WO2013118113A2

WO2013118113A2 - Method and system for optimization of deployment of battery service stations for electric vehicles

Info

Publication number: WO2013118113A2
Application number: PCT/IL2013/050075
Authority: WO
Inventors: Rebecca SHLISELBERG; Eyal PEREZ
Original assignee: Better Place GmbH; Better Place Labs Israel Ltd.
Priority date: 2012-02-06
Filing date: 2013-01-28
Publication date: 2013-08-15
Also published as: WO2013118113A3; TW201351134A

Abstract

A method for computer-implemented management of deployment of a network of battery service stations for electric vehicles is provided. The method includes dividing the certain territory into topographical subareas and selecting all pairs of the topographical subareas within a certain territory. Then, for each pair of selected topographical subareas, data are provided on a distance and average traffic intensity for electric vehicles therebetween during a predetermined time interval. Further, for each such pair of the topographical subareas, an average number of battery charge replenishments required for the electric vehicles travelling between the corresponding two subareas is estimated during the predetermined time interval. Further, the method includes establishing a relation between the average number of power replenishments and the topographic data. The relation between the average number of power replenishments and the topographic data over all the pairs of the subareas is optimized and a distribution of the power replenishment facilities within the certain territory is established.

Description

METHOD AND SYSTEM FOR OPTIMIZATION OF DEPLOYMENT OF BATTERY SERVICE STATIONS FOR ELECTRIC VEHICLES

FIELD OF THE INVENTION

This invention relates to deployment of battery service stations for electrical vehicles.

BACKGROUND OF THE INVENTION

Electric vehicles (EVs) that use electric energy to drive the vehicles have been attracting attention for solving problems associated with exhaustion of combustion fuel, such as oil on earth, and to develop environment friendly technology.

Electric vehicles contain electric storage batteries, to store electricity until required to power the vehicles. The electric storage batteries require periodic charging to replenish the electric charge for continued operation. Charging stations can be used to provide charging service for electric vehicles. The charging stations may include battery charging services to electric vehicles as well as battery exchange services where the user can simply replace (i.e., swap) the exhausted battery by a charged battery which can be available for replacement at the station.

Charging stations may, for example, be located in designated charging locations (e.g., similar to locations of gas stations), parking spaces (e.g., public parking spaces and/or private parking space), etc.

GENERAL DESCRIPTION

One of the major challenges to the widespread adoption of vehicles using electric storage batteries for powering the vehicle is the lack of infrastructure support in the form of an organized network of publicly accessible electric vehicle battery charging and/or replacement facilities. For conciseness of the description, the term "charging and/or replacement facility" should be interpreted broadly as a battery service station that can include inter alia such power replenishment facilities, as battery charging and/or battery replacement facilities. Thus, the battery service station may include charging spots providing charging services to electric vehicles, as well as battery exchange stations where the user can simply replace the exhausted battery by a charged battery which can be available for replacement.

Deployment of such a network of battery service stations having charging and/or replacement facilities can be rather costly and require the cooperation, collaboration, and support of electric utility companies, vehicle manufacturers and governments to develop the infrastructure needed to make electric vehicles more practical to consumers.

There is a need for a technique that can optimize deployment of a network of battery service stations on a certain territory that can provide minimization of the number of charging spots and replacement (exchange) stations that should be provided, while still supplying the requested service to electric vehicle users allowing them, with high level of certainty, to complete their scheduled travels by electric vehicle without running out of battery charge.

Thus, it would be advantageous to have the method and system allowing the electric vehicle users to recharge or exchange batteries in locations that are close to their standard travel route when the exhausted battery is to be quickly replaced and/or stop points for a battery charging service, which might require significantly longer time then a battery replacement procedure.

