Managing charging of electric vehicles in a depot

Abstract

A method and charging management system for managing charging of electric vehicles (4a…n) in a depot (40) comprising a plurality of chargers, each having one or more charging connectors (42) for enabling connection of the electrical vehicle to the charger, is disclosed. The method comprises receiving a request for mapping each of one or more electric vehicles (4a…n) to one of a plurality of chargers (42) in the depot (40) from a source (43). The request comprises vehicle data and parking data associated with the one or more electric vehicles (4a…n). Further, a sorted list of the one or more electric vehicles (4a…n) is generated based on respective arrival or departure time. Furthermore, a heuristic algorithm is iteratively implemented to map each of the one or more electric vehicles (4a…n) in the sorted list, to one of the chargers (42). The heuristic algorithm optimizes mapping of the electric vehicle (4a…n) to the charger (42), for one or more objectives, based on a predefined charger configuration data associated with each of the chargers (42), and the vehicle data and the parking data associated with the electric vehicle (4a…n), wherein at least one of the objectives is minimizing undercharging of the electric vehicle (4a…n). A mapping of each of the one or more the electric vehicles (4a…n) to the respective chargers (42) is provided on a target device (46, 47).

Description

[0001] Beschreibung / Description

[0002] MANAGING CHARGING OF ELECTRIC VEHICLES IN A DEPOT

[0003] The present invention generally relates to electric vehicles, and more particularly relates to a method and system for managing charging of electric vehicles in a depot.

[0004] Electric vehicles such as electric buses, electric cars, and electric bikes / scooters are gaining popularity due to their reduced emissions and diminished reliance on fossil fuels. As the adoption of electric vehicles continues to surge, depots serve as essential charging hubs for various types of EVs, from private cars to fleets. The electrification of depots represents a crucial step in the journey towards a more sustainable and eco-friendly public transportation system. However, one of the main challenges impeding such electrification of depots is the issue of parking infrastructure within the depots. As electric vehicle fleets grow, depots must also focus on effective parking of electric vehicles for efficient charging. Inefficient vehicle-to-charger mapping can lead to undercharging of the electric vehicle, inefficient cabin preconditioning, overloading of connectors, longer downtimes, and congestion within the depots. This ultimately hampers the smooth operation of electric public transportation. A well-designed parking algorithm is crucial for optimizing resource allocation, minimizing congestion, and enhancing the overall efficiency of the charging infrastructure. Moreover, it contributes to grid stability, energy cost savings, and environmental sustainability by intelligently managing charging loads.

[0005] In existing art, there are different algorithms that are used in smart parking for handling traffic and parking related situation. EP23193285.6 discloses a method, a computing system, and a computer-program product for robust optimization of charging of a plurality of electric vehicles scheduled for charging at a charging station comprising a plurality of chargers, based on vehicle data and station data. In particular, it is determined if a set of EVs from amongst the plurality of EVs has failed to achieve a pre-defined target State-of-Charge (SoC), after a pre-defined charging duration. If the set of EVs has failed to achieve the pre-defined target SoC, a target power is computed. Further, at least one of the maximum power of each charger associated with charging of each EV from the set of EVs and the maximum power of the set of EVs as per the target power are adjusted, to reach the target SoC of the set of EVs.

[0006] However, none of the algorithms consider connector charging rate, vehicle departure time, vehicle length and load on charging connectors simultaneously to manage electric vehicles in depot operations like preconditioning, charging and load balancing. In light of the above, there exists a need for a mechanism to allocate parking space to electric vehicles in a depot in order to best align the electric vehicles to suitable chargers for enabling efficient charging of the electric vehicles.

[0007] Therefore, it is an object of the present invention to provide a system and method for managing charging of electric vehicles in a depot.

[0008] In an aspect, the present invention includes a method for managing charging of electric vehicles in a depot comprising a plurality of chargers, each charger having one or more charging connectors for enabling connection of the electrical vehicle to the charger. The method comprises receiving a request for mapping each of one or more electric vehicles to one of the chargers in the depot from a source. The request comprises vehicle data and parking data associated with the one or more electric vehicles. The method further comprises generating a sorted list of the one or more electric vehicles based on respective arrival or departure time.

[0009] The method further comprises iteratively implementing a heuristic algorithm to map each of the one or more electric vehicles in the sorted list, to one of the chargers. The heuristic algorithm optimizes mapping of the electric vehicle to the charger, for one or more objectives, based on a predefined charger configuration data associated with each of the chargers, and the vehicle data and the parking data associated with the electric vehicle, wherein at least one of the objectives is minimizing undercharging of the electric vehicle. In an embodiment, wherein iteratively implementing the heuristic algorithm to map the each of the one or more electric vehicles to one of the chargers, comprises selecting an electric vehicle on top of the sorted list. Further, it is determined whether the electric vehicle is already connected to a charger among the plurality of chargers. If the electric vehicle is already connected to a charger among the plurality of chargers, a mapping is created between the electric vehicle to the already connected charger.

