Adaptive charge scheduling for energy storage devices of electric vehicles

Abstract

Approaches for adaptive charge scheduling in electric vehicles are described. As per the method, user preferences with respect to a desired charging time, state-of-charge (SoC), or both are obtained. Based on the user preferences and real-time operational data, a linear programming script may determine optimal charging parameters for the electric vehicle (102, 202). A control unit (108, 204) of the electric vehicle (102, 202) may generate a charging schedule based on the optimized charging parameters. Accordingly, charging of the electric vehicle (102, 202) is initiated in view of the charging schedule. In addition, the charging session is continuously monitored and the charging schedule may be adjusted based on real-time feedback from the energy storage device (106, 206).

Description

ADAPTIVE CHARGE SCHEDULING FOR ENERGY STORAGE DEVICES OF ELECTRIC VEHICLESTECHNICAL FIELD

[0001] The subject matter described herein, in general relates to charging of electric vehicles, and more particularly, to adaptive charge scheduling for energy storage devices of electric vehicles.BACKGROUND

[0002] Electronic devices, such as smart phones, laptops, PC’s, and other electrically powered systems, such as electric vehicles, may use an energy storage device, such as a rechargeable battery for powering. The energy storage device provides electrical energy to power different components of such devices or systems. Once discharged, the electronic system needs to be connected to a power supply source for recharging the energy storage device up to a certain state of charge (SoC). The energy storage device of the electronic devices may be charged by providing appropriate charging current.BRIEF DESCRIPTION OF FIGURES

[0003] The detailed description is provided with reference to the accompanying figures, wherein:

[0004] FIG. 1 illustrates an exemplary charging environment depicting an electric vehicle connected to a power supply source for charging an energy storage device, in accordance with an example of the present subject matter;

[0005] FIG. 2 illustrates a block diagram depicting components of an electric vehicle, in accordance with an example of the present subject matter; and

[0006] FIG. 3 illustrates an exemplary method for scheduling charging session of an energy storage device of the electric vehicle based on user input, in accordance with an example of the present subject matter.

[0007] Throughout the drawings, identical reference numbers designate similar, but not necessarily identical, elements. The figures are not necessarily to scale, and the size of some parts may be exaggerated to more clearly illustrate the example shown. Moreover, the drawings provide examples and / or implementations consistent with the description; however, the description is not limited to the examples and / or implementations provided in the drawings.DETAILED DESCRIPTION

[0008] Different components of electronic or mechanical devices, e.g., electric motor in an electric vehicle, operate on electric energy which may be provided by an energy storage device, such as a rechargeable battery. Such components draw power from the energy storage device to function appropriately. With passage of time as the device is used, the energy storage device may get discharged. Once discharged, due to it rechargeable capacities, the energy storage device may be recharged by connecting it to a power supply source. With increasing demand of device which are powered through electrical source of energy, specifically, electric vehicles, quick charging times for charging the energy storage device has become an essential requirement.

[0009] Electric vehicles (EVs) have gained significant popularity in recent years as a more environment friendly alternative to traditional combustion engine vehicles. However, the widespread adoption of EVs faces several challenges, one of which is the efficient and safe charging of their batteries. Charging an EV battery is not as simple as plugging the battery in and leaving the battery unattended. Various factors may affect the charging process, including the battery's current state of charge (SoC),temperature, and a user's specific charging needs. Improper charging may lead to reduced battery life, decreased efficiency, and in extreme cases, safety hazards.

[0010] Generally, prolonged charging sessions, such as overnight charging, may cause challenges and issues with respect to safety and battery life. Users often plug in their vehicles before going to bed, intending to have a fully charged battery by morning. However, leaving an EV plugged in for extended periods without proper management may lead to overcharging, which may damage the battery and reduce its overall lifespan. Additionally, users may have specific charging requirements based on their schedules and usage patterns. For instance, a user might need their vehicle to be charged to a certain level by a specific time, or they may want to take advantage of off-peak electricity rates. Existing charging techniques lack the flexibility to accommodate these varied user preferences while still maintaining optimal charging conditions for the battery of the EV.

[0011] Furthermore, the charging process is dynamic, with factors such as battery temperature, charging rate, and SoC changing throughout the charging session. Many existing charging systems do not adequately account for these real-time changes, potentially leading to suboptimal charging performance and reduced battery health over time.

