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Advanced vehicular thermal management system can improve engine performance, minimize fuel consumption, and reduce emissions by harmoniously operating computer-controlled servomotor components. In this paper, a neural network-based optimal control strategy is proposed to regulate the engine temperature through the advanced cooling system. Presenter Asma Al Tamimi, Hashemite University

Advanced vehicular thermal management system can improve engine performance, minimize fuel consumption, and reduce emissions by harmoniously operating computer-controlled servomotor components. In this paper, a neural network-based optimal control strategy is proposed to regulate the engine temperature through the advanced cooling system. The proposed optimization algorithm introduces a cost function of a predefined temperature error and a control input that is developed to minimize the introduced cost function. The main objective of the proposed optimal control design is to minimize the temperature error and power consumption of the system actuators. The development of the optimal controller utilizes a multi-layer neural network to approximate the proposed cost function. A representative numerical simulation is introduced in this paper to demonstrate the performance of the developed optimal controller.