Related papers: A Novel SOC Estimation for Hybrid Energy Pack usin…
This article proposes an offline Energy Management System (EMS) for Parallel Hybrid Electric Vehicles (PHEVs). Dividing the torque between the Electric Motor (EM) and the Internal Combustion Engine (ICE) requires a suitable EMS. Batteries…
This work investigates the real-time estimation of the state-of-charge (SoC) of Lithium-ion (Li-ion) cells for reliable, safe and efficient utilization. A novel attractive ellipsoid based sliding-mode observer (AESMO) algorithm is designed…
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…
Traditional long-term microgrid planning models assume constant power charging for battery energy storage systems (BESS), overlooking efficiency losses that occur toward the end of charge due to rising internal resistance. While this issue…
This paper presents a machine learning approach to model the electric consumption of electric vehicles at macroscopic level, i.e., in the absence of a speed profile, while preserving microscopic level accuracy. For this work, we leveraged a…
In the evolving landscape of vertical heterogeneous networks, the practice of cell switching particularly for small base stations faces a significant challenge due to the lack of accurate data on the traffic load of sleeping SBSs. This…
The electric vehicle (EV) and electric vehicle charging station (EVCS) have been widely deployed with the development of large-scale transportation electrifications. However, since charging behaviors of EVs show large uncertainties, the…
Energy harvesting (EH) IoT devices that operate intermittently without batteries, coupled with advances in deep neural networks (DNNs), have opened up new opportunities for enabling sustainable smart applications. Nevertheless, implementing…
Electric Vehicle (EV) penetration and renewable energies enables synergies between energy supply, vehicle users, and the mobility sector. However, also new issues arise for car manufacturers: During charging and discharging of EV batteries…
As the electric vehicle (EV) market continues to prioritize dynamic performance and rapid charging, battery configuration has rapidly evolved. Despite this, current literature has often overlooked the complex, non-linear relationship…
Being able to predict battery internal states that are related to battery degradation is a key aspect to improve battery lifetime and performance, enhancing cleaner electric transportation and energy generation. However, most present…
The heterogeneity in pack voltages and capacity of aged packs limits the performance and economic viability of second-use battery energy storage systems (2-BESS) due to issues of reliability and available energy. Overcoming these…
The integrated use of non-terrestrial network (NTN) entities such as the high-altitude platform station (HAPS) and low-altitude platform station (LAPS) has become essential elements in the space-air-ground integrated networks (SAGINs).…
Accurate state of charge (SOC) estimation is critical for ensuring the safety, reliability, and efficiency of lithium-ion batteries in electric vehicles and energy storage systems. Electrochemical models provide high fidelity for SOC…
In order to accurately estimate the SOC and SOH of a lithium-ion battery used in an electric vehicle (EV), we propose an Adaptive Diagonal Forgetting Factor Recursive Least Square (ADFF-RLS) for accurate battery parameter estimation.…
The Extreme Operating Conditions Search (EOCS) problem is one of the key problems in relay setting calculation, which is used to ensure that the setting values of protection relays can adapt to the changing operating conditions of power…
In deep learning, the load data with non-temporal factors are difficult to process by sequence models. This problem results in insufficient precision of the prediction. Therefore, a short-term load forecasting method based on convolutional…
A key function of battery management systems (BMS) in e-mobility applications is estimating the battery state of health (SoH) with high accuracy. This is typically achieved in commercial BMS using model-based methods. There has been…
Lithium ion batteries are widely used in many applications. Battery management systems control their optimal use and charging and predict when the battery will cease to deliver the required output on a planned duty or driving cycle. Such…
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…