[0010] In an embodiment, if the electric vehicle is not already connected to a charger, a parking feasibility metric corresponding to each of the chargers is determined based on a length of the electric vehicle and occupancy associated with the charger. Further, a charging rate feasibility metric corresponding to each of the chargers is determined, based on a power rating associated with the charger and the energy requirement of the electric vehicle. Furthermore, a final feasibility metric corresponding to each of the chargers is computed based on at least the parking feasibility metric, and the charging feasibility metric. In an embodiment, if the one or more objectives further comprise a preconditioning requirement associated with the electric vehicle, the method further comprises determining a departure feasibility metric corresponding to each of the chargers, based on the preconditioning requirement and a time duration for which the electric vehicle is expected to be in the depot. In a further embodiment, if the one or more objectives further comprise optimal load balancing, the method further comprises computing a cumulative charging load associated with each of the chargers, based on an energy requirement of the electric vehicle, and energy supplied to one or more electric vehicles that were previously connected to the charger over a predetermined time interval. The final feasibility metric corresponding to each of the chargers is computed based on the parking feasibility metric, the charging feasibility metric, and at least one of the departure feasibility metric and the cumulative charging load.

[0011] In an embodiment, the method further comprises, if the highest value of final feasibility metric is unique amongst the value of final feasibility metric corresponding to the plurality of chargers, assigning the charger associated with highest value of final feasibility metric to the electric vehicle. Else, if the highest value of the final feasibility metric is equal for two or more of the chargers, the charger with the least cumulative charging load, amongst the two or more chargers corresponding to the highest value of the final feasibility metric, is assigned to the electric vehicle. In a further embodiment, the method comprises identifying a next electric vehicle in the sorted list for next iteration of the heuristic algorithm, upon mapping of the electric vehicle to the respective charger. In an embodiment, the method further comprises identifying departure of at least one of the electric vehicles from the depot, and updating the mapping to indicate availability of the charger used for charging the departed electric vehicle.

[0012] The method further comprises providing the mapping of each of the one or more the electric vehicles to the respective chargers on a target device. In an embodiment, the target device is at least one of a dynamic signage, a display device onboard the electric vehicle and a mobile communication device associated with a driver of the electric vehicle.

[0013] In a second aspect, the present invention also provides a charging management system comprising a user input device adapted to receive a request for mapping each of one or more electric vehicles (4a... n) to one of a plurality of chargers in the depot, wherein the request comprises vehicle data and parking data associated with the one or more electric vehicles (4a... n) ; a data processing device adapted to carry out the above-described method steps; a database adapted to be accessible by the data processing device and to hold vehicle data and parking data, based the request received from the user input device; and at least one target device adapted to receive the mapping information. In an embodiment, the target device is at least one of a dynamic signage, a display device onboard the electric vehicle and a mobile communication device associated with a driver of the electric vehicle.

[0014] In a third aspect, a data processing device is arranged and configured to execute the method described above. The object of the present invention is also achieved by a computer-readable medium, on which program code sections of a computer program are saved, the program code sections being loadable into and / or executable by a processor which performs the method as described above when the program code sections are executed.

[0015] The realization of the invention by a computer program product and / or a non-transitory computer- readable storage medium has the advantage that computer systems can be easily adopted by installing computer program in order to work as proposed by the present invention.

[0016] The computer program product can be, for example, a computer program or comprise another element apart from the computer program. This other element can be hardware, for example a memory device, on which the computer program is stored, a hardware key for using the computer program and the like, and / or software, for example a documentation or a software key for using the computer program.

[0017] The above-mentioned attributes, features, and advantages of the present invention and the manner of achieving them, will become more apparent and understandable (clear) with the following description of embodiments of the invention in conjunction with the corresponding drawings. The illustrated embodiments are intended to illustrate, but not limit the invention.

[0018] The present invention is further described hereinafter with reference to illustrated embodiments shown in the accompanying drawings, in which:

[0019] FIG 1 is a schematic diagram of the layout of an exemplary electric vehicle charging lot or depot;

[0020] FIG 2 shows a flow chart of a method for managing charging of EVs in a depot comprising a plurality of chargers, in accordance with an embodiment of the present invention;

[0021] FIG 3 is a flow chart outlining the steps in a method of implementing the heuristic algorithm, in accordance with an exemplary embodiment of the present invention;

[0022] FIG 4 is a schematic representation of an electric vehicle charging lot or depot equipped with a charging management system in accordance with embodiments of the present invention. Hereinafter, embodiments for carrying out the present invention are described in detail. The various embodiments are described with reference to the drawings, wherein like reference numerals are used to refer to like elements throughout. In the following description, for purpose of explanation, numerous specific details are set forth in order to provide a thorough understanding of one or more embodiments. It may be evident that such embodiments may be practiced without these specific details.

