Three‐Dimensional Thermal Modeling of Lithium‐Polymer Batteries under Galvanostatic Discharge and Dynamic Power Profile

Chen, Yufei; Evans, James W.

doi:10.1149/1.2059263

Cited by 160 publications

(92 citation statements)

References 6 publications

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“…One bottleneck is energy storage, as the peak time of energy harvesting is not necessarily the same as that of energy consuming. The battery is probably the most widely used energy storage device [1,2]. Despite its ever-increasing importance, many challenges remain unsolved to characterize and manage the battery.…”

Section: Introductionmentioning

confidence: 99%

A universal state-of-charge algorithm for batteries

Xiao

Shi

2010

Proceedings of the 47th Design Automation Conference

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State-of-charge (SOC) measures energy left in a battery, and it is critical for modeling and managing batteries. Developing efficient yet accurate SOC algorithms remains a challenging task. Most existing work uses regression based on a time-variant circuit model, which may be hard to converge and often does not apply to different types of batteries. Knowing open-circuit voltage (OCV) leads to SOC due to the well known mapping between OCV and SOC. In this paper, we propose an efficient yet accurate OCV algorithm that applies to all types of batteries. Using linear system analysis but without a circuit model, we calculate OCV based on the sampled terminal voltage and discharge current of the battery. Experiments show that our algorithm is numerically stable, robust to history dependent error, and obtains SOC with less than 4% error compared to a detailed battery simulation for a variety of batteries. Our OCV algorithm is also efficient, and can be used as a real-time electro-analytical tool revealing what is going on inside the battery.

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Section: Introductionmentioning

confidence: 99%

A universal state-of-charge algorithm for batteries

Xiao

Shi

2010

Proceedings of the 47th Design Automation Conference

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“…The first N + 1 coefficients, B j,k (t) ( j, k = 0 to N), are determined by using the method of weighted residuals (MWR) which aims to find the coefficients which minimize the error. 52 The coefficients corresponding to j, k = N + 1 and N + 2 are calculated using the boundary conditions and will be discussed shortly. First, consider a general differential equation of the form (for example, the governing equations given in Table II):…”

Section: Reformulation and Simulationmentioning

confidence: 99%

Efficient Simulation and Model Reformulation of Two-Dimensional Electrochemical Thermal Behavior of Lithium-Ion Batteries

Northrop

Pathak

Rife

et al. 2015

J. Electrochem. Soc.

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Lithium-ion batteries are an important technology to facilitate efficient energy storage and enable a shift from petroleum based energy to more environmentally benign sources. Such systems can be utilized most efficiently if good understanding of performance can be achieved for a range of operating conditions. Mathematical models can be useful to predict battery behavior to allow for optimization of design and control. An analytical solution is ideally preferred to solve the equations of a mathematical model, as it eliminates the error that arises when using numerical techniques and is usually computationally cheap. An analytical solution provides insight into the behavior of the system and also explicitly shows the effects of different parameters on the behavior. However, most engineering models, including the majority of battery models, cannot be solved analytically due to non-linearities in the equations and state dependent transport and kinetic parameters. The numerical method used to solve the system of equations describing a battery operation can have a significant impact on the computational cost of the simulation. In this paper, a model reformulation of the porous electrode pseudo three dimensional (P3D) which significantly reduces the computational cost of lithium ion battery simulation, while maintaining high accuracy, is discussed. This reformulation enables the use of the P3D model into applications that would otherwise be too computationally expensive to justify its use, such as online control, optimization, and parameter estimation. Furthermore, the P3D model has proven to be robust enough to allow for the inclusion of additional physical phenomena as understanding improves. In this paper, the reformulated model is used to allow for more complicated physical phenomena to be considered for study, including thermal effects. There is an increasing societal pressure to utilize alternative energy sources to supplant the high use of fossil fuels. As energy and power demand is continually increasing, both in terms of grid usage and for transportation, there has been more interest in developing renewable energy sources. One problem with renewable energy sources is the intermittent nature and short-term unpredictability of supply of sources such as wind and solar. Thus, in order to match supply and demand, some form of energy storage is required, and lithium-ion battery technologies are one possible solution. Furthermore, electric vehicles are increasing in popularity as the price of liquid fuels generally increase. Lithium-ion batteries are a popular choice for electric vehicles because of their high energy and power density compared to other battery chemistries.The performance of lithium-ion batteries is highly dependent on the conditions at which they are exposed as well as the state of the internal variables. This has led to the development of several mathematical models to simulate battery behavior, ranging from simple empirical-based models or circuit based models 1,2 to computationally expensive mole...

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“…This model was a continuing work from another previous adaptive thermal modelling investigation by one of the authors [67]. Previously some other models assumed the change in entropy was constant [68][69][70]. Rad, et al [44] speculated that the SOC dependent change in entropy is an important heat source that is essential to include in order accurately predict the thermal behaviour of Li-ion ESS batteries under a wide range of operating conditions.…”

Section: Adaptive Thermal Modelmentioning

confidence: 99%

Theoretical Modelling Methods for Thermal Management of Batteries

Shabani¹,

Biju²

2015

Energies

102

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Abstract:The main challenge associated with renewable energy generation is the intermittency of the renewable source of power. Because of this, back-up generation sources fuelled by fossil fuels are required. In stationary applications whether it is a back-up diesel generator or connection to the grid, these systems are yet to be truly emissions-free. One solution to the problem is the utilisation of electrochemical energy storage systems (ESS) to store the excess renewable energy and then reusing this energy when the renewable energy source is insufficient to meet the demand. The performance of an ESS amongst other things is affected by the design, materials used and the operating temperature of the system. The operating temperature is critical since operating an ESS at low ambient temperatures affects its capacity and charge acceptance while operating the ESS at high ambient temperatures affects its lifetime and suggests safety risks. Safety risks are magnified in renewable energy storage applications given the scale of the ESS required to meet the energy demand. This necessity has propelled significant effort to model the thermal behaviour of ESS. Understanding and modelling the thermal behaviour of these systems is a crucial consideration before designing an efficient thermal management system that would operate safely and extend the lifetime of the ESS. This is vital in order to eliminate intermittency and add value to renewable sources of power. This paper concentrates on reviewing theoretical approaches used to simulate the operating temperatures of ESS and the subsequent endeavours of modelling thermal management systems for these systems. The intent of this review is to present some of the different methods of modelling the thermal behaviour of ESS highlighting the advantages and disadvantages of each approach. OPEN ACCESSEnergies 2015, 8 10154

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Three‐Dimensional Thermal Modeling of Lithium‐Polymer Batteries under Galvanostatic Discharge and Dynamic Power Profile

Cited by 160 publications

References 6 publications

A universal state-of-charge algorithm for batteries

A universal state-of-charge algorithm for batteries

Efficient Simulation and Model Reformulation of Two-Dimensional Electrochemical Thermal Behavior of Lithium-Ion Batteries

Theoretical Modelling Methods for Thermal Management of Batteries

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