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Battery pack capacity estimation for electric vehicles based on enhanced machine learning and field data

Journal of Energy Chemistry 2024年第5期92卷 605-618页

作者： Qingguang Qi Wenxue Liu Zhongwei Deng Jinwen Li Ziyou Song Xiaosong Hu College of Mechanical and Vehicle Engineering Chongqing UniversityChongqing 400044China School of Mechanical and Electrical Engineering University of Electronic Science and Technology of ChinaChengdu 611731SichuanChina Department of Mechanical Engineering National University of SingaporeSingapore 117575Singapore

Accurate capacity estimation is of great importance for the reliable state monitoring,timely maintenance,and second-life utilization of lithium-ion *** numerous works on battery capacity estimation using laboratory datasets,most of them are applied to battery cells and lack satisfactory fidelity when extended to real-world electric vehicle(EV)battery *** challenges intensify for large-sized EV battery packs,where unpredictable operating profiles and low-quality data acquisition hinder precise capacity *** fill the gap,this study introduces a novel data-driven battery pack capacity estimation method grounded in field *** proposed approach begins by determining labeled capacity through an innovative combination of the inverse ampere-hour integral,open circuit voltage-based,and resistance-based correction ***,multiple health features are extracted from incremental capacity curves,voltage curves,equivalent circuit model parameters,and operating temperature to thoroughly characterize battery aging behavior.A feature selection procedure is performed to determine the optimal feature set based on the Pearson correlation ***,a convolutional neural network and bidirectional gated recurrent unit,enhanced by an attention mechanism,are employed to estimate the battery pack capacity in real-world EV ***,the proposed method is validated with a field dataset from two EVs,covering approximately 35,000 *** results demonstrate that the proposed method exhibits better estimation performance with an error of less than 1.1%compared to existing *** work shows great potential for accurate large-sized EV battery pack capacity estimation based on field data,which provides significant insights into reliable labeled capacity calculation,effective features extraction,and machine learning-enabled health diagnosis.

关键词： Electricvehicle Lithium-ion battery pack capacity estimation Machine learning Field data

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A hierarchical enhanced data-driven battery pack capacity estimation framework for real-world operating conditions with fewer labeled data

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Journal of Energy Chemistry 2024年第4期91卷 417-432页

作者： Sijia Yang Caiping Zhang Haoze Chen Jinyu Wang Dinghong Chen Linjing Zhang Weige Zhang National Active Distribution Network Technology Research Center(NANTEC) Beijing Jiaotong UniversityBeijing 100044China

Battery pack capacity estimation under real-world operating conditions is important for battery performance optimization and health management,contributing to the reliability and longevity of batterypowered ***,complex operating conditions,coupling cell-to-cell inconsistency,and limited labeled data pose great challenges to accurate and robust battery pack capacity *** address these issues,this paper proposes a hierarchical data-driven framework aimed at enhancing the training of machine learning models with fewer labeled *** traditional data-driven methods that lack interpretability,the hierarchical data-driven framework unveils the“mechanism”of the black box inside the data-driven framework by splitting the final estimation target into cell-level and pack-level intermediate targets.A generalized feature matrix is devised without requiring all cell voltages,significantly reducing the computational cost and memory *** generated intermediate target labels and the corresponding features are hierarchically employed to enhance the training of two machine learning models,effectively alleviating the difficulty of learning the relationship from all features due to fewer labeled data and addressing the dilemma of requiring extensive labeled data for accurate *** only 10%of degradation data,the proposed framework outperforms the state-of-the-art battery pack capacity estimation methods,achieving mean absolute percentage errors of 0.608%,0.601%,and 1.128%for three battery packs whose degradation load profiles represent real-world operating *** high accuracy,adaptability,and robustness indicate the potential in different application scenarios,which is promising for reducing laborious and expensive aging experiments at the pack level and facilitating the development of battery technology.

