Battery Performance Degradation Research Articles

Layer-Resolved Mechanical Degradation of a Ni-Rich Positive Electrode

The effects of electrochemical aging on the mechanical properties of electrodes in lithium-ion batteries are challenging to measure and are largely unknown. Mechanochemical degradation processes occur at different scales within an electrode and understanding the correlation between the degradation of mechanical properties, electrochemical aging, and morphological changes is crucial for mitigating battery performance degradation. This paper explores the evolution of mechanical and electrochemical properties at the layer level in a Ni-rich positive electrode during the initial stages of electrochemical cycling. The investigation involves complementary cross-section analyses aimed at unraveling the connection between observed changes on both macroscopic and microscopic scales. The macroscopic constitutive properties were assessed using a U-shaped bending test method that had been previously developed. The compressive modulus exhibited substantial dependency on both the porous structure and binder properties. It experienced a notable reduction with electrolyte wetting but demonstrated an increase with cycling and aging. During the initial stages of aging, electrochemical impedance spectra revealed increased local resistance near the particle–electrolyte interface. This is likely attributable to factors such as secondary particle grain separation and the redistribution of carbon black. The swelling of particles, compression of the binder phase, and enhanced particle contact were identified as probable factors adding to the elevation of the elastic modulus within the porous layer as a result of cycling.

Experimental study on self-heating strategy of lithium-ion battery at low temperatures based on bidirectional pulse current

Preheating is an effective solution to the severe degradation of lithium-ion battery (LIB) performance at low temperatures. In this study, a bidirectional pulse-current preheating strategy for LIBs at low temperatures without external power is proposed, which involves the incorporation of a direct current/direct current converter and a series of resistances, inductances, and switches. The effects of ambient temperature, initial state of charge, and preheating strategy on the voltage and current evolution, heating rate, and heating efficiency are experimentally analysed in a climatic chamber, in addition to the effects of various preheating strategies on battery degradation. A comparative analysis is conducted using the classical pulse self-heating strategy. The results indicate that the bidirectional pulse-current preheating strategy enables preheating under an ambient temperature of −15 °C at a rate of 6.38 °C/min and affords a thermal efficiency of 31.9%. By contrast, the pulse heating method affords a heating rate of 3.46 °C/min and a thermal efficiency of 24.2%. As the battery temperature decreases, both the charge/discharge switching period and heating rate decrease, whereas the thermal efficiency and energy consumption ratio improve. No significant degradation occurs after 30 heating cycles, and the bidirectional pulse-current preheating strategy is demonstrated through capacity testing, incremental capacity curves, and electrochemical impedance spectroscopy testing. This study proposes a new approach for preheating LIBs internally and provides experimental evidence for a bidirectional-pulse preheating strategy.

Analysis of Performance Degradation in Lithium-Ion Batteries Based on a Lumped Particle Diffusion Model.

The analysis of performance degradation in lithium-ion batteries plays a crucial role in achieving accurate and efficient fault diagnosis as well as safety management. This paper proposes a method for studying the degradation pattern of lithium-ion batteries and establishing the structure-activity relationship between internal and external parameters by employing a lumped particle diffusion model. To simulate real-world operating conditions, a cycle life test was conducted with the constant current-constant voltage (CC-CV) charge mode and the discharge mode under New European Driving Cycle (NEDC) working condition. The test aimed to analyze the variations in the external macroscopic characteristic parameters of the battery. Building upon this analysis, a lumped particle diffusion model was constructed, and the model parameters were identified using the Levenberg-Marquardt (L-M) algorithm. Subsequently, the ohmic, activation, and concentration losses of the battery under different aging conditions were determined, revealing the internal state evolution during the degradation process of lithium-ion batteries. The findings indicate that the lumped particle diffusion model provides a comprehensive explanation of the internal mechanisms contributing to the performance degradation of lithium-ion batteries. Moreover, the proposed method offers a novel perspective for the real-time quantitative analysis of lithium-ion battery performance degradation.

