Hello, everyone! It is my pleasure to present my work on ACM UbiComp Conference.
I'm Songyuan Li from Zhejiang University.
The title of my presentation is "Data-Driven Battery-Lifetime-Aware Schedulig for Electric Bus Fleets".
First, I will introduce the background
According to the research, about 8.4 million EVs will be in use globally in 2025.
However, battery degradation is a severe problem of EVs, which hinders its development.
Especially for electric bus, compared with the other EVs, it has more frequent overload.
This leads to booming discharge current. Thus, the battery degradation problem is more severe on electric bus.
For example, BYD K9 has a 10% battery capacity degradation in about two years.
Motivated by this, we propose such a battery-lifetime-aware electric bus scheduling system.
Let's see the motivation extracted from our datasets.
Firstly, the passengers show boarding demand imbalance from both temporal and spatial view.
Then, the boarding demand imbalance relects to passenger load imbalance of electric bus.
Passenger load imbalance leads to large discharge current on batteries, thus result in quick battery degradation rate.
So in this work, we intend to schedule the electric bus fleet properly to balance the passenger load, so that the battery degradataion rate can be improved.
This is the system overview.
Taking these design challenges into consideration, we propose the system architecture.
It is composed of three parts: sensing, computing and operation.
Then, let's introduce the detailed design of our system.
Here is the battery-lifetime-aware scheduling modeling. First, we have the passenger load analysis.
Then, we have battery degradation modeling. This process includes electric motor behavior and battery capacity degradation.
Including these constraints, we have the following optimization problem. The objective is to minimize the average battery rate.
In order to provide real-time shceduling scheme, we must precisely predict the passenger arrival rate.
Here, we first apply K-means method to extract bus station loaction from our datasets, then we obtain the passenger arrival data.
We select time stamp, weather and holiday as our predict features.
We apply Prophet as our prediction model. This is a modular regression time-series prediction framework.
Here are the prediction results.
Then, we introduce the electric bus scheduling algorithm based on Lyapunov optimization.
Lyapunov optimization is network optimization method which can solve spatio-temporal coupling problems.
It has two usage conditions. According to our problem, we select passenger waiting queues at each bus station as the virtual queues.
And the objective is the average battery degradation rate. This is a time-average form. So we can apply this method here.
According to our derivation, we reduce the primal problem to a min concave problem, which can be solved in polynomial time with deterministic bound queue and performance guarantee.
We also notice that, in practical implement, we have estimation errors of some important parameters.
We prove that our system can tolerate these errors.
Then, we present the data-driven evaluation.
It's clear that our system has a 20% battery degradation rate reduction with pretty good passenger service quality.
Our system shows stable performance on different bus lines.
This means that our system can extend the battery lifetime of electric bus for about one year.
It also shows good robustness under different available bus settings and time horizon.
We also present insight analysis.
Our system successfully forecasts the incoming peak hours and provides relative adjustment in advance.
Good performance is due to smooth discharge current caused by small passenger load variance.
Finally, we conclude this paper.
In this paper, we propose a novel data-driven battery-lifetime-aware scheduling system. We provide precise estimation of passenger behavior.
We design near-optimal solution with reliable performance guarantee. And finally, we provide extensive evaluation based on practical datasets.
That's all for my presentation. Thanks for your attention!