Description:BACKGROUND
Field of Invention
[001] Embodiments of the present invention generally relate to a battery charger and particularly to a system for ultra-fast charging of an electric vehicle (EV) battery.
Description of Related Art
[002] The adoption of electric vehicles has grown due to stricter environmental regulations and improvements in rechargeable battery technology. Despite advances in overall electric vehicle performance, limitation of extended battery charging duration continues to restrict user convenience. Drivers often face long waiting periods at charging stations, that reduces efficiency of electric mobility when compared to conventional fuel-based transportation.
[003] Commercial ultra-fast charging networks have been introduced to address this limitation. Examples include the Tesla Supercharger Network, the Electrify America Hyper-Fast Charging System, and the Ionity Ultra-Fast Charging Network. Industry standards such as the CHArge de MOve (CHAdeMO) protocol and the Combined Charging System (CCS) have also been established to support high-power charging. These technologies allow higher power transfer rates and shorter charging sessions. However, such methods frequently result in accelerated wear of battery cells, excessive heat generation, and loss of storage capacity due to high thermal stress during rapid charge cycles.
[004] Existing charging infrastructure demonstrates drawbacks in terms of compatibility and adaptability. Many charging systems follow rigid charging profiles without consideration of varying battery states, that causes non-uniform power distribution and overall inefficiencies. A lack of predictive capability often results in unexpected failures, reduced lifespan of battery, and increased replacement costs. Widespread adoption remains limited due to inconsistent integration with electrical grids and variations in voltage standards across different charging stations.
[005] There is thus a need for an improved and advanced system for ultra-fast charging of an electric vehicle (EV) battery that can administer the aforementioned limitations in a more efficient manner.
SUMMARY
[006] Embodiments in accordance with the present invention provide a system for ultra-fast charging of an electric vehicle (EV) battery. The system comprising a battery management unit, coupled with batteries of an electric vehicle, configured to extrapolate real-time data from the batteries. The system further comprising a controller communicatively connected to the battery management unit. The controller is configured to receive the real-time data of the batteries from the battery management unit; dynamically adjust charging parameters by applying an artificial intelligence (AI) based predictive model; exert a multi-stage charging algorithm comprising a constant current (CC) charging stage, a constant voltage (CV) charging stage, and an AI-regulated pulse charging. Transition between the charging stage is determined based on the received real-time data of the batteries; activate a thermal management unit to regulate and modulate a temperature of the batteries. The thermal management unit is regulated by the artificial intelligence (AI) based predictive model; and optimize charging schedules by predicting power availability and grid demand to reduce degradation and improve charging efficiency of the batteries.
[007] Embodiments in accordance with the present invention further provide a method for ultra-fast charging of an electric vehicle (EV) battery. The method comprising steps of receiving a real-time data of batteries from a battery management unit; dynamically adjusting charging parameters by applying an artificial intelligence (AI) based predictive model; exerting a multi-stage charging algorithm comprising a constant current (CC) charging stage, a constant voltage (CV) charging stage, and an AI-regulated pulse charging. Transition between the charging stage is determined based on the received real-time data of the batteries; activating a thermal management unit to regulate and modulate a temperature of the batteries. The thermal management is regulated by the artificial intelligence (AI) based predictive model; and optimizing charging schedules by predicting power availability and grid demand to reduce degradation and improve charging efficiency of the batteries.
[008] Embodiments of the present invention may provide a number of advantages depending on their particular configuration. First, embodiments of the present application may provide a system for ultra-fast charging of an electric vehicle (EV) battery.
[009] Next, embodiments of the present application may provide a charger that dynamically adjusts charging parameters based on a state of charge, temperature, and usage history.
[0010] Next, embodiments of the present application may provide a charger that reduces stress on battery cells and minimize degradation over time.
[0011] Next, embodiments of the present application may provide a charger that uses artificial intelligence and predictive algorithms to enable real-time power distribution.
[0012] Next, embodiments of the present application may provide a charger that avoids energy wastage and ensuring faster charging without sacrificing battery health.
[0013] Next, embodiments of the present application may provide a charger that using liquid cooling, phase change materials, and active air circulation prevents overheating.
[0014] Next, embodiments of the present application may provide a charger that lowers risk of thermal runaway, and ensures stable operation.
[0015] Next, embodiments of the present application may provide a charger that detect early signs of cell imbalance or failure, allowing timely maintenance actions.
[0016] Next, embodiments of the present application may provide a charger that reduces unexpected breakdowns and increases overall reliability.
[0017] Next, embodiments of the present application may provide a charger that supports various charging standards, including high-voltage fast chargers, inductive wireless charging, and grid-connected energy management.
