Method for sizing a battery for an electric vehicle

Abstract

The invention relates to a computer-implemented method for sizing and constructing an electric vehicle battery. The sizing method comprises at least the following steps: a) several potential capacities (Capo) of the battery are defined; b) for each potential battery capacity, a route planner (Plan) is used to determine at least one possible route for said journey, each possible route being defined by a succession of road segments, and the travel time and / or energy consumption, as well as the location of the charging stations to be used by the electric vehicle on each possible route to recharge its battery and the associated charge level, are determined; c) from this data for each potential battery capacity, the battery capacity (Dim) is identified from among said potential capacities. Figure 1 to be published

Description

Title of the invention: Method for sizing a battery for an electric vehicle technical field

[0001] The invention relates to a method for sizing a battery for an electric vehicle.

[0002] It also relates to a method of constructing an electric vehicle where the battery, sized according to the sizing method, is installed in the electric vehicle.

[0003] The invention also relates to a computer program product.

[0004] Recent studies show that a small electric vehicle with a range of approximately 160 km could meet the energy needs of about 90% of driving days in various US cities. Furthermore, the needs of most single-car households would be met without requiring any behavioral changes for more than 5% of driving days. However, range anxiety is undoubtedly unavoidable for users. To overcome this issue and avoid slowing sales and the adoption of electric vehicles, route planners are becoming increasingly widespread.

[0005] While route optimization and charging strategy are important features for electric vehicle users, other factors can help them transition to electromobility and guide them in choosing the electric vehicle best suited to their needs. In particular, vehicle choice is often influenced by battery size, which is frequently selected for a few occasional long-distance trips. In other words, users want the vehicle's battery to be large enough to cover the entire journey (from home to their holiday destination, for example) with the fewest possible charges. This leads to the purchase of vehicles with large batteries that are hardly suitable for everyday use and have equivalent CO2 emissions (considering the vehicle's entire life cycle) comparable to conventional internal combustion engine vehicles.Indeed, oversizing the battery for a few long-distance journeys has a significant environmental impact. Previous technique

[0006] French patent application FR3092181 A1 relates to a method for sizing the battery of an electric vehicle. However, this method is based on the energy-consuming components and the vehicle's energy consumption are taken into account, but long-distance energy needs are not considered.

[0007] The invention aims to verify the capabilities of vehicles, i.e. their needs, depending on the size of the batteries (their capacity in short), in terms of travel time, energy consumption and financial and environmental cost, in order to choose (size) the optimal battery while avoiding oversizing the battery. Summary of the invention

[0008] The object of the invention is to propose a method for sizing the battery taking into account journeys, in particular long-distance journeys, in order to optimize travel time and / or energy consumption, while avoiding oversizing the battery.

[0009] To this end, the invention relates to a method for sizing a battery for an electric vehicle, for implementing the battery in said electric vehicle, based on the physical characteristics of said electric vehicle, at least one route from an origin to a destination in a predetermined space, the locations of charging stations in the predetermined space, the power of each of said charging stations, and a route planner, the predetermined space comprising a road network including road segments connected by intersections, the sizing method comprising at least the following steps: a) We define several potential capacities of the battery, said potential capacities being between a first value and a second value; b) for each potential battery capacity, the route planner is used to determine at least one possible route for said journey, each possible route being defined by a succession of road segments, and the travel time to travel the possible route by said electric vehicle and / or the energy consumption by said electric vehicle of each possible route is determined, as well as the position of the charging stations to be used by the electric vehicle on each possible route to recharge its battery and the associated charging level, c) from the travel times and energy consumption determined for each potential battery capacity and the size of each potential battery capacity, the battery capacity is identified among said potential capacities; and in which at least step b) is implemented by computer, and preferably all steps are implemented by computer.

[0010] Preferably, the route planner performs at least the following steps: - A routing graph is constructed from the road network, with the vertices of the routing graph representing the intersections of each road segment and the arcs of the routing graph representing the road segments. - The charging stations are positioned on the routing graph, - The routing graph is extended by multiplying at least some arcs according to predefined parameters, the predefined parameters including the traffic speed on the section of road considered and / or the charging level when stopping at a charging station, - an energy consumption model is determined, the energy model defining the energy consumption of the electric vehicle according to predetermined parameters, taking into account any potential recharging of the electric vehicle's battery, - For each route, the energy consumption model is applied to the different arcs of the extended routing graph, and the final route of the possible journey is defined as the set of arcs that minimize, using an optimization algorithm such as a Bellman-Ford shortest path algorithm, the travel time, or the energy consumption, or a compromise defined as a relative weight between these two parameters. - preferably, from the final route defined, the positions of the charging stations used by the electric vehicle and the level of charging achieved at each charging station used by the electric vehicle for the journey considered are determined.

[0011] Advantageously, we consider several traffic speeds of at least one portion of road, each traffic speed considered being between a first speed and a second speed and we repeat step b) for each traffic speed.

[0012] According to a variant of the invention, the road segments take into account the topography of the road network, in particular the slope of the road segment, the infrastructure and the signage by means of a geographic information system, the traffic data by means of a geolocation system or a geographic information system and preferably the meteorological data by means of an application programming interface system.

[0013] Preferably, a measurement of the speed profile of several vehicles is acquired on each section of the road network, using a geolocation system or an on-board phone, to determine, on the one hand, the average speed of the vehicles on the section of road, and on the other hand, the probability of vehicles stopping at the end of the section of road considered, the probability of stopping being determined by the percentage of vehicles for which a stop is considered at the end of the section of road, a vehicle being considered to have stopped if, at least at one point measurement of the second half of the road section, a vehicle speed is less than a predefined value, the predefined value preferably being less than or equal to 10 km / h.