Environmental Benefit Assessment of Second-Life Use of Electric Vehicle Lithium-Ion Batteries in Multiple Scenarios Considering Performance Degradation and Economic Value.

Second-life use of electric vehicle lithium-ion batteries (LIBs) is an inevitable trend; however, battery performance degradation increases environmental loads. This study evaluated the life cycle environmental impacts of second-life use of LIBs in multiple scenarios, considering performance degradation and economic value. The results showed that a component replacement rate of retired LIBs below 50% made the batteries worthy of repurposing. Reusing whole packs of retired LIBs was better than using only cells or modules owing to the environmental loads from diagnosis, disassembly, replacement, and test processes. The battery energy density and performance degradation significantly affect the maximum return on the environmental input. Compared with lithium iron phosphate (LFP) batteries, new lithium nickel manganese cobalt oxide (NMC) batteries, or lead-acid batteries, using retired NMC-811 batteries with capacities as low as 60.7% for energy storage systems to store wind electricity rather than hybrid or photovoltaic electricity, had substantial environmental benefits, including a low global warming potential. Considering the costs of battery recycling, labor, and electricity, using whole packs of retired LIBs could simultaneously achieve high economic and environmental values in energy storage and peak shaving scenarios.

State of Charge Estimation of Lead Acid Battery using Neural Network for Advanced Renewable Energy Systems

The Solar Dryer Dome (SDD), an independent energy system equipped with Artificial Intelligence to support the drying process, has been developed. However, inaccurate state-of-charge (SOC) predictions in each battery cell resulted in the vulnerability of the battery to over-charging and over-discharging, which accelerated the battery performance degradation. This research aims to develop an accurate neural network model for predicting the SOC of battery-cell level. The model aims to maintain the battery cell balance under dynamic load applications. It is accompanied by a developed dashboard to monitor and provide crucial information for early maintenance of the battery in the SDD. The results show that the neural network estimates the SOC with the lowest MAE of 0.175, followed by the Random Forest and support vector machine methods with MAE of 0.223 and 0.259, respectively. A dashboard was developed to help farmers monitor batteries efficiently. This research contributes to battery-cell level SOC prediction and the dashboard for battery status monitoring. Doi: 10.28991/ESJ-2023-07-03-02 Full Text: PDF

Preparation of argyrodite Li6–2xZnxPS5−xOxCl with improved electrochemical performance and air stability for all-solid-state batteries

The argyrodite Li6PS5Cl (LPSCl) solid electrolyte applied to all-solid-state batteries is gaining increasing attention as an attractive next-generation material, but in humid conditions it causes degradation of battery performance as a result of instability and poor contact with electrodes. In this experiment, LPSCl solid electrolyte was modified by substituting Zn for Li, and O for S through dry ball milling and heat-treatment processing. As evidence of ZnO substitution, X-ray diffraction, Laser-Raman, field emission scanning electron microscopy, nuclear magnetic resonance spectroscopy, and X-ray photoelectron spectroscopy analysis of the synthesized solid electrolyte were performed. The Li5.95Zn0.025PS4.975O0.025Cl (LPSCl-ZnO) solid electrolyte showed a high ionic conductivity at 30 °C. Moreover, it showed a higher critical current density value of 0.55 mA·cm−2 compared to the of 0.3 mA·cm−2 of LPSCl. It also exhibited high compatibility with Li metal as a cathode in cyclic voltammetry and in DC profile. In addition, the H2S gas generated after exposure to 10% humid air was a very low 0.38 cm3·g−1. Notably, an all-solid-state battery assembled with the ZnO-substituted solid electrolyte (x = 0.025) showed a specific capacity of 148.5 mAh·g−1 (0.1 C) and a high capacity retention rate of 99.7%.

