Understanding the constant-voltage fast-charging process using a high-rate Ni-rich cathode material for lithium-ion batteries

This study proposes constant-voltage charging as a promising fast-charging protocol and reveals the origin of capacity degradation in constant-voltage charging.

Coaxial Electrospinning Construction Si@C Core–Shell Nanofibers for Advanced Flexible Lithium-Ion Batteries

Silicon (Si) is expected to be a high-energy anode for the next generation of lithium-ion batteries (LIBs). However, the large volume change along with the severe capacity degradation during the cycling process is still a barrier for its practical application. Herein, we successfully construct flexible silicon/carbon nanofibers with a core–shell structure via a facile coaxial electrospinning technique. The resultant Si@C nanofibers (Si@C NFs) are composed of a hard carbon shell and the Si-embedded amorphous carbon core framework demonstrates an initial reversible capacity of 1162.8 mAh g−1 at 0.1 A g−1 with a retained capacity of 762.0 mAh g−1 after 100 cycles. In addition, flexible LIBs assembled with Si@C NFs were hardly impacted under an extreme bending state, illustrating excellent electrochemical performance. The impressive performances are attributed to the high electric conductivity and structural stability of the porous carbon fibers with a hierarchical porous structure, indicating that the novel Si@C NFs fabricated using this electrospinning technique have great potential for advanced flexible energy storage.

The Battery Life Estimation of a Battery under Different Stress Conditions

The prediction of capacity degradation, and more generally of the behaviors related to battery aging, is useful in the design and use phases of a battery to help improve the efficiency and reliability of energy systems. In this paper, a stochastic model for the prediction of battery cell degradation is presented. The proposed model takes its cue from an approach based on Markov chains, although it is not comparable to a Markov process, as the transition probabilities vary with the number of cycles that the cell has performed. The proposed model can reproduce the abrupt decrease in the capacity that occurs near the end of life condition (80% of the nominal value of the capacity) for the cells analyzed. Furthermore, we illustrate the ability of this model to predict the capacity trend for a lithium-ion cell with nickel manganese cobalt (NMC) at the cathode and graphite at the anode, subjected to a life cycle in which there are different aging factors, using the results obtained for cells subjected to single aging factors.

Evaluation of the influence of high-power charging cycles on the capacity degradation of lithium-ion batteries under various temperatures

High-power-charging (HPC) behavior and extreme ambient temperature not only pose security risks on the operation of lithium-ion batteries but also lead to capacity degradation. Exploring the degradation mechanism under these two conditions is very important for safe and rational use of lithium-ion batteries. To investigate the influence of various charging-current rates on the battery-capacity degradation in a wide temperature range, a cycle-aging test is carried out. Then, the effects of HPC on the capacity degradation at various temperatures are analyzed and discussed using incremental capacity analysis and electrochemical impedance spectroscopy. The analysis results show that a large number of lithium ions accelerate the deintercalation when the HPC cycle rate exceeds 3 C, making the solid electrolyte interphase at the negative surface unstable and vulnerable to destruction, which results in irreversible consumption of active lithium. In addition, the decomposition of electrolyte is significantly promoted when the HPC temperature is more than 30°C, resulting in accelerated consumption of electrode materials and active lithium, which are the main reasons for the capacity degradation of lithium-ion batteries during HPC under various temperatures.

Data-Driven Remaining Useful Life Prediction for Lithium-Ion Batteries Using Multi-Charging Profile Framework: A Recurrent Neural Network Approach

Remaining Useful Life (RUL) prediction for lithium-ion batteries has received increasing attention as it evaluates the reliability of batteries to determine the advent of failure and mitigate battery risks. The accurate prediction of RUL can ensure safe operation and prevent risk failure and unwanted catastrophic occurrence of the battery storage system. However, precise prediction for RUL is challenging due to the battery capacity degradation and performance variation under temperature and aging impacts. Therefore, this paper proposes the Multi-Channel Input (MCI) profile with the Recurrent Neural Network (RNN) algorithm to predict RUL for lithium-ion batteries under the various combinations of datasets. Two methodologies, namely the Single-Channel Input (SCI) profile and the MCI profile, are implemented, and their results are analyzed. The verification of the proposed model is carried out by combining various datasets provided by NASA. The experimental results suggest that the MCI profile-based method demonstrates better prediction results than the SCI profile-based method with a significant reduction in prediction error with regard to various evaluation metrics. Additionally, the comparative analysis has illustrated that the proposed RNN method significantly outperforms the Feed Forward Neural Network (FFNN), Back Propagation Neural Network (BPNN), Function Fitting Neural Network (FNN), and Cascade Forward Neural Network (CFNN) under different battery datasets.

Prognostics and Health Management of Renewable Energy Systems: State of the Art Review, Challenges, and Trends

The purpose of this study is to highlight approaches for predicting a system’s future behavior and estimating its remaining useful life (RUL) to define an effective maintenance schedule. Indeed, prognosis and health management (PHM) strategies for renewable energy systems, with a focus on wind turbine generators, are given, as well as publications published in the recent ten years. As a result, some prognostic applications in renewable energy systems are emphasized, such as power converter devices, battery capacity degradation, and damage in wind turbine high-speed shaft bearings. The paper not only focuses on the methodologies adopted during the early research in the area of PHM but also investigates more current challenges and trends in this domain

Remaining useful life prediction for lithium-ion battery based on the particle filter considering temperature effect

Temperature would affect the degradation process of lithium-ion battery. Therefore, considering the influence of temperature, this paper proposes method to predict the Remaining useful life (RUL) of the lithium-ion battery based on Arrhenius and double exponential model. And update the parameter by particle filter. Firstly, we establish a capacity degradation model with considering the influence of temperature, which is based on Arrhenius model and double exponential model. And then, in order to obtain the initial value of the parameters, we process the fitted the lithium-ion battery degradation data. Next, we use the particle filter (PF) algorithm to update the model parameters to realize the capacity estimation and the RUL prediction. Finally, according the experiment, we prove that the accuracy of the method proposed in this paper is better than that the method without considering the influence of temperature change. The result shows that the lithium-ion battery capacity degradation model established in this paper has great potential in the RUL prediction of the lithium-ion battery.