Study on the reconfigurable remaining useful life estimation system for satellite lithium-ion battery

This paper proposes a FPGA based reconfigurable system design methodology to realize the remaining useful life (RUL) estimation for the lithium-ion battery on satellite. With Relevance Vector Machine (RVM), which possesses the uncertainty expression capacity of the prediction results, the RUL estimation of lithium-ion battery is realized. Then, the dynamic Reconfigurable Computing (RC) technique based on FPGA is applied to realize the embedded RVM computation. The key challenges, including the computing method and architecture design of the kernel function matrix formulation and matrix inversion, are also solved. The research work contributes a novel idea for the computing of machine learning algorithms under limited hardware resource condition. Experimental results on the battery data set show that with almost the same computing accuracy as that on a PC platform, the proposed method can get a 4x speed up over the PC solution. And this also indicates that the reconfigurable computing method can be well applied to the embedded computing of machine learning algorithms and has bright prospect.