NSGA-III-SD based Fuzzy energy management system optimization for lithium battery/supercapacitor HEV

Hybrid energy storage system (HESS), a combination of lithium battery (LIB) and supercapacitor (SC) in hybrid electric vehicles (HEV), can slow down the degradation of battery performance and improve the peak current characteristics by using an optimal energy management system (EMS). A fuzzy EMS controller is first designed to allocate the energy between LIB and SC, then an improved NSGA-III algorithm based on similarity and diversity selection (NSGA-III-SD) is proposed to reduce energy consumption, lithium battery output current, peak power and prolong the life of LIB as well as to meet the SOC of SC constraint by optimizing the parameters of the fuzzy rule base and membership functions (MFs). Four typical driving patterns including busy city, urban, suburban and highway are selected to demonstrate the efficiency of the proposed method. The experimental results indicate that the optimal fuzzy EMS exhibits good adaptive power distribution capability under all driving conditions.

Attention-based BILSTM for the degradation trend prediction of lithium battery

There is an irreversibility in the decline of Li-ion batteries, and the performance of individual cells in the battery pack will gradually decline as the number of times the on-board Li-ion battery is charged and discharged increases This situation can significantly affect the daily use of electric vehicles, for example by shortening the driving range, and in addition, the deterioration of the battery performance increases the probability of electric vehicle breakdowns. Very little work has been done on the prediction of lithium battery performance degradation in long-mileage states, accurate prediction of future battery performance degradation can significantly reduce the probability of EV failure, making battery performance prediction very important. In this paper, we propose a BILSTM network based on an attention mechanism and utilize grey relation analysis and empirical modal decomposition in the input link of the network to address the shortcomings exposed by deep learning in the work on temporal prediction. The adopted approach can effectively address the impact of data noise and redundant features on the prediction work that occurs in deep learning. According to the experimental results, the prediction performance of the model proposed in this paper is found to be higher than other networks in both types of data sets

Self‐Adaptive and Electric Field‐Driven Protective Layer with Anchored Lithium Deposition Enable Stable Lithium Metal Anode

Lithium metal battery has great development potential because of its lowest electrochemical potential and highest theoretical capacity. However, the uneven deposition of Li+ flux in the process of deposition and stripping induces the vigorous growth of lithium dendrites, which results in severely battery performance degradation and serious safety hazards. Here, the tetragonal BaTiO3 polarized by high voltage corona was used to build an artificial protective layer with uniform positive polarization direction, which enables uniform Li+ flux. In contrast to traditional strategies of using protective layer, which can guide the uniform deposition of lithium metal. The ferroelectric protective layer can accurately anchor the Li+ and achieve bottom deposition of lithium due to the automatic adjustment of the electric field. Simultaneously, the huge volume changes caused by Li+ migration change of the lithium metal anode during charging and discharging is functioned to excite the piezoelectric effect of the protective layer, and achieve seamless dynamic tuning of lithium deposition/stripping. This dynamic effect can accurately anchor and capture Li+. Finally, the layer‐modified Li anode enables reversible Li plating/stripping over 1500 h at 1 mA cm−2 and 50 °C in symmetric cells. In addition, the assembled Li‐S full cell exhibits over 300 cycles with N/P ≈ 1.35. This work provides a new perspective on the uniform Li+ flux at the Li‐anode interface of the artificial protective layer.

Performance Degradation Evaluation of a Lithium-Ion Battery from Multiple SoC Measurements

Lithium-Ion (Li-ion) battery is essential in today's energy systems and electric vehicles (EVs). Although Li-ion battery can be charged quickly and have a high energy density, it has several drawbacks, including the rapid degradation of battery performance, especially in terms of battery capacity. Therefore, evaluating its performance degradation is necessary to understand its characteristics. In this paper, the performance degradation of a Li-ion battery is monitored and evaluated from multiple SoC measurements. A simple and low-cost experimental setup consisting of sensors, a microcontroller, and a PC is developed to measure and record the real-time data of Li-ion battery voltage and current. Then, the battery state of charge (SoC) is determined using the Coulomb Counting method, which is based on the incoming and outgoing currents of the battery. As a result, this study derives three parameters that indicate the performance degradation of a Li-ion battery, i.e., SoC, battery capacity, and discharge time. From multiple direct measurements with constant load and C20 discharge process, the minimum SoC value increases from 11% to 18%, while battery capacity decreases from 8.8Ah to 8.3 Ah and, discharge time decreases from 16.9 hours to 16.4 hours. All of those parameters indicate a degradation of around 7% in battery performance. Therefore, this research paves the way for finding a solution to mitigate the quick performance degradation of Li-ion batteries.

