Method and preconditioning system for optimal preconditioning of electric vehicles in depot

Abstract

A preconditioning system (200) and a method (100) for optimal preconditioning of EVs in a depot. The method (100) includes determining a preconditioning energy and a preconditioning duration for performing preconditioning of a cabin and a battery of EV and determining a preconditioning availability matrix for the EV based on the preconditioning energy and the preconditioning duration. The method also includes determining a plurality of preconditioning constraints associated with the preconditioning of the battery and the cabin of the EV and determining a charging model based on the plurality of preconditioning constraints and the preconditioning availability matrix for the EV. Further, the method also includes generating a charger profile comprising a plurality of power set points associated with the preconditioning of the cabin and the battery of the EV and causing to display the determined charging profile on a screen of a charger (1000).

Description

[0001]202319304 METHOD AND PRECONDITIONING SYSTEM FOR OPTIMAL PRECONDITIONING OF ELECTRIC VEHICLES IN DEPOT DESCRIPTION The present disclosure relates generally to optimization of preconditioning of electric vehicles (EVs), and more specifically to a method and preconditioning system for optimal preconditioning of EVs in a depot with battery balancing and charging. Vehicle fleet owners may have a multitude of vehicles being operated out of a depot. The multitude of vehicles may include for example but not limited to buses of different length, trucks, pickup vans, taxis, etc. All the vehicles have to be parked in the depot before and after a trip. A parking layout of the depot is generally designed to accommodate a large number of vehicles in a limited space. As the adoption of Electric Vehicles (EVs) becomes more prevalent, there is a growing need for smarter and more efficient charging solutions to support the demand. However, charging an EV is not a straightforward process and can be impacted by a range of factors, one such challenge is the temperature of an EV's battery and cabin that have a significant impact on the charging process and overall driving experience. Preconditioning of vehicles of electric vehicles (EVs) is a process of pre-heating or pre-cooling an interior of an EV before the EV begins its scheduled journey through a specific predetermined route. Here, the pre-heating or the pre-cooling of the EV includes the pre-heating or the pre- cooling of a cabin of the EV which typically accommodates passengers therewithin. The preconditioning of the EV ensures that a temperature of the cabin is either brought up or brought down to a temperature that is desirable for the passengers of the EV on an average, during the journey. An E-Depot is a depot that typically manages a fleet of EVs including e-Buses, e-Trucks, e-Cars, etc. To ensure smooth operations of the e-Depot, it is essential that the EVs are preconditioned before their scheduled departure from the e-Depot. Typically, a depot operator would prefer to use electricity from a grid to perform the preconditioning of the EVs, instead of energy stored in a battery of the EV to save a State of Charge (SoC) of the battery of the EV. However, the EVs are 202319304 required to be preconditioned according to their departure schedules. If the preconditioning of the EV is not achieved, then the EV is declared unfit for its assigned trip and hence this EV needs to be replaced by another EV which is fit for service and undertaking the trip. Moreover, an e-Depot may have various chargers deployed therein including, for example, sequential chargers, parallel chargers, etc. The sequential chargers are capable of charging one EV attached to its connectors at a time. When there are multiple EVs connected to different connectors of the sequential charger, one or more of these EVs may not be preconditioned within time of their scheduled departure or will have lower than expected SoC due to energy from the battery being utilized for preconditioning. Furthermore, an e-Depot may have resource limitations such as power constraints, schedule constraints, infrastructure constraints, etc., due to which preconditioning of all scheduled EVs may not be possible. Charging an EV with a cold battery can be less efficient and result in longer charging times, decreased range, and reduced battery life. Similarly, driving with a cold cabin can be uncomfortable and result in reduced range due to increased use of heating. For countries with hotter climates, batteries getting exposed to very high temperatures has the risk of thermal runout and also the comfort levels inside EV compartment goes down, due to excessive heat due to high heat transfer load. The preconditioning in this scenario should address to cool the battery systems and the bus compartment alike. Generally, the above-mentioned challenges are addressed by planning preconditioning while the EV is still plugged into grid's power source at the depot before leaving along with charging and balancing operations. Thus, the proposed solution provides a smart EV charging algorithm with cabin and battery preconditioning capabilities which is based on grid demand, weather conditions, and battery temperature, determines optimal preconditioning power and duration, and make sure EVs are preconditioned at their departure time in an optimal way both in terms of charging cost and peak power. This would result in improved range, increased battery life, and a more comfortable driving experience, making them more likely to continue using EVs as a sustainable transportation option. The object of the present disclosure is achieved by a computer-implemented method for 202319304 optimal preconditioning of electric vehicles (EVs) in a depot. The method includes determining a preconditioning energy and a preconditioning duration for performing preconditioning of a cabin and a battery of an EV of the plurality of EVs and determining a preconditioning availability matrix for the EV based on the preconditioning energy and the preconditioning duration. The method also includes determining a plurality of preconditioning constraints associated with the preconditioning of the battery and the cabin of the EV and determining a charging model based on the plurality of preconditioning constraints and the preconditioning availability matrix for the EV. Further, the method includes generating a charger profile comprising a plurality of power set points associated with the preconditioning of the cabin and the battery of the EV and causing to display the determined charging profile on a screen of a charger connected to the EV for charging. In one or more embodiments, the preconditioning availability matrix indicates a time slot for performing the preconditioning of the cabin and the battery of a corresponding EV. In one or more embodiments, the plurality of preconditioning constraints includes constraint associated with a maximum power at the depot and a maximum power of the EV, constraint associated with charging of the EV, constraint associated charging of the EV and maximum power of a connector of the charger and constraint associated with battery balancing. In one