The State of Charge Estimation of Lithium-Ion Batteries Based on a Proportional-Integral Observer

By Jun Xu, Chunting Chris Mi, Bing-gang Cao, Junjun Deng, Zheng Chen, Siqi Li. IEEE Transactions on Vehicular Technology, 2014

327 citations recorded by the source corpus.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/71a912295a0e31bf58ce2682453877586f847c30

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Xiaosong Hu
2. Rui Xiong
3. Lei Zhang
4. Michael G. Pecht
5. Changfu Zou
6. Zhenpo Wang
7. Hongwen He
8. Fengchun Sun

In Electrical and Electronic Engineering

1. Xiaosong Hu
2. Rui Xiong
3. Michael G. Pecht
4. Changfu Zou
5. Lei Zhang
6. Poh Chiang Loh
7. Zhenpo Wang
8. Fengchun Sun

Citations in the corpus

• Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles (2018, methodology): “The typical algorithms are Kalman filter [23]–[25], Luenberger observe [26], PI (proportion integration) observer [27], H∞ observer [28], sliding−mode observer [29], [30], et al.”
• A review of fractional-order techniques applied to lithium-ion batteries, lead-acid batteries, and supercapacitors (2018, background): “In integer-order models (IOMs) of Li-ion batteries, CPEs are either approximated by ideal capacitors [50], or a number of resistor-capacitor (RC) networks [13, 51], or relaxation times [52].”
• A Generalized SOC-OCV Model for Lithium-Ion Batteries and the SOC Estimation for LNMCO Battery (2016, methodology): “Principle of the PI observer has been explained in [36], and will hence not be discussed with the HIL platform described below.”
• Lithium Polymer Battery State-of-Charge Estimation Based on Adaptive Unscented Kalman Filter and Support Vector Machine (2016, background): “The absolute error ea in the literature [10], [17] is confined to ±2% error band and ±3% in [11] and [16].”
• State of Charge Estimation for Lithium-Ion Battery Based on Nonlinear Observer: An H∞ Method (2017, methodology): “Therefore, various observer-based SOC estimate approaches, such as the Luenberger observer [19,20], H∞ observer [21,22], nonlinear observer [23–26], proportional-integral observer [27,28] and sliding model observer [29–31], are proposed, which do not demand knowledge on noise distributions.”
• An Overview and Comparison of Online Implementable SOC Estimation Methods for Lithium-Ion Battery (2018, methodology): “4 use a different algorithm to calculate the gain L, such as proportional integral (PI) [50], [51], H-infinity filter [22]–[24], Kalman filter [10], [12], [13], [17],”