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This manuscript presents an algorithm for individual Lithium-ion (Li-ion) battery cell state of charge (SOC) estimation in a large-scale battery pack under minimal sensing, where only pack-level voltage and current are measured. For battery…
Recently, there has been an increasing interest in the roll-out of electric vehicles (EVs) in the global automotive market. Compared to conventional internal combustion engine vehicles (ICEVs), EVs can not only help users reduce monetary…
Accurate fault detection in lithium-ion batteries is essential for the safe and reliable operation of electric vehicles and energy storage systems. However, existing methods often struggle to capture complex temporal dependencies and cannot…
Electric vehicles and renewable energy systems need batteries that charge quickly, last many years and still store a lot of energy, but current chemistries struggle to deliver all three. Inspired by electric fish that deliver bursts of…
While the use of energy storage combined with grid-scale photovoltaic power plants continues to grow, given current lithium-ion battery prices, there remains uncertainty about the profitability of these solar-plus-storage projects. At the…
Renewable energy is critical for combating climate change, whose first step is the storage of electricity generated from renewable energy sources. Li-ion batteries are a popular kind of storage units. Their continuous usage through…
X-ray absorption spectroscopy (XAS) is a leading technique for understanding structural changes in advanced battery materials such as lithium-excess cathodes. However, extracting critical information like oxidation states from the…
High-throughput computing (HTC) is a pivotal asset in many scientific fields, such as biology, material science and machine learning. Applying HTC to the complex physics-based degradation models of lithium-ion batteries enables efficient…
Battery degradation significantly impacts the reliability and efficiency of energy storage systems, particularly in electric vehicles and industrial applications. Predicting the remaining useful life (RUL) of lithium-ion batteries is…
Lithium-ion batteries degrade due to usage and exposure to environmental conditions, which affects their capability to store energy and supply power. Accurately predicting the capacity and power fade of lithium-ion battery cells is…
System energy models are important for energy optimization and management in mobile systems. However, existing system energy models are built in lab with the help from a second computer. Not only are they labor-intensive; but also they will…
In this report, future performance demands of batteries for various vehicular applications are modeled. Vehicles ranging in size from electric bikes to heavy trucks are assessed using driving cycle data which allows key performance…
Accurate electrochemical models are essential for the safe and efficient operation of lithium-ion batteries in real-world applications such as electrified vehicles and grid storage. Reduced-order models (ROM) offer a balance between…
The ever-increasing number of spent lithium-ion batteries (LIBs) has presented a serious waste-management challenge. Aluminium and copper current collectors are important components in LIBs and take up a weight percentage of more than 15%.…
The prediction of battery rate performance traditionally relies on computation-intensive numerical simulations. While simplified analytical models have been developed to accelerate the calculation, they usually assume battery performance to…
Lithium-ion batteries (LIBs) have found wide applications in a variety of fields such as electrified transportation, stationary storage and portable electronics devices. A battery management system (BMS) is critical to ensure the…
The battery passport is proposed as a method to make the use and remaining value of batteries more transparent. The future EU Battery Directive requests this passport to contain the round-trip energy efficiency and its fade. In this paper,…
Rechargeable lithium metal batteries (LMBs) with an ultrahigh theoretical energy density have attracted more and more attentions for their crucial applications of portable electronic devices, electric vehicles, and smart grids. However, the…
Developing and testing algorithms for autonomous vehicles in real world is an expensive and time consuming process. Also, in order to utilize recent advances in machine intelligence and deep learning we need to collect a large amount of…
Mathematical modeling of lithium-ion batteries (LiBs) is a primary challenge in advanced battery management. This paper proposes two new frameworks to integrate physics-based models with machine learning to achieve high-precision modeling…