Abstract
The traffic and user have significant impacts on the electric vehicle (EV) charging load but are not considered in the existing research. We propose a novel integrated simulation framework considering the traffic, the user, and power grid as well as the EV traveling, parking and charging based on cellular automaton (CA). The traffic is modeled by the traffic module of the proposed framework based on CA, while the power grid and user are modeled in the EV charging module. The traffic flow, user’s charging preference, user’s charging satisfaction, and the total supply capability (TSC) in the surveyed region are considered in the proposed framework. Two cases are carried out to show the interactions between the user and power grid. It is shown that the proposed framework can accurately simulate the interactions among traffic situation, user’s behavior and TSC, which are significantly lacking in the existing research. The proposed framework is scalable in considering additional interrelated elements.
ELECTRIC vehicle (EV) is critical for alleviating energy dilemmas and the greenhouse effect [
The research on the influence of EV charging process of the traffic flow and power grid has been carried out in [
With the development of intelligent transportation and smart grid, researchers have further studied the relevance between the traffic flow and the EV charging load. The traffic flow data and power load profile are used to navigate EV users to best fit the charging station, where both the power grid and user can benefit based on the intelligent transportation system [
In addition to the multi-agent method, the cellular automaton (CA) method is able to simulate the bilateral effects between the traffic flow and EV charging load. Reference [
The traffic flow and the EV charging load are two principal elements in the EV charging process and are well studied by the research mentioned above, whereas the user’s behavior is not sufficiently considered. Some researchers have taken the EV user into account, but do not consider the interaction with the traffic flow and power grid. The traffic-constrained multi-objective planning method combining geographic location information and vehicle battery information to optimize the location of the charging station is proposed in [
The main contribution of this paper is the development of an integrated simulation framework considering the traffic, user, and power grid to analyze the interactions among them. To highlight the advantages of the proposed framework,
Note: √ means that the method has the function and × means that the method does not have the function.
The proposed framework has the advantage of microcosmic scale simulation, reflecting the behavior of each vehicle such as vehicle lane changing process, vehicle acceleration and deceleration processes and user’s charging preference, which all contribute to a more detailed and accurate simulation results. In addition, the proposed framework utilizes the concept of agent, and superimposes the user’s attributes as an agent upon the vehicle cell, which makes the proposed framework much more accurate in describing the user’s behavior. The advantages of the proposed framework will be further explained and highlighted in case studies. The two objectives of this paper are as follows.
1) Development of the proposed framework: detailed modeling of the traffic is based on CA, the consideration of user’s behavior as built-in attributes of each vehicle, and the interactions among the traffic, the user, and power grid during the charging process.
2) Case study based on the proposed framework: after constructing the proposed framework, two cases about the interactions are simulated and analyzed.
The rest of this paper is arranged as follows. Section Ⅱ introduces the design of the proposed framework. Section Ⅲ details the design and function of the modules which are used in the proposed framework. Section Ⅳ shows the results of two cases based on the proposed simulation framework. Discussions on the results are presented. At last, Section Ⅴ concludes the paper.
The proposed framework is based on a two-dimensional CA traffic model shown in

Fig. 1 Simulation based on CA framework.

