Battery Performance Degradation Research Articles

Localization of electrons within interlayer stabilizes NASICON-type solid-state electrolyte

Although Li1.3Al0.3Ti1.7(PO4)3 (LATP) solid electrolyte has the advantages of high ionic conductivity, low cost, and satisfying air stability, it is thermodynamically unstable with lithium metal, which could reduce the Ti4+ to Ti3+ and form a highly resistive interface, leading to the fast degradation of battery performance. Herein, through an in-depth study on the degradation mechanism of LATP by delocalized π electrons in succinonitrile (SN), we propose a novel plastic composite interlayer (PCI) based on succinonitrile and polyacrylonitrile, which can not only realize a rapid and uniform transmission of lithium ions but also localize π electrons, inhibit the continuous deterioration of LATP. Compared with previous battery tests at elevated temperatures or by adding liquid components, the PCI-based Li|Li symmetric cell has achieved a cyclability for more than 300 h at room temperature. Moreover, the discharge capacity of the all-solid-state battery based on PCI still retains 87.9% after 170 cycles at 0.1C, which is in sharp contrast to the 38.6% capacity retention after 50 cycles of LFP/SN/LATP/SN/Li battery. These results show that PCI, as an effective anode interlayer, paves a way for high-performance solid-state batteries based on NASICON-type electrolytes.

Experimental investigations on the correlations between the structure and thermal‐electrochemical properties of over‐discharged ternary/ Si‐C power batteries

The thermal safety of power lithium-ion batteries (LIBs) has seriously affected the booming development of electric vehicles (EVs). Especially, owing to the requirement of high energy density, thermal runaway (TR) easily occurs in LIBs, resulting in a higher heat generation rate. Over-discharging is recognized as a common cause for TR. In the present research, the correlations between the structure and thermal-electrochemical properties of an over-discharged ternary/Si-C battery at room and high temperatures were investigated. The heat generation mechanisms of the batteries due to the maximum surface temperature and peak temperature difference variations during fast charging and discharging processes were investigated. Moreover, the electrochemical performances parameters of the batteries, such as voltage changing trend, discharge time, discharge capacity, internal resistance, electrochemical impedance spectroscopy (EIS) spectra, were analyzed. When the battery was discharged at 2.0C and 55°C, its maximum temperature and highest temperature difference reached 91.34°C and 13.24°C, respectively, finally resulting in a sharp decline in electrochemical performance. Furthermore, the root reasons for performance degradation and heat generation intensification of the over-discharged battery were analyzed by scanning electron microscopy and X-ray diffraction. The cause of the aforementioned phenomenon is due to irreversible damage to the electrode materials. This research not only reveals the relevant relationship between the thermal behavior and the microscopic structure of the over-discharged ternary/Si-C battery under various temperature conditions but also provides valuable insights for improving the safety of LIBs modules even packs.

Residual Life Prediction of Lithium Battery Based on Small Sample Data Sets

In the recycling process of lithium-ion battery, the poor external environment will lead to the degradation of battery performance, so it is necessary to predict the residual life of lithiumion battery. However, the aging cycle of lithium-ion battery is long, so it is difficult to obtain sufficient aging data in a short time. Aiming at the problem of low prediction accuracy of lithium battery residual life under the condition of small samples, a BP neural network modelling algorithm fused with expert knowledge is proposed. Firstly, indirect health factors were extracted from the aging data of lithium battery in NASA PCOE laboratory. Secondly, the initial weight and threshold of BP neural network were optimized by genetic algorithm, and the initial multiplier value of the augmented Lagrange function was set according to Spearman correlation coefficient. Finally, the expert knowledge is integrated into the training process of BP neural network through the augmented Lagrange multiplier method. Simulation results show that the proposed algorithm has higher prediction accuracy than the traditional BP neural network under the condition of small samples.

Formation of Surface Impurities on Lithium-Nickel-Manganese-Cobalt Oxides in the Presence of CO2 and H2O.

