Current harmonics is one of the most significant power quality issues which has attracted tremendous research interest. Shunt active power filter (SAPF) is the best solution to minimize harmonic contamination, but its effectiveness is strictly dependent on how quickly and accurately its control algorithms can perform. This manuscript reviews various types of existing control algorithms which have been employed for controlling operation of SAPF. Harmonic extraction, DC-link capacitor voltage regulation, current control and synchronizer algorithms are examined and discussed. The most relevant techniques which have been applied for each control algorithm are described and contrasted in an organized manner to identify their respective strengths and weaknesses. It is found that the applied control algorithms differ in two conditions: (1) the condition where harmonic current distortion is treated by the SAPF in the presence of non-ideal source voltage; and (2) the condition where multilevel inverter is employed as the circuit topology of SAPF.

In this paper, a new reference current generation method is proposed for effective harmonics mitigation and reactive power compensation of three-level neutral-point diode clamped (NPC) inverter-based shunt active power filter (SAPF) under non-ideal grid voltage conditions. The proposed method is named as dual fundamental component extraction (DFCE) algorithm. In operation, the proposed algorithm extracts at the same time, the desired fundamental current and voltage components for generating reference current and synchronization phases respectively. As a result, the proposed algorithm is able to generate reference current which ensures in phase operation of SAPF with the operating power system, without depending on any phase-locked loop (PLL) elements. Besides, the proposed algorithm employs self-tuning filter (STF) for accurate computation of the fundamental components. Design concept and effectiveness of the proposed algorithm are thoroughly studied and evaluated in MATLAB-Simulink. Additionally, a laboratory prototype utilizing TMS320F28335 digital signal processor (DSP) is built to validate its feasibility. Encouraging findings obtained from both simulation and experimental works demonstrate effectiveness of the proposed algorithm under both ideal and non-ideal grid voltage conditions.

In electric vehicle technology, battery energy conservation is paramount due to the dependency of all system operations on the available battery. The proportional, integral and derivative (PID) controller parameters in the electric power assisted steering system for electric vehicle need to be tuned with the optimal performance setting so that less current is needed for its operation. This proposed two methods under the umbrella of swarm-intelligence technique namely particle swarm optimization (PSO) and ant colony optimization (ACO) in order to reduce current consumption and to improve controller performance. The investigation involves an analysis on the convergence behavior of both techniques in search for accurate controller parameters. A comprehensive assessment on the assist current supplied to the assist motor of the system is also presented. Investigation reveals that the proposed controllers, PID-PSO and PID-ACO are able to reduce the assist current supplied to the assist motor as compared to the conventional PID controller. This study also demonstrate the feasibility of applying both swarm-intelligence tuning method in terms of reduced time taken to tune the PID controller as compared to the conventional tuning method.

Electric vehicles and hybrid electric vehicles (EV) are increasingly common on roads today compared to a decade ago, driven by advancements in technology and a growing focus on sustainable transportation. These vehicles are powered by rechargeable lithium-ion batteries. A battery management system (BMS) is indispensable for ensuring the optimal performance, safety, and longevity of the EV’s batteries. In this review, the latest algorithm trends for BMS software are discussed. This work also focuses on several key functionalities of BMS like the state of charge (SOC) estimation, state of health (SOH) monitoring, state of energy (SOE), and state of power (SOP). Advanced algorithms for BMS are comprehensively reviewed, including those designed for specific functionalities, as well as those developed based on existing optimization, artificial intelligence, and estimation algorithms. These algorithms address critical challenges such as maintaining symmetry during charging and discharging, preventing thermal runaway, and managing battery faults in EV systems. This work provides valuable insights for researchers and practitioners in the field of EV design and development, particularly those focusing on the advancement of BMS technologies.

A new control technique known as inverted error deviation (IED) control is incorporated into the main DC-link capacitor voltage regulation algorithm of a three-level neutral-point diode clamped (NPC) inverter-based shunt active power filter (SAPF) to enhance its performance in overall DC-link voltage regulation so as to improve its harmonics mitigation performances. In the SAPF controller, DC-link capacitor voltage regulation algorithms with either the proportional-integral (PI) or fuzzy logic control (FLC) technique have played a significant role in maintaining a constant DC-link voltage across the DC-link capacitors. However, both techniques are mostly operated based on a direct voltage error manipulation approach which is insufficient to address the severe DC-link voltage deviation that occurs during dynamic-state conditions. As a result, the conventional algorithms perform poorly with large overshoot, undershoot, and slow response time. Therefore, the IED control technique is proposed to precisely address the DC-link voltage deviation. To validate effectiveness and feasibility of the proposed algorithm, simulation work in MATLAB-Simulink and experimental implementation utilizing a TMS320F28335 Digital Signal Processor (DSP) are performed. Moreover, conventional algorithms with PI and FLC techniques are tested too for comparison purposes. Both simulation and experimental results are presented, confirming the improvement achieved by the proposed algorithm in terms of accuracy and dynamic response in comparison to the conventional algorithms.