scholarly journals Adaptive Smooth Variable Structure Filter Strategy for State Estimation of Electric Vehicle Batteries

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8560
Author(s):  
Sara Rahimifard ◽  
Saeid Habibi ◽  
Gillian Goward ◽  
Jimi Tjong
Keyword(s):  
State Estimation ◽  
Estimation Error ◽  
Equivalent Circuit Model ◽  
Variable Structure ◽  
Internal Resistance ◽  
Accurate Estimation ◽  
Battery Management ◽  
Robust Adaptive ◽  
State Of Power ◽  
Smooth Variable Structure Filter

Battery Management Systems (BMSs) are used to manage the utilization of batteries and their operation in Electric and Hybrid Vehicles. It is imperative for efficient and safe operation of batteries to be able to accurately estimate the State of Charge (SoC), State of Health (SoH) and State of Power (SoP). The SoC and SoH estimation must remain robust and accurate despite aging and in presence of noise, uncertainties and sensor biases. This paper introduces a robust adaptive filter referred to as the Adaptive Smooth Variable Structure Filter with a time-varying Boundary Layer (ASVSF-VBL) for the estimation of the SoC and SoH in electrified vehicles. The internal model of the filter is a third-order equivalent circuit model (ECM) and its state vector is augmented to enable estimation of the internal resistance and current bias. It is shown that system and measurement noise covariance adaptation for the SVSF-VBL approach improves the performance in state estimation of a battery. The estimated internal resistance is then utilized to improve determination of the battery’s SoH. The effectiveness of the proposed method is validated using experimental data from tests on Lithium Polymer automotive batteries. The results indicate that the SoC estimation error can remain within less than 2% over the full operating range of SoC along with an accurate estimation of SoH.

scholarly journals On the Relation Between Smooth Variable Structure and Adaptive Kalman Filter

2020 ◽  
Vol 6 ◽  
Author(s):  
Mark Spiller ◽  
Dirk Söffker
Keyword(s):  
Kalman Filter ◽  
Nonlinear Systems ◽  
State Estimation ◽  
Parameter Optimization ◽  
Estimation Error ◽  
Variable Structure ◽  
Uncertain Nonlinear Systems ◽  
Adaptive Kalman Filter ◽  
Model Uncertainties ◽  
Smooth Variable Structure Filter

This article is addressed to the topic of robust state estimation of uncertain nonlinear systems. In particular, the smooth variable structure filter (SVSF) and its relation to the Kalman filter is studied. An adaptive Kalman filter is obtained from the SVSF approach by replacing the gain of the original filter. Boundedness of the estimation error of the adaptive filter is proven. The SVSF approach and the adaptive Kalman filter achieve improved robustness against model uncertainties if filter parameters are suitably optimized. Therefore, a parameter optimization process is developed and the estimation performance is studied.

Modeling and Estimation for Advanced Battery Management

2019 ◽  
Vol 2 (1) ◽  
pp. 393-426 ◽  
Author(s):  
Xinfan Lin ◽  
Youngki Kim ◽  
Shankar Mohan ◽  
Jason B. Siegel ◽  
Anna G. Stefanopoulou
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
The State ◽  
Consumer Electronics ◽  
Accurate Estimation ◽  
Health State ◽  
Battery Management ◽  
Space Applications ◽  
Internal States ◽  
State Of Power

The commercialization of lithium-ion batteries enabled the widespread use of portable consumer electronics and serious efforts to electrify trans-portation. Managing the potent brew of lithium-ion batteries in the large quantities necessary for vehicle propulsion is still challenging. From space applications a billion miles from Earth to the daily commute of a hybrid electric automobile, these batteries require sophisticated battery management systems based on accurate estimation of battery internal states. This system is the brain of the battery and is responsible for estimating the state of charge, state of health, state of power, and temperature. The state estimation relies on accurate prediction of complex electrochemical, thermal, and mechanical phenomena, which increases the importance of model and parameter accuracy. Moreover, as the batteries age, how should the parameters of the model change to accurately represent the performance, and how can we leverage the limited sensor information from the measured terminal voltage and sparse surface temperatures available in a battery system? With a frugal sensor set, what is the optimal sensor placement? This article reviews estimation techniques and error bounds regarding sensor noise and modeling errors, and concludes with an outlook on the research that will be necessary to enable fast charging, repurposing of batteries for grid energy storage, degradation prediction, and fault detection.