[0012] To this end, the present subject matter describes techniques for adaptive charge scheduling for electric vehicle batteries. The charge scheduling takes into account user preferences, real-time battery feedback, and uses advanced optimization techniques to ensure efficient and safe charging under various conditions.

[0013] The present subject matter describes techniques for adaptively charging an energy storage device of an electric vehicle. A control unit of the electric vehicle may obtain user input indicative of charging preferences. The user input may include at least one of a target state of charge (SoC) level, a desired charging duration, and an expected vehicle departure time.Based on the user input and real-time operational data of the energy storage device, the control unit may determine optimal charging parameters for the electric vehicle. These optimal charging parameters may include at least one of charging current and charging voltage. The real-time operational data may comprise at least one of current state of charge (SoC), target state of charge, charging time, and current temperature of the energy storage device.

[0014] The control unit may then generate a charging schedule based on the optimal charging parameters. Charging of the energy storage device may be initiated according to this charging schedule. In some aspects, the control unit may monitor the charging of the energy storage device in realtime. Based on this monitoring and updated operational data of the energy storage device, the control unit may adjust the charging schedule.

[0015] In an implementation, the control unit may accommodate changes in the user input during the charging of the energy storage device and recalculate the optimal charging parameters based on these changes. Additionally, the control unit may receive real-time feedback from a charger of the electric vehicle during the charging of the energy storage device. This feedback may include at least one of power input, charging rate, and power quality issues. The control unit may adjust the charging schedule based on this received real-time feedback to optimize power delivery from the charger to the energy storage device.

[0016] The adaptive approach as per the present subject matter allows the system to respond to changing conditions throughout the charging session, minimizing risks associated with extended charging periods, such as overcharging or battery damage, particularly during overnight charging or other prolonged durations. The approaches of the present subject matter not only optimizes battery charging to maintain battery health and longevity but also enhances user convenience by allowing for scheduled charging sessions according to user preferences. In some cases, the chargescheduling strategy may be implemented in electric vehicles, such as scooters, to improve the user experience and extend the life of the vehicle's battery.

[0017] The present subject matter is further described with reference to the accompanying figures. Wherever possible, the same reference numerals are used in the figures and the following description to refer to the same or similar parts. It should be noted that the description and figures merely illustrate principles of the present subject matter. It is thus understood that various arrangements may be devised that, although not explicitly described or shown herein, encompass the principles of the present subject matter. Moreover, all statements herein reciting principles, aspects, and examples of the present subject matter, as well as specific examples thereof, are intended to encompass equivalents thereof.

[0018] The manner in which the present subject matter is implemented are explained in detail with respect to FIG. 1 to FIG. 3. While aspects of described subject matter can be implemented in any number of different devices, environments, and / or implementations, the examples are described in the context of the following system(s). It is to be noted that drawings of the present subject matter shown here are for illustrative purposes and are not drawn to scale.

[0019] FIG. 1 illustrates an exemplary charging environment 100 depicting an electric vehicle 102 connected to a power supply source 104 for charging an energy storage device 106, in accordance with an example of the present subject matter. In an example, the energy storage device 106 is used as an electrical energy source to power different components of the electric vehicle 102. Examples of such electric vehicle 102 may include, but are not limited to, electric two-wheeler, electric car, electric bus, etc. Further, the energy storage device 106 may comprise various types of rechargeable batteries or battery packs. For instance, energy storage device 106 may include lithium-ion batteries, nickel-metal hydride (NiMH) batteries, solid-state batteries, and so on. The energy storage device 106 may supply power to various components of the electric vehicle 102, such as the electric motor for propulsion, a climate control system, infotainment systems, lights, and other onboard electronics. The capacity and configuration of the energy storage device 106 may vary depending on the specific requirements of the electric vehicle 102, including its size, range, and performance characteristics.

[0020] Although, the electric vehicle 102 in FIG. 1 is depicted as connected to the power supply source 104 (referred to as the power source 104) at the back end of the electric vehicle, the same may not be construed as a limitation. Further, although the description is provided with respect to the electric vehicle 102 (hereinafter referred to as vehicle 102) including the energy storage device 106, the same may also be implemented for any electronic device comprising the energy storage device without deviating from the scope of the present subject matter.