or more embodiments, determining the preconditioning energy and the preconditioning duration for the cabin and the battery of the EV includes receiving by a preconditioning heat load model a first set of inputs associated with the cabin of the EV. The first set of inputs associated with the cabin of the EV comprises an initial temperature of the cabin of the EV, an ambient temperature, an air conditioning load and a heater load. The method also includes receiving by the preconditioning heat load model a second set of inputs associated with the battery of the EV and determining the preconditioning energy and the preconditioning duration for the cabin and the battery of the EV based on the preconditioning heat load model. The second set of inputs associated with the battery of the EV includes initial battery temperature, ambient temperature and battery chemistry. In one or more embodiments, the method further includes determining that a sequential charger is connected to a set of EVs of the plurality of EVs, wherein the set of EVs comprise same 202319304 departure time and determining an overlap of time slots allocated for preconditioning of the EVs based on the same departure time. Further, the method includes determining a plurality of sub time slots of the time slots allocated for preconditioning of the plurality of EVs; allocating at least one sub time slot of the plurality of sub time slots to each of the EVs based on a weighted round robin technique; and performing the preconditioning of the cabin and the battery of the plurality of EVs in the allocated sub time slot. In one or more embodiments, the power required for preconditioning of the cabin of the EV and the battery of the EV is provided from one of: the charger and the EV battery. In one or more embodiments, the method further includes determining that a battery balancing is to be performed for at least one EV of the plurality of EVs; receiving balancing power and balancing duration associated with a battery of the EV; and performing the battery balancing based on the received balancing power and balancing duration. In one or more embodiments, the generating the charger profile further comprises ensuring that each EV gets the preconditioning power in a required time slot irrespective of a charging power allocated to any EV of the plurality of EVs in that time slot. In one or more embodiments, generating the charger profile further comprises ensuring that each EV is charged to a required SoC based on available charging facilities at corresponding depot. In one or more embodiments, the method further includes determining that a sequential charger is connected to the plurality of EVs and determining a difference between a required SoC and an attained SoC for each EV of the plurality of EVs. Further, the method includes determining a set of EVs with the difference between the required SoC and the attained SoC, greater than a SoC threshold and allocating a higher number of time slots for preconditioning of the cabin and the battery of the set of EVs with the difference between the required SoC and the attained SoC, greater than the SoC threshold. 202319304 In one or more embodiments, the plurality of power set points is associated with at least one of charging of the EV, battery balancing of the EV, precondition of the cabin and the battery of the EV. In one or more embodiments, causing to display the determined charging profile on the screen of the charger connected to the EV for charging includes sending the charging profile to the charger connected to the EV and causing to display the charging profile indicating the preconditioning duration and the preconditioning power allocated to the EV connected to the charger for charging. The object of the present disclosure is also achieved by a preconditioning system for optimal preconditioning of electric vehicles (EVs) in a depot. The preconditioning system includes a processor and a memory coupled to the processor (204). The memory includes instructions which, when executed by the processor, configures the processor to determine a preconditioning energy and a preconditioning duration for performing preconditioning of a cabin and a battery of an EV of the plurality of EVs and determine a preconditioning availability matrix for the EV based on the preconditioning energy and the preconditioning duration. The processor is also configured to determine a plurality of preconditioning constraints associated with the preconditioning of the battery and the cabin of the EV and determine a charging model based on the plurality of preconditioning constraints and the preconditioning availability matrix for the EV. The processor is then configured to generate a charger profile comprising a plurality of power set points associated with the preconditioning of the cabin and the battery of the EV and cause to display the determined charging profile on a screen of a charger connected to the EV for charging. The object of the present disclosure is also achieved by a charger for optimal preconditioning of electric vehicles (EVs) in a depot. The charger includes one or more connectors via which the one or more EVs (connect to the charger characterized by: a preconditioning system configured to carry out steps of the aforementioned method. The object of the present disclosure is further achieved by a computer program code which, when executed by a processor, causes the processor to carry out steps of the aforementioned method. 202319304 The object of the present disclosure is further achieved by a computer program product comprising computer program code which, when executed by a processor, causes the processor to carry out steps of the aforementioned method. Still other aspects, features, and advantages of the disclosure are readily apparent from the following detailed description, simply by illustrating a number of particular embodiments and implementations, including the best mode contemplated for carrying out the disclosure. The disclosure is also capable of other and different embodiments, and its several details may be modified in various obvious respects, all without departing from the scope of the disclosure. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive. A more complete appreciation of the present disclosure and many of the attendant aspects thereof will be readily obtained as the same becomes better understood by reference to the following description when considered in connection with the accompanying drawings: FIG 1 is a flowchart representation of a computer-implemented method for optimal preconditioning of EVs in a depot, in accordance with one or more embodiments of the present disclosure; FIG 2 is a block diagram representation of a preconditioning system for optimal charging of the EVs in the depot, in accordance with one or more embodiments of the present disclosure; FIG 3 is a flowchart representation of the method of determining a charging profile with battery balancing, preconditioning and charging power set points t, in accordance with one or more embodiments of the present disclosure; FIG 4 is an exemplary representation of heat exchange during preconditioning at the EV, in accordance with one or more embodiments of the present disclosure; FIG 5A is a graphical representation for preconditioning with a sequential charger or a standard charger connected to single EV and having time for performing all operations, in accordance with one or more embodiments of the present disclosure; 202319304 FIG 5B is a graphical representation for preconditioning with a sequential charger connected to multiple EVs having partial overlapping schedule, in accordance with one or more embodiments of the present disclosure; FIG 5C is a graphical representation for preconditioning with a standard charger connected to multiple EVs with partial overlapping schedule, in accordance with one or more embodiments of the present disclosure; and FIG 5D is a graphical representation for preconditioning with a standard charger connected to multiple EVs with similar departure time, in accordance with one or more embodiments of the present disclosure. 