Portable battery charging system and methods of employing thereof

Abstract

Provided is a portable battery charging system including a stackable battery charging device (100) that comprises an energy storage matrix (102), a set of sensors (104), a plurality of retractable photovoltaic energy conversion surfaces (106), an energy exchange unit (108), and a processing unit (110) The energy storage matrix (102) comprises a multiplicity of modular charging units (120), an enclosure configured to support stacking and nesting of similar modular charging units in a vertical or horizontal arrangement, and a first conductive port (122) and a second conductive port (124) attached with each of the modular charging units (120) configured to establish electrical and communication connections with adjacent modular charging units. The processing unit (110) is configured to charge the energy storage matrix (102), monitor and regulate the charging and discharging process of the connected modular charging units (120), and communicate with remote server.

Description

[0001] PORTABLE BATTERY CHARGING SYSTEM AND METHODS OF EMPLOYING THEREOF

[0002] FIELD OF THE INVENTION

[0003] The present invention relates to a portable battery charging system capable of charging battery powered devices with high adaptability and performance. More particularly, the invention relates to a portable battery charging system capable of charging electric vehicles anywhere on the land and in water or air.

[0004] BACKGROUND

[0005] With the prevailing increase in the use of vehicles across diverse sectors such as the aviation industry, military establishments, submarine deployments, public places, transportation of goods and numerous similar applications, there is a tremendous usage of fossil fuels needed to run such vehicles. Such multifold use of non-renewable resources is becoming a challenge as the supply of such resources cannot be met with their rising demands. Also, the use of fossil fuels such as petrol and diesel lead to pollution thereby harming the environment. To reduce the dependency on fossil fuels, vehicles currently in-use are being replaced by electric vehicles. Electric vehicles have almost zero emissions and are environment friendly to use. However, there are a few challenges that are being faced with the increasing use of electric vehicles. One of the challenges being faced is the operational constraint resulting from the need for frequent charging of the electric vehicle battery as these batteries have usually low charge capacity. Another challenge that is encountered is that the charging parameters vary among different devices and therefore, the charging resources cannot be standardized. The charging stations have to be customized according to the type of vehicles which increases the operational cost. Also, the dearth of readily accessible charging infrastructure and on-demand charging services hinders operational efficiency and impose logistic challenges. The currently used energy storage devices further lack energy management function which could be helpful to the user in emergency situations.

[0006] US7768229B2 discloses a charging system for electric cars which includes an AC electric grid power supply system and an electric car charging equipment. The electrical energy input of the system is controlled by the AC electric grid power supply system and the output is controlled by an electrical energy control module and coupled to the electric car charging equipment.

[0007] WO2011145939A9 discloses a charging system having plurality of charging ports and power converters wherein the power converters can be located at a remote location from the charging port, for charging an electric vehicle.

[0008] The charging stations and systems disclosed in the cited references are stationary where the users are required to reach the location of the charging station for charging their electric vehicles. Also, such systems are useful only for selected vehicles such as four wheelers.

[0009] KR20160108962A discloses amovable electric vehicle charger that can be run by a diesel generator or another power source when the system power is not available. The mobile EV charger herein can be mounted onto a vehicle to move it to the desired place.

[0010] There are other portable electric vehicle charging devices in the market which can be moved to the user’s location when desired. However, such portable devices have limited usability depending on the time taken to arrive at the user’s location and their capability to charge different kinds of vehicles simultaneously. Also, the charging systems may gradually increase the load on power grids which may affect the electricity supply to the people for their daily needs.

[0011] Therefore, there is a need to devise a mobile charging system which is fast, efficient can be operated remotely and can charge electric vehicles. Further, with the increasing use of electronic devices especially those operated by batteries, a charging system that can charge the batteries of such devices is desirable. Accordingly, the present invention provides a portable battery charging system that can be delivered on demand to the desired location instantaneously and can charge all types of electric vehicles capable of running on land, air or water and battery powered electronic devices.

[0012] SUMMARY OF THE INVENTION

[0013] The present disclosure relates to a portable battery charging system and a stackable battery charging device. The device comprises an energy storage matrix, a set of sensors, a plurality of photovoltaic energy conversion surfaces, an energy exchange unit, and a processing unit. The energy storage matrix comprises a multiplicity of modular charging units; an enclosure configured to support stacking and nesting of similar modular charging units in a vertical or horizontal arrangement; and a first and second conductive port attached with each of the modular charging units configured to establish electrical and communication connections with adjacent modular charging units. The photovoltaic energy conversion surfaces are extendable to envelop the stackable battery charging device or device that requires charging. The energy exchange unit is configured to supply and receive electrical power across multiple stacked modular charging units. The processing unit is communicably coupled with the energy storage matrix, the set of sensors, the plurality of retractable photovoltaic energy conversion surfaces, and the energy exchange unit. The processing unit is configured to charge the energy storage matrix, monitor and regulate the charging and discharging process of the connected modular charging units, and communicate with a remote server by using a wireless communication unit.

[0014] In another embodiment, the present disclosure also relates to a portable battery charging system. The system comprises one or more stackable battery charging devices, one or more end-user devices, and a processor. The one or more end-user devices is configured for generating energy requirement requests and receiving input regarding availability of at least one stackable battery charging device. The processor is communicably coupled with the one or more end-user devices, the one or more stackable battery charging devices, and a memory. The processor is configured to receive information related to each of the one or more stackable battery charging devices. Such information includes information of each of the stackable battery charging devices, information of rider of each of the stackable battery charging devices. The processor further receives an energy requirement request generated by at least one of end -users. The energy requirement request includes specification of device requiring charging, location of the vehicle, and time of charging requirement to be fulfilled. The processor is further configured to identify a rider with at least one stackable battery charging device having adequate energy to satisfy the received energy requirement request, while incurring minimum transportation time and cost based on optimized analysis of information of the received request and information of available riders along with the respective stackable battery charging devices. The processor generates notification regarding assignment of the determined rider to satisfy the received request from the at least one of end-users and transmits the status of the rider to the respective end-user device on real time basis. Upon fulfilling the received energy requirement request, the processor computes the power consumed from the respective stackable battery charging device based on information received from a processing unit of the stackable battery charging device and enables required payment transaction for completing the energy requirement request.

[0015] The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.

[0016] BRIEF DESCRIPTION OF DRAWINGS

[0017] The novel features and characteristics of the disclosure are set forth in the appended claims. The disclosure itself, however, as well as a preferred mode of use, further objectives, and advantages thereof, will best be understood with reference to the following detailed description of an illustrative embodiment when read in conjunction with the accompanying figures. One or more embodiments are now described, by way of example only, with reference to the accompanying figures wherein like reference numerals represent like elements and in which:

[0018] Figure 1 illustrates a schematic diagram of a stackable battery charging device, in accordance with some embodiments of the present disclosure;

[0019] Figure 1A illustrates a workflow of bi-directional energy transfer in the stackable battery charging device, in accordance with some embodiments of the present disclosure;

[0020] Figure 2A illustrates an exemplary architecture of a portable battery charging system, in accordance with some embodiments of the present disclosure;

[0021] Figure 2B illustrates a block diagram of the portable battery charging system, in accordance with some embodiments of the present disclosure;

[0022] Figure 3 illustrates a flowchart showing the workflow of the portable battery charging system, in accordance with an embodiment of the present disclosure.

