ANFIS-Based Maximum Power Point Tracking for Current-Controlled Charging of Electric Vehicle Batteries from Solar Photovoltaic Arrays
DOI:
https://doi.org/10.64882/ijrt.v13.i4.1849Keywords:
Maximum power point tracking, ANFIS, electric vehicle charging, solar photovoltaic array, DC-DC boost converter, Hill-Climbing, Perturb and Observe, battery state of charge.Abstract
Charging an electric vehicle (EV) battery directly from a solar photovoltaic (PV) array is complicated by the highly variable voltage, current and power delivered by the array under changing irradiance, which, if left uncontrolled, can degrade battery performance, response time and service life. Conventional hill-climbing maximum power point tracking (MPPT) methods, such as Perturb & Observe, respond poorly to fast-changing irradiance and cannot guarantee that the charging current stays within a safe band for the battery. This paper proposes a current-controlled charging scheme in which an Adaptive Neuro-Fuzzy Inference System (ANFIS) is used for MPPT, together with a de-rating mechanism, to hold the battery charging current close to a safe reference value of 14 A regardless of irradiance fluctuations. The ANFIS controller takes the PV array power–voltage (P–V) error and its rate of change as two inputs, each represented by seven membership functions, and combines them through a 49-rule Sugeno-type fuzzy inference system to generate the duty-cycle control signal for a DC-DC boost converter. When the array current is below the 14 A reference, the ANFIS-MPPT block drives the converter to operate the array at its maximum power point; when the array current exceeds the reference, a de-rating action reduces the converter duty cycle to pull the operating point back within the safe band. The proposed scheme is implemented and validated in MATLAB/Simulink and compared against a conventional Hill-Climbing (Perturb & Observe) MPPT controller under identical, time-varying irradiance profiles (600–1000 W/m²) and a fixed cell temperature of 25°C. Simulation results show that the proposed ANFIS-based scheme delivers panel output voltage and power that track irradiance changes smoothly (reaching about 390 V and 8000 W under 1000 W/m² irradiance) with markedly reduced oscillation compared with the Hill-Climbing method, keeps the array and battery current confined to a narrow 15–20 A and 0 to −12 A band respectively, and produces a smoother battery state-of-charge (SOC) trajectory than the conventional method.
References
M. Yilmaz and P. T. Krein, "Review of the Impact of Vehicle-to-Grid Technologies on Distribution Systems and Utility Interfaces," IEEE Transactions on Power Electronics, vol. 28, no. 12, pp. 5673–5689, 2013.
H. K. Singh and N. Kumar, "Current Controlled Charging Scheme for off Board Electric Vehicle Batteries from Solar PV Array," 2020 7th International Conference on Signal Processing and Integrated Networks (SPIN), pp. 935–940, 2020.
S. S. Satapathy and N. Kumar, "Derated MPPT Scheme for Grid Integrated Solar PV Energy Conversion System," 2019 International Conference on Computing, Power and Communication Technologies (GUCON), pp. 975–980, 2019.
M. Seyedmahmoudian et al., "State of the Art Artificial Intelligence-Based MPPT Techniques for Mitigating Partial Shading Effects on PV Systems – A Review," Renewable and Sustainable Energy Reviews, vol. 64, pp. 435–455, 2016.
T. Hiyama, S. Kouzuma, and T. Imakubo, "Identification of Optimal Operating Point of PV Modules using Neural Network for Real Time Maximum Power Tracking Control," IEEE Transactions on Energy Conversion, vol. 10, pp. 360–367, 1995.
S. Bhattacharya and S. Samanta, "Modified Incremental Conductance MPPT Algorithm for Very Fast Changing Atmospheric Condition for Solar Electric Vehicle Application," 2019 IEEE 16th India Council International Conference (INDICON), pp. 1–4, 2019.
Hussain, H. A. Sher, A. F. Murtaza, and K. Al-Haddad, "Revised Perturb and Observe Approach for Maximum Power Point Tracking of Photovoltaic Module using Finite Control Set Model Predictive Control," 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE), pp. 962–967, 2019.
J. Hahm, J. Baek, H. Kang, H. Lee, and M. Park, "Matlab-Based Modeling and Simulations to Study the Performance of Different MPPT Techniques Used for Photovoltaic Systems under Partially Shaded Conditions," International Journal of Photoenergy, vol. 2015, pp. 1–10, 2015.
G. Sarigiannidis, S. A. Stathis, and A. G. Kladas, "Performance Evaluation of MPPT Techniques for PV Array Incorporated into Electric Vehicle Roof," 2015 International Conference on Renewable Energy Research and Applications (ICRERA), pp. 1069–1073, 2015.
Nasir Hussein Selman, "Comparison Between Perturb & Observe, Incremental Conductance and Fuzzy Logic MPPT Techniques at Different Weather Conditions," International Journal of Innovative Research in Science, Engineering and Technology, vol. 5, no. 7, pp. 12556–12569, 2016.
I. Eker and Y. Torun, "Fuzzy Logic Control to be Conventional Method," Energy Conversion and Management, vol. 47, no. 4, pp. 377–394, 2006.
Jianming Xu, Longyun Kang, Changyi Zhong, and Binggang Cao, "Photovoltaic Power System with MPPT Functionality for a Small-Size Electric Vehicle," International Journal of Photoenergy, vol. 2014, pp. 1–9, 2014.
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