System and method for determining a charging point of a battery charging station for an electric vehicle

Abstract

The invention relates to a system (10) and a method for determining an available charging point (20) of a station for charging the battery of an electric vehicle. The invention makes it possible to assess the remaining charging time of the batteries of vehicles connected to the charging points (20) of a charging station (21). For this purpose, the battery charging speed is determined in the first minutes of charging, and the remaining charging time is estimated using the charging speed, a predictive model (15) of the charging duration of the electric vehicle battery and a target end-of-charging state. The invention also relates to a vehicle which comprises the determination system (10).

Description

Description Title of the invention: System and method for determining a charging point of a battery charging station for an electric vehicle Technical field

[0001] The invention relates to a system and a method for determining a charging point of a battery charging station of an electric vehicle. The invention also relates to a vehicle, in particular an automobile, which carries or uses a system and / or a method according to the invention. State of the prior art

[0002] The sudden boom in sales of electric vehicles, particularly electric cars, in the early 2020s brought new problems, particularly related to recharging the batteries of these vehicles.

[0003] Despite the massive deployment of public charging stations in urban areas, parking lots, highways, and shopping centers, the increase in the use of electric vehicles is such that the number of charging stations installed, particularly on major highways, is not sufficient to accommodate the growing number of electric vehicles on the road. In this context, electric vehicle users are having difficulty finding available charging stations.

[0004] Typically, electric vehicles are equipped with a navigation system that calculates the ideal route by considering the ideal charging points along the route. To do this, the navigation system considers the vehicle's SOC level, the planned route, and an estimate of the energy consumption along the route to define the ideal charging point based on these parameters.

[0005] Throughout this document, the vehicle's SOC refers to the state of charge of the vehicle's battery. Specifically, the acronym SOC is an abbreviation of the English expression "State of Charge." SOC is most often defined as a percentage of available energy relative to the total battery capacity.

[0006] The ideal route calculated by the navigation system also takes into consideration the ideal recharge time in terms of time and SOC gain.

[0007] Generally, the ideal charge of a vehicle on a journey corresponds to a SOC of 20% which is increased during charging to 80%. Note that the ideal charge depends on the chemistry used in the battery and the distance remaining to reach the final destination.

[0008] The navigation system will then identify a charging point on the route to optimize charging based on the remaining distance to reach the final destination. For example, in the case of an ideal charge of 20% to 80%, the navigation system will suggest to the driver the theoretical charging point that will be geographically closest when the vehicle's SOC reaches approximately 20%.

[0009] However, this estimate of the ideal charging point does not take into account various parameters which may be linked in particular to actual consumption during the journey, but also the availability of charging stations at the charging point in question.

[0010] First of all, it is not uncommon for the charging station identified as ideal by the navigation system to have a long queue when the driver arrives at this site. The waiting time for a charging point to become available is then added to the charging time and can considerably lengthen the break time and the journey time of the route. Moreover, consumer studies have shown in particular that during large traffic jams, the waiting time at charging stations can last for hours and therefore have a very significant impact on the viability of long-distance journeys for drivers using an electric vehicle.

[0011] In addition, the driver may find himself in a situation where the charging station initially identified as ideal is too far away due to excessive consumption, and it is then necessary to identify a new charging station. Although this is less common, in the case of more economical consumption than expected, it may also be necessary to identify a new charging station further away than the one identified at the beginning of the journey.

[0012] The question of the availability of charging points at this new charging station also arises in order to optimize the travel time to the final destination and reduce the driver's waiting time to recharge the vehicle.

[0013] In the remainder of this document, a charging station is understood to mean power electronics shared between several charging terminals, whereas the charging terminal is a physical totem that includes several charging points, each charging point corresponding to a socket configured to be connected to an electric vehicle. It should be noted that the charging points, for example, of the same charging terminal can pool the power available on said terminal. In this case, when several vehicles are connected to the same charging terminal, the available power drops significantly on all the charging points that pool the power.

[0014] In most states, charging stations are installed and operated by private operators and information on the charging time of the vehicle plugged into the charging point is therefore limited. Although most Electric vehicles provide the driver with an estimate of the remaining charging time, this information is not made public and shared.

[0015] It is also not easy to estimate this information because it requires several parameters that are not available to third parties, such as detailed knowledge of the battery chemistry, the battery temperature, the power limitations of the charging station, or the target level of the SOC programmed by the driver.

[0016] Currently, the only information available on the navigation system of an electric vehicle approaching a charging station is the instant availability of the charging points at the station, as in CN114689068 A. For charging points that are unavailable, especially when they are all unavailable, it is not possible to know the waiting time for them to become available.

[0017] The invention aims to overcome all or part of these drawbacks. Statement of the invention

[0018] The invention aims to estimate the release time of each charging point of a charging station.

[0019] The invention aims to evaluate the availability of charging points at a charging station upon the expected arrival of the vehicle at this station.

[0020] The invention aims to determine an alternative routing to a second charging station in the event of significant congestion of the charging points of a first charging station.

[0021] The invention aims to offer an ideal route to the driver of the electric vehicle, taking into account the recharging time of the vehicle's SOC.

