Journal of Control and Instrumentation Engineering

Smart Battery Management Framework for Electric Vehicles with Integrated Charge Tracking and Fire Risk Prevention

Mon, 04 May 2026 00:00:00 +0000

The rapid-fire relinquishment of electric vehicles (EVs) has increased the demand for advanced battery management systems (BMS) that ensure safety, effectiveness, and trustworthiness. This design presents the development and implementation of an intelligent EV battery management system integrated with charge monitoring and fire protection mechanisms. The primary idea is to enhance battery performance while minimizing pitfalls associated with overheating, overcharging, and thermal runaway. The proposed BMS continuously monitors crucial battery parameters such as voltage, current, temperature, and state of charge (SoC). By using bedded detectors and control algorithms, the system provides accurate real-time data, enabling effective battery application and longer lifetime. The charge covering unit ensures precise shadowing of charging and discharging cycles, precluding overcharging and deep discharge conditions that can degrade battery health. A major focus of this system is safety through fire prevention. Lithium-ion batteries, extensively used in EVs, are prone to thermal runaway under abnormal conditions. To address this, the system integrates temperature detectors and a fire discovery module that identifies abnormal heat rise at an early stage. When critical thresholds are exceeded, the BMS automatically disconnects the battery and activates advising systems, such as admonitions or announcements. This visionary approach significantly reduces the threat of fire hazards. Also, the system supports communication interfaces that allow data transmission to external bias or vehicle control units. This enables remote monitoring, diagnostics, and timely cautions to druggies or conservation brigades. The integration of smart communication improves overall system responsiveness and strengthens mindfulness. The proposed EV BMS with charge monitoring and fire protection offers a comprehensive result for ultramodern electric vehicles by combining performance optimization with enhanced safety features. It contributes to perfecting battery trustworthiness, extending functional life, and ensuring safe operation under various conditions. This system can be further developed with advanced technologies such as IoT and artificial intelligence for predictive conservation and smarter energy operation in upcoming EV operations.

Design and Implementation of a Delay and Energy-efficient Booth Multiplier

Y. Rama Krishna, K. Teja Sri, K. Srinivas Sai, K. Rama Koteswararao — Tue, 21 Apr 2026 00:00:00 +0000

The performance of digital multipliers plays a critical role in high-speed arithmetic operations used in modern digital signal processing and embedded systems. This project focuses on the design and implementation of a delay and energy-efficient booth multiplier, which is an optimized version of the traditional booth algorithm for signed multiplication. Booth multipliers reduce the number of partial products generated during multiplication, thereby decreasing computation time and hardware complexity. However, conventional designs often suffer from high propagation delay and increased power consumption. In this work, an enhanced booth multiplier architecture is proposed, incorporating optimized partial product generation, fast adders, and low-power design techniques to minimize delay and energy usage. The proposed design is implemented using Hardware Description Languages (HDL) such as VHDL or Verilog and validated on FPGA platforms to ensure real-time performance. Simulation results demonstrate significant improvements in speed and power efficiency compared to conventional multipliers. The proposed multiplier is highly suitable for applications in digital signal processing, embedded systems, and arithmetic-intensive computing environments where energy efficiency and low latency are critical. This work contributes to the development of high-performance arithmetic units for modern computational systems.

Microcontroller-based Fault Detection and Localization in Underground Transmission Lines

Alok Kumar — Mon, 04 May 2026 00:00:00 +0000

Cable fault localization is crucial for power transmission, distribution, communication systems, and vehicles. Reliable fault detection in underground power cables is essential for ensuring the continuity, safety, and operational efficiency of modern power distribution systems. This study proposes a microcontroller-based technique for precise fault localization using the Arduino Uno platform. The system estimates the distance to a fault by applying Ohm’s law, exploiting the proportional relationship between cable impedance and length. Variations in voltage caused by fault conditions are sensed and processed to determine the exact fault position in kilometres. The proposed method employs a regulated low DC test signal injected from the feeder end of the cable. When a fault occurs, the resulting change in current modifies the voltage distribution across a calibrated series resistor network that represents discrete cable segments. These analog voltage variations are captured by the Arduino Uno’s 10-bit analog-to-digital converter (ADC) and converted into digital signals for computational analysis. A mapping algorithm correlates the measured electrical parameters with the corresponding cable length, enabling accurate and real-time fault distance estimation. For experimental validation, a scalable resistor ladder model is developed to simulate underground cable sections, while switches positioned at predefined intervals emulate various fault scenarios, including short-circuit conditions. The detected fault distance and affected phase are displayed locally on an interfaced liquid crystal display (LCD). Additionally, a GSM communication module is integrated to transmit automated fault notifications via short message service (SMS) to authorized personnel, thereby supporting remote monitoring and faster response times. The proposed system provides a cost-effective, scalable, and efficient solution for underground cable fault detection. Its implementation can significantly reduce fault identification time, minimize service interruptions, and improve maintenance strategies in contemporary power transmission and distribution networks.