The present invention provides a novel method for computer-implemented management of deployment of a network of battery service stations for electric vehicles traveling within a certain territory. This method can be implemented at a computer system including one or more data processors integral in the same computer or distributed between multiple computers appropriately connectable to one another e.g. via a communication network such as the Internet or phone network. Such a management system comprises a memory utility for storing reference data comprising topographic data on roads and road junctions within the certain territory, and a processor utility configured and operable for determining optimized distribution of battery service stations within the certain territory. The method includes dividing the certain territory into topographical subareas and selecting all pairs of the topographical subareas within the certain territory. Then, for each pair of selected topographical subareas, data are provided on a distance and average traffic intensity for electric vehicles therebetween during a predetermined time interval. Further, for each such pair of the topographical subareas, an average number of battery charge replenishments required for the electric vehicles travelling between the corresponding two subareas is estimated during the predetermined time interval. Further, the method includes establishing a relation between the average number of power replenishments and the topographic data; and optimizing the relation between the average number of power replenishments and the topographic data over all the pairs of the subareas, and determining a distribution of the power replenishment facilities within the certain territory.

As indicated above, the power replenishment facilities may include charging spots and/or exchange (replacement) stations. Thus, the charge replenishments include battery charging and/or battery replacement.

According to some embodiments, the establishing of the relation between the average number of replenishments for the certain territory and the topographic data includes allocating places within the certain territory for location of the power replenishment facilities. The optimization of the relation between the average number of replenishments for the certain territory and the topographic data may include minimizing the total number of the power replenishment facilities required for deployment within the certain territory.

According to some embodiments, the optimizing of the relation between the average number of replenishments for the certain territory and the topographic data includes evaluating a capacity of the power replenishment facilities required to provide battery related service for the electric vehicles traveling within the certain territory.

According to some embodiments, the method of the present invention further includes providing updated topographic data on the roads and road junctions as well as updated input data on transportation characteristics on the certain territory corresponding to the updated topographic data. According to some embodiments, the method of the present invention further includes providing information about changes in the distribution of the power replenishment facilities within the certain territory. Then, the method may include establishing an updated relation between the specific number of replenishments for the certain territory and the topographic data; and further optimizing the updated relation to obtain an updated distribution of the power replenishment facilities within the certain territory. The changes in the distribution of the power replenishment facilities within the certain territory include either ceasing operation of at least one power replenishment facility or opening operation of at least one power replenishment facility.

It should be understood that the method of the present invention for computer- implemented optimization of deployment of a network of charging facilities for electric vehicles can be used both in the context of ongoing network optimization, and also for covering the planning phase. Thus, according to an embodiment of the present application, after obtaining a calculated distribution of the charging facilities, the method can further include a planning step that involves checking whether the theoretically calculated locations are really availability. The planning step may, for example, include obtaining approvals from the corresponding authorities to use the theoretically calculated locations, etc. If the theoretically calculated locations are not available for deployment of charging/replacement facilities, then another really suitable location(s) are selected within the territory. These really suitable locations can be taken into account for establishing an updated relation between the specific number of replenishments for the certain territory and the topographic data. After optimizing this updated relation, one can obtain an updated distribution of the power replenishment facilities within the certain territory that takes into account the planning step.

The present invention provides a computer-implemented system for managing deployment of a network of battery service stations for electric vehicles traveling within a certain territory. The system includes a memory utility storing reference data comprising topographic data on roads and road junctions within the certain territory; and a processor utility configured and operable for determining optimized distribution of power replenishment facilities within the certain territory.

The processor utility includes a segmenting module, an identifier module connected to and receiving data from the segmenting module, an estimator module connected to and receiving data from the identifier module, and an optimization module connected to and receiving data from the estimator module. The segmenting module is configured and operable for processing the topographic data for dividing the certain territory into topographical subareas of certain sizes and shapes. The identifier module is configured and operable for using such segmented topographic data and determining average traffic intensity between corresponding two subareas of each pair of the topographical subareas during a predetermined time interval. The estimator module is configured and operable for analyzing the average traffic intensity data and estimating average number of battery charge replenishments (charging or replacement of the battery) required for the electric vehicles travelling between two subareas, for each pair of the topographical subareas selected within the certain territory. The optimization module is configured and operable for carrying out the following:

(i) establishing a relation between the average number of power replenishments and the topographic data, (ii) optimizing the relation between the average number of power replenishments and the topographic data over all the pairs, and (iii) obtaining a distribution of the power replenishment facilities within the certain territory based on the relation between the average number of power replenishments and the topographic data.

