Experimental Measurements of Thermal Characteristics of LiFePO<sub>4</sub> Battery

Panchal, Satyam; Mathewson, Scott; Fraser, Roydon; Culham, Richard; Fowler, Michael

doi:10.4271/2015-01-1189

Cited by 23 publications

(12 citation statements)

References 7 publications

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“…The instantaneous OCV was calculated based on the nonlinear OCV-SOC function given in Equation 2, and the dynamic voltage fraction was reconstructed by extracting the impedance values using Equation (16). Finally, the estimated terminal voltage of the battery was established in the form of an observation equation, as shown in Equation 20.…”

Section: Soc Estimation Using a Nonlinear State Observermentioning

confidence: 99%

“…Hence, variation in impedance must be accurately determined under various operating conditions, and over the battery's lifetime, together with its dependence on current, SOC, temperature, and aging [14]. Notable analyses of the effect of variable temperature and aging under actual drive cycles on the performance of lithium-ion battery modules for an electric vehicle can be found in References [15,16]. In addition, to obtain data for this variation, many experiments are required that are not only time-consuming, but also labor intensive.…”

Section: Introductionmentioning

confidence: 99%

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SOC Estimation of Multiple Lithium-Ion Battery Cells in a Module Using a Nonlinear State Observer and Online Parameter Estimation

et al. 2018

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In recent years, electric vehicles (EVs), hybrid electric vehicles (HEVs), and plug-in electric vehicles (PEVs) have become very popular. Therefore, the use of secondary batteries exponentially increased in EV systems. Battery fuel gauges determine the amount of charge inside the battery, and how much farther the vehicle can drive itself under specific operating conditions. It is very important to provide accurate state-of-charge (SOC) information of the battery module to the driver, since inaccurate fuel gauges will not be tolerated. In this paper, a model-based approach is proposed to estimate the SOCs of multiple lithium-ion (Li-ion) battery cells, connected in a module in series, by using a nonlinear state observer (NSO) and an online parameter identification algorithm. A simple method of estimating the impedance and SOC of each cell in a module is also presented in this paper, by employing a ratio vector with respect to the reference value. A battery model based on an autoregressive model with exogenous input (ARX) was used with recursive least squares (RLS) for parameter identification, in an effort to guarantee reliable estimation results under various operating conditions. The validity and feasibility of the proposed algorithm were verified by an experimental setup of six Li-ion battery cells connected in a module in series. It was found that, when compared with a simple linear state observer (LSO), an NSO can further reduce the SOC error by 1%.

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Section: Soc Estimation Using a Nonlinear State Observermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SOC Estimation of Multiple Lithium-Ion Battery Cells in a Module Using a Nonlinear State Observer and Online Parameter Estimation

et al. 2018

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“…In addition, these pouch-type batteries are connected either in parallel or in series within the LIB packs or modules, which also generate high amount of heat during both discharging and charging. Therefore, a good battery thermal management system (BTMS) is necessary [11]. The heat of LIBs increases when EVs accelerate and experience fast charging.…”

Section: Introductionmentioning

confidence: 99%

High Reynold’s Number Turbulent Model for Micro-Channel Cold Plate Using Reverse Engineering Approach for Water-Cooled Battery in Electric Vehicles

et al. 2020

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The investigation and improvement of the cooling process of lithium-ion batteries (LIBs) used in battery electric vehicles (BEVs) and hybrid electric vehicles (HEVs) are required in order to achieve better performance and longer lifespan. In this manuscript, the temperature and velocity profiles of cooling plates used to cool down the large prismatic Graphite/LiFePO4 battery are presented using both laboratory testing and modeling techniques. Computed tomography (CT) scanning was utilized for the cooling plate, Detroit Engineering Products (DEP) MeshWorks 8.0 was used for meshing of the cooling plate, and STAR CCM+ was used for simulation. The numerical investigation was conducted for higher C-rates of 3C and 4C with different ambient temperatures. For the experimental work, three heat flux sensors were attached to the battery surface. Water was used as a coolant inside the cooling plate to cool down the battery. The mass flow rate at each channel was 0.000277677 kg/s. The k-ε model was then utilized to simulate the turbulent behaviour of the fluid in the cooling plate, and the thermal behaviour under constant current (CC) discharge was studied and validated with the experimental data. This study provides insight into thermal and flow characteristics of the coolant inside a cooing plate, which can be used for designing more efficient cooling plates.

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“…In the failure ratio of different material systems, NMC material batteries exceed 85%. The major challenge for the next generation of electric vehicles is how to manage battery pack heat generation according to its thermal characteristics [18].…”

Section: Introductionmentioning

confidence: 99%

Study on the Characteristics of a High Capacity Nickel Manganese Cobalt Oxide (NMC) Lithium-Ion Battery—An Experimental Investigation

Zhang

Xia

et al. 2018

Energies

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The use of high-capacity batteries as the battery pack of electric vehicles is the current development trend. In order to better design battery packages and battery management systems and develop related battery estimation technology, the related characteristics of high capacity battery cells need to be studied in depth. Capacity and pulse tests of batteries at different temperatures are carried out in this paper. Through these experimental data, the electrical characteristics of different parameters of the high capacity battery, such as capacity characteristic data, internal resistance characteristic data, OCV-SOC characteristic relation curve, power data and temperature rise are analyzed. The specific parameters of the battery in the second order equivalent circuit model are obtained by using the off-line parameter identification method. These parameters results can be used as comparison data and reference data. It is beneficial to the on-line parameter identification of battery model and the estimation of battery state, so as to shorten the development time and improve the quality of the development.

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Experimental Measurements of Thermal Characteristics of LiFePO₄ Battery

Cited by 23 publications

References 7 publications

SOC Estimation of Multiple Lithium-Ion Battery Cells in a Module Using a Nonlinear State Observer and Online Parameter Estimation

SOC Estimation of Multiple Lithium-Ion Battery Cells in a Module Using a Nonlinear State Observer and Online Parameter Estimation

High Reynold’s Number Turbulent Model for Micro-Channel Cold Plate Using Reverse Engineering Approach for Water-Cooled Battery in Electric Vehicles

Study on the Characteristics of a High Capacity Nickel Manganese Cobalt Oxide (NMC) Lithium-Ion Battery—An Experimental Investigation

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Experimental Measurements of Thermal Characteristics of LiFePO4 Battery

Cited by 23 publications

References 7 publications

SOC Estimation of Multiple Lithium-Ion Battery Cells in a Module Using a Nonlinear State Observer and Online Parameter Estimation

SOC Estimation of Multiple Lithium-Ion Battery Cells in a Module Using a Nonlinear State Observer and Online Parameter Estimation

High Reynold’s Number Turbulent Model for Micro-Channel Cold Plate Using Reverse Engineering Approach for Water-Cooled Battery in Electric Vehicles

Study on the Characteristics of a High Capacity Nickel Manganese Cobalt Oxide (NMC) Lithium-Ion Battery—An Experimental Investigation

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Experimental Measurements of Thermal Characteristics of LiFePO₄ Battery