[0023] The embodiments of the present invention utilise a heuristic parking algorithm that determines the optimal electric vehicle to charger mapping. The computer-implemented method comprises initially mapping the occupancy of each electric vehicle charger within a depot. Each electric vehicle charger is provided with one or more charging connectors for enabling connection of the electrical vehicle to the charger, and may be in a sequential or parallel charging scheme. Firstly, a request for mapping each of one or more electric vehicles to one of a plurality of chargers in the depot from a source. The request comprises vehicle data and parking data associated with the one or more electric vehicles. Further, a sorted list of the one or more electric vehicles is generated based on respective arrival or departure time. Furthermore, a heuristic algorithm is iteratively implemented to map each of the one or more electric vehicles in the sorted list, to the chargers. The heuristic algorithm optimizes mapping of the electric vehicle to the charger, for one or more objectives, based on a predefined charger configuration data associated with each of the chargers, and the vehicle data and the parking data associated with the electric vehicle, wherein at least one of the objectives is minimizing undercharging of the electric vehicle. Finally, the electric vehicle is mapped to the assigned charger. This information is then communicated to the driver of the electric vehicle, either via a mobile communications platform or dynamic signage (such as liquid crystal or light emitting diode screens), who parks the electric vehicle, connects the mapped charger and initiates the charging process. The mapping information is also output to a user, such as a depot manager. These and other features of the embodiments of the present invention are described in more detail below.

[0024] FIG 1 is a schematic diagram of the layout of an exemplary electric vehicle (EV) charging lot or depot 1. The charging lot 1 is provided with an entrance 2 and an exit 3 to enable EVs 4a... n to access a plurality of parking lanes 5a... n. Each of the parking lanes is provided with a number of chargers (not shown), each having one or more charging connectors (also not shown). EVs 4a...4n enter the park-ing lot 1 via the entrance 2 and follow a one-way system 6 round the charging lot 1 to reach the entrances 7a... n to the parking lanes 5a... n in order to access a charger. Once charged, the EVs 4a... n exit via the parking lane exits 8a... n and exit the charging lot 1 via the exit 3 and into service. The infrastructure of EV charging depots includes chargers of various ratings and types. The following embodiments and examples are described with respect to sequential or standard / parallel charging connectors, but the invention may be applied to any type of charging connector. Each EV arriving at a depot has a schedule that includes its arrival and departure times. In addition, the State of Charge (SoC) upon arrival of the EV in the depot and the SoC that the EV needs to attain before leaving the depot are also included. The depot manager needs to be able to charge the EVs to their respective departure charging status before the scheduled departure time, and therefore also needs to know how much power is required to charge the EVs. However, rather than manually assigning an EV to a charger in a parking spot in an effort to maximise the number of electric vehicles that can be parked in the depot and optimise the flow of EVs in and out of the depot, the embodiments of the present invention make use of a heuristic algorithm as explained below.

[0025] FIG 2 shows a flow chart 200 of a method for managing charging of EVs in a depot comprising a plurality of chargers, each charger having one or more charging connectors for enabling connection of the electrical vehicle to the charger, in accordance with an embodiment of the present invention. A data processing device may be arranged and configured to execute the steps of the method according to steps 205 to 220 described below.

[0026] At step 205, a request for mapping each of one or more EVs to one of a plurality of chargers in the depot is received from a source, wherein the request comprises vehicle data and parking data associated with the one or more EVs, and charger configuration data associated with each of the plurality of chargers. The vehicle data of an EV includes, for example, a vehicle identifier, SoC, battery capacity, and preconditioning requirement of the EV. The preconditioning requirement includes an indication of whether preconditioning of the EV is required, and energy required for such preconditioning. The term ‘preconditioning’ as used herein refers to heating or cooling of a cabin of the EV to a specific temperature. The parking data includes, for example, length, arrival time, departure time, and a current parking location of the EV. The charger configuration data includes, for example, a type of charging connector associated with the charger, a power rating of the charger, energy already supplied by the charger during previous charging cycles over a predetermined time interval, and length of an EV that may be accomodated for charging at the charger. For example, if the length of the EV is greater than a predefined length, then a particular charger may not have space to accommodate the EV. In an example, some chargers may accommodate small cars, but not buses due to space constraints. At step 210, a sorted list of the one or more EVs based on respective arrival or departure times is generated. At step 215, a heuristic algorithm is iteratively implemented to map (or assign) each of the one or more EVs, in the sorted list, to one of the chargers, wherein the heuristic algorithm optimizes, for one or more objectives, mapping of the EV to the charger based on a predefined charger configuration data associated with each of the chargers, and the vehicle data and the parking data associated with the EV, wherein at least one of the objectives is minimizing undercharging of the EV. In an embodiment, iteratively implementing the heuristic algorithm to map the each of the one or more EVs to one of the chargers includes firstly selecting an EV on top of the sorted list. Further, it is determined whether the EV is already connected to a charger among the plurality of chargers. If the EV is already connected to a charger, a mapping between the EV to the already connected charger is created. In an embodiment, if the EV is not already connected to a charger, a parking feasibility metric and a charging feasibility metric corresponding to each of the chargers in the depot are determined. The parking feasibility metric is determined based on a length of the EV and occupancy associated with the charger. The charging rate feasibility metric is determined based on a power rating associated with the charger and the energy requirement of the EV. The energy requirement is determined based on the SoC and the battery rating of the EV. If a preconditioning energy is specified within the preconditioning requirement of the EV, the energy requirement is determined based on the preconditioning energy, the SoC and the battery capacity. For example, if SoC is 40% and battery capacity is 200 kWh, the battery requires 120 kWh for complete charging. In addition, if preconditioning energy is 50 kWh, the energy requirement is 170 kWh (120 kWh + 50 kWh). Further, a final feasibility metric corresponding to each of the chargers is computed based on at least the parking feasibility metric and the charging feasibility metric.

