CN107392336B - Reservation-based distributed electric vehicle charging scheduling method in intelligent transportation - Google Patents
Reservation-based distributed electric vehicle charging scheduling method in intelligent transportation Download PDFInfo
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Abstract
The invention provides a reservation-based distributed electric vehicle charging scheduling method in intelligent traffic. Firstly, the method comprises the following steps: the charging station uses the 'CSSI Update' message to periodically release local state information to all the road side units in a period T; II, secondly: the electric automobile acquires the published Update service through the road side unit, and subscribes the real-time state information of the charging station published in each publishing period by using an 'Aggregated CSSI Update' message; thirdly, the method comprises the following steps: according to the information obtained from the road side unit, the electric automobile needing to be charged makes an autonomous decision, and uses 'ReservationAggregation' to issue a charging reservation to the road side unit encountered in the moving process; fourthly, the method comprises the following steps: the Charging station accesses the road side unit aggregating the reservation information of the electric vehicle through the 'Aggregated-Charging-reserving-Update' message. The invention can reduce the communication cost of the charging station; the optimal charging station can be found by the child of the moving electric automobile needing the charging service.
Description
Technical Field
The invention relates to an electric vehicle charging scheduling method, in particular to a distributed electric vehicle charging scheduling method based on reservation in intelligent transportation.
Background
The tail gas of motor vehicles becomes the main factor of air pollution in China, and is particularly obvious in many large and medium-sized cities. However, the driving range of the electric vehicle is generally about 200 km because the electric vehicle is limited by the energy density of the battery. For taxies, municipal vehicles and other vehicles with higher requirements on cruising ability, the current battery capacity is difficult to meet the daily driving requirement. When the vehicle travels to a distant destination, the electric vehicle needs to be charged in the traveling process and needs a certain time for charging, and generally, about 20 minutes is needed for completing quick charging. These public charging stations are typically deployed in places where the density of electric vehicles is high, such as shopping malls and parking lots. In this case, the electric vehicle requiring the charging service while moving may select the optimal charging station for charging.
If the electric vehicle is random out of order when selecting the time and place of charging, it will increase the vehicle operating costs and cause a charging station load imbalance. The operation cost of the charging station with the overhigh load is greatly increased; otherwise, the charging station part with too low load is left unused, and the profit is difficult to guarantee. And when the electric automobile arrives at a high-load charging station, the phenomenon of queuing for charging for a long time is likely to occur. The shortage of the driving mileage and the long-time charging queue cause the user of the electric vehicle to have mileage anxiety, and worry about whether the user can arrive at the destination on time. In view of this, research on the electric vehicle charging scheduling strategy is receiving wide attention. Due to the rapid development of intelligent transportation in recent years, the problem is solved. High-speed moving vehicles and roadside units may communicate via short-range V2R, making mass vehicle information collection cheaper and faster relative to cellular networks. More importantly, the roadside unit in the intelligent traffic can be utilized to improve the data collection and transmission efficiency.
Communication in pull mode via V2R is in information distribution. And the charging station issues related information to the electric automobile through the road side unit in the pull mode. In a mobile electric vehicle charging scenario, if the time required to reach a target charging station is long, a charging decision is made in the event that the electric vehicle cannot obtain a charging decision for another vehicle, then there are a large number of vehicles that select the same high performance charging station but have not yet arrived to travel to that charging station at the same time. There is a probability that a false decision is made ignoring the potential latency. Therefore, before reaching the selected charging station, the electric vehicle can obtain the reservation information of other vehicles through the road side unit, and simultaneously upload the reservation information of the electric vehicle after completing the charging decision, thereby solving the problems.
Disclosure of Invention
The invention aims to provide a reservation-based distributed electric vehicle charging scheduling method in intelligent transportation, which can enable an electric vehicle which needs a charging service in motion to select an optimal charging station for charging.
