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	<title>remaining useful life &#8211; BIOENGINEER.ORG</title>
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		<title>AI Model Predicts Battery Lifespan Across Different Factory Formation Protocols</title>
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		<pubDate>Fri, 09 Oct 2026 05:32:00 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[battery degradation]]></category>
		<category><![CDATA[battery manufacturing]]></category>
		<category><![CDATA[CATL]]></category>
		<category><![CDATA[formation protocols]]></category>
		<category><![CDATA[gated recurrent unit]]></category>
		<category><![CDATA[lithium-ion batteries]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[prognostics]]></category>
		<category><![CDATA[remaining useful life]]></category>
		<category><![CDATA[state of health]]></category>
		<category><![CDATA[University of Michigan Battery Lab]]></category>
		<category><![CDATA[unscented particle filter]]></category>
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					<description><![CDATA[Researchers have developed a hybrid particle-filter and neural network framework that predicts lithium-ion battery remaining useful life with 99.41 percent accuracy even across different manufacturing formation protocols.]]></description>
		
		
		
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