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 the purpose of explanation, numerous specific details are set forth in order to provide thorough understanding of one or more embodiments. It may be evident that such embodiments may be practiced without these specific details. Examples of a method, a system, and a computer-program product for optimal preconditioning of electric vehicles (EVs) in a depot are disclosed herein. In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It is apparent, however, to one skilled in the art that the embodiments of the disclosure may be practiced without these specific details or with an equivalent arrangement. In other instances, well-known structures and devices are shown in block diagram form in order to avoid unnecessarily obscuring the embodiments of the disclosure. Conventionally preconditioning and balancing power are provided at different intervals of time. This can be cumbersome and when operating with a tight schedule the time constraint may act as a drawback. Unlike to the conventional methods and systems, in the proposed solution the preconditioning and balancing power are provided simultaneously. Further, the preconditioning and charging power are also provided at the same time while still achieving the goals of minimizing peak power and charging costs. 202319304 The proposed solution ensures that the preconditioning can occur during other processes, such as charging and balancing. When charging and preconditioning take place simultaneously, instead of providing maximum power, it will provide the optimum power settings based on peak power and charging cost objectives, which will lower the total energy demand of the depot. Referring now to FIG 1, illustrated is a flowchart of a method (as represented by reference numeral 100) for optimal preconditioning of electric vehicles (EVs) in a depot, in accordance with an embodiment of the present disclosure. As used herein, optimal preconditioning of the EVs in the depot refers to a process of preconditioning the EVs just before departure without compromising on a required SoC of the EV. The preconditioning is performed optimally such that the charging and the battery balancing of the EV are also taken care-off in the proposed solution. In the proposed solution the preconditioning of the battery is carried out before the EV begins its route, there is less risk of unexpected downtime due to battery issues. This can help increase the reliability and availability of the electric bus fleet. Further, when the battery is preconditioned, it can operate at peak efficiency, resulting in an increased range. This means that the electric bus can travel further on a single charge, which is particularly important for longer routes. Preconditioning can also help extend the life of the battery. By operating at the optimal temperature range, the battery is less likely to experience stress or wear, which can cause it to degrade over time. As a result, the proposed solution increases battery life of the EV, ensures a comfortable travel for passengers travelling in the EV at the same time meeting all the requirements of preconditioning, charging and battery balancing. Referring to FIG 2, illustrated is a block diagram of a preconditioning system 200 for optimal preconditioning of the EVs in the depot, in accordance with one or more embodiments of the present disclosure. It may be appreciated that the preconditioning system 200 described herein may be implemented in various forms of hardware, software, firmware, special purpose processors, or a combination thereof. One or more of the present embodiments may 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, processors, or instruction execution system. For the purpose of this description, a computer-usable or computer-readable medium may be any apparatus that may contain, store, 202319304 communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The medium may 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 digital versatile disc (DVD). Both processors and program code for implementing each aspect of the technology may be centralized or distributed (or a combination thereof) as known to those skilled in the art. In an example, the preconditioning system 200 may be embodied as a computer-program product 200 programmed for performing the said purpose. The preconditioning system 200 may be incorporated in one or more physical packages (e.g., chips). By way of example, a physical package includes an arrangement of one or more materials, components, and / or wires on a structural assembly (e.g., a baseboard) to provide one or more characteristics such as physical strength, conservation of size, and / or limitation of electrical interaction. It is contemplated that in certain embodiments the computing device may be implemented in a single chip. As illustrated, the preconditioning system 200 includes a communication mechanism such as a bus 202 for passing information among the components of the preconditioning system 200. The preconditioning system 200 includes a processor 204 and a memory 206. Herein, the memory 206 is communicatively coupled to the processor 204. In an example, the memory 206 may be embodied as 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 in a system to make the system 200 execute the steps for performing the said purpose. Generally, as used herein, the term “processor” refers to a computational element that is operable to respond to and processes instructions that drive the preconditioning system 200. Optionally, the processor includes, but is not limited to, a microprocessor, a microcontroller, a complex instruction set computing (CISC) microprocessor, a reduced instruction set (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, or any other type of processing circuit. Furthermore, the term “processor” may refer to one or more individual 202319304 processors, processing devices and various elements associated with a processing device that may be shared by other processing devices. Additionally, the one or more individual processors, processing devices and elements are arranged in various architectures for responding to and processing the instructions that drive the preconditioning system 200. Herein, the memory 206 may be volatile memory and / or non-volatile memory. The memory 206 may be coupled for communication with the processor 204. The processor 