| Issue |
E3S Web Conf.
Volume 118, 2019
2019 4th International Conference on Advances in Energy and Environment Research (ICAEER 2019)
|
|
|---|---|---|
| Article Number | 02005 | |
| Number of page(s) | 4 | |
| Section | Energy Equipment and Application | |
| DOI | https://doi.org/10.1051/e3sconf/201911802005 | |
| Published online | 04 October 2019 | |
Multi-objective Parameters Optimization for HEV Based on improved Particle Swarm Algorithm
1
Wuhan donghu new technology development zone power supply company
2
Hubei power company material company
3
Wuhan Product Quality Supervision and Inspection Institute
Hybrid electric vehicle fuel consumption and emissions are closely related to its energy management strategy. A fuzzy controller of energy management using vehicle torque request and battery state of charge (SOC) as inputs, engine torque as output is designed in this paper foe parallel hybrid electric vehicle. And a multi-objective mathematical function which purpose on maximize fuel economy and minimize emissions is also established, in order to improve the adaptive ability and the control precision of basic fuzzy controller, this paper proposed an improved particle swarm algorithm that based on dynamic learning factor and adaptive inertia weight to optimize the control parameters. Simulation results based on ADVISOR software platform show that the optimized energy management strategy has a better distribution of engine and motor torque, which helps to improved the vehicle’s fuel economy and exhaust emission performance.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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