Functional Fractional Calculus for System Identification of Battery Cells

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.

“…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%

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

“…An example of a fractional order battery model is described in [2], which is introduced using impedance measurements. 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.…”

Section: A Fractional Systemsmentioning

confidence: 99%

“…For the following sections, it can be assumed that the buffer length is chosen sufficiently large. It should be noted that, according to [18], the fractional system described by (3) to 7does not form a classical state space representation. It is rather a pseudo state space description, because it depends not only on the current state x k , but also on the values of x from the past as discussed in this section.…”

Section: Fractional Calculusmentioning

confidence: 99%

Distributed and decentralized Kalman filtering for Cascaded Fractional order systems

2017 American Control Conference (ACC)

Gil

Hohmann

2017

Self Cite

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.

“…In [4] a fractional battery model is introduced using impedance measurements. A late-lumping parameter identification method for a fractional battery model is presented in [7].…”

Section: Introduction a Fractional Systemsmentioning

confidence: 99%

Distributed and decentralized state estimation of Fractional order systems

2016 American Control Conference (ACC)

Gil

Hohmann

2016

Self Cite

Abstract-This paper is about the decentralization and distribution of a Kalman filter for fractional order systems. A fractional order discrete state space for a global system is introduced and divided into different submodules. The distribution of the model and of the state estimation algorithm into submodules leads to small and scalable units, which do not need a central processing node. Each submodule performs its computation locally. All information required by other nodes is communicated between the nodes directly. Finally, an example is given to compare the fractional Kalman filter (FKF) for the overall system with the distributed and decentralized fractional Kalman filter (DDFKF).