A system and a method for real-time monitoring and adaptive control of a battery

Abstract

A system (100) for real-time monitoring and adaptive control of a battery is disclosed A pre-installed battery management system (105) measures battery data including cell voltage, pack voltage, current measurements, state of charge, cell temperature, and state of health. The supervisory control board (115) receives an operational command from the host (110) to manage battery operations including regulating battery parameters, controlling charge and discharge processes, managing safety mechanisms, and optimizing battery performance. A data acquisition module (130) captures real-time battery data, while a processing module (135) analyzes it using a combination of one or more methods to re-estimate battery state parameters. The processing module generates a renewed control strategy and modifies operational commands to enhance battery longevity. A control module (140) transmits these commands. A connectivity module (145) enables cloud-based remote monitoring and analytics. A plurality of integration tools (150) configures the supervisory control board for first-time use.

Description

[0001] A SYSTEM AND A METHOD FOR REAL-TIME MONITORING AND ADAPTIVE CONTROL OF A BATTERY EARLIEST PRIORITY DATE:

[0002] This Application claims priority from a complete patent application filed in India having Patent Application No. 202541024296, filed on 18th day of March 2025, and titled “A SYSTEM AND A METHOD FOR REAL-TIME MONITORING AND ADAPTIVE CONTROL OF A BATTERY”.

[0003] FIELD OF INVENTION

[0004] Embodiments of the present disclosure relate to the field of battery management systems, and more particularly, a system and a method for real-time monitoring and adaptive control of a battery.

[0005] BACKGROUND

[0006] A Battery Management System (BMS) is an electronic system that manages and monitors the performance, safety, and health of a battery pack. BMS ensures optimal charging and discharging, protects the battery from damage, and provides crucial data about the battery's state of charge (SoC), state of health (SoH), etc. However, existing BMS solutions have several limitations that impact battery performance, lifespan, and adaptability, particularly as batteries age.

[0007] Most BMS solutions are tailored specifically to work with new or fresh battery packs and are not optimized to manage battery performance throughout its aging process, leading to less efficient usage and reduced lifespan over time. As a lithium-ion battery or other new chemistry batteries degrade over time, they need specialized mechanisms to ensure performance and safety.Further, many existing BMS solutions are rigid when it comes to adapting to system requirements over the lifetime of a battery. This lack of flexibility makes it difficult to tailor battery management strategies for different applications, including secondaryuse scenarios. Some existing BMS solutions struggle with accurately estimating State of Charge (SoC) and State of Health (SoH) due to limitations in algorithms especially over the lifetime of the battery. The existing BMS solutions typically lack predictive maintenance capabilities, cloud connectivity, and Al-based analytics. These advanced features are critical for modern energy storage systems, and which can limit their effectiveness in modern applications.

[0008] Additionally, cost constraints pose another challenge. While high-quality commercial BMS solutions are expensive, lower-cost alternatives often compromise on reliability, accuracy, and safety. Many BMSs are also designed as closed systems with proprietary firmware, restricting customization, software updates, and third-party integration, thereby hindering long-term adaptability.

[0009] Original Equipment Manufacturers (OEMs) seeking to repurpose retired electric vehicle battery packs for stationary storage or other applications face significant challenges. The existing BMS in these battery packs is often a black box, preventing firmware modifications and failing to address the unique needs of aged battery packs, which require sophisticated algorithms for secondary use-case management. As a result, OEMs are often forced to replace the BMS entirely, increasing costs and complexity.

[0010] Hence, there is a need for an improved system and a method for real-time monitoring and adaptive control of a battery which addresses the aforementioned issue(s).

[0011] OBJECTIVES OF THE INVENTION

[0012] The primary objective of the invention is to introduce a supervisory control board as an add-on to existing Battery Management Systems (BMS) to enhance batterymonitoring, optimization, and predictive control or with minimal modification to the existing BMS.

[0013] Another objective of the invention is to continuously monitor battery parameters such as voltage, current, state of charge (SoC), state of health (SoH), cell temperature and the like, in real-time to ensure efficient and safe battery operation.

[0014] Yet another objective of the invention is to utilize a combination of one or more methods, including model-based approaches (such as Equivalent Circuit Models (ECM) and physics-based models) and artificial intelligence (Al)- Machine learning (ML) based techniques, to analyze battery data, and re-estimate battery state parameters.

[0015] Yet another objective of the invention is to determine and apply an updated control strategy to prevent unsafe charging, discharging, operation outside the safe operating area, ensuring battery longevity and safety.

[0016] Yet another objective is to apply advanced and more sophisticated control mechanisms such as fast charging, adaptive charging and other optimized battery management techniques.

[0017] Yet another objective is to enable predictive maintenance and anomaly detection both edge devices and the cloud using a combination of ALML and other methods, ensuring real-time fault identification, optimized performance, and enhanced battery safety without relying solely on cloud processing.

