A fast-charging policy is widely employed to alleviate the inconvenience caused by the extended charging time of electric vehicles. However, fast charging exacerbates battery degradation and shortens battery lifespan....A fast-charging policy is widely employed to alleviate the inconvenience caused by the extended charging time of electric vehicles. However, fast charging exacerbates battery degradation and shortens battery lifespan. In addition, there is still a lack of tailored health estimations for fast-charging batteries;most existing methods are applicable at lower charging rates. This paper proposes a novel method for estimating the health of lithium-ion batteries, which is tailored for multi-stage constant current-constant voltage fast-charging policies. Initially, short charging segments are extracted by monitoring current switches,followed by deriving voltage sequences using interpolation techniques. Subsequently, a graph generation layer is used to transform the voltage sequence into graphical data. Furthermore, the integration of a graph convolution network with a long short-term memory network enables the extraction of information related to inter-node message transmission, capturing the key local and temporal features during the battery degradation process. Finally, this method is confirmed by utilizing aging data from 185 cells and 81 distinct fast-charging policies. The 4-minute charging duration achieves a balance between high accuracy in estimating battery state of health and low data requirements, with mean absolute errors and root mean square errors of 0.34% and 0.66%, respectively.展开更多
The performance of lithium-ion batteries(LIBs)gradually declines over time,making it critical to predict the battery’s state of health(SOH)in real-time.This paper presents a model that incorporates health indicators ...The performance of lithium-ion batteries(LIBs)gradually declines over time,making it critical to predict the battery’s state of health(SOH)in real-time.This paper presents a model that incorporates health indicators and ensemble Gaussian process regression(EGPR)to predict the SOH of LIBs.Firstly,the degradation process of an LIB is analyzed through indirect health indicators(HIs)derived from voltage and temperature during discharge.Next,the parameters in the EGPR model are optimized using the gannet optimization algorithm(GOA),and the EGPR is employed to estimate the SOH of LIBs.Finally,the proposed model is tested under various experimental scenarios and compared with other machine learning models.The effectiveness of EGPR model is demonstrated using the National Aeronautics and Space Administration(NASA)LIB.The root mean square error(RMSE)is maintained within 0.20%,and the mean absolute error(MAE)is below 0.16%,illustrating the proposed approach’s excellent predictive accuracy and wide applicability.展开更多
State of health(SoH) estimation plays a key role in smart battery health prognostic and management.However,poor generalization,lack of labeled data,and unused measurements during aging are still major challenges to ac...State of health(SoH) estimation plays a key role in smart battery health prognostic and management.However,poor generalization,lack of labeled data,and unused measurements during aging are still major challenges to accurate SoH estimation.Toward this end,this paper proposes a self-supervised learning framework to boost the performance of battery SoH estimation.Different from traditional data-driven methods which rely on a considerable training dataset obtained from numerous battery cells,the proposed method achieves accurate and robust estimations using limited labeled data.A filter-based data preprocessing technique,which enables the extraction of partial capacity-voltage curves under dynamic charging profiles,is applied at first.Unsupervised learning is then used to learn the aging characteristics from the unlabeled data through an auto-encoder-decoder.The learned network parameters are transferred to the downstream SoH estimation task and are fine-tuned with very few sparsely labeled data,which boosts the