Search

Thermal runaway propagation of the power battery pack is an essential factor affecting the safety of electric vehicles. The commonly adopted propagation inhibition methods mainly include adding heat insulation materials and enlarging battery spacing, which could cause problematic heat dissipation and lower the system energy density. Herein, an innovative battery thermal management system composed of non-uniform thermal conductivity phase change materials and assisted liquid cooling is proposed. Combining the phase change materials with high and low thermal conductivity balances heat transfer and heat insulation requirements. The cooling performance and the ability of thermal runaway propagation mitigation of the proposed schemes are numerically studied. The results show that the proposed strategy can meet the heat dissipation requirements under normal operation and control the thermal runaway in a safe range by transferring the heat generated from the battery thermal runaway in the set direction. The maximum battery temperature and the temperature difference are 38.1 °C and 2.1 °C, respectively, under 3C discharge. Under thermal runaway conditions, the strategy successful confines the thermal runaway propagation within the middle row. The maximum battery temperature in other rows can be controlled under the irreversible thermal runaway reaction temperature of 200 °C. Further study found that increased thermal conductivity benefits the battery heat dissipation and reduces the risk of thermal runaway. However, it propagates faster and broader once the thermal runaway is triggered. In comparison, the decrease of thermal conductivity is beneficial to the mitigation of propagation but may reduce the overall heat dissipation of the battery module. This study can provide a new way to solve the contradiction between battery temperature control and thermal runaway spread suppression. • An innovative strategy is proposed to mitigate thermal runaway propagation. • Non-uniform phase change materials with different thermal conductivity are adopted. • The thermal runaway generated heat can be limited to transfer in a specific direction. • A suitable combination of high and low thermal conductivity for PCM is necessary. • Temperature control and thermal runaway suppression can be achieved simultaneously. [ABSTRACT FROM AUTHOR]

The demand for high energy density in lithium-ion battery packs for electric vehicles poses a challenge to maintaining its optimum operating temperature while reducing the risk of thermal runaway (TR) propagation. This study proposes a novel hybrid TR propagation mitigation system that balances heat transfer and thermal insulation requirements using low and high thermal conductivity phase change materials (PCM), heat pipes (HP), and air-cooling. The design and optimization of such a mitigation system are complex due to the many design parameters involved. The Adaptive-Kriging-High dimensional model representation (Adaptive-Kriging-HDMR) is used to establish a surrogate model of the system, and the sensitivity of the system's design parameters is evaluated with the maximum battery temperature and the system weight as the targets, thereby improving the efficiency of model calculation and reducing the dimension of optimization parameters. Then, the design of the sensitive parameters is optimized using an extended elitist non‐dominated sorting genetic algorithm (E-NSGA-II) multi-objective optimization algorithm. The results show that the modeling difficulty and optimization calculation time are significantly reduced by using a surrogate model. The calculation time for a single surrogate model only takes a few seconds instead of several hours for the original three-dimensional heat transfer and flow calculation. The thermal conductivity of high thermal conductivity-PCM, the distance between battery and low thermal conductivity-PCM, the battery spacing, and the HP length significantly affect the system. The optimized system substantially reduces the overall weight of the battery system while ensuring its good heat dissipation capability. In the case of TR in a single battery, the system succeeds in limiting the TR propagation to the same row, with the maximum battery temperature in the second row being only 64.3 °C, well below the battery TR trigger point. Under more severe conditions, such as TR occurring in two batteries simultaneously, the maximum battery temperature in the second row is 155.5 °C, and no TR spreads to the adjacent row. This study provides a rapid and effective method for designing a TR propagation mitigation system. It can serve as a reference for the engineering design and optimization of battery thermal management systems. [ABSTRACT FROM AUTHOR]

A data-driven multi-objective predictive control approach is proposed to increase the power production and reduce fatigue loads on a wind farm level using evolutionary optimization. The FLORIS (FLOw Redirection and Induction in Steady-state) tool is employed to characterize the wake characteristics within a wind farm and generate necessary data for data-driven prediction. A data driven wind farm predictor (WFP) is then constructed by using the turbine yaw angles as inputs and the wind farm power and thrust load as outputs under different inflow wind speeds and wind directions. Based on the WFP, a constrained optimization problem is formulated and the multi-objective predictive controller (MOPC) is designed based on wake steering and evolutionary optimization while considering the yaw angle control constraints. Extensive design experiments are conducted under various wind speeds and wind directions, and the results indicate that the wind farm thrust can be reduced by up to 12.96% while the wind farm power production can be well maintained at almost the same level by using the proposed control in comparison with a conventional model predictive control. The yaw angles optimized from the proposed control are more responsive and active in tuning the wind farm power production and thrust load mitigation than that in the conventional control method. • Designing a data-driven multi-objective predictive controller. • Designing a data driven wind farm predictor. • Providing data from the FLORIS for the multi-objective optimization of yaw angles. [ABSTRACT FROM AUTHOR]