Description:FIELD OF THE INVENTION

[0001] The present invention relates to battery management in electric vehicles. More particularly, the present invention relates to a system and a method for estimating a charging time of a battery of an electric vehicle.

BACKGROUND

[0002] The promising electric vehicle (EV) market is reshaping the automotive landscape, emphasizing the need for accurate state-of-charge (SOC) prediction and efficient charging solutions with minimal charging time. The ability to estimate charging time precisely is critical for EV users, enabling them to plan their journeys, anticipate wait times, and estimate charging costs.
[0003] The EV ecosystem is characterized by diverse charging infrastructures, multiple cell chemistries, and dynamic charging profiles across different EV variants and battery manufacturers. Traditional charging time estimation methods rely on simplistic approaches, such as using battery capacity, predefined average current values, and/or precomputed maps based on the vehicle’s SOC. However, these methods fail to account for the intricate dynamics involved in charging the EV.
[0004] Electric vehicle batteries come in various cell chemistries, including Nickel Manganese Cobalt Oxide (NMC), Nickel Cobalt Aluminum Oxide (NCA), Lithium Iron Phosphate (LFP), and more. Even within a specific cell chemistry, different combinations of cells in series and parallel configurations exist, providing varied power and capacities in a battery pack. This diversity adds a layer of complexity to estimate charging time accurately, as the charging characteristics depend on the specific chemistry and configuration of the battery being charged.
[0005] Further, the charging current limit, a critical parameter controlled by the Battery Management System (BMS), undergoes dynamic variations based on conditions such as cell temperatures, cell voltages, and SOC. The dynamic charging current profiles are designed to optimize the charging process while safeguarding the longevity of the battery. However, the ever-changing nature of these profiles poses a challenge for conventional charging time estimation methods.
[0006] Furthermore, the conventional charging time estimation algorithms fail to address the nuanced challenges of charging the EV. For instance, the conventional charging time estimation algorithms do not consider critical future factors like changes in charging current limits due to varying cell temperatures and voltages, as well as the long-term degradation of the battery over its lifetime. As a result, estimations based solely on current conditions may lack accuracy, especially when such future factors could significantly alter the charging profile. Consequently, the conventional charging time estimation algorithms have failed to consider the future factors while estimating battery charging time.
[0007] Therefore, in view of the problems mentioned above, it is advantageous to provide a method for accurately estimating the charging time of the battery of the EV to overcome the limitations known in the method used in the state of the art and also to provide a system for achieving this method.
SUMMARY

[0008] This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention. This summary is neither intended to identify key or essential inventive concepts of the invention nor is it intended for determining the scope of the invention.
[0009] To overcome, or at least mitigate, one of the problems mentioned above in the state of the art, a system and a method for estimating a charging time of a battery of an electric vehicle (EV) is needed. It is preferable to have a robust way of estimating the charging time when the EV is plugged into a charging infrastructure, to ensure that the user can plan their journey, wait times, and charging cost accordingly, providing them more control and security in using the EV.
[00010] In an aspect of the present invention, a method for estimating a charging time of a battery of an electric vehicle (EV) is disclosed. The method includes receiving, a plurality of initial battery parameters including at least one of an initial state of charge (SOC), an initial cell temperature, an initial state of health, and an initial charging current value associated with the battery. Further, the method includes simulating, using a first virtual twin model, a plurality of characteristics of the battery based on the initial SOC, the initial cell temperature, and the initial state of health, wherein the first virtual twin model mimics a behaviour of the battery in a digital environment. Further, the method includes, simulating, using a second virtual twin model, a plurality of characteristics of a charging controller associated with the EV based on a correlation of the initial charging current-value and the simulated plurality of characteristics of the battery, wherein the second virtual twin model mimics a behaviour of the charging controller in the digital environment. Furthermore, the method includes estimating the charging time of the battery based on the simulation of the plurality of characteristics of the battery and the charging controller respectively, wherein the charging time corresponds to one or more SOCs.
[00011] In another aspect of the present invention, a system for estimating a charging time of a battery of an electric vehicle (EV) is disclosed. The system includes a memory and at least one processor in communication with the memory. Further, the at least one processor is configured to receive, a plurality of initial battery parameters including at least one of an initial state of charge (SOC), an initial cell temperature, an initial state of health, and an initial charging current value associated with the battery. Further, the at least one processor is configured to simulate, using a first virtual twin model, a plurality of characteristics of the battery based on the initial SOC, the initial cell temperature, and the initial state of health; wherein the first virtual twin model mimics a behaviour of the battery in a digital environment. Further, the at least one processor is configured to simulate, using a second virtual twin model, a plurality of characteristics of a charging controller associated with the EV based on a correlation of the initial charging current-value and the simulated plurality of characteristics of the battery, wherein the second virtual twin model mimics a behaviour of the charging controller in the digital environment. Further, the at least one processor is configured to estimate the charging time of the battery based on the simulation of the plurality of characteristics of the battery and the charging controller respectively, wherein the charging time corresponds to one or more SOCs.
[00012] To further clarify the advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which are illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[00013] These and other features, aspects, and advantages of the present invention will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:
[00014] Figure 1 illustrates an environment for an implementation of a system for estimating a charging time of a battery of an electric vehicle (EV), according to an embodiment of the present disclosure;
[00015] Figure 2 illustrates a block diagram of the system for estimating the charging time of the battery, according to an embodiment of the present disclosure;
[00016] Figure 3 illustrates a detailed block diagram of a vehicle computation unit of the system for estimating the charging time of the battery, according to an embodiment of the present disclosure;
[00017] Figure 4 illustrates a process flow for simulating a plurality of characteristics of the battery, by a simulating module of the system, according to an embodiment of the present disclosure;
[00018] Figure 5a-5b illustrates a process flow for simulating a plurality of characteristics of a charging controller, by the simulating module of the system, according to an embodiment of the present disclosure;
[00019] Figure 6 illustrates a process flow for estimating the charging time of the battery, by an estimation module of the system, according to an embodiment of the present disclosure;
[00020] Figure 7 illustrates a representative use case of the system of Figure 3, implemented in the EV, for displaying the estimated charging time, according to an embodiment of the present disclosure; and
[00021] Figure 8 illustrates a flowchart depicting an exemplary method for method for estimating the charging time of the battery of the EV, according to an embodiment of the present disclosure.