[0027] In an embodiment, if the one or more objectives further comprise a preconditioning requirement, a departure feasibility metric corresponding to each of the chargers is determined, based on the preconditioning requirement and a time duration for which the EV is expected to be in the depot. In an example, the time duration for which the EV is expected to be in the depot is computed based on the arrival time and the departure time of EV. In another embodiment, if the one or more objectives further comprise optimal load balancing, a cumulative charging load associated with each of the chargers is computed, based on an energy requirement of the EV, and energy supplied to one or more EVs that were previously connected to the charger over a predetermined time interval. In a further embodiment, the final feasibility metric corresponding to each of the chargers is computed based on the parking feasibility metric, the charging feasibility metric, and at least one of the departure feasibility metric and the cumulative charging load. In a further embodiment, the charger configuration data includes a connector priority value assigned to each of the chargers. In this case, the connector priority value of the charger is also used in computing final feasibility metric, along with the above-mentioned metrics.

[0028] If the highest value of final feasibility metric is unique amongst the value of final feasibility metric corresponding to the plurality of chargers, the charger associated with highest value of final feasibility metric is assigned to the EV. Otherwise, if the highest value of the final feasibility metric is equal for two or more of the chargers, the charger with the least cumulative charging load, amongst the two or more chargers corresponding to the highest value of the final feasibility metric, is assigned to the EV. In an embodiment, a next EV is identified from the sorted list for next iteration of the heuristic algorithm, upon mapping of the EV to the respective charger. In an embodiment, departure of at least one of the EVs from the depot is identified. Further, the mapping is updated to indicate availability of the charger used for charging the departed EV.

[0029] At step 220, a mapping of each of the one or more the EVs to the respective chargers, is provided on a target device. The target device is at least one of a dynamic signage, a display device onboard the EV and a mobile communication device associated with a driver of the EV.

[0030] FIG 3 is a flow chart 300 outlining the steps in a method of implementing the heuristic algorithm, in accordance with an exemplary embodiment of the present invention. Herein, the heuristic algorithm is implemented for optimizing the assignment of chargers for minimal undercharging of the EVs, a preconditioning requirement, and for optimal load balancing on the chargers. Therefore, the final feasibility metric is therefore, computed based on a parking feasibility metric, a charging feasibility metric, a departure feasibility metric, a cumulative charging load and a connector priority vector associated with each of the chargers.

[0031] The computer-implemented method 300 initially comprises 302 to 326.

[0032] At step 302, an input comprising vehicle data and parking data is obtained in real-time from an input device.

[0033] At step 304, a mapping matrix (B2C matrix), a parking feasibility vector, a charging connector load vector and a connector priority vector are initialized based on the input obtained from the source. For example, if there are five chargers C1 , C2, C3, C4 and C5, an ordered sequence of the chargers is created. The chargers may be in the order C1 , C2, C3, C4 and C5. Further, a square B2C matrix is created based on the ordered sequence of the chargers as shown below:

[0034] Herein, number of columns and rows in the B2C matrix are equal to the number of chargers. For ease of understanding, the columns are numbered B1 , B2, B3, B4, B5 and are used to uniquely identify EVs that come to the depot. The value T in any cell of the B2C matrix represents that the EV of the respective column is mapped to a charger of the respective row, and the value ‘0’ indicates absence of a mapping respectively between the EV and the charger. In the present example, the EV B2 is connected to C3, the B2C matrix is initialized with a 1 corresponding to cell C3-B2