The present invention also provides a program storage device readable by computer, tangibly embodying a program of instructions executable by the computer to perform method steps for optimization of deployment of a network of power replenishment facilities for electric vehicles traveling within a certain territory, the method steps comprising: dividing the certain territory into topographical subareas; selecting all pairs of the topographical subareas within the certain territory; for each pair of the topographical subareas selected within the certain territory, providing data on a distance and an average traffic intensity therebetween during a predetermined time interval; for each pair estimating an average number of power replenishments required for the electric vehicles travelling between any two subareas during the predetermined time interval; for each pair establishing a relation between the average number of power replenishments and the topographic data; and optimizing the relation between the average number of power replenishments and the topographic data over all the pairs, and determining a distribution of the power replenishment facilities within the certain territory. The present invention also provides a computer program product comprising a computer useable medium having computer readable program code embodied therein for optimization of deployment of a network of power replenishment facilities for electric vehicles traveling within a certain territory, the computer program product comprising: computer readable program code for dividing a map of the certain territory into topographical subareas; computer readable program code for each pair of the topographical subareas selected within the certain territory, providing data on a distance and an average traffic intensity therebetween during a predetermined time interval; computer readable program code for each pair, estimating an average number of power replenishments required for the electric vehicles travelling between any two subareas during the predetermined time interval; computer readable program code for each, establishing a relation between the average number of power replenishments and the topographic data; computer readable program code for optimizing the relation between the average number of power replenishments and the topographic data over all the pairs, and determining a distribution of the power replenishment facilities within the certain territory.

There has thus been outlined, rather broadly, the more important features of the invention in order that the detailed description thereof that follows hereinafter may be better understood. Additional details and advantages of the invention will be set forth in the detailed description, and in part will be appreciated from the description, or may be learned by practice of the invention.

BRIEF DESCRIPTION OF THE DRAWING

In order to understand the invention and to see how it may be carried out in practice, embodiments will now be described, by way of non-limiting example only, with reference to the accompanying drawing, in which:

Fig. 1 is a flow diagram of a method of the present invention for managing deployment of a network of battery service stations; and

Fig. 2 is a block diagram of an example of a computer-implemented system of the present invention for managing deployment of a network of battery service stations for electric vehicles traveling within a certain territory. DETAILED DESCRIPTION OF EMBODIMENTS

The principles and operation of a system and method for computer-implemented management of deployment of a network of battery service stations for electric vehicles traveling within a certain territory according to the present invention may be better understood with reference to the drawings and the accompanying description, it being understood that this drawings and examples in the description are given for illustrative purposes only and are not meant to be limiting.

Some portions of the detailed descriptions, which follow hereinbelow, are presented in terms of algorithms and symbolic representations of operations on data represented as physical quantities within registers and memories of a computer system. An algorithm is here conceived to be a sequence of steps requiring physical manipulations of physical quantities and leading to a desired result. Usually, although not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. In the present description, these signals will be referred to as values, magnitudes, elements, symbols, numbers, or the like. Unless specifically stated otherwise, throughout the description, utilizing terms such as "processing" or "computing" or "calculating" or "determining" or "generating" or "estimating" or the like, refer to the action and processes of an electronic calculating circuit or a computer system or similar electronic computing device, that manipulates and transforms data.

According to some embodiments of the present invention, deployment of a network of battery service stations for electric vehicles by a service center begins from defining a certain territory through which the electric vehicles will travel. The term "territory" is broadly used in the present description and the claims to include various areas, such as states, countries, districts, cities and even parts of the cities. Each kind of territory can, for example, be characterized by topographical data on the roads, road junctions and the number of potential travelers who may use the electric vehicles for transportation within the territory.

In order to replenish the power of electric vehicles (by charging or replacement of the batteries), a network of power replenishment facilities (i.e., battery service stations) for the electric vehicles should be deployed. The power replenishment facilities include charging spots for providing charging services to electric vehicles as well as battery exchange/replacement facilities where the user can simply replace the exhausted battery by a charged battery which can be available for replacement at the station.

