METHOD FOR ESTIMATING THE CHARGE STATE OF A BATTERY, IN PARTICULAR FOR RECHARGING MANAGEMENT OF ELECTRIC VEHICLES

Description

[0001] The present invention claims priority from French application No. 2005535 filed on 05 / 26 / 2020.

[0002] The invention generally relates to the electrical recharging of electric batteries of electrified vehicles, such as plug-in hybrid vehicles and electric vehicles. More particularly, the invention relates to a method for estimating the state of charge of an electric battery, in particular for the energy management of the recharging of an electrified vehicle.

[0003] Electric charging infrastructure for electrified vehicles is designed as part of future smart grids for electric power transmission, known as "Smart Grids", in order to optimize the energy balance and availability of electrified vehicles, and to reduce their carbon footprint. In the context of a smart grid, the charging equipment on board the vehicle must be reversible in order to allow the network not only to provide electrical energy for recharging the vehicle's electric traction battery, but also, in a "V2G" mode, to draw available energy from it to meet high demand, for example, during peak electricity consumption.Electric charging of electrified vehicles essentially involves electric converters on board the vehicles, a deployed fleet of electric charging stations, an energy management system, an electric energy supplier or aggregator and data communication networks such as the Internet network and the cellular radiotelephone network or others.

[0004] The intelligent management of the charging of an electrified vehicle is a discontinuous operation over time which ensures a spreading of the electric charging in particular according to different objectives and constraints linked for example to energy availability, a “V2G” mode or others.

[0005] The discontinuity of the electric battery charging process leads to significant stress on several components of the on-board electronics of the electrified vehicle, significant stress which can cause premature aging of these components. Thus, when starting an electric charging session of the vehicle's electric battery or before each resumption of charging after interruptions in these, the energy management system needs in particular to know the state of charge of the electric battery and requests the vehicle for this through its on-board telematics module. The vehicle user can also, through a software application installed on his smartphone, request the vehicle several times to know the progress of the charging of the electric battery of his vehicle.

[0006] All these demands result in a multitude of repetitive waking / sleeping operations of different on-board electronic components of the vehicle to retrieve information and transmit it to the requester, i.e., to the energy management system or to the user of the vehicle. The on-board telematics module and the smart antenna associated with it, as well as the electric battery management module known as "BMS", the vehicle supervisor module known as "VSM", and the vehicle control unit known as "VCU" are the on-board electronic components of the vehicle mainly used for reading the state of charge of the electric battery.

[0007] There Fig.1 shows as an example a sequence of wake-up / sleep operations of the on-board telematics module and the smart antenna during an electric charging session of the vehicle's electric battery, the "ON" and "OFF" states respectively representing the waking up and sleeping of these devices. A multitude of RI wake-ups occur between start RD and end RF wake-ups of the electric charging session. Connecting the electric charging cable to the vehicle causes a first RB wake-up of the devices. Disconnecting the electric charging cable from the vehicle causes a final RDB wake-up of the devices.

[0008] The vehicle's telematics module and smart antenna are designed for a certain number of wake-ups per day, for example, one to two dozen wake-ups per day. A significant number of wake-ups compared to this number of daily wake-ups, due for example to an anxious user repeatedly checking the electric battery's charge status, can lead to failures and reduced durability of this on-board equipment.

[0009] Document FR3041782A1 discloses a method for managing the wake-up of a computer in a vehicle connected to an electric charging station. In this method, characteristics of a control signal are continuously detected to define a wake-up vector. The computer wake-up is decided on the basis of the wake-up vector and an ordered list of different patterns of the control signal.

[0010] DE 10 2011 107055 A1 discloses a method for estimating the state of charge of an electric battery of an electrified vehicle implemented in an electric charging system where a server and said electrified vehicle are in data communication, said method comprises, at the start of an electric charging session, a first communication of data from said electrified vehicle to said server, comprising electric charging session data including vehicle identification data, a first effective state of charge of said electric battery, a state of health of said electric battery and a charging mode.