[0018] BRIEF DESCRIPTION

[0019] In accordance with an embodiment of the present disclosure, a system for real-time monitoring and adaptive control of a battery is provided. The system includes a preinstalled battery management system configured to measure and manage a plurality ofbattery data. The plurality of battery data includes cell voltage, pack voltage, a plurality of current measurements, state of charge, cell temperature and state of health. The system includes a supervisory control board operatively coupled to the pre-installed battery management system and a host. The supervisory control board adapted to receive an operational command from the host for the pre-installed battery management system. The operational command includes an instruction to perform at least one battery operation. The battery operation includes at least one of regulating battery parameters, controlling charge and discharge processes, managing safety mechanisms, and optimizing battery performance. The pre-installed battery management system is enabled with a communication protocol to interface with the supervisory control board. The supervisory control board includes a processing subsystem hosted on a server. The processing subsystem is configured to execute on a network to control bidirectional communications among a plurality of modules. The processing subsystem includes a data acquisition module configured to receive the plurality of battery data from the preinstalled battery management system in real-time. The processing subsystem includes a processing module operatively coupled to the data acquisition module. The processing module is configured to analyze the plurality of battery data using a combination of one or more methods to re-estimate a plurality of battery state parameters in real-time. The one or more methods include a plurality of model-based approaches and artificial intelligence and machine learning-based techniques. The processing module is configured to generate a renewed control strategy based on realtime estimations and predictions of the battery’s health. The processing module is configured to modify the operational command to ensure longevity of the battery. The modification is based on the re-estimated plurality of battery state parameters, and the renewed control strategy. The processing subsystem includes a control module operatively coupled to the processing module. The control module is configured to transmit the modified operational command to the pre-installed battery management system for execution. The control module is configured to communicate the modified operational command and execution output to the host. The processing subsystemincludes a connectivity module operatively coupled to the control module. The connectivity module is configured to transmit the plurality of battery parameters to a cloud for remote monitoring and analytics and reporting using the combination of one or more methods and a cloud-based platform. The cloud-based platform performs one or more functions using the combination of one or more methods. The processing subsystem includes a plurality of integration tools operatively coupled to the supervisory control board. The plurality of integration tools is configured to configure the supervisory control board for a first-time use. The configuration includes initialization, calibration, and setup of the communication protocol with the preinstalled battery management system, the host, and the cloud-based platform.

[0020] In accordance with another embodiment of the present disclosure, a method for realtime monitoring and adaptive control of a battery is provided. The method includes measuring and managing, by a pre-installed battery management system, a plurality of battery data. The plurality of battery data includes cell voltage, pack voltage, a plurality of current measurements, state of charge, cell temperature, and state of health. The method includes receiving, by a supervisory control board, an operational command from the host for the pre-installed battery management system. The operational command includes an instruction to perform at least one battery operation. The battery operation including at least one of regulating battery parameters, controlling charge and discharge processes, managing safety mechanisms, and optimizing battery performance. The pre-installed battery management system is enabled with a communication protocol to interface with the supervisory control board. The method includes receiving, a data acquisition module, the plurality of battery data from the preinstalled battery management system in real-time. The method includes analysing, by a processing module, the plurality of battery data using a combination of one or more methods to re-estimate a plurality of battery state parameters in real-time. The one or more methods include a plurality of model-based approaches and artificial intelligence and machine learning-based techniques. The method includes generating, by theprocessing module, a renewed control strategy based on real-time estimations and predictions of the battery’s health. The method includes modifying, by the processing module, the operational command to ensure longevity of the battery. The modification is based on the re-estimated plurality of battery state parameters, and the renewed control strategy. The method includes transmitting, by a control module, the modified operational command to the pre-installed battery management system for execution. The method includes communicating, by the control module, the modified operational command and execution output to the host. The method includes transmitting, by a connectivity module, the plurality of battery parameters to a cloud for remote monitoring and analytics and reporting using the combination of one or more methods and a cloud-based platform. The cloud-based platform performs one or more functions using the combination of one or more methods. The method includes configuring, by a plurality of integration tools, the supervisory control board for a first-time use. The configuration including initialization, calibration, and setup of the communication protocol with the pre-installed battery management system, the host, and the cloudbased platform.

[0021] To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will follow by reference to specific embodiments thereof, which are illustrated in the appended figures. It is to be appreciated that these figures depict only typical embodiments of the disclosure and are therefore not to be considered limiting in scope. The disclosure will be described and explained with additional specificity and detail with the appended figures.

[0022] BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:FIG. 1 is a block diagram representation of a system for real-time monitoring and adaptive control of a battery in accordance with an embodiment of the present disclosure;

[0024] FIG. 2 is a block diagram of a computer or a server in accordance with an embodiment of the present disclosure;

[0025] FIG. 3(a) illustrates a flow chart representing the steps involved in a method for realtime monitoring and adaptive control of a battery in accordance with an embodiment of the present disclosure; and

[0026] FIG. 3(b) illustrates continued steps of the method of FIG. 3(a) in accordance with an embodiment of the present disclosure.

[0027] Further, those skilled in the art will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the figures with details that will be readily apparent to those skilled in the art having the benefit of the description herein.

[0028] DETAILED DESCRIPTION

[0029] For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe them. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Such alterations and further modifications in the illustrated system, and such further applications of the principles of the disclosure as would normally occur to those skilled in the art are to be construed as being within the scope of the present disclosure.The terms “comprises”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such a process or method. Similarly, one or more devices or subsystems or elements or structures or components preceded by "comprises... a" does not, without more constraints, preclude the existence of other devices, sub-systems, elements, structures, components, additional devices, additional sub-systems, additional elements, additional structures or additional components. Appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment.

[0030] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are only illustrative and not intended to be limiting.

