Toward a Wireless Charging for Battery Electric Vehicles at Traffic Intersections Shahram Mohrehkesh, Tamer Nadeem Department of Computer Science Old Dominion University {smohrehk, nadeem}@cs.odu.edu Abstract— In near future, Battery Electric Vehicles (BEVs) will become widely accepted and used. One of the main challenges of BEVs is their limited energy capacity. Current battery technologies require BEVs to make frequent trips, in comparison to traditional refuel, to charging stations for recharging. In this paper, we envision a new scheme for charging of BEV based on wireless charging. Our scheme exploits the frequent BEVs stops at traffic intersections to charge their batteries via wireless charging device. We study how to integrate control strategy at traffic intersections for maximizing charging while minimizing waiting delays. Simulation results are provided to show the effectiveness of proposed charging schemes. Keywords-component; battery; charging; vehicles; electric; intersection;traffic light. I. INTRODUCTION Number of operated battery electric vehicles (BEVs) is increasing every day. One main challenge of the current BEVs is limited capacity of current batteries. Due to this limitation, BEV’s battery requires frequent recharging [1]. Currently, BEVs stops by special charging stations for recharging. Due to current battery technologies, the number of stops for recharging is more than the number needed for traditional gas refueling. Smart recharging solutions are required to encourage the BEVs usage. One smart solution is to utilize the frequent stops of vehicles at traffic lights for recharging. In urban cities, the amount of time a vehicle spends at traffic lights on a daily basis is significant. This intersection delay or stop time could be utilized for BEVs recharging. In doing this, wireless recharging scheme is required rather the traditional recharging schemes that require physical plugging. Stop time can be very significant in some scenarios like Manhattan in New York City because of its large grid of intersections. In such cities, there is a huge number of vehicles (e.g., cabs and buses) that keep running most of the day. Stopping and parking at charging stations for long duration for recharging is not a practical solution for these vehicles. Utilizing the short duration stops at traffic light for wireless recharging instead of parking at charging stations is more practical and appealing for such scenarios in terms of time and cost saving. We name this scheme a Vehicular Wireless Charging (VWC). To realize the VWM scheme; two main questions need to be answered. The first question is how much energy VWC scheme could provide to BEVs? The second one is how to control intersection delays to maximize recharging while maintaining low delays? This paper tries to answer these questions by showing how to exploit intersection delays in recharging BEVs using wireless charging. To our knowledge, this is the first work that studies wireless charging of BEVs at intersections. The remainder of this paper is organized as follows. In the next section, we describe the specification of batteries in BEVs. Then, we introduce the charging and discharging properties of batteries. Next, in section III developed modules and methods are described. In Section IV, simulation results are shown and analyzed and followed by discussion in section V. Finally, Section VI concludes the paper and discusses future work. II. BACKGROUND A. Battery’s Technology for BEVs Batteries are mainly classified to different types according to their chemical material (e.g., Ni—MH and Li-ion). For BEVs, Lithium-ion is the best choice and outperforms other types in terms of different metrics such as safety, performance, life span, specific energy, and cost [2]. The most important parameter is specific energy, or simply Energy, shown in scale of kilo Watt hour (kWh). Typical values for current batteries are from 5 to 50 kWh. This value is for a pack of battery cells and is also related directly to another specification of battery that is shown in mile scale. As an example, a battery of 7 kilowatt-hours means that the vehicle can go for 14-28 miles without stop. The quantity of energy consumption per each mile varies. Many parameters such as vehicle’s weight, weight of battery pack, weather conditions and road condition affect energy consumption [3]. However, as a rule of thumb based on different experiments, 325 Watt-hours energy is consumed for every mile (1600 meters). Other important parameter in batteries is their capacity which is shown in terms of Ampere-hour (Ah). This parameter and voltage of battery specify the charging time. Charging of batteries depends on many other parameters like battery’s type. However, generally it is assumed that there is a linear charging model. For example, if a battery with capacity of 10Ah and voltage of 1 kV is connected to a 1kW charging system, it takes 10 hours to full charge. There are different levels and connection types for charging. On 1998, the California Air Resources Board classified levels of charging power that have been codified in title 13 of the California Code of Regulations, the U.S. 1999 National Electrical Code section 625 and SAE International standards. They define three levels of charging which are categorized based on ampere and voltage of charging connection. In this paper, we focus on the characteristics of wireless charging B. Wireless Charging for BEVs Wireless charging uses induction technology or magnetic charging. We discuss three methods of wireless charging which two of them have developed particularly for BEV. Mats: Mats use induction charging method. Nowadays, several existing mats are used to offer charging for devices like cell phones, laptops and etc. These mats provide voltage of 18v and ampere of 0.83A. Although, there is no existing mats for charging BEVs, making charging mats with high ampere for BEVs is feasible. Mats can be a potential choice for BEVs’ wireless charging. Plugless power: it is developed by Evatran in March 2010[4]. They claim it is the world’s first hands-free, plugless, and proximity charging system for electric vehicles. Charging is conducted while the BEV is parked. Their system currently uses a pole that transfers the