204 may execute instructions and / or code stored in the memory 206. A variety of computer-readable storage media may be stored in and accessed from the memory 206. The memory 206 may include any suitable elements for storing data and machine-readable instructions, such as read only memory, random access memory, erasable programmable read only memory, electrically erasable programmable read only memory, a hard drive, a removable media drive for handling compact disks, digital video disks, diskettes, magnetic tape cartridges, memory cards, and the like. In particular, the processor 204 has connectivity to the bus 202 to execute instructions and process information stored in the memory 206. The processor 204 may include one or more processing cores with each core configured to perform independently. A multi-core processor enables multiprocessing within a single physical package. Examples of a multi-core processor include two, four, eight, or greater numbers of processing cores. Alternatively, or in addition, the processor 204 may include one or more microprocessors configured in tandem via the bus 202 to enable independent execution of instructions, pipelining, and multithreading. The processor 204 may also be accompanied with one or more specialized components to perform certain processing functions and tasks such as one or more digital signal processors (DSP), and / or one or more application-specific integrated circuits (ASIC). Other specialized components to aid in performing the inventive functions described herein include one or more field programmable gate arrays (FPGA) (not shown), one or more controllers (not shown), or one or more other special- purpose computer chips. The preconditioning system 200 may further include an interface 208, such as a communication interface (with the said terms being interchangeably used) which may enable the preconditioning system 200 to communicate with other systems for receiving and transmitting information. The communication interface 208 may include a medium (e.g., a communication channel) through 202319304 which the preconditioning system 200 communicates with other system. Examples of the communication interface 208 may include, but are not limited to, a communication channel in a computer cluster, a Local Area Communication channel (LAN), a cellular communication channel, a wireless sensor communication channel (WSN), a cloud communication channel, a Metropolitan Area Communication channel (MAN), and / or the Internet. Optionally, the communication interface 208 may include one or more of a wired connection, a wireless network, cellular networks such as 2G, 3G, 4G, 5G mobile networks, and a Zigbee connection. The preconditioning system 200 also includes a database 210. As used herein, the database 210 is an organized collection of structured data, typically stored in a computer system and designed to be easily accessed, managed, and updated. The database 210 may be in form of a central repository of information that can be queried, analysed, and processed to support various applications and business processes. In the preconditioning system 200, the database 210 provides mechanisms for storing, retrieving, updating, and deleting data, and typically includes features such as data validation, security, backup and recovery, and data modelling. The database 210 here includes schedule of the multiple EVs which arrive and depart from the depot. The database 210 may be designed using relational or non-relational database management systems, depending on the specific requirements and preferences of the preconditioning system 200. The preconditioning system 200 further includes an input device 212 and an output device 214. The input device 212 may take various forms depending on the specific application of the preconditioning system 200. In an example, the input device 212 may include one or more of a keyboard, a mouse, a touchscreen display, a microphone, a camera, or any other hardware component that enables the user to interact with the preconditioning system 200. Further, the output device 214 may be in the form of a display. It is to be understood that, when reference is made in the present disclosure to the term “display” this refers generically either to a display screen on its own or to a screen and an associated housing, drive circuitry and possibly a physical supporting structure, of which all, or part of is provided for displaying information. In the present preconditioning system 200, the processor 204 and accompanying components have connectivity to the memory 206 via the bus 202. The memory 206 includes both dynamic 202319304 memory (e.g., RAM, magnetic disk, writable optical disk, etc.) and static memory (e.g., ROM, CD- ROM, etc.) for storing executable instructions that when executed perform the method steps described herein for route planning of the EVs. In particular, the memory 206 includes a module arrangement 216 to perform steps for managing the parking of the EVs in the depot. Also, in the preconditioning system 200, the memory 206 may be configured to store the data associated with or generated by the execution of the inventive steps. Referring to FIGS 1 and 2 in combination, the various steps of the method 100 as described hereinafter may be executed in the preconditioning system 200, or specifically in the processor 204 of the preconditioning system 200, for - optimal preconditioning of the EVs in the depot. For purposes of the present disclosure, optimal preconditioning of the EVs in the depot in the present method 100 is embodied as a decision algorithm for deciding based on a Mixed-Integer Linear Programming (MILP) based technique power to be allocated for performing preconditioning of the EV, charging of the EV and battery balancing of the EV. It also decides on time slot for each of the process considering various scenarios of overlapping of processes, etc. It may be appreciated that although the method 100 is illustrated and described as a sequence of steps, it may be contemplated that various embodiments of the method 100 may be performed in any order or a combination and need not include all of the illustrated steps. In embodiments of the present disclosure, at step 101, the method 100 includes, determining a preconditioning energy and a preconditioning duration for performing preconditioning of a cabin and a battery of the EV. The method includes receiving by a preconditioning heat load model a first set of inputs associated with the cabin of the EV and a second set of inputs associated with the battery of the EV and using them to determine the preconditioning energy and the preconditioning duration for performing preconditioning. The first set of inputs associated with the cabin of the EV can be for example but not limited to an initial temperature of the cabin of the EV, an ambient temperature, an air conditioning load and a heater load. The second set of inputs associated with the battery of the EV can be for example but not limited to initial battery temperature, ambient temperature and battery chemistry. Here, the preconditioning heat load model is a conventional heat model which may be used to arrive at the time and power required for performing certain process, which in the proposed