Exploring impedance spectrum for lithium-ion batteries diagnosis and prognosis:A comprehensive review

Lithium-ion batteries have extensive usage in various energy storage needs,owing to their notable benefits of high energy density and long lifespan.The monitoring of battery states and failure identification are indispensable for guaranteeing the secure and optimal functionality of the batteries.The impedance spectrum has garnered growing interest due to its ability to provide a valuable understanding of material characteristics and electrochemical processes.To inspire further progress in the investigation and application of the battery impedance spectrum,this paper provides a comprehensive review of the determination and utilization of the impedance spectrum.The sources of impedance inaccuracies are systematically analyzed in terms of frequency response characteristics.The applicability of utilizing diverse impedance features for the diagnosis and prognosis of batteries is further elaborated.Finally,challenges and prospects for future research are discussed.

Alternating current heating techniques for lithium-ion batteries in electric vehicles:Recent advances and perspectives

The significant decrease in battery performance at low temperatures is one of the critical challenges that electric vehicles(EVs)face,thereby affecting the penetration rate in cold regions.Alternating current(AC)heating has attracted widespread attention due to its low energy consumption and uniform heating advantages.This paper introduces the recent advances in AC heating from the perspective of practical EV applications.First,the performance degradation of EVs in low-temperature environments is introduced briefly.The concept of AC heating and its research methods are provided.Then,the effects of various AC heating methods on battery heating performance are reviewed.Based on existing studies,the main factors that affect AC heating performance are analyzed.Moreover,various heating circuits based on EVs are categorized,and their cost,size,complexity,efficiency,reliability,and heating rate are elaborated and compared.The evolution of AC heaters is presented,and the heaters used in brand vehicles are sorted out.Finally,the perspectives and challenges of AC heating are discussed.This paper can guide the selection of heater implementation methods and the optimization of heating effects for future EV applications.

Online Identification of Lithium-ion Battery Model Parameters with Initial Value Uncertainty and Measurement Noise

Online parameter identification is essential for the accuracy of the battery equivalent circuit model(ECM).The traditional recursive least squares(RLS)method is easily biased with the noise disturbances from sensors,which degrades the modeling accuracy in practice.Meanwhile,the recursive total least squares(RTLS)method can deal with the noise interferences,but the parameter slowly converges to the reference with initial value uncertainty.To alleviate the above issues,this paper proposes a co-estimation framework utilizing the advantages of RLS and RTLS for a higher parameter identification performance of the battery ECM.RLS converges quickly by updating the parameters along the gradient of the cost function.RTLS is applied to attenuate the noise effect once the parameters have converged.Both simulation and experimental results prove that the proposed method has good accuracy,a fast convergence rate,and also robustness against noise corruption.

Lithium-ion battery degradation trajectory early prediction with synthetic dataset and deep learning

Knowing the long-term degradation trajectory of Lithium-ion(Li-ion) battery in its early usage stage is critical for the maintenance of the battery energy storage system(BESS) in reality. Previous battery health diagnosis methods focus on capacity and state of health(SOH) estimation which can receive only the short-term health status of the cell. This paper proposes a novel degradation trajectory prediction method with synthetic dataset and deep learning, which enables to grasp the characterization of the cell's health at a very early stage of Li-ion battery usage. A transferred convolutional neural network(CNN) is chosen to finalize the early prediction target, and the polynomial function based synthetic dataset generation strategy is designed to reduce the costly data collection procedure in real application. In this thread, the proposed method needs one full lifespan data to predict the overall degradation trajectories of other cells. With only the full lifespan cycling data from 4 cells and 100 cycling data from each cell in experimental validation, the proposed method shows a good prediction accuracy on a dataset with more than 100 commercial Li-ion batteries.

Understanding the mechanism of capacity increase during early cycling of commercial NMC/graphite lithium-ion batteries

A capacity increase is often observed in the early stage of Li-ion battery cycling.This study explores the phenomena involved in the capacity increase from the full cell,electrodes,and materials perspective through a combination of non-destructive diagnostic methods in a full cell and post-mortem analysis in a coin cell.The results show an increase of 1%initial capacity for the battery aged at 100%depth of discharge(DOD)and 45℃.Furthermore,large DODs or high temperatures accelerate the capacity increase.From the incremental capacity and differential voltage(IC-DV)analysis,we concluded that the increased capacity in a full cell originates from the graphite anode.Furthermore,graphite/Li coin cells show an increased capacity for larger DODs and a decreased capacity for lower DODs,thus in agreement with the full cell results.Post-mortem analysis results show that a larger DOD enlarges the graphite dspace and separates the graphite layer structure,facilitating the Li+diffusion,hence increasing the battery capacity.

