TY - JOUR
T1 - Optimizing wireless charging locations for battery electric bus transit with a genetic algorithm
AU - Chen, Gang
AU - Hu, Dawei
AU - Chien, Steven
AU - Guo, Lei
AU - Liu, Mingzheng
N1 - Funding Information:
This research was funded in part by National Nature Science Foundation of China under Grant U1664264 and 51878066, in part by the Funds for Central Universities and Colleges of Chang’an University under grant 300102229304, 300102229201 300102220204 and 300102229111, in part by National Key R&D Program of China under grant 2020YFC1512004, in part by China Scholarship Council under grant 201806560035 and in part by Xi’an Technology Program under grant 2019218514GXRC021CG022-GXYD21.5. We would like to express our gratitude to two anonymous reviewers for their precious comments to improve our research.
Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2020/11/1
Y1 - 2020/11/1
N2 - Electrifying bus transit has been deemed as an effective way to reduce the emissions of transit vehicles. However, some concerns about on-board battery hinder its further development. Recently, dynamic wireless power transfer (DWPT) technologies have been developed, which enable buses to charge in-motion and overcome the drawback (short service range) with opportunity charging. This paper proposes a mathematic model which optimizes the locations for DWPT devices deployed at stops and size of battery capacity for battery electric buses (BEB) in a multi-route network, which considers the battery’s service life, depth of discharge and weight. A tangible solution algorithm based on a genetic algorithm (GA) is developed to find the optimal solution. A case study based on the bus network from Xi’an China is conducted to investigate the relationship among optimized costs, greenhouse gas (GHG) emissions, battery service life, size of the battery capacity and the number of DWPT devices. The results demonstrated that a bus network powered by DWPT shows better performance in both costs (a 43.3% reduction) and emissions (a 14.4% reduction) compared to that with stationary charging at bus terminals.
AB - Electrifying bus transit has been deemed as an effective way to reduce the emissions of transit vehicles. However, some concerns about on-board battery hinder its further development. Recently, dynamic wireless power transfer (DWPT) technologies have been developed, which enable buses to charge in-motion and overcome the drawback (short service range) with opportunity charging. This paper proposes a mathematic model which optimizes the locations for DWPT devices deployed at stops and size of battery capacity for battery electric buses (BEB) in a multi-route network, which considers the battery’s service life, depth of discharge and weight. A tangible solution algorithm based on a genetic algorithm (GA) is developed to find the optimal solution. A case study based on the bus network from Xi’an China is conducted to investigate the relationship among optimized costs, greenhouse gas (GHG) emissions, battery service life, size of the battery capacity and the number of DWPT devices. The results demonstrated that a bus network powered by DWPT shows better performance in both costs (a 43.3% reduction) and emissions (a 14.4% reduction) compared to that with stationary charging at bus terminals.
KW - Dynamic wireless power transfer
KW - Electric bus
KW - Genetic algorithm
KW - Transportation network planning
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U2 - 10.3390/su12218971
DO - 10.3390/su12218971
M3 - Article
AN - SCOPUS:85094608611
SN - 2071-1050
VL - 12
SP - 1
EP - 20
JO - Sustainability (Switzerland)
JF - Sustainability (Switzerland)
IS - 21
M1 - 8971
ER -