[0022] To this end, the invention relates to a system for determining an available charging point of a charging station for the battery of an electric vehicle comprising a navigation module configured to select a first charging station located near or on a route of the vehicle, the navigation module comprising, on the one hand, a predictive model of the charging time of the electric vehicle battery, the predictive model being configured to estimate the remaining charging time of the electric vehicle battery, and on the other hand, a determination module configured to determine the charging speed, at least during the first minutes of charging, of the batteries of the electric vehicles connected to the plurality of charging points of the first charging station,

[0023] the navigation module comprises a predetermined target end-of-charge state of charge and the navigation module is configured to estimate the remaining recharge time of said batteries based on: the states of charge of said batteries, the speed of change in the charge states of said batteries at least during the first minutes of recharging, - the predictive model of battery recharge duration, and - said target end-of-charge state of charge.

[0024] The system according to the invention makes it possible to evaluate the remaining charging time of the SOC of vehicles connected to the charging points of a charging station. It is thus possible to estimate whether a charging point will be available upon the expected arrival of the vehicle at said station. If the charging points are unavailable upon the expected arrival of the vehicle, the driver can choose another charging station and modify his route. It is thus possible to optimize the duration of the route and reduce the waiting time at the charging stations.

[0025] In embodiments, in order to evaluate the speed of evolution of the states of charge of the batteries of said electric vehicles, the determination module can be configured to record states of charge of said batteries, and to sample said states of charge at least during the first minutes of recharging, in particular at least during the first five minutes. The speed of evolution of the states of charge of the SOCs is thus evaluated on the basis of real data of recharging of the SOC, this makes it possible to evaluate the remaining charging time more precisely.

[0026] In embodiments, the determination system may comprise a location means configured to geolocate the vehicle in real time, when the first selected charging station has charging points that are unavailable and / or with too long a waiting time, the navigation module is configured, on the one hand, to offer the driver alternative charging stations located near the geographical position of the vehicle and / or on its route, and on the other hand, to estimate the remaining charging time of the electric vehicles connected to the plurality of charging points of these alternative charging stations. This makes it possible to optimize the vehicle's route in real time to allow optimal charging of the SOC while reducing the waiting time at the charging stations.

[0027] In embodiments, the predetermined target state of charge at the end of charging of the battery may be between 70 and 90% and better between 75% and 85% of the state of charge of the vehicle battery. Such a target SOC at the end of charging is defined based on the charging statistics measured by the inventors but also based on the recommendations of electric vehicle manufacturers. It makes it possible to estimate the charging time by approximating the target SOC at the end of charging.

[0028] In embodiments, in practice, the determination system comprises means of connection to the Internet and a processing interface configured to communicate with databases accessible online which lists the charging stations installed in a given territory, the processing interface informing, in real time real, of the charge states of the batteries of the vehicles connected to the plurality of charging points of the charging stations. The processing interface provides access to reliable real data which is provided by the operators of the charging stations and relayed by dedicated databases.

[0029] In embodiments, the determination method may comprise predictive estimation means for the vehicle waiting times at all charging stations before the start of charging, the navigation module being configured to calculate a total waiting time at the charging stations as a function of said waiting time before the start of charging and the remaining charging times of said batteries. The predictive estimation means also make it possible to take into consideration the vehicle queue at the charging station and therefore to provide the driver with more information, in particular concerning the waiting time at the charging station.

[0030] In embodiments, the predictive model of the charging time of the electric vehicle battery corresponds to a statistical model constructed from data measured at charging stations, this data making it possible to plot curves of the evolution of the state of charge as a function of the charging time. The predictive model is thus based on actual measured data and makes it possible to provide an estimate by comparing the speeds measured at the start of charging to estimate the remaining charging time on the basis of actual measured data.

[0031] The invention also relates to an electric vehicle comprising a system for determining a charging point of a charging station for the battery of an electric vehicle defined according to the invention.

[0032] The invention also relates to a method for determining an available charging point of a charging station for the battery of an electric vehicle, the method comprising the following steps: selecting a first charging station located on a route and / or close to the vehicle, the first charging station comprising a plurality of vehicle charging points, determining the states of charge of the batteries of the electric vehicles connected to the plurality of charging points of the charging station, evaluating the rate of change of the states of charge of said batteries, at least during the first minutes of recharging, and - estimating the remaining charging time of said batteries as a function of the rate of change in the states of charge evaluated for said batteries during the first minutes of charging, of a predictive model of battery charging duration up to a target state of charge for the end of charging of the batteries of the electric vehicles connected to the plurality of charging points of the first charging station.

[0033] The method includes characteristics corresponding to the characteristics of the determination system, it responds to the same technical problem and therefore presents the same advantages.

[0034] In embodiments, the rate of change of the states of charge of said batteries during the first minutes can be evaluated by sampling, before the start of recharging, states of charge of said batteries, and by sampling the states of charge of the batteries at least during the first minutes of charging and in particular during the first five minutes of charging. The evaluation of the rate of change of the states of charge is therefore based on real data which makes it possible to improve the precision of the method.

[0035] In embodiments, the remaining charging time may be estimated by using the initial states of charge and the rate of change of the states of charge as input data of the predictive model. It is thus possible to rely on a restricted measurement sampling to determine the speed at the start of charging of the battery SOCs to estimate the remaining charging time.

