Online Capacity Estimation of Lithium-Ion Batteries Based on Novel Feature Extraction and Adaptive Multi-Kernel Relevance Vector Machine

Yang Zhang,Bo Guo

doi:10.3390/en81112320

Abstract

Prognostics is necessary to ensure the reliability and safety of lithium-ion batteries for hybrid electric vehicles or satellites. This process can be achieved by capacity estimation, which is a direct fading indicator for assessing the state of health of a battery. However, the capacity of a lithium-ion battery onboard is difficult to monitor. This paper presents a data-driven approach for online capacity estimation. First, six novel features are extracted from cyclic charge/discharge cycles and used as indirect health indicators. An adaptive multi-kernel relevance machine (MKRVM) based on accelerated particle swarm optimization algorithm is used to determine the optimal parameters of MKRVM and characterize the relationship between extracted features and battery capacity. The overall estimation process comprises offline and online stages. A supervised learning step in the offline stage is established for model verification to ensure the generalizability of MKRVM for online application. Cross-validation is further conducted to validate the performance of the proposed model. Experiment and comparison results show the effectiveness, accuracy, efficiency, and robustness of the proposed approach for online capacity estimation of lithium-ion batteries.

Highlights

A lithium-ion battery is a critical component of power systems in satellites, hybrid electric vehicles, and portable electronic devices because of its desirable characteristics, such as high energy density, absence of memory effect, low loss of electrical energy, and long service time [1,2]
Battery capacity is a main indicator of cell aging, and monitoring of the actual capacity values can be used for state of health (SOH) evaluation [1]
The results show that the iterative numbers of accelerated particle swarm optimization (APSO) are less than that of the latter approach, thereby confirming the efficiency of the proposed method

Summary

Introduction

A lithium-ion battery is a critical component of power systems in satellites, hybrid electric vehicles, and portable electronic devices because of its desirable characteristics, such as high energy density, absence of memory effect, low loss of electrical energy, and long service time [1,2]. The failure of a lithium-ion battery may result in operational disability, or even catastrophic failure of the entire system. The state of health (SOH) of online lithium-ion batteries, which have widespread applications and high reliability requirement, must be monitored. Battery capacity is a main indicator of cell aging, and monitoring of the actual capacity values can be used for SOH evaluation [1]. The monitoring process is challenging for data collection of online capacity because internal state variables are inaccessible via general sensors [3]. Feature extraction was based on the full charging/discharging state of a battery [5], thereby ignoring partial charge/discharge states during operation. Six novel features are extracted from charge/discharge (C-D) cycles with consideration of the partial discharging state and convenient data collection during online operation

Methods

Results

Conclusion