Fig. 2 Structure of proposed framework.
CA is a discrete system where space, time, and state are discrete [
(1) |
The dynamic evolution is determined by the local evolution rule of each cell. For one-dimensional CA, the local function of the cell and its neighbor is:
(2) |
where is the state of the cell at time ; and , , and stand for neighbor cells.
Using CA as the connection is suitable to simulate the integrated system since the nature of CA is suitable for traffic simulation and the discrete characteristics of each cell are suitable for modelling the user’s behavior. There is an advantage of using CA to simulate the integrated system, e.g., CA has a clear physical meaning which means that the framework based on CA has strong interpretability compared with other probabilistic-based methods, where the cells stand for a part of the road, and the states of the cells stand for the properties of the vehicle.
The critical step in developing the proposed framework is to convert the traffic rules to CA generation update rules. The proposed framework is constructed based on CA framework, as is shown in
The structural of the proposed framework is shown in
This module is used to generate the new vehicles at the left boundary in
The maximum speed of the vehicle starts from 0 to the maximum speed where the number is normalized, and 1 stands for 5 km/h. The vehicle type is determined by EV ratio (EVR). The destination of vehicles is generated randomly. The initial SOC is generated by a function relevant to the time. The charging requirement is calculated by considering the energy consumption from the present position to the destination. The charging preference is a part of the user’s behavior and is formulated according to the generation of vehicles. In this module, the structure of traffic attributes is developed and the initial states of the vehicle are set.
In this module, the surrounding conditions of vehicles are scanned. Five distances are measured for one vehicle, which are rear left distance, rear right distance, front left distance, front right distance and front distance. Left label and right label are used to show whether two-side road is occupied or not. The distance element is used to show the distances with other vehicles in the respective directions. The surrounding information is used in the vehicle motion process and vehicle exit zone/CS process to ensure the safety.
The vehicle motion module includes two parts, which are vehicle lane changing module and vehicle moving forward module. This module is used to realize the vehicle motion function with traffic rules.
The vehicle lane changing module is used to investigate the lane changing of the vehicle. Lane changing is the fundamental behavior of vehicles in traffic flow. This module takes the data generated by the vehicle scan module in order to judge the safety of changing.
1) Condition of lane changing is presented as ; and .
2) Condition of safety is presented as .
With a unit of power grid as shown in
The vehicle motion module is used to control the velocity and movement of vehicles according to the velocity-dependent-randomization (VDR) model. Considering the practical driving principle, the vehicle accelerates or decelerates on the premise of driving safety. is the vehicle position of the
Step 1: detect random decelerate probability.
Step 2: accelerate .
Step 3: decelerate .
Step 4: randomly decelerate .
Step 5: move forward .
Meanwhile, the battery SOC is updated in this module according to the speed-time sequence of EV running status. The vehicle running equation [
(3) |
where is the vehicle tractive force, which is a function of ; is the velocity of the vehicle, which is a function of ; is the vehicle kerb weight; and g are the rolling resistance coefficients; is the road incline; is the correction coefficient of rotating mass; is the air density; is the coefficient of air resistance; is the coefficient of the wind; and is the velocity of the vehicle. Based on (3), the energy consumption formula for vehicle running is derived as (4).
(4) |
Assume the additional consumption is (5).
(5) |
The total energy consumption can be derived as (6).
(6) |
where is the energy consumption of vehicle running; is the beginning discrete simulation time step of vehicle motion, valued as the simulation time step when the vehicle is generated; is the latest simulation time step; is the additional power which is a constant value; and is the transmission efficiency. Based on (6), the SOC of EV is updated.
When the vehicle travels out of the simulated district, the vehicle is eliminated from the simulation system. The elimination process takes place at the right boundary of the road where all vehicle attributes are eliminated, and vital data are stored for analysis. The data gathered from this module will be used to analyze the congestion and the user’s average parking duration.
Vehicle parking modules are designed to simulate the user’s charging behavior in zones or charging stations, which is not included in previous research. The events that happen in zones are modeled as two processes which are vehicle charging process and vehicle parking process. The SOC of EV measures the vehicle charging process, and the parking duration measures the vehicle parking process.
In this module, the vehicle enters into the vehicle parking module from the traffic module as shown in
In this module, the parking duration is generated according to the normal distribution in (7), where the probability density function is shown in (8):
(7) |
(8) |
where is the time; is the standard deviation; and is the average parking time. The type of zone determines the average parking time. Typically, the residence zone has the longest , while the office zone has a shorter , and the commerce zone has the shortest . of commerce zone is the largest, and in the office zone is the smallest.
In this module, the charging mode for EV is determined using the user’s decision objective function considering the user’s preference , energy price , the urgency of charging time and the charging location .
(9) |
Three types of user’s preferences are modelled, which are speed-sensitive , price-sensitive and time-sensitive charging preferences.
(10) |
(11) |
(12) |
where is the charging duration of the
The speed-sensitive preference minimizes the charging time regardless of other elements. The time-sensitive preference selects the charging mode based on the time schedule. The price-sensitive preference selects the charging mode based on the charging price.
In this module, EVs are charged according to their charging mode. TSC limits the charging power of EV, affecting the charging process, the traffic flow, the user’s charging preferences and charging satisfactions. The parking time is calculated at the same time. The attributes of both vehicle and EV are updated.
Based on the simulation system, case studies can be carried out to analyze the interactions among the traffic, user, and power grid. The proposed framework can be easily expanded to investigate other elements due to the nature of the module design. For example, investigating the influence of EV charging load caused by severe weather can be realized by amending the modules and users in the proposed framework. For more specific user’s behavior, it can be modelled by altering the driving rules in traffic module and the rules in the vehicle parking module. The scalability cannot be easily achieved by other methods.
The simulation time duration is 86400 s (one day), and the simulation time step is set to be 6 s. EVs account for 50% of all vehicles. In this paper, the default maximum is set to be 200. The normalized vehicle number is the proportion of vehicle generation quantity of each hour to , which is shown in

Fig. 3 Daily traffic flow and power load profile.
The position of the office zone is at the right side of the second lane, and the entrance and exit of the office zone are at 480 road lengths and 481 road lengths. The position of the residence zone is at the same side of the office zone, and the entrance and exit are at 720 road lengths and 721 road lengths, respectively. The position of the commerce zone is at the left side of the first lane, and the entrance and exit are at 600 and 601 road lengths, respectively. Three charging stations locate at 20%, 30%, 70% of the road lengths, the first and third stations locate in the first lane, and the second station locates in the second lane. The per-hour traffic flow profile is based on the data from [
The initial SOC for EV are set to be:
(13) |
where is the normal distribution.
Assume the rational drivers with the time-sensitive charging preference are the majority. The typical ratio of three user’s preferences is 15% of speed-sensitive charging preference, 70% of time-sensitive charging preference and 15% of price-sensitive charging preference. The peak electricity demand happens during 07:00-19:00 according to the power load profile shown in
The fast charging power and the slow charging power are set to be 60 kW and 3.3 kW, respectively. There is no limitation on the number of charging devices, but the charging power in a zone or charging station is limited due to the power load profile shown in

