Equivalent circuit model parameters of a high-power Li-ion battery: Thermal and state of charge effects

Gomez, Jamie; Nelson, Ruben; Kalu, Egwu E.; Weatherspoon, Mark H.; Zheng, Jim P.

doi:10.1016/j.jpowsour.2010.12.107

Cited by 192 publications

(78 citation statements)

References 7 publications

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“…A single cell model of the parallel unit will have one SOC, but the relationship of this SOC to the individual cell SOC is not well defined. A single model of parallel cells was developed by [3], but this assumed that the cells were identical which in reality may not be true because of ageing [4] and extrinsic factors such as temperature [5]. Cells may age at different rates specifically because of parallelization, due to the different current throughput and the resultant temperature gradients.…”

Section: Introductionmentioning

confidence: 99%

Current Variation in Parallelized Energy Storage Systems

Bruen

Marco

Gama

2014

2014 IEEE Vehicle Power and Propulsion Conference (VPPC)

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Abstract-an electric vehicle battery pack may employ cells connected electrically in parallel to meet energy and power requirements. For the battery management system, cells connected in parallel are often treated as a single larger component. However, research has identified that the state of charge and cell current may not be equal due to differences in capacity, impedance and cell temperature. In this paper, the authors develop a parallel cell model validated against experimental data. The model is used to evaluate cell energy variation and the impact this can have on the design of highly parallelized battery systems.

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Section: Introductionmentioning

confidence: 99%

Current Variation in Parallelized Energy Storage Systems

Bruen

Marco

Gama

2014

2014 IEEE Vehicle Power and Propulsion Conference (VPPC)

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“…However, the accuracy of DEKF depends significantly on the accuracy of the parameter values in the battery model. As previously mentioned, battery parameters vary according to the operating conditions of the battery, such as the SOC, temperature, and charge/discharge current value [27], [28]. Thus, the parameter variations of the battery model have to be modeled carefully and taken into account for the DEKF operation.…”

Section: ) Variable Current Pulse Tests To Model Parametermentioning

confidence: 99%

State Estimation Technique for VRLA Batteries for Automotive Applications

Duong¹,

Tran²,

Choi³

et al. 2016

Journal of Power Electronics

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The state-of-charge (SOC) and state-of-health (SOH) estimation of batteries play important roles in managing batteries for automotive applications. However, an accurate state estimation of a battery is difficult to achieve because of certain factors, such as measurement noise, highly nonlinear characteristics, strong hysteresis phenomenon, and diffusion effect of batteries. In certain vehicular applications, such as idle stop-start systems (ISSs), significant errors in SOC/SOH estimation may lead to a failure in restarting a combustion engine after the shut-off period of the engine when the vehicle is at rest, such as at a traffic light. In this paper, a dual extended Kalman filter algorithm with a dynamic equivalent circuit model of a lead-acid battery is proposed to deal with this problem. The proposed algorithm adopts a battery model by taking into account the hysteresis phenomenon, diffusion effect, and parameter variations for accurate state estimations of the battery. The validity of the proposed algorithm is verified through experiments by using an absorbed glass mat valve-regulated lead-acid battery and a battery sensor cable for commercial ISS vehicles.

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“…The battery terminal voltage is affected by battery temperature, age, and State-Of-Charge (SOC) (Gomez et al, 2011;Johnson, 2002). Shown in in equation 13, the Equivalent Circuit Battery Model is one of the simplest battery models (Argonne National Labs, 2012;Johnson, 2002).…”

Section: Batterymentioning

confidence: 99%

Automated topology optimisation of hybrid electric vehicle powertrains

Ing

McPhee

2015

IJEHV

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Abstract:Hybrid Electric powertrains are comprised of gasoline and electric powertrains components. These components can be connected in numerous configurations; as the number of components increase, the number of configurations increase exponentially. In this research, an algorithm was developed to automatically assemble and compare all possible hybrid electric powertrain configurations. Combinatorics was used to discover all possible combinations of: an internal combustion engine, high-torque low-speed electric motor, low-torque high-speed electric motor, planetary gearset, five-speed discrete gearbox, and battery. The Graph Theoretic Method was used to generate the powertrain models. The steadystate system models were solved symbolically, and Dynamic Programming was used to determine the optimal control law that minimized fuel consumption. To save computation time, topologies were evaluated using a multi-stage screening process. The component sizes of the top 24 topologies were optimized and ranked by fuel consumption. It was found that the Parallel and Powersplit-like topologies with discrete gearboxes were most efficient. The best performing topology is a Powersplit Hybrid type, with a discrete gearbox connected to the final drive and the output gear of the planetary carrier and electric motor in parallel.Keywords: hybrid electric vehicles; hybrid electric powertrains; topology optimization; linear graph theory; Graph Theoretic Method; design automation Biographical notes: IntroductionConventional transportation vehicles use combustion engines for propulsion because fossil fuels are energy dense and relatively inexpensive. Rising fuel prices, depletion of fossil fuels, and public environmental awareness have made electric vehicles an attractive alternative. Electric vehicles use electricity instead of gas for propulsion. They are more efficient, quieter, and do not create tailpipe emissions. Unfortunately, conventional electric vehicles are heavily reliant on batteries for energy storage. Batteries are expensive, heavy, and require charging equipment. Fuel cells have been proposed as an alternative to batteries, but hydrogen fuelling infrastructure is not widely available, and high pressure hydrogen storage technology is not mature. As an intermittent step from gasoline to electric vehicles, Hybrid Electric Vehicles (HEVs) have emerged in the marketplace.Hybrid Electric powertrains combine elements from traditional gasoline powertrains and electric powertrains. This is advantageous because they operate 10-20% more efficiently, and can refuel at any gasoline station (Chen et al., 2009;del Re et al., 2010;Chan, 2007). Including additional powertrain components create challenges. Complex controllers are required to ensure the system is operating efficiently. Cost and weight also increase with the number of components.The number of potential powertrain configurations increase exponentially as more components are included. Novel powertrain architectures can be identified through human

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Equivalent circuit model parameters of a high-power Li-ion battery: Thermal and state of charge effects

Cited by 192 publications

References 7 publications

Current Variation in Parallelized Energy Storage Systems

Current Variation in Parallelized Energy Storage Systems

State Estimation Technique for VRLA Batteries for Automotive Applications

Automated topology optimisation of hybrid electric vehicle powertrains

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