关键词： Lithium-ion battery pack capacity estimation Label generation Multi-machine learning model Real-world operating

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Fast Remaining capacity estimation for Lithium-ion Batteries Based on Short-time Pulse Test and Gaussian Process Regression

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Energy & Environmental Materials 2023年第3期6卷 238-246页

作者： Aihua Ran Ming Cheng Shuxiao Chen Zheng Liang Zihao Zhou Guangmin Zhou Feiyu Kang Xuan Zhang Baohua Li Guodan Wei Tsinghua-Berkeley Shenzhen Institute(TBSI) Tsinghua UniversityShenzhen 518055China Tsinghua Shenzhen International Graduate School Tsinghua UniversityShenzhen 518055China

It remains challenging to effectively estimate the remaining capacity of the secondary lithium-ion batteries that have been widely adopted for consumer electronics,energy storage,and electric ***,by integrating regular real-time current short pulse tests with data-driven Gaussian process regression algorithm,an efficient battery estimation has been successfully developed and validated for batteries with capacity ranging from 100%of the state of health(SOH)to below 50%,reaching an average accuracy as high as 95%.Interestingly,the proposed pulse test strategy for battery capacity measurement could reduce test time by more than 80%compared with regular long charge/discharge *** short-term features of the current pulse test were selected for an optimal training *** at different voltage stages and state of charge(SOC)are collected and explored to find the most suitable estimation *** particular,we explore the validity of five different machine-learning methods for estimating capacity driven by pulse features,whereas Gaussian process regression with Matern kernel performs the best,providing guidance for future *** new strategy of combining short pulse tests with machine-learning algorithms could further open window for efficiently forecasting lithium-ion battery remaining capacity.

关键词： capacity estimation data-driven method Gaussian process regression lithium-ion battery pulse tests

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

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Journal of Energy Chemistry 2024年第1期88卷 409-420,I0010页

作者： Chuanping Lin Jun Xu Delong Jiang Jiayang Hou Ying Liang Xianggong Zhang Enhu Li Xuesong Mei State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong UniversityXi’an 710049ShaanxiChina Shaanxi Key Laboratory of Intelligent Robots School of Mechanical EngineeringXi’an Jiaotong UniversityXi’an 710049ShaanxiChina Department of Electrical Engineering and Automation Luoyang Institute of Science and TechnologyLuoyang 471023HenanChina Wuhan Institute of Marine Electric Propulsion China State Shipbuilding Corporation LimitedWuhan 430064HubeiChina Gresgying Digital Technology Ltd. Xi’an 710086ShaanxiChina

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.

关键词： Lithium-ion battery Partial charging curves capacity estimation Data-driven Sampling frequency

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A novel combined multi-battery dataset based approach for enhanced prediction accuracy of data driven prognostic models in capacity estimation of lithium ion batteries

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Energy and AI 2021年第3期5卷 141-149页

作者： Vijay Mohan Nagulapati Hyunjun Lee DaWoon Jung SalaiSargunan S Paramanantham Boris Brigljevic Yunseok Choi Hankwon Lim School of Energy and Chemical Engineering UNISTUlsanRepublic of Korea Xynergypower Corp Gyeonggi-doRepublic of Korea Department of Energy Engineering UNISTUlsanRepublic of Korea

To ensure smooth and reliable operations of battery systems,reliable prognosis with accurate prediction of State of Health of lithium ion batteries is of utmost ***,battery degradation is a complex challenge involving many electrochemical reactions at anode,separator,cathode and electrolyte/electrode ***,there is significant effect of the operating conditions on the battery *** machine learning tech-niques have been applied to estimate the capacity and State of Health of lithium ion batteries to ensure reliable operation and timely *** this paper,we study the Gaussian Process Regression(GPR)and Support Vector Machine(SVM)model-based approaches in estimating the capacity and State of Health of ***-tery capacity and State of Health estimations are carried out using GPR and SVM models and the predictions comparatively studied for accuracy based on RMSE *** prediction accuracy is further compared with re-spect to single sensor and multi sensor ***,a combined multi battery data set model is used to improve the prediction *** the data of multiple batteries with similar operating conditions for training a model resulted in higher prediction accuracy.