[0021] In addition, the power supply source 104 may include various types of charging infrastructure designed for electric vehicles, the power supply source 104 may be a residential charging station, typically installed in a home garage or driveway, providing Level 1 (120V AC) or Level 2 (240V AC) charging capabilities. Alternatively, the power supply source 104 could be a public charging station found in parking lots, shopping centers, or along highways, offering Level 2 or DC fast charging options. In some cases, the power supply source 104 may represent a high-power DC fast charging station capable of delivering up to 350 kW, enabling rapid charging for compatible electric vehicles.

[0022] In an example, the vehicle 102 may include a control unit 108 to manage and regulate various functions and operations of the vehicle 102, such as charging of the energy storage device 106 based on user preferences. The control unit 108 may obtain real-time operational data of the energy storage device 106, including parameters such as current stateof charge, temperature, and voltage levels. In an example, the control unit 108 may be implemented as a hardware or software-based application on the vehicle 102 (as described in FIG. 1 ). However, other implementations of the control unit 108 may also be possible, without deviating from the scope of the present subject matter. The control unit 108 may receive inputs from various sensors (not shown) and user interfaces, process this information using embedded software or firmware, and output control signals to different components of the electric two-wheeler to optimize performance, efficiency, and safety.

[0023] In an example, a user may provide input indicative of charging preferences for the electric vehicle 102. This user input may include a desired charging time, a target state-of-charge (SoC), an expected vehicle departure time, or a combination thereof. The user input may be facilitated through a user interface (not shown), which may be integrated into the electric vehicle 102 as part of a human machine interface (HMI) or accessible through a separate device, such as a mobile application. The user interface may provide options for the user to specify the charging preferences. For instance, a user may specify that the vehicle 102 should be charged for a certain number of hours, until the battery reaches a specific SoC, or be fully charged by a particular departure time.

[0024] Based on the user input and real-time operational data of the energy storage device 106, the control unit 108 may determine optimal charging parameters for the electric vehicle 102. The optimal charging parameters may include optimal current and voltage values for charging, while considering factors such as the current temperature of the energy storage device, charging time constraints, and current and target SoC. In an example, the control unit 108 may employ a linear programming script or other optimization algorithms to determine these optimal charging parameters. Using these parameters, the control unit 108 may generate a charging schedule tailored to the user's preferences and the vehicle'scurrent state. The control unit 108 may then initiate charging of the energy storage device 106 according to the charging schedule.

[0025] During the charging process, the control unit 108 may continuously monitor the charging and receive real-time feedback from the charger of the electric vehicle 102. This feedback may include data on power input, charging rate, and any power quality issues. The control unit 108 uses this information to dynamically adjust the charging schedule as needed, ensuring efficient and accurate charging of the energy storage device 106 while adhering to the user's preferences and maintaining optimal battery health.

[0026] The above description depicts the vehicle 102 as a four-wheeler; however, the same ought not to be considered as a limitation. Similar approaches may be applicable for even two-wheeled three-wheeled vehicles, without deviating from the scope of the present subject matter.

[0027] By obtaining user input indicative of charging preferences, the present subject matter provides a personalized charging experience to accommodates individual needs and schedules. The determination of optimal charging parameters based on both user input and real-time operational data allows for a dynamic and efficient charging process that adapts to changing conditions. This approach helps maximize charging efficiency while maintaining battery health and longevity. Furthermore, the ability to charge according to the charging schedule, while continuously monitoring and adjusting based on real-time feedback, facilitates optimizing power delivery and responding to any unexpected changes or issues during the charging session.

[0028] FIG. 2 illustrates a block diagram 200 depicting components of an electric vehicle 202, according to an example of the present subject matter. In an example, the electric vehicle 202 is similar to the electric vehicle 102, which includes a control unit 204, similar to control unit 108, for performing processes related to the functional aspects of the electric vehicle202, e.g., the control unit 204 is to receive inputs from a user for charging the battery of the vehicle 202.

[0029] In an example, the control unit 204 may be implemented within one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, micro-controllers, microprocessors, electronic devices, other electronic units designed to perform the functions described herein, or in combination thereof. For a firmware and / or software implementation, the methodologies may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein.