[0023] DETAILED DESCRIPTION OF THE INVENTION

[0024] In the present document, the word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any embodiment or implementation of the present subject matter described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.

[0025] While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however, that it is not intended to limit the disclosure to the forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the spirit and the scope of the disclosure.

[0026] The terms “comprises”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, device, or process that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or device or process. In other words, one or more elements in a system or apparatus proceeded by “comprises ... a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.

[0027] Embodiments of the present disclosure provide a stackable battery charging device. The device comprises an energy storage matrix, a set of sensors, a set of retractable photovoltaic energy conversion surfaces, an energy exchange unit, and a processing unit. The energy storage matrix comprises a plurality of modular charging units; an enclosure configured to support stacking and nesting of similar modular charging units in a vertical or horizontal arrangement; and a first and second conductive port attached with each of the modular charging units configured to establish electrical and communication connections with adjacent modular charging units. The photovoltaic energy conversion surfaces are extendable to envelop the stackable battery charging device or a device that requires charging. The energy exchange unit is configured to supply and receive electrical power across multiple stacked modular charging units. The processing unit is communicably coupled with the energy storage matrix, the set of sensors, the set of retractable photovoltaic energy conversion surfaces, and the energy exchange unit. The processing unit is configured to charge the energy storage matrix, monitor and regulate the charging and discharging process of the connected modular charging units, and communicate with a remote server by using a wireless communication unit.

[0028] In another embodiment, the present disclosure provides a portable battery charging system. The system comprises one or more stackable battery charging devices, one or more end-user devices, and a processor. The one or more end-user devices is configured for generating energy requirement requests and receiving input regarding availability of at least one stackable battery charging device. The processor is communicably coupled with the one or more end-user devices, the one or more stackable battery charging devices, and a memory. The processor is configured to receive information related to each of the one or more stackable batery charging devices. Such information includes information of each of the stackable batery charging devices, information of rider of each of the stackable batery charging devices. The processor further receives an energy requirement request generated by at least one of the end -users. The energy requirement request includes specification of a device requiring charging, location of vehicle, and time of charging requirement to be fulfilled. The processor is further configured to determine a rider with at least one stackable batery charging device having adequate energy to satisfy the received energy requirement request, and incurring minimum transportation time and cost based on optimized analysis of information of the received request and information of available riders along with the respective stackable batery charging devices. The processor generates notification regarding assignment of the determined rider to satisfy the received request from the at least one of end-users and transmits the status of the rider to the respective end-user device in real time basis. Upon fulfilling the received energy requirement request, the processor computes the consumed power from the respective stackable batery charging device based on information received from a processing unit of the stackable batery charging device and enables required payment transaction for completing the energy requirement request.

[0029] In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings that form a part hereof, and which are shown by way of illustration-specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.

[0030] Figure 1 illustrates a schematic diagram of a stackable batery charging device, in accordance with an embodiment of the present disclosure. As shown in Figure 1, the stackable batery charging device (100) comprises an energy storage matrix (102), a set of sensors (104), a set of retractable photovoltaic energy conversion surfaces (106), an energy exchange unit (108), and a processing unit (110). The energy storage matrix (102) comprises a set of modular charging units (120), an enclosure configured to support stacking and nesting of similar modular charging units in a vertical or horizontal arrangement, and a first conductive port ( 122) and a second conductive port (124) attached with each of the modular charging units (120) configured to establish electrical and communication connections with adjacent modular charging units. In one embodiment, the first conductive port (122) and the second conductive port (124) include a locking mechanism to ensure stable mechanical and electrical connections between stacked units. Further, the enclosure includes alignment features, such as grooves or magnets, to facilitate precise stacking and prevent displacement.

[0031] The energy storage matrix (102) comprises the modular charging units (120) that are functionally coupled and can be stacked over one another or alongside each other. The type of modular charging units i.e., battery packs that can be used in the present device will depend on the type of battery to be charged and can be selected from a group comprising liquid hydrogen and liquid oxygen, methane, hydrogen fuel cells, biofuels, hydrogen, hydrogen and ammonia, nuclear power, solar power, electricity, hydrogen storage, lithium-sulfur batteries, metal-air batteries, graphene batteries, quantum batteries, sodium-ion batteries, biodegradable batteries, multi-ion batteries, molten salt batteries and redox flow batteries. The modular charging units can be arranged in series or parallel depending on the required voltage and power. Accordingly, the modular charging units can be stacked over one another to form a vertical unit or can be arranged side by side to form a horizontal unit. For example, for a marine vessel requiring 600kw energy for its travel, a battery pack of 96v and 40ah can be arranged in the present system to achieve the desired charging voltage. The multiplicity of sensors (104) includes a set of data reading sensors, a set of data management sensors, a set of safety sensors, a set of input / output voltage sensors, a set of compatibility sensors, and a set of sensors for integration. The set of data reading sensors include voltage sensors, current sensors, and temperature sensors. The voltage sensors measure input and output voltage to ensure compatibility with different EV types and optimize charging parameters. The current sensors monitor the current flow during charging / discharging to prevent overcurrent and ensure efficient energy transfer. The temperature sensors track battery temperature to prevent overheating and ensure safe operation. The set of data management sensors include energy sensors and position sensors. The energy sensors collect real-time data on energy consumption, charging cycles, and battery health for analytics and performance optimization. The position sensors facilitate the alignment and connection in stackable battery units, ensuring precise stacking and safe operations. The set of safety sensors include overvoltage and overcurrent protection sensors, short- circuit sensors, and thermal sensors. The overvoltage and overcurrent protection sensors detect anomalies in voltage / current and trigger protective mechanisms to avoid damage. The short-circuit sensors monitor electrical circuits and shut down the system in case of a fault, ensuring user safety. The thermal sensors prevent thermal runaway by continuously monitoring and regulating battery temperatures. The set of input / output voltage sensors include dynamic voltage sensors, and bidirectional energy flow sensors. The dynamic voltage sensors automatically detect the type, capacity, and voltage requirements of connected devices, enabling adaptive charging for EVs. The bidirectional energy flow sensors monitor energy transfer for vehicle-to-vehicle charging and energy feedback into the system.The set of compatibility sensors include battery type identification sensors, and conductive port detection sensors. The battery type identification sensors automatically detect the connected device's battery type (Li-ion, NiMH, etc.) to adjust charging algorithms accordingly. The port detection sensors verify the compatibility of charging ports and initiate compliance checks before energy transfer. The set of sensors for integration include environmental sensors, load balancing sensors, and diagnostic sensors. The environmental sensors monitor ambient conditions like temperature and humidity, ensuring optimal operation in diverse environments. The load balancing sensors communicate with the processing unit to dynamically distribute power among stacked units based on real-time requirements. The diagnostic sensors continuously monitor system health and send error signals to the processing unit, enabling predictive maintenance.

