A Low-Cost MQTT-Based IoT Framework for Real-Time Monitoring of Battery Energy Storage Systems

Integrated IoT Platform with Web Interface for Wireless Health Monitoring and Battery Energy Management

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.

IoT-Based Energy Management and Monitoring System for Electric Vehicles

The transition to electric vehicles (EVs) is essential, as they represent a sustainable and forward-looking solution for modern transportation. However, the performance and longevity of EV batteries are highly dependent on effective management, as they are susceptible to degradation caused by overcharging and over-discharging. Accurate estimation of the battery’s state of charge (SoC) is therefore critical for prolonging battery lifespan and safeguarding associated components. This paper presents the design and implementation of a low-cost, Internet of Things IoT-based battery management and monitoring system for electric vehicles. The proposed system enables real-time monitoring and provides users with accessible information through a mobile application. Key battery parameters, including capacity, charging current, and discharging current, are continuously measured and displayed in real time. The system is developed using an ESP32 microcontroller and integrates the Blynk mobile application with the Blynk IoT platform to facilitate seamless data visualization and user interaction. The proposed solution offers an efficient, user-friendly, and cost-effective approach to enhancing battery performance and reliability in electric vehicles.

A Cloud-Enabled IoT Smart Energy Metering System for Smart Grid Applications

The existing energy metering systems lack real-time monitoring capabilities as well as cloud-based access. Smart grid technology helps in increasing energy efficiency and reliability of electrical power systems. This research work intends to design and develop an IoT-based smart energy metering system for real-time monitoring and visualization. The proposed system encompasses ESP32 microcontroller integrated with ZMPT101B voltage sensor and SCT-013 current sensors. However, the collected data is then processed using the EmonLib library in order to evaluate voltage, current, power, power factor, and energy consumption. The processed information is later sent to the Blynk cloud using Wi-Fi technology. As a result of the performed experimental tests, voltage, current, power, and energy consumption measurement errors has not exceeded 0.80%, 4.55%, 4.00%, and 2.06%, respectively.

An IoT-Based Real-Time Energy-Management System for Smart Load Control in a Residential Microgrid

The increasing complexity of residential energy systems and the growing penetration of distributed resources require practical energy-management solutions that extend beyond conventional metering. This paper presents the design and implementation of a real-time Internet of Things (IoT)-based energy-management system for monitoring and controlling household energy consumption under different operating conditions. The proposed system adopts a dual-processor architecture, in which a primary microcontroller performs time-critical electrical measurements and low-level load switching, while a secondary processor operates as a local IoT gateway for data handling, rule-based control decisions, local visualization, and message queuing telemetry transport (MQTT)-based cloud communication through a 4G link. The contribution of this work is not associated with the individual use of dual processing, cellular communication, cloud monitoring, load shedding, or backup power, as these technologies have been previously reported in smart-metering and home energy-management systems. Instead, the study focuses on their coordinated integration within a residential-scale prototype that combines calibrated per-load monitoring, priority-based load control, outage-resilient reporting, and credit-aware load restriction. The system measures voltage, current, active and apparent power, power factor, and energy consumption for individual loads and supports centralized visualization through a cloud-based dashboard. The prototype was experimentally evaluated under three representative scenarios: overload, main power outage, and low-credit operation. In the overload scenario, automatic priority-based load shedding reduced the total load by up to 75%. During power outages, a battery-supported subsystem maintained monitoring and communication for real-time outage reporting. In the low-credit scenario, non-essential loads were disconnected when the user balance fell below a predefined threshold, while essential loads remained energized. The results demonstrate that the implemented prototype can provide integrated monitoring, local rule-based control, cloud reporting, and backup-supported operation within a unified residential energy-management platform.

Internet of Things (IoT) Based Solar Tracking System With Automatic Power Off Mechanism

Design and Development of a Solar-Powered IoT-Based Energy Consumption Monitoring System

Residential energy conservation is frequently hindered by a lack of real-time visibility into usage, widening the "behavior-action gap" between daily electricity utilization and delayed monthly utility statements. This paper presents the development of a low-cost, open-source, residential solar-powered Internet of Things (IoT) monitoring system. The proposed architecture segments data collection tasks into a Direct Current (DC) renewable generation node and an Alternating Current (AC) appliance-level consumption interface using dual ESP32 microcontrollers. The system collects data from precision inline INA226 shunt sensors on the solar and battery storage paths, alongside a ZMPT101B transformer and an SCT-013 current transformer wrapped in a physical 5-turn wire loop to lift low-power current boundaries above standard resolution floors. Telemetry is streamed asynchronously via Message Queuing Telemetry Transport (MQTT) over a cloud HiveMQ broker. To optimize edge node efficiency, chronological timestamping is completely decoupled from the hardware layer and managed server-side upon database ingestion into InfluxDB. Real-time and historical analytics are visualised using a centralized Grafana web dashboard interface. Experimental results show that the hardware sensing layer maintained high measurement precision, yielding low absolute errors (Verror ≤ 0.38% and Ierror ≤ 32%) and successfully resolving low-power household standby states down to single-digit wattages (e.g., 9W). The event-driven alert system achieved zero-latency multi-tier warnings, verifying that the proposed framework delivers an accessible, high-fidelity sub-metering platform for urban electrification programs.