The control of the battery-thermal-management-system (BTMS) is key to prevent catastrophic events and to ensure long lifespans of the batteries. Nonetheless, to achieve a high-quality control of BTMS, several technical challenges must be faced: safe and homogeneous control in a multi element system with just one actuator, limited computational resources, and energy consumption restrictions. To address those challenges and restrictions, we propose a surrogate BTMS control model consisting of a classification machine-learning model that defines the optimum cooling-heating power of the actuator according to several temperature measurements. The la-belled-data required to build the control model is generated from a simulation environment that integrates model-predictive-control and linear optimization concepts. As a result, a controller that optimally controls the actuator with multi-input temperature signals in a multi-objective optimization problem is constructed. This paper benchmarks the response of the proposal using different classification machine-learning models and compares them with the responses of a state diagram controller and a PID controller. The results show that the proposed surrogate model has 35% less energy consumption than the evaluated state diagram, and 60% less energy consumption than a traditional PID controller, while dealing with multi-input and multi-objective systems.
Background: The thermal management of a battery pack designed for an electric vehicle is a key to prevent accidental events and ensure a long lifespan of the batteries. A typical accident is a thermal runaway of one or more cells in the battery which can cause fire or explosion of the battery pack. This paper presents a numerical modelling of a battery pack (BP) and its heat exchanger (HE) for an electric vehicle. The heat produced in the battery is evacuated by the HE. Methods: Two different kinds of modelling have been realized: a computational fluid dynamic (CFD) modelling and a coarse (called MOD3 for 3D Model) modelling. The CFD modelling allows the creation of fine numerical simulations of a BP, but uses large meshes, therefore the cost of each calculation is important. In order to make a large number of quite long transient simulations, a second tool called MOD3, employing only a coarse mesh, was developed in this study at the Commissariat à l’énergie atomique et aux énergies alternatives (CEA). Results: Two measurement campaigns corresponding to two different versions of the HE have been conducted at CEA. The temperature measurements allow comparisons of MOD3 to a real battery pack and to fit some heat exchange coefficients. The cells temperatures as well as the cooling liquid temperature are compared. Conclusions: The MOD3 tool has been fitted partly on CFD calculations, and partly on experimental measurements. It will be integrated in a machine learning environment by CIDETEC to take into account the thermal management of the BP in real car simulations.
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