State Of Health Assessment Research Articles

Real-Time Model-Based Estimation of SOC and SOH for Energy Storage Systems

To obtain a full exploitation of battery potential in energy storage applications, an accurate modeling of electrochemical batteries is needed. In real terms, an accurate knowledge of state of charge (SOC) and state of health (SOH) of the battery pack is needed to allow a precise design of the control algorithms for energy storage systems (ESSs). Initially, a review of effective methods for SOC and SOH assessment has been performed with the aim to analyze pros and cons of standard methods. Then, as the tradeoff between accuracy and complexity of the model is the major concern, a novel technique for SOC and SOH estimation has been proposed. It is based on the development of a battery circuit model and on a procedure for setting the model parameters. Such a procedure performs a real-time comparison between measured and calculated values of the battery voltage while a PI-based observer is used to provide the SOC and SOH actual values. This ensures a good accuracy in a wide range of operating conditions. Moreover, a simple start-up identification process is required based on battery data-sheet exploitation. Because of the low computational burden of the whole algorithm, it can be easily implemented in low-cost control units. An experimental comparison between SOC and SOH estimation performed by suggested and standard methods is able to confirm the consistency of the proposed approach.

Quantum assimilation based state-of-health assessment and remaining useful life estimation for electronic systems

State-of-health (SOH) assessment and remaining useful life (RUL) estimation are among the key issues in prognostics and health management (PHM) for electronic systems. Unlike mechanical systems, the homogeneity of the fault modes is quite low for electronic systems. Seen at the system level, there are also multiple and uncertain fault modes for electronic products. In this paper, we propose a novel methodology for system-level SOH assessment and RUL estimation inspired by the quantum mechanics disciplines, where there is no requirement to distinguish the fault modes from the fault development patterns. It is developed on the analogy that the healthy data points tend to move to the lower potential energy positions, and the subhealthy or unhealthy ones tend to be repelled from such positions. The fault development paths are located on the potential surface. The Fermi–Dirac health descriptor (FDHD) is defined based on the pseudowave function and potential function. Based on FDHD, the SOH assessment and RUL estimation algorithm are developed to track the existing fault development paths. Finally, the proposed method is verified in an experiment using power conversion board with a detailed analysis of the SOH assessment results and RUL estimation performances.