The purpose of the invention is realized as follows:
the method comprises the following steps: the charging station uses the 'CSSI Update' message to periodically release local state information to all the road side units in a period T;
step two: the electric automobile acquires the published Update service through the road side unit, and subscribes the real-time state information of the charging station published in each publishing period by using an 'Aggregated CSSI Update' message;
step three: according to the information obtained from the road side unit, the electric automobile needing to be charged makes an autonomous decision, and uses 'ReservationAggregation' to issue a charging reservation to the road side unit encountered in the moving process;
step four: the Charging station accesses the road side unit aggregating the reservation information of the electric vehicle through the 'Aggregated-Charging-reserving-Update' message.
The present invention may further comprise:
1. the first step specifically comprises the following steps: each of the charging stations is deployed at a specific location and issues its status information, which is used by an electric vehicle requesting charging to select a charging station; the road side unit is a road side unit deployed in the system and serves as an intermediate entity, information sent from each charging station is transmitted to the electric vehicles passing through the corresponding road side unit, and all the charging stations are connected with the road side unit through communication channels.
2. The autonomous decision making of the electric vehicle needing to be charged in the third step specifically comprises the following steps: before the autonomous charging decision is made, available charging stations are selected firstly, in the charging station selection process, potential available charging stations are selected firstly, then the optimal charging stations are selected, and when the number of the charging stations in a road network is small, all the charging stations in the region are calculated; when multiple charging stations are possibly arranged on a potential route, the positions of the charging stations and the driving range of the electric vehicle are considered, and then the vehicle cannot reach the destination and some charging stations are selected for charging, so that the charging stations need to be screened; after the available charging stations are obtained, the electric vehicle determines the utility value of the charging stations according to a multi-factor strategy, and a special charging station is selected for charging by using the information from the road side unit in an autonomous decision-making manner; the charging station selection strategy is determined according to three factors, namely travel duration, electric energy increment and charging station distance, a multi-factor weight coefficient is obtained by adopting a characteristic value vector method, in the process of charging selection, a user selects the importance of the three factors, the weight coefficient of each factor is obtained, normalization processing is carried out, the size of the utility value of each charging station is obtained, the optimal charging station is selected, and reservation information is sent to charging through a road side unit after the optimal charging station is obtained.
3. The fourth step specifically comprises: aggregating reservations of electric vehicles and then reporting them to the corresponding charging stations before the next release cycle of the charging stations, the reservations of each electric vehicle not being directly transmitted to the selected charging stations; the reservation information of the electric vehicle stored on the road side unit is to be issued to the next charging station before the timestamp is issued by the charging station, Tpre+ Δ given, TpreThe timestamp and the delta are issued by the charging station, the charging station is issued frequency, the charging station integrates the aggregation information of all road side units connected through wires, the reservation of all electric vehicles related to the charging station is issued, and meanwhile, the charging station state information is calculated and issued in the next issuing time period.
The invention provides a reservation-based distributed electric vehicle charging management scheme in intelligent traffic, which is required to complete charging information distribution of electric vehicles by deploying a plurality of road side units based on a publish/subscribe mechanism. The functions of the road side unit are expanded on the basis of the basic pull mode, and reservation information needs to be sent to the selected charging station through the road side unit for charging reservation. Unlike the basic pull mode, which only distributes the local information of the charging stations, the invention expands the functions of the road side unit, aggregates the reservations of the electric vehicles and then reports them to the corresponding charging stations before the next distribution cycle of the charging stations.
Through the reservation information of other electric vehicles, the current vehicle can adjust its charging schedule. When a charging station has been reserved by many electric vehicles, other electric vehicles requiring charging service will recognize the congestion state of the charging station for a future period of time, thereby selecting an alternative charging station.
The method has the following characteristics and advantages in the optimized charging performance of the electric automobile, and the pull mode capable of being reserved in the intelligent transportation is provided for the reason that a large number of electric automobiles drive to the same high-performance charging station due to the fact that information is not timely.
The invention extends the functionality of the roadside unit, aggregates reservations of electric vehicles and reports them to the corresponding charging stations before the next release cycle of the charging stations. The reservation of each electric vehicle is not directly transmitted to the selected charging station, and the proposed aggregation function can reduce the communication cost of the charging station.