[0031] In the following specification and the claims, reference will be made to a number of terms, which shall be defined to have the following meanings. The singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise.

[0032] Embodiments of the present disclosure relate to a system for real-time monitoring and adaptive control of a battery. The system includes a pre-installed battery management system configured to measure and manage a plurality of battery data. The plurality of battery data includes cell voltage, pack voltage, a plurality of current measurements, state of charge, cell temperature, and state of health. The system includes a supervisory control board operatively coupled to the pre-installed battery management system and a host. The supervisory control board adapted to receive an operational command from the host for the pre-installed battery management system. The operational command includes an instruction to perform at least one battery operation. The battery operation includes at least one of regulating battery parameters, controlling charge and dischargeprocesses, managing safety mechanisms, and optimizing battery performance. The preinstalled battery management system is enabled with a communication protocol to interface with the supervisory control board. The supervisory control board includes a processing subsystem hosted on a server. The processing subsystem is configured to execute on a network to control bidirectional communications among a plurality of modules. The processing subsystem includes a data acquisition module configured to receive the plurality of battery data from the pre-installed battery management system in real-time. The processing subsystem includes a processing module operatively coupled to the data acquisition module. The processing module is configured to analyze the plurality of battery data using a combination of one or more methods to re-estimate a plurality of battery state parameters in real-time. The one or more methods include a plurality of model-based approaches and artificial intelligence and machine learningbased techniques. The processing module is configured to generate a renewed control strategy based on real-time estimations and predictions of the battery’s health. The processing module is configured to modify the operational command to ensure longevity of the battery. The modification is based on the re-estimated plurality of battery state parameters, and the renewed control strategy. The processing subsystem includes a control module operatively coupled to the processing module. The control module is configured to transmit the modified operational command to the pre-installed battery management system for execution. The control module is configured to communicate the modified operational command and execution output to the host. The processing subsystem includes a connectivity module operatively coupled to the control module. The connectivity module is configured to transmit the plurality of battery parameters to a cloud for remote monitoring and analytics and reporting using the combination of one or more methods and a cloud-based platform. The cloud-based platform performs one or more functions using the combination of one or more methods. The processing subsystem includes a plurality of integration tools operatively coupled to the supervisory control board. The plurality of integration tools is configured to configure the supervisory control board for a first-time use. Theconfiguration includes initialization, calibration, and setup of the communication protocol with the pre-installed battery management system, the host, and the cloudbased platform.

[0033] FIG. 1 is a block diagram of a system (100) for real-time monitoring and adaptive control of a battery in accordance with an embodiment of the present disclosure. The system (100) includes a pre-installed battery management system (105) (BMS) configured to measure and manage a plurality of battery data. The pre-installed BMS refers to an existing BMS that comes integrated with a battery pack at the time of manufacturing. The battery pack consists of a plurality of battery cells electrically connected to function as a single power unit. Examples of battery pack include but are not limited to primary or first-life applications, secondary storage applications and the like. The system may be utilized at any stage of a battery life cycle, from new to second-life storage.

[0034] The plurality of battery data refers to sensor readings and electrical measurements collected from the battery pack. The plurality of battery data includes but is not limited to cell voltage, pack voltage, a plurality of current measurements, state of charge, cell temperature, state of health and the like.

[0035] The system (100) includes a supervisory control board (115) operatively coupled to the pre-installed battery management system (105) and a host (110). The supervisory control board (115) adapted to receive an operational command from the host (110) for the pre-installed battery management system (105). The operational command includes an instruction to perform at least one battery operation. The battery operation includes at least one of regulating battery parameters, controlling charge and discharge processes, managing safety mechanisms, and optimizing battery performance.

[0036] The pre-installed battery management system (105) is enabled with a communication protocol to interface with the supervisory control board (115). The host (110) is one ofan external control unit, including but not limited to an electronic control unit of a vehicle, a control unit of an energy storage system and the like. The communication protocol included but not limited to at least one of Controller Area Network, Universal Asynchronous Receiver-Transmitter or Ethernet, and the like.

[0037] One embodiment, the supervisory control board (115) functions as a "man-in-the-middle" system, meaning it acts as an intermediary control layer that re-evaluates battery parameters and introduces advanced management features while leveraging the pre-installed BMS for fundamental measurements and hardware controls.

[0038] It must be noted that connection between the supervisory control board (115), the preinstalled battery management system (105), and the host (110) can be established via wired or wireless communication.

[0039] The supervisory control board (115) includes a processing subsystem (120) (105) hosted on a server (125). In one embodiment, the server (125) may include a cloudbased server. In another embodiment, parts of the server (125) may be a local server coupled to a user device (not shown in FIG.l). The processing subsystem (120) is configured to execute on a network (130) to control bidirectional communications among a plurality of modules. In one example, the network (130) may be a private or public local area network (LAN) or Wide Area Network (WAN), such as the Internet. In another embodiment, the network (130) may include both wired and wireless communications according to one or more standards and / or via one or more transport mediums. In one example, the network (130) may include wireless communications according to one of the 802.11 or Bluetooth specification sets, or another standard or proprietary wireless communication protocol. In yet another embodiment, the network (130) may also include communications over a terrestrial cellular network, including, a global system for mobile communications (GSM), code division multiple access (CDMA), and / or enhanced data for global evolution (EDGE) network.In an embodiment, the server (125) is an edge controller that enables local execution of battery parameter re-estimation and adaptive control without requiring cloud connectivity.

