Aug 2026· International Conference on Circuit, Power and Computing Technologies· pp. 491-496· 0 citations· 17 references
Abstract
Modern energy storage systems depend on Battery Management Systems (BMS) to be safe, effective and long-lasting. In order to improve battery performance and reliability, this paper integrate a Smart BMS with Phase Change Material (PCM) Cooling Technique and Predictive Maintenance. For real-time monitoring and control, the system uses a number of sensors, including temperature, voltage, current and flame sensors, which are connected to an Arduino Uno and an ESP32-based Internet of Things module. Sensor data is analyzed to anticipate any malfunctions, allowing for proactive maintenance and preventing unplanned downtime. By efficiently controlling battery temperature, PCM cooling method reduces thermal runaway and lengthens battery life. Real-time warnings and remote monitoring are made possible by IoT architecture through a web or mobile interface. Improved safety, operational effectiveness and battery lifespan are demonstrated by experimental evaluation, which makes this strategy a viable option for cutting-edge energy storage applications.
The rapid growth of electric vehicles (EVs) has increased the demand for intelligent battery management systems capable of ensuring operational safety, reliability, and extended battery life. Battery failures caused by overvoltage, undervoltage, overcurrent, overheating, and gas leakage can significantly degrade battery performance and may lead to hazardous incidents such as thermal runaway and fire. This paper presents a Smart EV Battery Monitoring and Fire Prevention System that continuously monitors critical battery parameters and provides real-time protection against abnormal operating conditions. The proposed system employs an ESP32 microcontroller integrated with voltage, current (ACS712), temperature, and MQ-2 gas sensors to acquire and process battery data. A Battery Management System (BMS) is incorporated to provide overcharge, over-discharge, and short-circuit protection, while a relay module disconnects the battery during critical conditions. The system also utilizes a cooling fan, buzzer, LCD display, and GPS module to enhance user safety through immediate alerts, thermal management, and location tracking. Experimental evaluation under normal operation and multiple fault scenarios—including over-temperature, over-voltage, under-voltage, and over-current conditions—demonstrated reliable fault detection, rapid response, and effective activation of protective mechanisms. The developed prototype successfully enhanced battery safety, minimized the risk of fire hazards, and improved system reliability through continuous real-time monitoring. Owing to its low cost, scalability, and IoT-enabled architecture, the proposed system is well suited for electric vehicles, e-bikes, battery energy storage systems, and other smart energy applications requiring continuous battery health monitoring and fire prevention.
Sahitya Kiran P, Sujatha Kadagala, prasanna laxmi Kinthada et al.· International Journal of Sci...· 0 citations
Battery energy storage (BES) systems are vital in the enhancement of sustainability, performance and reliability in today’s power systems particularly for Backup Power Systems (BES), electric vehicles (EV) and for requiring renewable energy integration. This paper presents the development/evaluation of an ESP32-based smart battery energy storage and health monitoring system with remote access and real time monitoring capabilities. The proposed system uses the appropriate sensors and data collection/monitoring methods to provide continuous monitoring of critical battery parameters i.e. Voltage, Current, Temperature and State of Charge (SOC). The results of experiments proved the system operated as expected under the following parameters: A safe working voltage range for the batteries was maintained (12.1–12.8 V) the maximum allowed charging rate of the batteries was approximately +5 A; overcharging and discharging current rates were managed within acceptable limits. Additionally, accurate tracking of SOC confirmed that energy estimates were reliable, with remarkably consistent and almost linear fluctuations of approximately 5% to 7% during the regulated charging/discharge cycles. The system also identified risk factors such as Deep Discharge (below 40% SOC), Overcharging (above 85% to 90% SOC) and Excessive Rise in Temperature (above 45°C); therefore, the system enabled prompt preventive action to mitigate potential battery deterioration and failure. Due to the low power consumption, built-in Wi-Fi capability and cost-effectiveness of the ESP32, the system is suitable for scalable and remote deployment.
B. Rohini, S. Amosedinakaran, G. Hemachandra et al.· International Conference on...· 0 citations
In this document, an advanced intelligent energy storage management system solution for clean energy sources from solar power will be outlined through the use predictive controls. The integrated intelligent energy management architecture includes a solar panel array, battery bank, local users, two-way converter, and utility providers. Using the proposed control scheme, battery storage charging and discharging cycles will be determined based on solar generation and user load forecasts that comply with specified operating constraints (SOC, power limits, and depth of discharge). The goal of this research project is to minimize dependence on the grid as well as reduce the amount of wear-and-tear placed on the batteries due to storing and using renewable energy. After completing this study, it is expected that the findings will greatly enhance the amount of energy available to users from renewable energy sources as well as improving the reliability of these systems even during time periods when the operated equipment would normally experience significant variations in performance. This study also demonstrates how predictive battery management could assist with integrating renewable energy systems into the existing infrastructure used for producing electricity.
