Electrochemical-Engineering-Based Models for Lithium-Ion Batteries—Past, Present, and Future

<title>ABSTRACT</title> <p>Lithium-ion (Li-ion) batteries are increasingly targeted for electrical energy storage due to their exceptional energy and power density. However, Li-ion batteries operating in the hostile military environments are prone to catastrophic thermally induced failures or reduced lifetime if not maintained within narrow operating windows. There is great interest in advanced thermal management to optimize battery pack performance and enhance safety. In this numerical study, we examine the performance and safety benefits afforded by thermal management systems with effective thermal conductivities beyond the limits of traditional conductive cooling. Thermal runaway simulations based on NREL’s multi-scale multi-domain (MSMD) model illuminate how advanced thermal management can improve power density, while mitigating thermal runaway and cell fratricide potential. More efficient cooling increases the margin of safety, allowing end users to extend the pack operating envelope over an interrelated three parameter space encompassing the maximum permissible discharge rate, ambient operating temperature, and/or thermal runaway pack protection thresholds. Enhanced battery cooling will make Li-ion batteries safer for next generation military vehicles.</p>

Lithium-ion (Li-ion) batteries have been utilized increasingly in recent years in various applications, such as electric vehicles (EVs), electronics, and large energy storage systems due to their long lifespan, high energy density, and high-power density, among other qualities. However, there can be faults that occur internally or externally that affect battery performance which can potentially lead to serious safety concerns, such as thermal runaway. Thermal runaway is a major challenge in the Li-ion battery field due to its uncontrollable and irreversible nature, which can lead to fires and explosions, threatening the safety of the public. Therefore, thermal runaway prognosis and diagnosis are significant topics of research. To efficiently study and develop thermal runaway prognosis and diagnosis algorithms, thermal runaway modeling is also important. Li-ion battery thermal runaway modeling, prediction, and detection can help in the development of prevention and mitigation approaches to ensure the safety of the battery system. This paper provides a comprehensive review of Li-ion battery thermal runaway modeling. Various prognostic and diagnostic approaches for thermal runaway are also discussed.

With the rapid development of the new energy sector and lithium-ion (Li-ion) battery technologies, using Li-ion batteries with high energy density and high discharge rate to form the primary energy subsystem can further improve the compactness and miniaturization level of pulsed power systems. In traditional electric power systems, Li-ion batteries normally operate under the discharge condition of low current and long time. However, as the main power source of pulsed power systems, it is required that Li-ion batteries should have the capability for high-rate pulse discharge. To ensure the safe and reliable operation of the battery energy storage system, it is necessary to conduct the parameter identification of the Li-ion battery model to establish an accurate pulse discharge model under such working conditions. This paper focuses on the discharge characteristics of a cylindrical high-rate single Li-ion battery with a capacity of 3 Ah. At a 20 C rate, the discharge data were obtained when the pulse repetition frequency was 10 Hz and the duty cycle was 90%. By using the mathematical expressions of the Thevenin equivalent circuit model, the recursive relationship of the circuit parameters was obtained, and the parameter identification of the battery model was conducted based on the experimental data by using the genetic algorithm. The results show that the fitting accuracy of the parameter identification reaches 0.9995, indicating the high precision of the model for the Li-ion battery. This work provides a significant reference for the modeling and parameter identification of Li-ion batteries under high-rate pulse discharge conditions.

