In this paper, the online parameter identification of the battery model based on an intelligent adaptive particle swarm optimization (APSO) algorithm is proposed to improve the accuracy of parameter identification, in order to solve the problem of real-time internal parameters changes in the application on the electric vehicle(EV) power battery. APSO algorithm is able to effectively avoid the locally optimal solution, and achieve the globally optimal solution by converting parameter identification to parameter space optimization. Finally, a comparison analysis of simulation results between APSO algorithm and RLS method which is widely used has verified that APSO algorithm possess a faster response and accuracy in the application on parameter identification of battery model.
This paper reviews glowworm swarm optimization algorithm (GSO), which is a meta-heuristic swarm intelligence algorithm. The GSO algorithm is applied for solving optimization problems. Shortcoming of the GSO algorithm has been identified with the introduction and discussion of the improvement taken place in recent years. Adaptive step size and new movement rules have been widely used in the improvement of GSO algorithm. The application of GSO including clustering techniques is also presented. Very promising GSO clustering versions use MapReduce framework to improve the computational efficiency when the clustered data set is large and thereby reducing the time complexity.
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