An accurate state of health (SOH) estimation for lithium-ion batteries (LIBs) is crucial for reliable operations and extending service life. While electrochemical impedance spectroscopy (EIS) effectively characterizes LIBs degradation patterns, the high dimensionality of EIS data poses challenges for an efficient analysis. This study proposes a novel method that combines EIS with an equivalent circuit model (ECM) and distribution of relaxation time (DRT) analysis to extract low-dimensional health features from high-dimensional EIS data. A multi-scale kernel extreme learning machine (MS-KELM), optimized by the Sparrow Search Algorithm (SSA), estimates battery SOH with an average mean absolute error (MAE) of 1.37% and a root mean square error (RMSE) of 1.76%. In addition, compared with support vector regression (SVR) and Gaussian process regression (GPR), the proposed method reduces computational time by factors of 4 to 30 and lowers memory usage by approximately 18%.
Electric vehicle manufacturing firms face increasing supply chain vulnerability due to component shortages, technological interdependence, raw material volatility, and demand uncertainty. This study aims to examine whether digital transformation can be translated into a resilienc…
While the adoption of electric vehicles can reduce CO2 emissions, the extent of this reduction depends on the growth of the EV market. Sigmoid growth models, such as logistic or Gompertz function models, can be used to predict expected EV sales trends; however, their quality has…
To meet climate goals, the automotive industry is transitioning to electromobility, reshaping vehicle model variants, market composition and therefore influencing purchasing decisions. To cover the full range of possible vehicle models for the German passenger vehicle market, a m…
This study presents a novel methodology for optimizing energy management strategies in heavy-duty Fuel Cell Hybrid Electric Vehicles (FCHEVs) with pantograph charging systems. The approach integrates machine learning (ML) techniques to predict energy demand, optimize the power di…
The rapid development of autonomous vehicles is based mainly on their ability to accurately perceive their environment, where artificial intelligence and computer vision act as the core of environmental perception. In this regard, deep learning-based perception architectures have…
As a crucial pillar industry in the country, the automotive industry continues to evolve with the increasing number of vehicles in operation, leading to a continual rise in the need for aftermarket parts and repair services. Fluctuations in automotive spare part requirements are…