A Predictive Energy Management Strategy Using a Rule-Based Mode Switch for Internal Combustion Engine (ICE) Vehicles

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Event
WCX™ 17: SAE World Congress Experience
Authors Abstract
Content
With fuel efficiency becoming an increasingly critical aspect of internal combustion engine (ICE) vehicles, the necessity for research on efficient generation of electric energy has been growing. An energy management (EM) system controls the generation of electric energy using an alternator. This paper presents a strategy for the EM using a control mode switch (CMS) of the alternator for the (ICE) vehicles. This EM recovers the vehicle’s residual kinetic energy to improve the fuel efficiency. The residual kinetic energy occurs when a driver manipulates a vehicle to decelerate. The residual energy is commonly wasted as heat energy of the brake. In such circumstances, the wasted energy can be converted to electric energy by operating an alternator. This conversion can reduce additional fuel consumption. For extended application of the energy conversion, the future duration time of the residual power is exploited. The duration time is derived from the vehicle’s future speed profile. The future speed profile is non-deterministic in real driving environment. Therefore, the proposed EM applies a Markov chain model to stochastically predict the vehicle’s speed. Based on the predicted duration time of the residual power, a rule-based mode switching strategy is established. There are three types of control modes defined according to the target amount of battery charge. The proposed strategy of this paper was validated through simulation, and simulation results show an improvement in fuel efficiency compared to the results of a conventional EM.
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DOI
https://doi.org/10.4271/2017-01-0584
Pages
6
Citation
Kim, H., Shin, J., and Sunwoo, M., "A Predictive Energy Management Strategy Using a Rule-Based Mode Switch for Internal Combustion Engine (ICE) Vehicles," SAE Int. J. Engines 10(2):608-613, 2017, https://doi.org/10.4271/2017-01-0584.
Additional Details
Publisher
Published
Mar 28, 2017
Product Code
2017-01-0584
Content Type
Journal Article
Language
English