Prognostics of Lithium-Ion batteries using knowledge-constrained machine learning and Kalman filtering

• Propose a knowledge-constrained machine learning method for batteries prognostics • Propose a data-driven stochastic model for battery degradation • Employ an ANN-based DEKF method to capture the dynamics of lithium-ion batteries • Validate the proposed approach using NASA dataset for battery prognostics Accurately predicting the remaining useful life (RUL) of lithium-ion rechargeable batteries remains challenging as the battery capacity degrades in a stochastic manner given the internal complex electrochemical reactions of the battery and the external operational/environmental conditions. In this work, a knowledge-constrained machine learning framework is developed to learn the stochastic degradation of battery performance over working cycles for health prognostics of Lithium-Ion batteries. An artificial neural network (ANN) model is first trained and synchronized using a Dual Extended Kalman Filter (DEKF) to obtain critical health information of Lithium-Ion batteries. With the obtained health information, a knowledge-constrained machine learning method (KcML) is then developed to predict the stochastic degradation of the battery capacity in operation. Specifically, prior knowledge in battery capacity fade can be formulated as extra constraints to facilitate the development of machine learning models with improved fidelity level for battery capacity predictions. A dataset published by NASA is utilized to demonstrate the effectiveness of the proposed knowledge-constrained machine learning approach.

Limitation of Ni-rich layered cathodes in all-solid-state lithium batteries

The degradation of Ni-rich layered cathodes in all-solid-state lithium batteries is largely due to the morphology of the cathode particles. The degradation of battery performance is aggravated with increasing Ni content of the cathode material.

A Review of Inhibit the Growth of Lithium Dendrite Strategies

Nowadays, the development of electronic technology has driven the development of battery industry. Under the rigid demand for energy storage materials, lithium metal has received a lot of attention due to its excellent energy storage performance, however, the growth of lithium dendrite makes it difficult to recycle. This review introduces the principle of lithium dendrite growth and its negative impact leading to the degradation of battery performance, and then focuses on the methods to inhibit lithium dendrite growth e.g., fabrication of alloyed structure, regulation of solid electrolyte interface (SEI), application of solid electrolyte and recent research progress e.g., nanodiamond additive method, single-atom zinc anion skeleton method, battery self-heating method.

A Learning-Based Vehicle-Cloud Collaboration Approach for Joint Estimation of State-of-Energy and State-of-Health

The state-of-energy (SOE) and state-of-health (SOH) are two crucial quotas in the battery management systems, whose accurate estimation is facing challenges by electric vehicles' (EVs) complexity and changeable external environment. Although the machine learning algorithm can significantly improve the accuracy of battery estimation, it cannot be performed on the vehicle control unit as it requires a large amount of data and computing power. This paper proposes a joint SOE and SOH prediction algorithm, which combines long short-term memory (LSTM), Bi-directional LSTM (Bi-LSTM), and convolutional neural networks (CNNs) for EVs based on vehicle-cloud collaboration. Firstly, the indicator of battery performance degradation is extracted for SOH prediction according to the historical data; the Bayesian optimization approach is applied to the SOH prediction combined with Bi-LSTM. Then, the CNN-LSTM is implemented to provide direct and nonlinear mapping models for SOE. These direct mapping models avoid parameter identification and updating, which are applicable in cases with complex operating conditions. Finally, the SOH correction in SOE estimation achieves the joint estimation with different time scales. With the validation of the National Aeronautics and Space Administration battery data set, as well as the established battery platform, the error of the proposed method is kept within 3%. The proposed vehicle-cloud approach performs high-precision joint estimation of battery SOE and SOH. It can not only use the battery historical data of the cloud platform to predict the SOH but also correct the SOE according to the predicted value of the SOH. The feasibility of vehicle-cloud collaboration is promising in future battery management systems.