[0030] Further, the vehicle 202 includes an energy storage device 206. The energy storage device 206 may include a battery pack configured to store electrical energy. The battery pack may comprise multiple battery cells arranged in a housing. In some aspects, the energy storage device 206 may include a thermal management system to regulate the temperature of the battery pack. The energy storage device 206 may also incorporate a battery management system (BMS) (not shown) to monitor and control various parameters of the battery pack, such as state of charge, voltage, and current. The BMS acts as an intermediary between the energy storage device 206 and the control unit 204, facilitating communication through a dedicated data bus, such as a Controller Area Network (CAN) bus or a Local Interconnect Network (LIN). This allows for real-time transmission of operational data from the energy storage device 206 to the control unit 204. The BMS may continuously collect and process data from various sensors within the battery pack, including temperature sensors, voltage sensors, and current sensors. Thereafter, the BMS may package this information and send it to the control unit 204 at regular intervals, typically several times per second. This constant flow of real-time data enables the control unit 204 tomake informed decisions about the charging process and adjust parameters as needed. The energy storage device 206 may be designed to be rechargeable, allowing for replenishment of stored energy through various charging methods.

[0031] The vehicle 202 may be connected to a power supply source 208 for enabling charging of the energy storage device 206. The power supply source 208 may include residential power outlets, public charging stations, and dedicated EV charging infrastructure. The power supply source 208 may provide alternating current (AC) or direct current (DC) power, with the vehicle's onboard charging system adapting to the available power type.

[0032] Further, the vehicle 202 may include a human machine interface (HMI) 210 that may be configured to receive user input 212 regarding various aspects of the energy storage device 206 and charging process. The HMI 208 may allow users to input preferences related to the duration of charging and desired state of charge (SoC) of the battery pack. These inputs form the basis for personalized approach for adaptive charing. For instance, users can specify a target charging time (e.g., "charge for 6 hours"), a desired final SoC (e.g., "charge to 80%"), or an expected departure time (e.g., "fully charged by 7 AM tomorrow").

[0033] In some implementations, the HMI 210 may include a touchscreen display, physical buttons, voice recognition capabilities, or a combination thereof. The touchscreen display may present an intuitive graphical interface allowing users to input their preferences through sliders, dropdown menus, or numerical input fields. Physical buttons might provide quick access to common charging presets or navigation through menu options. Voice recognition capabilities could enable hands-free operation, allowing users to set charging preferences through voice commands.

[0034] The HMI 210 may also serve as an output device, displaying realtime charging information to the user as specified in the claims. This may include the current state of charge (SoC), estimated time to full charge, andthe current charging rate. Additionally, it might show other relevant information such as the optimized charging schedule, energy consumption data, and any adjustments made to the charging process in real-time.

[0035] The vehicle 202 may further include a charging circuit 214 configured to manage the flow of electrical energy from the power supply source 208 to the battery pack. The charging circuit 214 may comprise power electronics components, such as AC-DC converters, DC-DC converters, and rectifiers to condition the incoming power for battery charging. In some aspects, the charging circuit 214 may support multiple charging modes, including slow charging, fast charging, and wireless charging. The charging circuit 214 may communicate bidirectionally with the BMS and the control unit 204 to regulate the charging process based on the battery's current state and user preferences input through the HMI 210. The charging circuit 214 continuously provides real-time feedback to the control unit 204. This feedback may include data on power input, charging rate, and any power quality issues. For example, the charging circuit 214 may report the actual current and voltage being delivered to the energy storage device 206, allowing the control unit 204 to compare these values with the optimal parameters calculated.

[0036] The charging circuit 214 may also rapidly adjust its output based on instructions from the control unit 204. If the control unit 204 determines that the charging parameters need to be modified, due to changes in battery temperature, unexpected fluctuations in charging rate, or updates to user preferences, the control unit 204 may instruct the charging circuit 214 to alter the output accordingly. In an example, the charging circuit 214 may incorporate safety features to prevent overcharging, overheating, or other potentially harmful conditions.

[0037] In an example, the control unit 204 may further include a processor 216, interface(s) 218, a memory(s) 220, and data 222. The processor 216 may be implemented as microprocessors, microcomputers,microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and / or other devices that manipulate signals based on operational instructions. The interface(s) 218 may allow the connection or coupling of the control unit 204 with one or more other devices, through a wired (e.g., Local Area Network, i.e. , LAN) connection or through a wireless connection (e.g., Bluetooth®, Wi-Fi). The interface(s) 218 may also enable intercommunication between different logical as well as hardware components of the control unit 204.

[0038] The memory(s) 220 may be a computer-readable medium, examples of which include volatile memory (e.g., RAM), and / or non-volatile memory (e.g., Erasable Programmable read-only memory, i.e., EPROM, flash memory, etc.). The memory(s) 220 may be an external memory, or internal memory, such as a flash drive, a compact disk drive, an external hard disk drive, or the like. The memory(s) 220 may further include data which either may be utilized or generated during the operation of the control unit 204.

