Modular Battery Health Analytics for Student-Built Electric Go-Kart Platforms

Abstract

This article describes a low-cost analytics stack for tracking lithium-ion degradation in educational electric vehicle projects. The system combines impedance snapshots, temperature-normalized coulomb counting, and anomaly flags from a compact edge controller. Over a semester-long pilot, the model identified early cell imbalance events up to nine charge cycles before threshold alarms triggered in the stock battery management unit.

Figure 7 illustrates the lead time of analytics-based imbalance alerts relative to default battery management alarms.