Parameter Identification for a High-Performance Hydrostatic Actuation System Using the Variable Structure Filter Concept

2006 ◽  
Vol 129 (2) ◽  
pp. 229-235 ◽  
Author(s):  
S. R. Habibi ◽  
R. Burton
Keyword(s):  
Parameter Estimation ◽  
Early Detection ◽  
State Estimation ◽  
Condition Monitoring ◽  
High Performance ◽  
Variable Structure ◽  
Maintenance Costs ◽  
Estimation Strategy ◽  
Actuation System ◽  
Smooth Variable Structure Filter

Parameter estimation is an important concept that can be used for health and condition monitoring. Estimation or measurement of physically meaningful parameters and their evaluation against predetermined thresholds allows detection of gradual or abrupt deteriorations in the plant. This early detection of faults enables preventative unscheduled maintenance that is of benefit to industries concerned with reliability and safety. In this paper, a recently proposed state estimation strategy referred to as the smooth variable structure filter (SVSF) is reviewed and extended to parameter estimation. The SVSF is applied to a novel hydrostatic actuation system referred to as the electrohydraulic actuator (EHA). Condition monitoring of the EHA for preventative unscheduled maintenance would increase its safety in applications pertaining to aerospace and would reduce its operational and maintenance costs.

scholarly journals A Method to Identify Lithium Battery Parameters and Estimate SOC Based on Different Temperatures and Driving Conditions

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1391 ◽  
Author(s):  
Yongliang Zheng ◽  
Feng He ◽  
Wenliang Wang
Keyword(s):  
Kalman Filter ◽  
Electric Vehicles ◽  
Unscented Kalman Filter ◽  
Estimation Error ◽  
Equivalent Circuit Model ◽  
Battery Management System ◽  
Battery Management ◽  
Soc Estimation ◽  
Different Temperatures ◽  
Driving Conditions

State of charge (SOC) plays a significant role in the battery management system (BMS), since it can contribute to the establishment of energy management for electric vehicles. Unfortunately, SOC cannot be measured directly. Various single Kalman filters, however, are capable of estimating SOC. Under different working conditions, the SOC estimation error will increase because the battery parameters cannot be estimated in real time. In order to obtain a more accurate and applicable SOC estimation than that of a single Kalman filter under different driving conditions and temperatures, a second-order resistor capacitor (RC) equivalent circuit model (ECM) of a battery was established in this paper. Thereafter, a dual filter, i.e., an unscented Kalman filter–extended Kalman filter (UKF–EKF) was developed. With the EKF updating battery parameters and the UKF estimating the SOC, UKF–EKF has the ability to identify parameters and predict the SOC of the battery simultaneously. The dual filter was verified under two different driving conditions and three different temperatures, and the results showed that the dual filter has an improvement on SOC estimation.

scholarly journals A review of traction battery model and parameter identification in electric vehicle

2020 ◽  
Vol 185 ◽  
pp. 01040
Author(s):  
Chunyang Wang ◽  
Bo Xing ◽  
Jiaping Zhou
Keyword(s):  
Parameter Identification ◽  
State Estimation ◽  
Electric Vehicle ◽  
Equivalent Circuit Model ◽  
Energy Utilization ◽  
Battery Management System ◽  
Battery Management ◽  
Identification Methods ◽  
Battery Model ◽  
Electrochemical Mechanism

State estimation is a key issue of battery management system (BMS) to improve the energy utilization of traction battery in electric vehicle, which is usually achieved based on battery model. The commonly used models, equivalent circuit model (ECM) and electrochemical mechanism-based model (EMM), are reviewed in this paper. Besides, the corresponding parameter identification methods are analysed considering the target application background.