[0039] The data 222, on the other hand, includes a current SoC data 224, a target SoC data 226, a charging time data 228, a current temperature data 230, and other data 232. Further, the other data 232, amongst other things, may serve as a repository for storing data that is processed, or received, or generated as a result of the execution of instructions comprised in the control unit 204.

[0040] In operation, a user may provide input through the HMI 210. The user input 212 may indicate a desired charging time of the energy storage device 206, a desired state of charge (SoC) of the energy storage device 206, an expected vehicle departure time, or a combination thereof. In some cases, the user may specify a desired charging time without specifying a desired SoC or departure time. For example, a user may prefer to charge the vehicle 202 overnight, specifying a charging time of 8 hours starting from 10 PM. In this case, the control unit 204 may determine the optimalcharging parameters to charge the energy storage device 206 within the specified time, while ensuring that the energy storage device 206 does not overcharge or overheat.

[0041] Conversely, the user may specify a desired SoC or an expected departure time without specifying a desired charging time. For instance, a user may want to charge the vehicle 202 to 80% SoC or have it fully charged by 7 AM the next day. In this case, the control unit 204 may adjust the charging parameters to meet the specified requirements while optimizing for battery health and charging efficiency.

[0042] In other cases, the user may specify multiple parameters, such as both a desired charging time and a desired SoC, or an expected departure time and a desired SoC. Here, the control unit 204 may determine the optimal charging parameters to charge the energy storage device 206 to the specified SoC within the specified time. This may involve a balance between charging speed and battery health, as charging too quickly may overheat and overcharge, while charging too slowly may not achieve the desired SoC within the specified time.

[0043] In response to the user input 212, the processor 216 of the control unit 204 may determine optimal charging parameters for the electric vehicle based on both the user input and real-time operational data of the energy storage device 206. As used herein, the term "optimal charging parameters" may refer to a set of values or settings that govern the charging process of an energy storage device in an electric vehicle. These parameters are determined to achieve the most favorable balance between various factors including, but not limited to, charging efficiency, battery health, user preferences, and system constraints. Optimal charging parameters typically comprise at least one of charging current and charging voltage, and may also include other variables such as charging duration, charging rate profiles, and temperature thresholds. For example, the processor 216 may run optimization algorithms, such as a linearprogramming script, to determine the optimal charging parameters, including current and voltage, based on the user inputs 212 and real-time operational data of the energy storage device 206.

[0044] In an example, the optimization algorithm, such as the linear programming script may be implemented in a separate device, such as a server or a cloud-based system. The linear programming script may use mathematical models to optimize the charging parameters, considering the user-specified charging time and / or SoC, as well as the real-time feedback from the energy storage device 206. The linear programming script aims to maximize charging efficiency while meeting user requirements and ensuring battery health. This optimization process considers factors such as the target SoC 226, desired charging time 228, current SoC 224, current temperature 230, and other relevant data to maximize charging efficiency while meeting user requirements and ensuring battery health.

[0045] Based on the above, the processor 216 may generate a charging schedule based on the optimal charging parameters. The charging schedule may specify the optimal current and voltage values for each time interval during the charging session. The charging schedule may represent a comprehensive plan for the entire charging process, taking into account various factors and constraints. The charging schedule may divide the charging session into multiple time intervals, each with its own set of optimal charging parameters. For example, the charging schedule may specify a lower charging current during the initial stages to prevent stress on the battery cells, gradually increasing the current as the charging progresses. The charging schedule may also include periods of reduced charging rate or even brief pauses to allow for thermal management, ensuring the battery temperature remains within safe limits. The charging schedule may also account for expected variations in power supply, such as differences in electricity rates during peak and off-peak hours, to optimize charging costs if such information is available. In cases where the user has specified anexpected departure time, the charging schedule may be designed to reach the desired state of charge just in time for the departure, minimizing the time the battery spends at high charge levels.

