Toward Enhanced State of Charge Estimation of Lithium-ion Batteries Using Optimized Machine Learning Techniques.

State of charge (SOC) is a crucial index used in the assessment of electric vehicle (EV) battery storage systems. Thus, SOC estimation of lithium-ion batteries has been widely investigated because of their fast charging, long-life cycle, and high energy density characteristics. However, precise SOC assessment of lithium-ion batteries remains challenging because of their varying characteristics under different working environments. Machine learning techniques have been widely used to design an advanced SOC estimation method without the information of battery chemical reactions, battery models, internal properties, and additional filters. Here, the capacity of optimized machine learning techniques are presented toward enhanced SOC estimation in terms of learning capability, accuracy, generalization performance, and convergence speed. We validate the proposed method through lithium-ion battery experiments, EV drive cycles, temperature, noise, and aging effects. We show that the proposed method outperforms several state-of-the-art approaches in terms of accuracy, adaptability, and robustness under diverse operating conditions

Energy yield potentials from the anaerobic digestion of common animal manure in Bangladesh

This study provides previously unavailable field data relating to the biogas and methane yields from supervised authentic anaerobic digesters using the most common animal manure in Bangladesh: cow dung, poultry litter and town cattle market straw which are found to produce biogas yields of 0.034, 0.030 and 0.142 m3/kg respectively, with methane concentrations of 60% and 62% and 74% respectively and total solids of 19, 23 and 45 respectively. It also reports indications that in unsupervised plant issues with underfeeding, improper water mixing and irregular feeding are very common – all of which can significantly reduce yields. The figures above should thus be treated as maximum, optimum field values. These results provide reliable data for use in scaling up for national energy and investment planning, as they related directly to common scenarios of family smallholdings, common sized poultry farms and town cattle markets in Bangladesh where there is a reliance on combustion of local wood and dung biomass for cooking, creating air pollution, health and environmental degradation issues

Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model.

Accurate state of charge (SOC) estimation of lithium-ion (Li-ion) batteries is crucial in prolonging cell lifespan and ensuring its safe operation for electric vehicle applications. In this article, we propose the deep learning-based transformer model trained with self-supervised learning (SSL) for end-to-end SOC estimation without the requirements of feature engineering or adaptive filtering. We demonstrate that with the SSL framework, the proposed deep learning transformer model achieves the lowest root-mean-square-error (RMSE) of 0.90% and a mean-absolute-error (MAE) of 0.44% at constant ambient temperature, and RMSE of 1.19% and a MAE of 0.7% at varying ambient temperature. With SSL, the proposed model can be trained with as few as 5 epochs using only 20% of the total training data and still achieves less than 1.9% RMSE on the test data. Finally, we also demonstrate that the learning weights during the SSL training can be transferred to a new Li-ion cell with different chemistry and still achieve on-par performance compared to the models trained from scratch on the new cell