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Article summary:
1. Four velocity predictors based on artificial neural networks are designed and compared.
2. A predictive control strategy is proposed that considers electric motor thermal dynamics.
3. The proposed approach is verified for its computational efficiency and effectiveness in controlling the electric motor temperature rise.
Article analysis:
The article “Predictive energy management for plug-in hybrid electric vehicles considering electric motor thermal dynamics” provides a comprehensive overview of the current state of research into predictive energy management for PHEVs, with a focus on the consideration of electric motor thermal dynamics. The article is well-written and provides an in-depth analysis of the various aspects of this topic, including the design of four velocity predictors based on artificial neural networks, a Pontryagin's Minimum Principle-based model predictive control framework that includes EM thermal dynamics, and an analysis of different reference temperature thresholds and preview horizon sizes on fuel economy and EM temperature.
The article appears to be reliable and trustworthy overall, as it provides detailed information about the research conducted as well as references to other relevant studies in this field. It does not appear to contain any promotional content or partiality towards any particular viewpoint or opinion, nor does it present any unsupported claims or missing points of consideration. Furthermore, possible risks are noted throughout the article, such as potential issues with computational efficiency when using certain parameters for predictive control strategies. Additionally, both sides of the argument are presented equally throughout the article, providing a balanced view on this topic.