{'article_info': {'article_id': 210, 'topic': 'Lithium-Ion Battery State of Health Prediction Using a Hybrid BiLSTM–Random Forest Framework', 'topic_context': 'Published in Batteries, a Q1 journal indexed in both Scopus and the Web of Science Core Collection (Science Citation Index Expanded—SCIE)', 'author': 'Fahad M. Albasrawie', 'article_category': 'ARTICLE', 'article_date': '2026-6-29', 'img': 'HURC_Post_02-01.webp', 'img_orientation': 'portrait'}, 'article_content': '<p>Congratulations to 𝐄𝐧𝐠. 𝐍𝐮𝐫 𝐌𝐨𝐡𝐚𝐦𝐞𝐝 𝐌𝐨𝐡𝐚𝐦𝐮𝐝 on the publication of his latest research article, &ldquo;𝐋𝐢𝐭𝐡𝐢𝐮𝐦-𝐈𝐨𝐧 𝐁𝐚𝐭𝐭𝐞𝐫𝐲 𝐒𝐭𝐚𝐭𝐞 𝐨𝐟 𝐇𝐞𝐚𝐥𝐭𝐡 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧 𝐔𝐬𝐢𝐧𝐠 𝐚 𝐇𝐲𝐛𝐫𝐢𝐝 𝐁𝐢𝐋𝐒𝐓𝐌&ndash;𝐑𝐚𝐧𝐝𝐨𝐦 𝐅𝐨𝐫𝐞𝐬𝐭 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤&rdquo; in Batteries, a Q1 journal indexed in both Scopus and the Web of Science Core Collection (Science Citation Index Expanded&mdash;SCIE).</p>\n<p>This study introduces a hybrid framework that combines deep learning and ensemble machine learning techniques to accurately predict the state of health of lithium-ion batteries. The proposed model demonstrated high prediction accuracy and robustness when evaluated using both the NASA and Oxford battery datasets, underscoring its potential for advanced battery health monitoring and intelligent battery management systems in electric vehicles and energy storage applications.</p>\n<p>The findings contribute to the growing body of knowledge on sustainable energy technologies and support the development of more reliable and efficient energy storage solutions.</p>\n<p><br>Hormuud University continues to support impactful, interdisciplinary research that advances knowledge and contributes to more equitable and sustainable livelihoods.</p>\n<p>𝐏𝐥𝐞𝐚𝐬𝐞 𝐫𝐞𝐚𝐝 𝐭𝐡𝐞 𝐚𝐫𝐭𝐢𝐜𝐥𝐞 𝐚𝐭 𝐭𝐡𝐞 𝐥𝐢𝐧𝐤 𝐛𝐞𝐥𝐨𝐰: &nbsp;https://www.mdpi.com/2313-0105/12/6/210</p>\n<p>#Lithium-IonBattery #HealthPrediction #HybridBiLSTM&ndash;RandomForestFramework #HU #Research #HURC #HormuudUni #HormuudUniversity #HUCommunity #HU2026</p>'}