[0011] It is desirable to provide a method not having the aforementioned drawbacks of the prior art, for estimating the state of charge of an electric battery of an electrified vehicle and for providing this information without excessive stress on the on-board electronic equipment of the vehicle. According to a first aspect, the invention relates to a method for estimating the state of charge of an electric battery of an electrified vehicle implemented in an electric charging system for electrified vehicles comprising electric charging means connected to an electrical power supply network and ensuring electric charging of the electric battery, at least a first computer server hosting a software system for managing connected vehicles and a wide area data communication network through which the electrified vehicle and the first computer server are in data communication. According to the invention, the method comprises,at the start of an electric charging session at a time t 0 , a first communication of data from the electrified vehicle to the first computer server, this first communication of data comprising electric charging session data including vehicle identification data, a first effective state of charge of the electric battery, a state of health of the electric battery and a charging mode, and, during the electric charging session, an estimation and a provision by the first computer server of an estimated state of charge, the estimated state of charge being determined from the electric charging session data, an energy capacity of the electric battery and a first estimation calculation factor,the first estimation calculation factor being determined by selection from a database of estimation calculation factors accessible to the first computer server and from the electric charging session data.,

[0012] According to a particular characteristic, the method comprises a determination of the first estimation calculation factor using an artificial intelligence module.

[0013] According to another particular characteristic, the method comprises, during the electric recharging session, a second communication of data from the electrified vehicle to the first computer server, at a time t R1, this second communication of data comprising a second effective state of charge of the electric battery, and an intermediate correction of the estimated state of charge from the second effective state of charge and a determination by calculation of a second estimation calculation factor from the second effective state of charge according to a following calculation function F3: α R 1 = 1 − SOC E t R1 − SOC R1 t R1 SOC E t R1 − SOC 0 t 0 Or : SOC R1 is the second effective state of charge SOC E estimated state of charge, α R1 is the second estimation calculation factor, SOC 0 is the first effective state of charge, the first estimation calculation factor being replaced by the second estimation calculation factor for subsequent determinations of the estimated state of charge during the electric charging session.

[0014] According to yet another particular characteristic, the method comprises, at the end of the electric charging session at a time t R2, another communication of data from the electrified vehicle to the first computer server, this other communication of data comprising a third effective state of charge of the electric battery, and a final correction of the estimated state of charge from the third effective state of charge, a determination by calculation of a third estimation calculation factor from the second effective state of charge according to a following calculation function F3: α R2 = 1 − SOC E t R2 − SOC R2 t R2 SOC E t R2 − SOC 0 t 0 Or : α R2 is the third estimation calculation factor, SOC R2 is the third effective state of charge, SOC E estimated state of charge, and a record of the third estimate calculation factor in the estimate calculation factor database.

[0015] According to yet another particular characteristic, when the charging mode includes a constant power electric charging instruction, the estimated state of charge is determined using: of a first mapping giving an energy state as a function of a state of charge of said electric battery and vice versa, and of a first calculation function F1 giving an estimated energy state, according to a following calculation function F1: SOE t = SOE t0 + dt = SOE 0 t0 + α E ∫ t 0 t PC . dt SOH ∗ E ∗ 3600 Or : PC and SOH represent the constant power charging setpoint and the state of health of the electric battery, E represents the nominal energy capacity of the electric battery in kWh, and the number 3600 performs the conversion between the time unit “hour” of the capacity E and the time unit “second” of the time variable t. α E is the estimation calculation factor whose value is initially that of the first estimation calculation factor, this estimated energy state from which said estimated charge state is determined via the first mapping.

[0016] According to yet another particular characteristic, when the charging mode includes an electric charging instruction with a current profile, the estimated state of charge is determined using a second mapping between state of charge and charging current, a third mapping defining a time profile of charging current, and a second calculation function giving the estimated state of charge.

[0017] According to yet another particular characteristic, the method comprises providing the estimated state of charge to a second computer server hosting a software system for energy management of the electrical power supply network, the second computer server being in data communication with the first computer server through the wide area data communication network.

[0018] According to yet another particular characteristic, the method comprises providing the estimated state of charge to a computing device of a user of the electrified vehicle, the computing device being in data communication with the first computing server through the wide area data communication network.

[0019] According to yet another particular characteristic, the computing device is a so-called smartphone.

[0020] The invention also relates to a computer server comprising a data storage device storing program instructions for implementing the method as briefly described above.