[0036] In embodiments, the determination method may comprise a step of predictively estimating the availability of a charging point or a waiting time at the first charging station for a charging point to be available, the waiting time being estimated based on the remaining charging time of the batteries of the electric vehicles connected to the plurality of charging points of said first station, the waiting time being estimated at an expected arrival time of the vehicle at the first charging station. Depending on the waiting time, the driver may decide to modify his route to find an alternative charging station with a shorter waiting time.

[0037] In embodiments, the determining method may comprise estimating a queue of vehicles at the first charging station, the waiting time at the first charging station being determined based on the queue and the remaining charging time of said batteries. The evaluation of the vehicle queue at the charging station makes it possible to provide additional information to the method and / or the driver to determine the waiting time at the charging station.

[0038] In embodiments, the determination method may comprise a step of proposing alternative charging stations when: upon the estimated arrival of the vehicle, the first charging station has, according to the method, charging points that are not available and / or have too long a waiting time, and / or the autonomy of the vehicle changes such that the first selected charging station is too far or too close to the vehicle depending on the remaining charge state of the vehicle's battery;

[0039] the method then comprises a step of predictive estimation of the availability of charging points or waiting times at alternative charging stations upon the estimated arrival of the vehicle at each of these alternative charging stations.

[0040] The vehicle's route can thus be modified, by the driver or the navigation module, in real time to allow optimal charging of the SOC while reducing waiting time at charging stations. Description of the drawings

[0041] Other characteristics and advantages of the invention will become apparent upon reading the description which follows. This is purely illustrative and must be read in conjunction with the appended drawings in which: Figure 1 is a schematic representation of the information available on a public electric vehicle charging station accessible to electric vehicles of all manufacturers; Figure 2 is a schematic representation showing a Paris-Nice route and the public electric vehicle charging stations located on the route; Figure 3 is a schematic representation of a system for determining a charging point for the battery of an electric vehicle according to an embodiment of the invention;Figure 4 is a representation of two combined graphs which illustrate the value of the SOC at the start and end of recharging of the vehicle battery at a public charging point and the duration of the charge; Figure 5 is a representation of a graph reproducing the data from Figure 4 and showing more precisely the charging time of the recharges as a function of the SOC at the end of the charge; - Figure 6 is a representation of a distribution of errors in estimates of the end-of-charge SOC time, said estimates having been determined by the system and / or the method according to the invention; Figure 7 is a representation of a flowchart schematically illustrating a method of a charging point of the battery of an electric vehicle according to an embodiment of the invention. Description of the embodiments

[0042] With reference to Figure 3, the invention relates in particular to a system 10 for determining a charging point 20 of a charging station 21 for the battery of an electric vehicle. Indeed, at present the autonomy of an electric vehicle battery is altogether reduced compared to the autonomy of certain vehicles thermal. Thus, when a user of an electric vehicle wishes to travel a route whose distance is greater than the vehicle's range, it is necessary to plan stops along the route to recharge the SOC of the vehicle's battery. Unlike a thermal vehicle, it is preferable to schedule a stop to recharge the SOC of the vehicle's battery because the time it takes to charge the SOC is much longer than the time it takes to fill the tank of a thermal vehicle. The time it takes to recharge the SOC of an electric vehicle can generate significant queues at charging stations when road traffic is heavy, particularly during major holiday departures, but not only.

[0043] As illustrated in Figure 3, the determination system 10 includes a navigation module 11 configured to select a first charging station 21 located near or on a route of the vehicle. The determination system 10 may further include a human-machine interface 12 that is configured to allow the driver to program the route of the vehicle. The human-machine interface 12 may be integrated into an on-board console of the vehicle. Typically, the human-machine interface 12 may include a touchscreen, voice commands, manual controls such as buttons configured to select and change menus within the navigation module, etc.

[0044] The navigation module 11 can thus be an application stored and executed at the central console of the vehicle as illustrated in FIG. 3. The navigation module 11 can also be an application stored and executed on a mobile terminal belonging to the driver such as a smartphone.

[0045] In the embodiment illustrated in Figure 3, the determination system 10 comprises a computer 13 which is configured to estimate, in real time, the SOC of the vehicle battery. Advantageously, the computer 13 is also configured to estimate, in real time, the autonomy of the vehicle as a function of the distance remaining to reach the destination of the route. The autonomy of the vehicle is also estimated as a function of the consumption of the SOC which depends in particular on the speed at which the vehicle is traveling, the type of road taken, urban road, departmental road, national road, expressways, highway. The autonomy of the vehicle also depends on the behavior observed by the driver according to, for example, whether he drives smoothly or sportily. Other parameters such as the use of the vehicle's electrical / electronic equipment can influence the autonomy of the vehicle.

[0046] In the example of Figure 3, the determination system 10 comprises a location means 14 which is configured to geolocate the vehicle in real time. The location means 14 may comprise a transmitter / receiver compatible with GPS, Galileo, GLONASS, Beidou technologies or any other geolocation technologies. The location means 14 may be embedded directly on the vehicle or be included in a digital terminal belonging to the driver and connected with or without wires to the navigation module 11.

[0047] Figure 2 illustrates a Paris-Nice (France) route on which the geographical position of the charging stations located on the route is illustrated by a location point. This route has a large number of charging stations. In order to optimize the travel time of the route, the navigation module 11 can be configured to define an ideal route to reach a destination by selecting a first charging station 21 according to several parameters. The parameters can include the distance to be traveled to the selected charging station, a target SOC of the vehicle battery upon arrival at the selected charging station, for example a target SOC of between 12% and 20%. The price of the KWh applied by the charging station can also be a parameter for selecting the charging station 21.In practice, the driver can choose one or more parameters using the human-machine interface 12, the navigation module 11 calculates or recalculates the route according to the parameter(s) chosen by the driver.

