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The battery management system (BMS) is the main safeguard of a battery system for electric propulsion and machine electrification. It is tasked to ensure reliable and safe operation of battery cells connected to provide high currents at high voltage levels. In addition to effectively monitoring all the electrical parameters of a battery pack system, such as the voltage, current, and temperature, the BMS is also used to improve the battery performance with proper safety measures within the system. With growing acceptance of lithium-ion batteries, major industry sectors such as the automotive, renewable energy, manufacturing, construction, and even some in the mining industry have brought forward the mass transition from fossil fuel dependency to electric powered machinery and redefined the world of energy storage. Hence, the functional safety considerations, which are those relating to automatic protection, in battery management for battery pack technologies are particularly important to ensure that the overall electrical system, regardless of whether it is for electric transportation or stationary energy storage, is in accordance with high standards of safety, reliability, and quality. If the system or product fails to meet functional and other safety requirements on account of faulty design or a sequence of failure events, then the environment, people, and property could be endangered. This paper analyzed the details of BMS for electric transportation and large-scale energy storage systems, particularly in areas concerned with hazardous environment. The analysis covers the aspect of functional safety that applies to BMS and is in accordance with the relevant industrial standards. A comprehensive evaluation of the components, architecture, risk reduction techniques, and failure mode analysis applicable to BMS operation was also presented. The article further provided recommendations on safety design and performance optimization in relation to the overall BMS integration.

This work presents a novel technique which is simple yet effective in estimating electric model parameters and state-of-charge (SOC) of the LiFePO4 battery. Unlike the well-known recursive least-squares-based algorithms with single constant forgetting factor, this technique employs multiple adaptive forgetting factors to provide the capability to capture the different dynamics of model parameters. The validity of the proposed method is verified through experiments using actual driving cycles.

A novel algorithm based on fading Kalman filter to estimate the state of charge (SoC) of Li-ion battery used in electric vehicles is proposed and validated in this paper. Online identification of battery's electric model parameters followed by open circuit voltage estimation by fading Kalman filter resulted in accurate SoC estimation. The experimental results obtained from actual driving cycle in real-time reveal the robust performance of the proposed algorithm.