In an article recently published in the Journal of Energy Storage, researchers from China developed an optimized battery centralized scheduling strategy (BCSS) for a battery swapping-based electric vehicle (EV) charging method.
Study: A battery centralized scheduling strategy for battery swapping of electric vehicles. Image Credit: Black_Kira/Shutterstock.com
This novel strategy used a genetic algorithm that significantly reduced the battery purchase cost (BPC) and increased the battery charging peak load (BCPL) compared to optimized transportation-type strategies which are commonly used for plug-in-type EV charging methods viz. equal time interval transportation strategy (ETITS) and an equal number of batteries transportation strategy (ENBTS).
Background
EVs are soon going to completely replace the vehicles running on fossil fuels. However, they still face serious hurdles in large-scale usability. Rechargeable batteries are the core of EVs, which can be powered onboard by the plug-in charging method or through the battery swapping method. In the present scenario, charging the battery of an EV takes a much higher time than refilling fuel in a vehicle.
An increase in the number of EVs means the requirement of large parking spaces at the charging station, and shorter service life of batteries due to fast charging, lower BCPL due to high rush, and higher BPC. Additionally, all these issues become more complicated during peak traffic situations as the demand for battery charging is not uniform across a day, week, or season.
The battery swapping-based EV powering method can alleviate the above-mentioned problems in densely populated cities. For this, batteries can be charged separately and uniformly across a day and just need to replace the discharged ones quickly at the battery swapping and charging station (BSCS). It can significantly increase the BCPL and reduce the BPC owing to the increase in the service life of the battery. However, it requires an optimized battery charging scheduling strategy at the centralized battery charging station.
About the Study
In the present study, the researchers developed an optimized BCSS for battery swapping-based EV powering method and compared its effectiveness against plug-in-type optimized strategies such as ETITS and ENBTS. This strategy is particularly beneficial for densely populated cities with predictable high peak demand duration.
It can significantly reduce the peak load, load on the grid, BPC, and increase the BCPL. Moreover, this study incorporates a genetic algorithm that includes battery transportation quantity, battery transportation time, and the required number of transportations of battery.
The team first developed a formula to express the average battery demand time in terms of mileage. Subsequently, BPC and battery purchase quantity (BPQ) were calculated from maximum battery service cycles and average peak charging load. The specific focus of this strategy was to facilitate slow charging of batteries that can increase the service life of the rechargeable batteries.
Observations
The BPC was mainly comprised of the initial battery cost, battery charging cost, and logistics cost. Battery charging cost was directly dependent upon the battery charging load, whereas the logistics cost remained constant owing to constant battery transportation quantity for a predictable demand.
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The BCPL was 2418 kW and the BPC of the optimized BCSS was 4.15 million yuan, which is 24.7% and 9.5% less than corresponding BPC values of ETITS and ENBTS strategies. The battery transportation time also increased with an increase in battery transportation quantity.
The genetic algorithm incorporated an initial population of 100, a crossover rate of 0.85, and a mutation rate of 0.15. It showed an optimized battery transportation number of 7 times. Additionally, the BPC decreased with an increase in the number of transportations. The peak load of battery charging was reduced by 38.1% and 41.5% compared with the ETITS and ENBTS, respectively.
Conclusions
To conclude, the authors developed an optimized scheduling strategy for centralized battery charging in the case of the battery swapping-based EV powering method. The primary objective of this method was to minimize BPC and reduce the impact on the grid during peak demand conditions.
This strategy significantly reduced the normalized battery cost and a peak load of battery without a noticeable increase in battery transportation costs and quantity. Additionally, it reduced the requirement of parking space and parking time for charging and performed better than ETITS and ENBTS methods.
Source
Li, C., Wang, N., Li, W., Yi, Q., A battery centralized scheduling strategy for battery swapping of electric vehicles, Journal of Energy Storage, 51 (2022), 104327. https://www.sciencedirect.com/science/article/abs/pii/S2352152X22003516
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