Adaptive state of charge algorithm for nickel metal hydride batteries including hysteresis phenomena

Verbrugge, Mark W.; Tate, Edward

doi:10.1016/j.jpowsour.2003.08.042

Cited by 242 publications

(122 citation statements)

References 12 publications

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“…According to model complexity, accuracy and robustness, these twelve models have been evaluated. The authors concluded that first-order RC model with one-state hysteresis, proposed by [25], provides the best voltage prediction. The schematic of this model is shown in Figure 15.…”

Section: Modelingmentioning

confidence: 99%

“…The first Equation (3) is obtained from the definition of SOC, the second equation is from Kirchhoff's current law, and the last equation is proposed in [25] to take battery's hysteresis effects into consideration. In the last equation, the term S D denotes the battery self-discharge rate which is considered to be a function of temperature and battery SOC:…”

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: Modelingmentioning

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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“…Consisting in integrating the battery current, this open-loop and non-model based method is easy to implement online but it is affected by the uncertainty on the initial condition, by the measurement error accumulated during the battery life and by the battery capacity degradation due to usage [3,5,6]. To overcome these issues and improve the BMS functions, several approaches based on dynamic battery models have been investigated in [4,5,[7][8][9][10][11][12][13][14][15][16][17][18][19]. Regarding the SOC estimation, the main advantage of the model-based methods is that the initialization error can be recovered by means of output (voltage and temperature) feedback, the closed-loop estimation concept consisting in comparing the measurement of the cell voltage and temperature with model predictions.…”

Section: Ifp Energies Nouvelles International Conference Rencontres Smentioning

confidence: 99%

“…The error on SOC initialization is then propagated with time, invalidating the estimation. Furthermore, due to the large characteristic time associated to the battery relaxation, the OCV measurement can be unavailable in automotive applications [11,19]. It is also worth noting that such a map-based model is useless for power estimation as it can not predict the cell voltage during the non-zero current demands.…”

Section: Ampere-hour Countingmentioning

confidence: 99%

A Review of Approaches for the Design of Li-Ion BMS Estimation Functions

Domenico

Creff

Prada

et al. 2013

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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“…Similarly, [3] also assumes an electrical circuit but instead of trying to identify the circuit elements, it aims to compute the SOC directly from terminal voltage and terminal current. Combined with the Coulomb counting method, [12] uses online least squares regression to compute the parameters of an equivalent RC circuit. All these methods rely on electrical models to explain the battery behavior and thus have important wellknown drawbacks.…”

Section: Introductionmentioning

confidence: 99%

A Behavioral Algorithm for State of Charge Estimation

Balkan¹,

Gao²,

Tabuada³

et al. 2012

WEVJ

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In Hybrid Electricle Vehicles (HEV), performing online energy management is an important task to be achieved to reduce emissions, fuel consumption and increase vehicle performance. For this task, estimating the State of Charge (SOC) is needed since it serves as a measure of energy that is left inside an electrochemical battery. A variety of methods to solve this estimation problem have been proposed in the literature. However, most of these methods either assume equivalent circuit models for the battery and thus lose their validity under some discharge conditions or depend heavily on the choice of parameters in the algorithm. In this paper, we use behavioral framework to avoid postulation of a specific model for a battery and develop a new and simple SOC estimation algorithm. Once the problem is formulated as the computation of a specific free response of the battery, algorithm computes this response using only terminal current and terminal voltage measurements. We demonstrate the effectiveness of the algorithm with different discharge profiles using both simulated and real data.

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Adaptive state of charge algorithm for nickel metal hydride batteries including hysteresis phenomena

Cited by 242 publications

References 12 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

A Review of Approaches for the Design of Li-Ion BMS Estimation Functions

A Behavioral Algorithm for State of Charge Estimation

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