[00022] Further, skilled artisans will appreciate that elements in the drawings are illustrated for simplicity and may not have necessarily been drawn to scale. For example, the flow charts illustrate the method in terms of the most prominent steps involved to help to improve understanding of aspects of the present invention. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.
DETAILED DESCRIPTION OF FIGURES
[00023] For the purpose of promoting an understanding of the principles of the present disclosure, reference will now be made to the various embodiments and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the present disclosure is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the present disclosure as illustrated therein being contemplated as would normally occur to one skilled in the art to which the present disclosure relates.
[00024] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are explanatory of the present disclosure and are not intended to be restrictive thereof.
[00025] Whether or not a certain feature or element was limited to being used only once, it may still be referred to as “one or more features” or “one or more elements” or “at least one feature” or “at least one element.” Furthermore, the use of the terms “one or more” or “at least one” feature or element do not preclude there being none of that feature or element, unless otherwise specified by limiting language including, but not limited to, “there needs to be one or more…” or “one or more elements is required.”
[00026] Reference is made herein to some “embodiments.” It should be understood that an embodiment is an example of a possible implementation of any features and/or elements of the present disclosure. Some embodiments have been described for the purpose of explaining one or more of the potential ways in which the specific features and/or elements of the proposed disclosure fulfil the requirements of uniqueness, utility, and non-obviousness.
[00027] Use of the phrases and/or terms including, but not limited to, “a first embodiment,” “a further embodiment,” “an alternate embodiment,” “one embodiment,” “an embodiment,” “multiple embodiments,” “some embodiments,” “other embodiments,” “further embodiment”, “furthermore embodiment”, “additional embodiment” or other variants thereof do not necessarily refer to the same embodiments. Unless otherwise specified, one or more particular features and/or elements described in connection with one or more embodiments may be found in one embodiment, or may be found in more than one embodiment, or may be found in all embodiments, or may be found in no embodiments. Although one or more features and/or elements may be described herein in the context of only a single embodiment, or in the context of more than one embodiment, or in the context of all embodiments, the features and/or elements may instead be provided separately or in any appropriate combination or not at all. Conversely, any features and/or elements described in the context of separate embodiments may alternatively be realized as existing together in the context of a single embodiment.
[00028] Any particular and all details set forth herein are used in the context of some embodiments and therefore should not necessarily be taken as limiting factors to the proposed disclosure.
[00029] The terms “comprises”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such process or method. Similarly, one or more devices or sub-systems or elements or structures or components proceeded by “comprises... a” does not, without more constraints, preclude the existence of other devices or other sub-systems or other elements or other structures or other components or additional devices or additional sub-systems or additional elements or additional structures or additional components.
[00030] Embodiments of the present disclosure will be described below in detail with reference to the accompanying drawings.
[00031] For the sake of clarity, the first digit of a reference numeral of each component of the present disclosure is indicative of the Figure number, in which the corresponding component is shown. For example, reference numerals starting with digit “1” are shown at least in Figure 1. Similarly, reference numerals starting with digit “2” are shown at least in Figure 2.
[00032] Embodiments of the present disclosure disclose a system for estimating a charging time of a battery of an electric vehicle (EV) with a vehicle computation unit configured for estimating the charging time. The components of the disclosed system are configured to accurately estimate charging time while considering the battery configuration and dynamic charging current value to ensure the safety and longevity of the battery.
[00033] Figure 1 illustrates an environment 100 for an implementation of a system for estimating a charging time of a battery of an electric vehicle (EV), according to an embodiment of the present disclosure.
[00034] The Electric Vehicle (EV) or a battery-powered vehicle including, and not limited to two-wheelers such as scooters, mopeds, motorbikes/motorcycles; three-wheelers such as auto-rickshaws, four-wheelers such as cars and other Light Commercial Vehicles (LCVs) and Heavy Commercial Vehicles (HCVs) primarily work on the principle of driving an electric motor using the power from the batteries provided in the EV. Furthermore, the EV may have at least one wheel which is electrically powered to traverse such a vehicle. The term ‘wheel’ may be referred to any ground-engaging member which allows traversal of the electric vehicle over a path. The types of EVs include Battery Electric Vehicle (BEV), Hybrid Electric Vehicle (HEV) and Range Extended Electric Vehicle. However, the subsequent paragraphs pertain to the different elements of a Battery Electric Vehicle (BEV).
[00035] In construction, an EV 110 typically comprises a battery or battery pack 112 enclosed within a battery casing and includes a Battery Management System (BMS), an on-board charger 114, a Motor Controller Unit (MCU), an electric motor 116 and an electric transmission system 118. The primary function of the above-mentioned elements is detailed in the subsequent paragraphs: The battery 112 of the EV 110 (also known as Electric Vehicle Battery (EVB) or traction battery) is re-chargeable in nature and is the primary source of energy required for the operation of the EV 110, wherein the battery 112 is typically charged using the electric current taken from the grid through a charging infrastructure 120. The battery 112 may be charged using Alternating Current (AC) or Direct Current (DC), wherein in case of AC input, the on-board charger 114 converts the AC signal to DC signal after which the DC signal is transmitted to the battery via the BMS. However, in case of DC charging, the on-board charger 114 is bypassed, and the current is transmitted directly to the battery 112 via the BMS.
[00036] The battery 112 is made up of a plurality of cells which are grouped into a plurality of modules in a manner in which the temperature difference between the cells does not exceed 5 degrees Celsius. The terms “battery”, “cell”, and “battery cell” may be used interchangeably and may refer to any of a variety of different rechargeable cell compositions and configurations including, but not limited to, lithium-ion (e.g., lithium iron phosphate, lithium cobalt oxide, other lithium metal oxides, etc.), lithium-ion polymer, nickel metal hydride, nickel cadmium, nickel hydrogen, nickel-zinc, silver zinc, or other battery type/configuration. The term “battery pack” as used herein may refer to multiple individual batteries enclosed within a single structure or multi-piece structure. The individual batteries may be electrically interconnected to achieve the desired voltage and capacity for a desired application. The Battery Management System (BMS) is an electronic system whose primary function is to ensure that the battery 112 is operating safely and efficiently. The BMS continuously monitors different parameters of the battery such as temperature, voltage, current and so on, and communicates these parameters to the Electronic Control Unit (ECU) and the Motor Controller Unit (MCU) in the EV 110 using a plurality of protocols including and not limited to Controller Area Network (CAN) bus protocol which facilitates the communication between the ECU/MCU and other peripheral elements of the EV 110 without the requirement of a host computer.