[0032] The retractable photovoltaic energy conversion surfaces (106) are extendable to envelop the stackable battery charging device or a device that requires charging. Retractable photovoltaic energy conversion surfaces are designed to maximize efficiency while maintaining portability and ease of use. The surfaces are engineered to extend or retract based on usage, storage, or environmental conditions. This feature not only protects the surfaces during transport or adverse weather conditions but also allows users to adjust the surface area to optimize energy absorption throughout the day. The combination of retractable photovoltaic energy conversion surfaces and stackable batteries creates a scalable solution suitable for various applications, from personal gadgets to larger off-grid systems. In one embodiment, the processing unit (110) is configured to moderate the charging of the stackable battery charging device or the device that requires charging by using the power generated by the photovoltaic energy conversion surfaces. The number and dimensions of the photovoltaic energy conversion surfaces (106) depends on the size of the device or vehicle to be charged. The photovoltaic energy conversion surfaces (106) are retractable and can be extended to envelop the device or vehicle that requires charging. The photovoltaic energy conversion surfaces (106) serve to capture and store solar energy in the energy storage matrix (102). Based on the user input, the photovoltaic energy conversion surfaces (106) can be extended or folded in horizontal or vertical direction.

[0033] The energy exchange unit (108) is configured to supply and receive electrical power across multiple stacked modular charging units (120). The transfer of energy between the energy storage matrix (102) and the other device and between the source device and other device or vehicle takes place by an energy exchange unit (108). The energy exchange unit (108) may be in form of cables having different charging ports. The energy exchange unit (108) is capable of transferring energy upto hundreds of kilowatts and is compatible with wide range of devices. The energy exchange unit (108) can transfer energy in a wired or wireless manner. Also, the energy exchange unit (108) can function bidirectionally wherein the energy exchange unit (108) transfers energy to devices and can transfer the excess energy back to the stackable battery charging device. The processing unit (110) controls the functioning of the energy exchange unit (108).

[0034] The processing unit (110) is communicably coupled with the energy storage matrix (102), the set of sensors (104), the set of retractable photovoltaic energy conversion surfaces (106), and the energy exchange unit (108). The processing unit (110) is configured to charge the energy storage matrix (102), monitor and regulate the charging and discharging process of the connected modular charging units (120), and communicate with a remote server by using a wireless communication unit.

[0035] The processing unit (110) is configured to automatically detect the battery type, capacity, and voltage of the device that requires charging and adjust the charging parameters accordingly. The processing unit (110) can dynamically perform the compliance check of both internal and external units before initiation of energy transfer. The processing unit (110) is configured to dynamically allocate power among the stacked units based on their individual power requirements using the energy exchange unit. The processing unit (110) is further configured to dynamically manage the bi-directional energy transfer between the energy accumulation device and the device that requires charging. The processing unit (110) is configured to enable remote monitoring and control via a mobile application or a centralized control system by using the wireless communication unit. The processing unit (110) is configured to transmit notifications to the user via the wireless communication unit regarding charging status, errors, completed cycles. The processing unit (110) is further integrated with multi -fuel converters and controllers that adapt to diverse energy sources. The processing unit (110) is configured to automatically switch to solar energy as generated by the retractable photovoltaic energy conversion surfaces based on real time monitoring of availability of power for transmission.

[0036] In one embodiment, the bi-directional energy transfer in the stackable battery charging device involves managing the flow of energy between the energy storage matrix and the device requiring charging (e.g., EV). Such functionality is technically achieved through the steps illustrated in Figure 1A.

[0037] As depicted in Figure 1A, the workflow (150) includes a series of steps 152 through 162 for performing the execution of the proposed system. The details of the workflow (150) have been explained below in forthcoming paragraphs. 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.

[0038] At step 152, the device which requires charging is identified and the stackable battery charging device is initialized. In one embodiment, the processing unit is configured to detect the connected device's type, battery capacity, voltage, and energy requirements using the battery type identification sensors and voltage sensors. Further, the processing unit verifies compatibility of the device with the modular charging unit by using the connector detection sensors, and initializes the charging / discharging parameters based on the input device's specifications For example, required voltage, current, and state of charge). In another embodiment, if the energy transfer is bi-directional, for example, vehicle-to-device or device- to-device charging), the processing unit sets the system to allow energy flow in both directions.

[0039] At step 154, the energy flow direction is managed. In one embodiment, when the EV battery requires energy, the energy exchange unit (108) facilitates energy flow from the energy accumulation device to the device requiring charging. The voltage and current sensors monitor the energy flow in real-time to prevent overcharging or power losses. In another embodiment, if the stackable battery charging device itself needs energy for example from an EV, the processing unit reverses the energy flow. The bidirectional energy flow sensors dynamically adapt the energy accumulation device to act as the receiving unit.

[0040] At step 156, the energy is dynamically optimized. In one embodiment, the processing unit continuously reads data from current and voltage sensors to optimize energy transfer based on real-time requirements. Further, an adaptive technique aids in adjusting energy flow to maintain optimal efficiency and safety. In multi-device scenarios, the processing unit uses load balancing sensors to distribute energy among devices dynamically. Power is allocated based on priority, such as critical energy levels or device-specific requirements.

[0041] At step 158, the safety and compliance check are performed. In one embodiment, the temperature sensor monitors the stackable battery charging device to prevent overheating. If temperatures exceed safe thresholds, energy transfer is paused, and cooling measures are activated. The processing unit uses overcurrent and overvoltage sensors to detect anomalies and instantly cut off energy transfer to protect connected devices.

[0042] At step 160, the user interaction is performed over the communication network. In one embodiment, the stackable battery charging device logs all energy transactions and usage statistics in real-time using the respective user application and ensures transparency for users. The processing unit is further configured to generate notifications and send for actions such as charging completion, fault alerts, or energy transfer progress. The user application further allows users to toggle between charging and discharging modes, monitor status, and adjust parameters remotely.

[0043] At step 162, the charging process is terminated and necessary feedback is received. In one embodiment, the processing unit monitors the device's battery status, stopping energy transfer once the desired charge level is reached. Further, the users are notified via the respective user application or the digital display of stackable battery charging device. The processing unit performs a diagnostic check to ensure all units including the charger, device, and the conductive ports are functioning correctly post-transfer of power. Furthermore, the processing unit logs any faults detected, and provides maintenance recommendations as required.

[0044] The processing unit (110) comprises various communication means including transponders, antennae and frequency bands for information exchange. The processing unit (110) can operate in accordance with satellite-based communication and facilitates transmission of data via television broadcasting, internet connectivity, and global positioning systems. The processing unit (110) functions to monitor health status of the present device (100) and adjusts the power configurations of the system accordingly.