The issued reservation information does not disclose the location of the electric vehicle. Since the arrival time is predicted from the location of the electric car and its corresponding speed, these two pieces of information will not be released in any communication. In this case, the position information on the other electric vehicles is not obtained. The provided charging management scheme can effectively ensure the privacy of the electric automobile user. In the course of executing the above reservation, the reservation may be changed or cancelled during the running of the electric vehicle. To evaluate the stability of reservations in a multi-factor charging station selection strategy, a method of estimating the actual execution probability of each reservation is proposed.
In the charging station selection process, potential available charging stations are first selected, and then the optimal charging station is selected from among them. To reduce the calculation burden of an electric vehicle. A method for screening charging stations is provided by considering the positions of the charging stations and the driving range of an electric vehicle, and selecting some charging stations for charging when the vehicle cannot reach a destination. And after the available charging stations are obtained, the electric vehicle determines the utility value of the charging stations according to a multi-factor strategy, and autonomously decides to select a special charging station for charging by using the information from the road side unit. In the process of selecting the optimal charging station, an electric vehicle user faces the balance and selection of multiple factors such as charging time, charging station distance, expected charging amount and the like, and the charging station selection strategy is determined according to three factors of travel duration, electric energy increment and charging station distance.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a timing diagram of the electric vehicle, the charging station and the roadside unit according to the present invention.
FIG. 3 is a simulation of Helsinki.
4 a-4 b are possible charging station options, where FIG. 4a is an example of charging at only one charging station; fig. 4b is an example of a need for multiple charging.
Figure 5 a message table defined by the publish/subscribe system.
Fig. 6 charging station status information table.
FIG. 7EV-2 shows a charging reservation table.
Detailed Description
The method mainly comprises the following steps:
the method comprises the following steps: the charging station uses the "CSSI Update" message defined in the table of fig. 5 to periodically issue local status information to all of the roadside units at a period T.
Step two: the electric automobile obtains the issued updating service through the road side unit. The "Aggregated CSSI Update" message is used to subscribe to the charging station real-time status information published per publication cycle.
Step three: according to the information obtained from the road side unit, the electric automobile needing to be charged makes an autonomous decision, and uses 'ReservationAggregation' to issue a charging reservation to the road side unit encountered in the movement.
Step four: the Charging station accesses the roadside unit aggregating the electric vehicle reservation information through the "Aggregated-Charging-Reservations-Update" message defined in the table of fig. 5.
The invention is described in more detail below by way of example.
The second embodiment is as follows:
step one, the charging station uses the "CSSI Update" message defined in the table of fig. 5 to periodically issue local status information to all of the roadside units at a period T. The charging station distributes its status information to the road side units via a dedicated reliable communication channel connection. Each of which is deployed at a specific location and issues its status information, such as waiting time, location, price, etc., so that an electric vehicle requesting charging can use this information to select a charging station. The road side unit is a road side unit deployed in the system and acts as an intermediate entity, and information sent from each charging station can be transferred to electric vehicles passing through the corresponding road side unit. All charging stations are connected with each roadside unit through dedicated reliable communication channels.
The road side unit subscribes the release information of all the charging stations, and aggregates and caches the real-time state information released by each charging station. Each charging station integrates information on a plurality of electric vehicles of the charging reservation of the charging station with charging station local information for charging station information distribution. In the format of the table of fig. 6, the electric vehicle ID reserved for charging at the charging station is hidden. The target vehicle will not obtain information about which vehicles have reserved charging because only information containing the arrival time and the reserved charging time at that arrival is received. The issued reservation information does not disclose the location of the electric vehicle. Since the arrival time is predicted from the location of the electric car and its corresponding speed, these two pieces of information will not be released in any communication. In this case, the position information on the other electric vehicles is not obtained.
And step two, the electric automobile acquires the issued updating service through the road side unit. The "Aggregated CSSI Update" message is used to subscribe to the charging station real-time status information published per publication cycle. Particularly, when the electric automobile frequently encounters several road side units in a short time, the subscription mechanism can effectively reduce redundant access signaling.