Surface impurities involving parasitic reactions and gas evolution contribute to the degradation of high Ni content LiNixMnyCozO2 (NMC) cathode materials. The transient kinetic technique of temporal analysis of products (TAP), density functional theory, and infrared spectroscopy have been used to study the formation of surface impurities on varying nickel content NMC materials (NMC811, NMC622, NMC532, NMC433, NMC111) in the presence of CO2 and H2O. CO2 reactivity on a clean surface as characterized by CO2 conversion rate in the TAP reactor follows the order: NMC811 > NMC622 > NMC532 > NMC433 > NMC111. The capacity of CO2 uptake follows a different order: NMC532 > NMC433 > NMC622 > NMC811 > NMC111. Moisture pretreatment slows down the direct CO2 adsorption process and creates additional active sites for CO2 adsorption. Electronic structure calculations predict that the (012) surface is more reactive than the (1014) surface for CO2 and H2O adsorption. CO2 adsorption leading to carbonate formation is exothermic with formation of ion pairs. The average CO2 binding energies on the different materials follow the CO2 reactivity order. Water hydroxylates the (012) surface and surface OH groups favor bicarbonate formation. Water creates more active sites for CO2 adsorption on the (1014) surface due to hydrogen bonding. The composition of surface impurities formed in ambient air exposure is dependent on water concentration and the percentage of different crystal planes. Different surface reactivities suggest that battery performance degradation due to surface impurities can be mitigated by precise control of the dominant surfaces in NMC materials.

The Mechanism of Li Plating on Graphite Particles

Lithium ion battery is the mainstream technology that is powering most of mobile applications (consumer electronics, power tools etc.) and in the future, our transportation by electrifying the vehicles. Current lithium ion battery can only support 1C charging (~50 mins for 80% charge), which is much longer than refilling gasoline car ( < 5 min). Extreme fast charging ( <10 min for 80%) is a very attractive technology that can address this problem. However, Li plating happens in graphite at high charge currents or low temperature, which causes battery performance degradation or even safety hazard (short-circuit, thermal runaway and even fire).The core of addressing Li plating problem lies in understanding its mechanism. Despite Li ion battery has been invented for 30 years, a systematic understanding on the onset and growth of Li in graphite anode is still lacking. In this work, we elucidate the mechanism of Li plating, by investigating the interplay between Li intercalation and Li plating on a single graphite particle using in-situ optical microscopy coupled with electrochemical test. The results show Li plating happens when the surface of graphite saturates which shuts down the intercalation reaction. The saturation can happen much earlier than full lithiation of graphite particle due to solid diffusion limitation, resulting in severe under-utilization. The discovery sheds light on directions and guidelines of materials innovation or electrode design for achieving extreme fast charging.

Ion-conductive self-healing polymer network based on reversible imine bonding for Si electrodes

Si electrodes have attracted considerable attention as materials for next-generation lithium-ion batteries owing to their high theoretical capacity and natural abundance. However, many limitations must be overcome before Si electrodes can be commercialized, particularly the steady degradation in battery performance caused by large volume changes in the active material. Here, an ion-conductive self-healing polymer binder is developed for high-performance silicon electrodes. Glycol chitosan with dialdehyde-terminated polyethylene glycol as a macro-crosslinker is used in a simple process to form the crosslinked polymer network based on imine double bonds, which further improves ion conductivity. Strong and reversible imine double bonds in the crosslinked polymer provide self-healing ability. Si electrodes using the developed polymer network results in an initial Coulombic efficiency of 82.2%, a discharge capacity of 2141 mAh g−1 after 150 cycles, and a reversible capacity of 2700 mAh g−1 at a current density of 3C. These outstanding electrochemical performances demonstrate that the self-healable network and ion-conductive functionality of the developed polymeric binder significantly improves the operation of Si electrodes.

Stochastic modeling for tracking and prediction of gradual and transient battery performance degradation

As rechargeable battery-powered devices become a pervasive part of daily life, consumer standards for quality and reliability in these devices increase. One standard of particular interest is the ability to reliably track and predict the daily and lifetime capacity degradation of the batteries, over successive charge–discharge cycles. Tracking and predicting the degradation trend over batteries’ entire service lives can be challenging, not only due to the nonlinear degradation patterns subject to different usage conditions, but also because of the presence of transient regeneration events that can rapidly increase the battery's maximum capacity and affect degradation rates. Current methods in the literature commonly apply an exponential model in a Bayesian inference framework to track and predict global degradation trend, neglecting the transient behaviors and local fluctuations, hence leading to unsatisfactory performance. This paper presents a new customary model specifically designed to track the gradual battery degradation pattern, combined with a compound Poisson process-based model that aims to capture the transient behaviors. The integration of the two parts, forming a comprehensive degradation model, provides a more accurate description of capacity variation throughout battery's life cycle. During the model training phase, upon historical data, parameters involved in the two models are estimated through two Bayesian inference techniques: step-by-step estimation by a Particle Filter and batch estimation by a Markov Chain Monte Carlo algorithm, with their performance compared. The estimated parameters are then utilized for generating transient events and predicting future capacity degradation. NASA's lithium-ion battery data are analyzed to evaluate the effectiveness of the developed degradation model.

