Determination of multi-step constant current based fast charging method for Li-NMC battery cells using multi-swarm particle swarm optimization algorithm

Çarkıt T., ALÇI M.

International Journal of Energy Research, vol.46, no.15, pp.23219-23233, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 46 Issue: 15
  • Publication Date: 2022
  • Doi Number: 10.1002/er.8621
  • Journal Name: International Journal of Energy Research
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Aquatic Science & Fisheries Abstracts (ASFA), Communication Abstracts, Compendex, Environment Index, INSPEC, Metadex, Pollution Abstracts, Civil Engineering Abstracts
  • Page Numbers: pp.23219-23233
  • Keywords: battery, circuit model, fast charging, lithium-ion, optimization, ION, ENERGY, SEARCH, SYSTEM, STRATEGY, PATTERN
  • Kayseri University Affiliated: No


© 2022 John Wiley & Sons Ltd.Thanks to today's developing technology, lithium-ion batteries in commercial use have become important components in energy storage systems and electric vehicles. In lithium-ion battery-based systems, it is especially important that the electrochemical structures of electric vehicle batteries and rapid charging process as allowed by the manufacturers. In other respects, it is necessary to define the best quality, fastest and healthiest charging method within the limited time period determined in line with user requests. In this study, an intelligent charging algorithm is designed and adapted to the problem for optimizing the charging current capacity and charging time for INR 18650 cylindrical type 2000 mAh Li-NMC battery cells, taking into account the manufacturer's notes and the characteristics of the battery cells. To determine the optimum fast charging method by completing the total charging time within 30 min, the multi swarm-particle swarm optimization technique with both fixed and dynamic internal weights has been used. The algorithm with dynamic state variables has showed an average improvement of 0.03% and a maximum of 1.08% in the cellular chargeable capacity value, according to the test data consisting of 11 pulses and 1 A constant current. Otherwise, the algorithm with dynamic internal weights has achieved an average improvement of 1.14% and a maximum of 2.21% when compared to the fast charging information on the manufacturer's information page using the constant current-constant voltage method. Considering the 50-minute fast charging data in the manufacturer's information notes, the algorithm with dynamic weights improved the charging time by approximately 54.4%. As a result of running the dynamic weighted algorithm, a maximum improvement of 0.06% in terminal voltage and 0.92% in the state of charge value has been observed. When hundreds of cells in electric vehicle battery blocks and mobile energy storage systems come together, this increase provides significant energy savings. Similar to the increase in charging capacity, an improvement in equilibrium voltage, charging efficiency and state of charge has been also detected at the end of the charging process. Consequently, the method in the study has provided high charging capacity in a shorter time, while keeping the energy efficiency at high levels.