The novel state of charge estimation method for lithium battery using sliding mode observer

By Il-Song Kim. 2006

359 citations recorded by the source corpus.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/2aba9b15c6b490b2c6814150b382b3e8f880289b

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. Chunting Chris Mi
4. Lei Zhang
5. Fengchun Sun
6. Farzad Rajaei Salmasi
7. Mehdi Gholizadeh
8. Changfu Zou

In Electrical and Electronic Engineering

1. Rui Xiong
2. Xiaosong Hu
3. Chunting Chris Mi
4. Fengchun Sun
5. Farzad Rajaei Salmasi
6. Mehdi Gholizadeh
7. Changfu Zou
8. Lei Zhang

Citations in the corpus

• Estimation of State of Charge, Unknown Nonlinearities, and State of Health of a Lithium-Ion Battery Based on a Comprehensive Unobservable Model (2014, methodology): “Kim proposed a SMO based on a simple RC model for SoC estimation, which was robust to the modeling uncertainties [27].”
• Co-Estimation of State of Charge and State of Health for Lithium-Ion Batteries Based on Fractional-Order Calculus (2018, background): “Luenberger observer [16], sliding-mode observer [17], particle filter [18], and Kalman filter [19], as well as their variations, e.”
• The State of Charge Estimation of Lithium-Ion Batteries Based on a Proportional-Integral Observer (2014, methodology): “The sliding-mode observer was introduced to estimate the states of a battery [12].”
• 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.”