PVAnalytX: A MATLAB toolkit for techno-economic analysis and performance evaluation of rooftop PV systems

The adaptation of rooftop solar PV systems has increased significantly in recent years. Batteries have become an essential part of the rooftop solar PV systems, which ensures maximum utilisation of solar energy. With a view to present a clear insight to benefits of a rooftop solar PV system, considering both the technical and economic benefits, a MATLAB based PVAnalytX software toolkit has been presented in this paper. The end users can evaluate both the technical and economic benefits of a rooftop solar PV system considering solar generation and electricity demand variability, as well the battery performance degradation over the course of its usage. In addition, the mechanism to generate forecasted solar power generation and electricity demand data is also included as a feature in the proposed software toolkit. Furthermore, the toolkit considers actual electricity imports and export tariffs that allow the end consumers to know their financial expenditures for electricity consumption and income generated through the export of access rooftop PV energy to the grid. A thorough validation of the proposed toolkit has been presented through two case studies using actual dataset of rooftop PV systems located at CBD region of Sydney, Australia.

Visualizing Local Electrical Properties of Composite Electrodes in Sulfide All-Solid-State Batteries by Scanning Probe Microscopy

Studies on local conduction paths in composite electrodes are essential to the realization of high-performance sulfide all-solid-state lithium batteries. Here, we directly evaluate the electrical properties of individual LiNi1/3Mn1/3Co1/3O2 (NMC) electrode active material particles in composite positive electrodes by scanning probe microscopy (SPM) techniques. Kelvin probe force microscopy (KPFM) and scanning spreading resistance microscopy (SSRM) are combined. The results indicate that all NMC particles exhibit a charged state with increasing potential, but low electronic conduction paths exist at point of contacts of some NMC particles. Furthermore, the I–V characteristics measured by conductive atomic force microscopy (C-AFM) suggest that these specific NMC particles show low charge–discharge reactivity. The results of the SPM techniques indicate that poor conduction locally limits the charge–discharge reactivity of electrode active materials, leading to the degradation of battery performance. Such an SPM combination accelerates the morphological optimization of composite electrodes by facilitating the investigation of the intrinsic electrical properties of the electrodes.

An Adaptive Control Strategy for a Low-Ripple Boost Converter in BLDC Motor Speed Control

Abstract Conventional boost converters are widely used for connecting low-voltage power sources and inverters in motor control. However, a large filter capacitor bank is often used to reduce DC-link ripples that occur when an inverter is connected to a boost converter. Otherwise, significant voltage and current perturbations can impact on battery performance degradation and cause torque ripple, speed ripple and vibration in brushless DC (BLDC) motors. To suppress the converter’s DC-link ripple, this paper proposes a new control strategy for boost converter controller to generate low-ripple DC-link voltage or current at different motor speeds. In the proposed method, observers are designed to adaptively estimate the DC-link voltage and current harmonics. The harmonic terms are used as feedback signals to calculate the DC converter’s duty cycle. The entire control model is implemented on an embedded system, and its robustness is verified by simulation and experimental results that show the DC-link voltage and current ripples can be reduced by about 50% and 30%, respectively.

Sensor based in-operando lithium-ion battery monitoring in dynamic service environment

In this work, a dynamic testing platform to analyze lithium-ion battery (LIB) performance degradation and safety during in-service vibration and impacts is presented. The LIBs are cycled until end of life (EOL) under various dynamic conditions, providing in-operando battery performance examination. A sensor network constructed consisting of a resistance temperature detector (RTD), an accelerometer, an eddy current sensor and a shunt resistor is incorporated into the setup. Mechanisms of LIB capacity fade, temperature rise, and deformation from cycling in representative dynamic environments are analyzed and compared with results from theoretical analyses as well as with electrode structure evaluation. The sensor network identifies critical periods with high heat generation and stress accumulation rates. By comparing single cell and battery pack performances, complex effects of examined boundary conditions on LIB heat generation are analyzed. Using the sensor network, an approach to identify critical cell(s) with the highest risk of safety hazards without the need for electrochemical modeling is presented. Raman spectroscopy analyses, SEM imaging, and electrochemical impedance spectroscopy (EIS) analyses are also applied to support the observations.

Degradation Mechanism Study and Safety Hazard Analysis of Overdischarge on Commercialized Lithium-ion Batteries.

With the continuous improvement of the energy density of traction batteries for electric vehicles, the safety of batteries over their entire lifecycle has become the most critical issue in the development of electric vehicles. Abuse of electricity encountered in the application of batteries has a great impact on the safety of traction batteries. In this study, focused on the overdischarge phenomenon that is most likely to be encountered in the practical use of electric vehicles and grid storage, the impact of overdischarge on battery performance degradation is analyzed by neutron imaging technology and its safety hazards is systematically explored, combined with multimethods including electrochemical analysis and structural characterization. Results reveal the deterioration of the internal structure of traction batteries due to the overdischarge behavior and play a guiding role in the testing and evaluation of the safety of traction batteries.

