###### Abstract  
In photovoltaic and wind power storage systems, the reliability of the battery directly affects the overall reliability of the energy storage system. Failed batteries can seriously affect the stable operation of energy storage systems. This paper aims to improve the reliability of the storage systems by accurately predicting battery life and identifying failing batteries in time. The current prediction models mainly use artificial neural networks, Gaussian process regression and hybrid models. Although these models can achieve high prediction accuracy, the computational cost is high due to model complexity. Least squares support vector machine (LSSVM) is a computationally efficient alternative. Hence, this study combines the improved chicken swarm optimization algorithm (ICSO) and LSSVM into a hybrid ICSO-LSSVM model for the reliability of photovoltaic and wind power storage systems. The following are the contributions of this work. First, the optimal penalty parameter and kernel width are determined. Second, the chicken swarm optimization algorithm (CSO) is improved by introducing chaotic search behavior in the hen and an adaptive learning factor in the chicks. The performance of the ICSO algorithm is shown to be better than CSO using standard test problems. Third, the prediction accuracy of the three models is compared. For NMC1 battery, the predicted relative error of ICSO-LSSVM is 0.94%; for NMC2 battery, the relative error of ICSO-LSSVM is 1%. These findings show that the proposed model is suitable for predicting the failure of batteries in energy storage systems, which can improve preventive and predictive maintenance of such systems. least squares support vector machine; chicken swarm optimization algorithm; storage system; sustainable lithium-ion battery; chaotic search +
Footnote †: journal: Applied Soft  
and end-of-pipe treatment technology [8; 9; 10] to reduce environmental pollution from fossil energy. Renewables have also become increasingly attractive. Due to the advantages of wind energy and solar energy, such as extremely low levels of emissions, more and more attention has been paid to such technologies for a power generation system in the world [11; 12]. On the other hand, solar power and wind power generation are intermittent, unstable and uncontrollable. Energy storage systems can reduce the impact of wind power on the power grid, thus ensuring the stability of power system output [13; 14]. Lithium-ion batteries have broad application prospects in wind and photovoltaic power generation systems because of its long service life, no environmental pollution, and fast charging speed [15; 16; 17]. As the batteries start to age, operational problems begin to occur which may affect the performance of integrated renewable energy systems [18]. For example, dead batteries may cause a security accident if not found in time. Predicting the end of life of a battery from early symptoms is thus essential. Battery capacity declines over an increasing number of charging cycles [19]. The physical model and data-driven model are used to predict the life of lithium-ion batteries [20]. The main methods of physical model prediction methods include the degradation mechanism model and equivalent circuit model [21; 22]. The data-driven methods mainly include support vector machine (SVM), artificial neural networks (ANNs) and Gaussian process regression [23; 24; 25]. [PERSON] et al. [26] proposed an ANN model for forecasting the life of a lithium-ion battery. An ANN model has a strong nonlinear mapping ability to capture the behavior of lithium-ion batteries, but the training time of ANN is long and can lose predictive capability by overfitting training data. [PERSON] et al. [27] proposed a new model for aging state analysis of lithium-ion batteries. In this model, symptoms such as capacity loss, solid electrolyte interphase (SEI) growth and deposited layer growth are used. The thickness of SEI was quantified by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). [PERSON] et al.