Early results from a systems approach to improving the performance and lifetime of lead acid batteries

Kellaway, Mike; Jennings, Paul; Stone, D.A.; Crowe, E.; Cooper, A.G.

doi:10.1016/s0378-7753(02)00714-0

Cited by 17 publications

(10 citation statements)

References 1 publication

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“…A good example is the incorporation of a second current take-off tab symmetrically placed opposite the normal one. When plates are connected using both tabs, HRPSoC performance can be boosted by two orders of magnitude (12).…”

Section: Adaptation Of Grid Designmentioning

confidence: 99%

Partial State-of-Charge Duty: A Challenge but Not a Show-Stopper for Lead-Acid Batteries!

Moseley

Rand

2012

ECS Trans.

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The mechanism by which conventional lead-acid batteries (both flooded and valve-regulated designs) fail when subjected to highrate partial-state-of-charge (HRPSoC) operation in hybrid electric vehicles is manifested as poor dynamic charge acceptance at the negative plate. This parameter is defined as the amount of charge that is actually delivered to the battery during regenerative braking phases of a driving cycle, divided by the amount of charge that

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Section: Adaptation Of Grid Designmentioning

confidence: 99%

Partial State-of-Charge Duty: A Challenge but Not a Show-Stopper for Lead-Acid Batteries!

Moseley

Rand

2012

ECS Trans.

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“…Road data collected from a Honda Insight HEV (RHOLAB driving cycle [1]), where peak charge-and discharge-cLurent delands of s_60A and -lOOA, respectively, are required from the battery pcack when subjected to a series of vehicle drivinlg tests [1]. The Sa-cof-Cltrg-cuad Sta-c-f-H1calthd prediction of Lad-acid Batties …”

Section: Introductionmentioning

confidence: 99%

State-of-charge and state-of-health prediction of lead-acid batteries for hybrid electric vehicles using non-linear observers

Bhangu

Bentley

Stone

et al. 2005

2005 European Conference on Power Electronics and Applications

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The paper describes the application of state-estimation techniiques for the real-time prediction of state-ofcharge (SoC) and state-of-heallth (SoH) of lead-acid cells. Approaches based on the Extended Kalman Filter (EKF) are presented to provide correction for offset, drift and state divergence-an unfortunate feature of more traditional coulomb-counting teelunques. Experiinental results are einployed to demonstrate the relative attributes of tle proposed methodology.

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“…An example is the RHOLAB driving cycle requirements, introduced in Section V-C, which shows road data collected from a Honda Insight HEV, where peak charge-and discharge-current demands of 60 A and 100 A, respectively, are required from the battery pack when subjected to a series of vehicle driving tests [1]. The accommodation of such operating conditions requires that the man- agement system have accurate knowledge of the peak power buffer's state of charge (SoC) to facilitate safe and efficient operation.…”

mentioning

confidence: 99%

Nonlinear Observers for Predicting State-of-Charge and State-of-Health of Lead-Acid Batteries for Hybrid-Electric Vehicles

Bhangu

Bentley

Stone

et al. 2005

IEEE Trans. Veh. Technol.

451

203

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Abstract-This paper describes the application of state-estimation techniques for the real-time prediction of the state-of-charge (SoC) and state-of-health (SoH) of lead-acid cells. Specifically, approaches based on the well-known Kalman Filter (KF) and Extended Kalman Filter (EKF), are presented, using a generic cell model, to provide correction for offset, drift, and long-term state divergence-an unfortunate feature of more traditional coulomb-counting techniques. The underlying dynamic behavior of each cell is modeled using two capacitors (bulk and surface) and three resistors (terminal, surface, and end), from which the SoC is determined from the voltage present on the bulk capacitor. Although the structure of the model has been previously reported for describing the characteristics of lithium-ion cells, here it is shown to also provide an alternative to commonly employed models of lead-acid cells when used in conjunction with a KF to estimate SoC and an EKF to predict state-of-health (SoH). Measurements using real-time road data are used to compare the performance of conventional integration-based methods for estimating SoC with those predicted from the presented state estimation schemes. Results show that the proposed methodologies are superior to more traditional techniques, with accuracy in determining the SoC within 2% being demonstrated. Moreover, by accounting for the nonlinearities present within the dynamic cell model, the application of an EKF is shown to provide verifiable indications of SoH of the cell pack.

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Early results from a systems approach to improving the performance and lifetime of lead acid batteries

Cited by 17 publications

References 1 publication

Partial State-of-Charge Duty: A Challenge but Not a Show-Stopper for Lead-Acid Batteries!

Partial State-of-Charge Duty: A Challenge but Not a Show-Stopper for Lead-Acid Batteries!

State-of-charge and state-of-health prediction of lead-acid batteries for hybrid electric vehicles using non-linear observers

Nonlinear Observers for Predicting State-of-Charge and State-of-Health of Lead-Acid Batteries for Hybrid-Electric Vehicles

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