[0014] According to one configuration of the invention, the charging stations considered by the route planner have a power greater than or equal to 50kW.

[0015] According to one embodiment of the invention, the physical characteristics of the electric vehicle include its mass and its energy consumption.

[0016] Advantageously, in step c), the battery capacity is identified as the smallest capacity among the potential capacities allowing a travel time less than or equal to a predetermined travel time and / or an energy consumption less than or equal to a predetermined consumption.

[0017] The invention also relates to a method of constructing an electric vehicle, in which the capacity of the battery is identified from the sizing method according to one of the variants or combinations of variants described above, and said battery of identified capacity is implanted in the electric vehicle.

[0018] The invention also relates to a computer program product downloadable from a communication network and / or recorded on a computer-readable medium and / or executable by a processor or a server, comprising program code instructions for implementing the sizing method according to one of the variants or combinations of variants described above, when said program is executed on a computer, a mobile phone or a computing device. List of figures

[0019] Other features and advantages of the method and product according to the invention will become apparent from the following description of non-limiting examples of embodiments, with reference to the figures attached and described below. [Fig 1]

[0020] Figure 1 represents one embodiment of the dimensioning method according to the invention. [Fig 2]

[0021] Figure 2 illustrates an example of a route planner adapted to the method of dimensioning according to the invention. [Fig 3]

[0022] Figure 3 represents an example of the speed profile of the electric vehicle on a section of road for determining the probability of stopping at the end of the section of road, according to the invention. Description of the implementation methods

[0023] The invention relates to a method for sizing a battery for an electric vehicle in order to implement the battery in said electric vehicle.

[0024] Thanks to the method of the invention, it is possible to optimize the size of the battery to the bare minimum, depending on the type of journey to be made, while avoiding oversizing the battery.

[0025] The method thus serves to size the battery according to one or more typical journeys (preferably long-distance journeys of at least 200 km and preferably at least 500 km) that one wishes to undertake with the electric vehicle within a predetermined area (a given physical territory corresponding to the predetermined area). It does not seek to optimize only the journey time but aims for a compromise between journey time, energy consumption, and battery size, for example, by aiming for a journey time acceptable to the user and avoiding oversizing the battery. It can, for example, take into account the frequency of the typical journey in question.

[0026] According to the invention, the predetermined space comprises a road network, the road network comprising road segments (also called road segments) connected to each other by intersections (or crossroads). The intersections can thus correspond to points of the road network and the road segments to arcs connecting the points of the road network.

[0027] To do this, the method uses the following input data: - the physical characteristics of said electric vehicle: for example its energy efficiency and the energy consumption of accessories such as air conditioning or the radiator to heat the passenger compartment; - the choice of at least one route, each route being defined by an origin and a destination in a predetermined space, the route going from the origin to the destination; - the locations of existing charging stations (also called "charging points") within the predetermined area: in other words, the locations where it is possible to recharge the vehicle's battery within the predetermined area: - the power output of each of these charging stations. Indeed, the power outputs of the different charging stations are not all identical. Thus, the charging time for the same electric vehicle can vary from one station to another depending on the available power.

[0028] For the purposes of the invention, "charging stations" (also called "charging points") refer to charging stations for electric vehicles, more specifically for recharging the batteries of electric vehicles.

[0029] An electric vehicle is defined as a vehicle comprising only an electric motor powered by a battery. In other words, this vehicle does not have an internal combustion engine, such as a gasoline or diesel engine.

[0030] The method also uses a route planner.

[0031] Figure 1 illustrates, schematically and without limitation, one embodiment of the method for sizing the battery of an electric vehicle according to the invention.

[0032] The dimensioning method comprises at least the following steps: a) We define several potential capacities of the battery (Capo), said potential capacities being between a first value and a second value; b) for each potential battery capacity, the route planner (Plan) is used to determine at least one possible route for said journey, each possible route being defined by a succession of road segments, and the travel time to travel the possible route by said electric vehicle and / or the energy consumption by said electric vehicle of each possible route is determined, as well as the position of the charging stations to be used by the electric vehicle on each possible route to recharge its battery and the associated charging level, c) from the travel times and energy consumption determined for each potential battery capacity and the size (its footprint and / or its weight and / or its capacity) of each potential battery capacity, the battery capacity (Dim) is identified among said potential capacities.

[0033] Furthermore, [Fig. 1] includes another optional step which consists of constructing (Cons) the electric vehicle by implementing the battery thus sized (i.e. by implementing the battery corresponding to the capacity identified in step c)) in the electric vehicle. This optional step is represented by the dashed lines.

[0034] According to the invention, at least step b) is carried out by computer means, in particular a computer or a server, and preferably, all steps are carried out by computer means. Thus, the process can be easily implemented in a computer or a server.

[0035] Step a) of defining the potential capacities of the battery

[0036] During this step, several potential capacities for the battery are selected. The potential capacities are those that can be implemented in the electric vehicle in question. These may be the various batteries offered by the vehicle manufacturer or batteries more widely available on the market and compatible with the electric vehicle in question (compatible batteries may, for example, be those that can be mounted in the compartment provided for this purpose, taking into account the available space).

[0037] The first value may correspond to the minimum capacity of the batteries offered by the vehicle manufacturer or to a minimum capacity to meet the main consumers of the vehicle.