And step three, according to the information obtained from the road side unit, the electric automobile needing to be charged makes an autonomous decision, and the charging reservation is issued to the road side unit encountered in the moving process by using 'conservation Aggregation'. The electric automobile autonomously decides to select a special charging station for charging according to a minimum waiting time strategy by using information from the road side unit.
For each electric vehicle parked at the charging station, the waiting time at the charging station is the sum of the charging times of the electric vehicles in the charging station. To calculate this time, we need the following information
Is chargingNumber of electric vehicles, NCAnd (4) showing.
Local charging time of each electric vehicle stopped on the charging station, for
And (4) showing.
The number of charging slots on the charging station, denoted as υ.
Number of electric vehicles still waiting for available charging slots, with NWAnd (4) showing.
Obtaining the shortest charging time
Queue time
value1+value2And selecting a charging station with the minimum utility value for the utility values of the charging stations.
Each roadside unit as a subscribing user sets the "Reservations Aggregation" message defined in the table of fig. 5 and accesses Reservations of electric cars encountered using pull mode communications. The number of this topic depends on the number of rsus, as each rsu uses its message topic to collect electric vehicle charging reservations.
In the process of the bookable pull mode, the booking information issued by the electric vehicle is one of the information required by the charging decision. The electric vehicle that has made the charging station selection decision needs to distribute its own reservation information through communication with the roadside unit. The reservation information specifically includes (1) an ID of the selected charging station, (2) a time of arrival at the charging station, (3) a desired charging period for the electric vehicle, and (4) a stay period at the selected charging station. The reservation information is explained below:
time to reach charging station
Planning the shortest path from the current position to the selected charging station according to Dijkstra algorithmCalculating the travel time to the charging station based on the current position of the vehicle
Expected time of arrival
Given by:
desired length of charge time
Comprises the following steps:
here, the first and second liquid crystal display panels are,
is the energy consumed to move to the selected charging station, where αev(i)This value is related to the vehicle node's own performance for each meter of energy consumed by the vehicle. The length of stay at the charging station is the longest stay time that the user has set at the selected charging station according to his trip. When the dwell time reaches this value, the vehicle leaves the charging station, regardless of whether it is full, and leaves it earlier if charging is completed. For example, the charging reservation information in the table of fig. 7 is EV-2, and if the determination is made on the vehicle side and the ID of another vehicle is released to the target vehicle, the vehicle privacy is leaked, and therefore the electric vehicle ID is hidden.
And step four, the Charging station accesses the road side unit aggregating the electric vehicle reservation information through the 'Aggregated-Charging-reserving-Update' message defined in the table of fig. 5. The number of such topics depends on the number of charging stations, since the aggregated reservation information corresponds to the number of charging stations. The reservation information of the electric vehicle stored on the road side unit is to be released at the next charging stationThe time stamp is issued to the charging station before, by (T)pre+ Δ). T ispreIs the time stamp of the charging station release and Δ is the charging station release frequency. The charging station integrates the aggregate information of all road side units connected through the wire, issues the reservation of all electric vehicles related to the charging station, and simultaneously calculates the CSSI of the electric vehicles so as to issue the reservation in the next issuing time period. The roadside unit aggregation function expanded in the invention aggregates reservations of electric vehicles and then reports the reservations to corresponding charging stations before the next release cycle of the charging stations. The reservation of each electric vehicle is not directly transmitted to the selected charging station, and the proposed aggregation function can reduce the communication cost of the charging station.
In the course of executing the above reservation, the reservation may be changed or cancelled during the travel of the electric vehicle. To evaluate the stability of reservations in a multi-factor charging station selection strategy, a method of estimating the actual execution probability of each reservation is proposed.
