Parallel Battery Pack Charging Time Prediction via SOC Grouping

The battery system is segmented into multiple battery packs, each with its own state monitoring. The BMS divides the prediction task by individually monitoring SOC, temperature, and health status of each pack, then aggregating these segmented data points to determine overall system charge time. This segmentation allows parallel connection for increased power while managing prediction complexity through modular monitoring.

The system performs preliminary classification of battery packs into connected and available groups based on their current state before calculating charge time. By pre-categorizing packs according to their SOC levels, temperature conditions, and relay status, the BMS prepares the data structure in advance, simplifying the subsequent charge time prediction process for the parallel configuration.

The battery management system performs multiple functions using a unified monitoring architecture. The same sensor network and control logic that monitor individual pack states for safety and performance also provide the data needed for charge time prediction. This multi-functionality achieves high measurement precision without proportionally increasing device complexity, as the monitoring infrastructure serves both operational control and prediction purposes.

Each battery pack includes integrated sensors and monitoring circuits that automatically track its own state parameters (SOC, temperature, voltage, current). The packs self-report their status to the BMS, eliminating the need for external monitoring equipment. This self-service approach enables precise individual pack monitoring while minimizing the complexity of the overall monitoring system.