[0035] Further, a parking feasibility metric associated with each of the chargers is determined based on the vehicle data and the parking data. Based on the parking feasibility metric of the chargers, the parking feasibility vector Fp, = [Fp1 , Fp2, Fp3, Fp4, Fp5] is updated, wherein each of the elements Fpi(i= 1 , 2, 3, 4, 5) is parking feasibility metric corresponding to the chargers C1 , C2, C3, C4 and C5 respectively. In the present example, as charger C3 is occupied, Fp = [1 , 1 , 0, 1 , 1 ], wherein 1 indicates feasibility of parking, and 0 indicates infeasibility of parking. In particular, the parking at a specific charger, say C3, is infeasible if an EV is already connected to the charger C3. Similarly, the parking at a specific charger may be infeasible for a specific EV if a length of the EV more than a predefined length that may be accommodated at the charger. The length of the EV that may be accommodated at a charger is predefined as part of the charger configuration data. The actual length of the EV is obtained from the vehicle data.

[0036] Furthermore, a cumulative charging load associated with each of the chargers is determined based on at least a SoC associated with the EV connected to each of the chargers, based on the vehicle data. In another embodiment, the charging connector load associated with each of the chargers is determined based on the SoC and a preconditioning energy associated with the EV. In the present embodiment, the cumulative charging connector load associated with each of the chargers is determined in the form of a charging load vector Fc, a one-dimensional vector [FC1 , FC2, FC3, FC4, FC5] wherein each of the elements Fci(i= 1 , 2, 3, 4, 5] correspond to a charging load on each of the chargers C1 , C2, C3, C4 and C5 respectively. In the present example, if B3 requires 50 kWh power to attain full charging of its battery, the charging load vector Fc is initialised as Fc = [0, 0, 50, 0, 0]. With each new EV that is connected to the charger, the charging load vector is updated based on cumulative charging load experienced by the charger. For example, if another EV, say B1 , is charged using C3 following B3, and if B1 requires 10 kWh of power to fully charge, the cumulative charge load associated with C3 is updated to 60 from 50.

[0037] Similarly, a connector priority value associated with each of the chargers is also determined. The priority is predefined ranking associated with each of the chargers. In the present embodiment, the priority associated with the chargers is determined in the form of a priority vector P, which is a one-dimensional vector comprising an ordered list of priority values corresponding to each of the chargers C1 , C2, C3, C4 and C5. For example, the charger C3 may be given a high priority of 2 to ensure more utilization of C3, over the other chargers C1 , C2, C4 and C5 that have a lower priority of 1 . Here, P = [1 , 1 , 2, 1 , 1 ].

[0038] At step 306, a list of the EVs sorted based on an arrival time or a departure time is generated.

[0039] At step 308, if all the EVs present in the depot are mapped to chargers in the B2C matrix, the method ends at step 310. Otherwise, step 312 is performed.

[0040] At step 312, arrival of a new EV or departure of an existing EV from the depot is detected. In an embodiment, the movement is detected based on motion sensors installed near an entry or an exit point associated with the depot. Further, the sorted list of the EVs is updated based on the newly arrived / departed EV. In another embodiment, vehicle data and parking data of the EV are obtained to detect arrival or departure. For example, a fresh sorted list may be generated upon arrival or departure of an EV.

[0041] If departure of an EV from the depot is detected, the parking feasibility vector Fp is updated based on the updated availability of parking at the chargers as indicated by step 314. For example, if B2 leaves upon charging from C3, Fp is updated to [1 , 1 , 1 , 1 , 1], wherein the ‘0’ corresponding to C3 is updated to 1 to reflect renewed availability of parking for C3. Further, step 308 is repeated.

[0042] Alternatively, if arrival of a new EV Bi is detected, it is determined whether the new EV is already mapped to a charger in the B2C matrix, based on vehicle data (such as vehicle identifier) associated with the new EV, as indicated by step 316. In particular, the B2C matrix is queried to determine whether an entry corresponding to the EV Bi is present. If the B2C matrix indicates that the EV Bi is already mapped to a charger, step 308 is repeated. Otherwise, the parking feasibility vector Fp for the EV is updated based on the length of the EV bi, at step 318. In particular, the length of the EV Bi is less than a predefined length that may be accommodated at any charger and if the charger is unoccupied, the corresponding element in the parking feasibility vector Fp is set to 1 . Otherwise, the element is set to 0. For example, Fp may be [1 , 1 , 0, 1 , 1 ] indicating that it is feasible to connect Bi to C1 , C2, C4 or C5.