Due to their high energy density, long lifespan and light weight, lithium ion (Li-ion) batteries accounted for $11.8 billion dollars in sales in 2012, that makes up 60% of total portable battery sales and 37% of total battery sales. The same factors also make them the best candidate for use in hybrid electric vehicles (HEVs) and electric vehicles (EVs). Currently in the US almost all spent lithium ion batteries are land filled, compared to other types of batteries, such as lead acid batteries, in which 97% are recycled. This presents a large amount of environmental waste. Based on current trends in Li-ion battery use, if Li-ion batteries are not recycled, global lithium reserves are expected to be depleted by 2050. Additionally, recycling Li-ion batteries presents an economic opportunity in through the recovery of key valuable metals, such as cobalt, nickel and copper. Currently, most Li-ion battery recyclers focus on recovering cobalt from LiCoO2 cathode material or through the recovery of Co and Ni from the cathode materials via a high temperature pyrometallurgical approach. We propose a new methodology that uses a low temperature hydrometallurgical approach that has the advantage of having high efficiency of recovery. This process works regardless of the Li-ion batteries chemistry, shape and size and recovers Co, Ni, and Mn in the form of their hydroxides, which when sintered with recovered Li2CO3 produces new LiNixMnyCozO2 cathode materials. The regenerated cathode material, LiNixMnyCozO2 , has been tested and exhibits good electrochemical performance. In addition to the cathode material, copper, aluminium, steel, etc are also recycled. Figure 1

Lithium-ion (Li-ion) batteries as an energy storage device have drawn significant attention due to increasing demand especially in transportation, mobile, and renewable energy applications. Despite their wide utilization, the improvement of Li-ion batteries’ performance, including the enhancement of energy density, stability, and safety, remains a big challenge to overcome. Carbon nanostructures (1D, 2D, 3D) show potential as the anode materials for Li-ion batteries which possess high stability and Li-ion conductivity, yet they offer low capacity. Contrarily, metalloids and transition metal oxides materials, which show high capacity, suffer low Li-ion conductivity and exhibit volume expansion during charge/discharge. Combining these materials with carbon nanostructures to create carbon-based nanocomposites as the anode materials for Li-ion batteries is considered one of the most lucrative strategies to achieve improved performance. These composites form high stability, high conductivity, and high-capacity anode materials. Furthermore, the addition of heteroatoms to carbon nanostructures also significantly increases capacity. Herein, we intensively discuss several categories of carbon-based nanocomposites and the effect on their properties as well as performance (initial charge/discharge capacity, cycling performance). In addition, several future prospects and challenges are addressed.

The Lithium-ion (Li-ion) battery is a promising rechargeable battery that converts chemical energy to electrical energy for power consumption. Predicting the runtime and current-voltage (I-V) performance of Lithium-ion batteries is frequently employed in modern applications. The aim of the paper is to investigate the working principles of existing Li-ion battery electrical models and to assess the fidelity of the models. Contributions for electrical models of Li-ion batteries will be simulated by MATLAB/Simulink software to see in terms of mimicking the actions of real-life batteries if they are accurate and valid. The simulation results show that the three RC branches electrical Li-ion battery model is valid when it comes to predicting the behaviors during operations. The proposed strategy is emerging as an attractive technology option to be conducted in the Li-ion battery exchange stations in future power systems.

The state of energy (SOE) of Li-ion batteries is a key indicator for the energy optimization and management of energy storage devices (ESDs) in electric vehicles (EVs) and smart grids. To improve the SOE estimation accuracy, a hybrid Li-ion battery model is presented in this study against the dynamic loads and the rate energy effects of the battery. Firstly, in order to take the advantages of both models, a 2nd order RC battery model is merged with an analytical kinetic battery model for accurately predicting the battery voltage characteristics and capturing the nonlinear rate energy effects to realize the high-fidelity SOE and runtime prediction. Secondly, a new method to separate the fast and slow dynamics of the 2nd order RC model is developed and presented with high-performance accuracy. Thirdly, commercial Li-ion batteries are tested at dynamic loads under various temperature to validate the effectiveness of the proposed model. The experimental results show high accuracy and reliability of the proposed battery model on the estimation of the battery SOE and the battery terminal voltage responses under dynamic loads.

