首页|New Machine Learning Study Results Reported from University of Stuttgart (A Nove l Long Short-Term Memory Approach for Online State-of-Health Identification in L ithium-Ion Battery Cells)
New Machine Learning Study Results Reported from University of Stuttgart (A Nove l Long Short-Term Memory Approach for Online State-of-Health Identification in L ithium-Ion Battery Cells)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in artificial intelligence. According to news originating from Stuttgart, Germany, by NewsRx correspondents, research stated, "Revolutionary and cost-effective sta te estimation techniques are crucial for advancing lithium-ion battery technolog y, especially in mobile applications." Financial supporters for this research include Bundesministerium Fur Wirtschaft Und Energie. The news reporters obtained a quote from the research from University of Stuttga rt: "Accurate prediction of battery state-of-health (SoH) enhances state-of-char ge estimation while providing valuable insights into performance, second-life ut ility, and safety. While recent machine learning developments show promise in So H estimation, this paper addresses two challenges. First, many existing approach es depend on predefined charge/discharge cycles with constant current/constant v oltage profiles, which limits their suitability for real-world scenarios. Second, pure time series forecasting methods require prior knowledge of the battery's lifespan in order to formulate predictions within the time series. Our novel hyb rid approach overcomes these limitations by classifying the current aging state of the cell rather than tracking the SoH. This is accomplished by analyzing curr ent pulses filtered from authentic drive cycles."
University of StuttgartStuttgartGerm anyEuropeCyborgsEmerging TechnologiesMachine Learning
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