Abstract:
A System for predicting energy need of a plurality of electric-energy vehicles across a determined management area. 
     A processor in the system aggregates, from each electric-energy vehicle within the management area, positional information and energy-need information of each electric vehicle. 
     The processor is adapted to process the positional information and the energy-need information by way of a cognitive method, to determine predictions of the movement of the vehicle and endurance of the battery. 
     The processor is further adapted to combine this determined information to generate a predicted energy distribution model over said management area, indicative of future energy needs for said electric-energy vehicles.

Description:
[0001]    The present invention concerns a device and a method for predicting energy requirements of a plurality of electric energy sinks, for example electric vehicles, and a method for same. 
       BACKGROUND 
       [0002]    This section introduces aspects that may be helpful in facilitating a better understanding of the invention. Accordingly, the statements of this section are to be read in this light and are not to be understood as admissions about what is in the prior art or what is not in the prior art. 
         [0003]    A key limitation in the distribution of electricity is that it is difficult to store electrical energy on the scale generally generated for the needs of a residential population. A sophisticated system of control is therefore required to ensure that electricity generation very closely matches the electricity demand. 
         [0004]    In the residential and industrial electricity generation environment where a large number of users coexist and where the overall load varies smoothly and predictably, it is possible to predict the “average” energy load per user relatively accurately, and produce electrical energy accordingly. 
         [0005]    In the nascent environment of electrical-energy mobile vehicles temporarily connected to an electrical-energy distribution network via many recharging stations, it is far more difficult to accurately predict future energy-loads, as electric vehicle charging are punctual events, whereas electricity generation needs to be as constant as possible. 
         [0006]    Furthermore, the movement of electric vehicles and their electric storages causes unpredictable location and time of energy demand and supply. This leads to temporary load peaks and bottlenecks in the energy distribution network during the recharging process of the electric vehicles. 
         [0007]    The present invention seeks a way to predict future energy needs in a mobile electrical-vehicle network. 
       BRIEF SUMMARY 
       [0008]    An object of the present is a system as set out in claim  1 . 
         [0009]    Another object of the present is a method as set out in claim  8  or  10 . 
         [0010]    The present is an energy prediction model based upon cognitive learning models, and is particularly advantageous when fluctuating energy loads of uncertain magnitude and location poses difficulties for power generation equipment. 
         [0011]    It allows for selective distribution of energy around a geographic area, and may further be adapted to dynamically adapt prices at recharge stations to slow or spur energy demand. 
         [0012]    Advantageously, it may further be used to plan, dimension, and distribute a network of electric vehicle charging, repair or exchange infrastructure. 
         [0013]    The invention also concerns other objects as detailed in the appended claims. 
     
    
     
       BRIEF DESCRIPTION 
         [0014]    Some embodiments of devices and methods in accordance with embodiments of the present invention are now described, by way of example only, and with reference to the accompanying drawings, in which: 
           [0015]      FIG. 1  illustrates schematically an apparatus according to an embodiment of the invention; 
           [0016]      FIG. 2  illustrates a method of managing energy needs of  FIG. 1 ; 
           [0017]      FIG. 3  illustrates a representation of the geographic area  10  for the purposes of a method according to an embodiment of the invention; and 
           [0018]      FIG. 4  illustrates a sample energy need distribution map over the geographic area. 
       
    
    
     DETAILED DESCRIPTION 
       [0019]      FIG. 1  illustrates schematically a geographic area  10  within which circulate a plurality of mobile electric vehicles  12  along their respective path  14 , and at least one energy-need prediction device  50 . 
         [0020]    Each electric vehicle  12  comprises battery means  22 , also referred to as “a battery”, adapted to at least partially power the movement of the electric vehicles  12  along said path  14 . The battery  22  is adapted to determine the state of its energy level, and in particular the amount of energy necessary for a full charge, i.e. the difference in energy between the full-charge energy capacity and the instantaneous charge. 
         [0021]    Each electric vehicle  12  further comprises geolocation means  23  adapted for determining information relative to the position of the electric vehicle  12  within the geographic area  10 . Such information may for example comprise processed information such as GPS coordinates. Such information may also for example comprises information relative to one or more proximate landmarks, one or more proximate GSM base transceiver stations with known location data adapted to infer a location of the electric vehicle  12 . 
         [0022]    Furthermore, the electric vehicle  12  comprises communication means  24  adapted to communicate information wirelessly to the energy-need prediction device  50 , for example be a GSM, Wifi, radio, satellite, or others. 
         [0023]    Furthermore, the electric vehicle  12  comprises a controller  25  to couple the location information from the geolocation means  23  with the battery energy level information from the battery  22 . The controller  25  is further adapted to send this information, along with an identifying reference, for example a unique vehicle ID, via the communication means  25  to the energy-need prediction device  50 . 
         [0024]    The energy-need prediction device  50  comprises a communication device  26  for receiving information from each communication means  25  of the electric vehicles  12 . 
         [0025]    The device further comprises a processor  27  for processing the information received from the communication device  26 , and storage means  28  for storing all received information received from the communication device  26 . 
         [0026]    For example, the storage means  28  may comprise a database, whereby the battery energy information and the location is stored against a time stamp for each electric vehicle  12  tracked. 
         [0027]    Furthermore, the energy-need prediction device  50  may further comprise a storage database  29  comprising information relative to the transportation network, such as cartographic information. 
         [0028]      FIG. 2  illustrates schematically the process of predicting energy need across a geographic area. 
         [0029]    In the vehicle  12 , the controller  25  gathers periodically, for example every 15 minutes, information relative to the energy state of the battery  22  and of the location of the electric vehicle  12  from the geolocation means  23 , and combines this information with a unique identifier of the electric vehicle  12 . 
         [0030]    This combined information is sent from the controller  25  of the electric vehicle  12  to the processor  27  of the prediction device  50  via the communication means  24  of the electric vehicle  12  and via the communication device  26  of the energy-need prediction device  50 . 
         [0031]    The processor  27  stores the periodic information received from the electric vehicles  12  in the storage means  28 . 
         [0032]    Periodically, the processor  27  loads all the battery charge information for each electric vehicle  12  from the storage means  28  and, determines (step  40 ) the probable endurance of each electric vehicle  12  based upon:
       historically-determined energy usage rates, and   last-received battery energy capacity.       
 