With the ever-increasing demand for energy storage, lithium ion battery has been an attractive technology that has revolutionized the portable electronic industry [1]. With its large scale applications, such as in hybrid electric vehicles (HEV’s), and plug-in HEVs, there is great interest in developing multifunctional hybrid nanostructured electrode materials for high performance Li-ion batteries [2,3]. Novel architectures of hybrid nanomaterials as both electrodes and electrolytes, have been shown to improve the device performances. New electrode materials with high electrical conductivity and large surface area, resulting in improved electrochemical performances are highly desirable. Though metal oxides with high specific capacity have been extensively studied as advanced Li-ion battery anodes, their structural instability under lithium insertion has been of great concern [4]. Hence, several approaches have been undertaken to improve the electrochemical performance of metal oxides as efficient Li-ion battery anodes. One of the strategies to increase the conductivity is by incorporating the anode material on the conducting carbonaceous matrix including mesoporous carbon, amorphous carbon, carbon nanotubes and graphenes [5].Here, we report the synthesis of Nb2O5 and TiNb2O7 -anchored graphene hybrid nanocomposites through simple hydrothermal method and its electrochemical performance studies as advanced anodes for lithium ion battery. The nanocomposite electrodes have been characterized by X-ray diffraction (XRD), Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), X-Ray Diffraction (XRD) and Thermo Gravimetric Analysis (TGA). Using these hybrid nanostructures as positive electrodes in 1 M LiPF6–EC/DMC electrolyte vs. Li metal negative electrodes, Cyclic voltammetry and Galvanostatic charge-discharge cycling measurements have been performed.The self-supported nanostructured electrodes, with graphene as support with homogenously anchored orthorhombic Nb2O5 (T-Nb2O5) and monoclinic TiNb2O7 nanoparticles, exhibited excellent electrochemical performance with high reversible capacity and enhanced rate capability, arising from the synergistic effect of Nb2O5 nanocrystals anchored onto conducting graphene layers. While the presence of three-dimensional structure in Nb2O5 and TiNb2O7 provides open channels and vacant sites for lithium intercalation, presence of conducting graphene sheets improve the kinetics of lithium ion and electron transport, leading to improved battery performances. The hybrid nanocomposite electrodes showed exceptional power capability, with ~80% of the total capacity sustained at 16C rate (Figure 2). We believe these hybrid nanocomposites, with superior electrochemical performance, can be used as efficient anode for high performance lithium ion battery. Figure 1. SEM images of (a) Nb2O5 nanocrystals (b) Nb2O5/graphene (c) TiNb2O7 and (d) TiNb2O7/graphene nanocompositeFigure 2. (a) Galvanostatic cycling of TiNb2O7/graphene nanocomposite electrode, (b) Rate capability of Nb2O5/graphene nanocomposite electrode. References J.-M. Tarascon and M Armand, Nature. 414, 359, (2001).P. G. Bruce, B. Scrosati and J.-M. Tarascon, Angew. Chem. Int. Ed. 47, 2930 (2008)A.L.M. Reddy, S. Gowda, M.M. Shaijumon and P.M. Ajayan, Adv. Mater. 24, 5045-5064 (2012)P. Poizot , S. Laruelle , S. Grugeon , L. Dupont , J.-M. Tarascon , Nature, 407, 496 (2000).Z.-S. Wu, W. Ren, L. Xu, F. Li, H.-M. Cheng, ACS Nano 5, 5463-5471 (2011)

Effects of neutron and gamma radiation on lithium-ion batteries

The lithium-ion battery (LIB) has emerged as a key energy storage device for a wide range of applications, from consumer electronics to transportation. While LIBs have made key advancements in these areas, limitations remain for Li-ion batteries with respect to affordability, performance, and reliability. These challenges have encouraged the exploration for more advanced materials and novel chemistries to mitigate these limitations. The continued development of Li-ion and other advanced batteries is an inherently multiscale problem that couples electrochemistry, transport phenomena, mechanics, microstructural morphology, and device architecture. Observing the internal structure of batteries, both ex situ and during operation, provides a critical capability for further advancement of energy storage technology. X-ray imaging has been implemented to provide further insight into the mechanisms governing Li-ion batteries through several 2D and 3D techniques. Ex situ imaging has yielded microstructural data from both anode and cathode materials, providing insight into mesoscale structure and composition. Furthermore, since X-ray imaging is a nondestructive process studies have been conducted in situ and in operando to observe the mechanisms of operation as they occur. Data obtained with these methods has also been integrated into multiphysics models to predict and analyze electrode behavior. The following paper provides a brief review of X-ray imaging work related to Li-ion batteries and the opportunities these methods provide for the direct observation and analysis of the multiphysics behavior of battery materials.