performance of the estimation framework.The proposed method has been validated under different battery chemistries,formats,operating conditions,and ambient.The estimation accuracy can be guaranteed by using only three labeled data from the initial 20% life cycles,with overall errors less than 1.14% and error distribution of all testing scenarios maintaining less than 4%,and robustness increases with aging.Comparisons with other pure supervised machine learning methods demonstrate the superiority of the proposed method.This simple and data-efficient estimation framework is promising in real-world applications under a variety of scenarios.展开更多
Lithium-ion batteries are the most widely accepted type of battery in the electric vehicle industry because of some of their positive inherent characteristics. However, the safety problems associated with inaccurate e...Lithium-ion batteries are the most widely accepted type of battery in the electric vehicle industry because of some of their positive inherent characteristics. However, the safety problems associated with inaccurate estimation and prediction of the state of health of these batteries have attracted wide attention due to the adverse negative effect on vehicle safety. In this paper, both machine and deep learning models were used to estimate the state of health of lithium-ion batteries. The paper introduces the definition of battery health status and its importance in the electric vehicle industry. Based on the data preprocessing and visualization analysis, three features related to actual battery capacity degradation are extracted from the data. Two learning models, SVR and LSTM were employed for the state of health estimation and their respective results are compared in this paper. The mean square error and coefficient of determination were the two metrics for the performance evaluation of the models. The experimental results indicate that both models have high estimation results. However, the metrics indicated that the SVR was the overall best model.展开更多
锂离子电池健康状态(state of health,SOH)估计对确保能量存储系统的可靠性和安全性至关重要。然而,现有SOH估计方法在单一特征提取和固定充放电条件依赖方面存在局限性,难以适应多变的实际工作环境。为解决这一问题,本工作提出了一种...锂离子电池健康状态(state of health,SOH)估计对确保能量存储系统的可靠性和安全性至关重要。然而,现有SOH估计方法在单一特征提取和固定充放电条件依赖方面存在局限性,难以适应多变的实际工作环境。为解决这一问题,本工作提出了一种基于弛豫电压的并行多尺度特征融合卷积模型(multi-scale feature fusion convolution model,MSFFCM)结合极端梯度提升树(XGBoost)的SOH估计方法。MSFFCM通过多层堆叠卷积模块提取弛豫电压数据的深层特征,同时利用并行多尺度注意力机制增强了多尺度特征的捕捉能力,并将这些特征与统计特征进行融合,以提升模型的特征提取和融合能力。针对XGBoost模型,本工作应用贝叶斯优化算法进行参数调优,从而在多源融合特征基础上实现高精度SOH估计。实验验证基于两种商用18650型号电池的多温度和多充放电策略数据集,结果表明该方法的均方根误差(RMSE)和平均绝对误差(MAE)均小于0.5%,明显优于传统方法。本工作为锂电池健康管理提供了一种不依赖特定充放电条件的有效估计工具,有望在复杂的实际应用中发挥重要作用。展开更多
The state of health(SOH)is a critical factor in evaluating the performance of the lithium-ion batteries(LIBs).Due to various end-user behaviors,the LIBs exhibit different degradation modes,which makes it challenging t...The state of health(SOH)is a critical factor in evaluating the performance of the lithium-ion batteries(LIBs).Due to various end-user behaviors,the LIBs exhibit different degradation modes,which makes it challenging to estimate the SOHs in a personalized way.In this article,we present a novel particle swarm optimization-assisted deep domain adaptation(PSO-DDA)method to estimate the SOH of LIBs in a personalized manner,where a new domain adaptation strategy is put forward to reduce cross-domain distribution discrepancy.The standard PSO algorithm is exploited to automatically adjust the chosen hyperparameters of developed DDA-based method.The proposed PSODDA method is validated by extensive experiments on two LIB datasets with different battery chemistry materials,ambient temperatures and charge-discharge configurations.Experimental results indicate that the proposed PSO-DDA method surpasses the convolutional neural network-based method and the standard DDA-based method.The Py Torch implementation of the proposed PSO-DDA method is available at https://github.com/mxt0607/PSO-DDA.展开更多