[0048] In addition, as illustrated in Figure 3, the navigation module 11 comprises a predictive model 15 of the electric vehicle SOC charging time. This predictive model 15 is configured to estimate the remaining charging time of an electric vehicle connected to a charging point 20. According to one embodiment, the predictive model 15 corresponds to a statistical model constructed from data measured at different charging points. The data measured to construct the predictive model 15 essentially comprise the value of the initial SOC before charging, the SOC at the end of charging and also the duration of each charge. The predictive model 15 can be constructed in particular by non-linear regression applied to real data measured at different real charging points.For example, the predictive model 15 can be constructed by applying, to the actual measured data, the “Support Vector Regression (SVR) function using linear and non-linear kernels.

[0049] The graph in Figure 4 provides an overview of the type of results obtained during this data collection. The initial SOC value measured at the start of charging is read on the x-axis, which is labeled "soc start" on the graph. Conversely, the end-of-charge SOC value is read on the y-axis and is labeled "soc_stop". The bar charts in the graph represent a statistical distribution of the number of recharges that started at an initial SOC value readable on the x-axis and ended at an end-of-charge SOC value readable on the y-axis. In addition, the diameter of each point gives an indication of the duration of the charge; the larger the diameter, the longer the charge duration, and conversely, the smaller the diameter, the shorter the charge duration.

[0050] In Figure 4, two pairs of dotted axes have been plotted to highlight the recharges considered optimal in terms of charging duration compared to the SOC gain of the recharge. These data also show that the most optimal recharges are most frequently used by electric vehicle users. Here, a first pair of Si axes frames the interval of initial SOCs which are statistically the most frequent, and a second pair of Sf axes frames the end-of-charge SOCs which are also statistically the most frequent.

[0051] Figure 5 illustrates more explicitly the charging time in relation to an end-of-charge SOC "SOC stop". The charging time "charge duration" is expressed here in minutes. First of all, we can note on this graph that the charging time can also depend on the type of charging point 20 to which the vehicle battery is connected. Here, we count three different charging points, CCS 350 kWh, CCS 50 kWh and CHAdemO 50 kWh. Then, this graph shows that statistically a large number of recharges reach approximately 80% of SOC according to a charging time between 25 and 50 minutes. Conversely, most of the time to obtain an SOC of 100%, the charging time is often greater than 50 minutes. This allows us to determine that most of the time drivers program a target end-of-charge SOC at 80%.Car manufacturers also recommend recharging with a low initial SOC, less than or equal to 20% to reach a target SOC of 80% because this type of charging represents an efficient SOC / recharging time gain.

[0052] For example, four nonlinear regression algorithms can be tested to identify the one with the lowest error rate between the estimated end-of-charge times for a given target end-of-charge SOC and the actual measured charge times. The nonlinear regression algorithm with the lowest error rate will then be used preferentially to build the predictive model 15.

[0053] In addition, the predictive model 15 can be improved using machine learning such as machine learning called "random forest". The implementation in machine learning of a large number of data measured on charging stations (initial Soc, end of charge Soc, charging duration). A large number of data can make it possible to create recurring charging profiles for which it can be assumed that they correspond to a vehicle model. The evolution of the SOC in the first minutes of charging can then make it possible to recognize the charging profile of the vehicle by comparing it to the trained predictive model 15 and thus to predict more precisely the remaining charging time of the vehicle.

[0054] As illustrated in Figure 3, the navigation module 11 also comprises a module 16 for determining the remaining charging time of a plurality of vehicle charging points 20 of the first selected charging station 21. In this example, the determination module 16 is an algorithm that can be stored and executed locally, by the navigation module 11 and / or by the vehicle's computer 13. Alternatively, the determination module 16 can be stored on a remote server. In this case, the navigation module 11 requests the determination module 16 remotely via an internet connection. In the same way, the predictive model 15 can also be stored on a remote server.

[0055] In practice, the determination system 10 comprises means for connecting to the internet. When the predictive model 15 and the determination module 16 are stored remotely from the vehicle, the connection means allow in particular the interaction between the navigation module 11 and these two elements of the determination system 10. The connection means can be of several types and embedded for example a transmitter / receiver of 4G, 5G technology etc. The connection means can also embed a transmitter / receiver of Bluetooth and / or Wifi technology in order to interact with a router which can be constituted by the driver's smartphone.

[0056] According to the invention, the determination module 16 is configured to evaluate the speed of evolution of the states of charge, at least during the first minutes of charging, of the SOCs of the electric vehicles connected to the plurality of charging points of the first charging station. In particular, the speed of evolution of the states of charge of said SOCs can be determined, at least during the first five minutes of charging of said SOCs.

[0057] For this purpose and according to one embodiment, the determination module 16 can be configured to sample at regular intervals the SOCs of the vehicles connected to the plurality of charging points 20 of the charging stations. In particular, the determination module 16 can record the initial SOCs of the vehicles before the start of charging. Then, sampling, according to a regular time interval, of said SOCs can be carried out by the determination module 16. This sampling is carried out at least during the first minutes of charging. The rate of change of the states of charge can thus be determined as a function of the change in the SOCs during these first minutes.