power to the vehicle. So this method is not currently feasible to use in intersection because the need of this pole unless it is customized for intersection by putting the pole horizontally on the ground for example. This method also uses inductive charging method OLEV: Researchers at the Korea Advanced Institute of Science and Technology (KAIST) have developed an electric transport system (called Online Electric Vehicle, OLEV) where the vehicles get their power from cables underneath the surface of the road via noncontact magnetic charging. In such system, power source is placed underneath the road surface and power is wirelessly picked up by the vehicle itself [5]. This method is applicable for wireless charging at intersections, though it requires significant modifications to infrastructures. III. consumption for a non-moving vehicle is negligible. Charging rate defines how much energy a vehicle will be recharged during the intersection delay. Electric vehicles will get charged only when they are within charging zones. A charging zone is the area at that intersection that is equipped with wireless charging equipment. Only nonmoving vehicles at red traffic light and within the charging zone will be recharged wirelessly. Red phase duration of traffic light in addition to charging rate determine the recharged energy amount. Energy discharging model is a linear function of the distance traveled by the vehicle and the consumption rate. B. Control Strategy Module This is the main component of our system. As described in previous section, a vehicle is recharged while it is waiting at the intersection traffic light. Schedule and duration of traffic light phases (e.g., red phase) impacts the amount of recharged energy. Our objective is to adapt the traffic light’s control strategies at intersections in order to maximize the amount of recharging energy while minimizing the intersections delays. Adapted control strategy will be compared with the original pre-programmed control strategies in our simulation. Duration of traffic light’s stop time will be increased or decreased to provide more/less recharging time for vehicles based on various parameters such as vehicle’s level of energy. Other factors will be described later in our proposed schemes. Various methods and strategies have been proposed in literature (e.g. [6] , [7]) to control the traffic lights based on different traffic parameters. However, none of these strategies has considered the status of vehicle’s battery. In this paper, our goal is to show the impacts of the traffic light’s control strategy on the BEVs charging performance. Thus, we compare our methods with static pre-programmed control strategy. We believe that our methods can be used in combination with smart and sophisticated control methods. This is part of our future work. We develop three control strategies and compare them with each other and also with static pre-programmed traffic light scheme. Only Battery based Strategy (OBS): in this strategy, periodically for each intersection, we count number of non-moving vehicles with battery’s energy level above a minimum, energymin, and below a maximum threshold, energymax. In our model, the values of energymin and energymax are 5% and 20% of battery’s capacity respectively. The energymin is the threshold that we used to instantly fully recharge the vehicle’s battery as described earlier. If the portion of counted vehicles to the total number of vehicles at the intersection is above a threshold (default is 10%), we increase the remaining time of current traffic light’s phase (i.e., red phase) as a linear function of calculated portion. We limit the maximum increase to 20% of the original pre-programmed phase duration. On the other hand, if the calculated portion is zero, current phase’ time is reduced to compensate for increased delays at other intersections. Topology and Traffic based Strategy (TTS): in this strategy, it is assumed that the network topology of the VEHICULAR WIRELESS CHARGING (VWC) ARCHITECTURE In this section, we describe the components of VWC. First, we describe the implementation details of two modules that we have developed to evaluate the system. Next, we describe another module that is required in real implementation of system. A. Chargning and Discharging Models for BEVs We model the rechargeable battery in BEVs by four characteristics: energy capacity, initial energy of the battery, consumption rate and charging rate. The initial energy shows the initial energy level of the vehicle’s battery. In our model, we assume a vehicle with battery’s energy below 5% of its total capacity is at high risk to become disable because of running out of battery. Therefore, a full recharging at charging station is required. In this paper we abstract this assumption by instantly replace the battery with a full charged one whenever its current energy drops below its 5% capacity. Consumption rate is the amount of energy the vehicle consumes while moving. Energy road is known. In this scheme, non-moving vehicle with minimum level of battery at each intersection is found. Recharging duration to boost this vehicle battery’s energy to arrive to the next intersection is estimated. If the remaining time of traffic light’s intersection delay is less than the estimated duration, we increase the traffic light stop time provided that stop time will not increased more than two times of original stop time. Otherwise, traffic light’s stop time is not changed. high dense traffic scenarios, vehicles will stay longer at traffic intersections and higher chance for charging. Charging zone is assumed to be 20 meters in front of the intersections. Random values for battery’s initial energy are assigned to vehicles at the start of simulation. Ratio of charging rate to consumption rate is selected from 1, 0.5, 0.1, or 0.01. Summary of all simulation parameters is shown in Table I. TABLE I. Traffic Light based strategy (TLS): in this strategy, not only the topology and traffic information are available but also we know the traffic lights which has the charging ability. In the other methods, it was assumed that all intersections have the charging capability. This strategy is developed to study the effects of enabling some intersections with