[0040] The processing subsystem (120) includes a data acquisition module (130) configured to receive the plurality of battery data from the pre-installed battery management system (105) in real-time.

[0041] The processing subsystem (120) includes a processing module (135) operatively coupled to the data acquisition module (130). The processing module (135) is configured to analyze the plurality of battery data using a combination of one or more methods, to re-estimate a plurality of battery state parameters in real-time. The one or more methods include a plurality of model-based approaches and artificial intelligence and machine learning-based techniques. The plurality of model-based approaches includes Equivalent Circuit Models (ECM) and physics-based models. The re-estimated plurality of battery state parameters provides essential insights into the battery’s condition, performance, and longevity. The plurality of battery state parameters includes but is not limited to state of charge (SoC), which indicates the remaining charge in the battery, State of Health (SoH), which reflects the overall health including degradation trends and, an estimation of remaining useful life (RUL) of the battery over time, and the like. The processing module (135) continuously refines these state parameters in real-time using the combination of the one or more methods, thereby improving the accuracy of battery performance.

[0042] In one embodiment, the artificial intelligence and machine learning-based techniques employ at least one of the following battery estimations approaches empirical model, model-based approach, artificial intelligence-driven battery estimation algorithms, and the like. The empirical model is a model based on observations and data, rather than theoretical principles. It can be used to explain, predict, or simulate battery behavior based on past observations. The model-based approach utilizes mathematicalrepresentations and system identification techniques to estimate battery parameters based on electrochemical, thermal, physics based or equivalent circuit models. The artificial intelligence-driven approach employs machine learning and deep learning techniques to enhance battery state estimation, anomaly detection, and predictive analytics.

[0043] It must be noted that the artificial intelligence and machine learning-based techniques are configured with an artificial intelligence algorithm. Examples of the artificial intelligence algorithm include, but are not limited to, a Deep Neural Network (DNN), Convolutional Neural Network (CNN), Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN) and Deep Q-Networks.

[0044] The processing module (135) is configured to generate a renewed control strategy based on real-time estimations and predictions of the battery’s health. The renewed control strategy refers to an updated plurality of battery state parameters and control actions dynamically generated based on real-time estimations and predictive analytics. The renewed control strategy prevents unsafe charging, discharging, or operational conditions based on real-time predictive analytics.

[0045] It must be noted that the renewed control strategy can be either manually defined by engineers based on diagnostic data and expert analysis or automatically generated using artificial intelligence which autonomously adjusts settings by analyzing historical and real-time battery data.

[0046] The processing module (135) is configured to modify the operational command to ensure longevity of the battery. The modification is based on the re-estimated plurality of battery state parameters, and the renewed control strategy.

[0047] More specifically, the processing module (135) continuously monitors the plurality of battery data while also evaluating incoming operational commands. If the operational command requests a battery state parameter that exceeds the safe operating limits ofthe pre-installed BMS, the processing module (135) re-estimates the battery state parameter in real-time based on the current battery data. It then generates the renewed control strategy with adjusted, safe values and informs both the host (110) and the preinstalled BMS to execute the operational command revised.

[0048] For example, if the host (110) requests charging the battery pack to 245V, the processing module (135) analyzes the current state of charge (SoC), state of health (SoH), temperature, and the like. If the requested voltage is deemed unsafe due to the battery’s current condition, the processing module (135) dynamically adjusts the command to a safer charging voltage, for example 200V while ensuring optimal performance and longevity. The host (110) and the pre-installed BMS are then updated with the operational command revised for execution.

[0049] In another example, if the host (110) requests a discharge current of 150A, the processing module (135) analyzes the real-time state of charge (SoC), state of health (SoH), temperature, internal resistance, and other relevant parameters. If the requested current exceeds the safe operational limits based on the battery’s current condition, the processing module (135) dynamically adjusts the command to a safer discharge current, for example, 120A, to prevent excessive stress on the battery while ensuring optimal performance and longevity. The host (110) and the pre-installed BMS (105) are then updated with the revised operational command for execution.

[0050] The processing subsystem (120) includes a control module (140) operatively coupled to the processing module (135). The control module (140) is configured to transmit the modified operational command to the pre-installed battery management system (105) for execution. The control module (140) is configured to communicate the modified operational command and execution output to the host (110).

[0051] The processing subsystem (120) includes a connectivity module (145) operatively coupled to the control module (140). The connectivity module (145) is configured totransmit the plurality of battery parameters to a cloud for remote monitoring and analytics and reporting using the combination of one or more methods and a cloudbased platform. The cloud-based platform performs one or more functions using artificial intelligence and machine learning-based techniques. The one or more functions of the cloud-based platform include predicting battery degradation, remaining useful life, and potential failure, providing remote diagnostics and performance monitoring, generating real-time performance analytics, and enabling over-the-air updates of the supervisory control board (115) and the pre-installed battery management system (105).

[0052] Furthermore, the cloud-based platform continuously refines its artificial intelligence models by learning from data collected from a fleet of batteries deployed in various applications. The updated Al models can be downloaded and flashed onto the supervisory control board (115), allowing for continuous improvements in real-time decision-making and adaptive battery management strategies.

