Enhancing NICD and NIMH batteries charging efficiency: a MSCCC strategy using artificial intelligence control

Abstract

In the above essay, a smart multi-stage constant current charging (MSCCC) strategy has been proposed with an adaptive neuro-fuzzy inference system (ANFIS) to improve the charging efficiency of nickel metal hydride (NiMH) and nickel cadmium (NiCd) type batteries. The suggested charger uses a boost converter that is power-factor-corrected and variable current regulation according to real-time feedback of voltage and state of charge. MATLAB/Simulink is used to test the system with a 24 V23.5 Ah NiCd pack and 25.2 V49.4 Ah NiMH pack. Comparative simulations on conventional PI, fuzzy, and neural controllers show that ANFIS-MSCCC approach enhances state-of-charge (SoC) retention by about 5-8 percent, voltage overshoot by almost 20 percent and transitions between currents are smoother which results into lower electrical stress. Besides, the suggested approach has a shorter settling time, high charging stability, and safe thermal characteristics. These findings prove that the ANFIS-aided MSCCC provides a powerful and reconfigurable charging system to NiCd and NiMH batteries, which is applicable within the complex battery management systems that are already in use.