[0045] The processing unit (110) is adaptable to perform in accordance with various computing technologies such as quantum computing, neuromorphic computing and is compatible with edge computing. The processing unit (110) demonstrates the capacity to proactively recommend energy utilization strategies to end-users. The advanced computing features inherent to this unit enable automatic configuration of output parameters tailored to specific requirements. The processing unit (110) possesses the capability to autonomously compute energy losses and identify the underlying constraints responsible for such losses. The processing unit (110) is capable of transferring energy from the energy storage matrix (102) to multiple devices and also of transferring energy directly from selected source device to one or more devices.

[0046] The processing unit (110) controls the functioning of the device (100) of the present invention. The processing unit (110) serves to provide efficient energy management and is responsible for the distribution of load across the energy storage matrix (102) of the present device (100). The device (100) further comprises a digital display on the energy storage matrix to show the charging status and health of the modular charging units. The digital display can be a high- resolution touch-screen display that recognizes inputs from the user and displays the output as desired. The touch screen display unit typically encompasses the controls for the functioning of the stackable battery charging device (100) of the present invention. In an example, the digital display can be foldable and further encompasses various sensors that can detect and respond to inputs from the user.

[0047] In another embodiment, the stackable battery charging device (100) includes safety mechanisms such as overvoltage protection, overcurrent protection, and short-circuit protection for enhanced reliability. The processing unit (110) is configured to such protection by means of signals received from the multiple sensors (104).

[0048] In one embodiment, to achieve safety mechanisms such as overvoltage, overcurrent, and short-circuit protection in the stackable battery charging device, a multiplicity of sensors, control logic, and automated responses are implemented.

[0049] In one embodiment, the processing unit monitors the input and output voltage in real-time using the voltage sensors. The processing unit continuously compares the sensed voltage against predefined safe thresholds. If voltage exceeds the limit, the processing unit triggers an energy cutoff via a relay or circuit breaker, and sends notification through the user application. After stabilization, the stackable battery charging device resets automatically or prompts manual intervention.

[0050] Further, excessive current flow is prevented that could damage components or cause overheating. In one embodiment, the current sensors measure the real-time current flow during charging and discharging operations. The processing unit dynamically manages power distribution among stacked battery modules to prevent overload. If current exceeds the safe limit, the processing unit initiates a circuit interruption, and alerts the user via notifications or alarms. Further, if overheating is detected alongside overcurrent, the temperature sensors trigger cooling mechanisms.

[0051] The stackable battery charging device is protected against sudden, unintended direct connections between positive and negative terminals. In one embodiment, the voltage and current sensors, in combination with the processing unit, detect significant drops in resistance, indicative of a short circuit. The processing unit instantly disconnects the affected module or device from the energy flow. Further, the processing unit triggers safety alarms, and the user is informed of the fault location through the application or device display. In another embodiment, the processing unit logs the short-circuit event and isolates the faulty module, ensuring continuity for the rest of the system.

[0052] The temperature sensors provide thermal management, preventing overheating during overvoltage or overcurrent scenarios. The compatibility sensors ensure the connected device is within safe operating parameters before initiating energy transfer. The energy exchange unit halts charging automatically when any unsafe condition is detected.

[0053] Furthermore, the user application displays real-time safety status, alerts, and troubleshooting instructions, and notifications such as "overvoltage detected - charging halted" are sent immediately.

[0054] Figure 2A illustrates an exemplary architecture of a portable battery charging system, in accordance with some embodiments of the present disclosure.

[0055] As shown in Figure 2A, the exemplary system (200) comprises one or more components configured for enabling portable battery charging for different kind of vehicles and other devices. The exemplary system (200) comprises a portable battery charging system (102) (hereinafter referred to as PBCS), a data repository (220), one or more stackable battery charging devices (100), and one or more enduser devices (218) communicatively coupled via a communication network (216). The end-user devices (218) are configured with a user application to perform plurality of operations on the respective devices. The communication network (116) may include, without limitation, a direct interconnection, local area network (LAN), wide area network (WAN), wireless network (e.g., using Wireless Application Protocol), the Internet, etc.

[0056] The system of the present invention aims at portable battery charging system that is uniquely designed to cater to a wide spectrum of energy requirements for electric vehicles (EVs) and battery-powered devices. This invention is characterized by its versatility, mobility, and adaptability, targeting diverse operational environments including land, water, and air. By integrating renewable energy sources and advanced artificial intelligence (Al)-driven optimizations, the system redefines the conventional paradigms of EV charging, addressing the multifaceted challenges of scalability, efficiency, and sustainability.

[0057] The data repository (220) stores a plurality of information related to plurality of charging request as received from the end-users via respective end-user devices respectively. In one embodiment, the data repository (220) stores the information regarding modular charging units that includes but not limited to technical details such as dimensions, power storage capacity, charging compatibility, battery health details, etc. The data repository (220) can also store the historical information of modular charging units which includes but not limited to charge and discharge patterns, statistics of historical charging and discharging time etc. In one embodiment, the PBCS (102) processes the battery information to perform predictive analysis of respective future performance, and trace the probability of maintenance requirement of the batteries in order to achieve the proposed objective of the present invention. The data repository (220) may be integrated with the PBCS (102), in one embodiment. In another embodiment, the data repository (220) may be configured as a standalone device independent of the PBCS (102). In yet another embodiment, the data repository (220) may be coupled with the end-user device (218) in a standalone device independent of the PBCS (102).

[0058] The PBCS (202) comprises at least a processor (204) and a memory (206) coupled with the processor (204). The processor (204) may be for example, a specialized processing unit, a micro processing unit, a typical graphics processing unit (GPU) or any other processing unit capable of processing the charging request of the user and allocating at least one rider with sufficient modular charging units in order to fulfill the charging request. The PBCS (102) further comprises a data acquisition module (208), a data analysis module (210), a rider allocation module (212), and an alert and insights generation module (214).

[0059] The data acquisition module (208) is configured to receive information related to each of the one or more stackable battery charging devices, wherein such information includes information of each of the stackable battery charging devices, information of rider of each of the stackable battery charging devices. The data acquisition module (208) further receives an energy requirement request generated by at least one of the end-users. Wherein, the energy requirement request includes specification of a device requiring charging, the location of vehicle, and time of charging requirement to be fulfilled. The data acquisition module (208) retrieves historical and reference information from the data repository (220). The data analysis module (210) is configured to perform analysis of the battery data and user data. The rider allocation module (212) is configured to determine a rider with at least one stackable battery charging device having adequate energy to satisfy the received energy requirement request, and incurring minimum transportation time and cost based on optimized analysis of information of the received request and information of available riders along with the respective stackable battery charging devices. The alerts and insight generation module (214) generates notification regarding assignment of the determined rider to satisfy the received request from at least one of the end-user and transmits the status of the rider to the respective end -user device on real time basis. In another embodiment, upon fulfilling the received energy requirement request, the alerts and insights generation module (214) computes the consumed power from the respective stackable battery charging device based on information received from a processing unit of the stackable battery charging device and enables required payment transaction for completing the energy requirement request.

