Description:FIELD OF THE INVENTION

[0001] The present disclosure relates to electric vehicles. More particularly, the present disclosure relates to a system and a method for charging and discharging an electric vehicle.
BACKGROUND

[0002] In recent years, electric vehicles (EVs) such as two-wheeled vehicles have gained widespread popularity due to heightened environmental concerns and increased cost competitiveness with conventional gas vehicles. Typically, an electric vehicle (EV) includes a battery as a power source unit which provides power to an electric motor of the EV for propulsion. Further, the battery is a rechargeable Lithium-ion type battery that requires to be charged from a mains supply such that the battery may provide optimal power to propel the vehicle. Conventionally, the battery gets charged in different charging stages, for example, a trickle charge state, a pre-charge, a constant current charge state, and a constant voltage charge state. Further, the battery is required to be discharged up to a predetermined level to avoid over discharging of the battery. Conventionally, the charging and the discharging of the battery is maintained by a control unit which operates to deliver maximum performance while meeting a plurality of dynamic constraints of the battery.
[0003] However, in the conventional charging or discharging of the battery, the control unit of the battery works on a rule-based logic that compute real time values of inputs, for example, temperature, cell voltage, a state of charge of the cell based on the battery and provide a desired output, that is, a current while meeting the plurality of constraints of the battery. However, the conventional charging or discharging of the battery controlled by the control unit has limitations in that the rule-based logic requires the real-time inputs which may increase gradually depending on the battery. Further, as the real-time inputs increases, the number of constraints corresponding to the number of real time inputs also increases. Thus, in this case, the control unit requires additional code/logic to manage the increased number of constraints. This configuration increases difficulty and complexity for managing the additional code/logic. Moreover, the rule-based logic provides the desired output, that is, the current that satisfies the plurality of constraints in real-time only without predicting the future status of the battery and a value of the current to meet the plurality of constraints in a future time sample. Further, while charging or discharging the battery, a dynamic upper and lower limit constraint of the battery may change suddenly/abruptly depending on a plurality of states of the battery. Thus, the control unit, unable to predict these sudden changes, may violate at least one of the upper limit constraint and the lower limit constraint. The violation of the at least one of the upper limit constraint and the lower limit constraint occurs as the current is unable to change suddenly, because of hardware and safety restrictions, to be compatible with the upper and lower limit constraint of the battery. This configuration may increase the temperature of the battery gradually which may result in the thermal runaway in the battery in the long run. As such, this may impact the life of the battery and may compromise the overall safety of the EV.
[0004] Therefore, in view of the above-mentioned problems, it is desirable to provide a system and a method that can charge and discharge the battery while meeting the plurality of constraints and reducing one or more above-mentioned problems as disclosed.
SUMMARY

[0005] This summary is provided to introduce a selection of concepts, in a simplified format, that is further described in the detailed description of the invention. This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
[0006] The present disclosure aims to provide a system and a method for optimal charging and discharging of a battery of an electric vehicle.
[0007] In an embodiment of the present disclosure, a charging and discharging system for the electric vehicle is disclosed. The system includes a circuitry and a control unit. In an embodiment, the control unit is connected with a battery of the electric vehicle via the circuitry. The control unit is configured to determine a plurality of parameters by monitoring a plurality of predetermined factors associated with the battery. The plurality of parameters includes a target cost parameter and at least one constraint parameter imposed on a magnitude of a voltage of the battery, a current of the battery, and a rate of change of the current of the battery. The control unit is configured to compare a value of a corresponding parameter, from among the plurality of parameters, with a first predetermined threshold value corresponding to a prediction sample associated with the battery and a second predetermined threshold value corresponding to a controlled sample associated with the battery. The control unit is configured to predict a status of one of a charging current profile of the battery during a charging time period or a discharging current profile of the battery during a discharging time period, based on the comparison. The control unit is configured to control, based on the predicted status of the at least one of the charging current profile and the discharging current profile, the at least one of the charging current profile and the discharging current profile such that a target State of Charge (SoC) of the battery is achieved in an optimal time while meeting the at least one constraint parameter.