[0021] Other advantages and characteristics of the present invention will appear more clearly on reading the detailed description below of several particular embodiments of the invention, with reference to the appended drawings, in which: [ Fig.1 ] There Fig.1 shows a simplified timing diagram relating to wake-up / sleep operations in a prior art electric vehicle charging system. Fig.2 ] There Fig.2 is a general block diagram relating to the implementation of the method according to the invention in an electric recharging system for electrified vehicles. Fig.3 ] There Fig.3 shows an example of a charging curve with power profile of a charging mode called “Mode 2”. [ Fig.4 ] There Fig.4 shows a simplified timing diagram relating to wake-up / sleep operations in an electric charging system for electrified vehicles implementing the method according to the invention. Fig.5 ] There Fig.5 is a flowchart showing functional blocks relating to the method according to the invention. Fig.6 ] There Fig.6 schematically shows the calculation of the estimated state of charge in the case of a constant power electric charging instruction. Fig.7 ] There Fig.7 schematically shows the calculation of the estimated state of charge in the case of an electric charging instruction with a current profile. [ Fig.8 ] There Fig.8 schematically shows closed-loop corrections made to the estimated state of charge calculation. Fig.9 ] There Fig.9 shows a conversion curve between the open circuit voltage at the terminals of the electric battery and its state of charge.

[0022] The method according to the invention is described here in the context of an electric charging system for electrified vehicles based on static wired charging through a fleet of electric charging stations. The invention is not limited to this architecture of an electric charging system for electrified vehicles and may be implemented in other types of systems such as those based on static or dynamic induction charging.

[0023] In the electric charging system of the Fig.2 , marked SRE, an electrified EV is connected to a BRE electric charging station by a CE electric cable, through a PR charging socket. The BRE electric charging station is connected to an RE electricity supply network and is part of a fleet managed by an electric charging service provider.

[0024] The EV electrified vehicle is a vehicle connected by radio link to a wide area data communication network, such as the Internet, and includes in particular a high-voltage electric traction battery BAT and an on-board electric converter OBC of the alternating current / direct current type known as "AC / DC". High-voltage contactors CT are provided for connecting / disconnecting the electric battery with the OBC converter and the vehicle's power electrical circuit.

[0025] The management of the electric battery BAT is ensured by an electric battery management module BMS connected to a CAN data communication network of the electrified vehicle EV. The electric battery management module BMS is associated with current and voltage sensors (not shown) and is capable of providing data relating to the electric battery BAT, such as its state of charge “SOC” (for “State Of Charge” in English), its state of health SOH (for “State Of Health” in English) and its open circuit voltage OCV (for “Open Circuit Voltage” in English). Other on-board electronic components such as a telematics module BTA, a smart antenna BSRF, a vehicle supervisor module VSM and a vehicle control unit VCU are also visible at the Fig.2 and are connected to the CAN network.

[0026] The SRE electric charging system also uses the aforementioned wide area data communication network, such as the Internet, and computer systems hosted in server clouds NS1 to NS3 for its operation. The SRE electric charging system uses software and hardware resources available in these server clouds NS1 to NS3.

[0027] The NS1 server cloud hosts a SWC connected vehicle management software system, for example from the EV car manufacturer, providing several connectivity and data processing services for a fleet of connected vehicles. The SWC connected vehicle management software system is hosted on one or more SRC computer servers in the NS1 cloud.

[0028] The SRC computer server includes, in particular, a PROC processor which communicates with an HD data storage device, and conventional hardware devices such as NI network interfaces and other devices (not shown). The PROC processor includes one or more central data processing units (not shown) and volatile and non-volatile memories (not shown) for executing computer programs.

[0029] As visible at the Fig.2 , the connected vehicle management software system SWC comprises a software module MLE dedicated to implementing the method according to the invention for estimating the state of charge of the electric traction battery BAT. The method according to the invention is implemented in particular by the execution by the processor PROC of code instructions of the software module MLE.

[0030] The EV electrified vehicle is connected to the SWC connected vehicle management software system and the MLE software module by a LR1 data communication radio link, typically through a cellular radiotelephone network in “3G”, “4G” or “5G” mode, for example.

[0031] The USER user of the electrified vehicle EV, through a dedicated software application installed on his computer device SP, such as a smartphone, communicates with a consultation service of the SWC connected vehicle management software system. The SP smartphone is connected to the SWC system and to the MLE software module by an LR2 data communication radio link typically through the aforementioned cellular radiotelephone network. The USER user has the possibility of monitoring the recharging of the BAT electric battery of his vehicle.

[0032] The NS2 server cloud hosts an EMS energy management software system. The EMS system is responsible for managing intelligent charging of the BAT electric battery as part of a Smart Grid type electrical energy transmission network. The EMS system communicates with the SWC connected vehicle management software system via an LR3 data communication link.

