Assessment of Lithium-Ion Battery Types by Multi-Criteria Decision Making

  • Seyedkian Rezvanjou Department of Engineering, California State University East Bay, Hayward, California, 94542
  • Chang Li Faculty of Computer Science and Information System, Universiti Teknologi MARA (UiTM), Malaysia
  • Farzaneh Shoushtari Alumni of Industrial Engineering, Bu-Ali Sina University, Hamedan, Iran
Keywords: Lithium-Ion Battery, MCDM Approach, Assessment, Quantitative Analysis

Abstract

Lithium-ion batteries are the dominant energy storage technology for a wide range of applications, from portable electronics to electric vehicles and grid-scale energy storage. However, the diverse applications present conflicting demands on battery performance, leading to the development of numerous lithium-ion battery chemistries with distinct advantages and disadvantages. Choosing the optimal battery type for a specific application becomes a complex Multi-Criteria Decision-Making (MCDM) problem. This paper utilizes a MCDM framework to assess the performance of six common lithium-ion battery types: Lithium Cobalt Oxide (LCO), Lithium Manganese Oxide (LMO), Lithium Nickel Manganese Cobalt Oxide (NMC), Lithium Iron Phosphate (LiFePO4) and Lithium Titanate Oxide (Li4Ti5O12). The assessment considers technical criteria such as specific energy, specific power, cycle life, calendar life, safety, and environmental impact, alongside economic criteria like cost and availability. Through quantitative analysis and a weighted aggregation approach, the MCDM framework ranks the battery types based on their suitability for different application categories. The paper highlights the trade-offs and synergies between various criteria, providing valuable insights for engineers, researchers, and decision-makers navigating the diverse landscape of lithium-ion battery technology.

Published
2023-12-22
How to Cite
Rezvanjou, S., Li, C., & Shoushtari, F. (2023). Assessment of Lithium-Ion Battery Types by Multi-Criteria Decision Making. International Journal of Industrial Engineering and Operational Research, 5(5), 48-63. https://doi.org/10.22034/ijieor.v5i5.73