Abstract
This paper proposes a new form of algorithm environment for multi-objective optimization of energy management system in plug-in hybrid vehicles (PHEVs). The surrogate-assisted strength Pareto evolutionary algorithm (SSPEA) is developed to optimize the power-split control parameters guided by the data from the physical PHEV and its digital twins. By introducing a ''confidence factor'', the SSPEA uses the fused data of physically measured and virtually simulated vehicle performances (energy consumption and remaining battery state-of-charge) to converge the optimization process. Gaussian noisy models are adopted to emulate the real vehicle system on the hardware-in-the-loop platform for experimental evaluation. The testing results suggest that the proposed SSPEA requires less R&D costs than the model-free method that only uses the physical information, and more than 44.6% energy can be saved during the R&D process. Driven by the SSPEA, the optimized energy management system surpasses other non-DT-assisted systems by saving more than 4.8% energy.
Original language | English |
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Article number | 9580593 |
Number of pages | 10 |
Journal | IEEE Transactions on Industrial Informatics |
Early online date | 19 Oct 2021 |
DOIs | |
Publication status | E-pub ahead of print - 19 Oct 2021 |
Keywords
- Batteries
- Digital twin
- Energy management
- Fuels
- Informatics
- Optimization
- Torque