[0033] The NS3 server cloud hosts a UTP software system of the electric charging service provider that operates the fleet of electric charging stations, including the BRE station. The UTP electric charging service provider software system communicates with the EMS energy management software system via an LR4 data communication link.

[0034] In the EV electrified vehicle, the detection of the connection of the CE electric charging cable causes the transmission of DSC electric charging session data to the NS1 server cloud. The DSC electric charging session data includes, among other things, a vehicle / user identifier VE / USER, a recommended charging mode MC for the vehicle, an initial state of charge SOC 0 and the state of health SOH of the electric battery BAT. The initial state of charge SOC 0 and the state of health SOH of the electric battery BAT are those indicated at that time by the electric battery management module BMS.

[0035] Alternatively, the MC charging mode recommended for the vehicle can be retrieved from a database by the SWC connected vehicle management software system and / or the MLE software module from the vehicle / user identifier VE / USER.

[0036] Table 1 below shows different charging modes available, namely, constant power, with power profile P(t) or with current profile I(t). A charging curve with power profile P(t) of the charging mode called "Mode 2" is shown as an example in Fig.3 . [Tableau 1] Mode de recharge MC Puissance de recharge Mode 2 1,8-3,2 kW, P(t), I(t) Mode 3 7,3 kW, P(t), I(t) Mode 3 22 kW, P(t), I(t) Mode 4 50 kW, P(t), I(t) Mode 4 150 kW, P(t), I(t) Mode 4 350 kW, P(t), I(t) Mode « Smart Grid » P(t), I(t)

[0037] According to the invention, the aforementioned electric charging session data DSC= (VE / USER, MC, SOC 0 , SOH) define a particular profile for the electric charging session. These DSC data are processed by the MLE software module, in particular by an AI artificial intelligence module associated therewith, so as to obtain an appropriate estimation calculation factor α, as will become more clearly apparent later. Mass data may also be used by the AI ​​artificial intelligence module, together with the DSC data, to determine the appropriate estimation calculation factor α.

[0038] A charging setpoint CRE, which is a charging current setpoint or a charging power setpoint depending on the charging mode MC, is determined following data exchanges between the connected vehicle management software system SWC and the energy management software system EMS and is transmitted to the electrified vehicle EV. In the electrified vehicle EV, the charging setpoint CRE is used in particular for controlling the OBC converter and for management by the BMS module.

[0039] The EMS energy management software system exchanges data with the UTP electric charging service provider software system to establish and manage the progress of the electric charging session. The BRE electric charging station is controlled by the UTP electric charging service provider software system to charge the BAT electric battery according to the appropriate MC charging mode.

[0040] According to the invention, during the course of the electric charging session, it is an estimated state of charge SOC E, determined by the software module MLE, which is provided to the energy management software system EMS for its intelligent management of the charging and to the dedicated software application hosted in the smartphone SP of the user USER each time they are requested. The invention thus makes it possible during the electric charging session to drastically reduce the number of wake-up / sleep operations of the on-board electronic components concerned in the electrified vehicle EV.

[0041] There Fig.4 shows, by way of example, a sequence of wake-up / sleep operations of the BTA telematics module and the BSRF smart antenna occurring with the method of the invention during the electric recharging session of the BAT electric battery. As in the Fig.1 , the “ON” and “OFF” states represent respectively the waking up and sleeping of these devices. A single RP1 wake-up occurs between the start RD1 and end RF1 wake-ups of the electric charging session. Connecting the CE electric charging cable to the EV electrified vehicle causes a first RB1 wake-up of the devices. Disconnecting the CE electric charging cable from the vehicle causes a final RDB1 wake-up of the devices.

[0042] The electric charging session data DSC= (VE / USER, MC, SOC 0 , SOH) are typically transmitted during the first wake-up RB1 to the software module MLE. The charging instruction CRE is transmitted back to the electrified vehicle VE during the start wake-up of the electric charging session RD1.

[0043] The single RP1 wake-up call during the electric charging session corresponds to a request from the MLE software module for a reading of the actual state of charge, designated SOC R1, of the electric battery BAT. The actual state of charge SOC R1 is then provided by the vehicle's BMS module and this is transmitted to the MLE software module. This reading requested by the MLE software module of the actual state of charge SOC R1 during the electric charging session is not systematic, but can be decided by the MLE software module for the accuracy of the estimation, so as to recalibrate the estimated state of charge SOC E to the actual state of charge SOC R1 of the electric battery BAT and thus correct in a closed loop any possible drift in the SOC E estimation made by the MLE software module.

