Electrode Capacity Balancing for Accurate Battery State of Health Prediction and Degradation Analysis

Battery technology plays an increasingly vital role in portable electronic devices, electric vehicles, and renewable energy storage. During operation, batteries undergo performance degradation, which not only reduces device efficiency, but may also pose safety risks. The State of Health (SOH) is a crucial indicator for assessing battery condition. Traditional SOH prediction methods face limitations in real-time adjustment and accuracy under complex operating conditions. By determining electrode capacity loss and identifying complex patterns that traditional methods struggle to detect, prediction accuracy can be improved. Based on electrode capacity matching and compensation relationships, this paper proposes an electrode capacity balance model to evaluate battery development trends and degradation during cycling. We use qLi − qp state assessment as a trend criterion, qp to quantify aging, and Qc to identify thermal runaway risk levels, developing more efficient SOH prediction indicators and methods to ensure battery safety and performance.