scholarly journals A Physical Model-Based Observer Framework for Nonlinear Constrained State Estimation Applied to Battery State Estimation

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4402 ◽  
Author(s):  
Jonathan Brembeck
Keyword(s):  
State Estimation ◽  
Autonomous Vehicles ◽  
Service Oriented Architecture ◽  
Lithium Ion ◽  
Time Estimation ◽  
Inequality Constraint ◽  
Estimation Algorithms ◽  
Model Based ◽  
Nonlinear Inequality ◽  
System States

Future electrified autonomous vehicles demand higly accurate knowledge of their system states to guarantee a high-fidelity and reliable control. This constitutes a challenging task—firstly, due to rising complexity and operational safeness, and secondly, due to the need for embedded service oriented architecture which demands a continuous development of new functionalities. Based on this, a novel model based Kalman filter framework is outlined in this publication, which enables the automatic incorporation of multiphysical Modelica models into discrete-time estimation algorithms. Additionally, these estimation algorithms are extended with nonlinear inequality constraint handling functionalities. The proposed framework is applied to a constrained nonlinear state of charge lithium-ion cell observer and is validated with experimental data.

Comparison of Nonlinear Receding-Horizon and Extended Kalman Filter Strategies for Ground Vehicles Longitudinal Slip Estimation

2020 ◽  
Author(s):  
Elias Dias Rossi Lopes ◽  
Gustavo Simão Rodrigues ◽  
Helon Vicente Hultmann Ayala
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Extended Kalman Filter ◽  
Autonomous Vehicles ◽  
Contact Forces ◽  
Control Synthesis ◽  
Sideslip Angle ◽  
Estimation Algorithms ◽  
Longitudinal Slip ◽  
Tire Forces

Friction efforts are present in almost all mechanical applications, due to contact between bodies and there are many important situations, in which they must be properly controlled. Among these, there are tire contact forces, which is focus of many studies in autonomous vehicles and control applications on vehicle systems, since the tire forces and moments are nonlinear and may be modelled as friction efforts. Any control synthesis focused to optimize its performance must be associated to state estimators, since the efforts depend on slip variables, as longitudinal slip and sideslip angle, and it is not possible to accurately measure them. So, in this paper, two state estimation algorithms are evaluated: Extended Kalman Filter (EKF) and Moving Horizon State Estimation (MHSE), which are applied to a quarter-car model for longitudinal dynamics. It is presented that, for both traction and braking phases, the MHSE is more accurate, since it takes explicitly into account the nonlinear model in the estimation process, independently of Jacobian sensitivities to discontinuities as is the case here. So, it is demonstrated that the developed estimator may be successfully associated to controllers with the objective of optimize tire performance in traction and braking control.

scholarly journals Concurrent Real-Time Estimation of State of Health and Maximum Available Power in Lithium-Sulfur Batteries

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2133 ◽  
Author(s):  
Vaclav Knap ◽  
Daniel Auger ◽  
Karsten Propp ◽  
Abbas Fotouhi ◽  
Daniel-Ioan Stroe
Keyword(s):  
Large Scale ◽  
Initial Conditions ◽  
Specific Capacity ◽  
Lithium Ion ◽  
Time Estimation ◽  
Capacity Fade ◽  
State Of Health ◽  
Complex Behaviour ◽  
Estimation Algorithms ◽  
Lithium Sulfur

Lithium-sulfur (Li-S) batteries are an emerging energy storage technology with higher performance than lithium-ion batteries in terms of specific capacity and energy density. However, several scientific and technological gaps need to be filled before Li-S batteries will penetrate the market at a large scale. One such gap, which is tackled in this paper, is represented by the estimation of state-of-health (SOH). Li-S batteries exhibit a complex behaviour due to their inherent mechanisms, which requires a special tailoring of the already literature-available state-of-charge (SOC) and SOH estimation algorithms. In this work, a model of SOH based on capacity fade and power fade has been proposed and incorporated in a state estimator using dual extended Kalman filters has been used to simultaneously estimate Li-S SOC and SOH. The dual extended Kalman filter’s internal estimates of equivalent circuit network parameters have also been used to the estimate maximum available power of the battery at any specified instant. The proposed estimators have been successfully applied to both fresh and aged Li-S pouch cells, showing that they can accurately track accurately the battery SOC, SOH, and power, providing that initial conditions are suitable. However, the estimation of the Li-S battery cells’ capacity fade is shown to be more complex, because the practical available capacity varies highly with the applied current rates and the dynamics of the mission profile.

scholarly journals State estimation of lithium-ion cells using a physicochemical model based extended Kalman filter

2018 ◽  
Vol 223 ◽  
pp. 103-123 ◽  
Author(s):  
J. Sturm ◽  
H. Ennifar ◽  
S.V. Erhard ◽  
A. Rheinfeld ◽  
S. Kosch ◽  
...  
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Extended Kalman Filter ◽  
Lithium Ion ◽  
Physicochemical Model ◽  
Model Based ◽  
Lithium Ion Cells
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