[00037] The MCU primarily controls/regulates the operation of the electric motor based on the signal transmitted from the vehicle battery, wherein the primary functions of the MCU include starting the electric motor 116, stopping the electric motor 116, controlling the speed of the electric motor 116, enabling the EV 110 to move in the reverse direction and protect the electric motor 116 from premature wear and tear. The primary function of the electric motor 116 is to convert electrical energy into mechanical energy, wherein the converted mechanical energy is subsequently transferred to the transmission system of the EV to facilitate movement of the EV 110. Additionally, the electric motor 116 also acts as a generator during regenerative braking (i.e., kinetic energy generated during vehicle braking/deceleration is converted into potential energy and stored in the battery of the EV). The types of motors generally employed in EVs include, but are not limited to DC series motor, Brushless DC motor (also known as BLDC motors), Permanent Magnet Synchronous Motor (PMSM), Three Phase AC Induction Motors and Switched Reluctance Motors (SRM).
[00038] The transmission system 118 of the EV 110 facilitates the transfer of the generated mechanical energy by the electric motor 116 to the wheels 122a, 122b of the EV 110. Generally, the transmission systems 118 used in EVs 110 include a single-speed transmission system and a multi-speed (i.e., two-speed) transmission system, wherein the single-speed transmission system comprises a single gear pair whereby the EV 110 is maintained at a constant speed. However, the multi-speed/two-speed transmission system comprises a compound planetary gear system with a double pinion planetary gear set and a single pinion planetary gear set thereby resulting in two different gear ratios which facilitate higher torque and vehicle speed.
[00039] In one embodiment, all data pertaining to the EV 110 and/or charging infrastructure 120 may be collected and processed using a remote server 124 (known as cloud), wherein the processed data is indicated to the rider/driver of the EV 110 through a display unit present in the dashboard 126 of the EV 110. In an embodiment, the display unit may be an interactive display unit. In another embodiment, the display unit may be a non-interactive display unit.
[00040] In addition to the hardware components/elements, the EV 110 may be supported with software modules comprising intelligent features including and not limited to navigation assistance, hill assistance, cloud connectivity, Over-The-Air (OTA) updates, adaptive display techniques and so on. The firmware of the EV 110 may also comprise Artificial Intelligence (AI) & Machine Learning (ML) driven modules which enable the prediction of a plurality of parameters such as and not limited to driver/rider behaviour, road condition, charging infrastructures 120/charging grids 120 in the vicinity and so on. The data pertaining to the intelligent features may be displayed through the display unit present in the dashboard 126 of the EV 110. In one embodiment, the display unit may contain a Liquid Crystal Display (LCD) screen of a predefined dimension. In another embodiment, the display unit may contain a Light-Emitting Diode (LED) screen of a predefined dimension. The display unit may be a water-resistant display supporting one or more User-Interface (UI) designs. The EV 110 may support multiple frequency bands such as 2G, 3G, 4G, 5G, and so on. Additionally, the EV 110 may also be equipped with wireless infrastructure such as, and not limited to Bluetooth, Wi-Fi and so on to facilitate wireless communication with other EVs or the cloud. Further, the EV 110 may include a system 128 configured to estimate the charging time of the battery 112 of the EV 110, without departing from the scope of the present disclosure. In an example, the charging time indicates an amount of time to charge the battery up to one or more state-of-charges (SOCs) in response to detecting a connection of the EV 110 with the charging infrastructure 120. Furthermore, the one or more SOCs may indicate a measure of charge level or a measure of the remaining capacity of the battery 112.
[00041] In an alternative embodiment, the system 128 may alternatively reside in the remote server 124, without departing from the scope of the present disclosure. In an embodiment, the dashboard 126 of the EV 110 may be configured to display the charging time of the battery 112 of the EV 110 as estimated by the system 128. Furthermore, the system 128 may be configured to transmit the charging time of the battery 112 as estimated, to the remote server 124. Additionally, an application installed on a user device (not shown) and in communication with the remote server 124 may display the charging time of the battery 112 as estimated by the system 128, to a user. Further, the constructional and operational details of the system 128 are explained in subsequent paragraphs in conjunction with Figures 2 to 7, without departing from the scope of the present disclosure.
[00042] Figure 2 illustrates a block diagram of the system 128 for estimating the charging time of the battery 112, according to an embodiment of the present disclosure. The system 128 may be deployed in the EV 110 to estimate the charging time of the battery 112. The system 128 may be in communication with a load drive unit (LDU) 202 and the battery management system/unit 204 (BMS). The system 128 may include, but is not limited to, a vehicle computation unit (VCU) 206.
[00043] Referring to Figure 2, in an embodiment, the LDU 202 may be adapted to manage the charging process and facilitate communication between the EV 110 and the charging infrastructure 120.
[00044] In an example, the LDU 202 may be adapted to detect and identify a charger connected to the EV 110. In the example, the LDU 202 may be adapted to recognize the characteristics and specifications of the charging infrastructure 120 to initiate the charging process. Further, the LDU 202 may be adapted to receive configurations from Electronic Control Units (ECUs) installed within the EV 110. ECUs may be responsible for controlling various aspects of the vehicle, and their configurations are crucial for the LDU 202 to adapt to the specific characteristics and requirements of the EV 110. This ensures seamless integration and effective communication between the LDU 220 and other EV systems.
[00045] Furthermore, in an example, the LDU 202 may be adapted to determine a maximum charging current value based on the identification of the charger. Further, the LDU 202 may be adapted to transmit the maximum charging current value to the BMS 206. In the example, the maximum charging current value indicates the maximum allowable electric current that the charging infrastructure 120 may support during the charging process.