Phase-Field Simulation of Li-Ion Diffusion and Stress Evolution in Cathode Materials in NCA-Type Li-Ion Batteries

The crack propagation in a positive electrode of lithium (Li)-ion batteries degrades the performance of Li-ion batteries. The crack propagation is driven by the Li intercalation- and deintercalation-induced stress-field in the electrode. In order to prevent the degradation of Li-ion battery performance, it is essential to understand the Li-ion diffusion and stress evolution in the positive electrode during the charging and discharging processes. Phase-field (PF) method has been widely used as a powerful numerical simulation methodology for analyzing micro- and meso-scale phenomena including electrochemical reactions. In this study, we develop a PF model for simulating the Li-ion diffusion and stress evolution in a positive electrode in a LiNi1-x-y Co x Al y O2 (NCA)-type Li-ion battery, which attracts attentions as a high-capacity cathode material. In the PF model, the Li-ion diffusion in the NCA electrode is treated by the Cahn-Hilliard equation which incorporates the Butler-Volmer equation. The stress evolution is analyzed based on the PF microelasticity theory. The elastic constants and lattice misfit strains of NCA electrode used in the PF model were obtained from first-principles calculation. Using the developed PF model, we investigate the Li-ion diffusion and stress evolution in a NCA second-particle, which strongly depends on the distribution and crystal orientation of primary NCA particles in the second-particle.

Lithium-ion battery performance degradation evaluation in dynamic operating conditions based on a digital twin model

The performance of lithium-ion batteries degrades over time. Evaluating the performance degradation for lithium-ion batteries is essential to ensure the operational reliability and reduces the risk of host-system downtime. The battery capacity that is obtained by completely charging and discharging a battery cell, directly reflects the performance of a lithium-ion battery. But in practical applications, the battery is dynamically charged and discharged. This makes it difficult to measure the actual capacity and further evaluate battery performance degradation to ensure the battery operating safety. To address this challenging issue, this paper proposes a performance degradation evaluation model by estimating the battery actual capacity in dynamic operating conditions. A health indicator (HI) is extracted from the measurable parameters to reflect the battery performance degradation. A battery digital twin model that describes the relationship between the cell voltage and the cell state-of-charge (SOC) are modelled by the long short-term memory (LSTM) algorithm, which takes the HI as a temporal measurement. The battery actual capacity can be obtained by virtually completely discharging this digital twin model. The experimental results illustrate the potential of the proposed method applying in dynamic operating conditions.

Ultrasonic monitoring performance degradation of lithium ion battery

To monitor the performance and reliability of a lithium-ion battery during charge/discharge cycles, an ultrasonic time-of-flight (ToF) and attenuation measurement was performed. Ultrasonic ToF of the reflected pulse echo increased with the decrease in the state of charge (SoC) and with the increase in number of cycles (i.e., degradation). A health evaluation map relating SoC with ToF at various cycles was constructed. In this map, ultrasonic hysteresis was reported, of which area reflected the magnitude of irreversible electrochemical damage to the battery material. A linear correlation between the ToF and the number of cycles was observed.

Optimal energy management strategy of fuel‐cell battery hybrid electric mining truck to achieve minimum lifecycle operation costs

Proton exchange membrane fuel cell (PEMFC) electric vehicle is an effective solution for improving fuel efficiency and onboard emissions, taking advantage of the high energy density and short refuelling time. However, the higher cost and short life of the PEMFC system and battery in an electric vehicle prohibit the fuel cell electric vehicle (FCEV) from becoming the mainstream transportation solution. The fuel efficiency-oriented energy management strategy (EMS) cannot guarantee the improvement of total operating costs. This paper proposes an EMS to minimize the overall operation costs of FCEVs, including the cost of hydrogen fuel, as well as the cost associated with the degradations of the PEMFC system and battery energy storage system (ESS). Based on the PEMFC and battery performance degradation models, their remaining useful life (RUL) models are introduced. The control parameters of the EMS are then optimized using a meta-model based global optimization algorithm. This study presents a new optimal control method for a large mining truck operating on a real closed-road operation cycle, using the combined energy efficiency and performance degradation cost measures of the PEMFC system and lithium-ion battery ESS. Simulation results showed that the proposed EMS could improve the total operating costs and the life of the FCEV.

