With the improvement of battery performance and significant advantages in environmental protection, electric vehicles (EVs) are gradually replacing fuel vehicles as a development trend. Since batteries are the main source of power for EVs, ensuring energy supply is an important way to improve users' experience. At present, the power supply method for EVs is divided into two types: plug-in charging and battery swaps. Plug-in charging has disadvantages such as long charging time, fast charging will shorten the service life of the battery, and the parking space required for charging takes up a larger space. In addition, if the daily load of residents and the peak of EVs charging load are in the same time period, it will lead to a "peak-add-peak" state, which will affect the normal operation of the power grid (PG). On the contrary, the battery swapping scenario addresses these problems well. Battery swapping EVs can decrease user waiting time, reduce purchase cost, and improve battery useful life. Therefore, many companies adopt battery swapping scenario for EVs. However, there are still some challenges in the promotion of battery swapping scenario for EVs, such as the operating cost of the battery swapping and charging station (BSCS) and the centralized battery charging load (BCL).

The researches on BCL mainly include load demand forecasting and reduction of the impact of battery charging load on the PG. Reducing the impact of BCL mainly focuses on the charging method and improving the overload capacity of the PG. However, there are few studies on battery logistics and transportation strategies in BSCS. In addition, the past research has not proposed specific transportation strategies to reduce the load generated by large-scale charging. The operating cost of BSCS and large-scale BCL are the focuses of the implementation of the battery swapping scenarios.

In an attempt to eliminate the above gap, this project proposes a smart swapping scheduling algorithm strategy, which can significantly reduce battery cost and battery charging peak load. Based on the factors such as the number of battery packs, battery charging cost, logistics planning, electricity price of use time, energy storage profit and other factors affecting the cost of intelligent battery scheduling strategy, an optimization problem shall be established as a two-stage optimization problem. The first stage is the establishment of the battery purchasing cost model while the second stage is the establishment of the charging peak load model. These stages are interdependent on each other and change with time. Thus, the problem to be formulated in this project is two-stage dynamic optimization problem. For solving this dynamic time-dependent two-stage optimization problem, an approach is proposed that combines the analytical solution based on tropical algebra for a temporal scheduling problem and intelligent heuristic algorithms based on methods of artificial intelligent to solve a problem of cost minimization using the obtained solution of the scheduling problem. A simulation study of the process of private battery swaps EVs will be employed to simulate the optimization results in the prescribed battery swapping scenario.

The main contributions of this proposal are as follows. A BCSS is proposed in the battery swapping scenario. This strategy can effectively reduce the peak load during large-scale battery charging, thereby reducing the impact on the grid load. Furthermore, based on the BCSS strategy, this proposal establishes a two-stage time dependent temporal optimization problem of battery purchase cost considering the battery charging peak load, and uses analytical solution based on tropical algebra and surrogates-assisted particle swarm optimization to solve the optimization problem. Finally, the research results of this proposal give a specific transportation plan, including the number of transportations of battery, battery transportation quantity, battery transportation time.

This project proposes to solve the above-mentioned problem by combining both PI’s expertise in heuristic optimization of battery pack design using AI (Dr. Garg, HUST) and in analytical solutions based on tropical algebra for optimization of complex systems (Prof. Krivulin, SPBU). For implementing the project in an effective manner, the proposal has been structured into two phases.

Phase I consists of the following research activities:
1) Identifying important design variables (factors such as battery charging time, battery transport quantity, battery swapping, number of transports, charging power of battery, etc.) and constraints (peak period residential electricity consumption, number of charged batteries, number of replenishment batteries, etc.) that affects the functioning of battery swapping-charging station (process). Based on these factors and constraints, the models for battery peak charging load and battery purchasing cost are formulated (Project Objective for HUST).
2) By providing sufficient data and inputs (constraints, design variables and models), the next step is to frame the two-stage temporal scheduling optimization problem including constraints for minimizing the battery purchasing cost and battery peak charging load simultaneously (Project Objective for SPBU).

Phase II includes:
1) Markov Chain Monte Carlo for simulation of the battery swapping demand of EVs users at different times and obtaining the parameters such as battery swapping time, and number of EVs users who are swapping battery in each period. Surrogates-assisted particle swarm optimization is then applied to find the optimal values of design variables (residual power, swap threshold, battery charging time) that minimizes the battery purchasing cost and battery peak charging load simultaneously (Project Objective for HUST).
2) Tropical algebra solutions for solving the temporal scheduling optimization problem combined with that of surrogates assisted particle swarm optimization algorithm into to obtain final solutions (Project Objective for SPBU).

It is expected that numerous research papers on algebraic solutions of project scheduling problems published by N. Krivulin (SPBU), and on EVs battery charging-swapping scenarios published by A. Garg, G. Liang and W. Li (HUST) will serve as scientific groundwork of the study.
The collaboration findings will lead to the development of a smart swapping-charging scheduling software based on integration of principles of battery swapping-charging physical phenomenon, mathematics, battery fundamentals and AI, which satisfies the goal of smart city development and in line with strategy of Made in China 2025 for promoting adoption of new green energy vehicles.

The funding shall be sought from the following multiple sources as follows:
1) Sourcing funds from Governmental and Industry agencies in Russia (e.g., RSF, etc.).
2) Sourcing funds from Governmental and Industry agencies in China (e.g., NSFC., International NSFC; MOST).
3) Joint bilateral proposal calls between China and Russia (RSF-NSFC, etc.) and other international call for proposals such as announced in BRICS.
4) The project plan also involves working with the battery enterprises from both partner countries to secure funds for validation of the battery swapping-charging software for its actual commercial application.