[0046] Upon generation of the charging schedule, the control unit 204 may communicate with the charging circuit 214 to initiate the charging of the energy storage device 206. In an example, once the charging has initiated, the control unit 204, may continuously monitor various parameters associated with the charging session. For example, the control unit 204 may continuously collect data from a Battery Management System (BMS) to monitors battery parameters, such as state of charge (SoC), cell voltages, current, and temperature. Further, the control unit 204 may monitor the charging circuit 214 for data on power input, charging rate, and any power quality issues. The data collected by the control unit 204 may be further processed in real-time by the processor 216. The processor 216 may compare the data with user-defined parameters and system constraints. Based on the processed data, the control unit 204 may make real-time adjustments in the charge scheduling. For example, the control unit 204 may modify the charging speed to optimize for battery health, electricity costs, or time constraints.

[0047] For instance, if the current or voltage of the energy storage device 206 deviates from the optimal values determined by the optimization algorithm, the control unit 204 may adjust the charging parameters in realtime to bring them back to the optimal values. Similarly, if the SoC of the energy storage device 206 changes, the control unit 204 may adjust the charging parameters to ensure that the energy storage device 206 is charged to the user-specified SoC within the specified time. This may involve increasing the charging current or voltage if the battery's SoC is not increasing as expected or reducing the charging current or voltage if the battery's SoC is increasing too quickly.

[0048] In another example, the control unit 204 may adjust cooling or heating systems to maintain optimal battery temperature. In an example, the control unit 204 may reduce the charging current or voltage if the energy storage device 206 is overheating or increasing the charging current or voltage if the energy storage device 206 is not reaching the desired SoC within the specified time.

[0049] In an example, the control unit 204 may accommodate changes in user input mid-charging. If the user modifies the desired charging time, SoC, or expected departure mid-charging, the control unit 204 may recalculate the optimal charging parameters based on these changes. The optimization algorithm may re-calculate the new user input, and the charging circuit 214 may adjust a charger's output in real-time based on the updated charging parameters. This flexibility may allow the user to adapt the charging process to changing circumstances or needs, enhancing the user experience.

[0050] In an example, consider a scenario where a user initially sets the electric vehicle to charge to 80% state of charge (SoC) by 7:00 AM the next morning, expecting to leave for work at that time. The control unit 204 may calculate the optimal charging parameters and initiate the charging process accordingly. However, at 11 :00 PM, the user receives an urgent message about an early morning meeting and needs to leave by 6:00 AM instead. Using the HMI 210, the user may update their expected departure time from 7:00 AM to 6:00 AM. The control unit 204 immediately detects this change in the user input and triggers a re-calculation of the optimal charging parameters. Accordingly, the optimization algorithm may take into account the new time constraint, the current state of charge, the battery's temperature, and other relevant factors.

[0051] Based on this re-calculation, the control unit 204 may determine that to reach the desired 80% SoC by the new 6:00 AM deadline, the control unit 204 may need to increase the charging rate. As a result, the control unit2204 may generate a new charging schedule with updated optimal current and voltage values for the remaining time intervals. The control unit 204 may then communicate these new parameters to the charging circuit 214, which adjusts its output in real-time. The charging current is increased, accelerating the charging process while still maintaining safe operating conditions for the battery.

[0052] Throughout the charging process, the control unit 204 may continue to communicate with the charging circuit 214, sending updated instructions as specified in the charging schedule or as necessitated by realtime adjustments. This ongoing communication ensures that the charging process adheres to the optimized schedule while remaining flexible enough to adapt to any changes in conditions or user preferences. Further, the HMI 210 displays relevant charging information to the user. This may include the current state of charge (SoC), estimated time to full charge, charging rate, and any adjustments made to the charging schedule. This real-time feedback keeps the user informed and engaged with the charging process.

[0053] Accordingly, the present subject matter provides a dynamic and responsive solution for charging electric vehicles, allowing for continuous monitoring and adjustment of the charging process based on real-time feedback from the energy storage device, such as the battery. This may ensure that the battery is charged efficiently and accurately, while maintaining its health and longevity. The adaptive charging integrates user preferences, real-time battery data, and environmental factors to create an optimized charging schedule that can be adjusted on the fly. By balancing user needs with battery health considerations, the system not only enhances the user experience but also potentially extends the lifespan of the energy storage device, contributing to the overall sustainability and costeffectiveness of electric vehicle ownership.

[0054] FIG. 3 illustrates an exemplary method 300 for adaptively charging an energy storage device of an electric vehicle, in accordance withan example of the present subject matter. The order in which the method is described is not intended to be construed as a limitation, and any number of the described method blocks may be combined in any order to implement the methods, or an alternative method. Further, the method 300 may be implemented by processing resource or computing device(s) through any suitable hardware, non-transitory machine-readable instructions, or combination thereof.