[0018] Next, embodiments of the present application may provide a charger that is adaptable to different infrastructures worldwide.
[0019] These and other advantages will be apparent from the present application of the embodiments described herein.
[0020] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
BRIEF DESCRIPTION OF THE DRAWINGS
[0021] The above and still further features and advantages of embodiments of the present invention will become apparent upon consideration of the following detailed description of embodiments thereof, especially when taken in conjunction with the accompanying drawings, and wherein:
[0022] FIG. 1 illustrates a block diagram of a system for ultra-fast charging of an electric vehicle (EV) battery, according to an embodiment of the present invention;
[0023] FIG. 2 illustrates a connection diagram of the system for ultra-fast charging of an electric vehicle (EV) battery with a computer device, according to an embodiment of the present invention; and
[0024] FIG. 3 depicts a flowchart of a method for ultra-fast charging of an electric vehicle (EV) battery, according to an embodiment of the present invention.
[0025] The headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description or the claims. As used throughout this application, the word "may" is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Similarly, the words “include”, “including”, and “includes” mean including but not limited to. To facilitate understanding, like reference numerals have been used, where possible, to designate like elements common to the figures. Optional portions of the figures may be illustrated using dashed or dotted lines, unless the context of usage indicates otherwise.
DETAILED DESCRIPTION
[0026] The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the scope of the invention as defined in the claims.
[0027] In any embodiment described herein, the open-ended terms "comprising", "comprises”, and the like (which are synonymous with "including", "having” and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of", “consists essentially of", and the like or the respective closed phrases "consisting of", "consists of”, the like.
[0028] As used herein, the singular forms “a”, “an”, and “the” designate both the singular and the plural, unless expressly stated to designate the singular only.
[0029] FIG. 1 illustrates a block diagram of a system 100 for ultra-fast charging of an electric vehicle (EV) battery, according to an embodiment of the present invention. In an embodiment of the present invention, the system 100 may be adapted to analyze a battery health of a power unit of an electric vehicle 102. Based on the analyzed battery health of the power unit, the system 100 may indicate a charging station 104 to modulate a rate and method of charging of the power unit of the electric vehicle 102. The modulation in the rate and the method of charging of the power unit of the electric vehicle 102 may ensure a backup and a longevity of the power unit installed in the electric vehicle 102.
[0030] The system 100 may support high-power fast chargers up to 800 Volts (V), an inductive wireless charging, a grid-connected energy management, and so forth for seamless integration with commercially available charging infrastructures. The system 100 may be configured to engage with artificial computation and machine learning techniques for assistance in prediction of best charging times based on power availability and grid demand. The predictive power utilization may optimize charging schedules to minimize costs and environmental impact. The system 100 may be configured to dynamically adapt to different charging infrastructures. Thus, the system 100 may be versatile for a wide range of applications.
[0031] According to the embodiments of the present invention, the system 100 may incorporate non-limiting hardware components to enhance a processing speed and an efficiency such as the system 100 may comprise a battery management unit 108, batteries 110a-110n (hereinafter referred individually to as the battery 110, and plurally to as the batteries 110), a controller 112, a thermal management unit 114, and a network interface 116. In an embodiment of the present invention, the hardware components of the system 100 may be integrated with computer-executable instructions for overcoming the challenges and the limitations of the existing systems.
[0032] In an embodiment of the present invention, the charging station 104 may be adapted to charge the electric vehicle 102. The charging station 104 may receive electrical power from a grid and/or a generator. The charging station 104 may further adjust the received electrical power in compliance with the electric vehicle 102. The received electrical power may be adjusted using means such as, but not limited to, a capacitor, an inductor, a transformer, a set of logic gates, a rectifier, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the means in the charging station 104, including known, related art, and/or later developed technologies, that may adjust the received electrical power.
[0033] The adjusted electrical power may further be supplied to the electric vehicle 102 using a charging plug 106. The charging station 104 may physically be installed in establishments such as, but not limited to, a parking lot, a service garage, a home, a showroom, a workplace, and so forth. Embodiments of the present invention are intended to include or otherwise cover any establishments, including known, related art, and/or later developed technologies. for physical installation of the charging station 104.
