Mohammed Aman

Particle Swarm Optimization-Based Feature Weighting and Early-Cycle Remaining Useful Life Prediction of Lithium-Ion Batteries

Predicting the remaining useful life (RUL) of lithium-ion batteries is essential as it relates to the safety, reliability, and maintenance of both electric vehicles and energy storage systems. While machine learning algorithms have greatly enhanced the accuracy of prediction, a lot of the models available today still have some major flaws including redundant features, poor performance due to non-optimized model settings, and the necessity of the complete degradation process of the system. In this study, we develop a machine learning framework using particle swarm optimization (PSO) to perform hyperparameter tuning and feature optimization to improve lithium-ion battery RUL prediction. The framework allows for the automatic determination of the key degradation indicators, resulting in the optimization of the learning model and enhancement of both the accuracy and interpretability of the predictions. Furthermore, the framework is designed to be employed in an early-cycle learning environment wherein only the first 30% of battery discharge cycles are utilized in the model training. This method is intended to mimic the constraints of real-world applications in which complete lifecycle data is unavailable. Under a properly nested evaluation protocol, the results indicate that the model achieves a mean R2 of 0.354 ± 0.157 and RMSE of 19.13 ± 1.39 using only 30% of degradation cycles, compared to a mean R2 of 0.922 ± 0.014 and RMSE of 18.10 ± 1.11 under full-cycle training across five independent evaluations. While early-cycle prediction shows reduced and more variable explanatory power due to the smaller sample size, the model maintains reasonable error magnitude, supporting its potential utility for early diagnosis of lithium-ion batteries. The effectiveness of the framework is confirmed by feature importance analysis, evaluation of optimization convergence and multi-metric error assessment. In conclusion, the framework offers a highly interpretable and effective solution for predicting lithium-ion battery RUL, in alignment with the emerging data-driven approaches to battery management systems.

Causal trust aware federated multi agent reinforcement learning for 6G edge networks

Solar photovoltaic fault classifications under normal and shading conditions using machine learning models

This study suggested PV monitoring systems with lower maintenance costs and energy losses, and an accurate and efficient classifier for PV defects using normal and shading condition based on advanced ML techniques like Random Forest, K-Nearest Neighbors, Decision Tree, and SVM are proposed.

Machine learning approaches for electrocatalyst design in water splitting: a review for green hydrogen production

The production of green hydrogen through water splitting requires highly efficient electrocatalysts, but the current trial-and-error-based synthesis or discovery is time-consuming, costly and resource-intensive. Machine learning (ML) provides a powerful, data-driven alternative that can model complex structure-activity relationships across large chemical spaces at orders-of-magnitude speed. This review systematically overviews the life cycle of the electrocatalyst research and development application of ML. First, the thermodynamic and kinetic principles of the hydrogen and oxygen evolution reactions are summarised, along with some well-adopted and accepted activity descriptors. Then we explore data sources, featurization approaches, and algorithms, and discuss the model space, from a simple interpretable model to a graph neural network to a generative model, in the context of the ML toolkit. Strategic applications are discussed for high-throughput virtual screening of alloys and single-atom catalysts, as well as multifunctional activity prediction for overall water splitting, and stability optimisation under operating conditions. The topic of emerging frontiers is highlighted, including high-entropy alloys, amorphous materials, and linking atomic-scale understanding to device-level performance through integration with density functional theory. Finally, the problems of data scarcity, model interpretability and the discrepancy between computational predictions and industrial implementation are discussed, along with future directions for closed-loop discovery and self-driving laboratories. Incorporating ML into electrocatalyst design and combining it with autonomous experimentation will revolutionise this process, from simulation to energy solution, dramatically speeding it up.