[0053] In an embodiment, if industrial requirements prohibit cloud communication, the supervisory control board (115) may operate as a standalone system or in a fully wired mode with local processing. In this configuration, all battery state estimations, control strategy updates, and diagnostics are performed locally without reliance on cloud-based services.

[0054] In an embodiment, the cloud may also host a digital twin, enabling advanced battery modeling and simulation by replicating real-time battery behavior, which may be visualized through the user device.

[0055] In another embodiment, the system (100) includes one or more external circuits configured to provide additional safety mechanisms to enhance battery management and operational safety.The processing subsystem (120) includes a plurality of integration tools (150) operatively coupled to the supervisory control board (115). The plurality of integration tools (150) is configured to configure the supervisory control board (115) for a firsttime use. The configuration includes initialization, calibration, and setup of the communication protocol with the pre-installed battery management system (105), the host (110), and the cloud-based platform. When the supervisory control board (115) is installed for the first time, it needs to be configured based on the specific battery cells being used. By providing the plurality of integration tools (150), the system (100) reduces manual effort and minimizes errors.

[0056] In an embodiment, the system (100) may be implemented with or without the connectivity module (145) and the plurality of integration tools (150), depending on the specific requirements.

[0057] Let's consider an example where an operator assembles a battery pack using 12 retired EV battery cells, each with varying battery parameters or reuses a retired battery pack in an as-is condition. The pre-installed BMS that originally came with the cells remains functional. To enable intelligent monitoring and management, the operator integrates the supervisory control board (115), which first undergoes an initial configuration process using the plurality of integration tools (150) to establish communication with the pre-installed BMS. Once configured, the data acquisition module (130) of the supervisory control board (115) begins receiving real-time battery data from each cell, including voltage, temperature, current, state of charge (SoC), and state of health (SoH). The processing module (135) analyzes this data to re-estimate battery state parameters, detect potential degradation patterns, and predict the remaining useful life (RUL) of the pack. If an unsafe operational command is detected, for instance, if the host (110) commands the pack to charge to 245V, which could accelerate degradation, the processing module (135) autonomously adjusts the command to a safer voltage, for example 200 V while maintaining optimal performance and longevity. The controlmodule (140) then transmits the modified operational command to the pre-installed BMS or the host as applicable. The host utilizes some of the modified command and estimates to regulate the current it draws or sends, ensuring safer and more efficient battery operation.

[0058] It is to be noted that the system (100) may comprise, but is not limited to, a mobile phone, desktop computer, portable digital assistant (PDA), smart phone, tablet, ultrabook, netbook, laptop, multi-processor system, microprocessor-based or programmable consumer electronic system, or any other communication device that a user may use. In some embodiments, the system (100) may comprise a display module (not shown) to display information (for example, in the form of user interfaces). In further embodiments, the system (100) may comprise one or more of touch screens, accelerometers, gyroscopes, cameras, microphones, global positioning system (GPS) devices, and so forth.

[0059] In some embodiments, the system (100) may comprise one or more subsystems in a vehicle (for example, it may be integrated into any subsystem such as a motor or a central compute system) or a charging infrastructure (such as chargers where it enables safe and adaptive charging of the battery) or an energy management system (such as a system that uses batteries for storage from renewable sources).

[0060] In some embodiments, the system (100) may consist of other subsystems that allow for better management of the batteries, such as active balancing mechanisms, safety fuses, switches, and other protective features to enhance reliability and efficiency.

[0061] In one embodiment, the various functional components of the system may reside on a single computer, or they may be distributed across several computers in various arrangements. The various components of the system may, furthermore, access one or more databases, and each of the various components of the system may be in communication with one another. Further, while the components of FIG. 1 arediscussed in the singular sense, it will be appreciated that in other embodiments multiple instances of the components may be employed.

[0062] FIG. 2 is a block diagram of a computer or a server (125) in accordance with an embodiment of the present disclosure. The server (125) includes processor(s) (180), and memory ( 160) operatively coupled to the bus (170). The processor(s) ( 180), as used herein, means any type of computational circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex instruction set computing microprocessor, a reduced instruction set computing microprocessor, a very long instruction word microprocessor, an explicitly parallel instruction computing microprocessor, a digital signal processor, or any other type of processing circuit, or a combination thereof.

[0063] The memory (160) includes several subsystems stored in the form of an executable program which instructs the processor ( 180) to perform the method steps illustrated in FIG. 1. The memory (160) includes a processing subsystem (120) of FIG.l. The processing subsystem (120) further has following modules: a data acquisition module (130), aprocessing module (135), a control module (140), a connectivity module (145), and a plurality of integration tools (150).