[0034] In an embodiment of the present invention, the charging plug 106 may adapted to be plugged in a socket (not shown) on the electric vehicle 102. The charging plug 106 may enable a flow of the adjusted electrical power from the charging station 104 to the batteries 110 of the electric vehicle 102. The charging plug 106 may be, but not limited to, a Combined Charging System (CCS), a CHArge de MOve (CHAdeMO), a SAE J1772 (Type 1), an IEC 62196-2 (Type 2), a Guobiao/Tuījiàn China standard (GB/T), a Proprietary Connector Tesla (PCT), a National Electrical Manufacturers Association 14-50 (NEMA 14-50), an International Electrotechnical Commission 62196 (IEC 62196), a North American Charging Standard (NACS), and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the charging plug 106, including known, related art, and/or later developed technologies.
[0035] In an embodiment of the present invention, the battery management unit 108 may be connected to a positive terminal and a negative terminal of the batteries 110. The battery management unit 108 may be adapted to modulate, limit and/or stop a charging process of the batteries 110. Further, the battery management unit 108 may be adapted to extrapolate real-time data from the batteries 110. The real-time data may be, but not limited to, a state-of-charge (SoC), a voltage, a current, a temperature, charge cycles, environmental conditions, and so forth. Embodiments of the present invention are intended to include or otherwise cover any real-time data, including known, related art, and/or later developed technologies, that may be extrapolated by the battery management unit 108 from the batteries 110.
[0036] In an embodiment of the present invention, the battery management unit 108 may be configured to utilize the real-time data to detect cell imbalance in the batteries 110 and degradation trends. The real-time data may enable the battery management unit 108 to optimize charging and discharging cycles. In an embodiment of the present invention, the battery management unit 108 may be configured to interact with a vehicle onboard system (not shown) and the charging station 104 to coordinate optimal charging conditions. Hence, reducing downtime and efficiency. In an embodiment of the present invention, the battery management unit 108 may be configured to access AI-based analytics to generate predictive maintenance alerts.
[0037] In an embodiment of the present invention, the batteries 110 may be installed in the electric vehicle 102. The batteries 110 may be adapted to store the electrical power received from the charging station 104 via the charging plug 106. Further, the electrical power stored in the batteries 110 may be adapted to power on and facilitate movements of the electric vehicle 102. The batteries 110 may be installed in the electric vehicle 102 at locations such as, but not limited to, a floor bed, a trunk, a boot, a hood, a frunk, a rooftop, and so forth. Embodiments of the present invention are intended to include or otherwise cover any location for installation, including known, related art, and/or later developed technologies, of the batteries 110 in the electric vehicle 102.
[0038] In an embodiment of the present invention, the batteries 110 may include cells (not shown) such as, but not limited to, cylindrical cells, prismatic cells, pouch cells, solid-state cells, lithium-sulfur cells, lithium-air cells, sodium-ion cells, flow battery cells, nickel-metal hydride (NiMH) cells, ultracapacitor cells, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of cells constituted in the batteries 110, including known, related art, and/or later developed technologies, that may be installed in the electric vehicle 102.
[0039] In an embodiment of the present invention, the controller 112 may be operatively connected to the battery management unit 108. The operative communication may include, but not limited to, receiving, transmitting, processing, synchronizing, querying, updating, encrypting, decrypting, storing, retrieving, validating, logging, monitoring, alerting, authenticating, authorizing, compressing, decompressing, streaming, and rendering data or commands between the controller 112 and the battery management unit 108. The controller 112 may further be configured to execute computer-executable instructions to generate an output relating to the system 100.
[0040] In an embodiment of the present invention, the controller 112 may be configured to receive the real-time data of the batteries 110 from the battery management unit 108.
[0041] In an embodiment of the present invention, the controller 112 may be configured to dynamically adjust charging parameters by applying an artificial intelligence (AI) based predictive model. The charging parameters may be, but not limited to, a voltage, a current, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the charging parameters, including known, related art, and/or later developed technologies. The artificial intelligence (AI) based predictive model may be configured to avoid overcharging and maximizing charge cycles using predictive analytics. The avoidance of overcharging may result in enablement of energy distribution and reduction of wear and tear on the batteries 110. The artificial intelligence (AI) based predictive model may be configured to detect patterns of charging and user usage to optimize overall charging efficiency and predict likely failure of the batteries 110.
[0042] In an embodiment of the present invention, the controller 112 may be configured to exert a multi-stage charging algorithm. The multi-stage charging algorithm may be adapted to regulate a charging fashion of the batteries 110. The charging fashion may be regulated by transitioning between the charging states provided by the multi-stage charging algorithm. The charging states may be, but not limited to, a constant current (CC) charging stage, a constant voltage (CV) charging stage, and an AI-regulated pulse charging, and so forth. In a preferred embodiment of the present invention, the multi-stage charging algorithm may be configured to dynamically switch between fast and slow charging in an attempt to reduce stress on the batteries 110 and maximize energy storage efficiency. Embodiments of the present invention are intended to include or otherwise cover any type of the charging states, including known, related art, and/or later developed technologies. The multi-stage charging algorithm may be configured to calculate optimal transition points, for the charging stages, during charging cycles to trade speed with longevity. Further, the multi-stage charging algorithm may be configured to forecast optimum voltage and current levels that may be supplied to the batteries 110 to reduce degradation with optimum possible charge.
