Feature parameter extraction and intelligent estimation of the State-of-Health of lithium-ion batteries

Deng, Yuewen; Ying, Hejie; Jiaqiang, E; Zhu, Huangqiu; Wei, Kexiang; Chen, Jingwei; Zhang, Feng; Liao, Gaoliang

doi:10.1016/j.energy.2019.03.177

Cited by 149 publications

(44 citation statements)

References 67 publications

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“…Through the comparative analysis of differences between old and new batteries, the researchers found that the ohmic resistance and actual maximum battery capacity have more significant changes due to the SOH variations, and SOH is defined as follows from the perspective of ohmic resistance [23]:…”

Section: Setup Of Equivalent Circuit Model For the Lithium Batterymentioning

confidence: 99%

SOC and SOH Joint Estimation of the Power Batteries Based on Fuzzy Unscented Kalman Filtering Algorithm

et al. 2019

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In order to improve the convergence time and stabilization accuracy of the real-time state estimation of the power batteries for electric vehicles, a fuzzy unscented Kalman filtering algorithm (F-UKF) of a new type is proposed in this paper, with an improved second-order resistor-capacitor (RC) equivalent circuit model established and an online parameter identification used by Bayes. Ohmic resistance is treated as a battery state of health (SOH) characteristic parameter, F-UKF algorithms are used for the joint estimation of battery state of charge (SOC) and SOH. The experimental data obtained from the ITS5300-based battery test platform are adopted for the simulation verification under discharge conditions with constant-current pulses and urban dynamometer driving schedule (UDDS) conditions in the MATLAB environment. The experimental results show that the F-UKF algorithm is insensitive to the initial value of the SOC under discharge conditions with constant-current pulses, and the SOC and SOH estimation accuracy under UDDS conditions reaches 1.76% and 1.61%, respectively, with the corresponding convergence time of 120 and 140 s, which proves the superiority of the joint estimation algorithm.

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Section: Setup Of Equivalent Circuit Model For the Lithium Batterymentioning

confidence: 99%

SOC and SOH Joint Estimation of the Power Batteries Based on Fuzzy Unscented Kalman Filtering Algorithm

et al. 2019

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“…Second, processed external features are usually extracted from the differential charging curve under a constant current rate. Feature extraction [10]- [12], verification, and analysis are carried out on the curve under different aging conditions to establish the relationship between these health indicators and the SoH. According to the literature [10], [11], [13]- [19], feature extraction can be conducted on the IC/differential voltage (DV) or differential thermal voltammetry (DTV) curves.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, Li et al established a quantitative relationship between the SoH and three peak-valley value points along with their positions on the IC curve fitted by a Gaussian process regression algorithm [13]. Additionally, the processed external features extracted from other curves, such as the charge/discharge curve and the OCV curve, can also be used for this kind of analysis [12], [20]- [24]. Liu el.…”

Section: Introductionmentioning

confidence: 99%

Real-Time State-of-Health Estimation of Lithium-Ion Batteries Based on the Equivalent Internal Resistance

Tan

Zhan

et al. 2020

IEEE Access

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Real-time state-of-health (SoH) estimation is often difficult to obtain due to the unavailability of capacity measurements in real-time monitoring. The equivalent internal resistance (EIR), which is easily obtained and closely related to battery deterioration, is studied as a possible solution for achieving real-time and reliable SoH estimation for lithium-ion batteries. A novel real-time SoH estimation method based on the EIR is introduced for lithium-ion batteries. First, an experimental study of the relationship between the EIR and battery degradation is implemented, and this study is used to develop an empirical description of battery degradation using the EIR vector. Second, a fast extraction method for identifying the EIR in real time is proposed by leveraging the relationship between the EIR vector and state of charge (SoC). Third, a support vector regression (SVR)-based method for real-time SoH estimation is introduced by characterizing the hidden relationship between the EIR vector and battery SoH. The proposed method is demonstrated using laboratory test data. The results show that the proposed method can predict the battery SoH in real time with good accuracy and robustness.

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“…They proposed to feed the diagnostic features into GPR model to predict remaining useful life lithium-ion batteries. Similarly, Deng et al extracted four Diagnostic features from charging process and used them as SVM training data set to estimate SoH [13]. Some authors also proposed DFs by applying Incremental capacity (IC) and differential analysis.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Gradient Boosted Tree for SoH Estimation Based on Diagnostic Features

et al. 2020

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The success of electric vehicles (EVs) depends principally on their energy storage system. Lithium-ion batteries currently feature the ideal properties to fulfil the wide range of prerequisites specific to electric vehicles. Meanwhile, the precise estimation of batteries’ state of health (SoH) should be available to provide the optimal performance of EVs. This study attempts to propose a precise, real-time method to estimate lithium-ion state of health when it operates in a realistic driving condition in the presence of dynamic stress factors. To this end, a real-life driving profile was simulated based on highly dynamic worldwide harmonized light vehicle test cycle load profiles. Afterward, various features will be extracted from voltage data and they will be scored based on prognostic metrics to select diagnostic features which can conveniently identify battery degradation. Lastly, an ensemble learning model was developed to capture the correlation of diagnostic features and battery’s state of health (SoH). The result illustrates that the proposed method has the potential to estimate the SoH of battery cells aged under a distinct depth of discharge and current profile with a maximum error of 1%. This confirms the robustness of the developed approach. The proposed method has the capability of implementing in battery management systems due to many reasons; firstly, it is tested and validated based on the data which are equal to the real-life driving operation of an electric vehicle. Secondly, it has high accuracy and precision, and a low computational cost. Finally, it can estimate the SoH of battery cells with different aging patterns.

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Feature parameter extraction and intelligent estimation of the State-of-Health of lithium-ion batteries

Cited by 149 publications

References 67 publications

SOC and SOH Joint Estimation of the Power Batteries Based on Fuzzy Unscented Kalman Filtering Algorithm

SOC and SOH Joint Estimation of the Power Batteries Based on Fuzzy Unscented Kalman Filtering Algorithm

Real-Time State-of-Health Estimation of Lithium-Ion Batteries Based on the Equivalent Internal Resistance

Ensemble Gradient Boosted Tree for SoH Estimation Based on Diagnostic Features

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