A GPU-accelerated parallel Jaya algorithm for efficiently estimating Li-ion battery model parameters
A GPU-accelerated parallel Jaya algorithm for efficiently estimating Li-ion battery model parameters
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DOI：
10.1016/j.asoc.2017.12.041
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发表时间：
2018-04
期刊：
影响因子：
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通讯作者：
Long Wang;Zijun Zhang;Chao-Jung Huang;K. Tsui
中科院分区：
文献类型：
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作者：
Long Wang;Zijun Zhang;Chao-Jung Huang;K. Tsui
本文提出了一种在图形处理器（GPU-Jaya）上实现的并行Jaya算法来估计锂离子电池模型的参数。与通用Jaya算法（G-Jaya）类似，GPU-Jaya无需调整算法特定的参数。与G-Jaya算法相比，GPU-Jaya算法的解更新、适应值计算和最优解/最差解选择三个主要过程均通过统一计算设备架构（CUDA）在GPU上并行计算。CUDA的两种类型的存储器，全局存储器和共享存储器在执行中使用。通过真实的实验验证了GPU-Jaya算法在两种锂离子电池模型参数估计中的有效性，并与G-Jaya算法和其他基准测试算法进行了比较，证明了该算法的高效性。实验结果表明，GPU-Jaya算法可以准确地估计电池模型参数，同时大大减少了使用入门级和专业GPU的执行时间。
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.