A New Predictive Model for the State-of-Charge of a High-Power Lithium-Ion Cell Based on a PSO-Optimized Multivariate Adaptive Regression Spline Approach

Juan Carlos Alvarez Anton,Esperanza Garcia Gonzalo,Cecilio Blanco Viejo,Paulino J. Garcia Nieto,Manuela Gonzalez Vega,Juan Carlos Viera Perez

doi:10.1109/tvt.2015.2504933

Abstract

Batteries play a key role in achieving the target of universal access to reliable affordable energy. Despite their relevant importance, many challenges remain unsolved with regard to the characterization and management of batteries. One of the major issues in any battery application is the estimation of the state-of-charge (SoC). SoC, which is expressed as a percentage, indicates the amount of energy available in a battery. An accurate SoC estimation under realistic conditions improves battery performance, reliability, and lifetime. This paper proposes an SoC estimation method based on a new hybrid model that combines multivariate adaptive regression splines (MARS) and particle swarm optimization (PSO). The proposed hybrid PSO–MARS-based model uses data obtained from a high-power load profile (dynamic stress test) specified by the United States Advanced Battery Consortium (USABC). The results provide comparable accuracy to other more sophisticated techniques but at a lower computational cost.

Highlights

T HE rechargeable battery industry is experiencing significant growth driven by an upsurge in portable battery-powered devices, electric vehicles and other industrial applications
The main objective of a Battery Management System (BMS) is to maintain the health of all the cells in the battery within the manufacturer’s recommended operating conditions in order to prolong the lifespan of the battery pack
If we normalize the Generalized Cross Validation (GCV) column by dividing all its values by the highest value, multiplying them by 100 and sorting them, we obtain the results shown in Table VII, i.e., the normalized GCV values

Summary

Introduction

T HE rechargeable battery industry is experiencing significant growth driven by an upsurge in portable battery-powered devices, electric vehicles and other industrial applications. A number of different battery chemistries, such as lead-acid, nickel-metal-hydride and lithium-ion, among others, are used in these applications. One of the most important BMS functional requirements is that of estimating the State-of-Charge (SoC) of the battery or of the individual cells in the battery pack. The BMS needs to estimate the SoC in order to report the capacity left in the battery, typically called the “gas gauge”. The SoC is needed to control the battery charging or discharging process. This control can avoid situations such as over-discharging or overcharging, which lead to premature wear-out of the battery. The lithium-ion chemistry operates safely within the designed operating voltages; the battery becomes unstable and may pose a safety hazard if overcharged. Over-discharging stresses the battery and reduces its lifespan

Methods

Results

Conclusion