To quantify the reservation stability, the reservation stability of the electric vehicle at the ith charging station is set as mucs(i)The definition of reservation stability needs to consider two factors: on one hand, the difference value between the utility value of the selected charging station and the utility values of other alternative charging stations is large, the stability is high when the difference value is large, and the possibility that other more optimal charging stations appear in the period of time of the previous charging station is small; on the other hand, if the length of time for the charging reservation is allowed to be changed, the time required for the charging station to arrive is longer, and the possibility of changing the reservation is higher. According to the above basic idea mucs(i)Can be expressed as:
wherein
Is to select the arrival time, T, of the jth charging stationcurThe difference between these two times needs to be normalized for the current time, so divided by Dmax。
And
utility values, N, derived for the optimal charging station and the jth charging station, respectivelycsIs the number of available charging stations. Mu.scs(i)It can be seen that the probability of an electric vehicle not presenting a lower cost-of-charging option before reaching a selected charging station, i.e. not changing its opportunity to reserve a charge, is not present. The electric vehicle can perform reservation updating according to the received new charging station state information in the process of going to the selected charging station.
The second embodiment is as follows: the present embodiment is different from the specific embodiment in that, in the third step, available charging stations are first selected before making an autonomous charging decision. In the charging station selection process, potential available charging stations are first selected, and then the optimal charging station is selected from among them. When the number of the charging stations in the road network is small, all the charging stations in the region can be calculated. However, when there are potentially many charging stations on a potential route, it is not feasible to select all charging stations in the area one by one, which greatly increases the computational burden of the electric vehicle. Considering the positions of the charging stations and the driving range of the electric vehicle, when the vehicle cannot reach the destination and some charging stations are selected for charging, the charging stations need to be screened.
The details of the mechanism are described below, and the required parameters are electric vehicle energy consumption, remaining battery power, battery capacity, charging station location, departure point, and destination. Reach range R of a vehicleRUses the energy consumption alpha of the electric automobileevAnd the current remaining battery power Ee c vThe calculation results in that,
range R that can be reached when fully chargedFIs composed of
Four cases of electric vehicle charging options are analyzed as follows:
(1) situation without having to go to a charging station
If the vehicle can directly reach the destination from the current position by using the current battery power, performing shortest path planning on the source node and the target node by using the current position and the destination by using a Dijkstra algorithm;
(2) charging in a charging station
If the vehicle cannot reach the destination directly from the current position, the overlap area of the two circles shown in fig. 4a is first calculated. Circle 1 has current position Dep as center of circle and reachable range RRIs a radius. Circle 2 is centered on destination Des, and its range R that can be reached when fully chargedFIs a radius. In order to reduce the calculation cost, the Euclidean distance is used for calculating the overlapping area instead of actually planning the route distance, the available charging stations are obtained, then the routes are verified, and whether the selected charging stations meet the actual reach condition or not is checked according to the route distance. If only one route is found, the route is provided as an alternative. If several routes are available, the most cost-effective route is selected as the selection solution, and the selection of the optimal charging station will be explained in detail in the following section.
(3) Situations requiring multiple charging stations to charge
When the electric vehicle cannot reach the destination from the departure place depending on the current remaining capacity and there are no available charging stations in the overlapping area described in the above paragraph, a plurality of potentially available charging stations needs to be selected in the route planning. The distance L represents a distance between the departure point and the destination. L/2 and reachable Range RRA comparison is made and the larger of the radii of the two circles is used as the radius. Wherein the center of one circle is a starting point, the center of the other circle is a destination, and two common tangents are combined into a closed area. The charging stations within this closed area are potentially available charging stations, as shown in fig. 4 b. At L/2>RRDefining the radius of the circle as L/2 can avoid a smaller number of available charging stations due to the smaller area for searching for potential charging stations. On the other hand, at L/2<RRThe radius is defined as R in the case ofRAll remaining power at presentThe in-range charging station may serve as a potential charging station. Therefore, this method limits the area to search for potential charging stations.
