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.
Original language | English |
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Article number | 117853 |
Number of pages | 11 |
Journal | Applied Energy |
Volume | 305 |
Early online date | 17 Sept 2021 |
DOIs | |
Publication status | Published - 1 Jan 2022 |
Bibliographical note
Funding Information:The authors are grateful to the State Key Laboratory of Automotive Safety and Energy ( KF2029 ), the EPSRC Fellowship scheme ( EP/S001956/1 ), and the Natural Science Foundation of China ( 51775393 ).
Keywords
- Adaptive neuro fuzzy inference
- Energy management
- Gaussian process regression
- Hybrid vehicle
- Transfer learning
ASJC Scopus subject areas
- Mechanical Engineering
- Energy(all)
- Management, Monitoring, Policy and Law
- Building and Construction