[0008] In another embodiment, a method to charge and discharge an electric vehicle is disclosed. The method includes determining, by a control unit, a plurality of parameters by monitoring a plurality of predetermined factors associated with a battery. The plurality of parameters includes a target cost parameter and at least one constraint parameter imposed on a magnitude of a voltage of the battery, a current of the battery, and a rate of change of the current of the battery. The method includes comparing, by the control unit, a value of a corresponding parameter, from among the plurality of parameters, with a first predetermined threshold value corresponding to a prediction sample associated with the battery and a second predetermined threshold value corresponding to a controlled sample associated with the battery. The method includes predicting, by the control unit, based on the comparison, a status of one of a charging current profile of the battery during a charging time period or a discharging current profile of the battery during a discharging time period. The method includes controlling, based on the predicted status of the at least one of the charging current profile and the discharging current profile, the at least one of the charging current profile and the discharging current profile such that a target State of Charge (SoC) of the battery is achieved in an optimal time while meeting the at least one constraint parameter.
[0009] The present disclosure provides a configuration of the charging and discharging system along with the method to predict the charging current profile and/or the discharging current profile of the battery to determine the optimal charging and discharging current profile while meeting/satisfying the at least one constraint parameter. The system and method as disclosed also ensure charging of the battery till the target SoC in the shortest time possible. The system and method as disclosed ensures that the at least one constraint parameter is met, which eliminates the heating problem and thermal runaway problem of the battery in the long run, thereby ensuring efficient working of the battery with a long life expectancy.
[0010] 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

[0011] 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:
Figure 1 illustrates an environment of a system implemented in a vehicle, in accordance with an embodiment of the present disclosure;
Figure 2A illustrates a block diagram of the system, according to an embodiment of the present disclosure;
Figure 2B illustrates a detailed view of the block diagram depicting a control unit of the system, according to an embodiment of the present disclosure;
Figure 3 illustrates an equivalent operational module of a battery of the vehicle, according to an embodiment of the present disclosure;
Figure 4 illustrates a flowchart depicting the determination of a prediction sample and a controlled sample of the system, according to an embodiment of the present disclosure;
Figure 5 illustrates a comparison graph of operation of the system and a known art, according to an embodiment of the present disclosure;
Figure 6 illustrates a flowchart depicting the determination of the prediction sample and the controlled sample of the system, according to another embodiment of the present disclosure;
Figure 7 illustrates a flowchart depicting a method for charging and discharging the battery of the vehicle, according to an embodiment of the present disclosure; and
Figure 8 illustrates an experimental result of the system, according to an embodiment of the present disclosure.

[0012] 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

[0013] 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.
[0014] 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.
[0015] 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.”
[0016] 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.
[0017] 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.
[0018] 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.
[0019] 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.
[0020] Embodiments of the present disclosure will be described below in detail with reference to the accompanying drawings.
[0021] Figure 1 illustrates an environment 110 having a system 100 implemented in a vehicle, in accordance with an embodiment of the present disclosure. In an embodiment, the vehicle may be an Electric vehicle (EV) or a battery powered vehicle. The EV or the battery powered vehicle includes, and is not limited to a two-wheeler 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) that primarily work on the principle of driving an electric motor using the power from the batteries provided in the EV. Furthermore, the electric vehicle 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 that allows traversal of the electric vehicle over a path. The types of EVs include a Battery Electric Vehicle (BEV), a Hybrid Electric Vehicle (HEV), and a Range Extended Electric Vehicle. However, the subsequent paragraphs pertain to the different elements of the Battery Electric Vehicle (BEV). In an embodiment, the vehicle may be interchangeably referred as the electric vehicle or EV, without departing from the scope of the present disclosure.
[0022] The electric vehicle may be supported with software modules comprising intelligent features including and not limited to a navigation assistance, a hill assistance, a cloud connectivity, one or more Over-The-Air (OTA) updates, adaptive display techniques, and so on.
[0023] The firmware of the electric vehicle 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 behavior, road condition, charging infrastructures/charging grids in the vicinity, and so on. The data pertaining to the intelligent features may be displayed through a display unit present in a dashboard of the electric vehicle. 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 electric vehicle may support multiple frequency bands such as 2G, 3G, 4G, 5G, and so on. Additionally, the electric vehicle 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.
[0024] Further, in construction, the electric vehicle typically comprises hardware components such as a battery 108 or a battery module enclosed within a battery casing to form a battery pack and includes a Battery Management System (BMS), an on-board battery charger, a Motor Controller Unit (MCU), an electric motor, and an electric transmission system. The primary function of the above-mentioned elements is detailed in the subsequent paragraphs: The battery 108 of the electric vehicle (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 electric vehicle. The battery 108 is typically charged using the electric current taken from the grid through a charging infrastructure. The battery 108 may be charged using an Alternating Current (AC) or a Direct Current (DC). In the case of AC input, the on-board battery charger converts the AC signal to DC signal after which the DC signal is transmitted to the battery 108 via the BMS. However, in case of DC charging, the on-board battery charger is bypassed, and the current is transmitted directly to the battery 108 via the BMS. In an embodiment, the on-board battery charger may be interchangeably referred as a battery charger, without departing from the scope of the present disclosure.