[0038] The second value may correspond to the maximum capacity of the batteries offered by the vehicle manufacturer or to the maximum capacity of batteries available on the market and compatible with the vehicle.

[0039] For example, the potential capacities can be chosen incrementally between the first value and the second value, with a predetermined step, for example of 5 kWh. This approach makes it possible to size the battery as precisely as possible to the user's needs.

[0040] Alternatively, the potential capacities can correspond to all battery sizes offered by the vehicle manufacturer. Thus, the battery can be chosen according to the options offered by the manufacturer. Step b) Route planning

[0041] For each potential battery capacity defined in step a), we seek to determine several important parameters for sizing the battery and its final selection. These parameters to be determined include: - The travel time to complete the journey in question (from origin to destination) with the electric vehicle equipped with the battery for each of the potential capacities. Indeed, the potential capacity of the battery directly impacts the time (or distance) between two charges. Furthermore, the greater the potential capacity of the battery, the longer the charging time, starting from the same initial state of charge and ending with the same state of charge. - The energy consumption for completing the journey in question with the electric vehicle equipped with the battery for each of the potential capacities. Thus, we can see the impact of energy consumption depending on the choice of the potential battery capacity. - The number of charging stops along the route and the location of the charging stations used by the electric vehicle. Indeed, with a smaller potential capacity, the electric vehicle is likely to stop more often than with a vehicle equipped with a larger potential capacity. The location of the charging stations used impacts charging time because the power available at different stations can vary. Thus, depending on the locations of the charging stations used, the charging time, and therefore the travel time, can differ. - The charge level at each charging station used. Indeed, it may be wise not to charge the electric vehicle to 100%. In fact, the battery charging rate is generally not linear over time. For example, vehicle charging may be fast when the vehicle's charge level is between 30% and 80%, but slower after 80% and even slower after 90%.

[0042] The route planner thus proposes, for each journey considered and for each potential capacity of the electric vehicle battery, a possible route defined by a succession of road segments on the road graph and it proposes stops at charging stations, when charging the battery is necessary on the journey, the position of the charging stations used and the level of charging at each of the charging stations used.

[0043] According to one configuration of the invention, as illustrated in [Fig. 2], in step b), the route planner can, for example, perform at least the following steps: - a routing graph (Gr) can be constructed from the road network, the vertices of the routing graph representing the intersections of each road segment (PR) and the arcs of the routing graph representing the road segments (PR). In other words, the routing graph represents the road network of the territory under consideration (of the predetermined space). - Existing charging stations (Sp) can be positioned on the routing graph, and preferably, each charging station positioned on the routing graph is associated with a power level available for charging. Thus, the charging time can differ from one charging station to another, depending on the available power, for the same vehicle with the same potential battery capacity. - The routing graph (GrE) can be extended by multiplying at least some arcs according to predefined parameters. These parameters include the travel speed on the road segment in question and / or the charging level when stopping at a charging station. Each arc of the extended graph thus corresponds to a journey along each road segment with one or more distinct parameters: for example, several different travel speeds and / or several different charging levels for each arc connecting two identical vertices. Therefore, the route planner can search for the optimal charging level to limit travel time or energy consumption, for example, by avoiding systematically charging the vehicle to 100% battery capacity.

[0044] Different traffic speeds can be used by reducing this speed relative to the maximum authorized speed on at least certain sections of road or relative to the average traffic speed on certain sections of road. The sections of road affected by this speed reduction can be identified, for example, by an average travel speed exceeding a certain threshold, for Identify sections of highway, for example. Then, multiply the graph of each relevant road segment by the corresponding average speed for that segment. For instance, several possible speed reductions relative to the traffic speed or the maximum permitted speed can be considered. Advantageously, the speed reductions considered in the route planner can be 0, 5, 10, and 15 km / h. Thus, each arc of the relevant road segments can be multiplied by four arcs in the extended routing graph corresponding to the chosen possible speed reductions.

[0045] These certain road sections may include sections of motorway where the maximum permitted speed is generally greater than 100 km / h, for example. Reducing speed limits energy consumption, and therefore can eliminate some stops to recharge the battery. - We can determine (Conso) an energy consumption model, the energy model defining the energy consumption of the electric vehicle based on predetermined parameters, taking into account any potential battery charging. Thus, at each point on the route, we can know the battery charge level using the route planner. For each route, the energy consumption model can be applied to the different arcs of the extended routing graph, and the final route of the possible path can be defined as the set of arcs that minimize (Opt), using an optimization algorithm such as a Bellman-Ford shortest path algorithm, the travel time, the energy consumption, or a compromise defined as a relative weight between these two parameters. The algorithm minimizes the travel time or the energy consumption, or finds a mix between these two parameters. Optionally, the following can be determined (Pos_niv), based on the defined final route: the positions of the charging stations used by the electric vehicle and the charging level achieved at each station for the journey. This allows the user to know which charging stations to use along the route and the target charging level to achieve in order to optimize the route. They therefore know how many stops they need to make along their final route and where to stop to recharge their battery.

[0046] Advantageously, the first extension variable of the routing graph can be the travel speed to be adopted on certain road sections along the route in order to find the right compromise between travel time and energy consumption. In other words, on certain sections of the road, particularly at high speeds, it may be advantageous to drive at a speed lower than the average traffic speed or the maximum permitted speed in order to reduce energy consumption. This reduces the amount of energy required to complete the journey, which can translate into fewer stops for recharging the battery. The road segments affected by this speed reduction can be identified, for example, by an average travel speed exceeding a certain threshold, such as highway sections. The graph for each affected road segment is then multiplied by the corresponding number of average speeds considered for that segment. For example, several possible speed reductions can be considered relative to the traffic speed or the maximum permitted speed. Advantageously, the speed reductions considered in the route planner can be 0, 5, 10, and 15 km / h. Thus, each arc of the affected road segments is multiplied by four arcs in the extended routing graph corresponding to the chosen possible speed reductions.

