the optimization of the system network;
the charging center position model is as follows:
BPR function:
ensure no distribution paths between charging centers:
ensuring that a distribution path is inevitably formed between the charging center and the battery replacement station:
calculating the number of distribution vehicles used in the system:
Wvindicates whether the charging center v is established, xijkIndicating whether the batteries are distributed by the distribution vehicle k between (i, j);
wherein A represents the distribution cost of the battery distribution vehicle in unit time; m represents the number of battery delivery vehicles; m represents the cost of the delivery vehicle; alpha represents the undetermined parameter of the BPR function, and the value is 0.15; beta represents a BPR function undetermined parameter, and the value of beta is 4; t is tijRepresenting the actual passing time from the charging center i to the battery replacement station j; t is tijRepresenting the free running time from the charging center i to the battery replacement station j; hvRepresents a fixed fee for building the charging center v; qijRepresenting the actual traffic volume from the charging center i to the charging station j; cijThe traffic capacity from the charging center i to the battery replacement station j is represented; k represents a collection of battery delivery vehicles; v represents a set of charging centers; o denotes a set of selected charge centers O ═ { v | W ═ Wv＝1}。
2. The battery supply system and the network optimization method for electric taxis according to claim 1, wherein the selection scheme model based on the plurality of Logit models at least comprises taxis tendency to busy areas, the distance between the geographical location distribution and the gas station, the service level of the battery replacement station and the queuing penalty function of the battery replacement station; the weight coefficient of each influencing factor is theta1、θ2、θ3And theta4。
3. The battery supply system and network optimization method for electric taxis according to claim 2, further characterized in that the battery swapping station penalty function is:
the function is a relation function of the queue time of the power station and the customer satisfaction degree.
4. The battery supply system and network optimization method for electric taxis according to claim 2, wherein the selection scheme model based on the multiple Logit models is as follows:
the endurance is restrained, and the power change station, namely Bg, in the range of the endurance of each taxi is determined;
the utility function is used for determining the utility value of each taxi to each battery replacement station within the endurance capacity of the taxi;
the probability function is used for determining the probability of each taxi to each battery replacement station within the endurance capacity of the taxi;
wherein gamma isgbrThe r variable which influences the taxi g to select the u power change station; thetarA weight coefficient representing an r-th variable; vgbRepresenting a utility function of the electric taxi g for selecting the battery replacement station u; pgbRepresenting the probability of selecting the battery replacement station u by the electric taxi g; e0Representing the initial residual capacity of the electric taxi; e represents the average power consumption of the electric taxi per unit time; b denotes a set B ═ { u | w | of the selected power change stationsu＝1}；BgAnd the set G which represents the battery replacement stations within the range of the cruising ability of the taxi G represents the set of the taxies.
5. The battery supply system and the network optimization method for electric taxis according to claim 4, wherein the battery swapping station site selection model is as follows:
the fund is restricted, so that the total fund invested into the system is less than a certain limit;
battery capacity constraint, which means that the battery demand from every day to the u-th power change station does not exceed the battery capacity of the u-th power change station;
service rate constraint, ensuring that more than 90% of taxi battery replacement behaviors are met;
a variable value constraint;
wherein a represents the average cost of the electric taxi in unit time; h isuRepresenting the fixed cost of u for establishing the power conversion station; qb represents the battery capacity of the battery replacement station u; and U represents a set of power swapping stations.
6. The battery supply system and the network optimization method for electric taxis according to claim 5, wherein the process of selecting the optimal site selection scheme for the battery swap station satisfying each constraint from the multiple alternative site selection schemes through the site selection model is specifically as follows:
-generating a set of swapping station layout solutions M from the candidate swapping stations1,M2,,,,,,MnIn which M is1Is an alternative power changing station 1, M2Is an alternative power change station 2, by analogy, MnIs an alternative power changing station n; alternative switchTotal number of power station combinations
-taking a solution from said set of solutions;
-checking the battery number constraint, the capital constraint, the service rate constraint, respectively; calculating the cost of the charging center;
the taxi in the scheme is summed up with the cost of arriving at the battery replacement station, the construction cost of the battery replacement station and the cost related to the charging center;
and traversing each scheme to obtain the scheme with the lowest cost as the optimal site selection scheme of the power conversion station.
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