[0058] Several variations can be used, for example, it is possible to sample for the first five minutes at a frequency of one sample per minute. Another variation is to sample for the first nine minutes, with one sample every three minutes.

[0059] According to another embodiment, the SOCs of the vehicles connected to the charging points 20 can be sampled for the entire duration of the SOC charge. It is then possible to readjust the estimate of the remaining charging time in the event of a sudden drop in the power delivered by the charging point 20 which generally leads to an extension of the charging time. This type of drop can be explained by the connection of several vehicles to charging points which share the same power electronics, but also by overheating of the load point. It is therefore not uncommon to observe such a phenomenon.

[0060] According to an embodiment illustrated in Figure 3, the determination system 10 may comprise one or more processing interfaces 17 such as an “API” application interface. The processing interface 17 is hosted on a remote server and accessible via a conventional internet connection. The processing interface 17 communicates with one or more databases accessible via the internet which list the charging stations installed in a given territory.

[0061] As illustrated in Figure 1, the processing interface 17 can thus have access to a certain number of data relating to the charging points of a charging station such as the availability / unavailability of all the charging points 20 of the station in real time. Figure 1 illustrates the information which can be available for a charging terminal 22. The processing interface 17 can thus know the power of the charging terminal 22, the availability of each charging point 20, the price of the kWh distributed to each available charging point 20 is provided while the charging point 20 which is unavailable because a recharge is in progress, informs about the SOC of the vehicle which is being charged (see box). This information can thus be collected by the determination module 16 which is configured to communicate with the processing interface 17.For this, the determination module can use, for example, an OCPI protocol which is the acronym for “Open Charge Point interface”.

[0062] According to this embodiment, the determination module 16 can be configured to collect the SOC data from the charging points 20 of the first charging station 21 via algorithmic data collection protocols such as an OCPI Protocol in order in particular to measure the initial SOC of the vehicles connected to the plurality of charging points 20 of the charging stations.

[0063] According to the invention, the navigation module 11 comprises an estimated end-of-charge target SOC. According to one embodiment, the determined target SOC may be between 70 and 90%, better between 75% and 85% of the vehicle's SOC.

[0064] Reading the graph in Figure 4 shows that a large proportion of recharges begin with an initial SOC of around 20%, more precisely, between 15% and 25%. Although a significant proportion of recharges end with an SOC of 100%, the majority of end-of-charge SOCs are recorded around 80% of the SOC, and more specifically, between 77% and 91% of the SOC. The intersection of the two pairs of axes Si, Sf shows that the most efficient recharges in terms of SOC gain compared to the charging time start with an initial SOC of around 20% and end when the target end-of-charge SOC is around 80%. This type of charge is also the most frequent in the data measured by the inventors. The best SOC gain / charging time ratio charge is also illustrated on the graph of figure 4 by smaller points compared to those whose charge has a target SOC of 100%. This is why, within the framework of the invention, the target SOC at the end of charge can be chosen at around 80% according to the ranges of values ​​previously set out.

[0065] In this context, the navigation module 11 is configured to estimate the remaining charging time of the SOCs of the vehicles connected to the charging points 20 of the first charging station 21. In particular, the remaining charging time is estimated as a function of the estimated charging speed of said SOCs during the first minutes of charging, the predictive SOC charging duration model and the predetermined target end-of-charge SOC.

[0066] In practice, the remaining recharge time to reach the target SOC at the end of charge can be better estimated by using as input data, in the predictive model 15, the state of charge of the batteries and the rate of evolution of the states of charge determined at least at the start of charging.

[0067] It is thus possible to obtain an estimate of the remaining charging time of the electric vehicles connected to the charging points 20 of the first charging station 21 which determines a specific waiting time at each charging point 20 of said station. The table below is an example of how the estimated charging time can be presented to the driver for a terminal 22 of a first charging station 21 which has been selected, the terminal 22 having four charging points 20.

[0068] The driver is thus informed of the remaining charging time of the charging points 20 of the first charging station 21 that has been selected. The driver can thus assess, based on the expected arrival time at said station, whether a charging point 20 will be available or quickly available.

[0069] According to one embodiment, the navigation module 11 can be configured to correlate the expected arrival time at the first selected charging station 21 and display on the human-machine interface 12 the remaining waiting time for each charging point 20 at the estimated arrival time.

[0070] According to a particular embodiment, the determination system 10 may comprise predictive estimation means for vehicle waiting times before the start of charging at all the charging stations. For example, the estimation means may comprise vehicle detection sensors arranged at the charging stations, for example, optical sensors, magnetic vehicle sensors arranged on waiting spaces of the charging stations such as a magnetic loop integrated into the coating of the waiting space. A digital waiting ticket taken by drivers waiting at the charging station may also be used as an estimation means.

[0071] According to this embodiment, the navigation module 11 is configured to calculate a total predictive waiting time at the charging stations as a function of said waiting time and the remaining charging times of the electric vehicles connected to the plurality of charging points 20 of said first station 21. The total predictive waiting time is calculated as a function of an expected arrival time of the vehicle at these charging stations.

[0072] As illustrated in FIG. 3, in one embodiment, when the first selected charging station 21 has charging points 20 that are not available and / or with too long a waiting time.