[00046] Furthermore, in an example, the LDU 202 may be adapted to detect the presence of any faults or issues in the charging infrastructure 120 that might impede the charging process. In an advantageous aspect, verification of the charging infrastructure’s 120 integrity by the LDU 202 may be essential for ensuring safe and reliable charging.
[00047] Furthermore, in an example, the LDU 202 may be adapted to authenticate the charger. In the example, the LDU 202 may be adapted to confirm the identity and compatibility of the charger with the EV 110. In an advantageous aspect, by confirming the identity and compatibility of the charger the LDU 202 may prevent unauthorized charging and ensure that the EV 110 may interact correctly with the charging infrastructure 120.
[00048] Furthermore, in an example, the LDU 202 may be adapted to request an initial charging current value from the BMS 204, in response to checking the charging current limit, verifying faults, and authenticating the charger. In the example, the initial charging current value may correspond to the desired amount of electric current that the LDU 202 allows the charging infrastructure 120 to deliver to the battery 112 during the charging process. Consequently, the LDU 202 may act as a charging controller i.e., an electronic system whose primary function is to manage and control the charging process, by regulating the flow of electric current from the charging infrastructure 120 to the battery 112, ensuring efficient and safe charging.
[00049] In an embodiment, the BMS 204 may be an electronic system whose primary function is to ensure that the battery 112 is operating safely and efficiently. The BMS 204 may continuously monitor different parameters of the battery 112 such as temperature, voltage, current and so on, and communicates these parameters to the VCU 206 in the EV 110 using a plurality of protocols including but not limited to Controller Area Network (CAN) bus protocol which facilitates the communication without the requirement of the host computer. In an advantageous aspect, the BMS 204 ensures the safety, efficiency, and longevity of the battery 112.
[00050] In an example, the BMS 204 may be adapted to obtain details about the battery 112 for instance, a Battery Identification Number (BIN), which uniquely identifies the battery 112. Additionally, the BMS 204 may be adapted to acquire configuration of the battery 112, such as the number of cells in series and parallel, and the specific cell type used in the battery 112.
[00051] Further, in an example, the BMS 204 may be adapted to continuously monitor the charging current flowing into the battery 112 during the charging process. Consequently, the BMS 204 may be adapted to determine a plurality of initial battery parameters. In a non-limiting example, the plurality of initial battery parameters may include an initial state of charge (SOC) of the battery, an initial cell temperature, an initial State of Health (SOH), and the initial charging current value, and more, based on the real-time data received from sensors embedded in the battery 112.
[00052] In the example, the initial SOC may indicate a current charge level of the battery 112 as a percentage of its total capacity. The BMS 204 may be adapted to constantly calculate and update the initial SOC to provide an accurate representation of how much energy is stored in the battery 112 at any given time.
[00053] In the example, the BMS 204 may be adapted to monitor the initial cell temperature i.e., of individual cells of the battery 112 to prevent overheating and ensure the thermal stability of the battery 112.
[00054] In the example, the initial SOH may indicate the overall health and condition of the battery 112 over time. The BMS 204 may be adapted to assess factors such as charge-discharge cycles, temperature history, and other stressors to determine the battery’s 112 long-term health.
[00055] In the example, the BMS 204 may be adapted to provide the initial charging current value to the LDU 202, such that the amount of electric current based on the initial charging current value may be allowed to be delivered to the battery 112 during the charging process.
[00056] Further, the BMS 204 may be in communication with the VCU 206, to provide the plurality of initial battery parameters for estimating the charging time of the battery 112.
[00057] Figure 3 illustrates a detailed block diagram of the VCU 206 of the system 128 for estimating the charging time of the battery 112, according to an embodiment of the present disclosure.
[00058] Referring to Figure 3, the VCU 206 of the EV 110 is responsible for estimating the charging time of the battery 112 of the EV 110, wherein the key elements of the VCU 206 typically include (i) a microcontroller core (or processor unit) or a processor 302; (ii) a memory unit or a memory 304; (iii) a plurality of modules 306 and (iv) communication protocols including, but not limited to CAN protocol, Serial Communication Interface (SCI) protocol and so on. The sequence of programmed instructions and data associated therewith can be stored in a non-transitory computer-readable medium such as the memory unit 304 or storage device which may be any suitable memory apparatus such as, but not limited to read-only memory (ROM), programmable read-only memory (PROM), electrically erasable programmable read-only memory (EEPROM), random-access memory (RAM), flash memory, disk drive and the like. In one or more embodiments of the disclosed subject matter, non-transitory computer-readable storage media can be embodied with a sequence of programmed instructions for monitoring and controlling the operation of different components of the EV 110.
[00059] The processor 302 may include any computing system which includes, but is not limited to, Central Processing Unit (CPU), an Application Processor (AP), a Graphics Processing Unit (GPU), a Visual Processing Unit (VPU), and/or an AI-dedicated processor such as a Neural Processing Unit (NPU). In an embodiment, the processor can be a single processing unit or several units, all of which could include multiple computing units. The processor 302 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor is configured to fetch and execute computer-readable instructions and data stored in the memory. The instructions can be compiled from source code instructions provided in accordance with a programming language such as Java, C++, C#.net or the like. The instructions can also comprise code and data objects provided in accordance with, for example, the Visual Basic™ language, LabVIEW, or another structured or object-oriented programming language. The one or a plurality of processors control the processing of the input data in accordance with a predefined operating rule or artificial intelligence (AI) model stored in the non-volatile memory and the volatile memory. The predefined operating rule or artificial intelligence model is provided through training or learning algorithms which include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning.
