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

Abstract

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.

A Novel Kalman Filter Design and Analysis Method Considering Observability and Dominance Properties of Measurands Applied to Vehicle State Estimation

“…Here, the system modeling, as well as the measurements, are described by their statistical characteristics, and an optimal estimation [ 1 ] is performed by an iterative procedure (prediction and measurement update). This section closely follows [ 3 , 12 ]; the interested reader is referred to these sources for more detail.…”

“…The design of the ROMO is based on the so-called “wheel robot” concept [ 25 ] with all wheel by-wire steering capabilities, where the drivetrain, brakes, steering system, spring, and dampers are integrated into each of the four wheels. The dissemination of electric vehicles in the automotive market results in a variety of estimation problems related, e.g., to the battery state [ 3 ] as well as to the vehicle dynamics, see, e.g. [ 26 ].…”

“…In many cases, the properties of the measurands for the estimation problem are only evaluated by simple plausibility checks. For instance, when estimating the state-of-charge (see e.g., [ 3 ]) of a battery, it is evident that the battery voltage should be used as a measurand. But how would other measurands affect the observability, and might they be even more dominant?…”

Advanced Controller Development Based on eFMI with Applications to Automotive Vertical Dynamics Control

“…One important feature is the ability to set discretized continuous states before each call of the DoStep method. This functionality is crucial for certain Kalman filter algorithms that perform multiple integration steps from perturbated states (see e.g., [20]). For certain nonlinear Kalman filter algorithms, e.g., for the extended Kalman filter method, the computation of the state derivatives .…”

Nonlinear State Estimation with FMI: Tutorial and Applications

“…This work was significantly extended by Brembeck et al (2014) to cover a draft implementation of FMI 2.0 standard for co-simulation, and test implementations of an extended Kalman filter and a moving horizon estimator are demonstrated on an electric vehicle application. Brembeck (2019) further develops this work and implements a number of practical refinements on the state-of-charge estimator. Other recent work done by Vytvytskyi and Lie (2019) compares the performance of unscented Kalman filters and ensemble Kalman filters for state estimation in hydropower plants and finds that these methods work well in this application.…”