{'fraction_non_alphanumeric': 0.01971447994561523, 'fraction_numerical': 0.0, 'mean_word_length': 5.237288135593221, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
SOC Control of Lithium–Ion Battery Using Fuzzy Logic Controller and PID Controller Employed in DC Micro Grid

Integrating solar panels in DC micro grid is a big task, since it involves a battery for power storage and battery controller. The most tedious task is enhancement of battery life and efficiency. This paper introduces two separate models of DC micro grid, one regulated by fuzzy logic controller and the other one by a conventional PID controller. The fuzzy logic controller is implemented in MATLAB Simulink based upon Mamdani inference system. MPPT, fuzzy logic controller, lithium-ion battery and DC load are all integrated in one Simulink model. Best possible rule base is developed to regulate power flow through the DC micro grid, thus simultaneously enhancing the performance of lithium-ion battery. The fuzzy logic controller facilitates maintenance of SOC of lithium-ion battery within desired limits, which results in prevention of overcharging and over discharging. Also, conventional PID controller is implemented in MATLAB Simulink for maintaining SOC of lithium-ion battery within desired limits. This model involves integration of PID controller, MPPT, bi-directional DC-DC converter and lithium-ion battery. Based upon the obtained performance and results of both Simulink models, a comparison between the two controllers is deduced and corresponding results are analyzed. Transient response analysis is performed to compare the two controllers.