Thermal Management of Lithium-Ion Pouch Cell with Indirect Liquid Cooling using Dual Cold Plates Approach

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

doi:10.4271/2015-01-1184

Cited by 61 publications

(38 citation statements)

References 7 publications

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“…In a high temperature environment, lithium‐ion batteries quickly degrade, while in a cold temperature environment, the power output and energy are reduced, which eventually brings about the reduction of performance and driving range . The temperature estimations and the predictions of the lithium‐ion cell temperature are reported in various papers including analytical and numerical modeling . The following section gives a brief overview of lithium‐ion battery structure, components, and types.…”

Section: Introductionmentioning

confidence: 99%

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Cycling degradation testing and analysis of a LiFePO₄ battery at actual conditions

et al. 2017

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Summary This paper presents a degradation testing of a lithium‐ion battery developed using real world drive cycles obtained from an electric vehicle (EV). For this, a data logger was installed in the EV, and real world drive cycle data were collected. The EV battery system consists of 3 lithium‐ion battery packs with a total of 20 battery modules in series. Each module contains 6 series by 49 parallel lithium‐ion cells. The vehicle was driven in the province of Ontario, Canada, and several drive cycles were recorded over a 3‐month period. However, only 4 drive cycles with statistical analysis are reported in this paper. The reported drive cycles consist of different modes: acceleration, constant speed, and deceleration in both highway and city driving at −6°C, 2°C, 10°C, and 23°C ambient temperatures with all accessories on. Additionally, individual cell characterization was conducted using a C/25 (0.8A) charge‐discharge cycle and hybrid pulse power characterization (HPPC). The Thevenin battery model was constructed in MATLAB along with an empirical degradation model and validated in terms of voltage and SOC for all drive cycles reported. The presented model closely estimated the profiles observed in the experimental data. Data collected from the drive cycles showed that a 4.6% capacity fade occurred over the 3 months of driving. The empirical degradation model was fitted to these data, and an extrapolation estimated that 20% capacity fade would occur after 900 daily drive cycles.

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Section: Introductionmentioning

confidence: 99%

“…9 The temperature estimations and the predictions of the lithium-ion cell temperature are reported in various papers including analytical and numerical modeling. [10][11][12][13] The following section gives a brief overview of lithium-ion battery structure, components, and types.…”

mentioning

confidence: 99%

Cycling degradation testing and analysis of a LiFePO₄ battery at actual conditions

et al. 2017

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“…[6][7][8][9][10][11][12]. In battery management, the accurate estimation of various states of the battery, including state-of-charge (SOC), state-of-health (SOH), and state-of-power (SOP), is a priority [13][14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Lithium-Ion Battery Online Rapid State-of-Power Estimation under Multiple Constraints

Shun

Huang

et al. 2018

Energies

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Abstract:The paper aims to realize a rapid online estimation of the state-of-power (SOP) with multiple constraints of a lithium-ion battery. Firstly, based on the improved first-order resistance-capacitance (RC) model with one-state hysteresis, a linear state-space battery model is built; then, using the dual extended Kalman filtering (DEKF) method, the battery parameters and states, including open-circuit voltage (OCV), are estimated. Secondly, by employing the estimated OCV as the observed value to build the second dual Kalman filters, the battery SOC is estimated. Thirdly, a novel rapid-calculating peak power/SOP method with multiple constraints is proposed in which, according to the bisection judgment method, the battery's peak state is determined; then, one or two instantaneous peak powers are used to determine the peak power during T seconds. In addition, in the battery operating process, the actual constraint that the battery is under is analyzed specifically. Finally, three simplified versions of the Federal Urban Driving Schedule (SFUDS) with inserted pulse experiments are conducted to verify the effectiveness and accuracy of the proposed online SOP estimation method.

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“…Finally, an experimental setup was implemented for the validation of the proposed balancing system. Panchal et al [5,6] presented in situ measurements of the heat generation rate for a prismatic lithium-ion battery and a lithium-ion pouch cell (20 Ah capacity) at 1C, 2C, 3C, and 4C discharge rates and 5 • C, 15 • C, 25 • C, and 35 • C boundary conditions (BCs). The results show that the highest rate of heat generation was found to be 91 W for the 4C discharge rate and 5 • C BC, while the minimum value was 13 W measured at a 1C discharge rate and 35 • C BC.…”

Section: Introductionmentioning

confidence: 99%

Big-Data-Based Thermal Runaway Prognosis of Battery Systems for Electric Vehicles

2017

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A thermal runaway prognosis scheme for battery systems in electric vehicles is proposed based on the big data platform and entropy method. It realizes the diagnosis and prognosis of thermal runaway simultaneously, which is caused by the temperature fault through monitoring battery temperature during vehicular operations. A vast quantity of real-time voltage monitoring data is derived from the National Service and Management Center for Electric Vehicles (NSMC-EV) in Beijing. Furthermore, a thermal security management strategy for thermal runaway is presented under the Z-score approach. The abnormity coefficient is introduced to present real-time precautions of temperature abnormity. The results illustrated that the proposed method can accurately forecast both the time and location of the temperature fault within battery packs. The presented method is flexible in all disorder systems and possesses widespread application potential in not only electric vehicles, but also other areas with complex abnormal fluctuating environments.

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Thermal Management of Lithium-Ion Pouch Cell with Indirect Liquid Cooling using Dual Cold Plates Approach

Cited by 61 publications

References 7 publications

Cycling degradation testing and analysis of a LiFePO₄ battery at actual conditions

Cycling degradation testing and analysis of a LiFePO₄ battery at actual conditions

Lithium-Ion Battery Online Rapid State-of-Power Estimation under Multiple Constraints

Big-Data-Based Thermal Runaway Prognosis of Battery Systems for Electric Vehicles

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Thermal Management of Lithium-Ion Pouch Cell with Indirect Liquid Cooling using Dual Cold Plates Approach

Cited by 61 publications

References 7 publications

Cycling degradation testing and analysis of a LiFePO4 battery at actual conditions

Cycling degradation testing and analysis of a LiFePO4 battery at actual conditions

Lithium-Ion Battery Online Rapid State-of-Power Estimation under Multiple Constraints

Big-Data-Based Thermal Runaway Prognosis of Battery Systems for Electric Vehicles

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Cycling degradation testing and analysis of a LiFePO₄ battery at actual conditions

Cycling degradation testing and analysis of a LiFePO₄ battery at actual conditions