[0047] Additionally or alternatively, the second extension variable of the routing graph can be the charging level at each charging station used. Consequently, the routing graph can be extended to contain as many copies of the outgoing arcs from the vertices corresponding to a used charging station as there are predefined levels of battery energy recovery during charging. For example, the allowed charging levels could be {040, 20.....100}%, with a step of 10%. Thus, each outgoing arc from the vertices The potential charging stations in the extended routing graph are multiplied by eleven arcs in the extended routing graph for all possible predefined charging level values.

[0048] Preferably, the road segments can take into account the topography of the road network, in particular the slope of the road segment, the infrastructure and the signage by means of a geographic information system, the traffic data by means of a geolocation system or a geographic information system (GIS) and preferably the meteorological data by means of an application programming interface system (an API which means "application programming interface" in English).

[0049] Taking the slope into account directly impacts energy consumption.

[0050] Taking road infrastructure and signage into account allows for consideration of traffic lights, "yield" or "stop" signs where a stop may be required, speed limits, or speed bumps, for example. All these elements can impact the average speed and speed profile on a section of road.

[0051] By speed profile, we mean the variation (or curve) representing the instantaneous speeds along the section of road. The speed profile can be represented by A speed profile is a curve representing instantaneous speed as a function of distance along a section of road. This speed profile thus takes into account the accelerations and decelerations that can occur along the road segment, something that average speed alone cannot capture. This speed profile can be established, in particular, from measurements taken by electric or non-electric vehicles traveling on the road network. For example, this speed profile can be established using data from the Geco air® application (IFP Energies nouvelles, France), which records the speeds of road users along road segments. Geco air® is an application designed to reduce travel-related pollution, and to this end, it collects position and instantaneous speed data.

[0052] Traffic data allows for the consideration of traffic density and its impact on average speed. Traffic data may, in particular, correspond to the average speed and / or travel time of vehicles on a section of the road network in time slots, for example, every ten minutes. For this purpose, one can, for example, measure the average speed throughout the day, preferably over several days. In this way, one can retrieve a history of traffic and its daily variation recorded throughout the day.

[0053] By applying traffic data, it is possible to evaluate more precisely the travel time and / or energy consumption according to the day and time considered and / or according to real-time traffic data.

[0054] Weather data, particularly temperature data, is also useful for determining energy consumption when the vehicle's heating or air conditioning is activated. This can be done using a REST weather API, such as the "Météo France™" API, to retrieve real-time weather conditions at the starting point and update them regularly along the route, for example, at fixed time (e.g., hourly) or space (e.g., every 60 km) intervals. Interpolation methods can then be used to obtain weather information for each road segment. The ambient temperature can be obtained for each road segment by interpolating the temperature forecasts obtained from the weather API.

[0055] Topography can be assumed to be time-invariant or to vary over time. Therefore, this data can be stored offline in local databases for faster online route calculation. To populate such an offline database, one can, for example, use REST (Representational State Transfer) web services from map and location data providers, such as HERE Maps.

[0056] Each road segment can also be classified according to its topology, in particular with an associated "functional" class, ranging for example from 1 to The system includes major highways (5) and secondary urban streets. Furthermore, each road segment can be defined by a unique identifier and its geometry with three-dimensional coordinates (at least the coordinates of both ends of the road segment). These coordinates can preferably be expressed as latitude, longitude, and altitude above sea level. Finally, the type of intersection and / or signage at the downstream end of each road segment can also be retrieved: this includes road infrastructure and signage types such as traffic lights, stop signs, yield signs, roundabouts, and toll plazas.

[0057] According to one configuration of the invention, it is possible to acquire (or measure), on each section of the road network, the speed profile of several vehicles, by means of a geolocation system or an on-board phone to determine, on the one hand, the average speed of the vehicles on the section of road, and on the other hand the probability of stopping of the vehicles at the end of the section of road considered, the probability of stopping being determined by the percentage of vehicles for which a stop is considered at the end of the section of road, the stopping of the vehicle being considered if, on at least one measurement point of the second half of the section of road, a vehicle speed is less than a predefined value, the predefined value preferably being less than or equal to 10 km / h.Therefore, statistics on stops at the end of the road segment are taken into account, for example due to a traffic light, a "stop" or "yield" sign, or a motorway tollbooth. When a stop occurs, it directly impacts the speed profile, the average speed over the road segment, and thus influences the journey time and the vehicle's energy consumption.

[0058] Fig. 3 illustrates, schematically and without limitation, an example of a vehicle speed profile used to determine the probability of stopping.

[0059] In this figure, the black curve illustrates the vehicle's velocity profile Pv over a section of road PR. This velocity profile Pv defines the instantaneous velocity v of the vehicle over time t as it travels from intersection Pt1 to intersection Pt2. The section of road PR comprises a first half PI and a second half P2, downstream of PI in the direction of travel of the vehicle on the section of road PR. Point M represents the midpoint of the section of road PR and therefore corresponds to the point separating the first half PI from the second half P2.