[00060] Furthermore, the modules, processes, systems, and devices can be implemented as a single processor or as a distributed processor. Also, the processes, modules, and sub-modules described in the various figures of and for embodiments herein may be distributed across multiple computers or systems or may be co-located in a single processor or system. Further, the modules can be implemented in hardware, instructions executed by a processing unit, or by a combination thereof. The processing unit can comprise a computer, a processor, such as the processor, a state machine, a logic array, or any other suitable devices capable of processing instructions. The processing unit can be a general-purpose processor which executes instructions to cause the general-purpose processor to perform the required tasks or, the processing unit can be dedicated to performing the required functions. In another embodiment of the present disclosure, the modules may be machine-readable instructions (software) which, when executed by a processor/processing unit, perform any of the described functionalities. In an embodiment, the modules may include a receiving module 310, a simulating module 312, an estimating module 314, and a transmitting module 316. The receiving module 310, the simulating module 312, the estimating module 314, and the transmitting module 316 may be in communication with each other. The data serves, amongst other things, as a repository for storing data processed, received, and generated by one or more of the modules. Exemplary structural embodiment alternatives suitable for implementing the modules, sections, systems, means, or processes described herein are provided below.
[00061] In an embodiment, the receiving module 310 may be adapted to receive the plurality of initial battery parameters from the BMS 204. In an example, the plurality of initial battery parameters may include the initial SOC, the initial cell temperature, the initial state of health, and the initial charging current value associated with the battery 112. The receiving module 310 may be in communication with the simulating module 312.
[00062] In an embodiment, the simulating module 312 may be adapted to simulate, using a first virtual twin model 312-1, a plurality of characteristics of the battery 112. In an example, the first virtual twin model 312-1 may correspond to a logic, adapted to mimic the behaviour of the battery 112 in a digital environment based on the initial SOC, the initial cell temperature, the initial state of health, and the initial charging current value. The detailed working of the simulating module 312, using the first virtual twin model 312-1 is explained with reference to Figure 4.
[00063] Figure 4 illustrates a process flow 400 for simulating the plurality of characteristics of the battery 112, by the simulating module 312 of the system 128, according to an embodiment of the present disclosure.
[00064] At block 402, the simulating module 312, using the first virtual twin model 312-1, may be adapted to select from a pre-stored database, an Equivalent Circuit Model (ECM). In an example, the ECM may be selected based on the plurality of initial battery parameters. The ECM may include components such as resistors, capacitors, and voltage sources that collectively model the battery’s behaviour for representing the electrical behaviour of the battery 112. Thus, the ECM indicates the characteristics of the cell associated with the battery 112.
[00065] In the example, the pre-stored database may reside in the memory 304 and may be generated based on conducting laboratory tests for every possible cell type. In the example, the cell types may correspond to batteries with different chemistries and power densities. Thus, via laboratory tests, data on the behaviour of each cell type under various conditions, including different initial states and different current inputs, may be gathered and saved in the pre-stored database. Consequently, the pre-stored database may include the ECM corresponding to each cell type, representing a specific type of battery cell with its unique electrical characteristics.
[00066] Further, in the example, the ECM may be selected based on correlating the characteristics of the ECM with the plurality of initial battery parameters, i.e., but not limited to, the initial SOC and the initial charging current value. Consequently, the selected ECM closely matches the characteristics of the battery 112 being simulated using the first virtual twin model 312-1. Furthermore, the selected ECM may be utilized to simulate the plurality of characteristics of the battery 112 during the charging process.
[00067] At block 404, the simulating module 312, using the first virtual twin model 312-1, may be adapted to predict a cell temperature of the battery 112 using a thermal model.
[00068] In an example, the thermal model may be selected from the memory 304, which includes a variety of thermal models derived from laboratory tests using the following equation (1):
… (1)
The above equation (1) incorporates various parameters to simulate the thermal behaviour of the battery 112 during charging. Wherein Tcell is the temperature of the cell, Tamb is the ambient temperature, h is the heat transfer coefficient, A is the surface area for the heat transfer, Rcell is the cell resistance, m is the thermal mass and Cp is the specific heat capacity of the cell.
[00069] Further, the thermal model selected from the memory 304, enables the first virtual twin model 312-1 to predict the cell temperatures (future). Thus, with the ambient temperature and the initial cell temperature, the first virtual twin model 312-1 may be adapted to estimate the evolution of the cell temperature over time for any given current input to the cell during the charging process.
[00070] At block 406, the simulating module 312, using the first virtual twin model 312-1, may be adapted to simulate the plurality of characteristics of the battery 112. In an example, a correlation of the ECM model, the plurality of initial battery parameters (the initial SOC and the initial current charging value), and the predicted cell temperature, consequently, generate the simulation of the plurality of characteristics of the battery 112.
[00071] In an example, the simulated plurality of characteristics may include a predicted SOC indicating the current charge level of the battery 112 as a percentage of its total capacity. In the example, the simulated plurality of characteristics may include a predicted terminal voltage indicating the voltage at terminals of the battery 112. Thus, the predicted SOC and the predicted terminal voltage provide an expected behaviour of the battery 112 under the specific conditions defined by the initial battery parameters and the predicted cell temperature.
[00072] Now, referring back to Figure 3, in an embodiment, the simulating module 312 may be adapted to simulate, using a second virtual twin model 312-2, a plurality of characteristics of the charging controller. In an example, the second virtual twin model 312-2 may correspond to a logic, adapted to mimic the behaviour of the charging controller in the digital environment based on a correlation of the initial charging current value and the simulated plurality of characteristics of the battery 112. The detailed working of the simulating module 312, using the second virtual twin model 312-2 is explained with reference to Figure 5a-5b.
[00073] Figure 5a-5b illustrates a process flow 500 for simulating the plurality of characteristics of the charging controller, by the simulating module 312 of the system 128, according to an embodiment of the present disclosure.
[00074] At block 502, the second virtual twin model 312-2 may be adapted to receive the simulated plurality of characteristics of the battery 112 and the predicted cell temperature from the first virtual twin model 312-1.
[00075] At block 504, the second virtual twin model 312-2 may be adapted to correlate the simulated plurality of characteristics of the battery 112 and the predicted cell temperature with a pre-defined charging matrix.
[00076] In an example, the pre-defined charging matrix may include predefined relationships between the plurality of characteristics of the battery 112 and a permissible charging current value, stored in the memory 304. Further, the pre-defined charging matrix indicates a structured set of data that establishes relationships between the plurality of characteristics of the battery 112 and the permissible charging current value. Each entry or cell in the pre-defined charging matrix may correspond to a specific combination of parameters, such as State of Charge (SOC), terminal voltage, and cell temperature, associated with the permissible charging current value.
