Smart Battery Management System Using AI and CAN Protocol

Lithium-ion batteries power electric vehicles, energy storage and portable electronics, but they are sensitive to overvoltage, deep discharge, overcurrent and elevated temperature. This paper presents the design and implementation of a low-cost smart Battery Management System (BMS) for a 3S lithium-ion pack built around the ESP32 microcontroller. The system measures pack voltage, current and temperature, estimates State of Charge (SOC) using [method: Coulomb counting / AI model], detects overvoltage, undervoltage, overcurrent and overtemperature faults, and isolates the load through a relay. Measurements and fault states are shared with other nodes over a Controller Area Network (CAN) bus using an MCP2515 controller, and a dashboard displays live data. An AI component is used for [SOC/SOH estimation / anomaly detection] to improve on fixed-threshold decisions. Experimental results show [SOC error of X %, fault response time of Y ms, CAN message rate of Z Hz]. The system offers an inexpensive and extensible platform for battery monitoring and protection.