A Novel Smart Molecular Fuzzy Decision Support System for Solid-State Battery Investments in Grid-Level Renewable Energy Storage

It is very necessary to determine the most critical indicators to improve the performance of solid-state battery investments. Although there are many factors affecting these investments in the literature, there is no consensus on the most important factors. Investors’ failure to focus on the most critical factors may cause businesses to fail to use their financial and human resources effectively. This situation significantly reduces the performance and efficiency of investments. To eliminate this deficiency, a priority analysis of performance indicators for solid-state battery investments is performed in this study. For this purpose, a new decision-making model is developed that includes expert weighting with the entropy game, evaluation balancing with the Q-learning algorithm, calculation of criterion weights with the least squares optimization (LSO) and alternative ranking with the molecular ranking (MORAN) method. Furthermore, molecular fuzzy sets are also integrated into the model to manage uncertainties. This study contributes to the literature by presenting an integrated multi-criteria decision-making model to define essential indicators to improve the performance of solid-state battery investments. The proposed model offers several advantages over existing methods. Unlike traditional multi-criteria decision-making methods such as AHP or MOORA, this study integrates entropy-based game theory, reinforcement learning, least squares optimization, and molecular fuzzy set theory to provide a more dynamic, precise, and uncertainty-aware evaluation framework tailored for solid-state battery investments. The proposed MORAN method introduces a novel molecular geometry-inspired ranking logic, which enables a more flexible modeling of performance indicators compared to linear or ratio-based methods. In addition, while methods such as AHP have been widely applied for battery technology selection, they often rely on fixed pairwise comparisons. They do not accommodate learning-based optimization or flexible uncertainty modeling. This proposed model integrates different techniques to address these gaps. The findings show that charging efficiency and thermal stability are the most important performance criteria. Financial incentives with technological advancements and direct funding of community-scale battery storage projects are the most critical alternative investment policies.