[0055] It may also be understood that the method 300 may be performed by programmed devices, such as the control unit 108 and 204, as depicted in FIGS. 1 and 2. Furthermore, the method 300 may be executed based on instructions stored in a non-transitory computer-readable medium, as will be readily understood. The non-transitory computer-readable medium may include, for example, digital memories, magnetic storage media, such as one or more magnetic disks and magnetic tapes, hard drives, or optically readable digital data storage media. While the method 300 is described below with reference to the control units 108 and 204 as described above; other suitable systems for the execution of these methods may also be utilized. Additionally, implementation of the method is not limited to such examples.

[0056] At block 302, the method 300 may include obtaining a user input indicative of charging preferences of a user of an electric vehicle, such as the vehicle 102. In an example, the user input may be obtained through the HMI 210. The user input may indicate a desired charging time of the energy storage device 206, a desired state of charge (SoC) of the energy storage device 206, an expected vehicle departure time, or a combination thereof. In some cases, the user may specify a desired charging time without specifying a desired SoC or departure time. Conversely, the user may specify a desired SoC or an expected departure time without specifying a desired charging time. In an example, the control unit 204 may obtain the charging preferences from the user.

[0057] At block 304, the method 300 may include determining, based on the user input and real-time operational data of the energy storage device 206, optimal charging parameters for the electric vehicle. In an example, the optimal charging parameters may include a charging current, a charging voltage, or both for charging the energy storage device. In an example, the control unit 204 may process the user input through a linear programming script to determine the optimal charging parameters for charging the energy storage device 206. The linear programming script may process the user input by incorporating the user input into mathematical models. The linear programming script may also consider constraints related to temperature, time, and SoC during the optimization process. For instance, the linear programming script may aim to minimize the charging time while ensuring that the temperature of the energy storage device does not exceed a certain limit, and that the final SoC does not exceed the user-specified SoC.

[0058] At block 306, the method 300 may include generating a charging schedule based on the optimal charging parameters. In an example, the control unit 204 may generate the charging schedule that divides the charging session into multiple time intervals. Further, the charging schedule may specify the optimal current and voltage values for each time interval during the charging session. For example, the charging schedule may specify a lower charging current during the initial stages to prevent stress on the battery cells, gradually increasing the current as the charging progresses.

[0059] At block 308, the method 300 may include initiating the charging of the energy storage device based on the charging schedule. In an example, the control unit 204 may communicate with the charging circuit 214 to initiate charging of the energy storage device 206 based on the charging schedule.

[0060] At block 310, the method 300 may include monitoring the charging of the energy storage device in real-time. For example, once thecharging has initiated, the control unit 204 may continuously monitor various parameters associated with the charging session.

[0061] At block 312, the method 300 may include adjusting the charging schedule based on the monitored charging and updated operational data of the energy storage device. In an example, the control unit 204 may make real-time adjustments in the charge scheduling. For example, the control unit 204 may modify the charging speed to optimize for battery health, electricity costs, or time constraints. In an example, the control unit 204 may adjust cooling or heating systems to maintain optimal battery temperature. In another example, if the current or voltage of the energy storage device 206 deviates from the optimal values determined by the linear programming script, the control unit 204 may adjust the charging parameters in real-time to bring them back to the optimal values.

[0062] In an implementation, the control unit may receive real-time feedback from a charger of the electric vehicle during the charging process of the energy storage device. This feedback may include various parameters related to the charging operation, such as power input, charging rate, and power quality issues. For instance, the charger may provide data on the actual current and voltage being delivered to the energy storage device, allowing the control unit to compare these values with the optimal parameters calculated. The feedback may also include information on any fluctuations in the power supply, unexpected changes in the charging rate, or potential power quality issues such as voltage sags or harmonics. Based on this received real-time feedback, the control unit may dynamically adjust the charging schedule. These adjustments may involve modifying the charging current or voltage, altering the charging rate, or implementing protective measures in response to detected power quality issues. By continuously monitoring and responding to this real-time feedback, the control unit may optimize power delivery from the charger to the energystorage device, ensuring efficient charging while maintaining the safety and longevity of the energy storage device.