Porous Electrode Phase Transition Kinetics in Li-Ion Batteries

Traditional Porous Electrode Theory has provided an impressive amount of insight on the description of the intercalation kinetics of rechargeable batteries by describing the porous electrochemically active electrode as a diffusion and ohmic system with a spatial distribution of reversible interfacial reactions. Here, events such as phase transformations are fit into a curve by using the open circuit potential (OCP), and mass and interfacial reactions are described by starting from the classic reaction theory and fitted to experimental data. Recent experimental results highlight that the kinetics associated to phase transformations occurring during the intercalation/deintercalation process, are key to specifying battery performance and long term degradation. These kinetics, however, are theoretically and numerically complex to spatially resolve at a level that would make them practical to use in commercial applications. In this paper, we report on progress in the formulation of a coarse grained description that naturally accounts for the volumetric electrochemical phase transformations and reactions in a porous rechargeable batteries by starting from fundamental materials science principles. The effect of stresses in the intercalation kinetics is incorporated by upscaling the free energy-based phase field representations of the constituent materials is being developed. Comparisons against available experimental data are presented.

Simulating dendrite growth in lithium batteries under cycling conditions

Studies have shown that the lifetime and performance of lithium batteries are greatly influenced by the charge/discharge cycles of the battery. The number of cycles over which a battery operates depends on the rate and depth of charge/discharge. Degradation in battery performance over multiple cycles is directly related to dendrite growth at the electrode-electrolyte interface in the battery. Understanding how cycling effects dendrite growth rates and morphology requires resolution of the chemical-physical processes at the electrode-electrolyte interface. In this study, a numerical model of dendrite growth at this electrode-electrolyte interface over multiple charge/discharge cycles is presented. In this work, dendrite growth over multiple charge/discharge cycles is simulated at the interfacial level where the effects on dendrite growth rate and morphology can be resolved. The simulations are able to predict qualitative dendrite morphologies presented in experimental studies, and the effects of fast charging scenarios on dendrite growth rate and morphology are discussed.

Li-Ion Battery Performance Degradation Modeling for the Optimal Design and Energy Management of Electrified Propulsion Systems

Heavy-duty hybrid electric vehicles and marine vessels need a sizeable electric energy storage system (ESS). The size and energy management strategy (EMS) of the ESS affects the system performance, cost, emissions, and safety. Traditional power-demand-based and fuel-economy-driven ESS sizing and energy management has often led to shortened battery cycle life and higher replacement costs. To consider minimizing the total lifecycle cost (LCC) of hybrid electric propulsion systems, the battery performance degradation and the life prediction model is a critical element in the optimal design process. In this work, a new Li-ion battery (LIB) performance degradation model is introduced based on a large set of cycling experiment data on LiFePO4 (LFP) batteries to predict their capacity decay, resistance increase and the remaining cycle life under various use patterns. Critical parameters of the semi-empirical, amended equivalent circuit model were identified using least-square fitting. The model is used to calculate the investment, operation, replacement and recycling costs of the battery ESS over its lifetime. Validation of the model is made using battery cycling experimental data. The new LFP battery performance degradation model is used in optimizing the sizes of the key hybrid electric powertrain component of an electrified ferry ship with the minimum overall LCC. The optimization result presents a 12 percent improvement over the traditional power demand-driven hybrid powertrain design method. The research supports optimal sizing and EMS development of hybrid electric vehicles and vessels to achieve minimum lifecycle costs.

Optimal energy management with balanced fuel economy and battery life for large hybrid electric mining truck

With the addition of an energy storage system (ESS) and advanced controls, a hybrid electric propulsion system can considerably improve the fuel economy over a pure mechanical powertrain. However, the high cost and relatively short operating life of the battery ESS constitute a significant portion of the total operation cost (TOC) of an electrified vehicle, particularly for heavy-duty vehicles with a larger ESS. In this work, a new method for generating the optimal energy management strategy (EMS), considering the TOC of a hybrid electric mining truck (HEMT), is introduced. The cost associated with battery performance degradation and operation life-shortening under different battery use patterns is added to form the globally optimal, TOC-based EMS. The optimal EMS under different vehicle operation profiles are identified using dynamic programming (DP) to serve as benchmarks. An intelligent optimal ESS energy management method for achieving the minimum TOC during real-time, open-pit HEMT operations is introduced by combining an artificial neural network (ANN) model and a fuzzy-logic controller (FLC). The new, real-time intelligent optimal EMS led to twenty-one percent TOC reduction of the HEMT over the traditional, pure fuel economy-oriented optimal EMS, and formed the foundation of TOC-based, optimal EMS development for hybrid electric vehicles (HEVs).