Optimization of the lumped parameter thermal model for hard-cased li-ion batteries

Xifeng, Cui; Zeng, Jian; Zhang, Hongliang; Yang, Jianhong; Qiao, Jia; Li, Jie; Li, Wangxing

doi:10.1016/j.est.2020.101758

Cited by 25 publications

(10 citation statements)

References 34 publications

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“…[17] At a 1C discharge rate, the temperature increase is small thereby the natural convection heat transfer coefficient in this article is estimated to be 5 W m À2 K À1 . [18][19][20][21][22] The stress state of the battery can be estimated using solid mechanics. Furthermore, The total strain is equal to the sum of the mechanical strain and the strain caused by the temperature deviation.…”

Section: Boundary Conditionsmentioning

confidence: 99%

3D Simulation Study on Thermal Behavior and Thermal Stress of Lithium‐Ion Battery

Zhai

Kong

et al. 2022

Energy Tech

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Temperature and thermal stress are crucial factors affecting the working performance and thermal safety of lithium‐ion batteries (LIBs). During the discharge process of the battery, the temperature increases. The thermal expansion stress caused by the entropy heat and the ohmic heat can lead to the battery's lifetime reduction and even thermal safety issues. Herein, based on the coupled electrochemical model, the LIB's thermal behavior and thermal stress distribution at a 1C discharge rate are simulated and analyzed. The results show that the temperature distribution inside the battery is mainly affected by the heat exchange surface and the position of the tabs during the discharging process, in which the localized high‐temperature region occurs in the positive tab. The thermal stress inside the battery enhances rapidly at both the beginning and end of the discharging process while increasing slowly in the middle.

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Section: Boundary Conditionsmentioning

confidence: 99%

3D Simulation Study on Thermal Behavior and Thermal Stress of Lithium‐Ion Battery

Zhai

Kong

et al. 2022

Energy Tech

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“…The dynamic thermal model is designed considering only the heat transfer by convection while neglecting the conductive component. The heat source, 𝑃 of the thermal model is derived from 𝑣 and discrete-time temperature 𝜃 of the cell surface is expressed in Equation ( 13) [51]. We assumed that the cell is cooled by natural convection without an active cooling system.…”

Section: Cell Electric Modelmentioning

confidence: 99%

Adaptive Model Predictive Control Including Battery Thermal Limitations for Fuel Consumption Reduction in P2 Hybrid Electric Vehicles

Ezemobi

Yakhshilikova

Ruzimov

et al. 2022

WEVJ

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The primary objective of a hybrid electric vehicle (HEV) is to optimize the energy consumption of the automotive powertrain. This optimization has to be applied while respecting the operating conditions of the battery. Otherwise, there is a risk of compromising the battery life and thermal runaway that may result from excessive power transfer across the battery. Such considerations are critical if factoring in the low battery capacity and the passive battery cooling technology that is commonly associated with HEVs. The literature has proposed many solutions to HEV energy optimization. However, only a few of the solutions have addressed this optimization in the presence of thermal constraints. In this paper, a strategy for energy optimization in the presence of thermal constraints is developed for P2 HEVs based on battery sizing and the application of model predictive control (MPC) strategy. To analyse this approach, an electro-thermal battery pack model is integrated with an off-axis P2 HEV powertrain. The battery pack is properly sized to prevent thermal runaway while improving the energy consumption. The power splitting, thermal enhancement and energy optimization of the complex and nonlinear system are handled in this work with an adaptive MPC operated within a moving finite prediction horizon. The simulation results of the HEV SUV demonstrate that, by applying thermal constraints, energy consumption for a 0.9 kWh battery capacity can be reduced by 11.3% relative to the conventional vehicle. This corresponds to about a 1.5% energy increase when there is no thermal constraint. However, by increasing the battery capacity to 1.5 kWh (14s10p), it is possible to reduce the energy consumption by 15.7%. Additional benefits associated with the predictive capability of MPC are reported in terms of energy minimization and thermal improvement.

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“…However, large format cells often used in automotive applications are facing the challenge of temperature gradients inside the cells and the core temperature can significantly differ from the temperature value measured by the external sensors [114]. Unfortunately, measuring the core temperature, e.g., by integrating sensors inside the cell, is still an unresolved challenge and thus a current research topic [115]. Another possibility is to use data or modeling based determination approaches to determine the core temperature of the individual cells.…”

Section: Online Identification Of Core Temperaturementioning

confidence: 99%

Critical Review of Intelligent Battery Systems: Challenges, Implementation, and Potential for Electric Vehicles

et al. 2021

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This review provides an overview of new strategies to address the current challenges of automotive battery systems: Intelligent Battery Systems. They have the potential to make battery systems more performant and future-proof for coming generations of electric vehicles. The essential features of Intelligent Battery Systems are the accurate and robust determination of cell individual states and the ability to control the current of each cell by reconfiguration. They enable high-level functions like fault diagnostics, multi-objective balancing strategies, multilevel inverters, and hybrid energy storage systems. State of the art and recent advances in these topics are compiled and critically discussed in this article. A comprising, critical discussion of the implementation aspects of Intelligent Battery Systems complements the review. We touch on sensing, battery topologies and management, switching elements, communication architecture, and impact on the single-cell. This review contributes to transferring the best technologies from research to product development.

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Optimization of the lumped parameter thermal model for hard-cased li-ion batteries

Cited by 25 publications

References 34 publications

3D Simulation Study on Thermal Behavior and Thermal Stress of Lithium‐Ion Battery

3D Simulation Study on Thermal Behavior and Thermal Stress of Lithium‐Ion Battery

Adaptive Model Predictive Control Including Battery Thermal Limitations for Fuel Consumption Reduction in P2 Hybrid Electric Vehicles

Critical Review of Intelligent Battery Systems: Challenges, Implementation, and Potential for Electric Vehicles

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