[0043] In an embodiment of the present invention, the controller 112 may be configured to activate the thermal management unit 114 to regulate and modulate a temperature of the batteries 110. The thermal management unit 114 may be regulated by the artificial intelligence (AI) based predictive model. In an embodiment of the present invention, the controller 112 may be configured to optimize charging schedules by predicting power availability and grid demand to reduce degradation and improve charging efficiency of the batteries 110.
[0044] In an exemplary scenario, an electric powered car may be plugged in a household charging point. As an overall power load may be less in night times, therefore, the controller 112 may enable a charging of the electric powered car in period of 2:00 AM to 4:00 AM. Further, an ambient temperature at said period of time may be low, thus, preventing thermal stress on the batteries 110 of the electric powered car. As, the thermal stress may be low, the controller 112 may switch to a fast charging stage. However, if said electric powered car may be plugged in the household charging point at times of high household power usage, and high ambient temperature, the controller 112 may switch to a slow charging stage. Additionally, the controller 112 may cut-off charging of the electric powered car intermittently, and may activate the thermal management unit 114 to regulate and modulate the temperature of the batteries 110.
[0045] The controller 112 may be, but not limited to, a STMicroelectronics (STM32) embedded with a low-power control engine, a Raspberry Pi 4 installed with a general-purpose Linux, a Jetson Nano configured with an edge Machine Learning (ML) interface, a Programmable Logic Control (PLC) unit, a microprocessor, a development board, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the controller 112, including known, related art, and/or later developed technologies.
[0046] In an embodiment of the present invention, the thermal management unit 114 may be configured to regulate and modulate the temperature of the batteries 110. The thermal management unit 114 may be coupled with the batteries 110. The thermal management unit 114 may be operated by the multi-stage charging algorithm. The multi-stage charging algorithm may enable the thermal management unit 114 to dynamically monitor heat production and varies a degree of cooling accordingly to avert thermal runaway and provide working safety in the batteries 110. The multi-stage charging algorithm may enable the thermal management unit 114 to rationally redistribute heat to batteries 110 to provide stable thermal performance. The thermal management unit 114 may comprise a liquid cooling, a phase change material, an active air cooling, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of cooling methodologies, including known, related art, and/or later developed technologies, implemented by the thermal management unit 114.
[0047] In an embodiment of the present invention, the network interface 116 may be adapted to establish a communication link between the controller 112 and a computer device 200 (As shown in FIG. 2). The communication link may enable a transmission of the predictive maintenance alerts from the controller 112 to the computer device 200. The communication link may be established using protocols such as, but not limited to, a Message Queuing Telemetry Transport (MQTT) protocol, a Remote Procedure Call (gRPC) protocol, a Representational State Transfer (REST) protocol for edge-cloud, a WebSocket protocol, a Zero Message Queue (ZeroMQ) protocol, and so forth. Embodiments of the present invention are intended to include or otherwise cover any protocol, including known, related art, and/or later developed technologies, for establishing the communication link between the controller 112 and a computer device 200.
[0048] The network interface 116 may be, but not limited to, a Bluetooth communication interface, a millimetre waves communication interface, an Ultra-High Frequency (UHF) communication interface, a wired communication interface, a data cable-based communication interface, an Internet of Things (IoT) protocols, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the network interface 116, including known, related art, and/or later developed technologies.
[0049] FIG. 2 illustrates a connection diagram of the system 100 with the computer device 200, according to an embodiment of the present invention. In an embodiment of the present invention, the computer device 200 may be an electronic device. The computer device 200 may be used by a subject. The subject may be, but not limited to, an owner, a driver, a rider, a service person, a sales person, and so forth of the electric vehicle 102. The computer device 200 may enable the subject to receive the predictive maintenance alerts. The received predictive maintenance alerts may indicate a maintenance process that may be required to be carried out on the batteries 110.
[0050] The notification may be, but not limited to, a ping, a ring, a flash, a haptic, a reminder through an application, a Short Messaging Service (SMS), and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the notification, including known, related art, and/or later developed technologies. The computer device 200 may be, but not limited to, a smartphone, a laptop, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the computer device 200, including known, related art, and/or later developed technologies.