[0064] In accordance with an embodiment of the present disclosure, a system (100) for realtime monitoring and adaptive control of a battery is provided. The system (100) includes a pre-installed battery management system (105) configured to measure and manage a plurality of battery data. The plurality of battery data includes cell voltage, pack voltage, a plurality of current measurements, state of charge, cell temperature, and state of health. The system (100) includes a supervisory control board (115) operatively coupled to the pre-installed battery management system (105) and a host (110). The supervisory control board (115) adapted to receive an operational command from the host (110) for the pre-installed battery management system (105). The operational command includes an instruction to perform at least one battery operation. The batteryoperation includes at least one of regulating battery parameters, controlling charge and discharge processes, managing safety mechanisms, and optimizing battery performance. The pre-installed battery management system (105) is enabled with a communication protocol to interface with the supervisory control board (115). The supervisory control board (115) includes a processing subsystem (120) hosted on a server (125). The processing subsystem (120) is configured to execute on a network (130) to control bidirectional communications among a plurality of modules. The processing subsystem (120) includes a data acquisition module (130) configured to receive the plurality of battery data from the pre-installed battery management system (105) in real-time. The processing subsystem (120) includes a processing module (135) operatively coupled to the data acquisition module (130). The processing module (135) is configured to analyze the plurality of battery data using a combination of one or more methods to re-estimate a plurality of battery state parameters in real-time. The one or more methods include a plurality of model-based approaches and artificial intelligence and machine learning-based techniques. The processing module (135) is configured to generate a renewed control strategy based on real-time estimations and predictions of the battery’s health. The processing module (135) is configured to modify the operational command to ensure longevity of the battery. The modification is based on the re-estimated plurality of battery state parameters, and the renewed control strategy. The processing subsystem (120) includes a control module (140) operatively coupled to the processing module (135). The control module (140) is configured to transmit the modified operational command to the pre-installed battery management system (105) for execution. The control module (140) is configured to communicate the modified operational command and execution output to the host (110). The processing subsystem (120) includes a connectivity module (145) operatively coupled to the control module (140). The connectivity module (145) is configured to transmit the plurality of battery parameters to a cloud for remote monitoring and analytics and reporting using the combination of one or more methods and a cloud-based platform. The cloud-based platform performs one or more functions using the combination ofone or more methods. The processing subsystem (120) includes a plurality of integration tools (150) operatively coupled to the supervisory control board (115). The plurality of integration tools (150) is configured to configure the supervisory control board (115) for a first-time use. The configuration includes initialization, calibration, and setup of the communication protocol with the pre-installed battery management system (105), the host (110), and the cloud-based platform.

[0065] The bus (170) as used herein refers to be internal memory channels or computer network that is used to connect computer components and transfer data between them. The bus (170) includes a serial bus or a parallel bus, wherein the serial bus transmits data in bit-serial format and the parallel bus transmits data across multiple wires. The bus (170) as used herein, may include but not limited to, a system bus, an internal bus, an external bus, an expansion bus, a frontside bus, a backside bus and the like.

[0066] FIG. 3(a) illustrates a flow chart representing the steps involved in a method (200) for real-time monitoring and adaptive control of a battery in accordance with an embodiment of the present disclosure. FIG. 3(b) illustrates continued steps of the method (200) of FIG. 3(a) in accordance with an embodiment of the present disclosure. The method (200) includes measuring and managing, by a pre-installed battery management system, a plurality of battery data. The plurality of battery data includes cell voltage, pack voltage, a plurality of current measurements, state of charge, cell temperature, and state of health in step 205. The pre-installed BMS refers to an existing BMS that comes integrated with a battery pack at the time of manufacturing. The battery pack consists of a plurality of battery cells electrically connected to function as a single power unit. Examples of battery pack include but are not limited to primary or first-life applications, secondary storage applications and the like, and the like. The plurality of battery data refers to sensor readings and electrical measurements collected from the battery pack. The system may be utilized at any stage of a battery life cycle, from new to second-life storage to enhance battery performance, longevity, and safety.The system serves as a non-intrusive add-on solution to any pre-existing battery management system (BMS), enabling improved monitoring and control without requiring significant modifications to the existing setup. This ensures that even batteries experiencing performance degradation in electric vehicles or storage applications may be optimized effectively.

[0067] The method (200) includes receiving, by a supervisory control board, an operational command from the host for the pre-installed battery management system. The operational command includes an instruction to perform at least one battery operation. The battery operation including at least one of regulating battery parameters, controlling charge and discharge processes, managing safety mechanisms, and optimizing battery performance. The pre-installed battery management system is enabled with a communication protocol to interface with the supervisory control board in step 210. The host is one of an external control unit, including but not limited to an electronic control unit of a vehicle, a control unit of an energy storage system and the like. The communication protocol included but not limited to at least one of Controller Area Network, Universal Asynchronous Receiver-Transmitter or Ethernet, and the like.

[0068] One embodiment, the supervisory control board functions as a "man-in-the-middle" system, meaning it acts as an intermediary control layer that re-evaluates battery parameters and introduces advanced management features while leveraging the preinstalled BMS for fundamental measurements and hardware controls.

[0069] The method (200) includes receiving, a data acquisition module, the plurality of battery data from the pre-installed battery management system in real-time in step 215.

[0070] The method (200) includes analysing, by a processing module, the plurality of battery data using an artificial intelligence model to re-estimate a plurality of battery state parameters in real-time in step 220. The re-estimated plurality of battery stateparameters provides essential insights into the battery’s condition, performance, and longevity. The plurality of battery state parameters includes but is not limited to state of charge (SoC), which indicates the remaining charge in the battery, State of Health (SoH), which reflects the overall health and degradation level of the battery over time, and the like. The processing module continuously refines these state parameters in realtime using an artificial intelligence model, thereby improving the accuracy of battery performance.

