Al(NO3)3 Induced Morphological Changes in Nickel and Cobalt Salts as Advanced Electrodes for Supercapacitors

As one of the important components of the supercapacitor, electrode material has a significant influence on the electrochemical performance of the device. In this study, Ni2(NO3)2(OH)2 · 2H2O/(Co(NH3)5(N3))(N3)2 (NC) composite material was prepared by the microwave-assisted method. The effects of Al(NO3)3 contents on the morphology and structure of the composites were studied by scanning electron microscopy, X-ray diffraction, Raman spectrum, and laser particle size distribution analyzer. The capacitive properties of the composite were analyzed by cyclic voltammetry, constant current charge and discharge, and electrochemical impedance spectroscopy measurements. The results show that the Al(NO3)3 has a significant effect on the morphology and capacitance of NC. When the contents of Al(NO3)3 are 0, 5, 10, 15, and 20 wt%, the discharge capacities of NC, NCA-5, NCA-10, NCA-15, and NCA-20 samples obtained under 1 A/g current density are 1674, 1759, 2645, 1098, and 1321 F/g, respectively. At higher current densities of 4, 7, and 10 A/g, the discharge specific capacities reached 1350, 1064, and 894 F/g, indicating that the NCA-10 composite is a promising electrode material for supercapacitors.

An Electro-Thermal Model based fast optimal charging strategy for Li-ion batteries

<abstract> <p>This paper utilizes an integrated electro-thermal model of a lithium-ion battery to search for an optimal multistage constant current charge pattern that will minimize the total charging time of the battery, while restricting its temperature rise in each stage within safe limits. The model consists of two interlinked components, an electrical equivalent circuit model to continuously predict the battery's terminal voltage and a thermal model to continuously predict its temperature rise as charging progresses. The proposed optimization algorithm is based on a novel stepwise single-variable search technique that is very easy to implement and converges quickly. The results of our extensive simulation studies clearly indicate that the proposed charging strategy offers a fast, safe and easy-to-implement alternative to many of the existing computationally intensive optimal charging strategies.</p> </abstract>

Capacity Fade Estimation of a Lithium-Ion Battery Through an Integrated Electrochemical Battery Model and Empirical Cycle Aging Model

An electrochemical model based capacity fade estimation method for a Li-Ion battery is investigated in this paper. An empirical capacity fade model for estimating the state of health of a LiFePO4 electric vehicle battery was integrated with electrochemical battery model in Matlab/Simulink platform. This combined model was then validated against experimental data reported in the literature for constant current charge / discharge cycling. An HPPC current profile was then applied to the validated electrochemical-empirical battery prognosis model which reflected a real-time operating condition for charge and discharge current fluctuations in an electric vehicle battery. The combined model was simulated under the two different HPPC current inputs for three different cycle times. Additionally temperature was taken in account in estimating the cycle aging under the applied current profile to assess the present capacity remaining in the battery. The simulation results provided the state of health (SOH) of the battery for these cycling times which were comparable to the published experimental SOH values for constant current charge/discharge profiles. Thus this model can potentially be used to predict the capacity fade status of an electric vehicle battery.

Comparative Analysis of Features for Determining State of Health in Lithium-Ion Batteries

Traditionally, capacity and resistance have been used as the features to determine the state of health of lithium-ion batteries. In the present study, two additional features, the length of time of the constant current and the constant voltage phases of charging were used as additional indicators of state of health. To compare the appropriateness of each state of health feature, batteries were subjected to different discharge profiles and tested to failure. For each cycle, capacity, resistance, length of the constant current charge time and length of the constant voltage charge time were measured and compared based on their usefulness to estimate the state of health. Lastly, all the features were combined to give a fusion result for state of health estimation.

Preparation and Electrochemical Properties of CeO2/rGO Composite Material

The CeO2/rGO composites were prepared by hydrothermal method. The microscopic phase structure and microstructure of the samples were characterized by SEM, XRD and FTIR. The cyclic voltammetry (CV) and constant current charge and discharge tests were used to study the electrochemical stability and cycle ability of the CeO2/rGO composite electrode. The results show that the incorporation of graphene effectively increases the reversibility of CeO2 as an electrode material. The special structure of graphene provides a continuous network and more active sites for redox reactions. In the 6mol/L KOH electrolyte, the specific capacitance of CeO2/rGO composite reached 114F·g-1 when the current density was 0.5A/g. After 1000 cycles, the specific capacitance only decreased by 12.66%, which proves that this material has good cycle performance.

Research on Online Capacity Estimation of Power Battery Based on EKF-GPR Model

Capacity degrading over repeated charge/discharge cycles is a main parameter for evaluating battery performance, which is commonly used for determining the state of health. However, it is difficult to measure the available capacity because it requires the normal operation to be terminated and a long time-consuming detection process. This study presents an online available-capacity estimation method by combining extended Kalman filter (EKF) with Gaussian process regression (GPR) for the daily partial charge data of lithium-ion batteries. First, GPR is used to establish an empirical model of the time-voltage curve in the constant current charge cases. Second, by analyzing the characteristics of the charge curve, the daily piecewise partially charge data are registered with the piecewise complete charge data to update GPR model and preestimate the equivalent complete charge time. On this basis, the equivalent complete charge time is refined by EKF. Furthermore, the available capacity estimation of the battery with constant current charge processes under different aging conditions is achieved. It is verified by experiments that the estimated error can be controlled within 5% when the actual available capacity is greater than 90% of the initial capacity.

Heat Loss Measurement of Lithium Titanate Oxide Batteries under Fast Charging Conditions by Employing Isothermal Calorimeter

To understand better the thermal behaviour of lithium-ion batteries under different working conditions, various experiments were applied to a 13 Ah Altairnano lithium titanate oxide battery cell by means of isothermal battery calorimeter. Several parameters were measured such as the battery surface temperature, voltage, current, power, heat flux, maximum temperature and power area. In addition, the efficiency was calculated. Isothermal battery calorimeter was selected as the most appropriate method for heat loss measurements. Temperatures on the surface of the battery were measured by employing four contact thermocouples (type K). In order to determine the heat loss of the battery, constant current charge and discharge pulses at sixteen different C-rates were applied to the battery. It was seen that the charge and discharge C-rates has a considerable influence on the thermal behaviours of lithium-ion batteries. In this research paper, the C-rate was linked to the peak temperature, efficiency and heat loss and it was concluded that they are linear dependent on the C-rate. In addition, the outcomes of this investigation can be used for battery thermal modelling and design of thermal management systems.