[00077] In an example, consequently, the correlation establishes a relationship between the simulated plurality of characteristics of the battery 112 and the permissible charging current value. The correlation includes identifying the corresponding row or cell in the pre-defined charging matrix that aligns with the predicted SOC, the predicted terminal voltage, and the predicted cell temperature. In the example, the permissible charging current value indicates a highest amount of electric current that the charging controller (LDU) may allow to flow in the battery 112. In an advantageous aspect, the predefined relationships in the pre-defined charging matrix are constructed with considerations for safety and battery health, such that the correlation ensures that the charging controller adapts to the predefined relationships to protect the battery 112 from potential harm, such as overheating or overcharging.
[00078] At block 506, the second virtual twin model 312-2 may be adapted to determine a plurality of permissible charging current values corresponding to each of the predicted SOC, the predicted terminal voltage, and the predicted cell temperature, based on correlation in the previous block. Thus, the plurality of permissible charging current values indicates the highest amount of electric current that the charging controller allows to flow into the battery 112 corresponding to specific combinations of the predicted SOC, the predicted terminal voltage, and the predicted cell temperature.
[00079] At block 508, the second virtual twin model 312-2 may be adapted to determine a final charging current value to ensure the safety and longevity of the battery 112.
[00080] In an example, the final charging current value may be determined by selecting a minimum value from the plurality of permissible charging current values corresponding to each of the predicted SOC, the predicted terminal voltage, and the predicted cell temperature. Thus, the selected minimum value may correspond to the highest amount of electric current at which the battery 112 can be charged while maintaining safety standards.
[00081] At block 510, the second virtual twin model 312-2 may be adapted to send the determined final charging current value back to the first virtual twin model 312-1. The final charging current value replaces the initial charging current value while simulating the plurality of characteristics of the battery 112.
[00082] Consequently, the first virtual twin model 312-1 and the second virtual twin model 312-2 may iteratively predict the future states of the battery 112. The future states include estimating future SOC, terminal voltage, and other relevant parameters based on the final charging current value. Additionally, the first virtual twin model 312-1 and the second virtual twin model 312-2 may facilitate dynamic adaptation of the charging process, allowing for real-time adjustments based on the evolving conditions of the battery 112. In one example, the first virtual twin model 312-1 and the second virtual twin model 312-2 may iteratively simulate at fixed intervals of time, for instance, every four seconds. In one example, the the simulating module 312 maya be adapted to retrieve the first virtual twin model 312-1 and the second virtual twin model 312-2 among a set of virtual digital twin models based on the battery configuration including the cell type, the battery type, and other EV parameters.
[00083] Now, referring back to Figure 3, in an embodiment, the estimation module 314 may be adapted to estimate the charging time of the battery 112 based on the simulation of the plurality of characteristics of the battery 112 and the charging controller, respectively. The detailed working of the estimation module 314 is explained with reference to Figure 6.
[00084] Figure 6 illustrates a process flow 600 for estimating the charging time of the battery 112, by the estimation module 314 of the system 128, according to an embodiment of the present disclosure.
[00085] At block 602, the estimation module 314 may be adapted to determine a charging rate based on correlating the simulation of the plurality of characteristics of the battery 112 (by the first virtual twin model 312-1) and the charging controller (by the first virtual twin model 312-2), respectively. In an example, the charging rate indicates the maximum allowed charging current, suggesting the fastest rate (based on the final charging current value) at which the battery 112 may be charged without causing any harm. The estimation module 314 may be adapted to consider the charging rate in estimating the charging time required for the battery 112 to reach the one or more SOCs.
[00086] At block 604, the estimation module 314 may be adapted to estimate the charging time of the battery 112. In an example, the charging time is calculated based on the charging rate and provides an estimate of the time remaining to charge the battery 112 up to the one or more SOCs, for instance, time (estimated) to 80% charge and time (estimated) to 100% charge. In the example, the one or more SOCs may be a measure of the battery’s 112 charge level or remaining capacity, thus providing a reference point for the charging process. In the example, the measure of the charge level may indicate the percentage of the battery’s 112 full capacity that has been reached. In another example, the measure of the remaining capacity may indicate the amount of charge that may still be added to the battery 112. Thus, the measure of the battery’s 112 charge level or remaining capacity enables users to gauge the progress of the charging process.
[00087] Now, referring back to Figure 3, in an embodiment, the transmitting module 316 may be adapted to transmit the estimated charging time of the battery 112 to the remote server 124 and a Human Machine Interface (HMI) associated with the EV i.e., the dashboard 126 for displaying the estimated charging time corresponding to the one or more SOCs.
[00088] Figure 7 illustrates a representative use case of the system of Figure 3, implemented in the EV 110, for displaying the estimated charging time, according to an embodiment of the present disclosure.
[00089] In a scenario depicted as an example, displaying 702 the estimated charging time of the battery 112 for the one or more SOCs (80% and 100%) on the dashboard 126 of the EV 110. In another scenario depicted as an example, displaying 704 the estimated charging time of the battery 112 for the one or more SOCs on the application installed in the user device.
[00090] Figure 8 illustrates a flowchart depicting an exemplary method 800 for method for estimating the charging time of the battery 112 of the EV 110, according to an embodiment of the present disclosure. The method 800 may be a computer-implemented method executed, for example, by the system 128 and the modules 306. For the sake of brevity, the constructional and operational features of the system 128 that are already explained in the description of Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, and Figure 7, are not explained in detail in the description of Figure 8.
[00091] At step 802, the method 800 may include receiving, the plurality of initial battery parameters including at least one of the initial SOC, the initial cell temperature, the initial state of health, and the initial charging current value associated with the battery 112.
[00092] At step 804, the method 800 may include simulating, using the first virtual twin model 312-1, the plurality of characteristics of the battery 112 based on the initial SOC, the initial cell temperature, and the initial state of health. The first virtual twin model 312-1 mimics the behaviour of the battery 112 in the digital environment.
