In Situ Imaging of Electrode Thickness Growth and Electrolyte Depletion in Single-Crystal vs. Polycrystalline LiNixMnyCozO2/Graphite Pouch Cells using Multi-Scale Computed Tomography

Single-crystal LiNixMnyCozO2 (NMC) materials have recently garnered significant academic and commercial interest as they have been shown to provide exceptional long-term charge-discharge cycling stability in Li-ion cells. Understanding the degradation mechanisms occurring in conventional polycrystalline NMC materials in comparison to the more stable single-crystal equivalents has become a topic of significant interest. In this study, we demonstrate how multi-scale, in-situ computed tomography can be used to characterize important changes occurring in wound pouch cells containing polycrystalline or single-crystal NMC. These changes include cell-level phenomena (such as deformation of the jelly roll and electrolyte depletion) as well as electrode-scale phenomena (such as electrode thickness growth and electrode cracking). A set of twenty-one cells were scanned in total, consisting of three different electrodes: polycrystalline NMC622, single-crystal NMC811, and single-crystal NMC532. These studies were designed to characterize the effects of varying C-rate, depth of discharge, and duty cycle, so this work includes an analysis of these factors as they relate to physical changes taking place at the cell and electrode level.

How do Depth of Discharge, C-rate and Calendar Age Affect Capacity Retention, Impedance Growth, the Electrodes, and the Electrolyte in Li-Ion Cells?

Lithium-ion cells testing under different state of charge ranges, C-rates and cycling temperature have different degrees of lithium inventory loss, impedance growth and active mass loss. Here, a large matrix of polycrystalline NMC622/natural graphite Li-ion pouch cells were tested with seven different state of charge ranges (0-25, 0-50, 0-75, 0-100, 75-100, 50-100 and 25-100%), three different C-rates and at two temperatures. First, capacity fade was compared to a model developed by Deshpande and Bernardi. Second, after 2.5 years of cycling, detailed analysis by dV/dQ analysis, lithium-ion differential thermal analysis, volume expansion by Archimedes’ principle, electrode stack growth, ultrasonic transmissivity and x-ray computed tomography were undertaken. These measurements enabled us to develop a complete picture of cell aging for these cells. This then led to an empirical predictive model for cell capacity loss versus SOC range and calendar age. Although these particular cells exhibited substantial positive electrode active mass loss, this did not play a role in capacity retention because the cells were anode limited during full discharge under all the tests carried out here. However, the positive electrode mass loss was strongly coupled to positive electrode swelling and electrolyte “unwetting” that would eventually cause dramatic failure.

Estimation of Depth of Discharge of Li-ion Battery Using Neural Network

This paper deals with the estimation of depth of discharge for Li-ion batteries. Estimation is based on the knowledge of discharging curves measured for discrete values of loading currents. The estimator of the depth of discharge is a form of feedforward neural network which is trained with the measured data of discharge curves. Accuracy of estimation of the depth of discharge is shown for arbitrary generated and measured loading characteristics, where the depth of discharge is estimated by the designed neural network and measured by using the Coulomb counting method.

Coherent and Compact Zinc Electrodeposition Enabled by Compressing the Electric Double Layer of the Deposits

The porous hexagonal-platelet Zinc (Zn) deposits exacerbate the chemical corrosion and deteriorate the reversibility of the Zn electrodes in aqueous electrolytes. Based on the Derjaguin-Landau-Verwey-Overbeek (DLVO) theory, to turn the messy Zn deposits into agglomerate ones, the challenge is to weaken the electric double layer repulsive force, which is the main reason preventing the dense Zn deposits, between the electrodeposited Zn particles. Here, we proposed a strategy to compress the electric double layer and regulate the forces between the electrodeposited Zn particles by introducing inert charges to the surface of the Zn deposits. The results of the electron microscopies revealed dense and coherent electrodeposition of Zn, indicating that the van der Waals attraction between the deposits becomes governing during electrodeposition. Such results could be attributed to the adsorbed inert charges on Zn deposits decrease the net charges and weaken the electric double layer repulsive force. This design enables the Zn||Zn cells a long-term plating/stripping stability for > 1200 h, a high average Coulombic Efficiency of 99.9% for > 2100 h, and steady charge/discharge responses even under a draconian deep-discharge condition of 80% depth of discharge of Zn (DODZn). In addition, the Zn||VS2 full cells demonstrate significantly improved electrochemical reversibility and capacity retention.