[0063] Thus, the present subject matter provides a flexible and user- friendly solution for adaptive charge scheduling for batteries of electric vehicles. By enabling users to set individualized charging preferences, such as desired state of charge, departure times, etc, the present subject matter may dynamically adjust charging sessions to meet these needs without compromising safety or efficiency. In addition, by leveraging optimization algorithms and real-time battery monitoring, the present subject matter optimizes charging rates and timings to minimize battery degradation, thereby extending the battery's overall health and operational lifespan.

[0064] Although aspects for the present disclosure have been described in a language specific to structural features and / or methods, it is to be understood that the appended claims are not limited to the specific features or methods described herein. Rather, the specific features and methods are disclosed as examples of the present disclosure.

Claims

l / We Claim:1 . A method for adaptively charging an energy storage device (106, 206) of an electric vehicle (102, 202), the method comprising: obtaining, by a control unit (108, 204), a user input indicative of charging preferences of a user of the electric vehicle (102, 202); determining, based on the user input and real-time operational data of the energy storage device (106, 206), optimal charging parameters for the electric vehicle (102, 202), the optimal charging parameters comprising at least one of charging current and charging voltage; generating a charging schedule based on the optimal charging parameters; and initiating charging of the energy storage device (106, 206) according to the charging schedule.

2. The method as claimed in claim 1 , wherein the user input comprises at least one of a target state of charge (SoC) level, a desired charging duration, and an expected vehicle departure time.

3. The method as claimed in claim 1 , wherein the real-time operational data comprises at least one of current state of charge (SoC), target state of charge, charging time, and current temperature of the energy storage device (106, 206).

4. The method as claimed in claim 1 , further comprising: monitoring, by the control unit (10, 204), the charging of the energy storage device (106, 206) in real-time; and adjusting the charging schedule based on the monitored charging and updated operational data of the energy storage device (106, 206).

5. The method as claimed in claim 1 , further comprising displaying charging information to a user via a human machine interface (HMI) (210), whereinthe charging information comprises at least one of current state of charge (SoC), estimated time to full charge, and a charging rate.

6. The method as claimed in claim 1 , further comprising: accommodating changes in the user input during the charging of the energy storage device (106, 206); and recalculating the optimal charging parameters based on the changes in user input.

7. The method as claimed in claim 1 , further comprising: receiving real-time feedback from a charger of the electric vehicle (102, 202) during the charging of the energy storage device (106, 206), wherein the feedback includes at least one of power input, charging rate, and power quality issues; and adjusting the charging schedule based on the received real-time feedback to optimize power delivery from the charger to the energy storage device (106, 206).

8. A control unit (108, 204) for adaptively charging an energy storage device (106, 206) of an electric vehicle (102, 202), the control unit (108, 204) is to: obtain a user input indicative of charging preferences of a user of the electric vehicle (102, 202); determine, based on the user input and real-time operational data of the energy storage device (106, 206), optimal charging parameters for the electric vehicle (102, 202), the optimal charging parameters comprising at least one of charging current and charging voltage; generate a charging schedule based on the optimal charging parameters; and initiate charging of the energy storage device (106, 206) according to the charging schedule.

9. The control unit (108, 204) as claimed in claim 8, wherein the user input comprises at least one of a target state of charge (SoC) level, a desiredcharging duration, and an expected vehicle departure time.

10. The control unit (108, 204) as claimed in claim 8, wherein the real-time operational data comprises at least one of current state of charge (SoC), target state of charge, charging time, and current temperature of the energy storage device (106, 206).

11. The control unit (108, 204) as claimed in claim 8, wherein the control unit (108, 204) is to: monitor the charging of the energy storage device (106, 206) in realtime; and adjust the charging schedule based on the monitored charging and updated operational data of the energy storage device (106, 206).

12. The control unit (108, 204) as claimed in claim 8, wherein the control unit (108, 204) is to communicate with a human machine interface (HMI) (210) to display charging information to a user.

13. The control unit (108, 204) as claimed in claim 8, wherein the control unit (108, 204) is to: accommodate changes in the user input during the charging of the energy storage device (106, 206); and recalculate the optimal charging parameters based on the changes in user input.

14. The control unit (108, 204) as claimed in claim 8, wherein the control unit (108, 204) is to: receive real-time feedback from a charger of the electric vehicle (102, 202) during the charging of the energy storage device (106, 206), wherein the feedback includes at least one of power input, charging rate, and power quality issues; andadjust the charging schedule based on the received real-time feedback to optimize power delivery from the charger to the energy storage device (106, 206).

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