Extreme learning machine model for state-of-charge estimation of lithium-ion battery using salp swarm algorithm

Abstract

Accurate capacity estimation of lithium-ion batteries can effectively avoid over charge/discharge and improve life span. This paper proposes a state-of-charge (SOC) estimation model based on an improved extreme learning machine (ELM). ELM is a single hidden layer network with fast learning speed and good generalization performance, suitable for SOC estimation. For the ELM performance is highly dependent on network weights and hidden layer biases, a salp swarm algorithm (SSA) is applied to search for these parameters. Chaotic mapping is introduced in SSA to make the initialized individuals uniformly distributed. And a sine cosine algorithm (SCA) is embedded in swarm position formulation to promote communication. The model robustness and computational cost are verified by energy storage devices. The mean absolute error is 0.538, and the mean absolute percentage error is 0.887%. The results verified that the proposed model has better performance than other popular models, and shows good traceability and generality.