This paper presents a more accurate battery state of charge(SOC)and state of health(SOH)estimation method.A lithium battery is represented by a nonlinear two-order resistance-capacitance equivalent circuit model.The m...This paper presents a more accurate battery state of charge(SOC)and state of health(SOH)estimation method.A lithium battery is represented by a nonlinear two-order resistance-capacitance equivalent circuit model.The model parameters are estimated by searching least square error optimization algorithm.Precisely defined by this method,the model parameters allow to accurately determine the capacity of the battery,which in turn allows to specify the SOC prediction value used as a basis for the SOH value.Application of the extended Kalman filter(EKF)removes the need of prior known initial SOC,and applying the fuzzy logic helps to eliminate the measurement and process noise.Simulation results obtained during the urban dynamometer driving schedule(UDDS)test show that the maximum error in estimation of the battery SOC is 0.66%.Battery capacity is estimate by offline updated Kalman filter,and then SOH will be predicted.The maximum error in estimation of the battery capacity is 1.55%.展开更多
To ensure a long-term safety and reliability of electric vehicle and energy storage system,an accurate estimation of the state of health(SOH)for lithium-ion battery is important.In this study,a method for estimating t...To ensure a long-term safety and reliability of electric vehicle and energy storage system,an accurate estimation of the state of health(SOH)for lithium-ion battery is important.In this study,a method for estimating the lithium-ion battery SOH was proposed based on an improved extreme learning machine(ELM).Input weights and hidden layer biases were generated randomly in traditional ELM.To improve the estimation accuracy of ELM,the differential evolution algorithm was used to optimize these parameters in feasible solution spaces.First,incremental capacity curves were obtained by incremental capacity analysis and smoothed by Gaussian filter to extract health interests.Then,the ELM based on differential evolution algorithm(DE-ELM model)was used for a lithium-ion battery SOH estimation.At last,four battery historical aging data sets and one random walk data set were employed to validate the prediction performance of DE-ELM model.Results show that the DE-ELM has a better performance than other studied algorithms in terms of generalization ability.展开更多
为提高对动力电池的荷电状态(state of charge, SOC)估算精度、动力电池的健康状态(state of health, SOH)对锂电池性能的影响,提出一种扩展卡尔曼滤波(extended kalman filtering, EKF)联合估算算法。根据现有的实验数据,分析锂电池特...为提高对动力电池的荷电状态(state of charge, SOC)估算精度、动力电池的健康状态(state of health, SOH)对锂电池性能的影响,提出一种扩展卡尔曼滤波(extended kalman filtering, EKF)联合估算算法。根据现有的实验数据,分析锂电池特性,构建二阶RC等效电路模型,并进行参数辨识,搭建MATLAB仿真平台联合EKF算法进行SOC估算,将仿真结果与真实数据进行对比,结果表明,EKF联合估算SOC比EKF估算SOC误差精度约高1.2%,且抗干扰能力更强。展开更多
健康状态(state of health,SOH)是电池管理系统的重要参考依据,准确的SOH估计对保证电池安全稳定运行具有重大意义,其中提取可靠有效的健康特征描述电池老化状态以及构建精确稳定的估计模型是目前面临的主要问题。为了提高SOH估计精度,...健康状态(state of health,SOH)是电池管理系统的重要参考依据,准确的SOH估计对保证电池安全稳定运行具有重大意义,其中提取可靠有效的健康特征描述电池老化状态以及构建精确稳定的估计模型是目前面临的主要问题。为了提高SOH估计精度,提出了一种基于模糊熵和粒子滤波(particle filter,PF)的锂离子电池SOH估计方法。首先,通过分析电池老化过程中的放电电压数据,提取模糊熵值作为电池的老化特征;其次,基于代谢灰色模型(metabolic grey model,MGM)和时间卷积网络(temporal convolutional network,TCN)构建描述锂电池老化特征的非参数状态空间模型;最后,通过PF实现锂电池SOH的闭环估计。此外,利用NASA锂电池数据集对所提出的SOH估计方法进行了验证,并与该领域其他方法进行对比实验。结果表明,所提方法最大估计误差在5%左右,相比于同类方法其估计精度提升了约50%,且在不同训练周期数条件下表现出较好的鲁棒性,验证了所提方法的可行性与优越性。展开更多
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 ...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.展开更多
基金National Key Research and Development Program of China (Grant No. 2022YFE0102700)National Natural Science Foundation of China (Grant No. 52102420)+2 种基金research project “Safe Da Batt” (03EMF0409A) funded by the German Federal Ministry of Digital and Transport (BMDV)China Postdoctoral Science Foundation (Grant No. 2023T160085)Sichuan Science and Technology Program (Grant No. 2024NSFSC0938)。
