Kalman filter (KF) is an effective way to estimate the state-of-charge (SOC), but its performance is heavily dependent on the state-space model parameters. One of the factors that causes the model parameters to change is battery aging, which is individually and non-uniformly experienced by the cells inside the battery pack. To mitigate this issue, this paper proposes an online calibration method considering the impact of cell aging and cell inconsistency. In this method, the state-of-health (SOH) levels of the individual cells are estimated using the deep learning method, and the historical parameter loop-up table is constructed to update the state-space model. The proposed calibration framework provides enhanced accuracy for cell-by-cell SOC estimation by lightweight computing devices. The SOC estimation errors of the calibrated EKF reduce to 1.81% compared to 12.1% of the uncalibrated algorithms.

Determining charge and discharge behavior is essential for optimizing charging strategies and evaluating balancing algorithms in battery energy storage systems and electric vehicles. Conventionally, a sequence of circuit simulations or tedious hardware tests is required to evaluate the performance of the balancing algorithm. To mitigate these problems, this paper proposes a variable capacitor model that can be easily built from the incremental capacity curve. This model provides a direct and insightful R-C time constant method for the charge/discharge time calculation. After validating the model accuracy by experimental results based on the cylindrical lithium-ion cell test, a switched-capacitor active balancing and a passive cell balancing circuit are implemented to further verify the effectiveness of the proposed model in calculating the cell balancing time within 2% error.

Energy management systems incorporating battery energy storage systems (BESSs) are an effective way to deal with peak power demand in power systems, contributing to sustainability and energy management. In these systems, BESS interface converters encounter many challenges, such as achieving high efficiency, reliability, and scalability. A partial power processing (PPP) converter is one of the promising methods for reducing system energy losses and increasing reliability, thereby optimizing energy conversion between the BESS and the dc bus. However, implementing PPP in a BESS requires a high input-to-output voltage ratio and bidirectional operation over the battery voltage variation, which increases the design complexity. This paper proposes a half-bridge push–pull converter and provides guidance on its design for that purpose. A design procedure considering system efficiency and operating principles is comprehensively investigated and is applied to a PPP converter operating with a 15 kW BESS. The simulation and experimental results show that the efficiency of the system reaches up to 99% with a converter efficiency of 88%, which shows that the proposed structure achieves bidirectional operation and high efficiency based on the partial power processing concept.