Related papers: Deep learning for state estimation of commercial s…
Aiming at the state of health (SOH) prediction of lithium-ion batteries (LiBs) for electric vehicles (EVs), this paper proposes a fusion model of a dual-module bidirectional gated recurrent unit (BiGRU) and sparrow search algorithm (SSA)…
The entropic coefficient of a lithium-ion battery cell is used to calculate the reversible heat of a battery during operation, which is a non-negligible part for the battery thermal modelling. The contribution of this article is to propose…
We introduce a local machine-learning method for predicting the electron densities of periodic systems. The framework is based on a numerical, atom-centred auxiliary basis, which enables an accurate expansion of the all-electron density in…
Efficiency of Battery Energy Storage Systems (BESSs) is increasingly critical as renewable energy generation becomes more prevalent on the grid. Therefore, it is necessary to study the energy efficiency of lithium-ion batteries, which are…
Energy loss of energetic ions in solid is crucial in many field, and accurate prediction of the ion stopping power is a long-time goal. Though great efforts have been made, it is still very difficult to find a universal prediction model to…
Accurate prediction of battery state of charge is needed for autonomous robots to plan movements without using up all available power. This work develops a physics and data-informed model from a simulation that predicts SOC depletion as a…
Sodium-ion batteries (SIBs) share similar electrochemistry with Li but offer several advantages, including high abundance in nature and low cost, as well as suitability for fast charging due to a Na-ion mobility higher than that of Li. The…
Recent proliferation in electric vehicles (EVs) are posing profound impacts over the operation of electrical grids. In particular, due to the physical constraints on charging stations' capacity and uncertainty in charging demand, it becomes…
Core-shell electrode particles are a promising morphology control strategy for high-performance lithium-ion batteries. However, experimental observations reveal that these structures remain prone to mechanical failure, with shell fractures…
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…
Capacity and coulombic efficiency are often used to assess the performance of Li-ion batteries, under the assumption that these quantities can provide direct insights about the rate of electron consumption due to growth of the solid…
Essential to various practical applications of lithium-ion batteries is the availability of accurate equivalent circuit models. This paper presents a new coupled electro-thermal model for batteries and studies how to extract it from data.…
A data-driven solution is provided for the fast-charging problem of lithium-ion batteries with multiple safety and aging constraints. The proposed method optimizes the charging current based on the observed history of measurable battery…
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…
Charge unbalance is one of the key issues for series-connected Lithium-ion cells. Within this context, model-based optimization strategies have proven to be the most effective. In the present paper, an ad-hoc electrochemical model, tailored…
Accurate knowledge of temperatures in power semiconductor modules is crucial for proper thermal management of such devices. Precise prediction of temperatures allows to operate the system at the physical limit of the device avoiding…
We propose a forecasting technique based on multi-feature data fusion to enhance the accuracy of an electric vehicle (EV) charging station load forecasting deep-learning model. The proposed method uses multi-feature inputs based on…
Porous electrode theory (PET) provides essential insights into electrochemical states, but its computational complexity hinders real-time control and obscures scaling relations. To bridge the gap between high-fidelity simulations and…
Machine learning (ML) techniques have rapidly found applications in many domains of materials chemistry and physics where large data sets are available. Aiming to accelerate the discovery of materials for battery applications, in this work,…
The solid-state electrolyte is critical for achieving next-generation high energy density and high-safety batteries. Solid polymer electrolytes (SPEs) possess great potential for commercial application owing to their compatibility with the…