Search

Accurate estimation of the state of charge (SoC) of lithium-ion batteries is crucial for battery management systems, particularly in electric vehicle (EV) applications where real-time monitoring ensures safe and robust operation. This study introduces three advanced algorithms to estimate the SoC: deep neural network (DNN), gated recurrent unit (GRU), and long short-term memory (LSTM). The DNN, GRU, and LSTM models are trained and validated using laboratory data from a lithium-ion 18650 battery and simulation data from Matlab/Simulink for a LiCoO2 battery cell. These models are designed to account for varying temperatures during charge/discharge cycles and the effects of battery aging due to cycling. This paper is the first to estimate the SoC by a deep neural network using a variable current profile that provides the SoC curve during both the charge and discharge phases. The DNN model is implemented in Matlab/Simulink, featuring customizable activation functions, multiple hidden layers, and a variable number of neurons per layer, thus providing flexibility and robustness in the SoC estimation. This approach uniquely integrates temperature and aging effects into the input features, setting it apart from existing methodologies that typically focus only on voltage, current, and temperature. The performance of the DNN model is benchmarked against the GRU and LSTM models, demonstrating superior accuracy with a maximum error of less than 2.5%. This study highlights the effectiveness of the DNN algorithm in providing a reliable SoC estimation under diverse operating conditions, showcasing its potential for enhancing battery management in EV applications. [ABSTRACT FROM AUTHOR]

Structural, magnetic, electronic, elastic, thermodynamic, optical, and thermoelectric properties of full-Heusler alloys Co2YSn (Y = Ti; Zr; Hf) were determined using density functional theory-based WIEN2k code within GGA and GGA + U approximations for exchange correlation functions. The calculated formation energies and elastic parameters demonstrate the stability of these alloys. It was also observed that the studied compounds have a ductile structure and exhibit anisotropic behavior. The magnetic moment of these systems is equal to 2 μB, which conforms to the Slater–Pauling rule. In addition, the half-metallic ferrimagnetic behavior and good bandgap in the minority spin are showed for all compounds. The optical properties are systematically studied by computing the optical parameters. The existence of bandgaps with minimal energy loss in IR and visible regions makes these materials suitable candidates for optoelectronic devices. The thermoelectric properties of these systems were also examined in terms of temperature and chemical potential. [ABSTRACT FROM AUTHOR]