Journal
APPLIED SOFT COMPUTING
Volume 65, Issue -, Pages 12-20Publisher
ELSEVIER
DOI: 10.1016/j.asoc.2017.12.041
Keywords
Computational intelligence; Efficient computation; Parallel computing; Li-ion battery; Model parameter estimation
Categories
Funding
- Hong Kong RGC General Research Fund [11272216]
- Hong Kong Theme-based Research Scheme [T32-101/15-R]
- National Natural Science Foundation of China [11471275, 61603032]
- Fundamental Research Funds for the Central Universities [06500078]
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A parallel Jaya algorithm implemented on the graphics processing unit (GPU-Jaya) is proposed to estimate parameters of the Li-ion battery model in this paper. Similar to the generic Jaya algorithm (G-Jaya), the GPU-Jaya is free of tuning algorithm-specific parameters. Compared with the G-Jaya algorithm, three main procedures of the GPU-Jaya, the solution update, fitness value computation, and the best/worst solution selection are all computed in parallel on GPU via a compute unified device architecture (CUDA). Two types of memories of CUDA, the global memory and the shared memory are utilized in the execution. The effectiveness of the proposed GPU-Jaya algorithm in estimating model parameters of two Li-ion batteries is validated via real experiments while its high efficiency is demonstrated by comparing with the G-Jaya and other considered benchmarking algorithms. The experimental results reflect that the GPU-Jaya algorithm can accurately estimate battery model parameters while tremendously reduce the execution time using both entry-level and professional GPUs. (c) 2018 Elsevier B.V. All rights reserved.
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