Performance analysis of lead acid battery health using Kalman residuals through the development of smart monitoring system for electric three-wheelers

Monitoring battery health is an important aspect of accurately predicting the operational lifespan of batteries in electric vehicles. The battery terminal voltage, current, and temperature are the primary factors influencing battery durability and determine how long it will last before failure occurs. For commercially viable Electric Three Wheeler (E3W), an economical yet highly precise data acquisition system (DAQ) is crucial. Currently, commercially available lead-acid (PbA) battery-powered E3Ws lack a dedicated DAQ for the continuous monitoring of battery performance. In this research we develop ?SMDAQ? a smart multichannel DAQ to address this gap, enabling real-time monitoring of these critical parameters. The system is specifically designed for the PbA batteries commonly used in E3Ws. The prototype uses a noninvasive, dualpolarity-based current sensing technology to distinguish between charging and discharging cycles, detecting high current discharges up to 60A. It also incorporates custom temperature sensor probes capable of measuring temperatures up to 60?C in multiplexed mode. The system records terminal voltage up to 16.5V and traction battery pack voltage up to 66V using the node voltage subtraction method for accurate monitoring. Integrated with a data transmission and backup mechanism, the SMDAQ system stores data locally in flash memory and remotely on the cloud, offering userfriendly queries for battery parameters. It features discharge analysis and State of Health (SOH) assessment using a data-driven Root Mean Square Error (RMSE) and Kalman Filter Residual approach under varying discharge conditions. Battery anomalies are analyzed under different C-rate and depth of discharges. With 98.6% accuracy, prototypes have been tested for over 1000 hours, ensuring reliable performance.