N and S co-doped graphene enfolded Ni-Co-layered double hydroxides: an excellent electrode material for high-performance energy storage devices

Insight into the Effect of Glycerol on Dielectric Relaxation and Transport Properties of Potassium-Ion-Conducting Solid Biopolymer Electrolytes for Application in Solid-State Electrochemical Double-Layer Capacitor

The increased interest in the transition from liquid to solid polymer electrolytes (SPEs) has driven enormous research in the area polymer electrolyte technology. Solid biopolymer electrolytes (SBEs) are a special class of SPEs that are obtained from natural polymers. Recently, SBEs have been generating much attention because they are simple, inexpensive, and environmentally friendly. In this work, SBEs based on glycerol-plasticized methylcellulose/pectin/potassium phosphate (MC/PC/K₃PO₄) are investigated for their potential application in an electrochemical double-layer capacitor (EDLC). The structural, electrical, thermal, dielectric, and energy moduli of the SBEs were analyzed via X-ray diffractometry (XRD), Fourier transforms infrared spectroscopy (FTIR), electrochemical impedance spectroscopy (EIS), transference number measurement (TNM), and linear sweep voltammetry (LSV). The plasticizing effect of glycerol in the MC/PC/K₃PO₄/glycerol system was confirmed by the change in the intensity of the samples' FTIR absorption bands. The broadening of the XRD peaks demonstrates that the amorphous component of SBEs increases with increasing glycerol concentration, while EIS plots demonstrate an increase in ionic conductivity with increasing plasticizer content owing to the formation of charge-transfer complexes and the expansion of amorphous domains in polymer electrolytes (PEs). The sample containing 50% glycerol has a maximal ionic conductivity of about 7.5 × 10^-4 scm^-1, a broad potential window of 3.99 V, and a cation transference number of 0.959 at room temperature. Using the cyclic voltammetry (CV) test, the EDLC constructed from the sample with the highest conductivity revealed a capacitive characteristic. At 5 mVs^-1, a leaf-shaped profile with a specific capacitance of 57.14 Fg^-1 was measured based on the CV data.

Deep Reinforcement Learning-Based Real-Time Joint Optimal Power Split for Battery–Ultracapacitor–Fuel Cell Hybrid Electric Vehicles

Hybrid energy storage systems for hybrid electric vehicles (HEVs) consisting of multiple complementary energy sources are becoming increasingly popular as they reduce the risk of running out of electricity and increase the overall lifetime of the battery. However, designing an efficient power split optimization algorithm for HEVs is a challenging task due to their complex structure. Thus, in this paper, we propose a model that jointly learns the optimal power split for a battery/ultracapacitor/fuel cell HEV. Concerning the mechanical system of the HEV, two propulsion machines with complementary operation characteristics are employed to achieve higher efficiency. Additionally, to train and evaluate the model, standard driving cycles and real driving cycles are employed as input to the mechanical system. Then, given the inputs, a temporal attention long short-term memory model predicts the next time step velocity, and through that velocity, the predicted load power and its corresponding optimal power split is computed by a soft actor–critic deep reinforcement learning model whose training phase is aided by shaped reward functions. In contrast to global optimization techniques, the local velocity and load power prediction without future knowledge of the driving cycle is a step toward real-time optimal energy management. The experimental results show that the proposed method is robust to different initial states of charge values, better allocates the power to the energy sources and thus better manages the state of charge of the battery and the ultracapacitor. Additionally, the use of two motors significantly increases the efficiency of the system, and the prediction step is shown to be a reliable way to plan the HESS power split in advance.