solution is the preconditioning of the EV. 202319304 In a scenario where the preconditioning need not be performed to the EV, then the preconditioning system 200 picks the charging model and determines the charging profile to determine power set points which are associated with charging of the EV only. Hence in the proposed method there may be a preconditioning indication provided in the form a flag which is raised to indicate that the preconditioning needs to be performed. In embodiments of the present disclosure, at step 102, the method 100 includes determining a preconditioning availability matrix (AvaPreCon^,^) for the EV based on the preconditioning energy and the preconditioning duration. The preconditioning availability matrix indicates a time slot for performing the preconditioning of the cabin and the battery of a corresponding EV. The preconditioning availability matrix provides number of EVs × number of time slot. Values in the matrix can take up the value either 0 or 1. Value 1 is taken for every EV at the time slot where they must precondition and rest all zero. In embodiments of the present disclosure, at step 103, the method 100 includes determining a plurality of preconditioning constraints associated with the preconditioning of the battery and the cabin of the EV. Here, the preconditioning constraints can be for example but not limited to constraint associated with a maximum power at the depot and a maximum power of the EV, constraint associated with charging of the EV, constraint associated charging of the EV and maximum power of a connector of the charger (1000) and constraint associated with battery balancing. The preconditioning constraints are further described in FIG.4. The proposed solution uses a Mixed-Integer Linear Programming (MILP) based optimization technique and the charging model. MILP is a type of optimization problem that combines linear programming (LP) techniques with integer constraints. In a mixed-integer linear programming problem, some of the decision variables are required to take integer values, while others can take continuous values. This combination of integer and continuous variables allows MILP models to address a wide range of real-world problems, such as scheduling, routing, and resource allocation. The optimization model employed in the present method 100 seeks to optimally determine the time slots for scheduling of preconditioning of the EVs along with ensuring that the charging and battery balancing targets are efficiently achieved. 202319304 In embodiments of the present disclosure, at step 104, the method includes determining a charging model based on the preconditioning constraints and the preconditioning availability matrix for the EV. Here, the charging model incorporates the preconditioning constraints so that the preconditioning is performed in all scenarios irrespective of the power allocated for charging in a given time slot. It also includes the preconditioning availability matrix which clearly indicates the power allocated for preconditioning of the EV and the timeslots for the same. The charging model is capable of managing the precondition in various scenarios including when there are overlapping of the preconditioning schedules of multiple EVs which may have a same departure time. Such scenarios are covered in detail from FIG.5A-5D. In embodiments of the present disclosure, at step 105, the method includes generating a charger profile which includes power set points associated with the preconditioning of the cabin and the battery of the EV. The power set points are associated with charging of the EV, battery balancing of the EV, precondition of the cabin and the battery of the EV. Further, the charging profile is sent to the charger 1000 connected to the EV and the charging profile is caused to display on a screen of a charger 1000 connected to the EV for charging. The charging profile also includes details of the power required for preconditioning of the cabin of the EV and the battery of the EV along with details of the duration of preconditioning for the EV. Further, the charging profile also provides details of from where the power is extracted i.e., the charger 1000 or the EV battery. When the charger profile is generated, the preconditioning system 200 ensures that each EV gets the preconditioning power in a required time slot irrespective of a charging power allocated to any EV of the plurality of EVs in that time slot. It also ensures that each EV is charged to a required SoC based on available charging facilities at corresponding depot. Further, the scenarios encountered by a standard charger and a sequential charger may be very different. In an embodiment, the method includes determining that a sequential charger is connected to a set of EVs having the same departure time and determining an overlap of time slots allocated for preconditioning of the EVs. Then the method 100 includes determining sub time slots of the time slots allocated for preconditioning of the plurality of EVs and using a weighted round robin technique for allocating the sub time slot to each of the EVs for performing the preconditioning. 202319304 In embodiments of the present disclosure, the method 100 further includes determining that a battery balancing is to be performed for at least one EV of the plurality of EVs. This is in addition to charging and preconditioning of the EV. Then the preconditioning system 200 receives balancing power and balancing duration associated with a battery of the EV and performs the battery balancing. The battery balancing ensures better battery lifecycle and better charging over a period of time. In embodiments of the present disclosure, the method 100 further includes determining that a sequential charger is connected to the EVs and determining a difference between a required SoC and an attained SoC for each EV of the plurality of EVs. Further, the method includes determining a set of EVs with the difference between the required SoC and the attained SoC, greater than a SoC threshold and allocating a higher number of time slots for preconditioning of the cabin and the battery of the set of EVs with the difference between the required SoC and the attained SoC, greater than the SoC threshold. This ensures a better distribution of the charge at the depot. The proposed solution proposes the mathematical optimization approach based on MILP for optimal charging, balancing, and preconditioning that satisfy the power, battery capacity constraints and minimize the charging cost and peak power. To make sure buses are preconditioned while the bus is still plugged into the grid's power source at the depot before leaving, optimal preconditioning power and duration is calculate based on the charging schedule, grid demand, environment conditions, and battery temperature by using the heat transfer theory. Therefore, the proposed solution provides optimal power set points to carry out EV charging, balancing, and preconditioning simultaneously to minimize the peak power and charging costs, which will cut the total energy demand of the depot. FIG 3 is a flowchart representation of the method 300 of determining a charging profile with battery balancing, preconditioning and charging power set points t, in accordance