Fig. 4 Daily EV charging load of different zones. (a) Commerce zone. (b)Office zone. (c) Residence zone.
The peak charging loads are around 19:00 p.m., which is also the peak time of traffic flow. Since the parking duration of residence zone is much longer than other two zones, the parking in residence zone is inclined to use slow charging based on the charging preference, where the commerce zone has the maximum load fluctuation. After the initialization, the interactions among the traffic, user, and power grid can be investigated in the following section. The computing time for running the simulation is 226 s.
Case 1 aims to investigate the impact on the power grid caused by the user’s charging preferences and traffic flows.
The user’s charging preference has apparent impacts on EV charging load, while the existing method cannot simulate the change of user’s charging preference. The different setting scenario stands for the situation for different users and charging situations. The speed-sensitive preference mainly happens in the business department or office.
The daily charging load in the commerce zone is considered as an example. Three different ratios of the user’s charging preferences, i.e., price-sensitive, time-sensitive and speed-sensitive, are simulated to investigate the impact on the charging load of the power grid, as shown in

Fig. 5 Daily EV charging load of different user’s charging preferences. (a) The highest charging load fluctuation. (b) The smallest charging load fluctuation. (c) The highest charging load. (d) Monte Carlo method.
As shown in
Two different traffic flow scenarios are simulated in the proposed framework and compared with the traditional Monte Carlo method. In scenario 1, is set to be 250, and in scenario 2, it is set to be 150. Other framework parameters are based on the initialization in Section III. The daily charging load at the office zone is chosen to analyze the impact on the charging load of the power grid caused by different traffic flows, which is simulated and shown in Figs.

Fig. 6 Charging load of commerce zone by two simulation methods ().

Fig. 7 Charging load of commerce zone by two simulation methods ().
However, the proposed framework has a better dynamic performance, especially when the traffic flow increases up to the maximum flow of the road, since the proposed framework can simulate traffic jam condition as shown in

Fig. 8 Charging load of commerce zone by two simulation methods when traffic jam happens around 16:00 p.m..
To analyze the relationship between traffic flow and charging load, additional cases with different traffic flows are simulated to obtain the relationship between traffic flow and charging load.
Using the linear function to fit these two curves, the fitting polynomial equations are obtained as:
(15) |
where is the average charging load; and is the peak charging load. The curve fitting results are shown in
Note: SSE stands for the sum of squares for error and RMSE stands for root mean squared error.
To sum up, if the traffic flow in a specific district increases, the electricity demand grows simultaneously. In this paper, the impact on the power grid, especially on the peak charging load caused by the traffic and user, is investigated using the proposed framework, which modularizes the behaviors of the traffic, user, and power grid and can simulate the coupling of the system precisely. Besides, the proposed framework is beneficial for transportation planning and demand forecasting.
Case 2 investigates the impact on the user’s satisfaction with the charging process caused by different TSCs and traffic flows. According to the previous research, user’s satisfaction is related to the waiting time [
By setting different TSCs of the proposed framework, the average value and median value of charging overtime can be obtained in
The average overtime increases remarkably when TSC decreases. However, the maximum overtime has no significant change unless TSC is too small to satisfy the demand in

Fig. 9 Charging load profile of different TSCs in commerce zone.
By setting different traffic flows of the proposed framework, the average, median, and maximum values of charging overtime can be obtained as shown in

Fig. 10 Charging load profile of different traffic flows with 1000 kW TSC in commerce zone.
In general, increasing TSC or reducing the traffic flow can boost the user’s charging satisfaction rate. More than 40% designed TSC can be saved by using the proposed framework to simulate the user’s satisfaction rate as well as the investment.
We propose a framework for EV based on CA to investigate the interactions among the traffic, user, and power grid. The proposed framework lays the theoretical and modelling foundation for analyzing the complex interactions among the three elements. Based on the developed framework, the results from two case studies have been presented, which show that the user’s charging preferences have significant impact on the charging load, e.g., more than 15% of the charging load fluctuations. Meanwhile, the traffic flow has a nearly proportional relationship with the average and peak values of the charging load. Case 2 shows that increasing TSC or reducing the traffic flow has the promotion effect on the user’s charging satisfaction. However, the relationship between the user’s charging satisfaction and traffic flow or TSC is nonlinear, which is similar to a hyperbolic function. By using the proposed framework, the user’s satisfaction with different traffic flows under different power grid conditions can be investigated.
One of the future research directions is to consider the detailed topology of traffic and power grid. The traffic module can be further extended by combining with the geographic information system to simulate a more diversified traffic situation.
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