Extreme Learning Machine Based Prognostics of Battery Life

Abstract

This paper presents a prognostic scheme for estimating the remaining useful life of Lithium-ion batteries. The proposed scheme utilizes a prediction module that aims to obtain precise predictions for both short and long prediction horizons. The prediction module makes use of extreme learning machines for one-step and multi-step ahead predictions, using various prediction strategies, including iterative, direct and DirRec, which use the constant-current experimental capacity data for the estimation of the remaining useful life. The data-driven prognostic approach is highly dependent on the availability of high quantity of quality observations. Insufficient amount of available data can result in unsatisfactory prognostics. In this paper, the prognostics scheme is utilized to estimate the remaining useful life of a battery, with insufficient direct data available, but taking advantage of observations available from a fleet of similar batteries with similar working conditions. Experimental results show that the proposed prognostic scheme provides a fast and efficient estimation of the remaining useful life of the batteries and achieves superior results when compared with various state-of-the-art prediction techniques.

An Adaptive Prediction Model for the Remaining Life of an Li-Ion Battery Based on the Fusion of the Two-Phase Wiener Process and an Extreme Learning Machine

“…The degradation data of CS2-36, CS2-37, and CS2-38 in the first phase were used to train the ELM with four input dimensions and six hidden nodes. With the trained ELM, we used Equation (12) to construct the HI of CS2-35, and the HI at different times is shown in Figure 10. shows that the RECTE of ELM with four input dimensions and six hidden nodes is the smallest; therefore, we chose this type of ELM to detect the changing point.…”

“…The degradation data of CS2-36, CS2-37, and CS2-38 in the first phase were used to train the ELM with four input dimensions and six hidden nodes. With the trained ELM, we used Equation (12) to construct the HI of CS2-35, and the HI at different times is shown in Figure 10. With the obtained HI, we could determine the position of by the evaluation index shown in Equation 13, and we found that = 62 cycles, which is the same as the offline estimation of .…”

“…Therefore, according to the degradation data shown in Figure 1, the real lifetime of CS2-35 is 85 cycles. To demonstrate the accuracy of the proposed method, the methods of Razavi et al [12], Li et al [21], and Zhang et al [20] were compared, and the results of the RUL prediction are shown in Figure 12. , and , in Figure 11 change with the degradation data, which shows that the proposed method of parameter updating has a fast response to the arrival of new degradation data.…”

“…ELM is a single hidden layer FFNN whose weights are obtained by random assignment and calculation; thus, we can determine its weights faster than the neural network described above [11]. Razavi-Far et al [12] used ELM to predict the RUL of LiB using one-step and multistep prediction. Moreover, Hossain et al [13] presented an improved ELM with the gravitational search algorithm (GSA) to estimate the SOC of LiB and experimentally showed that their method performed better than the back-propagation neural network (BPNN) and the radial basis function neural network (RBFNN).…”

Long Short-Term Memory Approach to Estimate Battery Remaining Useful Life Using Partial Data

“…Despite some success of model-based methods, in practice, it is difficult to have a precise and well-established model that would allow tuning and updating the parameters during the prediction phase with different operational conditions [17], and there are not well-established failure physical models [3]. Considering the above-mentioned drawbacks of model-based approaches to predict the RUL, data-driven methods have proven to be more efficient because there is no need for mathematical modeling to compute the battery degradation.…”