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	<title>ICA-based ageing features &#8211; BIOENGINEER.ORG</title>
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		<title>Optimized CNN-BiLSTM-Attention for Battery SOH Estimation</title>
		<link>https://bioengineer.org/optimized-cnn-bilstm-attention-for-battery-soh-estimation/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Mon, 12 Jan 2026 19:27:27 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[Battery SOH estimation]]></category>
		<category><![CDATA[Energy storage systems** **Açıklama:** 1. **CNN-BiLSTM-Attention:** Makalenin önerdiği ve optimize ettiği temel model mimarisini doğrudan belirtir. 2. **Battery SOH estimation:** Araştırmanın]]></category>
		<category><![CDATA[ICA-based ageing features]]></category>
		<category><![CDATA[İşte 5 uygun etiket: **CNN-BiLSTM-Attention]]></category>
		<category><![CDATA[Machine Learning]]></category>
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					<description><![CDATA[In recent years, the rapid advancement of battery technology has become a pivotal focus in the realm of energy storage systems. Researchers have been tirelessly working on improving battery longevity, efficiency, and reliability. Among the challenges faced is the need for accurate State of Health (SOH) estimation, which is essential for maximizing battery performance and [&#8230;]]]></description>
		
		
		
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