[0043] At step 320, the cumulative charging connector load is predicted, based on the state of charge associated with the EV bi, for each of the chargers. In the present embodiment, each element in the charging connector load vector is updated based on a charging connector load predicted for the EV bi, based on the state of charge of the battery. For example, if 10kWh power is required to completely recharge the battery of the EV bi, the cumulative charging connector load for each of the chargers is predicted by adding 10 to each of the existing cumulative charging connector load. Therefore, the predicted cumulative charging connector load for C1 , C2, C3, C4 and C5 is 10, 10, 60, 10 and 10. Therefore, the charging load vector Fc may be updated by selectively updating the cumulative charging load for one of the charger (s) with lowest cumulative charging load. In the present example, the cumulative charging load corresponding to any one of the chargers C1 , C2, C4 and C5 may be updated. For example, the charging load vector Fc is updated to [10, 0, 50, 0, 0], by updating the index charging load corresponding to the first charger with lowest cumulative charging load.

[0044] At step 322, an energy requirement and duration for which the EV is expected to at the depot are determined. The energy requirement is determined based on a battery capacity of the EV and the SoC. The duration for which the EV is expected to at the depot maybe determined based on arrival and departure times of the EV. In the present embodiment, a charging rate vector Fcr is updated based on a charging power requirement computed from the determined energy requirement and duration. For example, if the SoC of Bi is at 50%, with the battery capacity at 200 kWh, the energy requirement is 100 kWh (i.e., 50% of 200 kWh). If the duration for which Bi is expected to be in the depot is 2 hrs, the power to be delivered by the charger is minimum of 50 kW (100 kWh / 2h). Therefore, a power rating of the charger must be greater than or equal to 50 kW in order to fully charge bi. If the power rating of the chargers C1 , C2, C3, C4 and C5 are 70 kW, 60 kW, 40 kW, 30 kW and 100 kW respectively, the chargers C1 , C2 and C5 can meet the energy requirement of Bi within the specific duration of 2 hrs. Therefore, the charging rate vector Fcr is updated to [1 , 1 , 0, 0, 1 ] wherein T indicates that the respective chargers C1 , C2 and C5 can fulfil the energy requirement, and ‘0’ indicates that the respective chargers C3 and C4 cannot fulfil the energy requirement.

[0045] Further, a departure feasibility metric associated with each of the chargers is calculated based on a preconditioning requirement of the EV and the time duration for which the EV is in the depot. For example, if 10 kWh is required for preconditioning energy of the EV, and if the EV is in the depot for 1 hr only, then using a charger C3 with power rating of 40 kW does not allow complete preconditioning of the EV. Therefore, the departure feasibility metric may take binary value of ‘O’. If the power rating of the EV allows complete preconditioning of the EV, then the departure feasibility metric may take binary value of T. A departure feasibility vector Fdt is further generated based on the departure feasibility metric associated with each of the chargers. In the present example, the departure feasibility vector Fdt = [1 , 1 , 0, 0, 1]

[0046] At step 324, a final feasibility corresponding to each of the chargers is estimated. In the present embodiment, a final feasibility vector is computed as a Hadamard product of at least Fp, Fcr, Fdt and P. i.e., F = Fp * Fcr * Fdt *P

[0047] In another embodiment, if the precondition requirement is absent, the final feasibility vector is computed as

[0048] F = Fp * Fcr * P.

[0049] At step 326, the highest value in the final feasibility vector is identified, if the highest value in the final feasibility vector is unique, that is without repetitions at multiple indices, step 328 is performed, followed by step 334. Otherwise steps 330 and 332 are performed, followed by 334.

[0050] At step 328, an index corresponding to the highest value is identified.

[0051] At step 330, indices corresponding to the highest value are identified.

[0052] At step 332, elements in the charging connector load vector Fc are checked to identify the index for which there exists least charging connector load. The index thus identified is determined to be the index corresponding to the highest value in the final feasibility vector.

[0053] At step 334, an element in the parking feasibility vector Fp that corresponds to the index of the highest value (identified at step 328 or 332) is updated to 0.

[0054] At step 336, the B2C matrix is updated to map the charger corresponding to the index of the element updated in the parking feasibility vector Fp.

[0055] Depots or charging lots for which the embodiments of the present invention are particularly useful are those used for public transportation, municipal vehicles or larger vehicles such as HGV (heavy goods vehicles) and LGV (light goods vehicles), vans or fleets of rental vehicles. The EV in the above examples may therefore be one of: a bus, coach, mini-bus, van, HGV, LGV, automobile (including any passenger capacity or classification) or municipal vehicle (such as waste collection lorry, street cleaning vehicle). The embodiments of the present invention equip an EV depot with a charging management system. This charging management system comprises several elements, as shown in FIG 4.

