A lumped-parameter electro-thermal model for cylindrical batteries

Lin, Xinfan; Pérez, Hector E.; Mohan, Shankar; Siegel, Jason B.; Stefanopoulou, Anna G.; Ding, Yi; Castanier, Matthew P.

doi:10.1016/j.jpowsour.2014.01.097

Cited by 447 publications

(197 citation statements)

References 27 publications

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“…Inspection of the discharge curves ( Figure 15) indicates strong agreement between the model and experimental data for most voltage profiles. Within the 90% to 10% SOC range, the maximum RMSE value was determined to be 38 mV, which is similar to the values obtained by Lin et al [17]. The model does deviate from the experimental data, however, towards the end of the cycle.…”

Section: Voltage Validation Resultssupporting

confidence: 87%

“…The largest deviation between experimental data and model data was detected at 10 • C, where an RMSE value of 1 • C was observed. Although the error is higher for low temperatures, the accuracy of the model is similar to predictive models that have been proposed in the literature [17,29]. Finally, it should be noted that the sudden drop in temperature at the end of the test is due to the entropic heat generation term that resembles an endothermic process at low SOC values.…”

Section: Temperature Validation Resultssupporting

confidence: 74%

“…First, examining the temperature errors from the proposed approach (CCD), the highest error is observed for the US-06 drive cycle at a temperature of 10 • C. This error value is consistent with electro-thermal models proposed by Damay et al [19], Lin et al [17] and Sun et al [30], where maximum RMSE values around 1 K were observed. Therefore, the results from this table further confirm the fact that the electro-thermal model can adequately predict the temperature rise in the battery.…”

Section: Comparison To Existing Approachessupporting

confidence: 85%

“…Since ECM parameter response can be highly non-linear with respect to temperature and SOC [17], a more advanced model is required to account for this curvature. Therefore, this paper uses a face-centered CCD, shown in Figure 6, for developing the response surface.…”

Section: Ecm Parameter Sub-modelmentioning

confidence: 99%

“…In the literature, where a statistical analysis has been carried out on the parameters, it is often common practice to use the mean squared error (MSE) to obtain an estimate of the noise variance [17]. This assumption is only valid, however, if the model is assumed to fit the data perfectly and the residuals are found to contain only noise.…”

Section: Ecm Parameter Sub-modelmentioning

confidence: 99%

See 4 more Smart Citations

Development of an Electro-Thermal Model for Electric Vehicles Using a Design of Experiments Approach

et al. 2018

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An accurate and computationally efficient lithium-ion battery model is beneficial when developing state-of-charge (SOC) and state-of-health (SOH) algorithms for battery management systems (BMS). These models allow for software-in-the-loop (SIL) and hardware-in-the-loop (HIL) testing, where the battery pack is simulated in software. However, development of these battery models can be time-consuming, especially when trying to model the effects of temperature and SOC on the equivalent circuit model (ECM) parameters. Estimation of this relationship is often accomplished by carrying out many experiments, which can be costly and time consuming for BMS manufacturers. To address these issues, this paper makes two contributions to literature. First, a comprehensive battery model is developed, where the ECM parameter surface is generated using a design of experiments (DOE) approach. Second, replication runs are conducted to accurately estimate the measurement noise and determine which model parameters are significant. The technique is then compared with existing approaches from the literature, and it is shown that, by using the proposed method, the same degree of accuracy can be obtained while requiring significantly fewer experimental runs. This can be advantageous for BMS manufacturers that require a high-fidelity model but cannot afford to carry out many experiments.

