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Globally, the research on battery technology in electric vehicle applications is advancing tremendously to address the carbon emissions and global warming issues. The effectiveness of electric vehicles depends on the accurate assessment of key parameters as well as proper functionality and diagnosis of the battery storage system. However, poor monitoring and safety strategies of the battery storage system can lead to critical issues such as battery overcharging, over-discharging, overheating, cell unbalancing, thermal runaway, and fire hazards. To address these concerns, an effective battery management system plays a crucial role in enhancing battery performance including precise monitoring, charging-discharging control, heat management, battery safety, and protection. The goal of this paper is to deliver a comprehensive review of different intelligent approaches and control schemes of the battery management system in electric vehicle applications. In line with that, the review evaluates the intelligent algorithms in battery state estimation concerning their features, structure, configuration, accuracy, advantages, and disadvantages. Moreover, the review explores the various controllers in battery heating, cooling, equalization, and protection highlighting categories, characteristics, targets, achievements, benefits, and shortcomings. The key issues and challenges in terms of computation complexity, execution problems along with various internal and external factors are identified. Finally, future opportunities and directions are delivered to design an efficient intelligent algorithm and controller toward the development of an advanced battery management system for future sustainable electric vehicle applications.

In this paper, the performance of an active neutral point clamped (ANPC) inverter is evaluated, which is developed utilizing both silicon (Si) and gallium trioxide (Ga2O3) devices. The hybridization of semiconductor devices is performed since the production volume and fabrication of ultra-wide bandgap (UWBG) semiconductors are still in the early-stage, and they are highly expensive. In the proposed ANPC topology, the Si devices are operated at a low switching frequency, while the Ga2O3 switches are operated at a higher switching frequency. The proposed ANPC mitigates the fault current in the switching devices which are prevalent in conventional ANPCs. The proposed ANPC is developed by applying a specified modulation technique and an intelligent switching arrangement, which has further improved its performance by optimizing the loss distribution among the Si/Ga2O3 devices and thus effectively increases the overall efficiency of the inverter. It profoundly reduces the common mode current stress on the switches and thus generates a lower common-mode voltage on the output. It can also operate at a broad range of power factors. The paper extensively analyzed the switching performance of UWBG semiconductor (Ga2O3) devices using double pulse testing (DPT) and proper simulation results. The proposed inverter reduced the fault current to 52 A and achieved a maximum efficiency of 99.1%.

Three-phase induction motors (TIMs) are widely used for machines in industrial operations. As an accurate and robust controller, fuzzy logic controller (FLC) is crucial in designing TIMs control systems. The performance of FLC highly depends on the membership function (MF) variables, which are evaluated by heuristic approaches, leading to a high processing time. To address these issues, optimisation algorithms for TIMs have received increasing interest among researchers and industrialists. Here, we present an advanced and efficient quantum-inspired lightning search algorithm (QLSA) to avoid exhaustive conventional heuristic procedures when obtaining MFs. The accuracy of the QLSA based FLC (QLSAF) speed control is superior to other controllers in terms of transient response, damping capability and minimisation of statistical errors under diverse speeds and loads. The performance of the proposed QLSAF speed controller is validated through experiments. Test results under different conditions show consistent speed responses and stator currents with the simulation results.