[0025] The battery 108 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 any other battery type/configuration. The term “battery pack” as used herein may be referred to multiple individual batteries enclosed within a single structure or multi-piece structure. The individual batteries may be electrically interconnected to achieve a 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 108 is operating safely and efficiently. The BMS continuously monitors different parameters of the battery 108 such as temperature, voltage, current, and so on, and communicates these parameters to a control unit 102 and the Motor Controller Unit (MCU) in the electric vehicle 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 electric vehicle without the requirement of a host computer.
[0026] In an embodiment, the electric vehicle may be equipped with the system 100, without departing from the scope of the present disclosure. In another embodiment, the system 100 may be implemented on a cloud-based server in communication with the electric vehicle, without departing from the scope of the present disclosure. In the illustrated embodiment, the system 100 may be configured to control a least one of a current charging profile or a current discharging profile of the battery 108 such that the battery 108 reaches a target State of Charge (SoC) while meeting at least one constraint parameter.
[0027] In an embodiment, the system 100 may include, but is not limited to, a circuitry 106, and the control unit 102 as will be detailed further below, without departing from the scope of the present disclosure.
[0028] The constructional and operational aspects of the system 100 having the circuitry 106 and the control unit 102 may be explained with reference to Figures 2A, 2B, 3, 4, and 5 in conjunction with Figure 1.
[0029] Figure 2A illustrates a block diagram of the system 100, according to an embodiment of the present disclosure. Figure 2B illustrates a detailed view of the block diagram depicting the control unit 102 of the system 100, according to an embodiment of the present disclosure. Figure 3 illustrates an equivalent operational module of the battery 108 of the electric vehicle, according to an embodiment of the present disclosure. Figure 4 illustrates a flowchart depicting the determination of a prediction sample 206 and a controlled sample 208 of the system 100, according to an embodiment of the present disclosure. Figure 5 illustrates a comparison graph between operation of the system 100 and a known art, according to an embodiment of the present disclosure.
[0030] In an embodiment, the control unit 102 may be in communication with the electric vehicle. In the illustrated embodiment, the control unit 102 may be connected with the battery 108 of the electric vehicle via the circuitry 106, without departing from the scope of the present disclosure. In an embodiment, the circuitry 106 may be an Extended Kalman Filter, without departing from the scope of the present disclosure.
[0031] In an embodiment, a plurality of sensors (not shown) connected with the battery 108, may be configured to sense the plurality of predetermined factors of the battery 108 and send the sensed predetermined factors of the battery 108 to the control unit 102. The plurality of sensors sends the plurality of predetermined factors from the battery 108 to the control unit 102, directly and through the circuitry 106. In the illustrative embodiment, the plurality of predetermined factors may be based on information associated with an equivalent operational model of the battery 108. In another embodiment, the information associated with the equivalent operational model of the battery 108 may vary as per the type of the battery 108, without departing from the scope of the present disclosure.
[0032] In an embodiment, referring to Figure 3, the information associated with the equivalent operational model of the battery 108 includes at least an equivalent circuit resistance 302 of the battery 108, a polarization resistance 304 of the battery 108, a polarization capacitance 306 of the battery 108, a voltage 308 of the battery 108, an open circuit voltage 312, and a current 310 of the battery 108. In an embodiment, the control unit 102 may be configured to receive the plurality of predetermined factors. In an embodiment, the predetermined factors may include at least a type of the battery 108, a charging current required for charging the battery 108, the target SoC of the battery 108, a charging time required to reach the target SoC of the battery 108, a current limitation of the battery 108 during a charging operation, a temperature of the battery 108, and a voltage limitation of the battery 108 during the charging operation, without departing from the scope of the present disclosure. Additionally, the plurality of predetermined factors may also include resistance of a cell of the battery 108, humidity of the battery pack, gas, and pressure of the battery 108, without departing from the scope of the present disclosure.
