Abstract
SummaryA data-driven approach is developed to predict the future capacity of lithium-ion batteries (LIBs) in this work. The empirical mode decomposition (EMD), kernel recursive least square tracker (KRLST), and long short-term memory (LSTM) are used to derive the proposed approach. First, the LIB capacity data is split into local regeneration and monotonic global degradation using the EMD approach. Next, the KRLST is used to track the decomposed intrinsic mode functions, and the residual signal is predicted using the LSTM sub-model. Finally, all the predicted intrinsic mode functions and the residual are ensembled to get the future capacity. The experimental and comparative analysis validates the high accuracy (RMSE of 0.00103) of the proposed ensemble approach compared to Gaussian process regression and LSTM fused model. Furthermore, two times lesser error than other fused models makes this approach an efficient tool for battery health prognostics.
Highlights
Deterioration in the fossil fuel resources and problems related to climate change provides an excellent stimulus for the developers for focusing on green energy resources, green transportation (i.e., electric vehicles (EVs), hybrid EVs, etc.), and smart grids (Hu et al, 2020; Ahmed et al, 2021)
For all the NASA batteries (B0005, B0006, B0018, B0054, and B0055), the cyclic aging tests were performed with a programmable electric load, controllable temperature chamber, and power supply (Saha and Goebel, 2007)
The CX2-16 battery was discharged with a constant current of 1.1 A, see (Yu, 2018; Liu et al, 2020) for more details about the experimental setup
Summary
Deterioration in the fossil fuel resources and problems related to climate change provides an excellent stimulus for the developers for focusing on green energy resources, green transportation (i.e., electric vehicles (EVs), hybrid EVs, etc.), and smart grids (Hu et al, 2020; Ahmed et al, 2021). EVs and renewable energy resources will play an essential role in bending the greenhouse gas emission curve for climate mitigation (Creutzig et al, 2016). Owing to the high energy and power density, low self-discharge rate, and high life cycle (Hu et al, 2020; Mannan et al, 2021), lithium-ion batteries (LIBs) have emerged as the leading power source to actuate all the variants of EVs (Ali et al, 2019a; Khan et al, 2021). Because of frequent charging and discharging cycles of LIBs, the battery capacity degraded until its end of life (Umair Ali et al, 2018). Developing a smart battery health prognostic system (SBHPS) for a smooth and reliable battery operation is essential
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