with one or more embodiments of the present disclosure. Referring to the FIG.3, at step 302, the system 200 initiates the optimal charging technique and at step 306 the system 200 determines the EV 202319304 to connector mapping based on the inputs received from the step 304 which includes the EV list and the charger list. At step 308, the system 200 determines if a balancing flag is raised indicating that the battery balancing of the EV is to be performed. On determining that the battery balancing of the EV needs to be performed, the system 200 at step 310 receives the inputs related to balancing which includes the balancing power and the balancing duration. Further, at step 312, the system 200 determines whether the preconditioning flag is raised indicating that the preconditioning of the EV cabin and the EV battery needs to be performed. On determining that the preconditioning need not be performed then the system 200, at step 314 executes the charging model and at step 316 uses the charging profile to determine the power set points. At step 312, the system 200 on determining that the preconditioning of the EV cabin needs to be performed determines at step 318, if the cabin preconditioning energy and the duration of performing the preconditioning are available. On determining that the cabin preconditioning energy and the duration of performing the preconditioning are not available, the system 200 step 324 uses a preconditioning heat load model. Here, the preconditioning heat load model uses the heat load equation (2) for determining the preconditioning parameters. The preconditioning heat load model at step 322 receives initial cabin temperature, ambient temperature, air conditioning load and heater load. The preconditioning heat load model also at step 328 receives inputs including initial battery temperature, ambient temperature and battery chemistry. At step 326 the system 200 uses the preconditioning heat load model and determines the preconditioning requirements the preconditioning energy and the preconditioning duration. Further, at step 320, the system 200 determines if the battery preconditioning energy and the duration for performing the battery preconditioning is available. If the battery preconditioning energy and the duration for performing the battery preconditioning is not available, then the system 200 loops back to the step 324. At step 320, once the battery preconditioning energy and the duration for performing the battery preconditioning is available, then the system 200 at step 330 determines a preconditioning availability matrix. 202319304 At step 332, the system 200 determines the minimum preconditioning power required for performing the preconditioning of the EV using the equation: PreCon_Power = min (calculated PreCon_power, bus, charger, connector, depot power) Further, at step 334, the system 200 updates the preconditioning charging model with preconditioning constraints, further described in FIG.4. At step 336, the system 200 determines if sequential charger is connected to the EVs with similar departure time. If the sequential charger is not connected then at step 340, the system 200 determines the charging profile with balancing, preconditioning and charging power set points. If the sequential charger is connected then at step 338, the system 200 uses the weighted round robin technique and at step 340 the system 200 determines the charging profile with balancing, preconditioning and charging power set points. FIG 4 is an exemplary representation of heat exchange 400 during preconditioning at the EV, in accordance with one or more embodiments of the present disclosure. A power required for performing the preconditioning of the EV is determined mathematically using a plurality of equations. Here, heat from a system 200 i.e., the EV is exchanged based on heat transfer three mechanisms which includes conduction, convection, radiation. An EV parked at a charging depot will experience all three. Multiple heat loads are experienced by the EVs and are as follows: 1. External heat load (Q̇ext): this includes a. Solar radiation (Q̇solar) (402) b. Ambient temperature gradient (Q̇amb) (406) 2. Internal heat load (Q̇in): this includes a. Battery pack while charging (Q̇bat) (404) b. Air conditioning load for EV preconditioning (Q̇pre-con) (408) Net heat gain or loss (Q̇net) is shown in the FIG. 4 and is determined using the given equation below: Q̇net = Q̇solar + Q̇amb + Q̇bat + Q̇pre-con (1) To determine the pre-conditioning heat load, the net heat transfer should be zero. Given environment condition, characteristics of the structural material used in the EV and 202319304 geometry, ambient and solar heat loads can be determined. Heat load from the battery can be obtained based on the battery pack characteristics and temperature profile while charging. Therefore, the preconditioning heat load is determined such that required cabin temperature and optimal battery pack temperature is obtained before departure and maximum range in given condition can be obtained. Since, the preconditioning needs to be completed just before departure, the preconditioning availability matrix is determined and has a shape equal to number of EVs × number of time slot. Values in the matrix can take up the value either 0 or 1. Value 1 is taken for every EV at the time slot where they must precondition and rest all zero. The preconditioning availability matrix is used to formulate the optimization problem with following constraints to make sure the EV gets the preconditioning power in the required time slot irrespective of the power allocated in that time slot. Notation: 1. Preconditioning availability matrix: AvaPreCon^,^2. Preconditioning power vector: ^^^^^^^^^^^^3. Preconditioning availability factor for sequential chargers: ^^^^^^^^^^^^^,^Constraints: 1. Depot and Bus max power constraint: The first equality constraint calculates the total power input in the time slot ^, provided in equation (2). The second inequality constraint make sure the power input in a time slot for a bus is less than the respective bus max power, provided in equation (3). +%^^,^ ⋅ %^"^^^*^^^   ∀^ ∈ ! (2)-^.^^^^^^^ ≥ ^^^^^%^^ + ^^^^^^^^^ ⋅ ^^^^^^ ^^^ ^,^ ^,^ ^^^^ ∀ ∈ %, ^ ∈ ! 2. Bus charging constraint: 202319304 The first inequality constraint allocates the power in time slots based on the bus availability, provided in equation (4). The second constraint makes sure two buses connected to same sequential charger does not charge in the same time slot, provided in equation (5). ^^^^^%^^^,^ + ^^^^^^^^^^,^ ⋅ ^^^^^^^^^^^^ ≤ ^^,^ ⋅ ^^^^^^^^^^^^^,^ ⋅^^^^^%-^.