文摘A fast-charging policy is widely employed to alleviate the inconvenience caused by the extended charging time of electric vehicles. However, fast charging exacerbates battery degradation and shortens battery lifespan. In addition, there is still a lack of tailored health estimations for fast-charging batteries;most existing methods are applicable at lower charging rates. This paper proposes a novel method for estimating the health of lithium-ion batteries, which is tailored for multi-stage constant current-constant voltage fast-charging policies. Initially, short charging segments are extracted by monitoring current switches,followed by deriving voltage sequences using interpolation techniques. Subsequently, a graph generation layer is used to transform the voltage sequence into graphical data. Furthermore, the integration of a graph convolution network with a long short-term memory network enables the extraction of information related to inter-node message transmission, capturing the key local and temporal features during the battery degradation process. Finally, this method is confirmed by utilizing aging data from 185 cells and 81 distinct fast-charging policies. The 4-minute charging duration achieves a balance between high accuracy in estimating battery state of health and low data requirements, with mean absolute errors and root mean square errors of 0.34% and 0.66%, respectively.
基金supported by Fundamental Research Program of Shanxi Province(No.202203021211088)Shanxi Provincial Natural Science Foundation(No.202204021301049).
文摘The performance of lithium-ion batteries(LIBs)gradually declines over time,making it critical to predict the battery’s state of health(SOH)in real-time.This paper presents a model that incorporates health indicators and ensemble Gaussian process regression(EGPR)to predict the SOH of LIBs.Firstly,the degradation process of an LIB is analyzed through indirect health indicators(HIs)derived from voltage and temperature during discharge.Next,the parameters in the EGPR model are optimized using the gannet optimization algorithm(GOA),and the EGPR is employed to estimate the SOH of LIBs.Finally,the proposed model is tested under various experimental scenarios and compared with other machine learning models.The effectiveness of EGPR model is demonstrated using the National Aeronautics and Space Administration(NASA)LIB.The root mean square error(RMSE)is maintained within 0.20%,and the mean absolute error(MAE)is below 0.16%,illustrating the proposed approach’s excellent predictive accuracy and wide applicability.
基金funded by the “SMART BATTERY” project, granted by Villum Foundation in 2021 (project number 222860)。
文摘State of health(SoH) estimation plays a key role in smart battery health prognostic and management.However,poor generalization,lack of labeled data,and unused measurements during aging are still major challenges to accurate SoH estimation.Toward this end,this paper proposes a self-supervised learning framework to boost the performance of battery SoH estimation.Different from traditional data-driven methods which rely on a considerable training dataset obtained from numerous battery cells,the proposed method achieves accurate and robust estimations using limited labeled data.A filter-based data preprocessing technique,which enables the extraction of partial capacity-voltage curves under dynamic charging profiles,is applied at first.Unsupervised learning is then used to learn the aging characteristics from the unlabeled data through an auto-encoder-decoder.The learned network parameters are transferred to the downstream SoH estimation task and are fine-tuned with very few sparsely labeled data,which boosts the performance of the estimation framework.The proposed method has been validated under different battery chemistries,formats,operating conditions,and ambient.The estimation accuracy can be guaranteed by using only three labeled data from the initial 20% life cycles,with overall errors less than 1.14% and error distribution of all testing scenarios maintaining less than 4%,and robustness increases with aging.Comparisons with other pure supervised machine learning methods demonstrate the superiority of the proposed method.This simple and data-efficient estimation framework is promising in real-world applications under a variety of scenarios.
