Insight Understanding of External Pressure on Lithium Plating in Commercial Lithium‐Ion Batteries

Advanced Functional Materials, Journal Year: 2024, Volume and Issue: unknown

Published: July 1, 2024

Abstract Lithium‐ion batteries (LIBs), as efficient electrochemical energy storage devices, have been successfully commercialized. Lithium plating at anodes has attracting increasing attention advance toward high density and large size, given its pivotal role in affecting the lifespan, safety, fast‐charging performance of LIBs. mostly happens during fast charging or low temperatures. However, external pressure is often overlooked an essential factor that influences lithium This review analyzes discusses influence on for commercial LIBs, with a particular focus plating. Recent advances this topic, including experimental results mechanism analyses, are reviewed. explored by examining internal morphology behavior batteries. It emphasized affects through ion transport, electron their heterogeneities, thereby risk Subsequently, rationale mitigating elucidated from perspective optimization inside Overall, provides valuable insights into practically guiding rational design development.

Language: Английский

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Interpretable Learning of Accelerated Aging in Lithium Metal Batteries

Journal of the American Chemical Society, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 25, 2024

Lithium metal batteries (LMBs) with high energy density are perceived as the most promising candidates to enable long-endurance electrified transportation. However, rapid capacity decay and safety hazards have impeded practical application of LMBs, where entangled complex degradation pattern remains a major challenge for efficient battery design engineering. Here, we present an interpretable framework learn accelerated aging LMBs comprehensive data space containing 79 cells varying considerably in chemistries cell parameters. Leveraging only from first 10 cycles, this accurately predicts knee points starts accelerate. Leaning on framework's interpretability, further elucidate critical role last 10%-depth discharging LMB rate propose universal descriptor based solely early cycle electrochemical evaluation electrolytes. The machine learning insights also motivate dual-cutoff discharge protocol, which effectively extends life by factor up 2.8.

Language: Английский

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A battery capacity trajectory prediction framework with mileage correction for electric buses

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 110, P. 115301 - 115301

Published: Jan. 13, 2025

Language: Английский

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Application of physics-informed neural networks in fault diagnosis and fault-tolerant control design for electric vehicles: A review

Measurement, Journal Year: 2025, Volume and Issue: unknown, P. 116728 - 116728

Published: Jan. 1, 2025

Language: Английский

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Integration of lithium-ion battery recycling into manufacturing through digitalization: A perspective

Journal of Power Sources, Journal Year: 2025, Volume and Issue: 631, P. 236158 - 236158

Published: Jan. 25, 2025

Language: Английский

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Battery fault diagnosis methods for electric vehicle Lithium-ion batteries: correlating codes and battery management system

Process Safety and Environmental Protection, Journal Year: 2025, Volume and Issue: unknown, P. 106919 - 106919

Published: Feb. 1, 2025

Language: Английский

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A hybrid methodology for assessing hydropower plants under flexible operations: Leveraging experimental data and machine learning techniques

Applied Energy, Journal Year: 2025, Volume and Issue: 383, P. 125402 - 125402

Published: Jan. 28, 2025

Language: Английский

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Battery health prognosis in data-deficient practical scenarios via reconstructed voltage-based machine learning

Cell Reports Physical Science, Journal Year: 2025, Volume and Issue: unknown, P. 102442 - 102442

Published: Feb. 1, 2025

Language: Английский

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Artificial Intelligence-Driven Electric Vehicle Battery Lifetime Diagnostics

IntechOpen eBooks, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 24, 2025

Ensuring the reliability, safety, and efficiency of electric vehicles (EVs) necessitates precise diagnostics battery life, as degradation batteries directly influences both performance sustainability. The transformative role artificial intelligence (AI) in advancing EV is explored herein, with an emphasis placed on complexities predicting managing health. Initially, we provide overview challenges associated lifetime diagnostics, such issues accuracy, generalization, model training. following sections delve into advanced AI methodologies that enhance diagnostic capabilities. These methods include extensive time-series AI, which improves predictive accuracy; end-to-end simplifies system complexity; multi-model ensures generalization across varied operating conditions; adaptable strategies for dynamic environments. In addition, explore use federated learning decentralized, privacy-preserving discuss automated machine streamlining development AI-based models. By integrating these sophisticated techniques, present a comprehensive roadmap future AI-driven prognostics health management. This underscores critical importance scalability, sustainability fostering advancement. Our interdisciplinary framework offers valuable insights can accelerate electrification transportation advance evolution energy storage systems, tackling key at intersection technology AI.

Language: Английский

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Coupling a capacity fade model with machine learning for early prediction of the battery capacity trajectory

Applied Energy, Journal Year: 2025, Volume and Issue: 389, P. 125703 - 125703

Published: March 28, 2025

Language: Английский

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