[00093] At step 806, the method 800 may include simulating, using the second virtual twin model 312-2, the plurality of characteristics of the charging controller associated with the EV 110 based on the correlation of the initial charging current value and the simulated plurality of characteristics of the battery 112. The second virtual twin model mimics the behaviour of the charging controller in the digital environment.
[00094] At step 808, the method 800 may include estimating the charging time of the battery 112 based on the simulation of the plurality of characteristics of the battery 112 and the charging controller respectively, wherein the charging time corresponds to the one or more SOCs.
[00095] While the above-discussed steps in Figures 2-7 are shown and described in a particular sequence, the steps may occur in variations to the sequence in accordance with various embodiments. Further, a detailed description related to the various steps of Figure 8 is already covered in the description related to Figures 2-7 and is omitted herein for the sake of brevity.
[00096] It will be appreciated that the modules, processes, systems, and devices described above can be implemented in hardware, hardware programmed by software, software instruction stored on a non-transitory computer-readable medium or a combination of the above. Embodiments of the methods, processes, modules, devices, and systems (or their sub-components or modules), may be implemented on a general-purpose computer, a special-purpose computer, a programmed microprocessor or microcontroller and peripheral integrated circuit element, an ASIC or other integrated circuit, a digital signal processor, a hardwired electronic or logic circuit such as a discrete element circuit, a programmed logic circuit such as a programmable logic device (PLD), programmable logic array (PLA), field-programmable gate array (FPGA), programmable array logic (PAL) device, or the like. In general, any process capable of implementing the functions or steps described herein can be used to implement embodiments of the methods, systems, or computer program products (software program stored on a non-transitory computer readable medium).
[00097] Furthermore, embodiments of the disclosed methods, processes, modules, devices, systems, and computer program products may be readily implemented, fully or partially, in software using, for example, object or object-oriented software development environments that provide portable source code that can be used on a variety of computer platforms. Alternatively, embodiments of the disclosed methods, processes, modules, devices, systems, and computer program products can be implemented partially or fully in hardware using, for example, standard logic circuits or a very-large-scale integration (VLSI) design. Other hardware or software can be used to implement embodiments depending on the speed and/or efficiency requirements of the systems, the particular function, and/or particular software or hardware system, microprocessor, or microcomputer being utilized.
[00098] In this application, unless specifically stated otherwise, the use of the singular includes the plural and the use of “or” means “and/or.” Furthermore, use of the terms “including” or “having” is not limiting. Any range described herein will be understood to include the endpoints and all values between the endpoints. Features of the disclosed embodiments may be combined, rearranged, omitted, etc., within the scope of the invention to produce additional embodiments. Furthermore, certain features may sometimes be used to advantage without a corresponding use of other features.
[00099] List of reference numerals:
Components Reference Numerals
Electric Vehicle 110
Battery 112
On-board charger 114
Electric motor 116
Electric Transmission System 118
Charging Infrastructure 120
Wheels 122a,122b
Remote Server 124
Dashboard 126
System 128
Load Drive Unit 202
BMS 204
Vehicle Computation Unit (VCU) 206
Processor 302
Memory 304
Modules 306
Data 308
Receiving Module 310
Simulating Module 312
Estimating Module 314
Transmitting Module 316
Method 800 , Claims:1. A method (800) for estimating a charging time of a battery (112) of an electric vehicle (EV) (110), the method (800) comprising:
receiving (802), a plurality of initial battery parameters including at least one of an initial state of charge (SOC), an initial cell temperature, an initial state of health, and an initial charging current value associated with the battery (112);
simulating (804), using a first virtual twin model (312-1), a plurality of characteristics of the battery (112) based on the initial SOC, the initial cell temperature, and the initial state of health; wherein the first virtual twin model (312-1) mimics behavior of the battery (112) in a digital environment;
simulating (806), using a second virtual twin model (312-2), a plurality of characteristics of a charging controller associated with the EV (110) based on correlation of the initial charging current-value and the simulated plurality of characteristics of the battery (112), wherein the second virtual twin model (312-2) mimics the behavior of the charging controller in the digital environment; and
estimating the charging time of the battery (112) based on the simulation of the plurality of characteristics of the battery and the charging controller respectively, wherein the charging time corresponds to one or more SOCs.
2. The method (800) as claimed in claim 1, wherein simulating, using the first virtual twin model (312-1), the plurality of characteristics of the battery (112) comprises:
selecting from a pre-stored database, an Equivalent Circuit Model (ECM) based on the plurality of initial battery parameters, wherein the ECM indicates characteristics of a cell associated with the battery (112);
predicting a cell temperature of the battery (112) using a thermal model and based on correlation of the initial charging current value, an ambient cell temperature, and the initial cell temperature; and
simulating the plurality of characteristics of the battery (112) based on correlating the ECM model, the plurality of initial battery parameters, and the predicted cell temperature, wherein the simulated plurality of characteristics of the battery (112) includes a predicted SOC and a predicted terminal voltage corresponding to the predicted cell temperature such that a future state of the battery (112) is predicted.
3. The method (800) as claimed in claim 1, wherein simulating, using the second virtual twin model (312-2), the plurality of characteristics of the charging controller comprises:
receiving the simulated plurality of characteristics of the battery (112) and the predicted cell temperature;
correlating each of the plurality of characteristics of the battery (112) and the predicted cell temperature with a predefined charging matrix;
determining, based on the correlation, a plurality of permissible charging current-values corresponding to each of the plurality of characteristics of the battery (112) and the predicted cell temperature, wherein the plurality of permissible charging current-values indicates a highest amount of electric current that the charging controller allows to flow in the battery (112) corresponding to the predicted SOC, the predicted terminal voltage, and the predicted cell temperature;
determining a final charging current-value based on selecting a minimum value from the permissible charging current-values such that the final charging current-value indicates a highest amount of electric current at which the battery (112) can be charged while maintaining safety and longevity of the battery (112); and
sending the final charging current-value to the first virtual twin model such that the initial charging current-value is replaced with the final charging current-value.