[0073] In this embodiment, the navigation module 11 is configured to offer the driver alternative charging stations 23, 24, 25, 26 located near the position of the vehicle and / or on its route. A magnifying glass is shown in FIG. 3 to diagram this search for alternative stations 23, 24, 25, 26, when the first station 21 selected does not have charging points 20 available quickly upon arrival of the vehicle at this first station 21.

[0074] When one or more alternative stations 23, 24, 25, 26 are identified by the navigation module 11, the navigation module 11 is configured to estimate the remaining charging time of the electric vehicles connected to the plurality of charging points 20 of these alternative charging stations 23, 24, 25, 26. This estimation uses the same estimation method as that described for the charging points 20 of the first station 21.

[0075] According to one embodiment, the human-machine interface 12 can be configured to allow the driver to select an alternative charging station 23, 24, 25, 26 when the first station 21 selected has charging points 20 that are not available and / or with too long a waiting time.

[0076] According to one embodiment, the navigation module 11 is also configured to propose alternative charging stations 23, 24, 25, 26 when the autonomy of the vehicle changes so that the first charging station 21 selected is too far or too close to the vehicle depending on the remaining SOC of the vehicle.

[0077] Figure 6 shows a comparison of the estimated end of charge time according to the invention with the actual end of charge time. For this, real data was used as input. of the predictive model 15. In particular, initial states of charge (initial SOC) and the rate of change of these states of charge (SOC) of these real data made it possible to produce estimates of the remaining charging time in accordance with the invention, these estimates were then compared to the actual measured data. The graph in Figure 6 illustrates this comparison according to four variables with the deviation of the estimated remaining charging time from the actual remaining charging time on the abscissa. Namely, all negative values ​​mean that the actual remaining charging time was ultimately shorter than the estimated remaining charging time, and conversely, all positive values ​​mean that the estimated remaining charging time ultimately turned out to be longer than the actual remaining charging time. On the ordinate of this graph is the probability that the estimated remaining charging time will have a given deviation from the actual charging time.

[0078] The graph makes it possible to determine that by using the system according to the invention there is an 80% probability that the estimate will be 10 minutes shorter than the actual remaining charging time, i.e., the user must wait 10 minutes upon arrival at the charging point. There is also a 60% probability that the estimate will be approximately 5 minutes shorter, which represents an acceptable margin of error for motorists who then only have to wait 5 minutes upon arrival at the charging point.

[0079] Of course, the invention also relates to an electric vehicle comprising a system 10 for determining a charging point of the battery according to the invention.

[0080] As illustrated in Figure 7, the invention relates to a method 50 for determining an available charging point 20 of a charging station 21 for the battery of an electric vehicle. The determination method 50 can in particular be implemented by the determination system 10 according to the invention as described previously.

[0081] In the example illustrated in Figure 7, the determination method 50 comprises a step 51 of selecting a first charging station 21 located on a route and / or near the vehicle. As described previously, the driver or passenger of the vehicle can determine the route using the navigation module 11 and / or the human-machine interface 12.

[0082] According to one embodiment, the first station 21 is selected as a function of the distance to be traveled, the SOC available at the time of programming the route and a target SOC for discharging the vehicle battery upon arrival at the first charging station 21. The selection 51 of the first station 21 can be carried out by the navigation module 11 after the driver has defined a certain number of parameters as described previously.

[0083] Most of the time a charging station 21 comprises a plurality of charging points 20 as illustrated in FIG. 3. The determination method 10 aims to define whether one of these charging points will be available when the vehicle arrives at the first selected station 21.

[0084] For this purpose, the determination method 50 comprises a step 52a of determining the initial states of charge of the batteries of the vehicles or initial SOCs. The initial states of charge correspond to the states of charge of the batteries of the electric vehicles connected to the plurality of charging points 20 of the first charging station 21 after the selection of the first station 21. The initial states of charge are, in practice, obtained via the determination module 16 and the processing interface 17 which are configured to communicate in real time with one or more databases listing the charging stations as described previously.

[0085] The determination method 50 comprises a step 52b of evaluating the rate of change of the states of charge or SOC of the electric vehicles connected to the plurality of charging points 20 of the first charging station 21. Advantageously, the rate of change of the states of charge can be determined at least during the first minutes of charging. According to one embodiment, the rate of change of the SOC of said vehicles is determined by sampling the SOC of the electric vehicles connected to the plurality of charging points 20 of the first charging station 21. First of all, the initial SOC is recorded before the start of charging during step 52a, then the change in the SOC is followed by sampling of the SOC at regular time intervals at least during the first minutes of charging. As described above, the estimation step 52b can be carried out by the navigation module 11.

[0086] According to one embodiment, the SOC may be sampled every minute for the first five minutes of the load. Other sampling variations are possible as described earlier in this document.

[0087] The rate of change of the states of charge can be determined by calculation based on the sampling time interval and the change in the SOC during sampling.

[0088] According to a particular embodiment, the rate of change of the states of charge or SOC can be evaluated throughout the duration of the charge as described previously.

[0089] According to one embodiment, the SOC readings during the first minutes of charging can be carried out on a processing interface hosted 17 on a website. As described previously, this type of processing interface lists the charging stations installed in a given territory, but also the real-time SOCs of the batteries of the vehicles connected to the plurality of charging points of the charging stations.

