A comparative study of data-driven battery capacity estimation based on partial charging curves

With its generality and practicality, the combination of partial charging curves and machine learning(ML) for battery capacity estimation has attracted widespread attention. However, a clear classification,fair comparison, and performance rationalization of these methods are lacking, due to the scattered existing studies. To address these issues, we develop 20 capacity estimation methods from three perspectives:charging sequence construction, input forms, and ML models. 22,582 charging curves are generated from 44 cells with different battery chemistry and operating conditions to validate the performance. Through comprehensive and unbiased comparison, the long short-term memory(LSTM) based neural network exhibits the best accuracy and robustness. Across all 6503 tested samples, the mean absolute percentage error(MAPE) for capacity estimation using LSTM is 0.61%, with a maximum error of only 3.94%. Even with the addition of 3 m V voltage noise or the extension of sampling intervals to 60 s, the average MAPE remains below 2%. Furthermore, the charging sequences are provided with physical explanations related to battery degradation to enhance confidence in their application. Recommendations for using other competitive methods are also presented. This work provides valuable insights and guidance for estimating battery capacity based on partial charging curves.

Diagnosing health in composite battery electrodes with explainable deep learning and partial charging data

Lithium-ion batteries with composite anodes of graphite and silicon are increasingly being used. However, their degradation pathways are complicated due to the blended nature of the electrodes, with graphite and silicon degrading at different rates. Here, we develop a deep learning health diagnostic framework to rapidly quantify and separate the different degradation rates of graphite and silicon in composite anodes using partial charging data. The convolutional neural network (CNN), trained with synthetic data, uses experimental partial charging data to diagnose electrode-level health of tested batteries, with errors of less than 3.1% (corresponding to the loss of active material reaching ∼75%). Sensitivity analysis of the capacity-voltage curve under different degradation modes is performed to provide a physically informed voltage window for diagnostics with partial charging data. By using the gradient-weighted class activation mapping approach, we provide explainable insights into how these CNNs work;highlighting regions of the voltage-curve to which they are most sensitive. Robustness is validated by introducing noise to the data, with no significant negative impact on the diagnostic accuracy for noise levels below 10 mV, thus highlighting the potential for deep learning approaches in the diagnostics of lithium-ion battery performance under real-world conditions. The framework presented here can be generalised to other cell formats and chemistries, providing robust and explainable battery diagnostics for both conventional single material electrodes, but also the more challenging composite electrodes.

Transfer learning from synthetic data for open-circuit voltage curve reconstruction and state of health estimation of lithium-ion batteries from partial charging segments

Data-driven models for battery state estimation require extensive experimental training data,which may not be available or suitable for specific tasks like open-circuit voltage(OCV)reconstruction and subsequent state of health(SOH)estimation.This study addresses this issue by developing a transfer-learning-based OCV reconstruction model using a temporal convolutional long short-term memory(TCN-LSTM)network trained on synthetic data from an automotive nickel cobalt aluminium oxide(NCA)cell generated through a mechanistic model approach.The data consists of voltage curves at constant temperature,C-rates between C/30 to 1C,and a SOH-range from 70%to 100%.The model is refined via Bayesian optimization and then applied to four use cases with reduced experimental nickel manganese cobalt oxide(NMC)cell training data for higher use cases.The TL models’performances are compared with models trained solely on experimental data,focusing on different C-rates and voltage windows.The results demonstrate that the OCV reconstruction mean absolute error(MAE)within the average battery electric vehicle(BEV)home charging window(30%to 85%state of charge(SOC))is less than 22 mV for the first three use cases across all C-rates.The SOH estimated from the reconstructed OCV exhibits an mean absolute percentage error(MAPE)below 2.2%for these cases.The study further investigates the impact of the source domain on TL by incorporating two additional synthetic datasets,a lithium iron phosphate(LFP)cell and an entirely artificial,non-existing,cell,showing that solely the shifting and scaling of gradient changes in the charging curve suffice to transfer knowledge,even between different cell chemistries.A key limitation with respect to extrapolation capability is identified and evidenced in our fourth use case,where the absence of such comprehensive data hindered the TL process.

Partial Charging of Capacitors for Improving Voltage Profiles of CSI Fed Motor Drives

The fast switching behaviors of wide bandgap devices bring some challenges such as high du/dt and limited short-circuit current withstand capability to the reliable operation of the motor drives.The current-source-inverter(CSI)provides a promising solution in mitigating those challenges by owning the DC-link choke,the reverse-voltage blocking switches and AC commutation capacitors.To further reduce du/dt on switches of CSI fed motor drives,the technique of partial charging of capacitors have been investigated in this paper.By designing the series-connected and the parallel-connected partial-charging circuit for capacitors in DC-link,the voltage profile of CSI could be improved.Specifically,the zero-voltage-switching(ZVS)is achieved for main power switches,the du/dt is reduced and the overvoltage protection is presented.The working mechanism of the technique of partial charging of capacitor is described and one example is discussed on the dual three-phase motor drive.The experimental verification is presented to show the performance of partial charging technique for improving voltage profile of CSI fed motor drives.

Lead‑Carbon Batteries toward Future Energy Storage:From Mechanism and Materials to Applications

The lead acid battery has been a dominant device in large-scale energy storage systems since its invention in 1859.It has been the most successful commercialized aqueous electrochemical energy storage system ever since.In addition,this type of battery has witnessed the emergence and development of modern electricity-powered society.Nevertheless,lead acid batteries have technologically evolved since their invention.Over the past two decades,engineers and scientists have been exploring the applications of lead acid batteries in emerging devices such as hybrid electric vehicles and renewable energy storage;these applications necessitate operation under partial state of charge.Considerable endeavors have been devoted to the development of advanced carbon-enhanced lead acid battery(i.e.,lead-carbon battery)technologies.Achievements have been made in developing advanced lead-carbon negative electrodes.Additionally,there has been significant progress in developing commercially available lead-carbon battery products.Therefore,exploring a durable,long-life,corrosion-resistive lead dioxide positive electrode is of significance.In this review,the possible design strategies for advanced maintenance-free lead-carbon batteries and new rechargeable battery configurations based on lead acid battery technology are critically reviewed.Moreover,a synopsis of the lead-carbon battery is provided from the mechanism,additive manufacturing,electrode fabrication,and full cell evaluation to practical applications.