[0056] FIG 4 is a schematic representation of an EV charging lot or depot 40 equipped with a charging management system in accordance with embodiments of the present invention. The depot 40 is provided with a number of EV parking lanes 41 and charging stations with chargers 42 therein, much like in FIG 1 above. A user input device 43 (source) is adapted to receive information relating to at least the EV and availability of charging infrastructure. This enables the collection of data regarding existing parked and charging EVs as well as data input by a user. For information that is stored in a memory, such as historical parking and charger configuration data, this may include accessible memory storage. A keyboard, trackpad, mouse, tablet, mobile device or similar may be provided for a user. A database 44, as described above, is adapted to be accessible from the depot and to hold the identity and location of EVs parked and charging within the depot at least one external input received from the user input device. The database need not be at the depot itself, but may be in a distributed, cloud or edge computing environment. A data processing device 45, such as a computer, is adapted to carry out the methods of the embodiments of the invention described above. Again, this may be local, or in a distributed, cloud or edge computing environment. Finally, at least one user output device 46 and / or 47 (target device) adapted to display mapping information is provided. This enables the display of mapping information to both a depot manager and an EV driver. For the depot manager, this may be a display such as a screen 46 provided with the data processing device 45, or a screen provided at a terminal connected to the data processing device 45 if this is provided remotely. For an EV driver, this may be an LCD or LED screen 47 as described above. Where elements of the system are provided remotely from the depot these are adapted to communicate with at least the user input device 43 and the user output devices 46, 47 via a communications network 48.

[0057] The embodiments of the present invention described above provide a number of advantages when compared with existing EV depot management systems. Unlike existing heuristic algorithms that are based either on a First Come First Serve (FCFS) or a priority algorithm for EV allocation in a queue, and the Round Robin (RR) algorithm for load balancing in smart parking systems to reduce traffic congestion, the embodiments of the present invention also take into account the load balancing required for efficient charging. Given that EV depots are, as discussed above, often limited in size, by maximising the efficiency of the charging process, the embodiments of the present invention can also help determine the optimal parking of EVs in the depot, based on the vehicle data, parking data and charger configuration data. By providing an efficient EV management system, less time is required for fleet management using the embodiments of the present invention than would otherwise be required. Balancing the load on individual charging connectors also aids in balancing the overall power needs of installed energy infrastructure and minimises the effect of downtime of individual charging connectors on the overall functionality of the depot.

[0058] Various modifications of the embodiments of the invention described above falling within the scope of the appended claims will be apparent to those skilled in the art.

[0059] The present invention can take a form of a computer program product comprising program modules accessible from computer-usable or computer-readable medium storing program code for use by or in connection with one or more computers, processing units, or instruction execution system. For the purpose of this description, a computer-usable or computer-readable medium can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The medium can be electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation mediums in and of themselves as signal carriers are not included in the definition of physical computer-readable medium include a semiconductor or solid state memory, magnetic tape, a removable computer diskette, random access memory (RAM), a read only memory (ROM), a rigid magnetic disk and optical disk such as compact disk read-only memory (CD-ROM), compact disk read / write, and DVD. Both processing units and program code for implementing each aspect of the technology can be centralized or distributed (or a combination thereof) as known to those skilled in the art.

[0060] While the present invention has been described in detail with reference to certain embodiments, it should be appreciated that the present invention is not limited to those embodiments. In view of the present disclosure, many modifications and variations would be present themselves, to those skilled in the art without departing from the scope of the various embodiments of the present invention, as described herein. The scope of the present invention is, therefore, indicated by the following claims rather than by the foregoing description. All changes, modifications, and variations coming within the meaning and range of equivalency of the claims are to be considered within their scope. All advantageous embodiments claimed in method claims may also be apply to system / apparatus claims. Bezugszeichenliste / List of Reference

[0061] 1 charging lot or depot

[0062] 2 entrance to depot

[0063] 3 exit from depot

[0064] 4a..n Electric Vehicles (EVs)

[0065] 5a.. n parking lanes

[0066] 6 one-way system round the charging lot

[0067] 7a.. n entrances to parking lanes 5a.. n respectively

[0068] 8a.. n exits from parking lanes 5a.. n respectively

[0069] 40 charging lot or depot

[0070] 41 EV parking lanes

[0071] 42 EV charging stations / chargers

[0072] 43 user input device

[0073] 44 database

[0074] 45 data processing device

[0075] 46, 47 user output devices

[0076] 48 communications network

Claims

Patentanspruche / Patent claims1 . A computer-implemented method for managing charging of electric vehicles (4a... n) in a depot (40) comprising a plurality of chargers (42), each charger comprising one or more charging connectors for enabling connection of the electrical vehicle to the charger, the method comprising:• receiving a request for mapping each of one or more electric vehicles (4a... n) to one of the chargers (42), from a source (43), wherein the request comprises vehicle data and parking data associated with the one or more electric vehicles (4a... n);• generating a sorted list of the one or more electric vehicles (4a... n) based on respective arrival or departure time;• iteratively implementing a heuristic algorithm to map each of the one or more electric vehicles (4a... n) in the sorted list, to one of the chargers (42), wherein the heuristic algorithm optimizes mapping of the electric vehicle (4a... n) to the charger (42), for one or more objectives, based on a predefined charger configuration data associated with each of the chargers (42), and the vehicle data and the parking data associated with the electric vehicle (4a... n), and wherein at least one of the objectives is minimizing undercharging of the electric vehicle (4a... n) .• providing a mapping of each of the one or more the electric vehicles (4a... n) to the respective charger (42), on a target device (46, 47).

