Charge Management for an Inductively Charged On-Demand Battery-Electric Shuttle Service with High Penetration of Renewable Energy
| Autor: | Dylan Day, Andrew Meintz, Jun Myungsoo, Ahmed Mohamed |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Battery (electricity)
business.industry Computer science 020209 energy 020302 automobile design & engineering 02 engineering and technology Inductive charging Load profile Automotive engineering Renewable energy Demand response 0203 mechanical engineering Peak demand Control theory 0202 electrical engineering electronic engineering information engineering Wireless business |
| Zdroj: | 2020 IEEE Applied Power Electronics Conference and Exposition (APEC). |
| DOI: | 10.1109/apec39645.2020.9124145 |
| Popis: | This paper presents a charge management control strategy for an on-demand battery-electric shuttle van operating at the National Renewable Energy Laboratory (NREL) campus and supported by day-time inductive charging at the vehicle’s waiting spot. A new control algorithm has been proposed for reducing the demand charge costs incurred from wireless charging of the on-demand shuttle. A custom controller has been developed to monitor the shuttle, wireless charger, renewable energy generation, and various loads at NREL’s campus, and regulate charging behavior for demand response. The intermittent renewable generation and sporadic operation of the on-demand shuttle service contribute to a high level of uncertainty in expected campus load profile, which must be carefully managed. The control algorithm predicts energy profile to estimate the mobility needs of the vehicle and maintain uninterrupted service during operation while still minimizing peak demand. The proposed controller has been designed and optimized using a Simulink model for the entire system. Next, it has been implemented and tested in real-time on the NREL campus. Two primary vehicle-use cases, charge sustaining and charge depletion operation, are tested under different load profiles and drive cycles to assess the controller’s effectiveness at reducing peak demand and therefore demand charges. The proposed controller showed robust performance under different driving scenarios with high correlation between simulated and experimental data. The results showed that proper demand response can be achieved with an average of 94% reduction of charging loads during peak demand events. |
| Databáze: | OpenAIRE |
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