Remaining Useful Life Prediction and State of Health Diagnosis for Lithium-Ion Batteries Using Particle Filter and Support Vector Regression

By Jingwen Wei, Guangzhong Dong, Zonghai Chen. IEEE transactions on industrial electronics (1982. Print), 2018

513 citations recorded by the source corpus.

Accurate remaining useful life (RUL) prediction and state-of-health (SOH) diagnosis are of extreme importance for safety, durability, and cost of energy storage systems based on lithium-ion batteries. It is also a crucial challenge for energy storage systems to predict RUL and diagnose SOH of batteries due to the complicated aging mechanism. In this paper, a novel method for battery RUL prediction and SOH estimation is proposed. First, a novel support vector regression-based battery SOH state-space model is established to simulate the battery aging mechanism, which takes the capacity as the state variable and takes the representative features during a constant-current and constant-voltage protocol as the input variables. The estimated impedance variables are taken as the output due to the correlation between battery capacity and the sum of charge transfer resistance and electrolyte resistance. Second, in order to suppress the measurement noises of current and voltage, a particle filter is employed to estimate the impedance degradation parameters. Furthermore, experiments are conducted to validate the proposed method. The results show that the proposed SOH estimation method can provide an accurate and robustness result. The proposed RUL prediction framework can also ensure an accurate RUL prediction result.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/dd42e7148a4f8f7b219f63ce384c91a6a082ba62

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Citations in the corpus

Gaussian Process Regression With Automatic Relevance Determination Kernel for Calendar Aging Prediction of Lithium-Ion Batteries (2020, methodology): “vector regression [15], [16], Bayesian prediction [17], [18], and artificial neural network [19]–[21] have been successfully applied to build data-driven models for battery cyclic aging”
The challenge and opportunity of battery lifetime prediction from field data (2021, methodology): “For prognostics, methods based on incremental updates of capacity fade [35, 36, 45, 50, 51] may work well with field data since they are able to handle rapidly varying operating conditions, usually using some”
Lithium Battery State-of-Health Estimation via Differential Thermal Voltammetry With Gaussian Process Regression (2021, methodology): “,[22] fit battery resistance series with polynomial function, and the polynomial regression is employed to forecast battery long-term RUL [17].”
A Data-Driven Approach With Uncertainty Quantification for Predicting Future Capacities and Remaining Useful Life of Lithium-ion Battery (2021, methodology): “On the basis of battery historical cycling data, various intelligent techniques such as vector machine [9], [10], neural network [11],”
Transfer Learning With Long Short-Term Memory Network for State-of-Health Prediction of Lithium-Ion Batteries (2020, methodology): “[7] combined particle filter (PF) coupled with support vector regression (SVR) to diagnose SOH, which can simulate battery”