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Accuracy comparison and improvement for state of health estimation of lithium-ion battery based on random partial recharges and feature engineering

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Journal of Energy Chemistry 2024年第5期92卷 591-604页

作者： Xingjun Li Dan Yu søren byg vilsen Daniel Ioan stroe Department of Energy Aalborg UniversityAalborg 9220Denmark Department of Mathematical sciences Aalborg UniversityAalborg 9220Denmark Walker Department of Mechanical Engineering The University of Texas at AustinAustinTX 78712USA

state of health(sOH)estimation of e-mobilities operated in real and dynamic conditions is essential and *** of existing estimations are based on a fixed constant current charging and discharging aging profiles,which overlooked the fact that the charging and discharging profiles are random and not complete in real *** work investigates the influence of feature engineering on the accuracy of different machine learning(ML)-based sOH estimations acting on different recharging sub-profiles where a realistic battery mission profile is *** features were extracted from the battery partial recharging profiles,considering different factors such as starting voltage values,charge amount,and charging sliding ***,features were selected based on a feature selection pipeline consisting of filtering and supervised ML-based subset *** linear regression(MLR),Gaussian process regression(GPR),and support vector regression(sVR)were applied to estimate sOH,and root mean square error(RMsE)was used to evaluate and compare the estimation *** results showed that the feature selection pipeline can improve sOH estimation accuracy by 55.05%,2.57%,and 2.82%for MLR,GPR and sVR *** was demonstrated that the estimation based on partial charging profiles with lower starting voltage,large charge,and large sliding window size is more likely to achieve higher *** work hopes to give some insights into the supervised ML-based feature engineering acting on random partial recharges on sOH estimation performance and tries to fill the gap of effective sOH estimation between theoretical study and real dynamic application.

关键词： Feature engineering Dynamic forklift aging profile state of health comparison Machine learning Lithium-ion batteries

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维普期刊数据库

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The development of machine learning-based remaining useful life prediction for lithium-ion batteries

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Journal of Energy Chemistry 2023年第7期82卷 103-121,I0003页

作者： Xingjun Li Dan Yu vilsen søren byg store Daniel Ioan Department of Energy Aalborg UniversityAalborg 9220Denmark Department of Mathematical sciences Aalborg UniversityAalborg 9220Denmark

Lithium-ion batteries are the most widely used energy storage devices,for which the accurate prediction of the remaining useful life(RUL)is crucial to their reliable operation and accident *** work thoroughly investigates the developmental trend of RUL prediction with machine learning(ML)algorithms based on the objective screening and statistics of related papers over the past decade to analyze the research core and find future improvement *** possibility of extending lithium-ion battery lifetime using RUL prediction results is also explored in this *** ten most used ML algorithms for RUL prediction are first identified in 380 relevant *** the general flow of RUL prediction and an in-depth introduction to the four most used signal pre-processing techniques in RUL prediction are *** research core of common ML algorithms is given first time in a uniform format in chronological *** algorithms are also compared from aspects of accuracy and characteristics comprehensively,and the novel and general improvement directions or opportunities including improvement in early prediction,local regeneration modeling,physical information fusion,generalized transfer learning,and hardware implementation are further ***,the methods of battery lifetime extension are summarized,and the feasibility of using RUL as an indicator for extending battery lifetime is *** lifetime can be extended by optimizing the charging profile serval times according to the accurate RUL prediction results online in the *** paper aims to give inspiration to the future improvement of ML algorithms in battery RUL prediction and lifetime extension strategy.

关键词： Lithium-ion batteries Remaining useful lifetime prediction Machine learning Lifetime extension

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维普期刊数据库

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