Nonlinear state observers and extended Kalman filters for battery systems

Rauh, Andreas; Butt, Saif Siddique; Aschemann, Harald

doi:10.2478/amcs-2013-0041

Cited by 25 publications

(15 citation statements)

References 43 publications

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“…As the process included SOC estimation of multiple battery cells, the computational burden was a very important design consideration for the reduction of overall system cost. Therefore, the NSO was preferred due to its lower computation burden and better performance for multi-cell applications [18]. All details on the proposed method are given in the sections below.…”

Section: Battery Modelmentioning

confidence: 99%

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SOC Estimation of Multiple Lithium-Ion Battery Cells in a Module Using a Nonlinear State Observer and Online Parameter Estimation

et al. 2018

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In recent years, electric vehicles (EVs), hybrid electric vehicles (HEVs), and plug-in electric vehicles (PEVs) have become very popular. Therefore, the use of secondary batteries exponentially increased in EV systems. Battery fuel gauges determine the amount of charge inside the battery, and how much farther the vehicle can drive itself under specific operating conditions. It is very important to provide accurate state-of-charge (SOC) information of the battery module to the driver, since inaccurate fuel gauges will not be tolerated. In this paper, a model-based approach is proposed to estimate the SOCs of multiple lithium-ion (Li-ion) battery cells, connected in a module in series, by using a nonlinear state observer (NSO) and an online parameter identification algorithm. A simple method of estimating the impedance and SOC of each cell in a module is also presented in this paper, by employing a ratio vector with respect to the reference value. A battery model based on an autoregressive model with exogenous input (ARX) was used with recursive least squares (RLS) for parameter identification, in an effort to guarantee reliable estimation results under various operating conditions. The validity and feasibility of the proposed algorithm were verified by an experimental setup of six Li-ion battery cells connected in a module in series. It was found that, when compared with a simple linear state observer (LSO), an NSO can further reduce the SOC error by 1%.

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Section: Battery Modelmentioning

confidence: 99%

“…V(e x ) is a negative definite, and as a result the error system in Equation (25), is asymptotically stable. Each dynamic system in Equations (20) and (22) can be selected as an observer for the system in Equations (18) and (17), respectively.…”

Section: Soc Estimation Using a Nonlinear State Observermentioning

confidence: 99%

SOC Estimation of Multiple Lithium-Ion Battery Cells in a Module Using a Nonlinear State Observer and Online Parameter Estimation

et al. 2018

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“…Non-linear sliding mode observers use a quasi-Newtonian approach, applied after pseudo-derivations of the output signal (Veluvolu et al, 2007;Efimov and Fridman, 2011). State observers using extended Kalman filters (EKFs) provide another method of transforming non-linear systems (Boker and Khalil, 2013;Rauh et al, 2013). Finding an appropriate method for parameter synthesis remains one of the major difficulties with state observers for non-linear systems.…”

Section: Introductionmentioning

confidence: 99%

State estimation for MISO non–linear systems in controller canonical form

Schwaller

Ensminger

Dresp-Langley

et al. 2016

International Journal of Applied Mathematics and Computer Science

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We propose a new observer where the model, decomposed in generalized canonical form of regulation described by Fliess, is dissociated from the part assuring error correction. The obtained stable exact estimates give direct access to state variables in the form of successive derivatives. The dynamic response of the observer converges exponentially, as long as the nonlinearities are locally of Lipschitz type. In this case, we demonstrate that a quadratic Lyapunov function provides a number of inequalities which guarantee at least local stability. A synthesis of gains is proposed, independent of the observation time scale. Simulations of a Düffing system and a Lorenz strange attractor illustrate theoretical developments.

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“…for which a positive definite, symmetric matrix P = P T > 0 has to be determined (Rauh et al, 2013a;Åström, 1970;Stengel, 1994). Using this matrix P , the observer gain is given by…”

Section: State and Disturbance Observer Designmentioning

confidence: 99%

An integrodifferential approach to modeling, control, state estimation and optimization for heat transfer systems

Rauh

Senkel

Aschemann

et al. 2016

International Journal of Applied Mathematics and Computer Science

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In this paper, control-oriented modeling approaches are presented for distributed parameter systems. These systems, which are in the focus of this contribution, are assumed to be described by suitable partial differential equations. They arise naturally during the modeling of dynamic heat transfer processes. The presented approaches aim at developing finitedimensional system descriptions for the design of various open-loop, closed-loop, and optimal control strategies as well as state, disturbance, and parameter estimation techniques. Here, the modeling is based on the method of integrodifferential relations, which can be employed to determine accurate, finite-dimensional sets of state equations by using projection techniques. These lead to a finite element representation of the distributed parameter system. Where applicable, these finite element models are combined with finite volume representations to describe storage variables that are-with good accuracy-homogeneous over sufficiently large space domains. The advantage of this combination is keeping the computational complexity as low as possible. Under these prerequisites, real-time applicable control algorithms are derived and validated via simulation and experiment for a laboratory-scale heat transfer system at the Chair of Mechatronics at the University of Rostock. This benchmark system consists of a metallic rod that is equipped with a finite number of Peltier elements which are used either as distributed control inputs, allowing active cooling and heating, or as spatially distributed disturbance inputs.

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Nonlinear state observers and extended Kalman filters for battery systems

Cited by 25 publications

References 43 publications

SOC Estimation of Multiple Lithium-Ion Battery Cells in a Module Using a Nonlinear State Observer and Online Parameter Estimation

SOC Estimation of Multiple Lithium-Ion Battery Cells in a Module Using a Nonlinear State Observer and Online Parameter Estimation

State estimation for MISO non–linear systems in controller canonical form

An integrodifferential approach to modeling, control, state estimation and optimization for heat transfer systems

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