Design and Implementation of a Cost-Effective Battery Management System for Electric Vehicles Using Custom-Designed Sensors and Passive Cell Balancing

As electric vehicles (EVs) become more widely used, the need for battery management systems (BMS) that are safe, reliable, and affordable continues to grow. However, the high cost of commercial BMS solutions remains a major challenge for small-scale applications and markets with limited resources. This paper presents the design and hardware implementation of a low-cost Battery Management System (BMS) for a 5-series, 2-parallel (5S2P) lithium-ion battery pack. The system is intended for small electric vehicles, such as e-bikes and electric scooters, particularly in developing economies. It combines ten custom-designed resistive voltage-divider circuits to measure individual cell voltages while addressing the floating-reference issue in series-connected cells, a current-sensing module using parallel shunt resistors, a thermistor-based temperature sensor using the B-parameter equation, and a programmable TL431-based passive cell-balancing circuit with independent control for each cell. The Arduino Mega 2560 acts as the main controller, estimating the battery state of charge (SOC) from voltage while acknowledging the limitations of this method. It also displays real-time battery readings on a 20×4 LCD. An HC-06 Bluetooth module sends the data to a smartphone app developed using MIT App Inventor. Experimental results show that cell voltage measurements are within 2% of multimeter readings across the 3.0–4.2 V range. Current measurements are within 2.8% of reference readings under two load conditions, while temperature readings differ by no more than 0.8°C from a calibrated thermometer between 15°C and 40°C. The system also demonstrates successful passive cell balancing during CC-CV charging. The prototype’s hardware cost is approximately PKR 7,900 (around USD 28), making it a more affordable alternative to commercial multi-cell BMS modules for e-bikes and electric scooters in cost-sensitive markets.