This paper investigates an adaptive neuro-fuzzy inference system (ANFIS)-based maximum power point tracking (MPPT) technique applied to a reconfigurable photovoltaic (PV)-based battery charger. The proposed method uses training data collected from a dynamic model of the PV module to train the ANFIS to locate the maximum power point (MPP) under different environmental conditions. Based on the estimated MPP, the proposed method can select the optimal configuration of a PV array and the corresponding global MPP under the non-uniform distribution of the temperature and irradiance. In this way, the proposed method can guarantee the highest possible power harvesting to charge a lithium-ion battery under either partial shading conditions or characteristics mismatch, achieving a high system efficiency. The proposed method is compared with the conventional MPPT scheme to verify its feasibility and effectiveness. The verification results show that the proposed method provides higher accuracy, faster response and better tracking efficiency.
- Adaptive neuro-fuzzy inference system (ANFIS)
- battery charging
- maximum power point tracking (MPPT)
- non-uniform irradiance
- photovoltaic system (PV)
- partial shading
- reconfigurable PV system