关键词： capacity estimation State of health Gaussian process regression Support vector machine Lithium ion battery

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FAST capacity estimation ALGORITHMS FOR MANETS USING DIRECTIONAL ANTENNAS

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Journal of Electronics(China) 2007年第5期24卷 710-716页

作者： Li Ning Guo Yan Guo Li Institute of Sciences PLA Univ. of Sci. ＆ Tech. Nanjing 210007 China School of Information Engineering Beijing University of Posts and Telecommunications Beijing 100875 China

In this letter,capacity estimation for Mobile Ad hoc NETworks (MANETs) using direc- tional antennas are *** Matrix-based Fast Calculation Algorithms (MFCAs) are proposed to estimate the network capacity in a network scenario in which there is no channel sharing among multiple sessions and traffic is sensitive to delay with an end-to-end delay *** first algo- rithm MFCA-1 is used to estimate network capacity in a situation where all links have the same delay. It estimates the maximum number of k-hop sessions in a network based on the k-hop adjacency matrix of the *** second algorithm MFCA-2 is used to estimate network capacity in a situation where different links may have different *** calculates the maximum number of sessions in a network with an end-to-end delay constraint based on the adjacency matrix and the link-delay matrix of the *** and simulation results show that both MFCA-1 and MFCA-2 can calculate network capacity much faster than the well-known Brute-Force Search Algorithm (BFSA) but with the same accuracy.

关键词： capacity estimation End-to-end delay Directional antennas Mobile Ad hoc NETworks(MANETs)

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Residential PV capacity estimation and power disaggregation using net metering measurements

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Global Energy Interconnection 2022年第6期5卷 590-603页

作者： Bo Liu Jianmin Tian Wenpeng Luan Yi Gao Xiaohui Wang Shuai Luo School of Electrical and Information Engineering Tianjin UniversityTianjin 300072P.R.China Economic and Technical Research Institute State Grid Tianjin Electric Power CompanyTianjin 300171P.R.China Artificial Intelligence Application Department China Electric Power Research InstituteBeijing 100089P.R.China

As the intermittency and uncertainty of photovoltaic(PV)power generation poses considerable challenges to the power system operation,accurate PV generation estimates are critical for the distribution operation,maintenance,and demand response program implementation because of the increasing usage of distributed ***,most residential PVs are installed behind the meter,with only the net load available to the ***,a method for disaggregating the residential PV generation from the net load data is needed to enhance the grid-edge *** this study,an unsupervised PV capacity estimation method based on net metering data is proposed,for estimating the PV capacity in the customer’s premise based on the distribution characteristics of nocturnal and diurnal net load ***,the PV generation disaggregation method is *** on the analysis of the correlation between the nocturnal and diurnal actual loads and the correlation between the PV capacity and their actual PV generation,the PV generation of customers is estimated by applying linear fitting of multiple typical solar exemplars and then disaggregating them into hourly-resolution power ***,the anomalies of disaggregated PV power are calibrated and corrected using the estimated *** results on a real-world hourly dataset involving 260 customers show that the proposed PV capacity estimation method achieves good accuracy because of the advantages of robustness and low *** with the state-of-the-art PV disaggregation algorithm,the proposed method exhibits a reduction of over 15%for the mean absolute percentage error and over 20%for the root mean square error.

关键词： Behind-the-meter Residential photovoltaic capacity estimation Power disaggregation Net metering

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A Fast capacity estimation Approach for Retired Lithium-ion Batteries

A Fast Capacity Estimation Approach for Retired Lithium-ion ...