It should be understood that the power replenishment facilities cannot be distributed uniformly within the territory but should take into account the traffic volume, intensity and load within various parts of the territory (as well as limitations on deployment and land procurement, etc.). For example, in certain regions, such as cities, the traffic load may be high on primary roads, leading to long travel and wait time, whereas in less populated areas the load may be light and move smoothly. Depending on the charging requirements for electric vehicles, the power replenishment facilities may be overloaded, generating long delays, or could be idle due to low demand. Accordingly, for deployment of power replenishment facilities, the service center must be able to estimate the right number and distribution (relative accommodation) of power replenishment facilities to provide adequate service at reasonable costs.

Reference is made to Fig. 1 illustrating a flow diagram 100 of a method of the present invention for computer-implemented management of deployment of a network of power replenishment facilities for electric vehicles traveling within a certain territory. The method is based on a predetermined deployment model which takes into account various aspects of topographic data on the roads and road junctions as well as traffic characteristics.

According to an example of the deployment model, the certain territory chosen for deployment of power replenishment facilities is divided onto N topographical subareas A_i5 where i=l, 2, .. . N (step 101). For each pair AjA_j (where , j=l , 2, N) of the topographical subareas selected within the certain territory, a distance Dy between the subareas Aj and A_j, and an average traffic intensity for the electric vehicles travelling between the corresponding two subareas during a predetermined time interval (such as an hour, a day, a week, etc. ) is provided (step 102). It should be note that a size of the subareas Aj should preferably be much smaller than the distance Dy between the subareas Aj and A_j.

For obtaining the average traffic intensity, traffic data can be utilized. This may be "reference data", previously provided and stored data for various purposes including traffic planning targets such as road construction or public transportation. The system of the invention can thus connect to a respective storage device where such reference data is maintained.

The traffic data may be updated based on measurements of traffic intensity that can be carried out in short-term variations (individual or periodic measurements), with the help of measuring devices (vehicle loops, tube detectors, video detectors, and other counters). Such measuring devices are known per see and need not be described in details, except to note the following:

For example, a loop detector is an inductive loop buried in a deep groove cut into asphalt concrete pavement and covered with bitumen. During operation, a frequency signal of 100 kHz is transferred through the loop. With any vehicle passing over the loop, it functions as a metal cord of the loop and, thus, produces changes in the inductive resistance of the loop. Change in resistance is a signal for the detector about the passing vehicle. In order to detect not only a number of vehicles but also their speed and driving direction within vehicle classification regime, it is necessary to install two loops in each traffic lane at a certain fixed distance (e.g., 2 meters) from each other.

A rubber tube is an elastic tube laid across the street. One of his ends is blocked, whereas the other is put on a metal tube taken out in front of the device. With a vehicle passing over the tube, containing air pressure increases and this is the signal for a counter to register the passing vehicle. Counters with rubber tubes are usually used as easily transported devices for a short-term traffic count.

Annual average daily traffic intensity can, for example, be calculated using the data on the annual average daily traffic intensity of short-term measurement. Having traffic intensity data on short-term measurements, the traffic intensity of the day, the traffic intensity of the week, and annual average daily traffic intensity can be calculated. The daily traffic intensity (DTI) can, for example, be calculated as I_D = M x K_D, where: I_D is the DTI of the measured day, car/day; M is the number of vehicles moved during the measured period, KD is the coefficient of the measured day traffic intensity. Likewise, if traffic intensity is measured over a week without interruption, then, average weekly daily traffic intensity (AWDTI) can be calculated by averaging I_D over a week, month, year, etc. Moreover, for each pair AjA_j (where i, j = 1 , 2, N) of the topographical subareas selected within the certain territory an average number Ry of power replenishments (typically, battery charging and/or battery replacement events) required for the electric vehicles travelling between the corresponding two subareas is estimated (step 103). The number of the power replenishments depends on the distance between the subareas Aj and A_j, and possibly also on a time required to travel this distance Dy. The latter varies with a change in the vehicle's speed profile while travelling along the respective route, which in turn depends on the traffic along the route (which might result in that the vehicle practically dos not move while it battery supplies power to various other facilities in the vehicle, like air conditioner, radio, etc.), and topographic data of the route. Thus, average speed vy of the vehicle travelling between the subareas AjA_j might be considered as well as being indicative of traffic data. With regard to topographic data, it should be understood that while there is a typical number for electric vehicles following a certain route, the number of such vehicles can be refined taking into consideration the topography: this number increases if the road is uphill and/or there are multiple traffic lights that require stops, and decreases if the road is downhill and/or has little traffic. Hence, on the basis of data about the length of the route (a distance between the two subareas) and traffic data, as well as additional topographic information together with the assumptions regarding the initial charge level of the battery, the need for battery replacement/charging during the route can be predicated and the number of electric vehicles that would require such a change at any given time can be predicted.