[0060] The end-user devices (218) can be one of tablet, mobile, smartphone, or any other personal devices capable of communicating with the PBCS (202) via a user application installed in the respective end-user devices (218). The end-user devices (218) can communicate with the PBCS (202) both ways over the Internet or other appropriate communications network. In one embodiment, the PBCS (202) can be configured in the end-user devices (218) in order to enable the user to access the portable battery charging system.

[0061] The user application enables a user i.e., vendor, end-user etc. to perform plurality of operations related to product tracking and circularity impact analysis. The plurality operations include but not limited to register with the PTCS (102) for receiving services, interact with PTCS (102) to provide plurality of product information, retrieving life cycle information, circularity information, displaying circularity matrix, statistical representation of impact of circularity etc. In one embodiment, the user application can further be configured as a multilingual interactive system such as chat bot application. The multilingual interactive system is used to conduct an on-line chat conversation via text or text-to-speech, in lieu of providing direct contact with a live human agent. The multilingual interactive system is designed to convincingly simulate the way a human would behave as a conversational partner.

[0062] In an embodiment, PBCS (202) may be a typical PBCS (202) as illustrated in Figure 2B. The PBCS (202) comprises the processor (204), the memory (206) communicatively coupled with the processor (204). The PBCS (202) further includes data (240) and modules (260). In one implementation, the data (240) may be stored within the memory (206). In some embodiments, the data (240) may be stored within the memory (206) in the form of various data structures. Additionally, the data (240) may be organized using data models, such as relational or hierarchical data models. In one example, the data (240) may include battery data (242), rider data (244), insight data (246), historical data (248), alert data (250), fault data (252) and other data. The other data may store data, including temporary data and temporary files, generated by the modules (260) for performing the various functions of the PBCS (202).

[0063] The modules (260) may include, for example, the data acquisition module (208), the data analysis module (210), the rider allocation module (212), the alerts and insights generation module (214), and a fault detection module (262). The modules (260) may comprise other modules to perform various miscellaneous functionalities of the PBCS (102). It will be appreciated that such aforementioned modules may be represented as a single module or a combination of different modules. The modules may be implemented in the form of instructions executed by a processor, hardware and / or firmware.

[0064] In operation, one or more riders are registered with rider profile information and respective stackable battery charging device information. Data related to the battery charging device is filled in the user application of respective rider devices by the one or more riders. Such information gets verified based on data driven authenticity. Thereafter, upon receipt of riders’ request, each of the riders and respective battery charging devices is assigned with a unique ID. Consequently, the consumers can also register themselves by providing personal information and information of respective vehicles or devices along with the technical specification of the system.

[0065] The processor (204) is configured to receive request from an end-user with respect to availability of surplus energy in portable medium for distribution and enlist the end-user as a rider based on the analysis of end-user identification information and information of the respective portable medium and corresponding energy exchange unit. The processor (204) is configured to authenticate an end-user or a rider based on the biometric authentication by means of fingerprint scanners, facial recognition module integrated with the system. Further, the processor (204) is configured to analyse historical usage patterns including frequency of charging by users across the specific range, area, time of the one or more end-users and environmental data to forecast energy demands and maintenance needs by using a machine learning model.

[0066] The processor (204) is configured to generate feedback regarding the one or more stackable battery charging devices based on the input received from the processing unit, wherein the processing unit receives plurality of statistical information from sensors installed in the respective stackable battery charging devices. The processor (204) is configured to perform secure, immutable records of energy transactions and ensure peer-to-peer energy sharing in microgrids by using Blockchain enabled technique. The processor (204) is configured to generate actionable insights for optimization of charging and support by individual user in achieving zero carbon mission based on analysis of information received from the sensors and user interactions. In another embodiment, the processor (204) is configured to optimize energy delivery for unique electric vehicle specifications by using adaptive charging technique.

[0067] In one embodiment, the fault detection module (262) identifies potential faults and schedule required maintenance based on the analysis of real time data of system health as received from the set of sensors (104).

[0068] The development and implementation of the machine learning model for the stackable battery charging system involves several stages, including data collection, feature engineering, model training, deployment, and real-time operation. In data collection phase, relevant data is gathered for training the machine learning model. User behavior data including charging frequency, duration, preferred locations, and times through the user application interface and system sensors are gathered. Further, temperature, humidity, and other conditions affecting energy demand and battery performance are recorded using environmental sensors. Thereafter, device data including parameters such as battery health, voltage, current, and capacity from the modular charging units are retrieved. Further, historical logs of usage patterns, maintenance records, and demand peaks are compiled for analysis.

[0069] Thereafter, one or more meaningful features are extracted from raw data for the model. First, temporal features including time-based patterns such as peak charging hours, daily / weekly trends, and seasonal fluctuations are identified. Usage density in specific geographic areas are analyzed to predict high-demand zones. Thereafter, users are categorized based on charging behavior (e.g., frequent vs. occasional users). The system is configured to quantify how external conditions influence charging needs or maintenance schedules. The system also includes generating maintenance indicators raegarding voltage fluctuations, temperature spikes, or usage frequency to predict wear and tear. Thereafter, a suitable machine learning technique is chosen and trained. In order to make a suitable machine learning model, a time series models (e.g., ARIMA or USTM) is used to forecast demand based on temporal patterns. Further, supervised learning models (e.g., Random Forest, Gradient Boosting) are used to predict maintenance needs from labeled data. Thereafter, the collected data is split into training (70%), validation (20%), and test sets (10%). Then, the training data is fed into the model, optimizing hyperparameters to minimize error metrics such as RMSE (Root Mean Squared Error). The model is evaluated for accuracy and generalizability on unseen data. Then the trained model is integrated into the proposed system for real-time operation.

[0070] In another embodiment, the trained model is hosted on a cloud platform for scalability and remote updates. Further, the deployed model is connected with the processing unit of stackable battery charging device to enable data input / output flow. Further, the prediction results are incorporated into the user application for user and admin insights.

[0071] The model is also used to generate forecasts and actionable insights. The system integrated with the model predicts high-demand locations and time slots for deploying portable chargers effectively. The system also identifies components likely to fail and schedule preemptive maintenance. The system further notifies users of optimal charging times and locations to avoid congestion, and dynamically adjust resource allocation (e.g., charging units) based on predictions.

[0072] In one embodiment, the model is periodically updated with new data to account for evolving patterns. The system performance and user feedback is collected to refine the model, and prediction errors are tracked and make feature engineering or model design improved as needed.

[0073] Such implementation reduces downtime and ensures better resource allocation by predicting energy demands. Further, providing accurate recommendations to users improves system usability, proactive maintenance prevents costly failures, and optimized operations minimize energy waste and enhance system efficiency.