[0033] Further, the control unit 102 may further be configured to receive the information associated with the equivalent operational module of the battery 108 to determine a time constant associated with the battery 108. The time constant is defined as a growth or decay rate of a voltage dynamic of the battery 108, without departing from the scope of the present disclosure. In an embodiment, the control unit 102 may be a model predictive control optimizer-based controller, without departing from the scope of the present disclosure.
[0034] In an embodiment, the control unit 102 connected with the battery 108 of the electric vehicle may be responsible for managing all the operations of the battery 108 of the electric vehicle. Referring to Figure 2B, the key elements of the control unit 102 typically include (i) a microcontroller core (or processor unit(s)) 218; (ii) memory unit(s) 220; (iii) module(s) 212, and (iv) communication protocols including, but not limited to a 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(s) 220 or a 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 electric vehicle.
[0035] The processor may include any computing system which includes, but is not limited to, a 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 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.
[0036] Among other capabilities, the processor is configured to fetch and execute computer-readable instructions and data stored in the memory unit 220. 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.
[0037] Furthermore, the modules 212, processes, systems, and devices can be implemented as a single processor or as a distributed processor. Also, the processes, modules 212, 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 212 can be implemented in hardware, instructions executed by the processing unit 218, or by a combination thereof. The processing unit 218 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.
[0038] The processing unit(s) 218 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 212 may be machine-readable instructions (software) which, when executed by the processor/processing unit, perform any of the described functionalities. In an embodiment, the modules 212 may include a determining module 222, a comparing module 224, a predicting module 226, and a controlling module 228. The data serves, amongst other things, as a repository for storing data processed, received, and generated by the modules 212. Exemplary structural embodiment alternatives suitable for implementing the modules 212, sections, systems, means, or processes described herein are provided below.
[0039] In an embodiment, the control unit 102, after receiving the plurality of predetermined factors and determining the time constant, may be configured to perform specific operations to control the at least one of a charging current profile and a discharging current profile of the battery 108. In the illustrated embodiment, the processing unit 218, in conjunction with the determining module 222, the comparing module 224, the predicting module 226, and the controlling module 228 may be configured to perform the specific operations to control the at least one of the charging current profile and the discharging current profile of the battery 108 explained in subsequent paragraphs.
[0040] In the illustrative embodiment, the determining module 222 is configured to determine a plurality of parameters by monitoring the plurality of predetermined factors associated with the battery 108. In an embodiment, the plurality of parameters includes a target cost parameter and the at least one constraint parameter imposed on a magnitude of a voltage of the battery 108, a current of the battery 108, and a rate of change of current of the battery 108. In an embodiment, the plurality of predetermined factors received directly by the control unit 102 is utilized by the determining module 222 to determine the at least one constraint parameter by the determining module 222. Further, the plurality of predetermined factors received by the control unit 102 from the battery 108 via the circuitry 106 and a target SoC of the battery 108 are utilized by the determining module 222 to determine the target cost parameter. Thus, the determining module 222 may be configured to determine the target cost parameter and the at least one constraint parameter based on the plurality of predetermined factors.
[0041] In an embodiment, the at least one constraint parameter corresponds to an upper limit and a lower limit imposed on each of the magnitude of the voltage of the battery 108, the current of the battery 108, and the rate of change of the current of the battery 108, without departing from the scope of the present disclosure. The upper limit and lower limit as imposed may be defined by aging a life cycle test of the battery 108 and may change dynamically based on the type of the battery 108, without departing from the scope of the present disclosure. In a non limiting example, a temperature and voltage are sensed by the plurality of sensors associated with the battery 108. Then, there may be a limit on current based on the temperature from the aging and life cycle tests. Similarly, there may be a limit on current based on the voltage from the aging and life cycle tests. Further, a first dynamic current limit based on the temperature is generated and a second dynamic current limit based on the voltage is generated to charge the battery 108. Now, a minimum of the first and second dynamic current limits are considered to generate a dynamic current limit based on the temperature and voltage as one of the at least one constraint parameter. In an embodiment, the target cost parameter may include the target SoC associated with the battery 108, without departing from the scope of the present disclosure.
[0042] In an embodiment, prior to monitoring the plurality of predetermined factors, the determining module 222 is configured to determine the prediction sample (Np) 206 and a controlled sample (Nc) 208. In a non-limiting example, the Np 206 may be 10 and the Nc may be 8. In another example, the Np 206 and the Nc 208 may be any value as per requirement, without departing from the scope of the present disclosure. In an embodiment, the determining module 222 may have the time constant from the battery 108 and a sample time of the control unit 102 as inputs, as shown at steps 402 and 404 of the Figure 4. In an embodiment, the sample time may be based on a predetermined execution cycle of the control unit 102. In an embodiment, at step 406, the determining module 222 is configured to determine a value of the prediction sample 206 and a value of the controlled sample 208 by dividing the time constant with the sample time of the control unit 102.