The assumption regarding the battery charge level depends on the start and end locations of a trip travel. For example, if no further info is provided, then the assumption can be that the battery is half charged and this allows a full round trip. However, if it is known that the start point is the residential location and the end point is a work place equipped with a charging spot, then the assumption of a fully charged battery can be made for the analysis purposes.

Further, for each pair AjA_j, a relation between the average number Ry of power replenishments and the topographic data is established (step 104). For this purpose, topographic data on roads and road junctions within the certain territory are used for determining such a relation. As a result, a list of the preferred places between the subareas Aj and A_j within the territory are provided for location of the power replenishment facilities (e.g., battery service stations) with the average number Ry of power replenishments for each place between the subareas Aj and A_j. This relation is further optimized over all the pairs AjA_j to obtain the number of the replenishment facilities (charging spots and/or exchange stations) and their exact location for each subarea located on the way between Aj and A_j. For each subarea A_k located between the subareas Aj and A_j, the contribution due to the neighboring subareas is summed up to obtain the total number of replenishments per period of time required for the subarea A_k. This is used to determine size, type and number of replenishment stations in the subarea A_k. The calculation method can be carried out for all the pairs AjA_j (where i, j = 1, 2, N) within the territory under consideration. As a result, a distribution of the power replenishment facilities within the chosen territory (step 105) is provided.

Example

A certain territory is considered which is divided into N subareas. For subareas Ai and A₃ that are separated by a subarea A₂, all the original values for the power replenishments Ri₂(0), R₂₃(0) and Ri₃(0) were set to 0. During measurements, the average number R_i3 of the power replenishments received for the pair AiA₃ was 1000. The distance obtained between a central point of Ai and a central point of A₃ was 200km, that typicality requires one battery replenishment along the way. Suppose that for 70% of electrical vehicles passing through the subarea A₂ from Ai to A , battery replenishments have to be performed in the subarea A₂, while for the other 30% electrical vehicles, replenishments can be done in the subarea A₃. Accordingly, the updated average numbers Ri₂ and R₂₃ can be obtained by Ri₂ = Ri₂(0) +1000 x 0.7 = 700 and R₂ = R₂₃(0) + 1000 x 0.3 = 300, correspondingly. The calculation method can be carried out for all the pairs AjA_j (where i, j = 1, 2, N) within the territory under consideration.

According to some embodiments, the optimization of the relation between the specific number of replenishments and the topographic data includes minimizing the total number of the power replenishment facilities required for deployment within the territory. The optimization may also include evaluating a capacity of the charging facilities and an optimal number of charge sockets required to provide charging service for the electric vehicles traveling within the selected territory. When desired, the optimization may take into account the distribution of available capacity in batteries in various electrical vehicles at the start of a trip, due to the different battery charge states, battery type, size and/or capacity.