[0074] In yet another embodiment, the feedback mechanism in the stackable battery charging system relies on statistical information from a variety of sensors installed within the charging device. The system processes sensor data through the processing unit to generate feedback about system health, performance, and safety compliance. First, input and output voltage across the system is measured and voltage levels during charging and discharging, abnormal fluctuations indicating potential issues like overvoltage or under-voltage are retrieved by using the voltage sensors. Also, current levels in real-time, and anomalies such as overcurrent that could lead to overheating or system damage are retrieved from the current sensors. Temperature of each module in the stack, and sudden spikes indicating overheating, thermal runaway, or cooling failure are retrieved from the temperature sensors. Battery type and capacity of connected devices, and compliance of the connected device with system specifications are determined by the compatibility sensors. Ambient temperature, humidity, and air quality, and data influencing safe and efficient operation of the device are retrieved by the environmental sensors. Resistance drops in the circuit, and detection of unintended connections causing shorts are determined by the short-circuit sensors. Further, real-time power allocation among modules, and detection of imbalances indicating potential module failures are determined by the load balancing sensors.

[0075] Thereafter, the processing unit receives real-time data from all sensors and processes the received statistical information into system-wide metrics, including but not limited to average voltage and current levels, temperature trends over time, frequency and nature of anomalies. The processing unit further determines the health of the stackable battery charging system by using the system -wide metrices. Wherein, voltage and current data are analyzed for degradation signs, temperature trends are compared against thresholds to detect overheating risks, and energy distribution data identifies underperforming or faulty modules.

[0076] The system also verifies that operational parameters remain within safe limits i.e., overvoltage / undervoltage thresholds, maximum allowable current and temperature levels, environmental condition tolerances, etc. If a parameter exceeds the threshold, the processing unit logs the issue and triggers an alert. The system also generates actionable feedback, including user alerts for notifying via the user application, suggestions for servicing or replacing specific modules, and reports on overall efficiency, usage trends, and energy savings.

[0077] The blockchain-enabled energy trading platform for the stackable battery charging system ensures secure, transparent, and decentralized management of energy transactions. Every energy transaction (charging, discharging, or sharing) is recorded as a block in a decentralized ledger. Such records are tamper-proof and provide a transparent transaction history. The users can directly share or trade surplus energy with others in a microgrid without intermediaries. Smart contracts automate transactions based on predefined terms, ensuring fairness and efficiency. Blockchain technology encrypts transaction data, protecting against fraud and unauthorized access. All stakeholders (e.g., users, operators) can access transaction details in real-time wherein the payments and energy credits are settled instantly, reducing delays in energy trading processes. Such platform enables seamless energy trading while enhancing trust, security, and efficiency in the EV ecosystem.

[0078] Figure 3 illustrates a flowchart showing a workflow of the proposed system, in accordance with an embodiment of the present disclosure. As depicted in Figure 3, the method (300) includes a series of steps 302 through 310 for performing the execution of the proposed system.

[0079] The details of the method (300) have been explained below in forthcoming paragraphs. 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.

[0080] At step 302, information related to one or more stackable battery charging devices are received. In one embodiment, the data acquisition module (208) is configured to receive information related to each of the one or more stackable battery charging devices, wherein such information includes information of each of the stackable battery charging devices, information of rider of each of the stackable battery charging devices.

[0081] At step 304, an energy requirement request generated by one end-user is received. In one embodiment, the data acquisition module (208) further receives an energy requirement request generated by at least one of end-users. Wherein, the energy requirement request includes specification of device requiring charging, location of vehicle, and time of charging requirement to be fulfilled. The data acquisition module (208) retrieves historical and reference information from the data repository (220). The data analysis module (210) is configured to perform analysis of the battery data and user data.

[0082] At step 306, a rider with at least one stackable battery charging device is identified to satisfy the energy requirement according to the requirements in the request. In one embodiment, the rider allocation module (212) is configured to identify a rider with at least one stackable battery charging device having adequate energy to satisfy the received energy requirement request, and incurring minimum transportation time and cost based on optimized analysis of information of the received request and information of available riders along with the respective stackable battery charging devices.

[0083] At step 308, notification regarding assignment of the identified rider is generated. In one embodiment, the alerts and insights generation module (214) generate notification regarding assignment of the identified rider to satisfy the received request from at least one of the end-users and transmits the status of the rider to the respective end-user device in real time basis.

[0084] At step 310, power consumed from stackable battery charging device is computed and payment transaction is enabled. In one embodiment, upon fulfilling the received energy requirement request, the alerts and insights generation module (214) computes the consumed power from the respective stackable battery charging device based on information received from a processing unit of the stackable battery charging device and enables required payment transaction for completing the energy requirement request.

[0085] In an operation, the processing unit (110) is the mode for processing this energy flow. The processing unit (110) converts the DC to AC and AC to DC energy depending on the requirement of input and output. The energy storage matrix is used for energy storage or energy dissipation point with the help of energy exchange unit. The processing unit manages the energy flow and efficiency. Further, safety system ensures safety compliance. In an example, EV charging can be done via portable battery charging system or charging of portable battery charging system can be done via EV / via Photovoltaic energy conversion surfaces.

[0086] The processing unit closely integrated with a mobile platform integration and safety system is responsible for dynamic monitoring and adjustment. Such technique dynamically adjusts the energy distribution based on user EV vehicle demand and environmental conditions considering safety measures and availability of power in energy storage matrix. These modules synchronize with each other to ensure optimal energy delivery and protects against overloading or inefficiencies.

[0087] Further, the proposed system ensures smart connectivity between the processing unit, remote users, riders, energy exchange unit and user application. They utilize the loT modules for remote monitoring and management of portable battery charging system. The communication protocols like MQTT or CAN bus within processing unit and energy storage matrix ensure seamless data exchange between devices. This close communication among these modules enables remote diagnostics, scheduling, and updates of portable battery charging system.

[0088] The proposed system uses the feature of bidirectional energy flow and the energy exchange units ensure the flow of energy between vehicles. The processing unit via mobile platform integration prioritizes the state-of-charge (SOC) of both vehicles to optimize energy transfer. Such feature supports in roadside assistance and fleet energy balancing.

[0089] The proposed system is further configured to execute Artificial Intelligence (Al) technique for analyzing historical usage patterns and environmental data to forecast energy demands and maintenance needs. The Al models of the system refine predictions over time with respect to pattern of charging and frequency of charging by users across the specific range, area, time etc. The pattern analysis provides support in ensuring the availability of the proposed system in required areas. The proposed system optimizes energy allocation of energy storage matrices in milliseconds based on current load, battery health, and usage patterns. Integrated sensors of safety system ensure safety compliance and provide continuous feedback to the system.

[0090] The safety system further ensures overall compliance of portable battery charging system. These modules based on sensor data detect anomalies in voltage, temperature, or resistance that indicate potential faults. Alerts are generated for preemptive action. In one embodiment, sensors integrated into the stackable battery charging system continuously monitor critical parameters such as voltage, current, temperature, resistance, and system performance metrics. The collected data is transmitted to the processor in real-time for analysis. Such data includes both instantaneous readings and historical data stored in the system.

