Transferable representation modelling for real-time energy management of the plug-in hybrid vehicle based on k-fold fuzzy learning and Gaussian process regression. (1st January 2022) - Zhou, Quan; Li, Yanfei; Zhao, Dezong; Li, Ji; Williams, Huw; Xu, Hongming; Yan, Fuwu
Graphical abstract: Highlights: A transfer learning routine is proposed to reduce workload in control development. Fuzzy learning and Gaussian process regression are incorporated in a control model. Fuzzy learning achieves 27% higher distinction rate than a neural network. The 'deeper' architecture outpefroms the 'broader' one in transfer learning. Real-time control functionality is verified by hardware-in-the-loop testing. Abstract: Electric vehicles, including plug-in hybrids, are important for achieving net-zero emission and will dominate road transportation in the future. Energy management, which optimizes the onboard energy usage, is a critical functionality of electric vehicles. It is usually developed following the model-based routine, which is conventionally costly and time-consuming and is hard to meet the increasing market competition in the digital era. To reduce the development workload for the energy management controller, this paper studies an innovative transfer learning routine. A new transferable representation control model is proposed by incorporating two promising artificial intelligence technologies, adaptive neural fuzzy inference system and Gaussian process regression, where the former applies k-fold cross valudation to build a neural fuzzy system for real-time implementation of offline optimization result, and the later connects the neural fuzzy system with a 'deeper' architecture to transfer the offline optimization knowledge learnt at source domain to new target domains. By introducing a concept of control utility that evaluates vehicle energy efficiency with a penalty on usage of battery energy, experimental evaluations based on the hardware-in-the-loop testing platform are conducted. Competitive real-time control ultility values (as much as 90% of offline benchmarking results) can be achieved by the proposed control method. They are over 27% higher than that achieved by the neural-network-based model.