The network of the power replenishment facilities includes charging spots and exchange stations. Accordingly, the power replenishment may include either battery charging or battery replacement. A replacement of the battery is a rather quick process that can be carried out within a few minutes, while a charging of a battery is much longer process, which may require several hours for full replenishment of the battery charge. Therefore, battery exchange stations can, for example, be established in the locations which can be along the roads similar to locations of gas stations, parking, etc. On the other hand, battery charging spots can be located at the end points of the travel to allow for charging while not on travel. The locations that are used as frequent destinations where people spend many hours for business, i.e., business centers, parking places of large companies having many employers, etc. may also be suggested as charging spot locations. Likewise, charging spots can be located at the places where the EV users can entertain themselves during the time required for charging the battery, for example, at shopping molls, cinema theaters, bowling centers, and at other entertainment places. When desired, the cross effect of the exchange stations and charging spot deployment may be investigated and the best deployment strategy that optimizes cost, service quality and/or any other service parameters may be used for selection of these deployments. According to some embodiments, parking time of the vehicles on each location can be used to allow prediction of the electric power level and the value of the electric current supply required for a particular location of each charging facility.

The steps of the process described above can be first performed prior to initial deployment, and then the calculation and optimization steps of the process can be repeated when update topographic data on the roads and road junctions, as well as new input data on transportation characteristics on the selected territory, are received by the service center. In particular, when new roads and/or junctions are built on the territory as well as changes in the number of electric vehicles, vehicle travel patterns and other updated transportation characteristics are provided to the service center, these input data can be introduced in the deployment model for further optimizing the relation between the specific number of replenishments for this territory and the topographic data for obtaining an updated distribution of the power replenishment facilities within the territory.

For example, an original location of the charging spot and/or exchange station can be changed and a new location of the power replenishment facility can be away from the main routs of the EV users. If so, the change in the location may require increase in capacity of the battery that, in turn, may cause degradation in the service, since such changes of the location of a power replenishment facility (charging spot and/or exchange stations) result in a detour of the vehicles from their primary route. These effects can be taken into account for further optimizing the relation between the specific number of replenishments to obtain an updated distribution of the remaining power replenishment facilities within the territory. Once a new location of the power replenishment facility is fixed, it is fed back to the model and the effect of the new site location on the other required power replenishment facilities is analyzed. It should be understood that the updated outcome may require changes in the arrangement for deployment of the network of the service station, i.e., in the location of the other charging spot and/or exchange facility.

The effect on user perceived service quality can be further analyzed as it does not necessarily correlate linearly to the deviation from the normal travel path. For example, an EV user traveling a long leisure trip will less likely feel a few kilometers detour to an exchange station as a nuisance, than a business traveler traveling a short trip. The deployment model of the application takes this factor into consideration by providing different weights for detour distances according to the type of the traveler as well as the destinations type of the source and target.

According to some embodiments of the present application, the calculation and optimization steps of the process for optimization/management of the deployment of the network of the service stations can also be repeated when the output data of the method, such as the initial arrangement for deployment of the charging facilities has been changed. For example, when one or more charging spots and/or exchange facilities stopped to operate; likewise when one or more new charging spots and/or exchange facilities were built up within the territory, these data can be fed to the deployment model in order to obtain an updated arrangement for deployment of the network of the power replenishment facilities. The updated outcome may require changes in the arrangement for deployment of the network of the power replenishment facilities, i.e., in the location of the other charging spot and/or exchange stations.

The methods described herein may be governed by instructions that are stored in a computer readable storage medium and that are executed by one or more processors of one or more computer systems.

Referring to Fig. 2, there is exemplified, by way of a block diagram, a computer-implemented system 10 for management of deployment of a network of power replenishment facilities (battery service stations) 11 for electric vehicles (not shown) traveling within a certain territory 12. The management system 10 is associated with a service center 13, which is a data processor and analyzer system configured and operable for providing management of the power replenishment service to electric vehicles.

The service center 13 is typically a computer system and includes inter alia a data processor utility 130 which is connectable to a memory utility 131 (which may be an external storage device accessible via a communication network) storing reference data comprising topographic data on roads and road junctions within the certain territory as well as other required data. The processor utility 130 is configured and operable according to the invention for determining optimized distribution of power replenishment facilities within the certain territory selected for deployment of the network of such facilities 11.

