Novel battery state-of-health online estimation method using multiple health indicators and an extreme learning machine

Pan, Haihong; Liu, Zhiqiang; Wang, Huimin; Wei, Haiyan; Chen, Lin

doi:10.1016/j.energy.2018.06.220

Cited by 252 publications

(80 citation statements)

References 27 publications

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“…Two lithiumion batteries, numbered A and B, are tested. Main specifications of the investigated lithium-ion batteries are shown in Table 1, and more detailed equipment parameters are referred to the Pan et al 26 According to previous studies, 17,27 the experimental procedure is shown in Figure 1B. The whole experimental period includes three characterization tests (10°C, 25°C, and 40°C) and one aging experiment (45°C).…”

Section: Aging Experimentsmentioning

confidence: 99%

A model‐based and data‐driven joint method for state‐of‐health estimation of lithium‐ion battery in electric vehicles

Lyu

Gao

2019

Int J Energy Res

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Summary Lithium‐ion battery state‐of‐health estimation is one of the vital issues for electric vehicle safety. In this work, a joint model‐based and data‐driven estimator is developed to achieve accurate and reliable state‐of‐health estimation. In the estimator, an increase in ohmic resistance extracted from the Thevenin model is defined as the health indicator to quantify the capacity degradation. Then, a linear state‐space representation is constructed based on the data‐driven linear regression. Furthermore, the Kalman filter is introduced to trace capacity degradation based on the novel state space representation. A series of battery aging datasets with different dynamic loading profiles and temperatures are obtained to demonstrate the accuracy and robustness of the proposed method. Results show that the maximum error of the Kalman filter is 2.12% at different temperatures, which proves the effectiveness of the proposed method.

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Section: Aging Experimentsmentioning

confidence: 99%

A model‐based and data‐driven joint method for state‐of‐health estimation of lithium‐ion battery in electric vehicles

Lyu

Gao

2019

Int J Energy Res

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“…In [11,12], genetic algorithm (GA) and recursive least square (RLS) are applied to obtain the internal resistance based on standard equivalent circuit model (ECM). Additionally, the pulse approaches are employed to track the internal resistance under different depth of discharging (DOD) [13]. Then the relationship between the real-time resistance and two fixed resistances (the maximum and minimum resistances) indicates the battery degradation condition.…”

Section: Introductionmentioning

confidence: 99%

State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression

Yuan

et al. 2020

Energy

314

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“…The framework of data-driven SoH estimation generally includes three main steps: (1) acquiring data (2) exploring historical data such as current, voltage and temperature to extract promising features [9] (3) feeding the features to a machine learning model to capture the correspondence between battery SoH and extracted features. Among these steps, the features extraction step is of utmost importance [10] since the accuracy of SoH estimation largely depends on the features that express distinct trends as battery degrades. These features called diagnostic features.…”

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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Novel battery state-of-health online estimation method using multiple health indicators and an extreme learning machine

Cited by 252 publications

References 27 publications

A model‐based and data‐driven joint method for state‐of‐health estimation of lithium‐ion battery in electric vehicles

A model‐based and data‐driven joint method for state‐of‐health estimation of lithium‐ion battery in electric vehicles

State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression

Ensemble Gradient Boosted Tree for SoH Estimation Based on Diagnostic Features

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