A Data-Driven Predictive Prognostic Model for Lithium-ion Batteries based on a Deep Learning Algorithm

Phattara Khumprom,Nita Yodo

doi:10.3390/en12040660

Abstract

Prognostic and health management (PHM) can ensure that a lithium-ion battery is working safely and reliably. The main approach of PHM evaluation of the battery is to determine the State of Health (SoH) and the Remaining Useful Life (RUL) of the battery. The advancements of computational tools and big data algorithms have led to a new era of data-driven predictive analysis approaches, using machine learning algorithms. This paper presents the preliminary development of the data-driven prognostic, using a Deep Neural Networks (DNN) approach to predict the SoH and the RUL of the lithium-ion battery. The effectiveness of the proposed approach was implemented in a case study with a battery dataset obtained from the NASA Ames Prognostics Center of Excellence (PCoE) database. The proposed DNN algorithm was compared against other machine learning algorithms, namely, Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Artificial Neural Networks (ANN), and Linear Regression (LR). The experimental results reveal that the performance of the DNN algorithm could either match or outweigh other machine learning algorithms. Further, the presented results could serve as a benchmark of SoH and RUL prediction using machine learning approaches specifically for lithium-ion batteries application.

Highlights

In the past, nickel–cadmium batteries were generally the only electrical power source for various portable equipment, until nickel metal hybrid and lithium-ion batteries were developed in the 1990s [1].In the present-day, lithium-ion battery technology is rapidly growing, and it is the most reliable electrical power source for numerous appliances
remaining useful life (RUL) prediction focuses on projecting the degradation results from a certain cycle until the EoL of the batteries, which is different from that of the state of health (SoH) prediction, which focuses on detecting the pattern of degradation
The RUL predictions experiments were performed from three different starting points, which were at the 40th cycle, 80th cycle, and the 120th cycle of battery No 05

Summary

Introduction

Nickel–cadmium batteries were generally the only electrical power source for various portable equipment, until nickel metal hybrid and lithium-ion batteries were developed in the 1990s [1]. In the present-day, lithium-ion battery technology is rapidly growing, and it is the most reliable electrical power source for numerous appliances. Lithium-ion batteries are extensively equipped in both high-power applications and low-power electronics products, such as hybrid-motor engines, electric cars, smartphones, tablet, laptops, etc. Lithium-ion technology is considered to be a standard power source, and its performance continues to improve. There is currently no any other technology that has proven to perform better than the lithium-ion battery. The following are the main advantages of lithium-ion batteries: (1) high energy density (up to 23–70 Wh/kg), (2) high efficiency (close to 90%), and (3) long life cycle (provides 80% capacity at 3000 cycles) [2]

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Discussion

Conclusion