Operation-area-constrained Adaptive Primary Frequency Support Strategy for Electric Vehicle Clusters

Due to their fast response and strong short-term power throughput capacity, electric vehicles(EVs) are promising for providing primary frequency support to power grids. However, due to the complicated charging demands of drivers, it is challenging to efficiently utilize the regulation capacity of EV clusters for providing stable primary frequency support to the power grid. Accordingly, this paper proposes an adaptive primary frequency support strategy for EV clusters constrained by the charging-behavior-defined operation area. First, the forced charging boundary of the EV is determined according to the driver's charging behavior, and based on this, the operation area is defined. This ensures full utilization of the available frequency support capacity of the EV. An adaptive primary frequency support strategy of EV clusters is then proposed. The output power of EV is adaptively regulated according to the real-time distance from the EV operating point to the forced charging boundary. With the proposed strategy, when the EV approaches the forced charging boundary, its output power is gradually reduced to zero. Then, the rapid state-of-charge declines of EVs and sudden output power reductions in EV clusters caused by forced charging to meet the driver's charging demands can be effectively avoided. EV clusters can then provide sustainable frequency support to the power grid without violating the driver's charging demands. Simulation results validate the proposed operation-area-constrained adaptive primary frequency support strategy, which outperforms the average strategy in terms of stable output maintenance and the optimal utilization of regulation capacities of EV clusters.

An Enhanced Data-Driven Model for Lithium-Ion Battery State-of-Health Estimation with Optimized Features and Prior Knowledge

In the long-term prediction of battery degradation,the data-driven method has great potential with historical data recorded by the battery management system.This paper proposes an enhanced data-driven model for Lithium-ion(Li-ion)battery state of health(SOH)estimation with a superior modeling procedure and optimized features.The Gaussian process regression(GPR)method is adopted to establish the data-driven estimator,which enables Li-ion battery SOH estimation with the uncertainty level.A novel kernel function,with the prior knowledge of Li-ion battery degradation,is then introduced to improve the mod-eling capability of the GPR.As for the features,a two-stage processing structure is proposed to find a suitable partial charging voltage profile with high efficiency.In the first stage,an optimal partial charging voltage is selected by the grid search;while in the second stage,the principal component analysis is conducted to increase both estimation accuracy and computing efficiency.Advantages of the proposed method are validated on two datasets from different Li-ion batteries:Compared with other methods,the proposed method can achieve the same accuracy level in the Oxford dataset;while in Maryland dataset,the mean absolute error,the root-mean-squared error,and the maximum error are at least improved by 16.36%,32.43%,and 45.46%,respectively.

Purification and anti-inflammatory effect of selenium-containing protein fraction from selenium-enriched Spirulina platensis

Spirulina platensis is an excellent carrier of Se,and thus widely used in medical fields and as well as in the food industry.However,there is little information about the characteristics and bioactivity of selenium-containing S.platensis proteins(Se-SP).In this study,Se-SP with different molecular weights were isolated from selenium-enriched S.platensis,and the bioactivities(such as antioxidant and anti-inflammatory activities)of Se-SP were investigated.Se-SP3(with a molecular weight range of 20-48 kDa)showed better free radical scavenging ability(ABTS)than the other Se-SPs.In addition,Se-SP3 suppressed inflammatory cytokines,in which decreased by 74% interleukin 6(IL-6),42.28% tumor necrosis factor-α(TNF-α),69.07% content of malondialdehyde(MDA),40.45% interleukin-1β(IL-1β)relative to the LPS group.Moreover,Se-SP3 decreased the nitric oxide(NO)production by 64.84% compared with the LPS group and increased the activities of superoxide dismutase(SOD)and glutathione peroxidase(GSH-Px),indicating that Se-SP3 has excellent antioxidant and anti-inflammatory activities,and could be used as a functional food ingredient or natural medicine.

A Multitime-scale Deep Learning Model for Lithium-ion Battery Health Assessment Using Soft Parameter-sharing Mechanism

Efficient assessment of battery degradation is important to effectively utilize and maintain battery management systems.This study introduces an innovative residual convolutional network(RCN)-gated recurrent unit(GRU)model to accurately assess health of lithium-ion batteries on multiple time scales.The model employs a soft parameter-sharing mechanism to identify both short-d dT and long-term degradation patterns.The continuously looped(V),T(V),dQ/dV and dT/dV are extracted to form a four-channel image,dV dV from which the RCN can automatically extract the features and the GRU can capture the temporal features.By designing a soft parameter-sharing mechanism,the model can seamlessly predict the capacity and remaining useful life(RUL)on a dual time scale.The proposed method is validated on a large MIT-Stanford dataset comprising 124 cells,showing a high accuracy in terms of mean absolute errors of 0.00477 for capacity and 83 for RUL.Furthermore,studying the partial voltage fragment reveals the promising performance of the proposed method across various voltage ranges.Specifically,in the partial voltage segment of 2.8-3.2 V,root mean square errors of 0.0107 for capacity and 140 for RUL are achieved.