Simultaneous inference of Lithium-Ion battery polarising impedance surface and capacity degradation using a Hybrid Neural Adaptive State Space Model

2021 ◽  
Vol 36 ◽  
pp. 102370
Author(s):  
Christopher P. Ley ◽  
Marcos E. Orchard
Keyword(s):  
State Space ◽  
Lithium Ion Battery ◽  
State Space Model ◽  
Lithium Ion ◽  
Simultaneous Inference ◽  
Impédance Surface ◽  
Impedance Surface ◽  
Space Model ◽  
Capacity Degradation

State-space model with non-integer order derivatives for lithium-ion battery

2016 ◽  
Vol 161 ◽  
pp. 330-336 ◽  
Author(s):  
Yuan Zou ◽  
Shengbo Eben Li ◽  
Bing Shao ◽  
Baojin Wang
Keyword(s):  
State Space ◽  
Lithium Ion Battery ◽  
State Space Model ◽  
Lithium Ion ◽  
Space Model ◽  
Integer Order

scholarly journals SOC Estimation of Lithium-Ion Batteries Based on Parameter-Dependent State-Space Model

2016 ◽  
Vol 29 (10) ◽  
pp. 433-440
Author(s):  
Masaki Oya ◽  
Kiyotsugu Takaba ◽  
Lei Lin ◽  
Ryu Ishizaki ◽  
Naoki Kawarabayasi ◽  
...  
Keyword(s):  
State Space ◽  
Lithium Ion Batteries ◽  
State Space Model ◽  
Lithium Ion ◽  
Space Model ◽  
Soc Estimation ◽  
Parameter Dependent ◽  
Dependent State

Development of L1-norm sliding mode observer for sensor fault diagnosis of an industrial gas turbine

2021 ◽  
pp. 095965182199617
Author(s):  
Mahyar Akbari ◽  
Abdol Majid Khoshnood ◽  
Saied Irani
Keyword(s):  
Fault Detection ◽  
State Space ◽  
Gas Turbine ◽  
Sliding Mode ◽  
State Space Model ◽  
Sensor Fault ◽  
Space Model ◽  
Decision Logic ◽  
Sensor Fault Detection ◽  
Industrial Gas Turbine

In this article, a novel approach for model-based sensor fault detection and estimation of gas turbine is presented. The proposed method includes driving a state-space model of gas turbine, designing a novel L1-norm Lyapunov-based observer, and a decision logic which is based on bank of observers. The novel observer is designed using multiple Lyapunov functions based on L1-norm, reducing the estimation noise while increasing the accuracy. The L1-norm observer is similar to sliding mode observer in switching time. The proposed observer also acts as a low-pass filter, subsequently reducing estimation chattering. Since a bank of observers is required in model-based sensor fault detection, a bank of L1-norm observers is designed in this article. Corresponding to the use of the bank of observers, a two-step fault detection decision logic is developed. Furthermore, the proposed state-space model is a hybrid data-driven model which is divided into two models for steady-state and transient conditions, according to the nature of the gas turbine. The model is developed by applying a subspace algorithm to the real field data of SGT-600 (an industrial gas turbine). The proposed model was validated by applying to two other similar gas turbines with different ambient and operational conditions. The results of the proposed approach implementation demonstrate precise gas turbine sensor fault detection and estimation.

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