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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Advances in Battery Manufacturing and Operating Status Analysis: Filtering and Artificial Intelligence Strategy

Advances in Battery Manufacturing and Operating Status Analysis: Filtering and Artificial Intelligence Strategy

Wang, Ziyun
Wang, Yan
Ji, Zhicheng

Hardcover

Technology & EngineeringGeneral Science

PREORDER - Expected ship date October 5, 2026

ISBN10: 1394437161
ISBN13: 9781394437160
Publisher: Wiley-IEEE Press
Published: Oct 5 2026
Pages: 240
Language: English

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 environments
  • Hybrid filter and long short-term memory network models designed to predict remaining useful life with improved accuracy
  • Genetic algorithm and Q-learning strategies applied to intelligent battery grouping and manufacturing capacity forecasting
  • Technical case studies covering problem definitions, data preprocessing, model selection, implementation, and real-world result interpretation
  • Data-driven strategies for optimizing battery lifecycle stages from manufacturing through operation and sustainable energy storage

Researchers 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.

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