Electrochemical-Thermal Model of Lithium Polymer Batteries

Song, Li; Evans, James W.

doi:10.1149/1.1393490

Cited by 219 publications

(119 citation statements)

References 17 publications

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“…However, because no investigations actually report the heat rate on a volume basis, cross-study comparison requires significant manipulation of the reported data. This includes estimating the heat rate from the total cell volume, [54][55][56][59][60][61][62][63][64][65][66][67][68]70 from the thickness of the unit cell 49 or the entire battery, 69 or from standard densities of full cells. 57,58 Unfortunately, these assumptions can greatly influence the estimated volumetric heat generation rate.…”

Section: R8mentioning

confidence: 99%

A Critical Review of Thermal Issues in Lithium-Ion Batteries

Bandhauer

Garimella

Fuller

2011

J. Electrochem. Soc.

1,696

915

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Lithium-ion batteries are well-suited for fully electric and hybrid electric vehicles due to their high specific energy and energy density relative to other rechargeable cell chemistries. However, these batteries have not been widely deployed commercially in these vehicles yet due to safety, cost, and poor low temperature performance, which are all challenges related to battery thermal management. In this paper, a critical review of the available literature on the major thermal issues for lithium-ion batteries is presented. Specific attention is paid to the effects of temperature and thermal management on capacity/power fade, thermal runaway, and pack electrical imbalance and to the performance of lithium-ion cells at cold temperatures. Furthermore, insights gained from previous experimental and modeling investigations are elucidated. These include the need for more accurate heat generation measurements, improved modeling of the heat generation rate, and clarity in the relative magnitudes of the various thermal effects observed at high charge and discharge rates seen in electric vehicle applications. From an analysis of the literature, the requirements for lithium-ion thermal management systems for optimal performance in these applications are suggested, and it is clear that no existing thermal management strategy or technology meets all these requirements. Thomas-Alyea (kethomas@alumni.princeton.edu) and Kandler Smith (kandler.smith@gmail.com). This article was reviewed by KarenElectric and hybrid electric vehicles ͑EV and HEV͒ may present the best near-term solution for the transportation sector to reduce our dependence on petroleum and to reduce emissions of greenhouse gases and criteria pollutants. Rechargeable lithium-ion batteries are well-suited for these vehicles because they have, among other things, high specific energy and energy density relative to other cell chemistries. For example, practical nickel-metal hydride ͑NiMH͒ batteries, which have dominated the HEV market, have a nominal specific energy and energy density of 75 Wh/kg and 240 Wh/L, respectively. In contrast, lithium-ion batteries can achieve 150 Wh/kg and 400 Wh/L, 1 i.e., nearly 2 times the specific energy and energy density.Whereas lithium-ion batteries are rapidly displacing NiMH and nickel-cadmium secondary batteries for portable and hand-held devices, they have not yet been widely introduced in automotive products. The main barriers to the deployment of large fleets of vehicles on public roads equipped with lithium-ion batteries continue to be safety, cost ͑related to cycle and calendar life͒, and low temperature performance 2 -all challenges that are coupled to thermal effects in the battery. Since the recent introduction of HEV fleets, the industry trend is toward larger batteries required for plug-in hybrids, extended-range hybrids, and all-electric vehicles. These larger battery designs impose greater pressure to lower costs and improve safety.Furthermore, most of the research on these types of batteries has been related to fin...

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Section: R8mentioning

confidence: 99%

A Critical Review of Thermal Issues in Lithium-Ion Batteries

Bandhauer

Garimella

Fuller

2011

J. Electrochem. Soc.

1,696

915

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“…The blue line shows the actual capacity values calculated by equation (6). The measured capacity values are shown by the red dots, which were simulated as the actual capacity values at cycle k plus independent zero-mean Gaussian noise terms and expressed as measurement model equation (13). The green line represents the prediction points, which were estimated by particle filtering equations (10) to (12).…”

Section: State Transition Modelmentioning

confidence: 99%

Lithium-ion battery life prognostic health management system using particle filtering framework

Dalal

2011

Proceedings of the Institution of Mechanical Engineers, Part O:

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In this paper, a detailed implementation of a lithium-ion battery life prognostic system using a particle filtering framework is presented. A lumped parameter battery model is used to account for all the dynamic characteristics of the battery: a non-linear open-circuit voltage, current, temperature, cycle number, and time-dependent storage capacity. The internal processes of the battery are used to form the basis of this model. Statistical estimates of the noise in the system and the anticipated operational conditions are processed to provide estimates of the remaining useful life. The model is then subsequently used in the particle-filtering framework with a sequential importance resampling algorithm to predict the remaining useful life of the battery for individual discharge cycles as well as for the battery cycle life. The research presented in this paper provides the necessary steps towards a comprehensive battery health management solution for energy storage devices.

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“…Researchers worldwide have developed a wide variety of battery models for different purposes. Almost all the existing battery models can be classified into the following two types: (1) parametric models, e.g., electrochemical models [1,2] and electrical models [3][4][5][6]; and (2) nonparametric models, e.g., artificial neural network (ANN) models [7][8][9] and abstract mathematical models [10]. Parametric battery models have been developed in terms of the equivalent electric-circuit parameters (for electrical models) or electrochemical parameters (for electrochemical models).…”

Section: Introductionmentioning

confidence: 99%

Online Semiparametric Identification of Lithium-Ion Batteries Using the Wavelet-Based Partially Linear Battery Model

Jiang

Zhang

2013

Energies

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Abstract:Battery model identification is very important for reliable battery management as well as for battery system design process. The common problem in identifying battery models is how to determine the most appropriate mathematical model structure and parameterized coefficients based on the measured terminal voltage and current. This paper proposes a novel semiparametric approach using the wavelet-based partially linear battery model (PLBM) and a recursive penalized wavelet estimator for online battery model identification. Three main contributions are presented. First, the semiparametric PLBM is proposed to simulate the battery dynamics. Compared with conventional electrical models of a battery, the proposed PLBM is equipped with a semiparametric partially linear structure, which includes a parametric part (involving the linear equivalent circuit parameters) and a nonparametric part [involving the open-circuit voltage (OCV)]. Thus, even with little prior knowledge about the OCV, the PLBM can be identified using a semiparametric identification framework. Second, we model the nonparametric part of the PLBM using the truncated wavelet multiresolution analysis (MRA) expansion, which leads to a parsimonious model structure that is highly desirable for model identification; using this model, the PLBM could be represented in a linear-in-parameter manner. Finally, to exploit the sparsity of the wavelet MRA representation and allow for online implementation, a penalized wavelet estimator that uses a modified online cyclic coordinate descent algorithm is proposed to identify the PLBM in a recursive fashion. The simulation and experimental results demonstrate that the proposed PLBM with the corresponding identification algorithm can accurately simulate the dynamic behavior of a lithium-ion battery in the Federal Urban Driving Schedule tests.

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Electrochemical-Thermal Model of Lithium Polymer Batteries

Cited by 219 publications

References 17 publications

A Critical Review of Thermal Issues in Lithium-Ion Batteries

A Critical Review of Thermal Issues in Lithium-Ion Batteries

Lithium-ion battery life prognostic health management system using particle filtering framework

Online Semiparametric Identification of Lithium-Ion Batteries Using the Wavelet-Based Partially Linear Battery Model

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