Robust Sideslip Angle Estimation for Lightweight Vehicles Using Smooth Variable Structure Filter

2013 ◽  
Author(s):  
Xiaoyu Huang ◽  
Junmin Wang
Keyword(s):  
Filter Design ◽  
Variable Structure ◽  
Stability Control ◽  
Vital Role ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Sideslip Angle ◽  
Vehicle Stability Control ◽  
Active Research ◽  
Smooth Variable Structure Filter

In the design of vehicle stability control (VSC) systems for ground vehicles, sideslip angle plays a vital role and its estimation has long been an active research topic. Accurate estimation of sideslip angle is more difficult for lightweight vehicles (LWVs) because their parameters are prone to significant changes with loading conditions — the amount and position of the payload. In this paper, a robust sideslip angle estimator based on a recently emerging smooth variable structure filter (SVSF) is presented. This sideslip angle estimator is suitable for LWVs because it is almost non-sensitive to the changes of the system parameters. A four-state vehicle lateral dynamic model including a pseudo-Burckhardt tire model is employed in the filter design. Compared with the widely utilized extended Kalman filter (EKF), the SVSF shows much better robustness against modeling errors. It is also more favorable in terms of tuning effort and computational speed. Simulation studies were conducted based on a high-fidelity vehicle model in CarSim®, where the vehicle took the form of a lightweight electric ground vehicle with independent in-wheel motors. The performance of the SVSF was shown by comparisons against the EKF under different settings for model parameters.

Parameter Identification for a High Performance Hydrostatic Actuation System Using the Variable Structure Filter Concept

2004 ◽  
Author(s):  
S. R. Habibi ◽  
R. Burton
Keyword(s):  
Parameter Estimation ◽  
Early Detection ◽  
State Estimation ◽  
Condition Monitoring ◽  
High Performance ◽  
Variable Structure ◽  
Maintenance Costs ◽  
Estimation Strategy ◽  
Actuation System ◽  
Smooth Variable Structure Filter

Parameter estimation is an important concept that can be used for health and condition monitoring. Estimation or measurement of physically meaningful parameters and their evaluation against predetermined thresholds allows detection of gradual or abrupt deteriorations in the plant. This early detection of faults enables preventative unscheduled maintenance that is of benefit to industries concerned with reliability and safety. In this paper, a recently proposed state estimation strategy referred to as the Smooth Variable Structure Filter (SVSF) is reviewed and extended to parameter estimation. The SVSF is applied to a novel hydrostatic actuation system referred to as the ElectroHydraulic Actuator (EHA). Condition monitoring of the EHA for preventative unscheduled maintenance would increase its safety in applications pertaining to aerospace and would reduce its operational and maintenance costs.

scholarly journals State of Charge Estimation of Lithium Battery Based on Improved Correntropy Extended Kalman Filter

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4197
Author(s):  
Jiandong Duan ◽  
Peng Wang ◽  
Wentao Ma ◽  
Xinyu Qiu ◽  
Xuan Tian ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Gaussian Noise ◽  
Estimation Error ◽  
Weighted Least Squares ◽  
Equivalent Circuit Model ◽  
State Of Charge ◽  
Battery Management ◽  
Soc Estimation ◽  
Non Gaussian

State of charge (SOC) estimation plays a crucial role in battery management systems. Among all the existing SOC estimation approaches, the model-driven extended Kalman filter (EKF) has been widely utilized to estimate SOC due to its simple implementation and nonlinear property. However, the traditional EKF derived from the mean square error (MSE) loss is sensitive to non-Gaussian noise which especially exists in practice, thus the SOC estimation based on the traditional EKF may result in undesirable performance. Hence, a novel robust EKF method with correntropy loss is employed to perform SOC estimation to improve the accuracy under non-Gaussian environments firstly. Secondly, a novel robust EKF, called C-WLS-EKF, is developed by combining the advantages of correntropy and weighted least squares (WLS) to improve the digital stability of the correntropy EKF (C-EKF). In addition, the convergence of the proposed algorithm is verified by the Cramér–Rao low bound. Finally, a C-WLS-EKF method based on an equivalent circuit model is designed to perform SOC estimation. The experiment results clarify that the SOC estimation error in terms of the MSE via the proposed C-WLS-EKF method can efficiently be reduced from 1.361% to 0.512% under non-Gaussian noise conditions.

Sign in / Sign up

Export Citation Format

Share Document