[0044] Another reading of the effective state of charge, designated SOC R2 , may be requested from the BMS module by the MLE software module at the end of the electric charging session, typically during the RF1 wake-up. Like that occurring during the RP1 wake-up, this reading of the effective state of charge SOC R2 allows a closed-loop correction by the MLE software module of a possible drift in the SOC E estimate, this correction being carried out here at the end of the electric charging session.

[0045] Various functions of the MLE software module, necessary for implementing the method of the invention, are now described below with particular reference to: Figs.5 à 8 .

[0046] As visible in the flowchart of the Fig.5 , the method of the invention uses functional blocks B0 to B7.

[0047] Functional block B0 represents the initial transmission of the DSC electric charging session data and the subsequent transmissions of the actual charging states SOC R1 , SOC R2 , of the electrified vehicle EV to the NS1 server cloud and the MLE software module.

[0048] Functional block B1 represents the storage in a database BDα of a set of estimation correction factors α. The estimation correction factors α in the database BDα are sorted and assigned to the different electrified vehicles VE and / or users USER managed by the connected vehicle management software system SWC. The database BDα is hosted in the NS1 server cloud, more precisely in the HD data storage device (cf. Fig.2 ).

[0049] Function block B2 represents the start of an electric charging session. The DSC electric charging session data is then received and processed by the MLE software module processing process.

[0050] Functional block B3 is then executed and determines an appropriate estimation calculation factor α S for the considered electric charging session. The appropriate estimation calculation factor α S is determined based on the electric charging session data DSC which defines a particular profile for the electric charging session. As indicated above, the aforementioned artificial intelligence module AI may be used to select the appropriate estimation calculation factor α S from the database BDα from the profile deduced from the DSC data. Other methods may be used, while remaining within the scope of the invention, to determine the appropriate estimation calculation factor α S, such as interpolation methods, similarity selection methods and others.

[0051] Functional block B4 is the real-time calculation of the estimated state of charge SOC E (t). The calculation of the estimated state of charge SOC E (t) is carried out differently depending on the charging setpoint CRE which is available and which depends on the charging mode MC.

[0052] There Fig.6 illustrates the calculation of the estimated state of charge SOC E (t) in the case where the charging instruction CRE is a constant power electric charging instruction PC.

[0053] The state of charge SOC E (t) is estimated using a C1 mapping and a calculation function F1 visible in Fig.6 The C1 map, represented by a curve, gives the energy state SOE (in kWh) of the electric battery BAT as a function of its state of charge SOC (in %).

[0054] In the calculation function F1: PC and SOH represent the constant power charging setpoint and the state of health of the electric battery BAT as mentioned above, E represents the nominal energy capacity of the electric battery BAT in kWh, and the number 3600 performs the conversion between the time unit “hour” of the capacity E and the time unit “second” of the time variable t. α E is the estimation calculation factor whose value is initially that α S determined by the functional block B3 (see Fig.5 ) for the electric charging session in question.

[0055] The SOE 0 energy state is first determined using the C1 map from the available SOC 0 state of charge. Interpolation from values ​​close to the C1 map is used if necessary to obtain the SOE 0 energy state with good accuracy.

[0056] The function F1 allows us to estimate the value of the state SOE(t) at the time t considered, with dt = t-t0.

[0057] The estimated state of charge SOC E (t) is determined using the C1 mapping from the state SOE(t) obtained with the F1 function. Interpolation from values ​​close to the C1 mapping is used if necessary to obtain the estimated state of charge SOC E (t) with good accuracy.

[0058] There Fig.7 illustrates the calculation of the estimated state of charge SOC E (t) in the case where the charging instruction CRE is an electric charging instruction with current profile I(t), for example at a power of 7 kW.

[0059] The state of charge SOC E (t) is estimated using maps C2, C3 and a calculation function F2 visible in the Fig.7 . The C2 map, represented by a curve, gives the recharge current I (in A) as a function of the state of charge SOC (in %). The C3 map, represented by a curve, gives the current profile I(t) over time for recharging the electric battery BAT.

[0060] In the calculation function F2: I and SOH represent the current amplitude given by the mapping C3 and the health status of the electric battery BAT as mentioned above, C represents the nominal energy capacity of the electric battery BAT in Ah, and the number 3600 performs the conversion between the time unit “hour” of the capacity C and the time unit “second” of the time variable t. α C is the estimation calculation factor whose value is initially that α S determined by the functional block B3 (cf. Fig.5 ) for the electric charging session in question.

