Nonlinear Fault Detection and Isolation for a Lithium-Ion Battery Management System

2010 ◽  
Author(s):  
Jim Marcicki ◽  
Simona Onori ◽  
Giorgio Rizzoni
Keyword(s):  
Fault Detection ◽  
Hybrid Electric Vehicle ◽  
Sliding Mode ◽  
Estimation Error ◽  
Lithium Ion ◽  
Open Loop ◽  
Operating Conditions ◽  
Fault Detection And Isolation ◽  
Battery Management System ◽  
Loop Model

Lithium-ion batteries are a growing source for electric power, but must be maintained within acceptable operating conditions to ensure efficiency and reliability. Therefore, a robust fault detection and isolation scheme is required that is sensitive enough to determine when sensor or actuator faults present a threat to the health of the battery. A scheme suitable for a hybrid electric vehicle battery application is presented in this work. The diagnostic problem is formulated as a nonlinear parity equation approach, but is modified for the considered application. Sliding mode observers are designed for input estimation, while the output voltage estimation is performed using an open loop model. The selection of optimal thresholds given a maximum allowable probability of error is also considered. An assessment of the design using real-world driving-cycle data leads to the conclusion that the estimation error of the observers determines a lower bound on the minimum detectable fault magnitude.

scholarly journals Estimation and Fault Diagnosis of Lithium-Ion Batteries: A Fractional-Order System Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Shulan Kong ◽  
Mehrdad Saif ◽  
Guozeng Cui
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Fractional Order ◽  
Sliding Mode ◽  
Estimation Error ◽  
Lithium Ion ◽  
Fault Isolation ◽  
Sliding Mode Observer ◽  
Fault Detection And Isolation ◽  
Order System

This study investigates estimation and fault diagnosis of fractional-order Lithium-ion battery system. Two simple and common types of observers are designed to address the design of fault diagnosis and estimation for the fractional-order systems. Fractional-order Luenberger observers are employed to generate residuals which are then used to investigate the feasibility of model based fault detection and isolation. Once a fault is detected and isolated, a fractional-order sliding mode observer is constructed to provide an estimate of the isolated fault. The paper presents some theoretical results for designing stable observers and fault estimators. In particular, the notion of stability in the sense of Mittag-Leffler is first introduced to discuss the state estimation error dynamics. Overall, the design of the Luenberger observer as well as the sliding mode observer can accomplish fault detection, fault isolation, and estimation. The effectiveness of the proposed strategy on a three-cell battery string system is demonstrated.

scholarly journals State of Charge Estimation for Power Lithium-Ion Battery Using a Fuzzy Logic Sliding Mode Observer

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2491 ◽  
Author(s):  
Wenhui Zheng ◽  
Bizhong Xia ◽  
Wei Wang ◽  
Yongzhi Lai ◽  
Mingwang Wang ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Sliding Mode ◽  
Estimation Error ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Sliding Mode Observer ◽  
State Of Charge ◽  
Battery Management System ◽  
Test Results ◽  
Soc Estimation

State of charge (SOC) estimation is of vital importance for the battery management system in electric vehicles. This paper proposes a new fuzzy logic sliding mode observer for SOC estimation. The second-order resistor-capacitor equivalent circuit model is used to describe the discharging/charging behavior of the battery. The exponential fitting method is applied to determine the parameters of the model. The fuzzy logic controller is introduced to improve the performance of sliding mode observer forming the fuzzy logic sliding mode observer (FLSMO). The Federal Urban Driving Schedule (FUDS), the West Virginia Suburban Driving Schedule (WUBSUB), and the New European Driving Cycle (NEDC) schedule test results show that the average SOC estimation error of FLSMO algorithm is less than 1%. When the initial SOC estimation error is 20%, the FLSMO algorithm can converge to 3% error boundary within 2400 s. Comparison test results show that the FLSMO algorithm has better performance than the sliding mode observer and the extended Kalman filter in terms of robustness against measurement noise and parameter disturbances.

scholarly journals Fault detection and isolation for a multi-cellular converter based on sliding mode observer

2015 ◽  
Vol 48 (21) ◽  
pp. 164-170 ◽  
Author(s):  
H. Meziane ◽  
C. Labarre ◽  
S. Lefteriu ◽  
M. Defoort ◽  
M. Djemai
Keyword(s):  
Fault Detection ◽  
Sliding Mode ◽  
Sliding Mode Observer ◽  
Fault Detection And Isolation

scholarly journals Estimation of lithium-ion battery state-of-charge using an extended kalman filter

2021 ◽  
Vol 10 (4) ◽  
pp. 1759-1768
Author(s):  
Mouhssine Lagraoui ◽  
Ali Nejmi ◽  
Hassan Rayhane ◽  
Abderrahim Taouni
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Battery ◽  
Extended Kalman Filter ◽  
Lithium Ion ◽  
The State ◽  
Operating Conditions ◽  
State Of Charge ◽  
Ohmic Loss ◽  
Battery Management System ◽  
Transfer Resistance

The main goal of a battery management system (BMS) is to estimate parameters descriptive of the battery pack operating conditions in real-time. One of the most critical aspects of BMS systems is estimating the battery's state of charge (SOC). However, in the case of a lithium-ion battery, it is not easy to provide an accurate estimate of the state of charge. In the present paper we propose a mechanism based on an extended kalman filter (EKF) to improve the state-of-charge estimation accuracy on lithium-ion cells. The paper covers the cell modeling and the system parameters identification requirements, the experimental tests, and results analysis. We first established a mathematical model representing the dynamics of a cell. We adopted a model that comprehends terms that describe the dynamic parameters like SOC, open-circuit voltage, transfer resistance, ohmic loss, diffusion capacitance, and resistance. Then, we performed the appropriate battery discharge tests to identify the parameters of the model. Finally, the EKF filter applied to the cell test data has shown high precision in SOC estimation, even in a noisy system.

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