[0090] The determination method 50 comprises a step 53 of estimating the remaining charging time of said SOCs as a function of the estimation of the evolution of the speed of the states of charge of the batteries during the first minutes of charging. The determination method 50 also takes into account the predictive model 15 of SOC charging duration and a target SOC at the end of charge determined to evaluate the remaining charging time. As described previously, the target SOC at the end of charge can be chosen around 80% for the reasons which have been mentioned earlier in this document.

[0091] According to one embodiment, the remaining charging time can be estimated using as input data the initial states of charge or initial SOC and the estimated speed of evolution of the states of charge of the batteries of the vehicles connected to the plurality of charging points of the selected charging station.

[0092] In the context of the determination system 10 of the invention, it is the navigation module 11 which evaluates the remaining charging time of the SOCs of the batteries of the vehicles being recharged.

[0093] The method may include a step 54 of estimating the expected arrival time of the vehicle at the first selected charging station 21. For these purposes, the navigation module 11 may use the location means of the determination system 10. For this purpose, the navigation module 11 may integrate a conventional driver guidance system to obtain such information by taking into account the speed of the vehicle and the traffic on the planned route to the first selected charging station 21.

[0094] According to one embodiment, the determination method 50 may comprise a step of predictive estimation 55 of the availability of a charging point 20 or of a waiting time at the first charging station 21 for one of the charging points 20 to be available. According to the invention, the waiting time is estimated at an expected arrival time of the vehicle at the first charging station 21. The waiting time is estimated as a function of the remaining charging time of the electric vehicles connected to the plurality of charging points 20 of said first station 21. This charging time is defined at the end of the step of estimation 53 of the remaining charging time of the determination method 50.

[0095] Nevertheless, according to a particular embodiment, the determination method 50 comprises a step of estimating the waiting time 56 at the first charging station 21. In this embodiment, the waiting time at the first charging station 21 is determined as a function of the queue and the remaining charging time of the electric vehicles connected to the plurality of charging points 20 of said first station 21. The estimation of the waiting time 56 is carried out by counting the number of vehicles at the charging station 21. As described previously, several means can be used to estimate the queue of vehicles; it is possible to equip the stations with vehicle detection sensors (optical, magnetic, etc.) or by setting up a digital ticket. In the example of FIG. 3, the navigation module 11 operates step 56 in collaboration with the processing interface 17 and estimation means available at the recharging station.

[0096] When at the expected arrival time of the vehicle, a charging point 20 is available or the total waiting time is acceptable for the driver, the first charging station 21 selected is maintained in the route programming.

[0097] Conversely, when, at the estimated arrival time of the vehicle, the first charging station 21 has, according to the method, charging points 20 that are not available and / or have a total waiting time that is too long. The acceptability of the total waiting time can be evaluated by the driver or by the navigation module 11 after the driver has entered various parameters for evaluating the total waiting time such as a delay in the arrival time at the final destination greater than a determined delay duration, or even an optimal duration per break time or per cumulative break during the route.

[0098] In the event of unavailability or excessive total waiting time and according to one embodiment, the determination method 50 may comprise a step 57 of proposing alternative charging stations 23, 24, 25, 26 as illustrated in FIG. 3. Using the location of the vehicle, the determination method 50 proposes alternative stations 23, 24, 25, 26 which are located in the geographical vicinity of the vehicle or on its route. Several parameters may be taken into account to make these proposals, for example, the distance to be traveled in relation to the remaining SOC of the vehicle battery or the detour time to the alternative station 23, 24, 25, 26. Such information may be displayed on the human-machine interface 12 to allow the driver to choose the solution which suits him best.Alternatively, the determination method 50 can automatically choose an alternative station 23, 24, 25, 26 after the navigation module 11 has been configured according to certain aforementioned parameters.

[0099] According to another embodiment, the step 57 of proposing an alternative station 23, 24, 25, 26 can also occur when the autonomy of the vehicle changes so that the first selected charging station 21 is too far or too close to the vehicle depending on the remaining battery SOC of the vehicle. In the example of FIG. 3, this situation can be determined by the navigation module 11 in relation to the computer 13 which evaluates the SOC of the vehicle in real time and the location means 14 which allow the navigation module to calculate and follow the route.

[0100] The determination method 50 then comprises a step of predictive estimation 58 of the availability of the charging points 20 or of the waiting times at the alternative charging stations at the estimated arrival time of the vehicle at each of these alternative charging stations 23, 24, 25, 26. For this, the determination method 50 applies in particular the steps of determination 52a of the initial charging states, of estimation 52b of the speed of evolution of the states of charge, estimation 53 of the remaining charging time, estimation of the arrival time 54 at the alternative station. The determination method 30 may comprise a step 59 of selection or proposal of the most suitable alternative station 23, 24, 25, 26 according to various parameters already mentioned. Either the navigation module 11 is configured to choose the most suitable alternative station according to the remaining SOC of the vehicle and / or other parameters or, the navigation module 11 displays the different alternative stations 23, 24, 25, 26, which will present according to the method a charging point 20 available or with a short waiting time, and informing the driver of various parameters such as the estimated remaining SOC of the vehicle on arrival at each alternative station, the journey time and / or the detour quantified in time or distance.This information allows the driver to select the most suitable alternative station.

