###### Abstract
[28] proposed a hybrid method, which combines relevance vector machine and particle filter method. Experiments show that the proposed method can reduce the dependence on training samples. In comparison to ANN, SVM has strong generalization ability and better robustness [29]. Least squares support vector machine (LSSVM) is developed on the basis of SVM [30]. LSSVM simplifies the solution of Lagrange multiplier by transforming inequality constraints into equality constraints in SVM. This results in computational efficiency. [PERSON] and [PERSON] [31] used LSSVM to forecast telecommunication traffic. To enhance the prediction performance, an improved artificial bee colony algorithm was used to optimize the LSSVM parameters. [PERSON] et al. [32] propose a novel method that combines wavelet technique with LSSVM to forecast dissolved gases in oil-immersed power transformers; the parameters are optimized with particle swarm optimization (PSO) algorithm. [PERSON] and [PERSON] [24] proposed a novel multi-kernel SVM to forecast the useful life of lithium-ion batteries with better prediction accuracy. [PERSON] et al. [33] used support vector regression (SVR) to predict the remaining useful life of equipment. This method does not need to estimate degradation states or the failure threshold. The Chicken Swarm Optimization algorithm (CSO) is a novel swarm intelligence optimization algorithm based on PSO that simulates a hierarchical system with different ways of foraging. The CSO algorithm includes chicks, hens and roosters. It is a multi-population algorithm. Compared with classical PSO, CSO algorithm has stronger convergence ability by taking advantage of different foraging strategies used by three tiers of agents. This technique has proven to be useful for many engineering applications. [PERSON] and [PERSON] [34] proposed an improved CSO algorithm to solve a wireless sensor network node localization problem. [PERSON] et al. [35] used CSO algorithm for deep well monitoring. The complexity of CSO algorithm can be reduced by the clustering method of wheel graph theory. [PERSON] et al. [36] used a novel model based on improved boundary CSO to estimate the parameter of nonlinear systems. [PERSON] et al. [37] applied the CSO algorithm to optimize the economic operation of a micro-grid. This paper proposes the ICSO-LSSVM model to forecast the end of life of lithium-ion batteries as part of energy storage systems integrated with renewable energy systems. In order to enhance the prediction accuracy, the ICSO algorithm optimizes the parameters of the LSSVM model. In particular, hen particles are sorted. The first 10% of the hen particles with superior performance replace the rooster particles with poor fitness, which speeds up the search speed of the chickens and avoids the rooster particles falling into local minima. Furthermore, an adaptive learning factor is introduced into the chick location and the mutation operator is proposed. The adaptive learning factor makes the chicks move quickly to the best particles that speed up the convergence rate of the algorithm; the mutation operator broadens the shrinking population search space and makes the chicken swarm jump out of previous locally optimal locations. This feature enlarges the search space and reduces the risk of entrapment in local optima. By introducing chaotic search, mutation operator and adaptive learning factor in ICSO algorithm, the whole convergence performance is improved. The life of NMCI and NMC2 batteries is forecasted by using this ICSO-LSSVM model, and the prediction error of the model is analyzed. Two groups of test data are used to test the ICSO-LSSVM model. The test results showed that the proposed model can predict the life of lithium-ion batteries efficiently and reliably. This paper is organized as follows. Section 2 presents the ICSO-LSSVM method. Section 3 introduces the charge and discharge test data of lithium-ion battery. The results are discussed in detail in Section 4. Section 5 then gives the findings and conclusions.