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第40届中国控制会议

作者： Xuebin Cao Zhongkai Zhou Bin Duan Pingwei Gu Yunlong Shang Chenghui Zhang School of Control Science and Engineering Shandong University

capacity is one of the most critical parameters of lithium-ion batteries（LIBs）. Retired batteries generally also have 70%-80% of the nominal capacity, so it can bring considerable benefits through echelon use. However, the existing methods cannot simultaneously meet the capacity estimation accuracy and efficiency required for large-scale retired batteries. So this may lead to costing excessive time and energy. To solve the above problems, a fast and accurate capacity evaluation method based on support vector machine（SVM） is proposed in this paper. And the parameters（penalty coefficient and kernel function width） of the SVM model are optimized by the particle swarm optimization（PSO）. In order to expand the scope of application,capacity evaluation models in three different SOC are established. The inputs of the model are sampling voltages selected from the featured charging curve at three cases, and the output is the battery capacity. Experimental results demonstrate that the maximum error in the first case is less than 3.02%, and the errors in the other two cases are 1.88% and 1.97% respectively.

关键词： retired batteries capacity estimation support vector machine(SVM) particle swarm optimization(PSO)

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Co-estimation of State of Charge and capacity for Lithium-Ion Batteries with Multi-Stage Model Fusion Method

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Engineering 2021年第10期7卷 1469-1482页

作者： Rui Xiong Ju Wang Weixiang Shen Jinpeng Tian Hao Mu Department of Vehicle Engineering School of Mechanical EngineeringBeijing Institute of TechnologyBeijing 100081China Faculty of Science Engineering and TechnologySwinburne University of TechnologyHawthornVIC 3122Australia

Lithium-ion batteries(LIBs)have emerged as the preferred energy storage systems for various types of electric transports,including electric vehicles,electric boats,electric trains,and electric *** energy management of LIBs in electric transports for all-climate and long-life operation requires the accurate estimation of state of charge(SOC)and capacity in *** study proposes a multistage model fusion algorithm to co-estimate SOC and ***,based on the assumption of a normal distribution,the mean and variance of the residual error from the model at different ageing levels are used to calculate the weight for the establishment of a fusion model with stable ***,a differential error gain with forward-looking ability is introduced into a proportional–integral observer(PIO)to accelerate convergence ***,a fusion algorithm is developed by combining a multistage model and proportional–integral–differential observer(PIDO)to co-estimate SOC and capacity under a complex application ***,the convergence and anti-noise performance of the fusion algorithm are ***,the hardware-in-the-loop platform is set up to verify the performance of the fusion *** validation results of different aged LIBs over a wide range of temperature show that the presented fusion algorithm can realize a high-accuracy estimation of SOC and capacity with the relative errors within 2%and 3.3%,respectively.

关键词： State of charge capacity estimation Model fusion Proportional-integral-differential observer Hardware-in-the-loop

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

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Chinese Journal of Electrical Engineering 2024年第3期10卷 1-11页

作者： Lulu Wang Kun Zheng Yijing Li Zhipeng Yang Feifan Zhou Jia Guo Jinhao Meng North China Electric Power Test and Research Institute of China Datang Group Science and Technology Research Institute Co. Ltd.Beijing 100040China School of Future Technology Xi'an Jiaotong UniversityXi'an 710049China National Innovation Platform(Center)for Industry-Education Integration of Energy Storage Technology Xi'an Jiaotong UniversityXi'an 710049China School of Electrical Engineering Xi'an Jiaotong UniversityXi'an 710049China Department of Mechanical Engineering Imperial College LondonLondon SW72AZUK

Efficient assessment of battery degradation is important to effectively utilize and maintain battery management *** study introduces an innovative residual convolutional network(RCN)-gated recurrent unit(GRU)model to accurately assess health of lithium-ion batteries on multiple time *** model employs a soft parameter-sharing mechanism to identify both short-d dT and long-term degradation *** 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 *** designing a soft parameter-sharing mechanism,the model can seamlessly predict the capacity and remaining useful life(RUL)on a dual time *** 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 ***,studying the partial voltage fragment reveals the promising performance of the proposed method across various voltage ***,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.

关键词： Residual convolutional network-gated recurrent unit capacity estimation soft parameter sharing remaining useful life prediction

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