[0071] In one embodiment, the artificial intelligence and machine learning-based techniques employ at least one of the following battery estimations approaches empirical model, model-based approach, artificial intelligence-driven battery estimation algorithms, and the like. The empirical model is a model based on observations and data, rather than theoretical principles. It can be used to explain, predict, or simulate battery behavior based on past observations. The model-based approach utilizes mathematical representations and system identification techniques to estimate battery parameters based on electrochemical, thermal, physics based or equivalent circuit models. The artificial intelligence-driven approach employs machine learning and deep learning techniques to enhance battery state estimation, anomaly detection, and predictive analytics.

[0072] The method (200) includes generating, by the processing module, a renewed control strategy based on real-time estimations and predictions of the battery’s health in step 225. The renewed control strategy refers to an updated plurality of battery state parameters and control actions dynamically generated based on real-time estimations and predictive analytics. The renewed control strategy prevents unsafe charging, discharging, or operational conditions based on real-time predictive analytics.

[0073] It must be noted that the renewed control strategy can be either manually defined by engineers based on diagnostic data and expert analysis or automatically generated usingthe combination of one or more methods which autonomously adjusts settings by analyzing historical and real-time battery data.

[0074] The method (200) includes modifying, by the processing module, the operational command to ensure longevity of the battery. The modification is based on the re-estimated plurality of battery state parameters, and the renewed control strategy in step 230.

[0075] More specifically, the processing module continuously monitors the plurality of battery data while also evaluating incoming operational commands. If the operational command requests a battery state parameter that exceeds the safe operating limits of the pre-installed BMS, the processing module re-estimates the battery state parameter in real-time based on the current battery data. It then generates the renewed control strategy with adjusted, safe values and informs both the host and the pre-installed BMS to execute the operational command revised.

[0076] The method (200) includes transmitting, by a control module, the modified operational command to the pre-installed battery management system for execution in step 235.

[0077] The method (200) includes communicating, by the control module, the modified operational command and execution output to the host in step 240.

[0078] The method (200) includes transmitting, by a connectivity module, the plurality of battery parameters to a cloud for remote monitoring and analytics and reporting using the combination of one or more methods and a cloud-based platform. The cloud-based platform performs one or more functions using the combination of one or more methods in step 245. The one or more functions of the cloud-based platform include predicting battery degradation, remaining useful life, and potential failure, providing remote diagnostics and performance monitoring, generating real-time performance analytics, and enabling over-the-air updates of the supervisory control board and the pre-installed battery management system.Furthermore, the cloud-based platform continuously refines its artificial intelligence models by learning from data collected from a fleet of batteries deployed in various applications. The updated Al models can be downloaded and flashed onto the supervisory control board, allowing for continuous improvements in real-time decision-making and adaptive battery management strategies.

[0079] The method (200) includes configuring, by a plurality of integration tools, the supervisory control board for a first-time use. The configuration including initialization, calibration, and setup of the communication protocol with the preinstalled battery management system, the host, and the cloud-based platform in step 250. When the supervisory control board is installed for the first time, it needs to be configured based on the specific battery cells being used. By providing the plurality of integration tools, the system reduces manual effort and minimizing errors.

[0080] V arious embodiments of the system and method for real-time monitoring and adaptive control of a battery as described above provide several advantages. The supervisory control board offers a cost-effective add-on solution to enhance the pre-installed battery management system without requiring modifications to hardware, making it ideal for secondary storage applications using retired EV batteries, and the like. The system supports batteries throughout their entire lifecycle, from first-time use to aging batteries in EVs still under warranty and retired batteries repurposed for energy storage, by leveraging the data acquisition module and processing module for real-time state estimation, degradation prediction, and control strategy updates. The processing module, powered by Al and machine learning, continuously re-estimates battery parameters, predicts degradation, and generates a renewed control strategy to prevent unsafe charging, discharging, and operational conditions. The connectivity module enables cloud-based monitoring and analytics, while the system may also operate in a fully wired standalone mode, ensuring functionality in environments without cloud connectivity.The techniques described in this disclosure may be implemented, at least in part, in hardware, software, firmware, or any combination thereof. For example, various aspects of the described techniques may be implemented within one or more processors, including one or more microprocessors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or any other equivalent integrated or discrete logic circuitry, as well as any combinations of such components. The term “processor” or “processing subsystem” may generally refer to any of the foregoing logic circuitry, alone or in combination with other logic circuitry, or any other equivalent circuitry. A control unit including hardware may also perform one or more of the techniques of this disclosure.

[0081] Such hardware, software, and firmware may be implemented within the same device or within separate devices to support the various techniques described in this disclosure. In addition, any of the described units, modules, or components may be implemented together or separately as discrete but interoperable logic devices. Depiction of different features as modules or units is intended to highlight different functional aspects and does not necessarily imply that such modules or units must be realized by separate hardware, firmware, or software components. Rather, functionality associated with one or more modules or units may be performed by separate hardware, firmware, or software components, or integrated within common or separate hardware, firmware, or software components.

[0082] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the disclosure and are not intended to be restrictive thereof.

[0083] While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person skilled in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein.The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, the order of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts need to be necessarily performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples.