^^^ ∀ ∈ %, ^ ∈ ! (4)S^,^ ≤ A^,^ ⋅ ^^^^^^^^^^^^^,^ ∀b ∈ B, t ∈ T (5)3. Bus charging and connector max power constraint This constraint limits the power given in a time slot for a bus is not greater than the max connector power to which the bus is connected. PowerB78 + ^^^^^^^^^ ⋅ ^^^^^^^^^^^ ∑ =>^^,^ ^,^ ^ ≤ <8∈?8 PowerCnMax<8 ⋅B2Cn^,<8 ∀b ∈ B, t ∈ T (6)This equality constraint calculates the connector power input in the time slot ^. ⋅%2^^^,A^ + %^^,^ ⋅ %^"^^^*^^^ ⋅ %2^^^,A^ ∀D^ ∈ ^^, ^ ∈ ! (7)4. Battery balancing constraint. BS^,^ ≤ A^,^ ⋅ ^^^^^^^^^^^^^,^ ∀b ∈ B^EF, t ∈ T (8)The above considered constraint makes sure two EVs connected to same sequential charger does not do cell balancing in the same time slot as that of preconditioning. The above equations are solved, and an optimal power set points are obtained for charging, balancing, and preconditioning. The proposed solution results in improved range and increased battery life. 202319304 FIG 5A is a graphical representation for preconditioning with a sequential charger or a standard charger connected to single EV and having time for performing all operations, in accordance with one or more embodiments of the present disclosure. Consider a depot with a maximum power limit of 60kW, the time slot length of 30 minutes with time sub-slot length of 10 minutes. Consider a sequential or a standard charger is connected to a single bus, Bus 1 and have enough time for all the operations. Consider that the Bus 1 requires 10kWh of energy and has a scheduled departure time of 07:00 AM. The Bus 1 has a preconditioning device with a power rating of 5 kW and hence it requires 2 hours for preconditioning. Therefore, the Bus 1 will be allocated 4 slots for preconditioning just before the departure. In the FIG.5A, since the bus 1 has an arrival time of 01:00 AM, the bus 1 starts charging and the optimal power set points are updated at the end of the slot. After charging to desired SoC, balancing is also completed. Preconditioning power is allocated in the last 4 time slots. FIG 5B is a graphical representation for preconditioning with a sequential charger connected to multiple EVs having partial overlapping schedule, in accordance with one or more embodiments of the present disclosure. Consider the sequential charger is connected to two buses with partial overlapping schedule. The Bus 1 requires 10kWh of energy and has a scheduled departure time of 06:30 AM. The Bus 1 includes a preconditioning device with a power rating of 10kW, then it requires 1 hour for preconditioning. The Bus 2 requires 4kWh of energy and has a scheduled departure time of 08:30 AM. The Bus 2 includes a preconditioning device with a power rating of 8kW, then it requires 30 minutes for preconditioning. In the FIG.5B, the Bus 1 and the Bus 2 has an arrival time of 01:30 AM and 05:00 AM respectively. The Bus 1 completes its charging and balancing by 04:30 AM and then the Bus 2 starts charging. Since the bus 1 must be preconditioned, the power is reverted to the Bus 1 and after that the Bus 2 completes its charging. Here in this case balancing and preconditioning for the Bus 2 happens simultaneously. FIG 5C is a graphical representation for preconditioning with a standard charger connected to multiple EVs with partial overlapping schedule, in accordance with one or more embodiments of the present disclosure. Consider the standard charger is connected to two buses with partial 202319304 overlapping schedule. The Bus 1 requires 10kWh of energy and includes a scheduled departure time of 06:00 AM. The Bus 1 has a preconditioning device with a power rating of 10kW, then it requires 1 hour for the preconditioning. The Bus 2 requires 8kWh of energy and includes a scheduled departure time of 09:00 AM. The Bus 2 includes a preconditioning device with a power rating of 8kW, then it requires 1 hours for preconditioning. In the FIG.5C, the Bus 1 and the Bus 2 includes an arrival time of 01:30 AM and 03:30 AM respectively. Since the depot uses a standard charger, it can be observed that optimal charging power set point is allocated, and bus are preconditioned at their respective slots. FIG 5D is a graphical representation for preconditioning with a standard charger connected to multiple EVs with similar departure time, in accordance with one or more embodiments of the present disclosure. Consider a scenario where the sequential charger is connected to two EV buses having similar departure time. First bus, Bus 1 requires 7.5kWh and second bus, Bus 2 requires 10kWh of energy for preconditioning. Both the EV buses comprise a preconditioning device each with a power rating of 5kW and hence the Bus 1 requires 2 hours, and the Bus 2 requires 1.5 hours for preconditioning. Both the buses are connected to same charger and the charger type is sequential. Both the Bus 1 and the Bus 2 have a departure time of 08:30 AM. In this scenario, there is overlapping of the departure schedules of both the buses and hence the system 200 will not supply all the power from the grid draws some energy from the battery of the bus for preconditioning. To minimize the energy drawn from the battery of the bus, weighted round robin technique is used. The round robin is a network scheduling technique in which time slots are allocated to each process in equal segments and in circular rotation with equal weights to all the processes. Here the process concerned is preconditioning power allocation for a bus in given time slots. Giving equal weightage for preconditioning slots to buses departing at the same time is not an optimal solution when the buses have not attained their respective departure SoC. Therefore, the bus with a higher difference between required and attained SoC should be given more preconditioning slots, hence the idea of weighted round robin. The difference between the round robin and the weighted round robin is the weights given to all the preconditioning processes. Weighted round robin formulation is performed as provided below: 202319304 Consider two buses connected at same charger of sequential type. Then, Number of Sub-slots allocated for preconditioning for the bus 1 and the bus 2: a"^^a^CJK = ^^GHℎ^^CJK × ^bc ^^^d^^^^"^eab a"^^a (11)a"^^a^CJ` = ^^GHℎ^^CJ` × ^bc ^^^d^^^^"^eab a"^^a (12)For example: Required SoC Attained SoC Sub-slots for preconditioning (total 9) Bus 1 70 55 3 Bus 2 95 60 6 In the FIG.5D, both the bus 1 and the bus 2 are charged to their desired SoC and balanced also. The bus 1 needs 4 preconditioning slots and the bus 2 needs 3 preconditioning slots. Number of overlap slots are 3 and those three slots are then divided into 9 sub-slots of 10 min each. Using weighted round robin technique, the bus 1 gets 6 sub-slots and the bus 2 gets 3 sub-slots in which the power is provided from the charger. The other bus in the same slot will get energy from the EV battery. This helps minimize the power drawn from the EV battery in a normalized sense for both the buses. While the present disclosure has been described in detail with reference to certain embodiments, it should be appreciated that the present disclosure 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 disclosure, as described herein. The scope of the present disclosure 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. 202319304 Reference Numerals method 100 step 101 step 102 step 103 step 104 step 105 preconditioning system 200 bus 202 processor 204 memory 206 interface 208 database 210 input device 212 output device 214 module arrangement 216 charger 1000 method 300 step 302 step 304 step 306 step 308 step 310 step 312 step 314 step 316 step 318 202319304 step 320 step 322 step 324 step 326 step 328 step 330 step 332 step 334 step 336 step 338 step 340 representation of heat exchange 400 Solar radiation (Q̇solar) 402 Ambient temperature gradient (Q̇amb) 406 Battery pack while charging (Q̇bat) 404 Air conditioning load for EV preconditioning (Q̇pre-con) 408

