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This paper presents the development of machine learning-enabled data-driven models for effective capacity predictions for lithium-ion batteries under different cyclic conditions. To achieve this, a model structure is first proposed with the…
Incremental capacity analysis (ICA) and differential voltage analysis (DVA) are two effective approaches for battery degradation monitoring. One limiting factor for their real-world application is that they require constant-current (CC)…
Retired batteries (RBs) for second-life applications offer promising economic and environmental benefits. However, accurate and efficient sorting of RBs with discrepant characteristics persists as a pressing challenge. In this study, we…
Solid-state ionic conduction is a key enabler of electrochemical energy storage and conversion. The mechanistic connections between material processing, defect chemistry, transport dynamics, and practical performance are of considerable…
Electrochemical batteries are ubiquitous devices in our society. When they are employed in mission-critical applications, the ability to precisely predict the end of discharge under highly variable environmental and operating conditions is…
Porous electrodes composed of multiphase active materials are widely used in Li-ion batteries, but their dynamics are poorly understood. Two-phase models are largely empirical, and no models exist for three or more phases. Using a modified…
Ultrahigh energy density batteries based on alpha-Li_xBN2 (1<x<3) positive electrode materials are predicted using density functional theory calculations. The utilization of the reversible LiBN2 + 2 Li+ + 2 e- -> Li3BN2 electrochemical cell…
The unified 3D phase-field model for the description of the lithium-ion cell as a whole is developed. The model takes into account the realistic distribution of particles in porous electrodes, percolative transport of ions, and the…
Physics-based electrochemical battery models derived from porous electrode theory are a very powerful tool for understanding lithium-ion batteries, as well as for improving their design and management. Different model fidelity, and thus…
Halide solid-state electrolytes have emerged as promising candidates for all-solid-state lithium batteries due to their high oxidative stability and deformability, yet their moderate ionic conductivity remains a bottleneck. While…
Electrolyte filling is a time-critical step during battery manufacturing that also affects the battery performance. The underlying physical phenomena during filling mainly occur on the pore scale and are hard to study experimentally. In…
Recent years have seen a rapidly escalating demand for battery technologies capable of storing more energy, charging more quickly and having longer usable lifetimes, driven largely by increased electrification of transport and by grid-scale…
Higher energy density and longer lifetime are the requirements for next-generation lithium-ion batteries. A promising anode material is silicon, which offers high specific capacity, but its significant volume change during lithiation and…
Lithium metal has been an attractive candidate as a next generation anode material. Despite its popularity, stability issues of lithium in the liquid electrolyte and the formation of lithium whiskers have kept it from practical use.…
In this paper, we propose a robust optimization model that addresses both the cost-efficiency and fast charging requirements for electric vehicles (EVs) at charging stations. By combining elements from traditional cost-minimization models…
Primary human cells offer the most faithful representation of native human physiology, yet their practical utility is constrained by the difficulty of introducing exogenous genetic material. Electroporation provides a promising non-viral…
Understanding ionic transport in halide solid electrolytes is essential for advancing next-generation solid-state batteries. This work demonstrates the effectiveness of fine-tuning the Crystal Hamiltonian Graph Network (CHGNet) universal…
The electron density of a molecule or material has recently received major attention as a target quantity of machine-learning models. A natural choice to construct a model that yields transferable and linear-scaling predictions is to…
Artificial intelligence (AI) has emerged as a tool for discovering and optimizing novel battery materials. However, the adoption of AI in battery cathode representation and discovery is still limited due to the complexity of optimizing…
Solid ionic conductors are essential components of batteries and fuel cells. In many cases, ionic conduction through crystalline materials with substitutional disorder can be modeled with atomic-scale lattice model percolation simulations.…