[0060] The velocity profile Pv shows varying instantaneous speeds along the road segment PR. In particular, a portion Vc of the velocity profile Pv includes instantaneous speeds below criterion C. In this case, such as at least one instantaneous speed of the portion Vc located on the second portion P2 of the road segment Tl is less than the predefined criterion C, we consider that this vehicle stops in the second portion P2 of the road portion PR.

[0061] Advantageously, the type of intersection and / or signage can be used to determine the probability of stopping (i.e., the vehicle speed dropping to zero) at the corresponding infrastructure element. This probability of stopping can then replace the average traffic speed and be used to refine the predicted driving behavior. Such a simple probabilistic model can be obtained by a classification method relating stopping behavior to the type of intersection, the functional class of the road segment under consideration, and the time of day in order to capture the impact of peak and off-peak traffic.

[0062] According to an advantageous implementation of the invention, the route planner may include a travel time model, that is to say a model for estimating the time required to travel a portion of road.

[0063] The estimated travel time on an arc of the graph can be obtained from the routing API, for example based on real-time traffic conditions, this travel time being denoted Tj- On arcs where the speed is reduced compared to the average traffic speed V j (or compared to the maximum authorized speed), the travel time Tj v increases compared to Tpi can be calculated as follows:

[0064] ÿ, = T 4- S 1 i,v 1 i^uvvlVr6v)

[0065] With LJa the length of the road segment and ôv the reduction of the speed considered on the road segment compared to the average traffic speed.

[0066] Additionally or alternatively, on arcs where the vehicle battery is recharged at a charging station, the travel time estimation Tj c can also take into account several contributions as follows:

[0067] T if if 6 C =Q TP Tj+ T d + T s + T c , if oh c Q

[0068] Where the travel time from the routing API Tj could be replaced by Tj v to take into account the desired speed reduction on the considered arc if, on the arc, there is a speed reduction option as discussed previously

[0069] And where the second term Td is the detour time required to reach the location of the off-road charger. This term depends on the distance from the nearest vertex of the routing graph and the assumed average speed of the detour.

[0070] The third term Ts is constant and represents the time spent after the vehicle has stopped to interact with the charger and prepare the charge.

[0071] The fourth term Tc corresponds to the actual charging time, which depends on the initial state of charge of the battery at the start of charging, and on the final state of charge, which is not necessarily 100% as explained previously, of the battery capacity and the charger power.

[0072] Ôc is the charging level on the section of road considered.

[0073] In order to correctly estimate the charging time Tc, a simple model inspired by the CC-CV charging method (“constant-current constant-voltage” for constant current - constant voltage), with a pre-charge phase when the battery capacity is low, such as that described in the document “FAV Pinto, LHMK Costa, and MD de Amorim, “Modeling Spare Capacity Reuse in EV Charging Stations based on the Li-ion Battery Profile,” in International Conference on Connected Vehicles and Expo (ICCVE), 2014, pp. 92-98”, can be used.

[0074] Advantageously, the route planner can also include a driving model. In order to accurately estimate the energy consumption required for travel along an arc of the routing graph, the first step is to predict the driver's behavior on the road segment (also called a portion of the road), in order to forecast the vehicle's traction power demand. For example, a model such as the "Intelligent Driver Model" (IDM) described in the document: "[M. Treiber, A. Hennecke, and D. Helbing, “Congested Traffic States in Empirical Observations and Microscopie Simulations,” Physical Review E, vol. 62, pp. 1805-1824, 2000]" can be used to generate synthetic expected speed profiles on each arc of the routing graph as a function of dynamic traffic data.This model is a car-following model that describes the dynamics of vehicle position and speed in interaction with preceding vehicles. In a route planning framework according to the invention, since it is not possible to know precisely in advance the behavior of the vehicles preceding the vehicle for which the route is optimized using the method according to the invention, this model can be adapted for use with a vehicle that does not interact with actual preceding vehicles. To do this, the probability of stopping at the end of the road segment, as described earlier in this description, is determined, and a virtual preceding vehicle is then assumed to appear and disrupt the speed of the vehicle for which the route is calculated. Data from the Geco air® application can be used, for example, for this purpose.The presence of this virtual preceding vehicle forces a speed transition to zero, thus forcing a stop at the end of the road segment considered. Conversely, when the probability of stopping does not result in a stop at the end of the road segment considered, the vehicle for which the route is calculated reaches the average speed of the following road segment.

[0075] The speed dynamics depend on the boundary conditions of the road segment of the road network considered, and in particular on the presence of a stopping event at The upstream and / or downstream end (intersection) of this section of road. If no stop is planned at the downstream end (intersection) of the section of road, only the term "clear road" appears in the speed dynamics:

[0076] x(t) = v(t) . , . x { - / v(t) \ H = ) ) = Vfree

[0077] Where a is the maximum acceleration of the vehicle, v the speed of the vehicle and x its position, )7 can be considered as a driver responsiveness parameter, and V p is the target speed of the vehicle.

[0078] vfree corresponds to the free road speed, that is to say without stopping at the end (intersection) downstream of the section of road.

[0079] The target speed VT can be fixed as a function of the length L; of the portion of route and vehicle position for which the route is calculated as follows:

[0080] if x(t) < max{0, L r d b} if x(t) >max{0, L r d h}

[0081] Where is the average speed of travel on arc i, is the speed of travel on the next arc i + 1, db is the length of the driver's prediction horizon, i.e. a parameter that indicates the distance from which the driver begins to target the speed of the next road segment.

[0082] Furthermore, the initial speed can be set to zero if there is a probability of stopping at the end of the upstream arc (of the road segment), and to Vj otherwise.

