Developing a real-time data-driven battery health diagnosis method, using time and frequency domain condition indicators

Khaleghi, Sahar; Firouz, Yousef; Mierlo, Joeri Van; Bossche, Peter Van den

doi:10.1016/j.apenergy.2019.113813

Cited by 68 publications

(23 citation statements)

References 35 publications

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“…Additionally, SOC is generally defined as equation (2), and coupling the equation ( 1) and (2) results in the construction of battery model. Considering the operational application in state-space, the battery model can be desecrated as equation (3). It is worthy to mention that the constructed system model is associated with sampling intervals, and the performance will be influenced as the increasing of intervals.…”

Section: A Second-order Equivalent Circuit Modelmentioning

confidence: 99%

“…Considered to be preferable components for EVs, the lithium-ion batteries have been developed for operational applications owing to multiple facets such as stationary characteristics and reliant lifespan, and the fast-growing markets for applicational EVs which are closely associated with innovative technologies of battery management system (BMS). Deliberated as the crucial components, the management of batteries draws growing attention for both academia and industry due to the unpredictable safety and anxious driving mileage, and the precise modelling and stateof-charge (SOC) estimation are fundamental issues for improving the comprehensive performance of batteries [1], including energy management strategy [2], health diagnosis [3], lifespan prediction [4] and other related functions.…”

Section: Introductionmentioning

confidence: 99%

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A Comprehensive Evaluation on Variable Sampling Intervals of Power Battery System for Electric Vehicles

et al. 2020

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Considered to be the state-of-art solution for intelligent management of electric vehicles, cloud-control has been broadly investigated especially for parameterization and state estimation. Considering the operational cloud-database, the sampling intervals contribute to the precision and robustness of the battery management, and a balance between storage and performance is of crucial importance for real-time controlling. Unfortunately, the comprehensive performances on variable sampling intervals are doubtful for the development of cloud-control. Herein, the research of sampling intervals is carried out and the operational applications are simulated for validation including the precision, robustness and information content. 5 th -order spherical simplex-redial Kalman filter and particle swarm optimizationsimulated annealing methods are developed for researching the influences on precision of state of charge (SOC) estimation and parameterization under stable or dynamic conditions. Moreover, the information content of the desecrated database is evaluated based on the information entropy. The comparative results are carried out for separate characteristic, and the analysis exhibit the special performances for diverse sampling interval. The research confirms the differences on diverse intervals on operational applications, and the analysis might deliver effective guidance for future processing framework based on cloudcontrolling towards specific intentions.

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Section: A Second-order Equivalent Circuit Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Comprehensive Evaluation on Variable Sampling Intervals of Power Battery System for Electric Vehicles

et al. 2020

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“…Latterly, numerous machine learning techniques have been devised for battery SoH estimation, such as artificial neural network (ANN) [27,28,29,30], support vector machine (SVM) [31,32], regressive vector machine (RVM) [33,34], particle filter (PF) [35,36], Random Forests (RF) and Gaussian process regression (GPR) [37,9]. Utilizing extracted features from the terminal voltage response of the Li-ion battery under current pulse tests, a novel method is proposed in [31] together with an SVM model to estimate the SoH of an LFP type LIB.…”

Section: Introductionmentioning

confidence: 99%

State-of-health estimators coupled to a random forest approach for lithium-ion battery aging factor ranking

Mawonou

Eddahech

Dumur

et al. 2021

Journal of Power Sources

119

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Electrified vehicles users may expect their vehicle to have a steady autonomy range and available power throughout the lifetime of their cars. The health assessment of Lithium-ion batteries (LIBs), in that regard, represents a critical point for performance evaluation and lifetime prediction. Reliable state-of-health (SoH) assessment is essential to ensure cautious and suitable use of LIBs. To that end, several embedded solutions are proposed in the literature. In this paper, two new aging indicators are developed to enrich the existing diagnosis-based (DB-SoH) solutions. These indicators are based on collected data during charging (CDB-SoH) and driving (DDB-SoH) events overtime. The data are comprised of variables such as distance, speed, temperature, charging power, and more. Both solutions produce reliable state-of-health S oH assessment with significantly good accuracy. Additionally, a data-driven battery aging prediction using the random forest (RF) algorithm is introduced with respect to actual users' behavior and ambient conditions. The proposed solution produced an S oH estimation accuracy of 1.27%. Finally, a method for aging factors ranking is proposed. The obtained ranking is consistent with known aging root causes in the literature and can be used to mitigate fast LIB aging for electrified vehicle applications.

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“…Berecibar et al [31] estimated the SOH by tracking the location change of valley values in the DV curve. In addition, researchers also extracted HIs from the frequency domain [32], such as sample entropy [33] and mechanical parameters [34] to estimate SOH. The above methods mainly focus on the SOH estimation of battery cells, and HIs are mostly not simply extracted.…”

Section: Introductionmentioning

confidence: 99%

Joint Estimation of Inconsistency and State of Health for Series Battery Packs

et al. 2021

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Battery packs are applied in various areas (e.g., electric vehicles, energy storage, space, mining, etc.), which requires the state of health (SOH) to be accurately estimated. Inconsistency, also known as cell variation, is considered a significant evaluation index that greatly affects the degradation of battery pack. This paper proposes a novel joint inconsistency and SOH estimation method under cycling, which fills the gap of joint estimation based on the fast-charging process for electric vehicles. First, fifteen features are extracted from current change points during the partial charging process. Then, a joint estimation system is designed, where fusion weights are obtained by the analytic hierarchy process and multi-scale sample entropy to evaluate inconsistency. A wrapper is used to select the optimal feature subset, and Gaussian process regression is implemented to estimate the SOH. Finally, the estimation performance is assessed by the test data. The results show that the inconsistency evaluation can reflect the aging conditions, and the inconsistency does affect the aging process. The wrapper selection method improves the accuracy of SOH estimation by about 75.8% compared to the traditional filter method when only 10% of data is used for model training. The maximum absolute error and root mean square error are 2.58% and 0.93%, respectively.

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Developing a real-time data-driven battery health diagnosis method, using time and frequency domain condition indicators

Cited by 68 publications

References 35 publications

A Comprehensive Evaluation on Variable Sampling Intervals of Power Battery System for Electric Vehicles

A Comprehensive Evaluation on Variable Sampling Intervals of Power Battery System for Electric Vehicles

State-of-health estimators coupled to a random forest approach for lithium-ion battery aging factor ranking

Joint Estimation of Inconsistency and State of Health for Series Battery Packs

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