An artificial neural network (ANN) based multi-frequency electrical impedance spectroscopy (EIS) technique is proposed to estimate the static state of charge (SOC) of lithium-ion (Li-ion) battery in this paper. The proposed ANN-based multi-frequency EIS technique firstly collects the data of AC independence and their corresponding static SOC. With battery discharging current and multi-frequency EIS results, an ANN model is built and trained to estimate SOC. The measurement data is obtained using the potentiostats/galvanostats device, and the ANN is trained using the neural network toolbox in MATLAB. According to the experimental results, the performance of the proposed ANN model is dependent on the number of neurons in the hidden layer. The proposed method is validated with a set of random discharging processes. The high accuracy of SOC estimation is able to be achieved with the average error reduced to 1.92% when the number of neurons in the hidden layer is 35. Therefore, the proposed ANN-based multi-frequency EIS technique can be utilized to measure the static SOC of random discharge of Li-ion batteries.

In conventional adaptive variable step size (VSS) maximum power point tracking (MPPT) algorithms, a scaling factor is utilized to determine the required perturbation step. However, the performance of the adaptive VSS MPPT algorithm is essentially decided by the choice of scaling factor. In this paper, a neural network assisted variable step size (VSS) incremental conductance (IncCond) MPPT method is proposed. The proposed method utilizes a neural network to obtain an optimal scaling factor that should be used in current irradiance level for the VSS IncCond MPPT method. Only two operating points on the characteristic curve are needed to acquire the optimal scaling factor. Hence, expensive irradiance and temperature sensors are not required. By adopting a proper scaling factor, the performance of the conventional VSS IncCond method can be improved, especially under rapid varying irradiance conditions. To validate the studied algorithm, a 400 W prototyping circuit is built and experiments are carried out accordingly. Comparing with perturb and observe (P&O), α-P&O, golden section and conventional VSS IncCond MPPT methods, the proposed method can improve the tracking loss by 95.58%, 42.51%, 93.66%, and 66.14% under EN50530 testing condition, respectively.

This article proposes a multi-tiered fault detection system for series-connected lithium-ion battery modules. Improper use of batteries can lead to electrolyte decomposition, resulting in the formation of lithium dendrites. These dendrites may pierce the separator, leading to the failure of the insulation layer between electrodes and causing micro short circuits. When a micro short circuit occurs, the electrolyte typically undergoes exothermic reactions, leading to thermal runaway and posing a safety risk to users. Relying solely on temperature-based judgment mechanisms within the battery management system often results in delayed intervention. To address this issue, the article develops a multi-tiered fault detection algorithm for series-connected lithium-ion batteries. This algorithm can effectively diagnose micro short circuits, aging, and normal batteries using minimal battery data, thereby improving diagnostic accuracy and enhancing the flexibility of fault detection. Simulations and experiments conducted under various levels of micro short circuits validate the effectiveness of the algorithm, demonstrating its ability to distinguish between short-circuited, aged, and normal batteries under different conditions. This technology can be applied to electric vehicles and energy storage systems, enabling early warnings to ensure safety and prevent thermal runaway.

Wireless power transfer through metal enclosures remains a critical challenge because eddy currents induced in the metallic casing cause severe energy loss and temperature rise. Existing approaches either employ capacitive–inductive hybrid coupling, which yields extremely low efficiency, or require destructive modifications to the enclosure. This paper proposes a ventilation-slot-based inductive power transfer method that exploits the existing ventilation openings of a laptop enclosure to transmit magnetic flux, thereby preserving the mechanical integrity and electromagnetic shielding of the metal casing. A systematic asymmetric coil design methodology is developed: 25 primary–secondary coil gap combinations are evaluated using 3-D electromagnetic simulations, and a normalized multi-objective function that simultaneously maximizes the average coupling coefficient and minimizes misalignment sensitivity is formulated to select the optimal geometry. The optimized coil pair achieves stable coupling across 0 mm–30 mm lateral misalignment. A series–series (SS) resonant compensation topology is adopted, and the voltage gain of the complete fifth-order AC equivalent circuit is analytically derived. Closed-loop variable frequency control is implemented on a TMS320F280049C digital signal processor with wireless feedback via an nRF24L01 2.4 GHz transceiver, eliminating the need for a secondary-side DC–DC converter. A prototype rated at 100-W output (200-V input/20-V output) operating at 100 kHz is built and tested. Experimental results demonstrate that zero-voltage switching is achieved from 20 % to 100 % load. Without the metal barrier the full-load efficiency is 81.99 %; with the barrier, full-load efficiency remains 75.57 %—a decrease of only 6.42 %.