[0083] On the other hand, if a stop is predicted at the downstream intersection of the road segment, the speed dynamics may also include an interaction term and a virtual preceding vehicle may be assumed to influence the behavior of the vehicle for which the route can be calculated, as follows: [°084] . f d0+v(t)Th ^¢) = ^-3(^5^+^^),

[0085] where dQ is the minimum inter-distance relative to the virtual preceding vehicle, Tb is the minimum distance relative to the virtual preceding vehicle, and b is a predetermined braking acceleration. The speed of the virtual preceding vehicle V1 can be calculated as follows:

[0086] JÛi+1, if x(t) <max{0, Lrdh} V} ( 0, if x( t) > max{ 0, Lr dh}

[0087] If the virtual preceding vehicle is stopped (Vj = 0), the vehicle for which the route is calculated cannot stop exactly at the end of the road segment, given the This means that we assume the previous virtual vehicle stops at the end of the segment and that we take into account the inter-distance dQ and the length of the vehicle itself.

[0088] Advantageously, the route planner can also include an energy consumption model.

[0089] The energy consumption of the electric vehicle is calculated using its dynamics and powertrain modeling in order to accurately estimate the losses of the electric drive and the absorption of auxiliary power. The drive force Fw to rotate the vehicle's wheels, for a given speed yff) (i.e., the speed provided by the driving model described above) can be defined as follows:

[0090] Fw(t) = mv(t) + c0 + n2grsina

[0091] where 121 is the mass of the vehicle, 9 is the gravitational acceleration, a is the road slope which varies along the road segment, and the coefficients μ₀, μ₀ and μ₂ are identified for a given electric vehicle. The wheel force Fw can be converted into the mechanical power Pm required by the propulsion system:

[0092] p m ( t ) = Fw (t)v(t) F t siffn[F ^

[0093] where is the transmission efficiency.

[0094] Finally, the battery's energy consumption can be defined as follows: 100951 E b =f^P m (t)^ p M+P attI dt

[0096] where represents the efficiency of the electric propulsion, tf is the travel time of the road segment, and Paux is the absorption of auxiliary power along the road segment. For example, the demand for auxiliary power can be assumed to come mainly from the air conditioning of the passenger compartment or the driver's comfort needs, and it can be a convex function of the ambient temperature available on each road segment.

[0097] According to one embodiment of the invention, the optimization algorithm can be defined by an optimization problem designed to find the best compromise between travel time and / or energy consumption.

[0098] The optimization problem can be formulated as follows:

[0099] minX ( + ( 1 - 2 ) we ) xf f 1, if i = i° SXC =1-1 if i = id l 0, otherwise Cniin - CV i GA xcE{0A}

[0100] With

[0101] C denoting the arcs of a linear graph a(F) obtained from the extended routing graph F, the linear graph having a vertex representing each arc of the extended routing graph and each arc of the linear graph representing a pair of adjacent arcs of the extended routing graph.

[0102] f' the set of incoming arcs Ç,

[0103] i the set of outgoing arcs Ç,

[0104] i° the original arc,

[0105] [d the destination arc,

[0106] flj the route composed by all the arcs Ç connecting the origin j° to

[0107] the time cost,

[0108] the energy cost,

[0109] XZ the decision variable which is 0 if the arc in question does not belong to the route or 1 if it belongs to the route

[0110] The weight of compromise,

[0111] A' represents the set of arcs of the extended routing graph,

[0112] Cmin: a minimum acceptable battery charge state,

[0113] C: battery charge state

[0114] Thus, the optimization problem is an objective function that can be written as a weighted sum of the time and energy costs on each arc of the routing graph, as estimated by the travel time and energy consumption models described above. The weight of the trade-off is denoted A. The decision variable takes binary values ​​depending on whether the arc C belongs to the route or not. The second constraint is a classical flow conservation constraint. The third constraint requires each possible route segment to satisfy the physical limits of the battery capacity, denoted by C. To reduce concerns about range, the minimum battery state of charge Cmin can be strictly greater than zero, for example, greater than a predefined minimum charge value.

[0115] Preferably, the optimization algorithm can be a Bellman-Ford shortest path algorithm to guarantee the optimality of the routing solution in order to avoid, in particular, negative values ​​due to the presence of the energy term.

[0116] By considering different potential capacities for the battery, we can thus see the evolution in terms of travel time, energy consumption, number of stops, stopping positions for recharging and the level of recharging at each stop.

[0117] Preferably, when considering several traffic speeds on at least one section of road (to take into account speed reductions relative to the average traffic speed or the maximum authorized speed on certain sections of road), each traffic speed considered can be between a first speed and a second speed, and step b) can be repeated for each traffic speed. The first speed can correspond, for example, to the maximum authorized speed or the average traffic speed on the section of road considered, reduced by a predetermined criterion, for example, the predetermined criterion can be between 10 and 40 km / h. The second speed can correspond to the maximum authorized speed or the average traffic speed on the section of road considered.

[0118] Preferably, the charging stations considered by the route planner can have a power output greater than or equal to 50 kW. This allows for the exclusion of charging stations with a lower power output that would require excessive charging time. These charging stations would therefore be irrelevant. By eliminating these charging stations from the route planner, memory usage can be reduced, computation time accelerated, without affecting the relevance of the route planner's results.