4. The method (800) as claimed in claim 1, wherein estimating the charging time of the battery (112) based on the simulation of the plurality of characteristics of the battery (112) and the charging controller, respectively, comprises:
determining a charging rate based on correlating the simulation of the plurality of characteristics of the battery (112) and the charging controller, respectively, such that the charging rate indicates a maximum allowed charging current; and
estimating the charging time of the battery (112) based on the charging rate such that the charging time indicates an amount of time to charge the battery (112) up to the one or more SOCs, wherein the one or more SOCs indicate one of, a measure of charge level or a measure of remaining capacity of the battery.
5. The method (800) as claimed in claim 4, comprising: determining the charging rate iteratively at a fixed interval of time.
6. The method (800) as claimed in claim 4, comprising:
transmitting the estimated charging time of the battery (112) to at least one of a cloud storage (124) and a Human Machine Interface (HMI) (126) associated with the EV (110) for displaying the estimated charging time corresponding to the one or more SOCs.
7. The method (800) as claimed in claim 1, comprising:
retrieving the first virtual twin model (312-1) and the second virtual twin model (312-2) among a set of virtual digital twin models based on a pre-defined battery configuration including a cell type, a battery type, and other EV parameters.
8. The method (800) as claimed in claim 1, wherein receiving, the plurality of initial battery parameters associated with the battery in response to detecting a connection of the EV (110) with a charging infrastructure (120), from a Battery Management System (BMS) (204).
9. The method (800) as claimed in claim 8, wherein the connection of the EV (110) with the charging infrastructure (120) is detected by a Load Drive Unit (LDU) (202).
10. A system (128) for estimating a charging time of a battery (112) of an electric vehicle (EV) (110), the system (128) comprising:
a memory (304);
at least one processor (302) in communication with the memory (304), the at least one processor (302) configured to:
receive, a plurality of initial battery parameters including at least one of an initial state of charge (SOC), an initial cell temperature, an initial state of health, and an initial charging current value associated with the battery (112);
simulate, using a first virtual twin model (312-2), a plurality of characteristics of the battery based on the initial SOC, the initial cell temperature, and the initial state of health; wherein the first virtual twin model (312-2) mimics behavior of the battery (112) in a digital environment;
simulate, using a second virtual twin model (312-2), a plurality of characteristics of a charging controller associated with the EV (110) based on correlation of the initial charging current-value and the simulated plurality of characteristics of the battery (112), wherein the second virtual twin model (312-2) mimics the behavior of the charging controller in the digital environment; and
estimate the charging time of the battery (112) based on the simulation of the plurality of characteristics of the battery (112) and the charging controller respectively, wherein the charging time corresponds to one or more SOCs.
11. The system (128) as claimed in claim 10, wherein to simulate, using the first virtual twin model (312-1), the plurality of characteristics of the battery (112), the at least one processor (302) is configured to:
select from a pre-stored database, an Equivalent Circuit Model (ECM) based on the plurality of initial battery parameters, wherein the ECM indicates characteristics of a cell associated with the battery (112);
predict a cell temperature of the battery (112) using a thermal model and based on correlation of the initial charging current value, an ambient cell temperature, and the initial cell temperature; and
simulate the plurality of characteristics of the battery (112) based on correlating the ECM model, the plurality of initial battery parameters, and the predicted cell temperature, wherein the simulated plurality of characteristics of the battery includes a predicted SOC and a predicted terminal voltage corresponding to the predicted cell temperature such that a future state of the battery (112) is predicted.
12. The system (128) as claimed in claim 10, wherein to simulate, using the second virtual twin model (312-2), the plurality of characteristics of the charging controller, the at least one processor (302) is configured to:
receive the simulated plurality of characteristics of the battery (112) and the predicted cell temperature;
correlate each of the plurality of characteristics of the battery (112) and the predicted cell temperature with a predefined charging matrix;
determine, based on the correlation, a plurality of permissible charging current-values corresponding to each of the plurality of characteristics of the battery (112) and the predicted cell temperature, wherein the plurality of permissible charging current-values indicates a highest amount of electric current that the charging controller allows to flow in the battery (112) corresponding to the predicted SOC, the predicted terminal voltage, and the predicted cell temperature;
determine a final charging current-value based on selecting a minimum value from the permissible charging current-values such that the final charging current-value indicates a highest amount of electric current at which the battery (112) can be charged while maintaining safety and longevity of the battery; and
send the final charging current-value to the first virtual twin model such that the initial charging current-value is replaced with the final charging current-value.

13. The system (128) as claimed in claim 10, wherein to estimate the charging time of the battery (112) based on the simulation of the plurality of characteristics of the battery (112) and the charging controller, respectively, the at least one processor (302) is configured to:
determine a charging rate based on correlating the simulation of the plurality of characteristics of the battery (112) and the charging controller, respectively, such that the charging rate indicates a maximum allowed charging current; and
estimate the charging time of the battery (112) based on the charging rate such that the charging time indicates an amount of time to charge the battery up to the one or more SOCs, wherein the one or more SOCs indicate one of, a measure of charge level or a measure of remaining capacity of the battery (112).
14. The system (128) as claimed in claim 13, wherein the at least one processor (302) is configured to: determine the charging rate iteratively at a fixed interval of time.
15. The system (128) as claimed in claim 13, wherein the at least one processor (302) is configured to:
transmit the estimated charging time of the battery (112) to at least one of a cloud storage (124) and a Human Machine Interface (HMI) (126) associated with the EV (110) for displaying the estimated charging time corresponding to the one or more SOCs.
16. The system (128) as claimed in claim 10, wherein the at least one processor (302) is configured to:
retrieve the first virtual twin model (312-1) and the second virtual twin model (312-2) among the set of virtual digital twin models based on a pre-defined battery configuration including a cell type, a battery type, and other EV parameters.
17. The system (128) as claimed in claim 10, wherein the at least one processor (302) is configured to receive, the plurality of initial battery parameters associated with the battery (112) in response to detecting a connection of the EV (110) with a charging infrastructure (110), from a Battery Management System (BMS) (204).
18. The system (128) as claimed in claim 17, wherein the at least one processor (302) is configured to detect the connection of the EV (110) with the charging infrastructure (120) by a Load Drive Unit (LDU) (202).