Unraveling the LiNbO3 coating layer on battery performances of lithium argyrodite-based all-solid-state batteries under different cut-off voltages

The coating layer can effectively mitigate the undesirable side effects that occur at the active material/sulfide electrolyte interface in the cathode mixture. Plenty of research has reported the coating layer effect in the typical voltage window, while the influence at different voltage windows is unclear. Herein, the degradation mechanism of the bare and LiNbO3 coated LiCoO2 electrodes in all-solid-state batteries when cycled at different cut-off voltages has been systematically investigated. Thecoated electrodes exhibit superior electrochemical performance than the bare electrodes at different charging/discharging rates when the upper cut-off voltage is 3.6 V, while both electrodes show fast degradation of battery performance at higher cut-off voltages (3.9 and 4.2 V). The electrochemical performances are highly dependent on the interfacial stability between the active material and solid electrolyte in the cathode mixture and the structural instability of LiCoO2 at different voltage windows. The evolution of interfacial resistances is systematically investigated in combination of in-situ EIS, relaxation time distribution (DRT), TEM, and XPS. Structural changes of bare and coated LiCoO2 before and after cycled at different cut-off voltages are studied by XRD, TEM, and dQ/dV analysis. The clarification of complex interfaces and phase stability evolution of LiCoO2 provides a strong theoretical basis for constructing high-performance all-solid-state batteries.

Steric-hindrance effect and self-sacrificing template behavior induced PDA@SnO2-QDs/N-doped carbon hollow nanospheres: Enhanced structural stability and reaction kinetics for long-cyclic Li-ion half/full batteries

Tin-based anode materials with high theoretical specific capacity are subject to huge volume expansion and poor reaction reversibility, leading to degradation of battery performance. Herein, the steric-hindrance effect and self-sacrificing template behavior of polydopamine were firstly developed to induce the formation of hollow nanospheres assembled by ultrafine SnO2 quantum dots (SnO2-QDs) and nitrogen-doped carbon (NC), containing residual polydopamine (PDA) cores. The PDA@SnO2-QDs/NC hollow nanospheres could effectively accommodate the volume expansion and maintain structural stability. More importantly, the PDA core could capture oxygen free radicals produced by the charge/discharge process and be involved in the evolution of the SEI layer, achieving enhanced electrochemical reaction kinetics. The optimized PDA@SnO2-QDs/NC anode shows a specific capacity of 898 mAh g−1 after 300 cycles at 0.3 A g−1, and scarcely capacity attenuation after 1500 cycles at 1 A g−1. The long-cyclic life is up to 3000 cycles at 3 A g−1. Even after 200 cycles, the anode in the PDA@SnO2-QDs/NC||LFP full battery gives a reversible capacity of 489 mAh g−1 at 0.3 A g−1, with a capacity retention of 77 %. This work casts new light on tin-based anode materials and interface optimization.

Behaviour of battery separator under different charge rates according to poroelastodynamic model

A separator can influence the performance of a battery in several ways, and determining the mechanical response of the separator can allow to unveil the underlying reasons. We use quasi-static and fully dynamic poroelastic governing equations to simulate the separator of a lithium-ion battery under different charge/compression rates to characterise the displacements and pore pressure. The viscoelastic effect is also considered by adjusting the separator Young's modulus. Error analysis and independent mesh tests are used to validate the simulations. The numerical results show a wave pattern in the stress–strain curve of the separator when using the fully dynamic model, whereas this phenomenon cannot be observed when using the quasi-static model. The distribution of properties in the separator shows that the maximum displacement and pore pressure in the separator increase with the compression rate, and the pore pressure fluctuates during compression when using the fully dynamic model. The two variables in the poroelastodynamic model, namely, displacement and pore pressure, can establish an alternative for analysing battery separators. The simulation results provide a new insight into the separator behaviours and help one understand the battery performance degradation caused by separators better.