Energy efficiency of Li-ion battery packs re-used in stationary power applications

Lithium-ion (Li-ion) batteries are essential components for electric vehicles and smart grid systems that will become increasingly widespread in the future. However, the complexity of the capacity fade mechanism of Li-ion batteries has been a technical barrier to their practical use. This study proposes a capacity fade model composed of three factors: capacity loss, increase in internal resistance, and diffusion effect. The model derives sufficient information from battery data and conditions of use for evaluating and predicting capacity fade. The battery data includes discharge curves and electrode materials. The conditions of use are elements correlated to the three factors listed above and include the number of cycles and storage time. This paper discusses the availability of our proposed model by applying it to Li-ion battery data obtained from reference papers.

With the increasing popularity of electric vehicles (EVs), the demands for rechargeable and high-performance batteries like lithium-ion (Li-ion) batteries have soared. Li-ion battery systems require the use of a battery management system (BMS) to perform safely and efficiently. Accurate and reliable battery modeling is important for the BMS to function properly. Currently, many BMS applications use the equivalent circuit model due to its simplicity. However, with the development of a cloud BMS, machine learning battery models can be utilized, which can potentially improve the accuracy and reliability of the BMS. This work investigates the performance of four different machine learning models used to predict the thermal (temperature) and electrical (voltage) behaviors of Li-ion battery cells. A prismatic Li-ion battery cell with a capacity of 25 Ah was cycled under a constant current profile at three different ambient temperatures, and the surface temperature and voltage of the battery were measured. The four machine learning regression models—linear regression, k-nearest neighbors, random forest, and decision tree—were developed using the scikit-learn library in Python and validated with experimental data. The results of their performance were reported and compared using the R2 metric. The decision tree-based model, with an R2 score of 0.99, was determined to be the best model in this case study.

<div class="section abstract"><div class="htmlview paragraph">Lithium-ion (Li-ion) batteries have become a research focus in the field of electric and hybrid electric vehicles, due to key advantages such as high specific energy, high energy density and low self-discharge rate in comparison with other battery technologies. Nevertheless, key issues involving safety, performance, cost, charging time and durability restrict mass market adoption of electric vehicles. Cold-temperature operation of Li-ion batteries is currently a subject of major research efforts. The effects of low temperature reduce the available energy of the battery and increase its internal impedance. Moreover, Li-ion batteries experience serious degradation mechanisms at low temperatures, strongly affecting performance, which poses difficulties for cold-climate countries. In this work, a battery-in-the-loop experiment has been set up to estimate and analyze Li-ion battery cold start ranges at temperatures down to -40 °C. This experimental setup is connected to the open integration and test platform CarMaker from IPG Automotive to evaluate cold start range both through standardized automotive duty cycles and within a virtual test environment designed to replicate real-world test driving. In addition, a non-isothermal equivalent circuit battery model has been developed in CarMaker for Simulink and validated against experimental data. Effects of cold temperatures on the capacity and power fade of Li-ion battery technology are studied, as well as important thermal aspects, including effects of changing battery heat transfer coefficient to replicate different thermal management strategies. Range discrepancies between standardized duty cycles and a representative driving environment are also explored. Overall, this work contributes to identify key improvements in Li-ion battery thermal management for performance and durability enhancement during cold start.</div></div>

Quantifying the cost effectiveness of non-aqueous potassium-ion batteries