[0043] In an embodiment, the value of Nc may be smaller than or equal to the value of Np, without departing from the scope of the present disclosure. In an embodiment, at step 408, the determining module 222 determines whether each of the value of the prediction sample 206 and the value of the controlled sample 208 is within a threshold range of the predetermined execution cycle of the control unit 102. Further, when the determining module 222 determines that each of the value of the prediction sample 206 and the controlled sample 208 is within the threshold range of the predetermined execution cycle of the control unit 102, then, at step 412, the value of the prediction sample 206 and the controlled sample 208 is stored, in the memory unit 220, as the first predetermined threshold value and the second predetermined threshold value, without departing from the scope of the present disclosure.
[0044] In an embodiment, when the determining module 222 determines that each of the value of the prediction sample 206 and the controlled sample 208 exceeds the threshold range of the predetermined execution cycle of the control unit 102, then, the value of the prediction sample 206 and the controlled sample 208 may be reduced by a preset amount, for example, 1, as shown at step 410. This process continues till the time when the value of the prediction sample 206 and the controlled sample 208 is within the threshold range of the predetermined cycle of the control unit 102. This configuration ensures the operation of the control unit 102 within an available memory and computational power of the control unit 102, thus making the system 100 faster and more accurate.
[0045] In an embodiment, after determining the first predetermined threshold value, the second predetermined threshold value, and the plurality of parameters, the comparing module 224 may be configured to compare a value of a corresponding parameter, from among the plurality of parameters, with the first predetermined threshold value and the second predetermined threshold value. The comparing module 224 may compare based on the type of the battery 108. In an embodiment, the first predetermined threshold value corresponds to the prediction sample 206 associated with the battery 108. In an embodiment, the second predetermined threshold value corresponds to the controlled sample 208 associated with the battery 108, without departing from the scope of the present disclosure.
[0046] In an embodiment, after comparison, the predicting module 226 may be configured to predict a status of one of a charging current profile during a charging time period or a discharging current profile during a discharging time period. Additionally, the predicting module 226 also analyze the equivalent operational module of the battery 108, a goal/target of the control unit 102, for predicting the status of one of the charging current profile during the charging time period or the discharging current profile during the discharging time period, without departing from the scope of the present disclosure.
[0047] Thus, the predicting module 226 may predict the status based on the prediction samples 206, the equivalent operational module of the battery 108, the plurality of parameters, target of the control unit 102, and the controlled samples 208, without departing from the scope of the present disclosure. In an embodiment, the controlled samples 208 may be a control/limit on the prediction samples 206.
[0048] In an embodiment, based on the predicted status of the at least one of the charging current profile and the discharging current profile, the determining module 222 may determine at least one of the charging current profile of the battery 108 and the discharging current profile of the battery 108. The at least one of the charging current profile and the discharging current profile may vary depending on the at least one constraint parameter, without departing from the scope of the present disclosure.
[0049] Further, the controlling module 228 may be configured to control the at least one of the charging current profile and the discharging current profile. In an embodiment, the controlling module 228 gradually decreases a value of one of a charging current of the battery 108 or a discharging current of the battery 108, when the at least one of the charging current profile and the discharging current profile is about to reach/approaching a threshold current level. In an embodiment, the threshold current level may correspond to the at least one of the charging current profile or the discharging current profile, without departing from the scope of the present disclosure. This configuration ensures that the system 100 as disclosed controls the at least one of the charging current profile and the discharging current profile before reaching the threshold current level, thereby eliminating excess heat generation in the battery 108 due to overcharging and over discharging. In a non-limiting example, referring to Figure 5, the system 100 decreases the at least one of the charging current profile and the discharging current profile before reaching the threshold current level (shown by 502) unlike as known art, where a control unit controls the at least charging current profile and the discharging current profile, when the at least charging current profile and the discharging current profile exceeds the threshold current level (shown by 504). In yet another non limiting example, the Np 206 of the control unit 102 may be 10 and the Nc 208 of the control unit 102 may be 8. Further, the sample time of the control unit 102 may be 1 sec. In this case, the prediction module 226, based on the Np 206, may predict the state of the battery 108 for the next 10 seconds. Further, the Nc 208 may be 8 seconds. Thus, the controlling module 228 may vary/control the at least one of the charging current profile and the discharging current profile for the 8 seconds, so that the at least one constraint parameter may be satisfied for the next 10 seconds.