Claims

202319304 PATENTANSPRÜCHE / PATENT CLAIMS 1. A method (100) for optimal preconditioning of electric vehicles (EVs) in a depot, the method (100) comprising: determining, by a processor (204), a preconditioning energy and a preconditioning duration for performing preconditioning of a cabin and a battery of an EV of the plurality of EVs; determining, by the processor (204), a preconditioning availability matrix for the EV based on the preconditioning energy and the preconditioning duration; determining, by the processor (204), a plurality of preconditioning constraints associated with the preconditioning of the battery and the cabin of the EV; determining, by the processor (204), a charging model based on the plurality of preconditioning constraints and the preconditioning availability matrix for the EV; generating, by the processor (204), a charger profile comprising a plurality of power set points associated with the preconditioning of the cabin and the battery of the EV; and causing, by the processor (204), to display the determined charging profile on a screen of a charger (1000) connected to the EV for charging.

2. The method (100) according to claim 1, wherein the preconditioning availability matrix indicates a time slot for performing the preconditioning of the cabin and the battery of a corresponding EV.

3. The method (100) according to claim 1, wherein the plurality of preconditioning constraints comprises constraint associated with a maximum power at the depot and a maximum power of the EV, constraint associated with charging of the EV, constraint associated charging of the EV and maximum power of a connector of the charger (1000) and constraint associated with battery balancing.

4. The method (100) according to claim 1, wherein determining, by the processor (204), the preconditioning energy and the preconditioning duration for the cabin and the battery of the EV comprises: receiving by a preconditioning heat load model a first set of inputs associated with the cabin of the EV, wherein the first set of inputs associated with the cabin of the EV comprises an initial temperature of the cabin of the EV, an ambient temperature, an air conditioning load and a heater load;202319304 receiving by the preconditioning heat load model a second set of inputs associated with the battery of the EV, wherein the second set of inputs associated with the battery of the EV comprises initial battery temperature, ambient temperature and battery chemistry; and determining the preconditioning energy and the preconditioning duration for the cabin and the battery of the EV based on the preconditioning heat load model.

5. The method (100) according to claim 1, further comprising: determining, by the processor (204), that a sequential charger is connected to a set of EVs of the plurality of EVs, wherein the set of EVs comprise same departure time; determining, by the processor (204), an overlap of time slots allocated for preconditioning of the EVs based on the same departure time; determining, by the processor (204), a plurality of sub time slots of the time slots allocated for preconditioning of the plurality of EVs; allocating, by the processor (204), at least one sub time slot of the plurality of sub time slots to each of the EVs based on a weighted round robin technique; and performing, by the processor (204), the preconditioning of the cabin and the battery of the plurality of EVs in the allocated sub time slot.

6. The method (100) according to claims 1 and 5, wherein the power required for preconditioning of the cabin of the EV and the battery of the EV is provided from one of: the charger (1000) and the EV battery.

7. The method (100) according to claim 1, further comprising: determining, by the processor (204), that a battery balancing is to be performed for at least one EV of the plurality of EVs; receiving, by the processor (204), balancing power and balancing duration associated with a battery of the EV; and performing, by the processor (204), the battery balancing based on the received balancing power and balancing duration.

8. The method (100) according to claim 1, wherein the generating the charger profile further comprises ensuring that each EV gets the preconditioning power in a required time slot irrespective of a charging power allocated to any EV of the plurality of EVs in that time slot.

9. The method according to claim 1, wherein generating the charger profile further comprises ensuring that each EV is charged to a required SoC based on available charging facilities at corresponding depot.202319304 10. The method (100) according to claim 1, further comprising: determining, by the processor (204), that a sequential charger is connected to the plurality of EVs; determining, by the processor (204), a difference between a required SoC and an attained SoC for each EV of the plurality of EVs; determining, by the processor (204), a set of EVs with the difference between the required SoC and the attained SoC, greater than a SoC threshold; allocating, by the processor (204), a higher number of time slots for preconditioning of the cabin and the battery of the set of EVs with the difference between the required SoC and the attained SoC, greater than the SoC threshold.

11. The method (100) according to claim 1, wherein the plurality of power set points is associated with at least one of charging of the EV, battery balancing of the EV, precondition of the cabin and the battery of the EV.

12. The method (100) according to claim 1, wherein causing to display the determined charging profile on the screen of the charger (1000) connected to the EV for charging comprises: sending, by the processor (204), the charging profile to the charger (1000) connected to the EV; and causing, by the processor (204), to display the charging profile indicating the preconditioning duration and the preconditioning power allocated to the EV connected to the charger (1000) for charging.

13. A preconditioning system (200) for optimal preconditioning of electric vehicles (EVs) in a depot, the preconditioning system (200) comprising: a processor (204); and a memory (206) coupled to the processor (204), wherein the memory (206) comprises instructions which, when executed by the processor (204), configures the processor (204) to: determine a preconditioning energy and a preconditioning duration for performing preconditioning of a cabin and a battery of an EV of the plurality of EVs; determine a preconditioning availability matrix for the EV based on the preconditioning energy and the preconditioning duration; determine a plurality of preconditioning constraints associated with the preconditioning of the battery and the cabin of the EV;202319304 determine a charging model based on the plurality of preconditioning constraints and the preconditioning availability matrix for the EV; generate a charger profile comprising a plurality of power set points associated with the preconditioning of the cabin and the battery of the EV; and cause to display the determined charging profile on a screen of a charger connected to the EV for charging.

14. A computer program product, comprising computer program code which, when executed by a processor (204), cause the processor (204) to carry out the method (100) of one of the claims 1 to 12.

15. A charger (1000) for optimal preconditioning of electric vehicles (EVs) in a depot, wherein the charger (1000) comprises: one or more connectors via which the one or more EVs (connect to the charger (1000); characterized by: preconditioning system (200) configured to: determine a preconditioning energy and a preconditioning duration for performing preconditioning of a cabin and a battery of an EV of the plurality of EVs; determine a preconditioning availability matrix for the EV based on the preconditioning energy and the preconditioning duration; determine a plurality of preconditioning constraints associated with the preconditioning of the battery and the cabin of the EV; determine a charging model based on the plurality of preconditioning constraints and the preconditioning availability matrix for the EV; generate a charger profile comprising a plurality of power set points associated with the preconditioning of the cabin and the battery of the EV; and cause to display the determined charging profile on a screen of the charger (1000) connected to the EV for charging.

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