Reliable state of health condition monitoring of Li-ion batteries based on incremental support vector regression with parameters optimization

Abstract

State of health condition monitoring of Li-ion batteries is an important issue for safe and reliably operation of battery-powered products. Consequently, it remains a challenging subject for industrial and academic studies. In this article, an incremental support vector regression is proposed for battery state of health lifetime estimation. In order to improve the battery state of health forecasting accuracy, the quantum-behaved particle swarm optimization is proposed to define reliably the incremental support vector regression parameters. The validation of the proposed method was done based on the NASA battery data set, and it demonstrates that it yields good performance in remaining useful life estimation of Li-ion batteries. This case study shows that compared with the linear, polynomial regression methods, and compared to previous works, the proposed method can obtain more accurate state of health prediction results. Even for state of health prediction starting from the cycle near capacity regeneration, the proposed model can still accurately estimate the global degradation trend. Furthermore, the proposed quantum-behaved particle swarm optimization–incremental support vector regression combination has greater robustness when the training data contain noise and measurement outliers. This allows satisfactory prediction performances without pre-processing the data manually.

A Critical Review of Online Battery Remaining Useful Lifetime Prediction Methods

“…The particle filter method is used to estimate the impedance attenuation parameters and deal with the measurement noise of current and voltage. Ben Ali et al (2020) proposed a combined method based on Quantum Behavioral Particle Swarm Optimization (QBPSO) and Incremental Support Vector Regression (ISVR). The RMSE of this method is 0.0202 ah, and the MAPE is 0.0255%, which is more robust.…”

Prognostics and Health Management of Renewable Energy Systems: State of the Art Review, Challenges, and Trends

“…Moreover, it is the most used and most developed approach, with research based on the use of neural networks and their variants, support vector machine [1][2][3][4][5][6][7][8][9][20][21][22][23][24], probabilistic methods (Bayesian networks, Markov models and their derivatives) [1,4,[31][32][33][34][35][36], stochastic models [21,33,35,[37][38][39][40][41][42][43], state and filtering models (Kalman filter and their variants, particle filter, etc.) [4,15,[43][44][45][46][47][48][49][50][51][52][53][54], regression tools (support vector regression and their variants) [45][46]…”

“…[4,15,[43][44][45][46][47][48][49][50][51][52][53][54], regression tools (support vector regression and their variants) [45][46][47][48][49]54], or combinations of different methods [4]. In addition, the Gaussian process (GP) regression [4,17,[49][50][51][52][53][54] is a commonly used method among regression-based data-driven approaches, etc. A comprehensive review of various data-driven algorithms has been carried out by Nam-Ho et al ( 2017) in [4].…”

Towards robotizing the processes of testing lithium-ion batteries

“…5 Based on these factors, several studies have been conducted to find a trustworthy non-destructive method for SoH estimation in LIB. Among these methods, capacity-based methods, [9][10][11] impedance spectroscopy 7,[12][13][14][15] and Kalman filtering methods are commonly used to estimate the battery model parameters that can be considered as battery health indicators. [16][17][18][19][20] Capacity-based SoH methods can result in extremely accurate and reasonably straight forward measurements, 21 but this test is a very time-consuming process that requires the cell to be fully charged and discharged to carry out the current integration.…”

“…that reflects the steps involved in LIB during lithium intercalation and the frequency domain at which they are located. Ben Ali et al 11 proposed an incremental support vector regression to estimate the battery SoH lifetime estimation using NASA battery data set. Eddahech et al 28 presented an equivalent circuit model of the lithium-ion polymer cell using EIS measurements.…”