文摘Lithium-ion batteries are the most widely accepted type of battery in the electric vehicle industry because of some of their positive inherent characteristics. However, the safety problems associated with inaccurate estimation and prediction of the state of health of these batteries have attracted wide attention due to the adverse negative effect on vehicle safety. In this paper, both machine and deep learning models were used to estimate the state of health of lithium-ion batteries. The paper introduces the definition of battery health status and its importance in the electric vehicle industry. Based on the data preprocessing and visualization analysis, three features related to actual battery capacity degradation are extracted from the data. Two learning models, SVR and LSTM were employed for the state of health estimation and their respective results are compared in this paper. The mean square error and coefficient of determination were the two metrics for the performance evaluation of the models. The experimental results indicate that both models have high estimation results. However, the metrics indicated that the SVR was the overall best model.
基金supported in part by the National Natural Science Foundation of China(92167201,62273264,61933007)。
文摘The state of health(SOH)is a critical factor in evaluating the performance of the lithium-ion batteries(LIBs).Due to various end-user behaviors,the LIBs exhibit different degradation modes,which makes it challenging to estimate the SOHs in a personalized way.In this article,we present a novel particle swarm optimization-assisted deep domain adaptation(PSO-DDA)method to estimate the SOH of LIBs in a personalized manner,where a new domain adaptation strategy is put forward to reduce cross-domain distribution discrepancy.The standard PSO algorithm is exploited to automatically adjust the chosen hyperparameters of developed DDA-based method.The proposed PSODDA method is validated by extensive experiments on two LIB datasets with different battery chemistry materials,ambient temperatures and charge-discharge configurations.Experimental results indicate that the proposed PSO-DDA method surpasses the convolutional neural network-based method and the standard DDA-based method.The Py Torch implementation of the proposed PSO-DDA method is available at https://github.com/mxt0607/PSO-DDA.
基金Open Fund Project of State Key Laboratory of Large Electric Transmission Systems and Equipment Technology(No.SKLLDJ042017005)。
文摘This paper presents a more accurate battery state of charge(SOC)and state of health(SOH)estimation method.A lithium battery is represented by a nonlinear two-order resistance-capacitance equivalent circuit model.The model parameters are estimated by searching least square error optimization algorithm.Precisely defined by this method,the model parameters allow to accurately determine the capacity of the battery,which in turn allows to specify the SOC prediction value used as a basis for the SOH value.Application of the extended Kalman filter(EKF)removes the need of prior known initial SOC,and applying the fuzzy logic helps to eliminate the measurement and process noise.Simulation results obtained during the urban dynamometer driving schedule(UDDS)test show that the maximum error in estimation of the battery SOC is 0.66%.Battery capacity is estimate by offline updated Kalman filter,and then SOH will be predicted.The maximum error in estimation of the battery capacity is 1.55%.
文摘To ensure a long-term safety and reliability of electric vehicle and energy storage system,an accurate estimation of the state of health(SOH)for lithium-ion battery is important.In this study,a method for estimating the lithium-ion battery SOH was proposed based on an improved extreme learning machine(ELM).Input weights and hidden layer biases were generated randomly in traditional ELM.To improve the estimation accuracy of ELM,the differential evolution algorithm was used to optimize these parameters in feasible solution spaces.First,incremental capacity curves were obtained by incremental capacity analysis and smoothed by Gaussian filter to extract health interests.Then,the ELM based on differential evolution algorithm(DE-ELM model)was used for a lithium-ion battery SOH estimation.At last,four battery historical aging data sets and one random walk data set were employed to validate the prediction performance of DE-ELM model.Results show that the DE-ELM has a better performance than other studied algorithms in terms of generalization ability.
文摘为提高对动力电池的荷电状态(state of charge, SOC)估算精度、动力电池的健康状态(state of health, SOH)对锂电池性能的影响,提出一种扩展卡尔曼滤波(extended kalman filtering, EKF)联合估算算法。根据现有的实验数据,分析锂电池特性,构建二阶RC等效电路模型,并进行参数辨识,搭建MATLAB仿真平台联合EKF算法进行SOC估算,将仿真结果与真实数据进行对比,结果表明,EKF联合估算SOC比EKF估算SOC误差精度约高1.2%,且抗干扰能力更强。
文摘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.
