Challenges and outlook for lithium-ion battery fault diagnosis methods from the laboratory to real world applications
Lithium-ion batteries are the ideal energy storage device for numerous portable and energy
storage applications. Efficient fault diagnosis methods become urgent to address safety …
storage applications. Efficient fault diagnosis methods become urgent to address safety …
[HTML][HTML] Review of parameterisation and a novel database (LiionDB) for continuum Li-ion battery models
Abstract The Doyle–Fuller–Newman (DFN) framework is the most popular physics-based
continuum-level description of the chemical and dynamical internal processes within …
continuum-level description of the chemical and dynamical internal processes within …
[HTML][HTML] Collaborative and privacy-preserving retired battery sorting for profitable direct recycling via federated machine learning
Unsorted retired batteries with varied cathode materials hinder the adoption of direct
recycling due to their cathode-specific nature. The surge in retired batteries necessitates …
recycling due to their cathode-specific nature. The surge in retired batteries necessitates …
[HTML][HTML] Realistic fault detection of li-ion battery via dynamical deep learning
Accurate evaluation of Li-ion battery (LiB) safety conditions can reduce unexpected cell
failures, facilitate battery deployment, and promote low-carbon economies. Despite the …
failures, facilitate battery deployment, and promote low-carbon economies. Despite the …
High-efficient prediction of state of health for lithium-ion battery based on AC impedance feature tuned with Gaussian process regression
J Wang, R Zhao, QA Huang, J Wang, Y Fu, W Li… - Journal of Power …, 2023 - Elsevier
The safety of lithium-ion battery (LIB)-powered electric vehicles and stationary energy
storage devices relies on a high-efficient state of health (SOH) prediction of the LIB system …
storage devices relies on a high-efficient state of health (SOH) prediction of the LIB system …
Feature engineering for machine learning enabled early prediction of battery lifetime
Accurate battery lifetime estimates enable accelerated design of novel battery materials and
determination of optimal use protocols for longevity in deployments. Unfortunately …
determination of optimal use protocols for longevity in deployments. Unfortunately …
[HTML][HTML] Battery aging mode identification across NMC compositions and designs using machine learning
A comprehensive understanding of lithium-ion battery (LiB) lifespan is the key to designing
durable batteries and optimizing use protocols. Although battery lifetime prediction methods …
durable batteries and optimizing use protocols. Although battery lifetime prediction methods …
[HTML][HTML] Li-ion battery degradation modes diagnosis via Convolutional Neural Networks
Lithium-ion batteries are ubiquitous in modern society with a presence in storage systems,
electric cars, portable electronics, and many more applications. Consequently, to enable …
electric cars, portable electronics, and many more applications. Consequently, to enable …
Enabling battery digital twins at the industrial scale
Digital twins are cyber-physical systems that fuse real-time sensor data with models to make
accurate, asset-specific predictions and optimal decisions. For batteries, this concept has …
accurate, asset-specific predictions and optimal decisions. For batteries, this concept has …
Transferable data-driven capacity estimation for lithium-ion batteries with deep learning: A case study from laboratory to field applications
Capacity estimation plays a vital role in ensuring the health and safety management of
lithium-ion battery-based electric-drive systems. This research focuses on developing a …
lithium-ion battery-based electric-drive systems. This research focuses on developing a …