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In the realm of electric vehicles (EVs), effective battery thermal management is critical to avert thermal runaway, overheating, and extend the operational lifespan of batteries. The process of designing thermal management systems can be substantially expedited through the utilization of modeling and simulation techniques. However, the high-fidelity 3D computational fluid dynamics (CFD) simulations often demand significant computational resources to provide comprehensive results under varying conditions. In this paper, we develop a reduced order model (ROM) to capture the battery thermal dynamics employing a sub-space method. To construct this ROM, we use high-fidelity CFD simulations to generate step responses of battery temperature with respect to the heat generation and cooling power. These step responses are subsequently used as training data for the ROM.

The safety, performance, and operational life of power dense Lithium-ion batteries used in Hybrid and Electric Vehicles are dependent on the operating temperature. Modeling and simulation are essential tools used to accelerate the design process of optimal thermal management systems. However, high-fidelity 3D computational fluid dynamics (CFD) simulation of such systems is often difficult and computationally expensive. In this paper, we demonstrate a multi-part coupled system model for simulating the heating/cooling system of the traction battery at a module level. We have reduced computational time by employing reduced-order modeling (ROM) framework on separate 3D CFD models of the battery module and the cooling plate. The order of the thermal ROM has also been varied to study the trade-off between accuracy, fidelity, and complexity. The ROMs are bidirectionally coupled to an empirical battery model built from in-house test data.

High strength and thin materials are widely adopted in modern electric vehicles for lightweight design to achieve high energy efficiency. For battery modules, 5000 and 6000 aluminum are typically utilized as a structural material with a thickness range between 1 to 5 mm. Laser welding is one of the most optimum welding tools for joining such a thin material due to its unique advantages, e.g., high welding speed, high accuracy, high energy yet the smallest possible heat affect zone, etc. This paper aims to develop a simplified yet effective FE modeling procedure to simulate the laser welding effects on the aluminum structures used in electric vehicle battery modules. A sequentially-coupled thermo-mechanical analysis procedure is developed to determine the softened zone size for aluminum weldments. Then a tie-rupture weld model incorporates the softened zone to predict the weld failure strength.

Electric vehicles are becoming a rapid growing part of the automotive scene. Batteries are considered as one of the most important and challenging components in the development of electric vehicles. The mechanical performance of the battery module is of great interest and the crashworthiness analysis of the battery module is always a critical design aspect. In crash and other severe events, the battery module is subject to impact loads from different directions. The module is designed with a capability to be deformed and collapsed in a controlled manner to mitigate safety critical damage to battery cells inside the module. In the design process, it is necessary to consider the distribution of the impact loads during the crash to minimize the local damage. In this paper, a finite element model is developed and used as an efficient simulation tool to analyze the dynamic behavior of a generic battery module upon crushing and shocking.

Electric vehicle makers have largely relied on aluminum to make their cars lighter in hopes of offsetting the weight of the battery pack and reducing overall weight. Distortion of Aluminum welding is a big issue due to Aluminum’s high coefficient of expansion ratios. This paper presents an effective numerical approach to minimize weld-induced distortion in Electrical Vehicle Aluminum assembly structures using welding sequence optimization. A numerical optimization framework based on genetic algorithms and Finite Element Analysis (FEA) is developed and implemented. The shrinkage method calibrated using transient approach, is used for the weld sequence optimization to reduce the computation time. The optimization results show that the proposed calibration approach can contribute substantially to reduce distortion by optimizing weld sequences. It enhances final aluminum assembly quality while facilitating and accelerating design and development.