[0050] In an embodiment, the threshold current level may vary depending on the at least one constraint parameter, without departing from the scope of the present disclosure. The controlling module 228 controls in such a manner that the target SoC of the battery 108 may be achieved in the shortest/optimal time while meeting the at least one constraint parameter from the plurality of parameters. In an embodiment, particularly, the control unit 102 may have a time factor which enables the battery 108 to achieve the target SoC of the battery 108 in the optimal time. In a non limiting example, the Np 206 provides that after a predetermined time, for example 20 mins, the charging current or the discharging current has to be decreased up to 30 amperes as the current is about to exceed a threshold value of the charging current. In that case, the control unit 102 gradually decreases the charging current before reaching the threshold value of the charging current. This configuration ensures charging of the battery 108 to the target SoC while meeting the at least one constraint parameter, for example, the threshold current and thus, protects the life of the battery 108.
[0051] Figure 6 illustrates a flowchart depicting the determination of the prediction sample 206 and the controlled sample 208 of the system 100, according to another embodiment of the present disclosure.
[0052] In another embodiment, the determining module 222 may have a memory power constraint and computational power constraint of the control unit 102 as an input, as shown at step 602. Further, based on the input, a graph depicting the memory power constraint and the computation power constraint of the control unit 102 with respect to the prediction sample 206 and the controlled sample 208 is generated, at step 604. At step 606, the determining module 222 may determine the value of the prediction sample 206 and the value of the controlled sample 208 from predetermined table data that maps the memory power constraint and the computation power constraint required by the control unit 102 for a plurality of the prediction samples 206 and controlled samples 208. In another embodiment, the value of the prediction sample 206 and the value of the controlled sample 208 may be determined based on an available memory in the control unit 102, without departing from the scope of the present disclosure.
[0053] At step 608, the determining module 222 determines whether a resultant complexity of each of the prediction sample 206 and the controlled sample 208 is within the threshold range of the predetermined execution cycle of the control unit 102. In an embodiment, the resultant complexity may correspond to a complexity of the process performed by the control unit 102. Further, when the determining module 222 determines that the resultant complexity of each of the prediction sample 206 and the controlled sample 208 is within the threshold range of the predetermined execution cycle of the control unit 102, then, at step 610, the value of the prediction sample 206 and the value of the controlled sample 208 may be stored, in the memory unit 220, as the first predetermined threshold value and the second predetermined threshold value, respectively.
[0054] In another embodiment, the second threshold value may be less or equal to the first threshold value, without departing from the scope of the present disclosure. In another embodiment, when the determining module 222 determines that the resultant complexity of each of the value of the prediction sample 206 and the controlled sample 208 exceeds the threshold range of the predetermined execution cycle of the control unit 102, then, the value of the prediction sample 206 and the controlled sample 208 may be reduced by a preset amount, for example, 1, as shown at step 612. This process continues till the time when the value of the prediction sample 206 and the controlled sample 208 is within the threshold range of the predetermined cycle of the control unit 102. The value of the prediction sample 206 and the controlled sample 208 has to be within the threshold range of the predetermined cycle of the control unit 102, as when the value of the prediction sample 206 and the value of the controlled sample 208 increases, the complexity of the process to be performed in the control unit 102 also increases. This results in an increased computation power constraint and the memory power constraint on the control unit 102. Therefore, the process as disclosed in another embodiment provides a dynamic process for determining the prediction sample 206 and the controlled sample 208 which are within the threshold range of the predetermined cycle of the control unit 102, without departing from the scope of the present disclosure. This results in the efficient working of the control unit 102. Further, after determining the value of the prediction sample 206 and the controlled sample 208, the further process may be same as defined in conjunction with Figures 1 to 5, without departing from the scope of the present disclosure. The same is not disclosed for the sake of brevity of the present disclosure.
[0055] The present disclosure also relates to a method 700 for charging and discharging the battery 108 by the system 100 as shown in Figure 7. The order in which the method steps are described below is not intended to be construed as a limitation, and any number of the described method steps can be combined in any appropriate order to execute the method or an alternative method. Additionally, individual steps may be deleted from the method, without departing from the spirit and scope of the subject matter described herein.
