An Approach to Control Electric Automotive Water Pumps Deploying Artificial Neural Networks

With the global shift towards sustainability and technological advancements, electric hybrid vehicles (EHVs) are increasingly being seen as viable alternatives to traditional internal combustion (IC) engine vehicles, which also require efficient cooling systems. The electric automotive water pump (AWP) has been introduced as an alternative to IC engine belt-driven pump systems. However, current control methods for AWPs typically employ fixed gain settings, which are not ideal for the varying conditions of dynamic vehicle environments, potentially leading to overheating issues. To overcome the limitations of fixed gain control, this paper proposes implementing an artificial neural network (ANN) for managing the AWP in EHVs. The proposed ANN provides an intelligent, adaptive control strategy that enhances the AWP's performance, supported through MATLAB simulation work illustrated in this paper. Comparative analysis demonstrates that the ANN-based controller surpasses conventional PID and fuzzy logic controllers (FLC), exhibiting no overshoot, 0.1 secs rapid response, and 0.0696 integral absolute error (IAE) performance. Consequently, the findings suggest that ANNs can be effectively utilized in EHVs.