Shokhrukh Shoyimov, Zufar Qoryog’diyev· EPJ Web of Conferences· 0 citations
The rapid adoption of electric vehicles has significantly increased the demand for efficient and reliable energy storage systems. Among various energy storage technologies, lithium-ion batteries have emerged as the preferred choice for electric two-wheelers due to their high energy density, long service life, and superior charging characteristics. However, the safe operation of lithium-ion batteries remains a major challenge because they are highly sensitive to overcharging, deep discharge, excessive current flow, and abnormal temperature rise. These conditions can negatively affect battery performance, reduce operational life, and create serious safety risks.[1][2] A Battery Management System (BMS) plays a vital role in monitoring, protecting, and optimizing battery operation. Modern BMS technologies provide continuous supervision of cell voltage, current, temperature, State of Charge (SOC), and State of Health (SOH) while ensuring safe operation through various protection mechanisms[3][4]. Recent advancements in communication technologies, Internet of Things (IoT), Artificial Intelligence (AI), and cloud-based monitoring have further enhanced the capabilities of battery management systems[8][9]. This paper presents a comprehensive review of Battery Management Systems used in electric two-wheeler applications. Various battery monitoring techniques, cell balancing methods, fault diagnosis approaches, thermal management strategies, and intelligent battery management technologies are analyzed. The paper also discusses current research challenges and future developments that can improve the safety, efficiency, and reliability of electric vehicle battery systems.
Mr. Kharat Sagar Balasaheb, Mr. Sarwade Aniket Kai, Ms. Wale Yogita Kisan, Ms. Pawar Harshada Santosh· International Journal of Adv...· 0 citations
Renewable energy has become a key solution for addressing the increasing global demand for sustainable and environmentally friendly power generation. Effective monitoring of renewable energy systems is essential to ensure efficient operation, minimize energy losses, and improve system reliability. This paper presents an Embedded System-Based Renewable Energy Monitoring System designed for real-time acquisition, processing, and transmission of operational parameters from renewable energy sources such as solar photovoltaic (PV) panels and wind energy systems. The proposed system employs a microcontroller integrated with voltage, current, temperature, and environmental sensors to continuously monitor system performance. The collected data are processed locally and transmitted to a cloud-based monitoring platform through wireless communication technologies such as Wi-Fi or GSM, enabling remote supervision and data visualization. Threshold-based fault detection mechanisms generate alerts whenever abnormal operating conditions are identified, allowing timely maintenance and reducing system downtime. Experimental evaluation demonstrates that the proposed embedded monitoring system provides accurate sensor measurements, low power consumption, reliable wireless communication, and real-time performance monitoring. The system offers a cost-effective, scalable, and energy-efficient solution suitable for residential, industrial, and smart grid renewable energy applications, thereby enhancing operational efficiency and supporting sustainable energy management.
Lenkalapally Harika, Biram Rasagna, A. K. Rathod· International Journal of Sci...· 0 citations
This study explores an intelligent energy management framework for hybrid energy storage systems (HESS) that combine lithium-ion batteries and supercapacitors in electric vehicle applications. A Python-based simulation model incorporating a hierarchical AI controller was developed and evaluated using a 60-second real-world driving profile. The proposed method enables efficient power sharing between storage components, resulting in an overall system efficiency of 93.12%. Regenerative braking performance reached 88.5% ± 2.3%, indicating effective energy recovery. The strategy also reduces battery current stress by 42.3%, contributing to improved lifespan and operational reliability. Temperature regulation remains stable, with battery values maintained between 25.86°C and 29.93°C due to adaptive load allocation. The controller dynamically adjusts behavior across driving conditions such as cruising, acceleration, and braking. Comparative results confirm that integrating supercapacitors improves efficiency relative to battery-only systems. Economic evaluation further suggests a lifecycle cost reduction of 18.5% despite higher initial investment. These findings support the feasibility of AI-assisted HESS for next-generation electric mobility.
K. M, N. Latha, R. Devika et al.· 2026 6th International Confe...· 0 citations
