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An increasing number of software applications incorporate runtime Deep Neural Networks (DNNs) to process sensor data and return inference results to humans. Effective deployment of DNNs in these interactive scenarios requires meeting…

Predicting the motion of multiple traffic participants has always been one of the most challenging tasks in autonomous driving. The recently proposed occupancy flow field prediction method has shown to be a more effective and scalable…

We propose InsNet, an expressive insertion-based text generator with efficient training and flexible decoding (parallel or sequential). Unlike most existing insertion-based text generation works that require re-encoding of the context after…

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…

Accurate prediction of lithium-ion battery lifespan is vital for ensuring operational reliability and reducing maintenance costs in applications like electric vehicles and smart grids. This study presents a hybrid learning framework for…

Battery recycling is a critical process for minimizing environmental harm and resource waste for used batteries. However, it is challenging, largely because sorting batteries is costly and hardly automated to group batteries based on…

In the realm of battery charging, several complex aspects demand meticulous attention, including thermal management, capacity degradation, and the need for rapid charging while maintaining safety and battery lifespan. By employing the…

Lithium-ion batteries are becoming increasingly omnipresent in energy supply. However, the durability of energy storage using lithium-ion batteries is threatened by their dropping capacity with the growing number of charging/discharging…

Batteries are pivotal for transitioning to a climate-friendly future, leading to a surge in battery research. Scopus (Elsevier) lists 14,388 papers that mention "lithium-ion battery" in 2023 alone, making it infeasible for individuals to…

The degradation process of lithium-ion batteries is intricately linked to their entire lifecycle as power sources and energy storage devices, encompassing aspects such as performance delivery and cycling utilization. Consequently, the…

Early prediction of battery cycle life is essential for improving battery design, manufacturing, and deployment. However, despite encouraging results with machine learning, progress remains constrained by scarce data and data heterogeneity…

Accurate prediction of the Remaining Useful Life (RUL) is essential for enabling timely maintenance of lithium-ion batteries, impacting the operational efficiency of electric applications that rely on them. This paper proposes a RUL…

In this paper, we introduce an approach for the prediction of capacity for over 100,000 spinel compounds relevant for battery materials, from which we propose the 20 most promising candidate materials. In the design of batteries, selecting…

The Electric Vehicle (EV) Industry has seen extraordinary growth in the last few years. This is primarily due to an ever increasing awareness of the detrimental environmental effects of fossil fuel powered vehicles and availability of…

Battery life estimation is critical for optimizing battery performance and guaranteeing minimal degradation for better efficiency and reliability of battery-powered systems. The existing methods to predict the Remaining Useful Life(RUL) of…

Retentive Network (RetNet) represents a significant advancement in neural network architecture, offering an efficient alternative to the Transformer. While Transformers rely on self-attention to model dependencies, they suffer from high…

Ubiquitous use of lithium-ion batteries across multiple industries presents an opportunity to explore cost saving initiatives as the price to performance ratio continually decreases in a competitive environment. Manufacturers using…

Accurate prediction of lithium-ion battery capacity and its associated uncertainty is essential for reliable battery management but remains challenging due to the stochastic nature of aging. This paper presents a new method, termed the…

Accurate state-of-charge (SOC) estimation is critical for the safe and efficient operation of lithium-ion batteries in battery management systems (BMS). Although data-driven approaches can effectively capture nonlinear battery dynamics,…

Smartphones and smartphone apps have undergone an explosive growth in the past decade. However, smartphone battery technology hasn't been able to keep pace with the rapid growth of the capacity and the functionality of smartphones and apps.…