Cell state-of-charge (SoC) balancing within each branch of a three-phase battery energy storage system (BESS) and among three branches is crucial to overcome the inability to fully utilise the available capacity of a three-phase BESS. The proposed topology is constructed with one branch instead of three branches to take advantage of its idle cells/modules (Ms) (one-third of the total cells/ Ms) and to eliminate the need of SoC balancing among the branches. Contrary to conventional topologies, idle cells/ Ms can serve as redundant cells/ Ms or can be dropped out of a BESS, thereby leading to a reduction in the cost, control complexity, size, and losses of a BESS. A novel SoC balancing strategy for the proposed topology of a three-phase BESS is introduced in this paper. Moreover, the cell/M activation algorithm is implemented to minimise the duration needed to activate the cells/Ms required to generate voltage for the phases, thereby leading to an improvement of battery operational efficiency. Based on the simulation results, SoC balancing among 3996 cells, 2664 cells, 333/222 Ms, and 12 cells in M with the lowest and the highest average SoC is achieved in 53 min, 48 min, 38 min, 18 min and 53 min, respectively.

Electric vehicles (EVs) are experiencing rapid global adoption driven by environmental concerns and fuel security. This article presents a new dual-source converter based on a hybrid modular multilevel configuration (DCHMMC) designed for optimal cell utilisation in EV battery systems. Contrary to conventional converters that can either charge or discharge the cells using a single source, thereby leaving several cells/modules (Ms) idle during each time step, the proposed converter enables the integration of two sources that can utilise the cells simultaneously. This dual source feature minimises idle cells/Ms, enhances energy efficiency, and supports flexible bidirectional power flow. The proposed converter operates in three distinct modes. The first involves dual-source charging for fast charging and improved vehicle availability. The second involves one source charging while the other discharges for dynamic operation. Finally, the last involves dual-source discharging for maximum power delivery and support vehicle-to-grid (V2G) operation. The simulation results demonstrated smooth multilevel sinusoidal output voltages (Vout_a and Vout_b), each with a peak of 350 V, generated simultaneously using 132 cells (six cells per M, 22 Ms). The total harmonic distortion (THD) values for Vout_a and Vout_b were 0.42% and 2.25%, respectively, confirming the high-quality performance. Furthermore, only 0–36 cells and 0–6 Ms were idle during operation, showing improved cell utilisation.

This paper presents an enhanced fault-tolerant control framework for induction motor drive systems, emphasizing resilience, adaptability, and system-level reliability in interconnected industrial environments. A modified field-oriented control structure is developed to ensure decoupled flux and speed regulation under open-circuit fault conditions. The proposed control integrates an adaptive proportional–integral–resonant (PIR) controller within a reconfigured stationary reference frame to maintain stable operation even during partial phase loss. By continuously adjusting its resonant frequency in response to real-time feedback, the controller achieves superior transient and steady-state performance. Experimental validation on a dSPACE DS1104 real time platform compares the performance of PI, PR, and PIR controllers. Results reveal that the conventional PI controller fails to sustain post-fault operation, while the adaptive PR controller enhances system continuity and dynamicrecovery. The proposed adaptive PIR controller further mitigates torque pulsations, reducing torque ripple from over 60% (PI-based) to approximately 10%, demonstrating significant improvement in fault-tolerant performance and robustness. This approach contributes to the development of reliable, self-healing industrial drive subsystems for next-generation smart manufacturing systems.

It is crucial to operate photovoltaic (PV) systems at their maximum output under a variety of environmental conditions. Maximum power point tracking (MPPT) can be used to achieve the optimum power of a PV system, which is always dependent on the surrounding conditions. Performance evaluation of MPPT with opposition‐based learning–Levy flight optimization (OBLLFO) and a boost converter was presented in this paper. Several MPPT methods have recently been considered as alternatives to conventional MPPT methods, which have high steady‐state oscillation and low efficiency. In order to solve the aforementioned issues, a soft computing method OBLLFO is suggested. The verification of the conventional and soft computing algorithms was conducted using a standalone PV system. A total of four MPPT algorithms, namely, perturb and observe (P&O), particle swarm optimization (PSO), Levy flight optimization (LFO), and OBLLFO, were presented in this paper to prove that the OBLLFO has the best performance under uniform irradiances and varying environmental conditions. In this simulation, a standalone PV system with a resistive load, DC‐DC converter, MPPT algorithm, and PV modules was used. Under MATLAB/Simulink, the MPPT algorithms are tested under three main conditions which are under high irradiance (1000 W/m ² ), medium irradiance (600 W/m ² ), low irradiance (200 W/m ² ), and dynamic response under a step change of irradiance from partial shading condition (PSC) to uniform irradiance (1000 W/m ² ). Based on the simulation results, the OBLLFO method shows the best MPPT methods with better tracking efficiency of an average of 99.90%, a tracking speed of 4.0 s, and a steady‐state oscillation of 0.09%. In conclusion, the proposed OBLLFO demonstrates its ability in tracking local and global points with high efficiency and low steady‐state oscillation.