[0061] An initial current I0, at the start of charging, is determined using the C2 map from the available state of charge SOC 0. An interpolation from values ​​close to the C2 map is used if necessary to obtain the initial current I0 with good accuracy.

[0062] The integral of the curve I(t) of the C3 map between a time t0, corresponding to the initial current I0, and the time t considered is necessary for the calculation of the function F2. The surface S in the curve I(t) represents the value of this integral. Interpolations from values ​​close to the C3 map are used if necessary to obtain the surface S with good accuracy. The estimated state of charge SOC E (t) is given by the function F2.

[0063] The mechanisms intended to correct in closed loop the drift of the SOC E (t) estimate of the state of charge of the electric battery BAT are now described below, more particularly with reference to the Figs.5 And 8 .

[0064] Functional blocks B5 and B6 concern the correction during the electric charging session, mentioned above with reference to the Fig.4 (cf. RP1). This intermediate correction is decided by the functional block B5, for example beyond a certain duration of the electric charging session. It gives rise to a reading of the state of charge SOC R1 of the electric battery BAT which occurs at a time t R1 (cf. Fig.8 ).

[0065] Functional block B7 concerns a final correction at the end of the electric charging session, mentioned above with reference to the Fig.4 (cf. RF1). It is systematic and gives rise to a reading of the state of charge SOC R2 of the electric battery BAT which occurs at a time t R2 (cf. Fig.8 ).

[0066] A calculation function F3 shown in the Fig.8 is used to correct the value of the estimation calculation factor α and match it to the actual states of charge SOC R1 and SOC R2 which are read at times t R1 and t R2 , respectively.

[0067] During the electric charging session, at time t R1 , the estimation calculation factor α R1 is calculated from the value of SOC E with the factor α S (see B3) and SOC R1 . The factor α R1 is used by the functional block B4 for the rest of the calculation of the estimated state of charge SOC E (t). The factor α R1 is transmitted to the functional block B1 to enrich the database BDα.

[0068] Point A at the Fig.8 corresponds to the recalibration of the estimated state of charge SOC E (t) on the actual state of charge SOC(t)=SOC R1, the state of charge SOC(t) being shown in broken line at Fig.8 .

[0069] At the end of the electric charging session, at time t R2 , the estimation calculation factor α R2 is calculated from the value of SOC E with the factor α R1 (or α S if the intermediate correction above has not been carried out) and SOC R2 . The factor α R2 is transmitted to the functional block B1 to enrich the database BDα. Typically, the state of charge SOC E (t) = SOC R2 is transmitted at the end of the electric charging session to the energy management software system EMS.

[0070] Point B at the Fig.8 corresponds to the recalibration of the estimated state of charge SOC E (t) on the actual state of charge SOC(t)=SOC R2 at the end of the electric charging session.

[0071] In some applications, for various reasons, it is an open circuit voltage OCV at the terminals of the electric battery BAT that may be transmitted by the electrified vehicle VE to the state of charge estimation software module MLE instead of the state of charge SOC. In such a case, a C4 map, shown in Fig.9 in the form of a curve, can be used by the MLE software module to obtain the effective state of charge SOC from the open circuit voltage OCV. The method of the invention as described above can thus be used in these applications to obtain the estimated state of charge SOC E.

[0072] The invention is not limited to the particular embodiments which have been described here by way of example. Those skilled in the art, depending on the applications of the invention, will be able to make various modifications falling within the scope of protection of the invention.

Claims

1. Method for estimating the state of charge of an electric battery (BAT) of an electrified vehicle (EV) implemented in an electric charging system for electrified vehicles (SRE) comprising electric charging means (BRE) connected to an electrical supply network (RE) and ensuring an electric recharge of said electric battery (BAT), at least a first computer server (NS1, SRC) hosting a software system for managing connected vehicles (SWC) and a wide area data communication network through which said electrified vehicle (EV) and said first computer server (NS1, SRC) are in data communication (LR1), and where said method comprises, at the start of an electric charging session at a time t o , a first communication of data from said electrified vehicle (VE) to said first computer server (NS1, SRC), said first communication of data comprising electric charging session data (DSC) including vehicle identification data (VE / USER), a first effective state of charge (S0 C 0 ), of said electric battery (BAT), a state of health (SOH) of said electric battery (BAT) and a charging mode (MC); and characterized in that : during said electric charging session, an estimate and a provision by said first computer server (NS1, SRC) of a load status estimated (SOC E ), said estimated state of charge (SOC E ) being determined from said electric charging session data (DSC), an energy capacity (C, E) of said electric battery (BAT) and a first estimation calculation factor ( αs), said first estimation calculation factor ( αs) being determined by selection from a database of estimation calculation factors (DB α) accessible to said first computer server (NS1, SRC) and from said electric charging session data (DSC).