[0091] The processor processes the incoming data using advanced techniques, such as machine learning or rule-based analytics, to identify any deviations from normal operating conditions. For example, it may compare voltage levels against expected thresholds or detect anomalies in temperature patterns. If the analysis reveals unusual patterns or parameter anomalies - such as overheating, overvoltage, or irregular energy transfer - the system flags these as potential faults. The processor categorizes the identified faults based on severity, urgency, and the affected component. Such categorization helps to prioritize necessary actions. Based on the detected faults, the processor forecasts the need for maintenance using predictive models. For instance, if a sensor indicates wear and tear in a component, the system predicts when that component is likely to fail and schedules a service before it impacts the system’s performance. The processor communicates the maintenance requirements to the end-user or operator through a mobile app, dashboard, or notification system. Such processing provides actionable insights, such as the fault details, the urgency of the repair, and recommended next steps.

[0092] The system ensures that maintenance activities are performed proactively. It may even integrate with a centralized maintenance platform to book service appointments or order replacement parts automatically. After maintenance is performed, the processor verifies system performance to confirm that the faults have been addressed and the system is functioning optimally.

[0093] The proposed system aggregates data from sensors and user interactions, presenting insights via dashboards. The matrices also includes the energy consumption, cost savings, and carbon footprint reduction.

[0094] The proposed system optimizes energy allocation of energy storage matrix. The combination of data exchanged between Al based system, the processing unit and loT in energy storage matrices supports in closely monitoring of component and environmental conditions. The system predicts maintenance schedules and recommends parts replacement in portable battery charging device before failures occur.

[0095] The system integrated with quantum techniques leverages qubits to process data on energy demand, environmental conditions, and battery status in parallel. Additionally, machine learning models built on quantum computing frameworks dynamically optimize energy allocation. Such features enables real-time decisionmaking and reduces energy wastage significantly.

[0096] The proposed system may be accessed through robotics and drone technology with Al-powered navigation systems for supply of energy requirement in challenging terrains. Additionally, integrated GPS and mapping tools ensure precise delivery. This will be utilized in case of emergency or remote areas.

[0097] The proposed system collects real-time data on system health of the energy storage matrices and processing unit such as voltage fluctuations, temperature and the likes. This also identifies potential faults and schedules maintenance proactively.

[0098] The proposed system integrated with Blockchain technology ensures secure, immutable records of energy transactions and ensure peer-to-peer energy sharing in microgrids. Blockchain technology in the stackable battery charging system ensures secure, transparent, and tamper-proof energy transactions within the system. It operates as a decentralized ledger that records all energy exchanges between the stackable battery charging apparatus and end-users. Each transaction is securely encrypted and chronologically linked to previous transactions, preventing unauthorized modifications or fraud. By implementing blockchain, the system enables efficient peer-to-peer energy sharing within microgrids, allowing users to trade surplus energy securely and transparently. This eliminates the need for intermediaries, reducing costs and increasing trust among participants.

[0099] The blockchain-enabled system enhances the reliability of energy transactions by maintaining immutable records. When a rider delivers energy to an end-user, the transaction details such as the amount of energy transferred, timestamp, and payment confirmation are stored on the blockchain. This ensures that all transactions are verifiable and cannot be altered, providing both users and service providers with a trustworthy record of energy distribution. Smart contracts, self-executing digital agreements embedded in the blockchain can automate payments and enforce transaction terms without manual intervention.

[0100] Security and user authentication are critical components of the blockchain integration. The system utilizes blockchain to securely store biometric verification data, such as fingerprint scans or facial recognition information, ensuring that only authorized users can access and utilize the charging services. This enhances fraud prevention and eliminates identity theft risks, making the system more secure for riders and end-users. Additionally, blockchain enables real-time tracking of stackable battery charging apparatus, ensuring that their availability and location are accurately reflected in the system.

[0101] Blockchain also facilitates predictive maintenance and fault detection by securely logging sensor data from battery charging apparatus. The system continuously collects performance metrics, such as charge cycles, energy efficiency, and temperature variations. When anomalies are detected, blockchain ensures the integrity of these records, allowing maintenance teams to diagnose and address issues proactively. This helps prevent system failures, reduces downtime, and enhances the overall reliability of the energy distribution network.

[0102] Blockchain technology supports sustainability efforts by providing insights into carbon footprint reduction. Each energy transaction can be recorded with metadata detailing its energy source, efficiency, and environmental impact. Users can track their contributions toward achieving a zero-carbon mission and optimize their energy consumption accordingly. By leveraging blockchain’s transparency, the system promotes accountability and encourages users to make eco-friendly energy choices, driving the adoption of cleaner and more efficient energy solutions.

[0103] The system incorporates multi-fuel converters and controllers that adapt to diverse energy sources (e.g., liquid hydrogen, biofuels) and Al based platform optimizes source switching based on availability and efficiency. Such feature results in enhanced operational flexibility.

[0104] The portable battery charging device is made of advanced polymers with embedded microcapsules that release repairing agents when damaged. Additionally, the system performs necessary monitoring to detect and trigger self-repair mechanisms. This results in increases system reliability and reduces maintenance costs

[0105] Further, zero-gravity charging capability is achieved through processing unit by adapting electromagnetic energy transfer from energy storage matrix and Al based system for energy stabilization systems for zero-gravity environments. This feature results in smooth functioning of portable battery charging system in space missions and extraterrestrial energy management.

[0106] The energy exchange units supported by processing unit ensures the mechanism for taking care of unique energy requirements. The adaptive charging technique optimizes energy delivery for unique EV specifications like drones, submarines etc. and overall enhance portable battery charging system versatility. The adaptive charging technique in the stackable battery charging system customizes energy delivery based on the specific requirements of each electric vehicle (EV). When an EV is connected, the system’s sensors and processing unit automatically detect the vehicle’s battery type, capacity, and voltage specifications. Using this information, the processor adjusts the charging parameters, such as voltage, current, and charging speed, to match the EV's unique needs. For instance, larger batteries may receive higher power levels, while smaller batteries are charged more gently to prevent damage. The system also monitors real-time conditions, like battery health and temperature, to make continuous adjustments during charging, ensuring safety and efficiency. Such intelligent approach reduces energy waste, prolongs battery life, and provides faster, optimized charging tailored to each EV.

[0107] Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based here on. Accordingly, the disclosure of the embodiments of the invention is intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the claims.

[0108] While various aspects and embodiments have been disclosed herein, other aspects and embodiment will be apparent to those skilled in the art.

[0109] In the detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings that form a part hereof, and in which are shown by way of illustration specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The description is, therefore, not to be taken in a limiting sense.

