Professional Certificate in Commercial Battery Aging Prediction
-- ViewingNowThe Professional Certificate in Commercial Battery Aging Prediction is a course designed to equip learners with the essential skills needed to predict battery aging in commercial applications. This course is crucial in the current industry landscape, where there is a high demand for professionals who can optimize battery performance and lifespan in electric vehicles, renewable energy systems, and grid storage solutions.
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GBP £ 140
GBP £ 202
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โข Introduction to Battery Aging Prediction: Overview of the importance of battery aging prediction, the role of commercial batteries, and the key factors affecting battery aging.
โข Fundamentals of Battery Technology: Basics of battery types, chemistries, and their respective aging characteristics.
โข Data Collection for Battery Aging Prediction: Techniques and best practices for data collection, including sensor selection and placement, data preprocessing, and handling missing data.
โข Feature Engineering for Battery Aging Models: Overview of different features and their relevance, including voltage, current, temperature, and state of charge (SoC), and selection of appropriate features.
โข Advanced Machine Learning Techniques for Battery Aging Prediction: Exploration of various machine learning algorithms, such as support vector machines, decision trees, and artificial neural networks, to predict battery aging.
โข Deep Learning for Battery Aging Prediction: Introduction to deep learning techniques, including recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, and their application in predicting battery aging.
โข Model Validation and Evaluation: Techniques for validating and evaluating battery aging prediction models, including cross-validation, statistical testing, and performance metrics.
โข Practical Applications and Case Studies: Real-world applications of battery aging prediction, including energy storage systems, electric vehicles, and grid-scale energy storage, and case studies demonstrating the impact and benefits of accurate aging prediction.
โข Future Trends and Challenges in Battery Aging Prediction: Exploration of emerging trends and challenges, including the development of new battery chemistries, the integration of renewable energy sources, and the need for standards and regulations.
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