Description:FIELD OF THE INVENTION
The present invention relates to the field of energy storage and management, specifically to Battery Management Systems (BMS) for lithium-ion batteries. It focuses on optimizing battery performance, extending battery lifespan, and ensuring operational safety by monitoring critical parameters such as voltage, current, and temperature. The system leverages predictive algorithms and adaptive control mechanisms to enhance battery efficiency, making it particularly suitable for electric vehicles and other high-demand energy storage applications.
BACKGROUND OF THE INVENTION
In today’s context, Battery Management Systems (BMS) require efficient energy storage solutions to extend battery lifespan. This is achieved by optimizing and closely monitoring key parameters such as voltage, current, and temperature, especially in lithium-ion batteries. Proper management of these parameters is essential to enhance performance, prevent overheating, and ensure reliable operation, addressing critical energy storage durability and safety Challenges.
As energy storage solutions become increasingly essential for modern applications such as electric vehicles, renewable energy systems, and portable electronic devices, the demand for efficient and reliable Battery Management Systems (BMS) has grown. Lithium-ion batteries, due to their high energy density and efficiency, are widely used in these applications. However, they are highly sensitive to parameters such as voltage fluctuations, excessive current draw, and temperature variations. Poor management of these factors can result in reduced battery life, overheating, and even hazardous failures such as thermal runaway.
Traditional BMS solutions primarily focus on passive monitoring, where voltage, current, and temperature values are recorded without real-time intervention. While these systems provide basic protection through predefined cut-off limits, they do not proactively prevent risks or optimize battery performance dynamically. As a result, batteries often experience suboptimal charging and discharging cycles, leading to capacity degradation over time.
Another major challenge in conventional BMS technology is the lack of predictive maintenance. The absence of real-time diagnostics and early failure detection mechanisms leads to unexpected battery malfunctions. For applications like electric vehicles, where reliable and long-lasting battery performance is critical, these shortcomings significantly impact efficiency and safety.
To address these challenges, there is a need for an advanced BMS that not only monitors but also intelligently manages battery operation. A system that employs predictive analytics, adaptive control algorithms, and real-time risk assessment would substantially enhance lithium-ion battery performance and safety.
The proposed invention introduces an enhanced BMS capable of real-time monitoring, predictive modeling, and adaptive control. By continuously assessing critical battery parameters and proactively adjusting operating conditions, the system ensures optimal energy utilization while mitigating risks associated with overheating and overvoltage conditions.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The present invention provides an advanced Battery Management System (BMS) designed to optimize lithium-ion battery performance and longevity through real-time monitoring and predictive risk analysis. The system integrates intelligent algorithms with sensor-driven data collection to actively manage voltage, current, and temperature levels, thereby preventing potential battery failures and ensuring operational safety.
The proposed BMS continuously monitors battery parameters using high-precision sensors. The data collected is processed by a predictive modeling engine that identifies abnormal patterns indicative of overheating, overvoltage, or excessive current flow. In response to these detections, the system dynamically adjusts the battery's operating parameters by modifying charge and discharge rates or activating cooling mechanisms.
A key feature of this invention is its ability to conduct controlled charge and discharge cycles, ensuring optimal energy utilization. The system gradually increases and decreases voltage and current in a controlled manner over a predefined simulation period, optimizing performance for electric vehicle applications. This controlled discharge mechanism enhances driving range and promotes energy efficiency.
The user interface of the BMS provides real-time diagnostics and notifications. The system alerts users to potential risks while offering recommendations for corrective action. By leveraging predictive analytics and machine learning techniques, the system improves battery life and ensures safe operation.
The adaptive control mechanism of the system ensures that batteries operate safely across their entire lifecycle. This proactive approach minimizes unexpected failures and maximizes energy efficiency, making the invention a valuable enhancement to current battery management technologies.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
 
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The enhanced Battery Management System (BMS) consists of multiple interconnected components designed to monitor, analyze, and optimize battery performance. The system includes voltage, current, and temperature sensors that continuously capture data from lithium-ion battery cells. This data is processed by an embedded control unit equipped with predictive modeling algorithms to assess battery health and detect potential risks.
The control unit integrates machine learning-based predictive analysis, which enables the system to recognize deviations from normal operating conditions. When anomalies such as excessive current draw or overheating are detected, the system initiates corrective actions, including modifying the charge/discharge profile or activating thermal management mechanisms. These actions ensure that the battery remains within safe operating limits, thus preventing premature degradation and safety hazards.
The system implements an adaptive control strategy that dynamically adjusts battery charging and discharging cycles based on real-time operating conditions. By gradually increasing and decreasing current and voltage levels, the BMS prevents abrupt energy fluctuations, enhancing battery efficiency and longevity. This is particularly beneficial for applications such as electric vehicles, where efficient energy utilization directly impacts driving range and performance.
Additionally, the BMS features an intuitive user interface that provides real-time battery diagnostics, alerts, and recommendations. Users receive notifications regarding potential issues, such as overheating or voltage imbalances, along with suggested corrective measures. The system also logs historical performance data, enabling predictive maintenance and long-term battery health optimization.
The BMS can be integrated with various power sources, including renewable energy systems, to further enhance energy efficiency. Its modular design allows compatibility with different battery chemistries, making it a versatile solution for a wide range of applications.
The invention ensures that lithium-ion batteries operate within safe temperature, voltage, and current thresholds while maximizing their operational lifespan. By implementing a proactive, intelligent battery management approach, this system overcomes the limitations of conventional BMS technologies.
For present invention, inventors use an enhanced BMS that automatically tracks and manages the basic parameters of lithium-ion batteries, including voltage, current, and temperature using sensors and control algorithms. The system operates similarly: it collects data and applies to predictive models; therefore, it recognizes risks such as overheating or overvoltage and changes the output or activates cooling techniques in case of possible harm. In other words, a simplistic user interface offers diagnosis and notification of the problem, optimizes the battery’s life span, and improves its safety. This solution makes it possible for batteries to operate safely across time, thereby meeting challenges characteristic of energy storage solutions.;
The current and voltage gradually increase and then decrease in a controlled manner. This process lasts five hours in the simulation and shows how energy is effectively used. This controlled discharge helps improve performance in electric vehicles, allowing them to drive farther and promoting energy efficiency
, Claims:1. A Battery Management System (BMS) that monitors voltage, current, and temperature levels in lithium-ion batteries to optimize performance and safety; wherein a predictive modeling engine analyzes sensor data to detect overheating, overvoltage, and excessive current conditions; wherein an adaptive control mechanism dynamically adjusts charge and discharge cycles based on real-time battery conditions; wherein controlled charge and discharge cycles enhance energy efficiency and battery lifespan.
2. The system as claimed in claim 1, wherein a user interface provides real-time diagnostics, notifications, and recommended corrective actions.
3. The system as claimed in claim 1, wherein machine learning algorithms are utilized for predictive maintenance and failure prevention.
4. The system as claimed in claim 1, wherein thermal management techniques such as cooling activation are implemented when temperature thresholds are exceeded.
5. The system as claimed in claim 1, wherein the system logs historical performance data to support long-term battery health monitoring.
6. The system as claimed in claim 1, wherein it can be integrated with renewable energy sources for improved efficiency.
7. The system as claimed in claim 1, wherein modular compatibility allows integration with different battery chemistries.