[0119] Advantageously, the physical characteristics of the electric vehicle can include its mass and its energy consumption pattern. Indeed, the vehicle's mass directly impacts its energy consumption. The vehicle's energy consumption pattern can depend on its aerodynamic capabilities, its energy efficiency, and the various energy consumers of the vehicle, for example, the cabin air conditioning. Step c) Battery identification

[0120] Step b) made it possible to determine several parameters for each route for each potential battery capacity of the electric vehicle (and possibly for several traffic speeds for at least certain road sections). These parameters can be chosen from among the travel time which takes into account charging stops and charging times, energy consumption, the number of charging stops, the position of the charging stops, and the charging level of each charging stop.

[0121] These different parameters can then be compared to choose the potential capacity for implementation (installation) on the electric vehicle. Thus, among the different potential capacities defined in step a), one of these potential capacities is identified as the battery capacity for its implementation in the electric vehicle. Therefore, the battery capacity is sized accordingly.

[0122] For example, the battery capacity can be identified (sized) as the smallest capacity among the potential capacities that allows for a travel time less than or equal to a predetermined travel time and / or energy consumption less than or equal to a predetermined consumption. Indeed, in this way, a predetermined travel time and / or a predetermined consumption acceptable to the user can be targeted. The smallest battery that achieves this (or these) objective(s) can be identified in such a way as to avoid oversizing the battery, which has a greater environmental impact.

[0123] Alternatively, the capacity can be identified by other means. For example, the gain in travel time and / or energy consumption can be assessed, and the capacity can be taken from which the gain (in travel time and / or energy consumption, for example) of larger capacity batteries becomes less than a certain criterion.

[0124] By gain, we mean a reduction in travel time or energy consumption of a given capacity compared to potential capacities larger than that given capacity. A negative gain indicates an increase in travel time or energy consumption, which is irrelevant. Thus, for the purposes of the invention, only positive gains are considered.

[0125] The battery capacity can also be identified by a predetermined mathematical law that allows for a compromise between travel time, energy consumption, and / or possibly the size (capacity) of the battery. This mathematical law can also take into account the annual distribution of different journeys in order to consider the relative weight of different journeys.

[0126] Battery capacity can also be identified based on a criterion related to users' willingness to accept a predefined number of stops along the route, or based on a criterion of stopping to recharge the battery at regular intervals (for example, every 3 hours), particularly to meet safety criteria while driving. Therefore, the stops necessary for the driver to rest are used to recharge the battery. Optional construction step

[0127] According to one embodiment of the invention, after identifying the battery capacity using the sizing method described above, said battery of the identified capacity can be implanted (or mounted or implemented) in the electric vehicle. Therefore, the invention also relates to a method of constructing an electric vehicle in which the battery capacity is identified from the sizing method as described, and said battery of identified capacity is implanted in the electric vehicle.

[0128] Thus, the battery identified and mounted in the electric vehicle is optimized for the planned long-distance journey(s), avoiding oversizing the battery to limit CO2 emissions generated over the entire life of the battery, including the impact of battery manufacturing and the potential impact of battery recycling.

[0129] Furthermore, the invention relates to a method for retrofitting (reconfiguring) a vehicle that includes an original powertrain (for example, an internal combustion engine), in which the following steps are implemented: - The vehicle's powertrain is modified, for example by replacing a combustion engine with a machine, - An electric battery is implemented within the vehicle, the electric battery being sized using the method according to any of the variants or combinations of variants described previously.

[0130] The invention also relates to a computer program product downloadable from a communication network and / or stored on a computer-readable medium and / or executable by a processor or server, comprising program code instructions for implementing the sizing method as described above, when said program is executed on a computer, mobile phone, or computing device. Therefore, the method is simple and quick to use. Examples

[0131] The method for sizing an electric vehicle battery was used to size the battery of a Fiat 500E for a long-distance journey from Stuttgart, Germany to Nice, France, via the Alps. This journey is approximately 800 km. Topographical information was obtained from HERE Maps. The positions of the various existing charging stations were retrieved from OpenChargeMap, and only DC fast charging stations with a charging power of 50 kW or more were considered.

[0132] The potential capacities have been defined from 38kWh to 68 kWh, in increments of 5kWh.

[0133] The motorway sections were considered with several possible speeds from 90 km / h to 135 km / h in 5 km / h increments: ten speeds were therefore considered for this example. The graph was extended for each of the relevant road sections by multiplying each arc of the graph by ten.

[0134] The efficiency of the electric propulsion (the yield) was considered to be 85%.

[0135] Traffic conditions were also considered during the peak hour, at 8 a.m. from the morning, on a working day (March 15, 2022) and were retrieved from HERE Maps.

[0136] For this example, the ambient temperature is assumed to be constant at 20°C, which translated into a constant demand for auxiliary power of 650W to power the vehicle's auxiliary systems.

[0137] The parameters of the travel time model were set at T^=60 seconds and Ts=| 80 seconds.

[0138] The sizing method of the invention was used for two strategies: - By optimizing energy consumption by the route planner (we then have / .= I ); - By optimizing the travel time using the route planner (we then have 7.=0).

[0139] These strategies have been compared to a likely behavior of the user who would not have access to the method according to the invention and who would start looking for the next fast charging station as soon as the battery state fell below 40% and which constitutes the prior art.

[0140] Using the prior art method (stopping as soon as possible when the battery charge level drops below 40%), the user, who would only accept two stops during the journey, would need a battery of at least 55 kWh installed in the vehicle. With a 68 kWh battery, the journey still takes nearly 11 hours and the energy consumption is approximately 126 kWh (at 91 km / h on the motorway sections). With a 55 kWh battery, the journey takes approximately 12 hours and the energy consumption is approximately 126 kWh (at 91 km / h on the motorway sections).

