Fractional algebraic identification of the distribution of relaxation times of battery cells

2018 Annual American Control Conference (ACC)

Funk

Eckert

et al. 2018

Self Cite

Two state of charge estimation methods using fractional order extended and unscented Kalman filter and a nonlinear variable fractional order battery model are implemented. Both, battery model and Kalman filters are evaluated and compared using measurements of an actual lithium-ion polymer battery cell. The observability of the battery model and the influence of an initialization function on the estimation algorithms is investigated.

Section: Introductionmentioning

confidence: 99%

Fractional Extended and Unscented Kalman Filtering for State of Charge Estimation of Lithium-Ion Batteries

2018 Annual American Control Conference (ACC)

Funk

Eckert

et al. 2018

Self Cite

“…In this paper, we use the battery model from [6,8,11] for a single Li-ion cell of type SLPB 834374H from Kokam. The equivalent circuit model which is used in this paper is shown in Fig.…”

Section: Model Of a Single Cellmentioning

confidence: 99%

“…Using noninteger order differential equations, fractional models describe reality better than conventional models of the same order as they model accurately the internal impedance and the electrochemical dynamics of a battery cell. Hence, they are used more and more frequently in recent time [6][7][8][9][10][11][12]. As this model is easy to implement, the additional effort compared to integer order models is negligible and the results are convincing, the application for battery modeling is suitable.…”

Section: Introductionmentioning

confidence: 99%

Cascaded Fractional Kalman Filtering for State and Current Estimation of Large-Scale Lithium-Ion Battery Packs

2018 Chinese Control and Decision Conference (CCDC)

Brenneisen

Stark

et al. 2018

Self Cite

In this paper, a cascaded fractional Kalman filter for state of charge and branch current estimation of largescale battery systems is proposed. As a centralized approach for the estimation of a large-scale system is costly in terms of effort and time, a partition into smaller and, therefore, simpler subsystems is applied. Since the overall system is divided into smaller units, a local computation is allowed and complexity reduced. In these distributed systems, usually, the subsystems communicate with each other to exchange relevant data. Using a model based on mesh currents, we receive a cascaded system structure which results in a hierarchical arrangement of all subsystems. This concludes in a one-directional information flow and, therefore, reduces the overall communication effort. Using this proposed approach, it is not only possible to estimate the states of each branch locally but also to calculate the branch currents when the total current is known. Finally, a practical test with real measurement data is presented.

“…A late-lumping parameter identification method for a fractional battery model is presented in [3]. In [4] a model-based approach is derived which extends Mikusińskis operational calculus for fractional systems in order to identify the distribution of relaxation times of a battery cell. In [5] a spectral parameter estimation method is presented for the identification of a linear fractional state space system.…”

Section: A Fractional Systemsmentioning

confidence: 99%

Distributed and decentralized Kalman filtering for Cascaded Fractional order systems

2017 American Control Conference (ACC)

Gil

Hohmann

2017

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This paper presents a distributed Kalman filter algorithm for cascaded systems of fractional order. Certain conditions are introduced under which a division of a fractional system into cascaded subsystems is possible. A functional distribution of a large scale system and of the state estimation algorithm leads to smaller and scalable nodes with reduced memory and computational effort. Since each subsystem performs its calculations locally, a central processing node is not needed. All data which are required by subsequent nodes are communicated to them unidirectionally. Also a comparison between the Fractional Kalman Filter (FKF) and the Cascaded Fractional Kalman Filter (CFKF) is given by an example.