The processor utility 130 includes a segmenting module 132, an identifier module 133, an estimator module 134, and an optimization module. The segmenting module 132 is configured for dividing a map of the certain territory 12 into N topographical subareas Aj of certain sizes and shapes. The identifier module 133 is configured and operable for using data generated by the segmenting module 132 and data indicative of the traffic in the corresponding subareas, and determining an average traffic intensity between all two subareas Aj and A_j (where , j=l, 2, .. ., N) for each pair of the topographical subareas during a predetermined time interval. The average traffic intensity is calculated on the basis of the measurements of short-term traffic intensity carried out in short-term variations (individual or periodic measurements), with the help of measuring devices 14 e.g., vehicle loops, tube detectors, video detectors, and other counters) mounted on roads and road junctions (not shown) within the territory 12. The traffic intensity measurements, as well other relevant data regarding events in the territory of interest, can be transmitted from measurement equipment installed within the territory to the service center 13 through a communication network 15.

The estimator module 134 is configured and operable for estimating average number of power replenishments (battery charge replenishments) required for the electric vehicles travelling between two subareas Aj and A_j for each pair Aj A_j (where , j = 1 , 2, .. ., N) of the topographical subareas selected within the selected territory 12.

The optimization module 135 is configured and operable for determining a relation between the average number of power replenishments and the topographic data. The optimization module 135 is configured and operable for optimizing the relation between the average number of power replenishments and the topographic data over all the pairs AjA_j (where i, j = 1 , 2, .. ., N) of the topographical subareas selected within the territory 12. During the optimization step, the number of the replenishment facilities and their exact location is determined for each subarea located on the way between Aj and A_j. As described above, for each pair AjA_j, the average number Ry of the power replenishments and the location of these replenishment facilities are determined, thereby a list of the subareas A_k between Aj and A_j is provided at which the corresponding replenishments are to be made. The contribution from the neighboring subareas is added to each subarea A_k between Aj and A_j, thereby obtaining a total number of replenishment facilities required for each subarea A_k per a certain time period.

The optimization module 135 is also configured for obtaining a distribution of the power replenishment facilities within the territory 12 based on the relation between the average number of power replenishments for the territory 12 and the topographic data on the roads and road junctions within the territory 12.

It will also be understood that the system according to the invention may be a suitably programmed computer. Thus, the invention contemplates a computer program being readable by a computer for executing the method of the invention. The invention further contemplates a machine-readable memory (storage medium) tangibly embodying a program of instructions executable by the machine for executing the method of the invention. The machine -readable storage medium may include a magnetic or optical disk storage device, solid state storage devices such as Flash memory, or other non-volatile memory device or devices. The computer readable instructions stored on the computer readable storage medium are in source code, assembly language code, object code, or other instruction format that is interpreted by one or more processors.

As such, those skilled in the art to which the present invention pertains, can appreciate that while the present invention has been described in terms of preferred embodiments, the concept upon which this disclosure is based may readily be utilized as a basis for the designing of other structures, systems and processes for carrying out the several purposes of the present invention.

Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting.

It should be noted that the word "comprising" as used throughout the appended claims is to be interpreted to mean "including but not limited to".

It is important, therefore, that the scope of the invention is not construed as being limited by the illustrative embodiments set forth herein. Other variations are possible within the scope of the present invention as defined in the appended claims and their equivalents.

Claims

CLAIMS:

1. A method for computer-implemented management of deployment of a network of power replenishment facilities for electric vehicles traveling within a certain territory, the method implemented at a computer system associated with a memory utility storing reference data comprising topographic data on roads and road junctions within said certain territory, and comprising a processor utility configured and operable for determining optimized distribution of the power replenishment facilities within said certain territory, the method comprising:

dividing the certain territory into N topographical subareas A_i5 where i=\, 2,... N;

selecting pairs AjA_j of the topographical subareas within said certain territory, where i, j=l, 2, N;

for each pair AjA_j of the topographical subareas selected within the certain territory, providing data on a distance and an average traffic intensity therebetween during a predetermined time interval;

for each pair AjA_j, estimating an average number of power replenishments required for the electric vehicles travelling between Aj and A_j during the predetermined time interval;

for each pair AjA_j, establishing a relation between the average number of power replenishments and the topographic data; and

optimizing said relation between the average number of power replenishments and the topographic data over all the pairs AjA_j, and determining a distribution of the power replenishment facilities within said certain territory.

