相关论文: Improving Performance Prediction of Electrolyte Fo…

Surface adsorption is one of the fundamental processes in numerous fields, including catalysis, environment, energy and medicine. The development of an adsorption model which provides an effective prediction of binding energy in minutes has…

Generating a novel and optimized molecule with desired chemical properties is an essential part of the drug discovery process. Failure to meet one of the required properties can frequently lead to failure in a clinical test which is costly.…

We present a local and transferable machine learning approach capable of predicting the real-space density response of both molecules and periodic systems to external homogeneous electric fields. The new method, SALTER, builds on the…

Lithium-ion batteries are used to store energy in electric vehicles. Physical models based on electro-chemistry accurately predict the cell dynamics, in particular the state of charge. However, these models are nonlinear partial…

Accurate forecasting of solar power output is essential for efficient integration of renewable energy into the grid. In this study, an attention-based deep learning model, inspired by transformer architecture, is used for short-term solar…

In recent years, battery technology for electric vehicles (EVs) has been a major focus, with a significant emphasis on developing new battery materials and chemistries. However, accurately predicting key battery parameters, such as…

Humans are remarkably good at understanding and reasoning about complex visual scenes. The capability to decompose low-level observations into discrete objects allows us to build a grounded abstract representation and identify the…

This paper proposes an improved deep learning based maximum power point tracking (MPPT) in solar photovoltaic cells considering various time series based environmental inputs. Generally, artificial neural network based MPPT algorithms use…

Measuring the performance of solar energy and heat transfer systems requires a lot of time, economic cost and manpower. Meanwhile, directly predicting their performance is challenging due to the complicated internal structures. Fortunately,…

Optimal cooling that minimises thermal gradients and the average temperature is essential for enhanced battery safety and health. This work presents a new modelling approach for battery cells of different shapes by integrating Chebyshev…

Hybrid power trains (HPT) run on multiple energy sources, often involving energy storage systems/batteries (ESS). As a result, the risk of battery degradation and the reliability of energy storage elements pose a major challenge in…

Transformers are widely used in natural language processing due to their ability to model longer-term dependencies in text. Although these models achieve state-of-the-art performance for many language related tasks, their applicability…

In the manufacturing process, sensor data collected from equipment is crucial for building predictive models to manage processes and improve productivity. However, in the field, it is challenging to gather sufficient data to build robust…

Battery diagnosis, prognosis and health management models play a critical role in the integration of battery systems in energy and mobility fields. However, large-scale deployment of these models is hindered by a myriad of challenges…

A methodology that seeks to enhance model prediction performance is presented. The method involves generating multiple auxiliary models that capture relationships between attributes as a function of each other. Such information serves to…

Modelling electrolytes accurately on both a nanoscale and cell level can contribute to improving battery chemistries.[Armand and Tarascon, Nature, 2008, 451, 652-657] We previously presented a thermodynamic continuum model for…

Model identification of battery dynamics is a central problem in energy research; many energy management systems and design processes rely on accurate battery models for efficiency optimization. The standard methodology for battery…

Data-driven techniques are increasingly used to replace electronic-structure calculations of matter. In this context, a relevant question is whether machine learning (ML) should be applied directly to predict the desired properties or be…

Accurate load forecasting is crucial for maintaining the power balance between generators and consumers,particularly with the increasing integration of renewable energy sources, which introduce significant intermittent volatility. With the…

The increasing need for energy storage solutions to balance variable renewable energy sources has highlighted the potential of Pumped Thermal Electricity Storage (PTES). In this paper, we investigate the trade-offs between model accuracy…