         [0035]    For example, the processor  27  can determine an endurance with a likely probability factor, for example a range of one hundred kilometers at 90% certainty. Alternatively, the processor can determine a number of endurance figures for each electric vehicle, with each endurance corresponding to a probability figure, for example 80 kilometers at 95% probability, 100 kilometers at 90% probability, 120 kilometers at 70% probability, and 150 kilometers at 40% probability. 
         [0036]    We understand by endurance the range in distance that the electric vehicle  12  will be able to travel before emptying the energy reserves of the battery  22 . 
         [0037]    The processor  27  is thus able to determine, based upon historically-determined parameters and a cognitive method, the probable endurance of each electric vehicle  12  in the management area  10 . 
         [0038]    By cognitive method, the inventors mean a learning method that deduces future results by the analysis of past information. The method may, also, derive greater precision in the predictions with more data and over time. 
         [0039]    In parallel to the calculation of endurance data (step  40 ) for each electric vehicle  12 , the processor  27  analyses (step  42 ) the cartographic storage database  29  and the movement information of electric vehicles  12  as stored in the database  28 . 
         [0040]    Movement information is able to be determined using a learning algorithm by analyzing the known discrete geolocation points in the database  28 , to determine the routes and roads favoured by the electric vehicles  12 , and cartographic zones favoured by the electric vehicle  12 . 
         [0041]    This can be accomplished by:
       using historical analysis to analyze geographic zones where electric vehicles  12  are most often detected, and thus, where the vehicles  12  are likely to be detected,   analyzing cartographic information to determine road possible from a detected location, and the affluence of the respective roads to determine likelihood of electric vehicle  12  using that road from the present location,       
 
         [0044]    Alternatively, movement information is able to be determined by using a learning algorithm on historical geolocation information in storage means  28 , and by leveraging cartographic data from storage means  29 , to identify traffic orientation probabilities at each cross-road. 
         [0045]    For example, looking at  FIG. 3 , the processor  27  can can subdivide the geographic area  10  in orthogonal cells, such that each cell has an orthogonal reference (x 1 , y 1 ) to (xn, yn). At cross-road  31  then, it may be possible to determine from historical information what fraction of all electric vehicle traffic take which route, such that each cell (x 1 , y 1 ) to (xn, yn) has an associated probability value of being a destination for the electric vehicle  12  in question. 
         [0046]    The processor  27  determines an energy prediction distribution combining the estimated endurance of each electric vehicle  12  with its estimated route, as explained previously. 
         [0047]    Future energy distribution probability P can be established at each cell (x,y), at a given time t, as being the sum of the empty-charge capacity (ECC) at a given associated probability as explained previously, for all electric vehicles to be destined to cell (x,y): 
         [0000]    
       
         
           
             
               
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         [0048]    Where ECC is the empty-charge capacity, one is to understand the difference in energy between a battery  22  at full capacity and the instantaneous battery capacity at the time of the communication. In other words, the empty-charge capacity is the energy needed to refill the battery  22  to full capacity. 
         [0049]    Such an algorithm may for example produce a cartographic map of future energy needs for the electric vehicles, as shown in  FIG. 4 . This produces a map with zones  33  of future low energy demand, and zones  34  of high future energy demand. 
         [0050]    Zones of high energy demand concentration  34  may generally correspond to locations of a high density of electric vehicles  12  deemed to be in need of electric energy. Zones of low energy capacity concentration  33  are generally ones where few electric vehicles  12  are present or where few electric vehicles  12  are deemed to be in need of an electric recharge. 
         [0051]    In another embodiment, the movement prediction and endurance prediction processing may be performed in the electric vehicle  12  rather than in the energy management apparatus  50 . For example, the controller  25  of each electric vehicle  12  could aggregate and use a cognitive learning method to determine, for itself, the endurance and movement information associated with probability information. This processed information could then be sent to the energy management apparatus, which would then aggregate every electric vehicle&#39;s information to produce an energy-demand model. The process is thus the same, but the main processing is distributed over the electric vehicles. 
         [0052]    A person of skill in the art would readily recognize that steps of various above-described methods can be performed by programmed computers. Herein, some embodiments are intended to cover program storage devices, e.g., digital data storage media, which are machine or computer readable and encode machine-executable or computer-executable programs of instructions where said instructions perform some or all of the steps of methods described herein. The program storage devices may be, e.g., digital memories, magnetic storage media such as a magnetic disks or tapes, hard drives, or optically readable digital data storage media. The embodiments are also intended to cover computers programmed to perform said steps of methods described herein. 
         [0053]    The present inventions may be embodied in other specific apparatus and/or methods. The described embodiments are to be considered in all respects as only illustrative and not restrictive. In particular, the scope of the invention is indicated by the appended claims rather than by the description and figures herein. All changes that come within the meaning and range of equivalency of the claims are to be embraced within their scope.