Cloud-Battery management system based health-aware battery fast charging architecture using error-correction strategy for electric vehicles

Abstract

The health-aware battery fast charging (HABFC) strategy for electric vehicles (EVs) is predominantly dependent on the input conditions. These conditions are set using a set of various type of sensors (temperature, current, voltage, etc.), which can seldom be erroneous. Also, one of the primary challenges associated with physical battery management system (BMS) is the lack of memory required to execute intricate state estimation methodologies. Therefore, this paper proposes the Cloud-BMS based health-aware battery fast charging (HABFC) architecture with error correction strategy for reducing the charging time and increasing the cycle life of the EV battery. A cloud BMS has been integrated in the HABFC architecture to reduce the dependency on erroneous sensors. To design an effective cloud BMS, this paper reviews the seven most critical battery state estimation techniques (state of charge (SoC), state of health (SoH), state of power (SoP), state of energy (SoE), state of temperature (SoT), state of function (SoF), and state of safety (SoS)). Based on comparison of mathematical computational methods, its accuracy and challenges, the best computational method for each state estimation technique has been determined. The cloud BMS makes all state estimations based on parameters estimated using the virtual battery. Further, this paper presents an error correction strategy that helps correct the error in physical BMS employing a master-slave configuration. The proposed cloud BMS based HABFC architecture is validated using a cell cycling setup. It can be concluded that the proposed architecture produces 21.24% more cycle life as compared to fast charging without HABFC and 11% more cycle life as compared to cell charged using HABFC without cloud BMS.