Adaptive Piecewise Equivalent Circuit Model With SOC/SOH Estimation Based on Extended Kalman Filter

Huang, Zexin; Best, Matt C.; Knowles, James A.; Fly, Ashley

doi:10.1109/tec.2022.3218613

Cited by 31 publications

(3 citation statements)

References 26 publications

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“…In [21], the work was validated using a sequence of worldwide harmonized light-duty vehicles test procedure (WLTP) over 40 h. Ref. [22] proposed an electro-thermal model with two EKFs and a piecewise polynomial function to capture the SOC and parameter relationships. Here, the approach was validated on constant-current-degraded cells and, for the parameter estimation, on the WLTP and DST at a new cell.…”

Section: Research Objectivementioning

confidence: 99%

Model-Based State-of-Charge and State-of-Health Estimation Algorithms Utilizing a New Free Lithium-Ion Battery Cell Dataset for Benchmarking Purposes

Neupert

Kowal

2023

Batteries

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State estimation for lithium-ion battery cells has been the topic of many publications concerning the different states of a battery cell. They often focus on a battery cell’s state of charge (SOC) or state of health (SOH). Therefore, this paper introduces, on the one hand, a new lithium-ion battery dataset with dynamic validation data over degradation and, on the other hand, a model-based SOC and SOH estimation based on this dataset as a reference. An unscented Kalman-filter-based approach was used for SOC estimation and extended with a holistic ageing model to handle the SOH estimation. The paper describes the dataset, the models, the parameterisation, the implementation of the state estimations, and their validation using parts of the dataset, resulting in SOC and SOH estimations over the entire battery life. The results show that the dataset can be used to extract parameters, design models based on it, and validate it with dynamically degraded battery cells. The work provides an approach and dataset for better performance evaluations, applicability, and reliability investigations.

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Section: Research Objectivementioning

confidence: 99%

Model-Based State-of-Charge and State-of-Health Estimation Algorithms Utilizing a New Free Lithium-Ion Battery Cell Dataset for Benchmarking Purposes

Neupert

Kowal

2023

Batteries

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“…In [17], a new deep learning network framework is proposed to achieve SOH estimation. To improve the accuracy of the estimates, the researchers in [18] proposed a method based on a hybrid neural network called Gate Recursive Unit-Convolutional Neural Network, which can learn the shared information and time dependence of charging profiles through deep learning techniques, and can utilize the newly observed charging profiles (e.g., voltages, currents, and temperatures) to estimate SOH. In [19], proposed as a means of estimating SOC and SOH, an adaptive segmented equivalent circuit model based on the extended Kalman filter is illustrated in the degradation study and the model validation procedure.…”

Section: Introductionmentioning

confidence: 99%

SOC-SOH Estimation and Balance Control Based on Event-Triggered Distributed Optimal Kalman Consensus Filter

Fang,

Xu,

Fan

2024

Energies

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The inconsistency in state-of-charge (SOC) for electric vehicle batteries will cause component damage and lifespan reduction of batteries. Meanwhile, the consistency in the state-of-health (SOH) also negatively influences the consensus of SOC. To ensure the consensuses of SOC and SOH simultaneously, this paper introduces an innovative distributed optimal Kalman consensus filter (KCF) approach to battery management systems. In addition, at the stage where sensors transmit information to each other, a new event-triggering mechanism (ETM) based on dynamic information is proposed to reduce communication overhead effectively. Theoretical analysis verifies the optimality of the algorithm. By numerical simulations, the proposed event-triggered distributed optimal KCF (ET-DOKCF) method can improve the performance of SOC-SOH estimation and save communication resources.

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“…In terms of equivalent circuit models, Reference [6] used the 2-RC model to capture the degradation of the Li-ion battery, which was also considerably flexible for diverse cells of different chemistry. Reference [7] presentd a self-adaptive piecewise equivalent circuit model to estimate the battery SOH based on extended kalman filter. Furthermore, data models have also received attention.…”

mentioning

confidence: 99%

Life cycle capacity evaluation for battery energy storage systems

Sun,

Yue,

Zhuang

et al. 2024

Electr Eng

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Adaptive Piecewise Equivalent Circuit Model With SOC/SOH Estimation Based on Extended Kalman Filter

Cited by 31 publications

References 26 publications

Model-Based State-of-Charge and State-of-Health Estimation Algorithms Utilizing a New Free Lithium-Ion Battery Cell Dataset for Benchmarking Purposes

Model-Based State-of-Charge and State-of-Health Estimation Algorithms Utilizing a New Free Lithium-Ion Battery Cell Dataset for Benchmarking Purposes

SOC-SOH Estimation and Balance Control Based on Event-Triggered Distributed Optimal Kalman Consensus Filter

Life cycle capacity evaluation for battery energy storage systems

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