Quantitative Analysis of Lithium-Ion Battery Capacity Prediction via Adaptive Bathtub-Shaped Function

Yi Chen,Bin Zheng,Qiang Miao,Michael Pecht,Shaomin Wu

doi:10.3390/en6063082

Abstract

Batteries are one of the most important components in many mechatronics systems, as they supply power to the systems and their failures may lead to reduced performance or even catastrophic results. Therefore, the prediction analysis of remaining useful life (RUL) of batteries is very important. This paper develops a quantitative approach for battery RUL prediction using an adaptive bathtub-shaped function (ABF). ABF has been utilised to model the normalised battery cycle capacity prognostic curves, which attempt to predict the remaining battery capacity with given historical test data. An artificial fish swarm algorithm method with a variable population size (AFSAVP) is employed as the optimiser for the parameter determination of the ABF curves, in which the fitness function is defined in the form of a coefficient of determination (R2). A 4 x 2 cross-validation (CV) has been devised, and the results show that the method can work valuably for battery health management and battery life prediction.

Highlights

Estimating the remaining useful lifetime (RUL) of a lithium-ion battery is one of the most important requirements for mechatronics systems, such as portable devices, satellites, deep space probes, robotic systems and hybrid vehicles, in which prognostics and health management (PHM) technologies are used to assess the reliability and performance of a mechatronics product and, under its actual lifecycle conditions, to determine the advent of failure and mitigate system risk.Much research on estimating the remaining useful life of batteries has been carried out in recent years
The simulation results for the AFSA method with a variable population size (AFSAVP)-driven hybrid modelling of the battery capacity behaviours are performed by SwarmF ish, which is a toolbox for MATLAB developed by Chen [25]
The mean and standard deviation (MEAN ± STD) of α, β, ζ and γ of the four tests are listed, which indicate the optimised locations of the parameter settings within their initial ranges. error is the absolute error of the normalised battery capacity, between the predicted data and the experimental data of validation

Summary

Introduction

Estimating the remaining useful lifetime (RUL) of a lithium-ion battery is one of the most important requirements for mechatronics systems, such as portable devices, satellites, deep space probes, robotic systems and hybrid vehicles, in which prognostics and health management (PHM) technologies are used to assess the reliability and performance of a mechatronics product and, under its actual lifecycle conditions, to determine the advent of failure and mitigate system risk. Burgess [6] proposed a method to assess the RUL of valve regulated lead-acid batteries using capacity measurements and Kalman filtering. He et al [7] proposed an empirical model using Dempster-Shafer. A schooling fish can quickly respond to the changes in the direction and speed of their neighbours; information about their behaviours has been passed to others, which help them move from one configuration to another, almost as one unit By borrowing this intelligence of the social behaviours, the AFSA is parallel, independent of the initial values and able to achieve a global optimum.

Adaptive Bathtub-Shaped Functions

Swarm Fish Algorithm with Variable Population

Battery Capacity Prediction

Fitness Function

Simulation Results and Discussions

Conclusions and Future Work