[0051] FIG. 3 depicts a flowchart of a method 300 for ultra-fast charging of an electric vehicle (EV) battery, according to an embodiment of the present invention.
[0052] At step 302, the system 100 may receive the real-time data of the batteries 110 from the battery management unit 108.
[0053] At step 304, the system 100 may dynamically adjust the charging parameters by applying the artificial intelligence (AI) based predictive model.
[0054] At step 306, the system 100 may exert the multi-stage charging algorithm comprising the constant current (CC) charging stage, the constant voltage (CV) charging stage, and the AI-regulated pulse charging. The transition between the charging stage may be determined based on the received real-time data of the batteries 110.
[0055] At step 308, the system 100 may activate the thermal management unit 114 to regulate and modulate the temperature of the batteries 110. The thermal management unit 114 may be regulated by the artificial intelligence (AI) based predictive model.
[0056] At step 310, the system 100 may optimize charging schedules by predicting power availability and grid demand to reduce degradation and improve charging efficiency of the batteries 110.
[0057] At step 312, the system 100 may enable the artificial intelligence (AI) based predictive model to generate and transmit the predictive maintenance alerts to the computer device 200.
[0058] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it is to be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0059] This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined in the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements within substantial differences from the literal languages of the claims. , Claims:CLAIMS
I/We Claim:
1. A system (100) for ultra-fast charging of an electric vehicle (EV) battery, the system (100) comprising:
a battery management unit (108), coupled with batteries (110a-110n) of an electric vehicle (102), configured to extrapolate real-time data from the batteries (110a-110n); and
a controller (112) communicatively connected to the battery management unit (108), characterized in that the controller (112) is configured to:
receive the real-time data of the batteries (110a-110n) from the battery management unit (108);
dynamically adjust charging parameters by applying an artificial intelligence (AI) based predictive model;
exert a multi-stage charging algorithm comprising a constant current (CC) charging stage, a constant voltage (CV) charging stage, and an AI-regulated pulse charging, wherein transition between the charging stage is determined based on the received real-time data of the batteries (110a-110n);
activate a thermal management unit (114) to regulate and modulate a temperature of the batteries (110a-110n), wherein the thermal management unit (114) is regulated by the artificial intelligence (AI) based predictive model; and
optimize charging schedules by predicting power availability and grid demand to reduce degradation and improve charging efficiency of the batteries (110a-110n).
2. The system (100) as claimed in claim 1, wherein the real-time data is selected from a state of charge (SoC), a temperature, charge cycles, environmental conditions, or a combination thereof.
3. The system (100) as claimed in claim 1, wherein the charging parameters are selected from a voltage, a current, or a combination thereof.
4. The system (100) as claimed in claim 1, wherein the controller (112) is configured to enable the artificial intelligence (AI) based predictive model to generate and transmit predictive maintenance alerts.
5. The system (100) as claimed in claim 1, wherein the thermal management unit (114) comprises a liquid cooling, a phase change material, an active air cooling, or a combination thereof.
6. A method (300) for ultra-fast charging of an electric vehicle (EV) battery, the method (300) is characterized by steps of:
receiving a real-time data of batteries (110a-110n) from a battery management unit (108);
dynamically adjusting charging parameters by applying an artificial intelligence (AI) based predictive model;
exerting a multi-stage charging algorithm comprising a constant current (CC) charging stage, a constant voltage (CV) charging stage, and an AI-regulated pulse charging, wherein transition between the charging stage is determined based on the received real-time data of the batteries (110a-110n);
activating a thermal management unit (114) to regulate and modulate a temperature of the batteries (110a-110n), wherein the thermal management unit (114) is regulated by the artificial intelligence (AI) based predictive model; and
optimizing charging schedules by predicting power availability and grid demand to reduce degradation and improve charging efficiency of the batteries (110a-110n).
7. The method (300) as claimed in claim 6, comprising a step of enabling the artificial intelligence (AI) based predictive model to generate and transmit predictive maintenance alerts.
8. The method (300) as claimed in claim 6, wherein the real-time data is selected from a state of charge (SoC), a temperature, charge cycles, environmental conditions, or a combination thereof.
9. The method (300) as claimed in claim 6, wherein the charging parameters are selected from a voltage, a current, or a combination thereof.
10. The method (300) as claimed in claim 6, wherein the thermal management unit (114) comprises a liquid cooling, a phase change material, an active air cooling, or a combination thereof.
Date: August 28, 2025
Place: Noida

Nainsi Rastogi
Patent Agent (IN/PA-2372)
Agent for the Applicant