(4) No route is available to reach the destination
In both cases, it is not feasible to rely on the remaining battery power to reach the destination at the departure location: the destination is not within the range which can be reached by the departure point, and no charging station is in the range; when charging at more than two charging stations is required, the amount of electricity is not sufficient to travel to the next charging station. In this case the vehicle cannot reach the destination and the vehicle provides as a reference a route planning from the current position Dep to the destination Des via the nearest charging station.
Under the conditions, the available charging stations of the vehicle at the current moment can be acquired through the available charging station selection mechanism, and the calculation amount of the charging station effectiveness degree calculated by the electric vehicle when the optimal charging station is selected is effectively reduced.
The third concrete implementation mode: the difference between the present embodiment and the specific embodiment is that the electric vehicle in step three determines the utility value of the charging station according to the multi-factor strategy, and uses the information from the road side unit to autonomously decide and select a special charging station for charging. In the process of selecting the optimal charging station, an electric vehicle user faces the balance and selection of multiple factors such as charging time, charging station distance, expected charging amount and the like, and the charging station selection strategy is determined according to three factors of travel duration, electric energy increment and charging station distance.
Electric vehicle EViIts travel duration is calculated by the following method: definition of EViTime taken to travel from the current location to the selected charging station Dev，cs，EViTime taken to travel from charging station to destination Dcs,d. The electric vehicle uses Dijkstra' S algorithm to plan the shortest path from the charging station to the destination, at which path the speed S isevRunning can yield Dev，cs，Dcs,d. Due to the time spent at the selected charging station (including the time waiting for charging EWT)csAnd time of charging
) There are two cases, one is motoring when the condition of equation (4) is satisfied
The vehicle can be fully charged, which takes time at the charging station
Under the other condition, the staying time of the electric automobile in the charging station reaches Dev(r)But leaves the charging station when the charging is not finished, and the staying time of the charging station is Dev(r). According to the above situation, define
The electric vehicle charged halfway reaches the total time consumption of the destination.
It can be calculated from the following formula: the total time taken for a fully charged electric vehicle to travel to the destination in the selected charging station is:
in another case, due to Dev(r)Is calculated from equation (6) when not fully charged:
by counting charging stations
Electric vehicle EViThe power consumption from the current position to the selected charging station is Mev,cs。EViStarting from a charging station to a destinationPower consumption of Mcs,dI.e. EV after charging is completed at the charging stationiThe power consumption of the travel to the destination will continue. The electric automobile uses Dijkstra algorithm to plan the shortest path from the charging station to the destination, and the energy consumption alpha is consumed per meter according to the electric automobileevFrom the path length Mev,cs，Mcs,d. The amount of charge at the selected charging station can be divided into two cases. Electric vehicle EVrWill be at its last charging time Dev(i)When the charging is completed before, the net charge amount from the current position to the charging end is
The staying time of the electric automobile in the charging station reaches Dev(r)When the charging is not completely charged, the amount of charge in the charging station is (D)ev(i)-EWTcs) X beta, where beta is the charging station charging rate, Dev(r)-EWTcsIs the charging period. According to the above conditions, define
The net increment to reach the destination for the electric vehicle charged midway.
The method can be calculated by the following steps: the incremental electrical energy of the electric vehicle driving to the destination with the selected charging station fully charged is:
in another case, due to Dev(r)Without completing the charging, the power increment is:
by calculating heading to each charging station
And obtaining the net electric energy increment of each charging station. The distance to each charging station may be obtained from an onboard electronic map.
And a characteristic value vector method is adopted to obtain the weight coefficients of the multiple factors, and a user can select the importance of the three factors to obtain the weight coefficients of all the factors during charging selection. Due to the fact that the relevant factors are different in dimension, normalization processing needs to be carried out on the relevant factors, the utility value of each charging station can be obtained according to the formula (9), and the optimal charging station is selected. Wherein P isev(r)And PmaxThe distance of the vehicle node to the selected charging station and the distance to the farthest charging station respectively,
and DmaxFor the duration of the journey to be charged via the selected charging station and the longest journey time for the selected charging station respectively,
and
the power increment in the journey and the maximum battery capacity of the vehicle.
