State of health monitoring of a battery module using multilayer neural network and internal resistance

Lithium batteries are presently used in various applications, such as cell phones, electric vehicles, unmanned submarines, and energy storage systems, as main power sources. Therefore, for stable and safe use of this system, it is important to rapidly detect defects in the battery and accurately diagnose faults. Battery faults can be diagnosed by measuring their state of health (SOH), which is affected by various operating conditions. In this work, a battery SOH monitoring system is implemented to detect faults using a multilayer neural network state classifier (MNNSC) and an internal resistance state classifier (IRSC). In this system, the MNNSC uses discharge voltage data from a lithium battery operating at high temperatures. Further, the IRSC uses the open circuit voltage, terminal voltage, and current to calculate the internal resistance. Experimental results show that the proposed battery SOH monitoring method was high accuracy.