[0056] The method 700 may be performed by the control unit 102, without departing from the scope of the present disclosure.
[0057] The method 700 for charging and discharging the battery 108 by the system 100 begins at step 702 where the method includes determining 702, by the control unit 102, the plurality of parameters by monitoring the plurality of predetermined factors associated with the battery 108. In an embodiment, the plurality of parameters includes the target cost parameter and the at least one constraint parameter imposed on the magnitude of the voltage of the battery, the current of the battery 108, and the rate of change of the current of the battery 108.
[0058] At step 704, the method 700 includes comparing, by the control unit 102, the value of the corresponding parameter, from among the plurality of parameters, with the first predetermined threshold value corresponding to the prediction sample 206 associated with the battery 108 and the second predetermined threshold value corresponding to the controlled sample 208 associated with the battery 108.
[0059] At step 706, the method 700 includes predicting, by the control unit 102, based on the comparison, the status of one of the charging current profile of the battery 108 during the charging time period or the discharging current profile of the battery 108 during the discharging time period.
[0060] At step 708, the method 700 includes controlling, by the control unit 102, based on the predicted status of the at least one of the charging current profile and the discharging current profile, the at least one of the charging current profile and the discharging current profile such that a target State of Charge (SoC) of the battery 108 is achieved in an optimal time while meeting the at least one constraint parameter.
[0061] Figure 8 illustrates an experimental result of the charging and discharging system, according to an embodiment of the present disclosure. From Figure 8, it is clear that, at the Np= 3 and the Nc=5, the at least one constraint parameter, that is, the voltage of the cell of the battery 108 may vary the current limit, shown as A. Similarly, based on the at least one constraint parameter, for example, the temperature of the battery 108, the current limit may vary, shown as B. Further, the control unit 102 may control the at least one of the current charging profile and the discharging current profile such that the at least one of the current charging profile and the discharging current profile may not exceed the threshold value of the current limit. Further, the target SoC, for example, 92%, of the battery 108 is achieved while meeting/satisfying the at least one constraint parameter, shown as C.
[0062] As would be gathered, the present disclosure ensures the charging and discharging system 100 and the method 700 thereof. The system 100 as disclosed controls the at least one of the charging current profile and the discharging current profile before reaching the threshold value unlike as known art, thereby eliminating excess heat generation in the battery 108 due to overcharging and over discharging. Further, this configuration ensures efficient working of the battery 108 while maintaining longer life of the battery 108. This configuration ensures safe charging and discharging in the battery 108 while meeting the at least one constraint parameter. This configuration ensures charging of the battery 108 up to the target State of Charge (SoC) by the control unit 102 in the optimal/shortest time possible, thus ensuring comfort of user using the battery 108 in the vehicle and maintaining the life of the battery 108.
[0063] 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).
[0064] Furthermore, embodiments of the disclosed methods, processes, modules, devices, systems, and computer program product 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 product 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.
[0065] 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
, Claims:1. A charging and discharging system (100) for an electric vehicle, the system (100) comprising:
a circuitry (106); and
a control unit (102) connected with a battery (108) of the electric vehicle via the circuitry (106), the control unit (102) configured to:
determine a plurality of parameters by monitoring a plurality of predetermined factors associated with the battery (108), wherein the plurality of parameters includes a target cost parameter and at least one constraint parameter imposed on a magnitude of a voltage of the battery (108), a current of the battery (108), and a rate of change of the current of the battery (108);
compare a value of a corresponding parameter, from among the plurality of parameters, with a first predetermined threshold value corresponding to a prediction sample (206) associated with the battery (108) and a second predetermined threshold value corresponding to a controlled sample (208) associated with the battery (108);
predict, based on the comparison, a status of one of a charging current profile of the battery (108) during a charging time period or a discharging current profile of the battery (108) during a discharging time period; and
control, based on the predicted status of the at least one of the charging current profile and the discharging current profile, the at least one of the charging current profile and the discharging current profile such that a target State of Charge (SoC) of the battery (108) is achieved in an optimal time while meeting the at least one constraint parameter.

2. The system (100) as claimed in claim 1, wherein the plurality of predetermined factors comprises at least a type of the battery (108), a charging current required for charging the battery (108), the target SoC of the battery (108), a charging time required to reach the target SoC of the battery (108), a current limitation of the battery (108) during a charging operation, a temperature of the battery (108), and a voltage limitation of the battery (108) during the charging operation.

