Advances in Battery Manufacturing and Operating Status Analysis

Filtering and Artificial Intelligence Strategy

Inbunden, Engelska, 2026

1 691 kr

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Beskrivning

Advanced filtering and AI algorithms for battery system analysis Advances in Battery Manufacturing and Operating Status Analysis details zonotopic and particle filtering methods for robust real-time estimation of critical battery parameters, alongside hybrid models combining filters with long short-term memory networks for remaining useful life prediction. Coverage of genetic algorithms and Q-learning addresses intelligent battery grouping and manufacturing capacity forecasting. Technical case studies walk through problem definitions, data preprocessing, model selection, implementation, and interpretation of results. Key topics also include: Zonotopic and particle filtering approaches for achieving robust, real-time estimation of critical battery state parameters in operational environmentsHybrid filter and long short-term memory network models designed to predict remaining useful life with improved accuracyGenetic algorithm and Q-learning strategies applied to intelligent battery grouping and manufacturing capacity forecastingTechnical case studies covering problem definitions, data preprocessing, model selection, implementation, and real-world result interpretationData-driven strategies for optimizing battery lifecycle stages from manufacturing through operation and sustainable energy storageResearchers and industry professionals in energy storage, power electronics, and electrical engineering R&D will find targeted algorithmic strategies for battery system management. Graduate students studying energy storage and related disciplines gain exposure to filtering and AI methods applied directly to manufacturing and operational analysis challenges.

Produktinformation

Utgivningsdatum:2026-12-16
Format:Inbunden
Språk:Engelska
Antal sidor:240
Upplaga:27001
Förlag:John Wiley & Sons Inc
ISBN:9781394437160

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Mer om författaren

Ziyun Wang is a Professor and Doctoral Supervisor at Jiangnan University's School of Automation and Intelligent Science and Deputy Director of the Engineering Center for the Application of Internet of Things Technology. His research is focused on advanced manufacturing, battery operation analysis, and filter design. Yan Wang is a Professor and Doctoral Supervisor at Jiangnan University's School of Automation and Intelligent Science and Yangtze River Distinguished Professor of the Ministry of Education. Her research spans artificial intelligence, advanced control and system optimization, and industrial internet technology. Zhicheng Ji is a Professor and Doctoral Supervisor at Jiangnan University's School of Automation and Intelligent Science, and Director of Jiangsu Engineering Research Center for Intelligent Optimization Manufacturing of Industrial Internet, and former Vice Chancellor of Jiangnan University. His research is focused on energy system design, state estimation, and fault diagnosis.

Innehållsförteckning

Introduction 11.1 Research background and significance 11.2 Battery manufacturing 21.3 Battery operating status analysis 41.4 Principle of filter design methods 61.5 Artificial intelligence strategy 81.6 Outlines 92 Advances in battery grouping by artificial intelligence strategy 112.1 Introduction to battery grouping 112.2 Problem formulation and model establishment 122.3 Chromosome encoding 132.4 Crossover modes designing 132.5 Population mutations 142.6 Convergence analysis 162.7 Case study on battery grouping 182.8 Concluding remarks 203 Advances in forecasting of battery manufacturing capacity by artificial intelligence strategy 213.1 Introduction to battery manufacturing capacity 213.2 Problem formulation 243.3 Q-learning principle 243.4 Variational mode decomposition 273.5 Long short-term memory 293.6 Design of the forecasting method 303.7 Case study on forecasting of battery manufacturing capacity 343.8 Concluding remarks 394 Advances in battery operating status analysis by zonotopic filtering 414.1 Introduction to battery operating status analysis 414.2 Problem formulation 434.3 Zonotopic filter 474.4 Zonotope and Gaussian Kalman filters 534.5 Orthotope-search-expansion-based extended zonotopic Kalman filter 614.6 Constrained zonotopic Kalman filtering 734.7 Case study on battery operating status analysis 794.8 Concluding remarks 925 Advances in battery operating status analysis by filtering and artificial intelligence 935.1 Problem formulation 935.2 Introduction to particle filter and particle swarm optimization 955.3 Particle swarm optimization based orthometric hyperparallel space filtering 985.4 Improved particle filter algorithm based on the parallelotope 1075.5 Projected particle-confinement-based zonotopic space filtering 1195.6 Zonotopic feasible set optimized filter based on differential evolution 1305.7 Case study on battery operating status analysis 1365.8 Concluding remarks 1516 Advances in battery remaining useful life analysis by filtering and artificial intelligence 1536.1 Introduction to battery remaining useful life analysis 1536.2 Dynamic complexity reduction zonotopic Kalman filter 1566.3 Double exponential empirical particle filter 1636.4 Anti-aliasing filter and LSTM 1696.5 Case study on battery remaining useful life analysis 1736.6 Concluding remarks 1867 Summary and future outlook 1897.1 Summary 1897.2 Future outlook 191References 195