Claims (1)
1. A reservation-based distributed electric vehicle charging scheduling method in intelligent transportation is characterized by comprising the following steps:
the method comprises the following steps: the charging station uses the 'CSSI Update' message to periodically release local state information to all the road side units in a period T; the method specifically comprises the following steps: each of the charging stations is deployed at a specific location and issues its status information, which is used by an electric vehicle requesting charging to select a charging station; the road side unit is a road side unit deployed in the system and serves as an intermediate entity, information sent out from each charging station is transmitted to the electric vehicles passing through the corresponding road side unit, and all the charging stations are connected with the road side unit through communication channels;
step two: the electric automobile acquires the published Update service through the road side unit, and subscribes the real-time state information of the charging station published in each publishing period by using an 'Aggregated CSSI Update' message;
step three: according to the information obtained from the road side unit, the electric automobile needing to be charged makes an autonomous decision, and uses 'ReservationAggregation' to issue a charging reservation to the road side unit encountered in the moving process;
the autonomous decision making of the electric vehicle needing to be charged specifically comprises the following steps: before the autonomous charging decision is made, available charging stations are selected firstly, in the charging station selection process, potential available charging stations are selected firstly, then the optimal charging stations are selected, and when the number of the charging stations in a road network is small, all the charging stations in the region are calculated; when multiple charging stations are possibly arranged on a potential route, the positions of the charging stations and the driving range of the electric vehicle are considered, and then the vehicle cannot reach the destination and some charging stations are selected for charging, so that the charging stations need to be screened; after the available charging stations are obtained, the electric vehicle determines the utility value of the charging stations according to a multi-factor strategy, and a special charging station is selected for charging by using the information from the road side unit in an autonomous decision-making manner; the charging station selection strategy is determined according to three factors, namely travel duration, electric energy increment and charging station distance, a multi-factor weight coefficient is obtained by adopting a characteristic value vector method, in the process of charging selection, a user selects the importance of the three factors, the weight coefficient of each factor is obtained, normalization processing is carried out, the size of a utility value of each charging station is obtained, the optimal charging station is selected, and reservation information is sent to charging through a road side unit after the optimal charging station is obtained;
step four: the Charging station accesses a road side unit aggregating the reservation information of the electric vehicle through an Aggregated-Charging-reserving-Update message; the method specifically comprises the following steps: the reservations of aggregated electric vehicles are then reported to the corresponding charging stations before their next release cycle, each electric vehicle's reservation not being sent directly to the selected charging station(ii) a The reservation information of the electric vehicle stored on the road side unit is to be issued to the next charging station before the timestamp is issued by the charging station, Tpre+ Δ given, TpreThe timestamp and the delta are issued by the charging station, the charging station is issued frequency, the charging station integrates the aggregation information of all road side units connected through wires, the reservation of all electric vehicles related to the charging station is issued, and meanwhile, the charging station state information is calculated and issued in the next issuing time period.
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| CN109063876B (en) * | 2018-08-07 | 2022-03-11 | 张锐明 | Electric vehicle charging reservation method |
| CN109858653A (en) * | 2019-01-25 | 2019-06-07 | 恒大智慧充电科技有限公司 | Charge reserving method, computer equipment and storage medium |
| CN110319836B (en) * | 2019-04-09 | 2021-02-09 | 华东理工大学 | Path planning control method and device with lowest energy loss as target |
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| CN110207717B (en) * | 2019-06-21 | 2020-12-29 | 北京嘀嘀无限科技发展有限公司 | Automobile charging navigation method, electronic equipment and storage medium |
| CN111209493B (en) * | 2020-01-09 | 2023-12-12 | 杭州中恒电气股份有限公司 | Charging station recommendation method, device, equipment and medium |
| US11203268B2 (en) * | 2020-01-14 | 2021-12-21 | GM Global Technology Operations LLC | Method and apparatus for selecting a charging station |
| CN111209494A (en) * | 2020-01-15 | 2020-05-29 | 山东理工大学 | Dynamic searching method and device for electric vehicle charging station, storage medium and equipment |
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