2. Method according to claim 1, characterized in that it comprises a determination of said first estimation calculation factor ( αs) using a artificial intelligence (AI) module.

3. Method according to claim 1 or 2, characterized in that it comprises, during said electric recharging session, a second communication of data from said electrified vehicle (VE) to said first computer server (NS1, SRC), at a time t Ri , said second communication of data comprising a second effective state of charge (SOC R1 ) of said electric battery (BAT), and an intermediate correction of said estimated state of charge (SOC E ) from said second effective state of charge (SOC R1 ) and a determination by calculation of a second estimation calculation factor ( αR1) from said second effective state of charge (SOC R1 ) according to a function of Calculation (F3) following: α R 1 = 1 − SOC E t R1 − SOC R1 t R1 SOC E t R1 − SOC 0 t 0 said first estimation calculation factor ( αs) being replaced by said second estimation calculation factor ( αR1) for subsequent determinations of said estimated state of charge (SOC E ) during said electric charging session.

4. Method according to any one of claims 1 to 3, characterized in that it comprises, at the end of said electric recharging session at a time t R2 , another communication of data from said electrified vehicle (VE) to said first computer server (NS1, SRC), said other communication of data comprising a third effective state of charge (SOC R2 ) of said electric battery (BAT), and a final correction of said estimated state of charge (SOC E ) from said third effective state of charge (SOC R2 ), a determination by calculation of a third estimation calculation factor ( αR2) from said second effective state of charge (SOC R1 ) according to a function of calculation (F3) following: α R2 = 1 − SOC E t R2 − SOC R2 t R2 SOC E t R2 − SOC 0 t 0 and a record of said third estimation calculation factor ( αR2) in said database of estimation calculation factors (DB α).

5. Method according to any one of claims 1 to 4, said recharging mode (MC) including a constant power electric recharging instruction (PC), characterized in that said estimated state of charge (SOC E ) is determined using: -- of a first mapping (C1) giving an energy state as a function of a state of charge of said electric battery (BAT) and vice versa, - and a first calculation function (F1) giving an estimated energy state (SOE), this calculation function (F1) being as follows: SOE t + SOE t0 + dt = SOE 0 t0 + α E ∫ t0 t PC .dt SOH ∗ E ∗ 3600 Or: - PC and SOH represent the constant power charging setpoint and the state of health of the electric battery BAT, - - E represents the nominal energy capacity of the electric battery BAT in kWh, and the number 3600 performs the conversion between the unit of time " hour " of the capacity E and the time unit "second" of the time variable t. - αE is the estimation calculation factor whose value is initially that of the first estimation calculation factor αs, this estimated energy state (SOE) from which said state of estimated load (SOC E ) via the first mapping (C1).

6. Method according to any one of claims 1 to 4, said recharging mode including an electric recharging instruction with current profile (I(t)), characterized in that said estimated state of charge (SOC E ) is determined using a second mapping between state of charge and recharge current (C2), a third mapping (C3) defining a time profile of recharge current (I(t)), and a second calculation function (F2) giving said estimated state of charge (SOC E ).

7. Method according to any one of claims 1 to 6, characterized in that it comprises a provision of said estimated state of charge (SOC E ) to a second computer server (NS2) hosting an energy management software system (EMS) of said electrical supply network (RE), said second computer server (NS2) being in data communication with said first computer server (NS1, SRC) through said wide area data communication network.

8. Method according to any one of claims 1 to 7, characterized in that it comprises a supply of said estimated state of charge (SOC E ) to a device computer (SP) of a user (USER) of said electrified vehicle (EV), said computer device (SP) being in data communication with said first computer server (NS1, SRC) through said wide area data communication network.

9. Method according to claim 8, characterized in that said device computer is a smart phone called smartphone (SP).

10. Computer server (NS1, SRC) comprising a data storage device (HD) storing program instructions (MLE) for implementing the method according to any one of claims 1 to 9.