TinO2n-1/MXene Hierarchical Bifunctional Catalyst Anchored on Graphene Aerogel toward Flexible and High-Energy Li-S Batteries.

The development of lithium-sulfur (Li-S) batteries with high-energy density, flexibility, and safety is very appealing for emerging implantable devices, biomonitoring, and roll-up displays. Nevertheless, the poor cycling stability and flexibility of the existing sulfur cathodes, flammable liquid electrolytes, and extremely reactive lithium anodes raise serious battery performance degradation and safety issues. Herein, a metallic 1T MoS2 and rich oxygen vacancies TinO2n-1/MXene hierarchical bifunctional catalyst (Mo-Ti/Mx) anchored on a reduced graphene oxide-cellulose nanofiber (GN) host (Mo-Ti/Mx-GN) was proposed to address the above challenges. By applying a directional freezing process, the hierarchical architecture of a flexible GN scaffold composed of waved multiarch morphology with long-range alignment is achieved. The synergetic effects of 1T MoS2 and TinO2n-1/MXene are beneficial to suppress the shuttling behavior of lithium polysulfides (LiPSs), expedite the redox kinetics of sulfur species, and promote the electrocatalytic reduction of LiPSs to Li2S. The electrode demonstrates improved electrochemical properties with high sulfur-mass loading (8.4 mgs cm-2) and lean electrolyte (7.6 μL mgs-1) operation. We also explored the feasibility of producing pouch cells with such flexible electrodes, gel polymer electrolytes, and a robust lithium anode, which exhibited reversible energy storage and output, wide temperature adaptability, and good safety against rigorous strikes, implying the potential for practical applications.

An improved deep extreme learning machine to predict the remaining useful life of lithium-ion battery

To aim at the problem of inaccurate prediction of the remaining useful life of the lithium-ion battery, an improved grey wolf optimizer optimizes the deep extreme learning machine (CGWO-DELM) data-driven forecasting method is proposed. This method uses the grey wolf optimization algorithm based on an adaptive normal cloud model to optimize the bias of the deep extreme learning machine, the weight of the input layer, the selection of activation function, and the number of hidden layer nodes. In this article, indirect health factors that can characterize the degradation of battery performance are extracted from the discharge process, and the correlation between them and capacity is analyzed using the Pearson coefficient and Kendel coefficient. Then, the CGWO-DELM prediction model is constructed to predict the capacitance of the lithium-ion battery. The remaining useful life of lithium-ion batteries is indirectly predicted with a 1.44 A·h failure threshold. The prediction results are compared with deep extreme learning machines, long-term memory, other prediction methods, and the current public prediction methods. The results show that the CGWO-DELM prediction method can more accurately predict the remaining useful life of lithium-ion batteries.

Interfacial stability analysis between Ca-doped Na3PS4 solid electrolyte and Na anode from first-principles calculations

In this work, the chemical and mechanical stability of the interfaces between the Ca2+ doped Na3PS4 solid electrolyte and Na anode and between the un-doped Na3PS4 solid electrolyte and Na anode are systematically studied by first-principles calculations. The crystal structure, electronic structure, and mechanical properties of the two interface structures were analyzed respectively. When Ca2+ was doped into the Na3PS4 solid electrolyte, the decomposition of the solid electrolyte after contact with the Na anode was reduced, and the chemical stability of the interface structure was enhanced. Moreover, Ca2+ doping may significantly improve the mechanical strength (ultimate tensile strain and ultimate tensile strength) of the interface structure. The improvement of interface stability alleviates battery performance and capacity degradation. Therefore, the improved stability of the Na-Ca0.125Na2.75PS4 interface structure was the result of a mechanical-chemical coupling. This surface modification of Ca2+ doping enhanced the stability of the interface structure and provided direction for designing all-solid-state batteries (ASSB) with high capacity and long cycle life.