In series battery strings, cell-inconsistency is caused by the state-of-charge (SOC) mismatch, nonidentical battery impedance, or different self-discharging rates, and this leads to overcharge and overdischarge. Practically, switched-capacitor equalizers are the most promising means to eliminate the cell inconsistency by virtue of automatic equalization, but the performance is heavily dependent on the initial cell voltage distribution and the number of series connections due to inefficient switch utilization. This article proposes a direct cell-to-cell equalizer for a series-connected battery using a switch-matrix single-capacitor converter to further improve the switched-capacitor equalizer in term of performance consistency and Coulomb efficiency. By adopting one extra current sensor and an optimal pairing algorithm, energy is transferred directly between the highest-SOC cell and the lowest-SOC cell to eliminate the impact of battery-impedance difference and false voltage measurement in battery monitoring integrated circuit caused by polarization effect. The experimental results verify the feasibility of the proposed scheme. Real-time tests are also implemented to fairly compare the proposed with the conventional methods. It is found that the performance of the proposed method is independent from initial voltage distribution in a series string, where the performance indices are consistent regardless of initial conditions. Besides, the energy loss of the proposed equalizer is further reduced and its overall efficiency is high in all test scenarios.

As the number of parallel battery connections in an energy storage system is increased to extend the energy capacity and second-life batteries are actively adopted, the battery is more prone to cell inconsistency issues. The difference in the internal impedance and the mismatched state-of-charge accelerates the self-balancing effect between the parallel branches to reduce cell utilization and eventually results in harmful effects, both to the lifetime and to the safety of the batteries. However, conventional methods only partially mitigate the parallel inconsistency issue. This paper proposes a dynamic resistance equalizer for parallel-connected battery configurations to improve equalization performance. The optimal design procedure is also presented to minimize the power loss and equalization time. The overall performance is experimentally verified by a sequence of tests for a Li-ion battery in a 2S-4P configuration. The experimental results show that the proposed method dissipates less external power loss than the fixed resistor equalizer and less internal loss than the conventional sequencing method. When both total loss and balancing performance are considered together, as the number of series connections increases, the merits of the proposed method stand out. This is verified by additional hardware-in-the-loop tests, presenting a fascinating feature for most practical battery applications.

To mitigate standby power and safety issues, wireless power transfer system requires an algorithm to determine the presence of the target object. Even though various foreign object detection schemes have been investigated so far, such a study is still not so active for capacitive power transfer system. In this paper, we propose a method to detect the presence of the target object by monitoring the capacitance in the transmitter-side electrodes without adding additional distance or pressure sensors. This capacitance increases when the target object is completely aligned as compared with when separated. Therefore, the proposed method identifies the presence or absence of the target object by measuring the change in capacitance of transmitter-side electrodes using step pulse injection of micro controller unit. Verification has been made in two step processes. At first, performance of capacitance measurement has been compared with LCR meter. The performance of target receiver detection algorithm has been tested in a 5 W capacitive power transfer hardware. In our experimental set-ups, the inter-electrode capacitance increased 6 times when the target object was fully aligned and thus the proposed scheme successfully detected the presence of the target object.

Assessing the performance of active balancing methods poses a significant challenge due to the time required to replicate the equalization of various balancing techniques under identical initial cell conditions. Conventional circuit simulation methods, designed for high-frequency switching behavior, impose a considerable computational burden when applied to the long-term equalization of battery cells. To address this challenge, this paper presents an efficient performance evaluation method employing an average equivalent model of the equalizers. By representing the charge transfer mechanism inherent to the equalization process, the proposed approach is compatible with the most widely used switched-energy-tank equalizers. The validity of this method is confirmed through simulation and experimental results. In the case of four series-connected battery cells, our proposed approach can assess the performance of a three-hour equalization process in just one minute of execution time. The use cases in the paper highlight the practical feasibility of the AM in facilitating performance comparisons of SET-Es under various initial conditions.

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.