As electric vehicle adoption reaches an inflection point and the buildout of charging infrastructure lags behind, battery swap is back in fashion, according to a new crop of startups looking to give the technology another try. This is something different from battery reconditioning.
Later City News: Taxis are often motivated to drive in crowded areas to earn more money by serving customers.
A research recently published by journal of Sustainable Cities and Society confirms that battery swapping is an efficient and fast recharging method enabling taxi drivers to go to a battery swapping station (BSS) and replace their empty batteries with full ones.
This leads to commercial businesses of swapping batteries more beneficial than the other options, which inspires the service providers to invest in this recharging platform.
Observer reported that “Today, EVs are back and we have once again learned that for many drivers, plugging in and waiting isn’t a great solution,” Levi Tillemann, a former policy advisor to the U.S. Department of Energy and now head of policy and international outreach at Ample, a San Francisco-based battery swapping company, wrote in a recent op-ed for The Washington Post.
“This simple, powerful mode of EV refuelling uses robots to physically remove empty batteries and replace them with fully charged batteries—much as you would change the batteries in a flashlight.”
In fact, the drive-through battery-swapping technology killed by Tesla years ago is now benefiting Chinese electric car makers and drivers as the world's largest auto market moves to adopt electric mobility.
Have a look, how Nio ES* changing battery in less than 5 minutes.
Today, battery swap technology pioneered by Chinese EV companies, most notably Nio, which sells electric cars without batteries and instead charges buyers a monthly subscription fee for battery swap service.
"This one is fast," a Beijing taxi driver surnamed Wang, told CGTN. Wang said the battery-swapping service saved him the one and a half hours of downtime it would usually take to charge his car battery.
The driver's cab is from China's biggest maker of purely electric cars, Beijing Electric Vehicle Marketing Co., Ltd (BJEV), a unit of the state-owned BAIC Group. It owns 206 battery-swap stations in 19 cities in China. The stations look like car wash centers and mainly serve the company's taxi fleet.
Some electric car makers and tech companies want this option because it is the only super-fast way that an electric car can be charged and thus compete with gasoline cars. To implement this technology the car itself has to be designed to be able to be opened up with a chamber on the bottom, and by swiftly taking off the bolts underneath the car.
Fast charging stations can charge an electric car in around 30 minutes, which is obviously considerably longer than it would take to pump gas. A regular electric car charger can take 8 or even 12 hours to fully charge.
Clean taxicab fleets in cities will have a significant impact on reducing air pollution and cutting emissions.
Use of electric taxis is a highly efficient solution to address the issue of greenhouse effects, because electric cars are cleaner and cheaper than gasoline-powered cars.
Sayarshad from Cornell University and Mahmoodian from University of South Florida in their published paper proposed a novel dynamic programming (DP) model to incorporate a Markov decision process (MDP) with an actual demand function, operator cost, customer delay, and a dynamic pricing strategy using a social optimization function through a look-ahead policy.
The results indicate that the average response time to transfer batteries from BCS nodes to BSS nodes under the look-ahead policy is reduced by up to 9% compared to the myopic case.
Accordingly, depleted batteries that are swapped out can be charged when electricity is cheap or electricity demand is low. In addition, renewable energy sources may produce more electricity than other energy resources.
However, an online distribution of batteries from BCS nodes to BSS nodes needs to determine optimal routes of trucks, the optimal load of vehicles, and optimal loaded/unloaded batteries at demand nodes.
In order to replace the gasoline taxi fleet with a green taxi fleet, sufficient installation of charging infrastructure is critical. By monitoring of the density of stations, the BSS nodes keep sufficient stocks of charged batteries at each charging station, which leads to avoiding the risk of battery degradation.
The research indicates that the social welfare increases by 27% under a high density of customers when the average of inter-arrival time is 0.1, while it reduces after decreasing the inter-arrival time to 0.4.
The relative difference appears fixed when the density of customers goes up to 0.4 – 0.6. This makes sense as there is enough time to handle a given population demand function.
The battery swapping method is a good solution for taxi fleets where a driver swaps his battery in a BSS node, and also, returns from or goes to a particular location regularly. The method is also The number of battery beneficial for drivers who need to swap their batteries without delay at a BSS.
Long hours of waiting to charge a battery can now be eliminated through the proposed online battery supply chain by monitoring batteries in the transportation and distribution sector.
Therefore, response times are considered to make a balance between supply and demand.
Comparison of response times between the proposed policy and the myopic policy shows that the average response time is 37.178 min for a look-ahead policy, while it is 40.654 min for a myopic policy (without look-ahead).
The results showed that the average response time under the non-myopic policy for 60 customers is reduced by up to 9% compared to the myopic policy.
Also, average prices under the marginal and non-myopic pricing policies are $10.848 and $4.955, respectively. Thus, the average price under the proposed nonmyopic pricing policy decreases by 54% compared to the marginal price.
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Additionally, the average social welfare is 2467.674 under the marginal pricing policy where it is reduced by 22% compared to the proposed non-myopic strategy. The service level at BSSs can be improved using the proposed dynamic pricing policy.
Taxi drivers exchange their batteries with a socially efficient price other than the standard monopoly price, which inspires them to use EVs.
The study also observe that the average social welfare under the look-ahead policy increases by 22% compared to a policy without look-ahead. The numerical analyses highlighted the benefits of supplying batteries at a socially efficient price instead of the standard monopoly price.
Authors of this article belive the proposed model allows for many potential extensions. Other transient queueing measures under time-variant arrival rates are realistic scenarios that might be appropriate in realworld operations.
Furthermore, a scheduling model that performs job sequencing for charging batteries at BCSs can also be considered for future studies.
Applying facility location problems by taking into account the queueing delay and social welfare of districts to determine the optimal location of BSSs is a potential direction for future research.
By solving the logistic issues, we can employ electric taxis in cities to reduce emissions. The proposed model intended to show how to apply vehicles such as e-bike, electric car-sharing , and electric express buses.
Investigating a routing and delivery problem with a mixed fleet of electric and conventional freight vehicles is also a topic of interest.
Among the operations after disaster, the proposed model can be applied to allocate relief items to demand nodes.
Finally, it is recommended to conduct an empirical study on the BSCS network with performance validation through the use of the proposed model.
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