[0141] To reduce energy consumption for this prior art method, the main parameter is the traffic speed on the motorway sections, regardless of battery capacity. In other words, this method encourages users to invest in larger capacity batteries and therefore increases CO2 emissions.

[0142] The first optimized strategy (energy consumption optimization) of the method according to the invention shows that the route planner can improve both travel time and energy consumption compared to the prior art method. Indeed, with the method of the invention and the energy consumption optimization, the journey can be completed in approximately 11 hours (at a speed of 91 km / h on the motorway sections), with a consumption of approximately 123 kWh and with a 50 kWh battery. Furthermore, with a battery of a With a capacity of 40kWh, the journey time would be 12 hours and the consumption approximately 123 kWh (at a travel speed of 91 km / h on the motorway sections).

[0143] Thus, with a smaller battery capacity than that of the prior art, it is possible to achieve a shorter or equivalent travel time while consuming less energy. The reduction in battery capacity (for the same travel time and the same highway speed) is approximately 25% compared to the prior art method. This demonstrates the relevance of the method according to the invention.

[0144] The travel time optimization strategy of the method according to the invention also shows that the route planner can improve both travel time and energy consumption compared to the prior art method. With this strategy, the journey can be completed in approximately 10.5 hours with an energy consumption of 124 kWh (at a speed of 91 km / h on highway sections) using a 43 kWh battery. In this configuration, with a lower net battery capacity (43 kWh), four charging stops would be required along the route. With a 40 kWh battery, the journey can be completed in less than 11 hours with an energy consumption of approximately 125 kWh, also requiring four stops. Thus, with this strategy, travel time and energy consumption could be reduced compared to the prior art method while reducing the battery size by more than 40%.

Claims

1. Demands A method for constructing an electric vehicle in which a battery is sized for implementation in said electric vehicle, based on the physical characteristics of said electric vehicle, at least one route from an origin to a destination in a predetermined space, the locations of charging stations in the predetermined space, the power of each of said charging stations, and a route planner, the predetermined space comprising a road network including road segments (RS) connected by intersections, the method comprising at least the following steps: a) We define several potential capacities (Capo) of the battery, said potential capacities being between a first value and a second value; b) for each potential battery capacity, the route planner (Plan) is used to determine at least one possible route for said journey, each possible route being defined by a succession of road segments, and the travel time to travel the possible route by said electric vehicle and / or the energy consumption by said electric vehicle of each possible route is determined, as well as the position of the charging stations to be used by the electric vehicle on each possible route to recharge its battery and the associated charging level. c) from the travel times and energy consumption determined for each potential battery capacity and the size of each potential battery capacity, the capacity (Dim) of the battery is identified among said potential capacities as the smallest capacity among the potential capacities allowing a travel time less than or equal to a predetermined travel time and / or an energy consumption less than or equal to a predetermined consumption; d) the said battery of identified capacity is installed (Cons) in the electric vehicle; in which a measurement is acquired, on each section of road (PR) of the road network, of the speed profile (Pv) of several

2. vehicles, using a geolocation system or an on-board phone to determine, on the one hand, the average speed of vehicles on the section of road, and on the other hand, the probability of vehicles stopping at the end of the section of road considered, the probability of stopping being determined by the percentage of vehicles for which a stop is considered at the end of the section of road, the vehicle stopping being considered if, at at least one measurement point in the second half (P2) of the section of road (PR), a vehicle speed is less than a predefined value (C) and in which all steps are implemented by computer, and in which the road segments (RP) take into account the topography of the road network, including the slope of the road segment, infrastructure and signage by means of a geographic information system, traffic data by means of a geolocation system or a geographic information system and meteorological data by means of an application programming interface system. Construction method according to claim 1, wherein the route planner performs at least the following steps: - a routing graph (Gr) is constructed from the road network, the vertices of the routing graph representing the intersections of each road segment (PR) and the arcs of the routing graph representing the road segments (PR), - the charging stations are positioned (Sp) on the routing graph, - the routing graph is extended (GrE) by multiplying at least some arcs according to predefined parameters, the predefined parameters including the traffic speed on the section of road considered and / or the charging level when stopping at a charging station, - an energy consumption model is determined (Conso), the energy model defining the energy consumption of the electric vehicle according to predetermined parameters, taking into account any possible recharging of the electric vehicle's battery, - For each route, the energy consumption model is applied to the different arcs of the extended routing graph, and the set of arcs is defined as the final route of the possible journey. allowing to minimize (Opt), by an optimization algorithm such as a shortest path Bellman-Ford algorithm, the travel time, or the energy consumption or a compromise defined as a relative weight between these two parameters, - preferably, we determine (Pos_niv), from the final route defined, the positions of the charging stations used by the electric vehicle and the level of charging achieved at each charging station used by the electric vehicle for the journey considered.

3. A construction method according to claim 2, wherein several traffic speeds of at least one section of road are considered, each traffic speed considered being between a first speed and a second speed, and step b) is repeated for each traffic speed.

4. Construction method according to any one of the preceding claims, wherein the predefined value is less than or equal to 10 km / h.

5. A construction method according to any one of the preceding claims, wherein the charging stations considered by the route planner have a power greater than or equal to 50kW.

6. A construction method according to any one of the preceding claims, wherein the physical characteristics of the electric vehicle include its mass and energy consumption.

7. Product computer program downloadable from a communication network and / or stored on a computer-readable medium and / or executable by a processor or server, comprising program code instructions for implementing steps a) to c) of the construction method according to any one of claims 1 to 6, when said program is executed on a computer, mobile phone, or computing device.

Images