2. The method of claim 1, wherein said power replenishment facilities include battery charging facilities and battery replacement facilities.

3. The method of claim 1, wherein said power replenishments comprise charging and replacements.

4. The method of claim 1 , wherein said establishing a relation between the average number of power replenishments and the topographic data includes allocating places within said certain territory for location of the power replenishment facilities.

5. The method of claim 1, wherein said optimizing of the relation between the average number of power replenishments and the topographic data includes minimizing the total number of the power replenishment facilities required for deployment within said certain territory.

6. The method of claim 1, wherein said optimizing of the relation between the average number of power replenishments and the topographic data includes evaluating a capacity of the power replenishment facilities required to provide power replenishment service for the electric vehicles traveling within said certain territory.

7. The method of claim 1, further comprising:

providing updated topographic data on the roads and road junctions as well as updated input data on transportation characteristics on said certain territory corresponding to said updated topographic data;

establishing an updated relation between the average number of power replenishments and the topographic data; and

optimizing said updated relation to obtain an updated distribution of the power replenishment facilities within said certain territory.

8. The method of claim 1, further comprising:

providing information about changes in said distribution of the charging facilities within said certain territory;

9. The method of claim 8, wherein said changes in said distribution of the power replenishment facilities within said certain territory include either ceasing operation of at least one power replenishment facility or opening operation of at least one power replenishment facility.

10. A computer-implemented system for management of deployment of a network of power replenishment facilities for electric vehicles traveling within a certain territory, comprising: a processor utility being connectable to a memory utility for accessing reference data comprising topographic data on roads and road junctions within said certain territory, and being configured and operable for generating data indicative of optimized distribution of power replenishment facilities within said certain territory, the processor utility comprising:

a segmenting module configured for dividing a map of said certain territory into topographical subareas of certain sizes and shapes;

an identifier module configured and operable for determining average traffic intensity between paired subareas of selected pairs of the topographical subareas during a predetermined time interval

an estimator module configured and operable for estimating average number of power replenishments required for the electric vehicles travelling between the paired subareas;

an optimization module configured and operable for (i) establishing a relation between the average number of power replenishments and the topographic data, (ii) optimizing said relation between the average number of power replenishments and the topographic data over all the pairs, and (iii) obtaining a distribution of the power replenishment facilities within said certain territory based on the relation between the average number of power replenishments and the topographic data.

11. A program storage device readable by computer, tangibly embodying a program of instructions executable by the computer to perform method steps for optimization of deployment of a network of power replenishment facilities for electric vehicles traveling within a certain territory, the method steps comprising:

dividing the certain territory into N topographical subareas A_i5 where i=\ , 2, .. .

N;

selecting pairs AjA_j of the topographical subareas within said certain territory, where i, j=l , 2, .. . , Ν;

for each pair AjA_j of the topographical subareas selected within the certain territory, providing data on a distance and an average traffic intensity therebetween during a predetermined time interval; for each pair AjA_j, estimating an average number of power replenishments required for the electric vehicles travelling between Aj and A_j during the predetermined time interval;

12. A computer program product comprising a computer useable medium having computer readable program code embodied therein for optimization of deployment of a network of power replenishment facilities for electric vehicles traveling within a certain territory, the computer program product comprising:

computer readable program code for dividing a map of said certain territory into topographical subareas;

computer readable program code for each pair AjA_j of the topographical subareas selected within the certain territory, providing data on a distance and an average traffic intensity therebetween during a predetermined time interval;

computer readable program code for each pair AjA_j, estimating an average number of power replenishments required for the electric vehicles travelling between Aj and A_j during the predetermined time interval;

computer readable program code for each AjA_j, establishing a relation between the average number of power replenishments and the topographic data;

computer readable program code for optimizing said relation between the average number of power replenishments and the topographic data over all the pairs AjA_j, and determining a distribution of the power replenishment facilities within said certain territory.