Claims

I CLAIM:

1. A system (100) for real-time monitoring and adaptive control of a battery comprising:a pre-installed battery management system (105) configured to measure and manage a plurality of battery data, wherein the plurality of battery data comprises cell voltage, pack voltage, a plurality of current measurements, state of charge, cell temperature, and state of health;characterized in that,a supervisory control board (115) operatively coupled to the pre-installed battery management system (105) and a host (110), wherein the supervisory control board (115) adapted to receive an operational command from the host (110) for the preinstalled battery management system (105), wherein the operational command comprises an instruction to perform at least one battery operation, wherein the battery operation comprising at least one of regulating battery parameters, controlling charge and discharge processes, managing safety mechanisms, and optimizing battery performance, wherein the pre-installed battery management system (105) is enabled with a communication protocol to interface with the supervisory control board (115);a processing subsystem (120) hosted on a server (125) and configured to execute on a network (130) to control bidirectional communications among a plurality of modules, wherein the plurality of modules comprising:a data acquisition module (130) configured to receive the plurality of battery data from the pre-installed battery management system (105) in real-time;a processing module (135) operatively coupled to the data acquisition module (130), wherein the processing module (135) is configured to:analyse the plurality of battery data using a combination of one or more methods to re-estimate a plurality of battery state parameters in real-time, wherein the one or more methods comprise a plurality of model-based approaches and artificial intelligence and machine learning-based techniques;generate a renewed control strategy based on real-time estimations and predictions of the battery’s health; andmodify the operational command to ensure longevity of the battery, wherein the modification is based on the re-estimated plurality of battery state parameters, and the renewed control strategy;a control module (140) operatively coupled to the processing module (135), wherein the control module (140) is configured to:transmit the modified operational command to the pre-installed battery management system (105) for execution; andcommunicate the modified operational command and execution output to the host (110);a connectivity module (145) operatively coupled to the control module (140), wherein the connectivity module (145) is configured to transmit the plurality of battery parameters to a cloud for remote monitoring and analytics and reporting using the combination of one or more methods and a cloud-based platform, wherein the cloud-based platform performs one or more functions using the combination of one or more methods; anda plurality of integration tools (150) operatively coupled to the supervisory control board (115), wherein the plurality of integration tools (150) is configured to configure the supervisory control board (115) for a first-time use, wherein the configuration comprising initialization, calibration, and setup of the communication protocol with the pre-installed battery management system (105), the host (110), and the cloud-based platform.

2. The system (100) as claimed in claim 1, wherein the communication protocol comprises at least one of Controller Area Network, Universal Asynchronous Receiver-Transmitter or Ethernet.

3. The system (100) as claimed in claim 1, wherein the host (110) is one of an external control unit, comprising an electronic control unit of a vehicle, a control unit forming a part of an energy storage system, wherein the energy storage system comprising at least one of an external charger or a building management system, or any external entity configured to communicate with the battery .

4. The system (100) as claimed in claim 1, wherein the artificial intelligence and machine learning-based techniques utilizes at least one of empirical, model-based, or artificial intelligence-driven battery estimation algorithms.

5. The system (100) as claimed in claim 1, wherein the supervisory control board (115) is configured to operate as a standalone system, when performing local processing without requiring cloud connectivity.

6. The system (100) as claimed in claim 1 , wherein the one or more functions of the cloud-based platform comprise predicting battery degradation, remaining useful life, and potential failure, providing remote diagnostics and performance monitoring, generating real-time performance analytics, and enabling over-the-air updates of the supervisory control board (115) and the pre-installed battery management system (105).

7. The system (100) as claimed in claim 1, wherein the machine learning models are continuously updated in the cloud from the plurality of battery data received froma battery pack and are subsequently updated into the supervisory control board (115) to refine predictions.

8. The system (100) as claimed in claim 1, comprises one or more external circuits configured to provide additional safety mechanisms to enhance battery management and operational safety.

9. A method (200) for real-time monitoring and adaptive control of a battery comprising:measuring and managing, by a pre-installed battery management system, a plurality of battery data, wherein the plurality of battery data comprises cell voltage, pack voltage, a plurality of current measurements, state of charge, cell temperature, and state of health; (205)characterized in that,receiving, by a supervisory control board, an operational command from the host for the pre-installed battery management system, wherein the operational command comprises an instruction to perform at least one battery operation, wherein the battery operation comprising at least one of regulating battery parameters, controlling charge and discharge processes, managing safety mechanisms, and optimizing battery performance, wherein the pre-installed battery management system is enabled with a communication protocol to interface with the supervisory control board; (210) receiving, a data acquisition module, the plurality of battery data from the preinstalled battery management system in real-time; (215)analysing, by a processing module, the plurality of battery data using a combination of one or more methods to re-estimate a plurality of battery state parameters in real-time, wherein the one or more methods comprise a plurality ofmodel-based approaches and artificial intelligence and machine learning-based techniques; (220)generating, by the processing module, a renewed control strategy based on realtime estimations and predictions of the battery’s health; (225)modifying, by the processing module, the operational command to ensure longevity of the battery, wherein the modification is based on the re-estimated plurality of battery state parameters, and the renewed control strategy; (230) transmitting, by a control module, the modified operational command to the pre-installed battery management system for execution; (235)communicating, by the control module, the modified operational command and execution output to the host; (240)transmitting, by a connectivity module, the plurality of battery parameters to a cloud for remote monitoring and analytics and reporting using the combination of one or more methods and a cloud-based platform, wherein the cloud-based platform performs one or more functions using the combination of one or more methods; and (245)configuring, by a plurality of integration tools, the supervisory control board for a first-time use, wherein the configuration comprising initialization, calibration, and setup of the communication protocol with the pre-installed battery management system, the host, and the cloud-based platform. (250)

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