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Section: Voltage Validation Resultssupporting

confidence: 87%

Section: Temperature Validation Resultssupporting

confidence: 74%

Section: Comparison To Existing Approachessupporting

confidence: 85%

Section: Ecm Parameter Sub-modelmentioning

confidence: 99%

Section: Ecm Parameter Sub-modelmentioning

confidence: 99%

See 3 more Smart Citations

Development of an Electro-Thermal Model for Electric Vehicles Using a Design of Experiments Approach

et al. 2018

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An Integrated Flow–Electric–Thermal Model for a Cylindrical Li‐Ion Battery Module with a Direct Liquid Cooling Strategy

et al. 2022

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Lithium-ion batteries (LiBs) are on their way to becoming a dominating energy storage technology, especially for application in electric vehicles, [1,2] due to their excellent performance in terms of combinations of energy density, power capability, energy efficiency, and cycle life. In recent decades, there have been plenty of investigations, both using experimental measurements [3][4][5] and simulations, [6][7][8][9] aiming to improve the safety, energy, and power of LiBs. This has led to a consensus that the performance of LiBs is largely dependent on their operational temperature, [10][11][12][13][14] explaining the growing interest in thermal control of LiBs, both at the cell and system level and to increased efforts in coupled thermal and electrochemical modeling of cell behavior. [15][16][17][18][19] However, most of the existing coupled thermal and electrochemical models are either built on the single-cell level, which is not applicable in the investigation of the effect of the battery module design. Alternatively, they are built based on the most simply operational air-cooling system. The aim of the present study is, therefore, to establish an integrated model at the battery module level, which can take into account the simultaneous and interdependent thermal behavior of different cells and the effects from relevant multi-physical fields caused by different cooling strategies, incorporating fluid flow and heat transfer across both fluid and solid domains.Since the thermal behavior of the batteries/module is determined by the balance between heat generation and dispassion rate, the first step to building a convincing integrated model is to understand the heat generation within the battery, i.e., to build a precise thermal model. The existing thermal models for LiBs can be classified into lumped-parameter models, [20] electric-thermal models, [6,21] electrochemical-thermal models, [22,23] and thermal runaway models. [24,25] Among these, the electrochemical-thermal model can provide the most detailed information on the battery physics, which makes it possible to extract heat generated from different components, [26] making it favorable from a battery design point of view. For example, Kim et al. discussed the impact of geometry and position of the tabs on the temperature gradient in an operated battery, [27] while Lee et al. [28] found that the heat

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Recent Developments of Thermal Management Strategies for Lithium‐Ion Batteries: A State‐of‐The‐Art Review

2022

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The performance of lithium‐ion batteries is quite dependent on temperature and a series of investigations on the battery thermal management system (BTMS) have been reported during the past decades. Herein, the recent developments of BTMS are thoroughly summarized. First, the thermal characteristics of the battery caused by different temperature conditions and temperature nonuniformity are described. Then, the thermal models, including electro‐thermal coupled model and electrochemical–thermal coupled model are briefly presented. Subsequently, various traditional strategies like air cooling, liquid cooling, phase change material (PCM) cooling, and heat pipe cooling are elaborated. With the development of battery technology, BTMS based on a single strategy alone cannot meet the growth of thermal requirements, especially under dynamic and high‐power energy conditions. Hence, a hybrid BTMS that integrates two or more cooling strategies begins to be studied and provide a feasible solution for the problem. The related studies on hybrid BTMS are also systematically reviewed from the perspective of combinations, such as air–PCM, liquid–PCM, heat pipe–PCM, and other hybrid strategies. Besides, the current research on battery preheating strategies is also summarized. Finally, insufficient present studies are pointed out, aiming to provide comprehensive guidance toward the development of battery thermal management.

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A lumped-parameter electro-thermal model for cylindrical batteries

Cited by 447 publications

References 27 publications

Development of an Electro-Thermal Model for Electric Vehicles Using a Design of Experiments Approach

Development of an Electro-Thermal Model for Electric Vehicles Using a Design of Experiments Approach

An Integrated Flow–Electric–Thermal Model for a Cylindrical Li‐Ion Battery Module with a Direct Liquid Cooling Strategy

Recent Developments of Thermal Management Strategies for Lithium‐Ion Batteries: A State‐of‐The‐Art Review

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