Claims

CLAIMS:1) A stackable battery charging device (100), comprising: an energy storage matrix (102), said matrix comprising: a multiplicity of modular charging units (120); an enclosure dimensioned to permit the nesting and stacking of congruent modular charging units in vertical or horizontal orientations; a first conductive port (122) and a second conductive port (124) affixed to each of said modular charging units, said ports configured to establish both electrical and communicative adjacency with neighboring modular charging units; a set of sensors (104) including a set of data reading sensors, a set of data management sensors, a set of safety sensors, a set of input / output voltage sensors, a set of compatibility sensors, and a set of sensors for integration; a plurality of photovoltaic energy conversion surfaces (106), said surfaces (106) retractably deployable to circumscribe the modularly stackable battery charging apparatus or a device requiring electrical replenishment; an energy exchange unit (108) configured to enable the transmission and reception of electrical energy across a multiplicity of modular charging units (120); and a processing unit (110) communicably coupled with said energy storage matrix (102), said set of sensors (104), said plurality of photovoltaic energy conversion surfaces (106), and said energy exchange unit (108), wherein the processing unit (110) is configured to effect the charging of said energy storage matrix (102), to monitor and regulate the charging and discharging cycles of the interconnected modular charging units (120), and to establish communication with a remote computational resource via a wireless communication unit.2) The stackable battery charging device (100) as claimed in claim 1, wherein the first conductive port (122) and a second conductive port (124) are equipped with a locking mechanism to maintain secure mechanical and electrical connections between stacked units.3) The stackable battery charging device (100) as claimed in claim 1, wherein the enclosure incorporates alignment features, including grooves or magnets, to enable precise vertical and / or horizontal stacking and prevent unintended movement or displacement of the stacked units.4) The stackable battery charging device (100) as claimed in claim 1, wherein the set of data reading sensors include voltage sensors, current sensors, and temperature sensors; the set of data management sensors include energy sensors and position sensors; the set of safety sensors include overvoltage and overcurrent protection sensors, short-circuit sensors, and thermal sensors; the set of input / output voltage sensors include dynamic voltage sensors, and bidirectional energy flow sensors; the set of compatibility sensors include battery type identification sensors, and connector detection sensors; and the set of sensors for integration include environmental sensors, load balancing sensors, and diagnostic sensors.5) The stackable battery charging device (100) as claimed in claim 1, wherein the processing unit (110) is configured to automatically detect the battery type, capacity, and voltage, to adjust the charging parameters and to dynamically perform the compliance check of internal and external units before initiation of energy transfer.6) The stackable battery charging device ( 100) as claimed in claim 1 , further comprises a digital display on the energy storage matrix (102) that indicates the charging status and health of the modular charging units.7) The stackable battery charging device (100) as claimed in claim 1, wherein the processing unit (110) is configured to dynamically allocateenergy among the stacked units based on their individual power requirements using the energy exchange unit.8) The stackable battery charging device (100) as claimed in claim 1, wherein the processing unit (110) is further configured to dynamically manage the bi-directional energy transfer between the energy storage matrix and the device that requires charging.9) The stackable battery charging device (100) as claimed in claim 1, wherein the processing unit (110) is configured to enable remote monitoring and control via a mobile application or a centralized unit by using the wireless communication unit.10) The stackable battery charging device (100) as claimed in claim 1, wherein the device incorporates safety features including overvoltage protection, overcurrent protection, and short-circuit protection, to enhance its reliability.11) The stackable battery charging device (100) as claimed in claim 1, wherein the processing unit (110) is configured to transmit notifications to the user via the wireless communication unit regarding charging status, errors and completed cycles.12) The stackable battery charging device (100) as claimed in claim 1, wherein the processing unit (110) is further equipped with multi-fuel converters and adaptive controllers to allow utilization of different energy sources.13) The stackable battery charging device (100) as claimed in claim 1, wherein the processing unit (110) is configured to automatically switch to photovoltaic energy as generated by the retractable photovoltaic energy conversion surfaces based on real-time monitoring of availability of power for transmission.14) A portable battery charging system (202), comprising one or more stackable battery charging devices (100) as claimed in claims 1-13, further comprising: one or more end-user devices (218) for generating energy requirement requests and receiving input regarding availability of at least one modular, stackable battery charging device; a machine learning equipped processor (204) communicably coupled with the one or more end-user devices (218), the one or more modular, stackable battery charging devices (100), and a memory (206), wherein the processor (204) is configured to: receive information related to each modular, stackable battery charging device, wherein such information includes information of each of the modular, stackable battery charging device and information of rider of each of the modular, stackable battery charging device; receiving an energy requirement request generated by at least one of end-users, wherein the energy requirement request includes specification of the device that requires charging, location of the device, and time of charging requirement to be fulfilled; identifying a rider with at least one modular, stackable battery charging device possessing sufficient energy to fulfill the received energy requirement request, and incurring minimum transportation time and cost based on optimized analysis of the requested parameters, rider availability, and corresponding battery charging apparatus data; generating notification regarding assignment of the determined rider to satisfy the received request from at least one of end-users and transmitting the status of the rider to the respective end-user device in real time basis; andupon fulfilment of the received energy requirement request, computing the power consumed from the respective modular, stackable battery charging device based on information received from the processing unit and facilitating the necessary payment transaction to complete the request.15) The portable battery charging system (202) as claimed in claim 14, wherein the processor (204) is further configured to receive request from an end-user with respect to availability of surplus energy in portable energy storage medium for distribution and enlist the end-user as a rider based on the analysis of end-user identification information and information of the respective portable medium and corresponding energy transfer unit.16) The portable charging system (202) as claimed in claim 14, wherein the processor (204) is further configured to analyse historical usage patterns including frequency of charging by users across the specific range, area, time of the one or more end-users and environmental data to forecast energy demands and maintenance needs by using a machine learning model.17) The portable battery charging system (202) as claimed in claim 14, wherein the processor (204) is configured to generate feedback regarding the one or more modular, stackable battery charging devices based on the input received from the processing units, wherein the processing units receive plurality of statistical information from sensors installed in the respective stackable battery charging devices.18) The portable battery charging system (202) as claimed in claim 14, the processor (204) is configured to perform secure, immutable records of energy transactions and ensure peer-to-peer energy sharing in microgrids by using Block-chain enabled technology.19) The portable battery charging system (202) as claimed in claim 14, wherein the processor (204) is configured to generate actionable insights for optimization of charging and support by individual user in achieving zerocarbon mission based on analysis of information received from the sensors and user interactions.20) The portable battery charging system (202) as claimed in claim 14, wherein the processor (204) is configured to optimize energy delivery for unique electric vehicle specifications by using adaptive charging technique.21) The portable battery charging system (202) of claim 14, wherein the processor (204) is configured to authenticate an end-user or a rider based on the biometric authentication employing fingerprint scanners and a facial recognition module integrated into the system. 22) The portable battery charging system (202) of claim 14, wherein the processor (204) is further configured to identify potential faults and schedule necessary maintenance based on a real time analysis of the system health data.

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