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Summary This paper presents the design and implementation of a photovoltaic‐integrated shunt active power filter (SAPF) to improve the power quality and to generate clean power. The system uses adaptive neuro‐fuzzy inference system (ANFIS)‐based maximum power point tracking and control of synchronous reference frame theory–based SAPF. Various control schemes are implemented in MATLAB and then validated in real‐time using FPGA‐based computation engine of OPAL‐RT 4510. Control techniques built around the artificial neural network, fuzzy logic control, and ANFIS are compared for balanced and unbalanced loads on parameters like total losses with/without compensation, voltage drop, power factor, and total harmonic distortion.

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Summary Nonlinear load results in inferior power quality and a non‐uniform power demand curve. This gets aggravated due to the charging of electric vehicles. This paper presents a new control approach of integrating a photovoltaic (PV) cell with battery storage to a shunt active power filter (SAPF) for electric vehicle (EV) applications. The multifunctional PV‐Battery‐integrated SAPF (PV‐Battery‐SAPF) performs harmonic mitigation along with clean power generation, energy storage, uniform load demand curve, and battery swapping. It is achieved through a two‐stage topology. In the first stage, maximum power point (MPP) of a PV array is robustly tracked using metaheuristic algorithms like cuckoo search algorithm and particle swarm optimization. In the second stage, an ant colony optimization‐tuned controller is developed which adaptively controls the SAPF to improve the power quality. The suggested method provides efficient MPP tracking, lesser total harmonic distortion, better dynamic system performance, and appropriate charging/discharging of battery leading to increased system reliability. The proposed system is validated in real time on a hardware‐in‐the‐loop (HIL) testing platform.