Useful Energy Prediction Model of a Lithium-ion Cell Operating on Various Duty Cycles

The paper deals with the subject of the prediction of useful energy during the cycling of a lithium-ion cell (LIC), using machine learning-based techniques. It was demonstrated that depending on the combination of cycling parameters, the useful energy (<i>RUE_c</i>) that can be transfered during a full cycle is variable, and also three different types of evolution of changes in <i>RUE_c</i> were identified. The paper presents a new non-parametric <i>RUE_c</i> prediction model based on Gaussian process regression. It was proven that the proposed methodology enables the <i>RUE_c</i> prediction for LICs discharged, above the depth of discharge, at a level of 70% with an acceptable error, which is confirmed for new load profiles. Furthermore, techniques associated with explainable artificial intelligence were applied, for the first time, to determine the significance of model input parameters – the variable importance method – and to determine the quantitative effect of individual model parameters (their reciprocal interaction) on <i>RUE_c</i> – the accumulated local effects model of the first and second order. Not only is the <i>RUE_c</i> prediction methodology presented in the paper characterised by high prediction accuracy when using small learning datasets, but it also shows high application potential in all kinds of battery management systems.

Useful Energy Prediction Model of a Lithium-ion Cell Operating on Various Duty Cycles

The paper deals with the subject of the prediction of useful energy during the cycling of a lithium-ion cell (LIC), using machine learning-based techniques. It was demonstrated that depending on the combination of cycling parameters, the useful energy (<i>RUE_c</i>) that can be transfered during a full cycle is variable, and also three different types of evolution of changes in <i>RUE_c</i> were identified. The paper presents a new non-parametric <i>RUE_c</i> prediction model based on Gaussian process regression. It was proven that the proposed methodology enables the <i>RUE_c</i> prediction for LICs discharged, above the depth of discharge, at a level of 70% with an acceptable error, which is confirmed for new load profiles. Furthermore, techniques associated with explainable artificial intelligence were applied, for the first time, to determine the significance of model input parameters – the variable importance method – and to determine the quantitative effect of individual model parameters (their reciprocal interaction) on <i>RUE_c</i> – the accumulated local effects model of the first and second order. Not only is the <i>RUE_c</i> prediction methodology presented in the paper characterised by high prediction accuracy when using small learning datasets, but it also shows high application potential in all kinds of battery management systems.

Reliability and Energy Costs Analysis of a Rural Hybrid Microgrid Using Measured Data and Battery Dynamics: A Case Study in the Coast of Perú

Hybrid microgrids constitute a promising solution for filling the electricity access gap that currently exists in rural areas; however, there is still relatively little information about their reliability and costs based on measured data in real working conditions. This article analyzes data obtained from the operation of a 9 kW hybrid microgrid in the fishermen’s cove of Laguna Grande, Paracas, in the Ica region of Perú, which has been running for 5 years. This microgrid has been equipped with data acquisition systems that measure and register wind speed, solar radiation, temperatures, and all the relevant electric parameters. Battery dynamics considerations are used to determine the depth of discharge in a real-time operative situation. The collected data are used to optimize the design using the specialized software HOMER, incorporating state-of-the-art technology and costs as a possible system upgrade. This work aims to contribute to better understanding the behavior of hybrid rural microgrids using data collected under field conditions, analyzing their reliability, costs, and corresponding sensitivity to battery size as well as solar and wind installed power, as a complement to a majority of studies based on simulations.

Thermal Performance of Lithium Titanate Oxide Anode Based Battery Module under High Discharge Rates

A lithium titanate oxide (LTO) anode based battery has high power density, and it is widely applied in transportation and energy storage systems. However, the thermal performance of LTO anode based battery module is seldom studied. In this work, a heat generation theoretical model of the battery is explored. The thermal performance of LTO anode based battery modules under high discharge rates is studied by both experiment and simulation. It is found that the temperature rise of the battery can be estimated accurately with the calculation of the equivalent internal resistance under different discharge rates. In addition, under the same depth of discharge, both the temperature rise and the temperature difference in the battery module increase with the discharge rates.