Aim- On the fly estimation of SOH in practical environment.

Abstract- The battery management system is the most critical part of the EV because it is one of the component in which growth of the vehicle market is dependent upon. Over the few years many new technologies have made significant impact on battery capacity and power density. However, more development is needed to balance excess storage capacity and system balance to meet the functional requirements and to avoid catastrophic failures. Additionally, advance sensing and monitoring technologies required to forecast and control the battery functionality to detect potential health issues. Most of the battery management systems are limited to constrained assumption, they are considering partial and dynamic cycle, which highly affect the battery life. Present system is designed to accommodate this limitation.

This System aims to forecast the health of electrical vehicle battery and warns driver about possible failures. This system introduces the new technology to predict the state of health using expert system. This system integrates the driving pattern information with the existing battery health management system parameters to estimate the state-of-health (SOH) on the fly and give the information about possible faults. The expert system uses the training data from SOH estimated model stored in cloud which is created using the EV battery parameters like current, voltage, temperature, charging cycles, current driving pattern and navigation information. After training it will be used for real-time battery health prediction. Estimation is calculated for the next 5 km of driving and alert information is provided to the driver in the infotainment screen. This robust system can insure the heathy life of the battery and also ensures that calculated information regarding SOC is accurate. The idea is pertinent to multiple platforms which consist of infotainment and smartphones.