Adaptive sliding mode observers for lithium-ion battery state estimation based on parameters identified online

Fast and accurate state estimation is one of the major challenges for designing an advanced battery management system based on high-fidelity physics-based model. This paper evaluates the performance of a modified extended Kalman filter (EKF) for on-line state estimation of a pseudo-2D thermal-electrochemical model of a lithium-ion battery under a highly dynamic load with 16C peak current. The EKF estimation on the full model is shown to be significantly more accurate (< 1% error on SOC) than that on the single-particle model (10% error on SOC). The efficiency of the EKF can be improved by reducing the order of the discretised model while maintaining a high level of accuracy. It is also shown that low noise level in the voltage measurement is critical for accurate state estimation.

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A non-fragile observer-based adaptive sliding mode control for fractional-order Markovian jump systems with time delay and input nonlinearity

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331219891590 ◽

2020 ◽

Vol 42 (8) ◽

pp. 1448-1460 ◽

Cited By ~ 2

Author(s):

Majid Parvizian ◽

Khosro Khandani ◽

Vahid Johari Majd

Keyword(s):

Time Delay ◽

Sliding Mode Control ◽

State Estimation ◽

Fractional Order ◽

Sliding Mode ◽

Adaptive Sliding Mode Control ◽

Markovian Jump ◽

Input Nonlinearity ◽

Adaptive Sliding Mode ◽

Jump Systems

In this paper, state estimation and adaptive sliding mode control (SMC) of uncertain fractional-order Markovian jump systems (FO-MJSs) with time delay and input nonlinearity are considered. A non-fragile observer is proposed to estimate the system states, and an observer-based adaptive sliding mode controller is synthesized to ensure the reachability of the sliding surfaces in the state-estimation space in finite time. The sufficient condition for stochastic stability of the error system and sliding mode dynamics is derived in the form of linear matrix inequalities (LMIs). Finally, some numerical examples are presented to illustrate the effectiveness of the proposed method.

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State estimation method for power lithium-ion battery based on dynamic equilibrium voltage

International Journal of Electric and Hybrid Vehicles ◽

10.1504/ijehv.2018.10016789 ◽

2018 ◽

Vol 10 (2) ◽

pp. 95

Author(s):

Qiuting Wang ◽

Wei Qi

Keyword(s):

State Estimation ◽

Lithium Ion Battery ◽

Dynamic Equilibrium ◽

Estimation Method ◽

Lithium Ion

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State estimation of a SCARA Robot using sliding mode observers

2015 European Control Conference (ECC) ◽

10.1109/ecc.2015.7330805 ◽

2015 ◽

Cited By ~ 1

Author(s):

Latifa Boutat-Baddas ◽

Harouna Souley Ali ◽

Mohamed Darouach

Keyword(s):

State Estimation ◽

Sliding Mode ◽

Scara Robot ◽

Sliding Mode Observers

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A Health Monitoring Method Based on Multivariate State Estimation Technique

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.281.80 ◽

2013 ◽

Vol 281 ◽

pp. 80-85 ◽

Cited By ~ 5

Author(s):

Jian Peng ◽

Wei Dong Xiao ◽

Xiu Pin Huang

Keyword(s):

State Estimation ◽

Lithium Ion Battery ◽

Lithium Ion ◽

Training Data ◽

Ratio Test ◽

Estimation Technique ◽

Performance Parameters ◽

Monitoring Method ◽

Sequential Probability ◽

Health Monitor

The monitor of lithium-ion battery health is becoming a challenge because the performance of battery is effect by many environment factors. To address this problem, a new health monitor method based on Multivariate State Estimation Technique (MSET) and Sequential Probability Ratio Test (SPRT) is proposed in this paper. In order to demonstrate the performance gain of the method, a detailed experiment is performed based on a lithium-ion battery. By the comparison of performance parameters actual residuals and healthy residuals driven from the training data based on MSET, the fault detection can be implemented based on the SPRT.

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