[0101] Steps 55 to 59 are shown in dotted lines on the flowchart of Figure 7 because they constitute embodiments of the invention as described in the text.

Claims

Claims 1. System for determining (10) an available charging point (20) of a charging station (21) for the battery of an electric vehicle comprising a navigation module (11) configured to select the charging station (21) in the vicinity and / or on a planned route of the vehicle, the navigation module (11) comprising, on the one hand, a predictive model (15) of the duration of recharging of the electric vehicle battery, and on the other hand, a determination module (16) configured to determine the speed of recharging of the batteries of the electric vehicles connected to the plurality of charging points (20) of the charging station (21), the navigation module (11) being configured to estimate the remaining recharging time of said batteries as a function of: - the charge states of said batteries, - the speed of change in the charge states of said batteries at least during the first minutes of recharging, - estimation of the remaining charge duration of said batteries obtained by implementing the predictive model (15) of battery recharge duration on said charge states and said rate of change of the charge states, and - a predetermined end-of-charge target state of charge.

2. Determination system (10) according to claim 1, in which, in order to evaluate the speed of evolution of the states of charge of said batteries, the determination module (16) is configured to record states of charge before recharging of said batteries, and to sample said states of charge at least during the first minutes of recharging, in particular at least during the first five minutes.

3. Determination system (10) according to one of claims 1 and 2 wherein, when the selected charging station (21) has charging points (20) that are not available and / or with too long a waiting time, the navigation module (11) is configured, on the one hand, to offer the driver alternative charging stations (23, 24, 25, 26) located near the vehicle and / or on its planned route, and on the other hand, to estimate the remaining charging time of the batteries of the electric vehicles connected to the plurality of charging points (20) of these alternative charging stations (23, 24, 25, 26).

4. Determination system (10) according to one of claims 1 to 3, in which the target state of charge at the end of predetermined charge is between 70 and 90% and better between 75% and 85%.

5. Determination system (10) according to one of claims 1 to 4, which comprises means of connection to the internet and a processing interface (17) configured to communicate with databases accessible online which list the charging stations installed in a given territory, the processing interface (17) providing information, in real time, on the charge states of the batteries of the vehicles connected to the plurality of charging points (20) of the charging stations.

6. Determination system (10) according to claim 5, which comprises predictive estimation means for the waiting times of vehicles at all the charging stations before the start of charging their batteries, the navigation module (11) being configured to calculate a total waiting time at the charging stations as a function of said waiting time before the start of charging and the remaining charging times.

7. Determination system (10) according to one of claims 1 to 6, in which the predictive model (15) of battery recharge duration corresponds to a statistical model constructed from data measured on recharge stations, this data making it possible to plot curves of the evolution of the state of charge as a function of the recharge time.

8. Electric vehicle comprising a system (10) for determining an available charging point of a charging station defined according to one of claims 1 to 7.

9. Method for determining (50) an available charging point of a charging station for the battery of an electric vehicle, the method comprising the following steps: - selecting (51) a charging station (21) on a route and / or near the vehicle, the charging station (21) comprising a plurality of vehicle battery charging points (20), - determining (52a) the charge states of the batteries of the electric vehicles connected to the plurality of charging points (20) of the charging station (21), - assess the speed of evolution (52b) of the charge states of said batteries, at least during the first minutes of recharging, and - estimating the remaining charging time (53) of said batteries as a function of the states of charge of said batteries and the rate of change of the states of charge evaluated for said batteries during the first minutes of recharging, the method (50) using a predictive model of battery charging time up to a state of charge target end of charge of the batteries of electric vehicles connected to the plurality of charging points of the charging station.

10. Determination method (50) according to claim 9, in which the rate of change of the states of charge of said batteries during the first minutes is evaluated by recording their states of charge before the start of their charging, and by sampling said states of charge at least during the first minutes of charging and in particular during the first five minutes of charging.

11. Determination method (50) according to one of claims 9 and 10, in which the remaining charging time is estimated using the initial charging states and the rate of change of the charging states as input data of the predictive model, the predictive model having been determined, for example, by a non-linear regression applied to real measured data.

12. Determination method (50) according to one of claims 9 to 11, which comprises a step of predictive estimation (55) of the availability of a charging point (20) or of a waiting time at the charging station (21) for a charging point (20) to be available, the waiting time being estimated as a function of the remaining charging time of the batteries of the electric vehicles connected to the plurality of charging points (20) of said station (21), the waiting time being estimated at an expected arrival time of the vehicle at the charging station (21).

13. Determination method (50) according to claim 12, which comprises an estimation (56) of a queue of vehicles at the charging station, the waiting time at the charging station being determined as a function of the queue and the charging time of said batteries.

14. Determination method (50) according to one of claims 12 and 13, which comprises a step of proposing (57) alternative charging stations (23, 24, 25, 26) when: when, at the estimated arrival of the vehicle, the charging station (21) has, according to the method (50), charging points (20) which are not available and / or with too long a waiting time, and / or - when the autonomy of the vehicle evolves so that the selected charging station (21) is too far or too close to the vehicle depending on the remaining state of charge of the vehicle battery, the method then comprises a step of predictive estimation (58) of the availability of the charging points (20) or of the waiting times at alternative charging stations (23, 24, 25, 26) at the estimated arrival of the vehicle at each of these alternative charging stations (23, 24, 25, 26).

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