Lithium-ion battery state of charge estimation with a Kalman Filter based on a electrochemical model

Domênico, Daniela Di; Fiengo, Giovanni; Stefanopoulou, Anna G.

doi:10.1109/cca.2008.4629639

Cited by 174 publications

(115 citation statements)

References 14 publications

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“…Model-based methods, on the other hand, employ either an electrochemical model or an equivalent-circuit model. As mentioned earlier, [10,11,[28][29][30] are some of the works recently conducted on SOC estimation based on full-order or reduced-order electrochemical models. However, due to the aforementioned implementation simplicity of equivalent-circuit models, there is a large body of literature using equivalent-circuit models to estimate battery SOC.…”

Section: Indication Of Battery Statesmentioning

confidence: 99%

“…They introduced the single particle approximation of the full order electrochemical models and used it for SOC estimation. Domenico et al [29] and Moura et al [30] used this single particle model for SOC estimation. Smith et al [10] and Chaturvedi et al [11] employed model order reduction techniques to facilitate on-line implementation of electrochemical models in BMSs.…”

Section: Modelingmentioning

confidence: 99%

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Development of an Experimental Testbed for Research in Lithium-Ion Battery Management Systems

et al. 2013

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Advanced electrochemical batteries are becoming an integral part of a wide range of applications from household and commercial to smart grid, transportation, and aerospace applications. Among different battery technologies, lithium-ion (Li-ion) batteries are growing more and more popular due to their high energy density, high galvanic potential, low self-discharge, low weight, and the fact that they have almost no memory effect. However, one of the main obstacles facing the widespread commercialization of Li-ion batteries is the design of reliable battery management systems (BMSs). An efficient BMS ensures electrical safety during operation, while increasing battery lifetime, capacity and thermal stability. Despite the need for extensive research in this field, the majority of research conducted on Li-ion battery packs and BMS are proprietary works conducted by manufacturers. The available literature, however, provides either general descriptions or detailed analysis of individual components of the battery system, and ignores addressing details of the overall system development. This paper addresses the development of an experimental research testbed for studying Li-ion batteries and their BMS design. The testbed can be configured in a variety of cell and pack architectures, allowing for a wide range of BMS monitoring, diagnostics, and control technologies to be tested and analyzed. General considerations that should be taken into account while designing Li-ion battery systems are reviewed and different technologies and challenges commonly encountered in OPEN ACCESSEnergies 2013, 6 5232 Li-ion battery systems are investigated. This testbed facilitates future development of more practical and improved BMS technologies with the aim of increasing the safety, reliability, and efficiency of existing Li-ion battery systems. Experimental results of initial tests performed on the system are used to demonstrate some of the capabilities of the developed research testbed. To the authors' knowledge, this is the first work that addresses, at the same time, the practical battery system development issues along with the theoretical and technological challenges from cell to pack level.

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Section: Indication Of Battery Statesmentioning

confidence: 99%

Section: Modelingmentioning

confidence: 99%

Development of an Experimental Testbed for Research in Lithium-Ion Battery Management Systems

et al. 2013

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“…Di Domenico et al proposed the average electrode model of the battery in References [14,15], which was simplified on the basis of the micro-macroscopic coupled battery model advanced in the literature [8]. In order to reduce the complexity of the model, the electrolyte concentration in the average electrode model is considered as a constant value, and the electrode dynamics characteristic of the X axis in the cross section is considered only.…”

Section: Average Electrode Modelmentioning

confidence: 99%

Parameter Identification of Electrochemical Model for Vehicular Lithium-Ion Battery Based on Particle Swarm Optimization

Yang

Chen

et al. 2017

Energies

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Abstract:The dynamic characteristics of power batteries directly affect the performance of electric vehicles, and the mathematical model is the basis for the design of a battery management system (BMS).Based on the electrode-averaged model of a lithium-ion battery, in view of the solid phase lithium-ion diffusion equation, the electrochemical model is simplified through the finite difference method. By analyzing the characteristics of the model and the type of parameters, the solid state diffusion kinetics are separated, and then the cascade parameter identifications are implemented with Particle Swarm Optimization. Eventually, the validity of the electrochemical model and the accuracy of model parameters are verified through 0.2-2 C multi-rates battery discharge tests of cell and road simulation tests of a micro pure electric vehicle under New European Driving Cycle (NEDC) conditions. The results show that the estimated parameters can guarantee the output accuracy. In the test of cell, the voltage deviation of discharge is generally less than 0.1 V except the end; in road simulation test, the output is close to the actual value at low speed with the error around ±0.03 V, and at high speed around ±0.08 V.

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“…In order to properly evaluate the SOC estimation, a powerful and effective battery model, which has a fast calculation speed, enough precision, and limited storage size, is necessary to quantify the battery electrochemical reaction. Currently, the electrochemical model can precisely estimate battery SOC based on calculating a variety of differential equations [33,34]. Usually, these kinds of models can be finished by some particular finite element analysis software, such as Ansys.…”

Section: Introductionmentioning

confidence: 99%

A Novel State of Charge Estimation Algorithm for Lithium-Ion Battery Packs of Electric Vehicles

Chen

Shen

et al. 2016

Energies

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This paper focuses on state of charge (SOC) estimation for the battery packs of electric vehicles (EVs). By modeling a battery based on the equivalent circuit model (ECM), the adaptive extended Kalman filter (AEKF) method can be applied to estimate the battery cell SOC. By adaptively setting different weighed coefficients, a battery pack SOC estimation algorithm is established based on the single cell estimation. The proposed method can not only precisely estimate the battery pack SOC, but also effectively prevent the battery pack from overcharge and over-discharge, thus providing safe operation. Experiment results verify the feasibility of the proposed algorithm.

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Lithium-ion battery state of charge estimation with a Kalman Filter based on a electrochemical model

Abstract: Abstract-

Cited by 174 publications

References 14 publications

Development of an Experimental Testbed for Research in Lithium-Ion Battery Management Systems

Development of an Experimental Testbed for Research in Lithium-Ion Battery Management Systems

Parameter Identification of Electrochemical Model for Vehicular Lithium-Ion Battery Based on Particle Swarm Optimization

A Novel State of Charge Estimation Algorithm for Lithium-Ion Battery Packs of Electric Vehicles

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