Sensor fault diagnosis modeling of lithium-ion batteries for electric vehicles

Electric Vehicles (EVs) are considered the replacement for gas-emitting and environmentally polluting fuel vehicles. The development of EVs relies on electronic devices and communication circuits for ease of access. The heart of EVs is the battery that requires post-run-time charging and precise maintenance. Electronic sensors attached to the batteries monitor their health, discharging, and charging rate for user notification and prolonged functioning. Therefore, the operation of the sensors is to be monitored promptly for effective battery maintenance. This article introduces a Fault Detection Method (FDM) using Operational Data (OD) IP: 2038 10920 On: Tue 12 Sep 2023 03:53:35 accumulated from the EV. This data is first split into ctive and passive that references the run-time and idle Copyright: American Scientific Publishers time of the vehicle. The sensor operations during the idle time re considered similar due to minimal or no Delivered by Ingenta sensing function. Contrarily the run-time observation shows up variations that are not huge compared to the previous history. In this data verification process, recurrent learning is employed for identifying variations in the active and passive states of the EVs. If variations are continuous regardless of the active and passive states, then the sensor is identified as faulty post the manual battery life assessment. The learning is trained using the current and previous sensor observations and batter health for identifying the variations.