2. The method according to claim 1 , wherein iteratively implementing the heuristic algorithm to map the each of the one or more electric vehicles (4a... n) to one of the chargers (42), comprises:• selecting an electric vehicle (4a... n) on top of the sorted list;• determining whether the electric vehicle (4a... n) is already connected to a charger among the plurality of chargers (42);• if the electric vehicle (4a... n) is already connected to a charger among the plurality of chargers (42) , creating a mapping between the electric vehicle (4a... n) to the already connected charger.

3. The method according to claims 1 and 2, further comprising:• if the electric vehicle (4a... n) is not already connected to a charger,• determining a parking feasibility metric corresponding to each of the chargers (42) based on a length of the electric vehicle (4a... n) and occupancy associated with the charger;• determining a charging rate feasibility metric corresponding to each of the chargers (42), based on a power rating associated with the charger and the energy requirement of the electric vehicle (4a... n) ; and• computing a final feasibility metric corresponding to each of the chargers (42), based on at least the parking feasibility metric, and the charging feasibility metric.

4. The method according to claims 1 and 3, wherein if the one or more objectives further comprise a preconditioning requirement associated with the electric vehicle (4a... n), the method further comprises:• determining a departure feasibility metric corresponding to each of the chargers (42), based on the preconditioning requirement and a time duration for which the electric vehicle (4a... n) is expected to be in the depot.

5. The method according to claim 3 or 4, wherein if the one or more objectives further comprise optimal load balancing, the method further comprises:• computing a cumulative charging load associated with each of the chargers (42), based on an energy requirement of the electric vehicle (4a... n), and energy supplied to one or more electric vehicles (4a... n) that were previously connected to the charger over a predetermined time interval.

6. The method according to claims 3 to 5, wherein the final feasibility metric corresponding to each of the chargers (42) is computed based on the parking feasibility metric, the charging feasibility metric, and at least one of the departure feasibility metric and the cumulative charging load.

7. The method according to any of the claims 3 to 6, further comprising:• if the highest value of final feasibility metric is unique amongst the value of final feasibility metric corresponding to the plurality of chargers (42), assigning the charger associated with highest value of final feasibility metric to the electric vehicle (4a... n);• else if the highest value of the final feasibility metric is equal for two or more of the chargers (42),assigning the charger with the least cumulative charging load, amongst the two or more chargers (42) corresponding to the highest value of the final feasibility metric, to the electric vehicle (4a... n).

8. The method according to claims 1 and 7, further comprising:• identifying a next electric vehicle (4a... n) in the sorted list for next iteration of the heuristic algorithm, upon mapping of the electric vehicle (4a... n) to the respective charger.

9. The method according to any of the preceding claims, further comprising:• identifying departure of at least one of the electric vehicles (4a... n) from the depot (40);• updating the mapping to indicate availability of the charger used for charging the departed electric vehicle (4a... n).

10. The method according to any of the preceding claims, wherein the target device is at least one of a dynamic signage, a display device onboard the electric vehicle (4a... n) and a mobile communication device associated with a driver of the electric vehicle (4a... n).11 . A charging management system comprising: a user input device (43) adapted to receive a request for mapping each of one or more electric vehicles (4a... n) to one of a plurality of chargers (42) in the depot, wherein the request comprises vehicle data and parking data associated with the one or more electric vehicles (4a... n) ; a data processing device (45) adapted to carry out the method of claims 1 to 10; a database adapted to be accessible by the data processing device (45) and to hold vehicle data and parking data, based the request received from the user input device; and at least one target device (46, 47) adapted to receive the mapping information.

12. The charging management system according to claim 11 , wherein the target device is at least one of a dynamic signage, a display device onboard the electric vehicle (4a... n) and a mobile communication device associated with a driver of the electric vehicle (4a... n).

13. A data processing device (45) arranged and configured to execute the steps of the computer-implemented method according to any one of the method claims 1 to 10.

14. A computer program product, comprising computer program code which, when executed by a data processing device (45), causes the data processing device (45) to carry out the method of one of the claims 1 to 10.

15. A computer-readable medium comprising a computer program product comprising computer program code which, when executed by a data processing device (45), cause the data processing device (45) to carry out the method of one of the claims 1 to 10.

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