3. The system (100) as claimed in claim 1, wherein the at least one constraint parameter corresponds to an upper limit and a lower limit imposed on each of the magnitude of the voltage of the battery (108), the current of the battery (108), and the rate of change of the current of the battery (108).

4. The system (100) as claimed in claim 1, wherein the circuitry (106) is an Extended Kalman Filter (EKF).

5. The system (100) as claimed in claim 1, wherein:
the plurality of predetermined factors is based on information associated with an equivalent operational model of the battery (108), and
the control unit (102) is configured to determine the target cost parameter and the at least one constraint parameter based on the plurality of predetermined factors.
6. The system (100) as claimed in claim 5, wherein:
the information associated with the equivalent operational model of the battery (108) includes at least an equivalent circuit resistance (302) of the battery (108), a polarization resistance (304) of the battery (108), a polarization capacitance (306) of the battery (108), a voltage (308) of the battery (108), and a current (310) of the battery (108), and
the control unit (102) is configured to determine a time constant based on the information associated with the equivalent operational model of the battery (108).
7. The system (100) as claimed in claim 6, wherein, prior to monitoring of the plurality of predetermined factors, the control unit (102) is configured to:
determine a value of the prediction sample (206) and a value of the controlled sample (208) by dividing the time constant with a sample time of the control unit (102);
determine whether each of the value of the prediction sample (206) and the value of the controlled sample (208) is within a threshold range of a predetermined execution cycle of the control unit (102); and
store, in a memory unit (220) upon a determination that each of the value of the prediction sample (206) and the value of the controlled sample (208) is within the threshold range of the predetermined execution cycle of the control unit (102), the value of the prediction sample (206) and the value of the controlled sample (208) as the first predetermined threshold value and the second predetermined threshold value, respectively.

8. The system (100) as claimed in claim 6, wherein, prior to monitoring of the plurality of predetermined factors, the control unit (102) is configured to:
generate a graph depicting a memory power constraint and a computation power constraint of the control unit (102) with respect to the prediction sample (206) and the controlled sample (208);
determine a value of the prediction sample (206) and a value of the controlled sample (208) from predetermined table data that maps the memory power constraint and the computation power constraint required by the control unit (102) for a plurality of the prediction samples (206) and the controlled samples (208);
determine whether a resultant complexity of each of the prediction sample (206) and the controlled sample (208) is within a threshold range of a predetermined execution cycle of the control unit (102);
store, in a memory unit (220) upon a determination that the resultant complexity of each of the prediction sample (206) and the controlled sample (208) is within the threshold range of the predetermined execution cycle of the control unit (102), the value of the prediction sample (206) and the value of the controlled sample (208) as the first predetermined threshold value and the second predetermined threshold value, respectively.

9. The system (100) as claimed in claim 8, wherein the second predetermined threshold value is equal to or less than the first predetermined threshold value.

10. The system (100) as claimed in claim 1, wherein the control unit (102) is a model predictive control optimizer-based controller.
11. The system (100) as claimed in claim 1, wherein, to control the at least one of the charging current profile and the discharging current profile, the control unit (102) is configured to:
gradually decrease a value of one of a charging current of the battery (108) or a discharging current of the battery (108) when the at least one of the charging current profile and the discharging current profile is about to reach a threshold current level corresponding to the at least one of the charging current profile or the discharging current profile.

12. A method (600) to charge and discharge an electric vehicle, the method (600) comprising:
determining (702), by a control unit (102), a plurality of parameters by monitoring a plurality of predetermined factors associated with a battery (108), wherein the plurality of parameters includes a target cost parameter and at least one constraint parameter imposed on a magnitude of a voltage of the battery (108), a current of the battery (108), and a rate of change of the current of the battery (108);
comparing (704), by the control unit (102), a value of a corresponding parameter, from among the plurality of parameters, with a first predetermined threshold value corresponding to a prediction sample (206) associated with the battery (108) and a second predetermined threshold value corresponding to a controlled sample (208) associated with the battery (108);
predicting (706), by the control unit (102), based on the comparison, a status of one of a charging current profile of the battery (108) during a charging time period or a discharging current profile of the battery (108) during a discharging time period; and
controlling (708), by the control unit (102), based on the predicted status of the at least one of the charging current profile and the discharging current profile, the at least one of the charging current profile and the discharging current profile such that a target State of Charge (SoC) of the battery (108) is achieved in an optimal time while meeting the at least one constraint parameter.