charging capability on the control strategies. This strategy is an extension to topology and traffic based strategy (TTS). Therefore, the strategy of extending the stop time of a traffic light is similar to topology and traffic based strategy. However, the distance to next intersection used the topology and traffic based strategy is replaced with the distance to the next intersection enabled with charging capabilities. Note that this control strategy is used only at intersections enabled with charging capabilities. C. Communications and Billing Sub-systems One of the VMC’s requirements is the ability to communicate with BEVs to collect corresponding information such as battery level values of the vehicles, battery’s capacity, charging and consumption rate. In doing this, we assume vehicles are equipped with some communication mechanism such as DSRC technology that enable sending up-to-date information (e.g. location, speed, time, identification) periodically to intersection controller that is equipped with the corresponding communication unit. In addition, intersection controllers might be able to communicate with each other in order to exchange their information in addition to traffic statistics such as the traffic flow rate. This information could be used to improve the traffic lights control strategy. The infrastructure is also responsible for authentication and billing process. Vehicles approaching the intersection authenticate themselves to the intersection. Consequently, the intersection permits only authenticated vehicle to get charged through the wireless charging equipment. Also, charging rate and charging duration for each individual vehicle is sent to the central billing system. IV. PERFORMANCE EVALUATION Parameter Network and traffic Model Number of intersections Number of vehicles Number of routes Simulation duration Value(s) Charging zone Maximum speed at roads Distance between intersections Vehicle Model Vehicle Energy Capacity Energy consumption Battery cell capacity Battery output voltage Vehicle length Initial Energy of Vehicles Charging rate 20m 13 m/s 100 m 24 150 75 7200 second s 5 kWh 200 Wh per km 90 Ah 3.6 V 4m random 9.6, 4.8, 0.96, 0.096 kW B. Simulation Scenarios We evaluate scenarios using different charging strategies described in section III in addition to two charging schemes: No Charging and No Strategy described as follow: No Charging (NoC): in which no intersection is enabled with charging capabilities. Only energy consumption is calculated and reduced from total vehicles’ energy. No Strategy (NoS): in this method, schedule of traffic lights are not modified and they operate based on their pre-programmed static control program. Charging strategy: one of the OBS, TTS, and TLS are used. TLS will be compared only with NOS because it is a special strategy based on intersections with charging capability which is not included in the other methods. To compare these methods, four metrics are defined: Average Remaining Energy: this parameter shows how much each charging scheme could provide energy to the vehicles. Average Recharging Occurrence: it shows on average the number of times the vehicles’ energy level falls below the energymin threshold. In order to avoid stopping of vehicles with energy level below energymin, we charge their battery back to full capacity. This metric shows the effect of the charging schemes on the number of trips to charging stations for full recharge. Average Waiting Time: it shows the average waiting time experienced by vehicles at intersections. This parameter evaluates how a charging scheme is able to In this section, we describe our simulation tools, evaluation metrics, and the results of extensive experiments. A. Simulation Tools and Parameters We use SUMO [8] for our experiments and TracI [9] module to control the traffic lights and collect performance metrics. For simulation, a network grid of 24 intersections is developed that are connected to each other through single lane in both directional. Distance between two consecutive intersections is 100m. Scenarios are evaluated with 150 vehicles (20 vehicles per kilometer- low dense traffic). We use a low dense traffic scenario to show the effectiveness of mechanism even in low dense traffic scenarios. Obviously, in SIMULATION PARAMETERS No Strategy OBS TTS Figure 1. Average remaining energy using different methods with charging rate of 9.6kW TTS OBS No Strategy TTS OBS No Strategy TTS OBS 0.096 kW 3 0.96 kW 4.8 kW 0 kW TTS No Charging OBS 0 No Strategy 1 No Charging 2 Average Time to Rirst Recharging(s) 8000 7000 6000 5000 4000 3000 2000 1000 0 TTS 2.36 OBS 3 Different charging control strategies also improve the Average Time to First Recharge. In our calculation of the average first recharging metric, we use the total simulation duration (i.e., 7200sec) as the average first recharging for any vehicle that didn’t run out of energy during the simulation. Figure 4 shows that with high charging rates, charging schemes outperform NoC scheme significantly. In addition, OBS and TTS scheme outperform NoS scheme for high charging rates. With the very high charging rate 9.6kW, all charging schemes have similar performance. This is because only 3 vehicles (2% of all vehicles) ran out of energy during the simulation for this scenario. No Strategy 4 4.8 kW Figure 3. Average waiting time for differenet t charging schemes with different charging rates TTS 4.33 0.96 kW OBS Avg Remaining Energy(kWh) 4.43 4.26 5 0.096 kW No Strategy Figure 2 shows the Average Remaining Energy and Average Recharging Occurrence for different charging rates under charging schemes. In all cases, OBS and TTS outperform NoS by a significant decrease (10%-20%) in the Average Recharging Occurrence while maintaining very low increase in the Average Waiting Time as shown in Figure 3. Avg. Waiting time(s) TTS C. Simulation Results Figure 1 shows the significant impact of different charging schemes on the Average Remaining Energy of vehicles when the charging rate is 9.6 kW that is equal to the consumption rate. The figure shows that all charging scheme (NoS, OBS, TTS) have increased the Average remaining energy, compared to the no charging (NoC) scheme, with a factor of more than 1.8. In addition, the Average Recharging Occurrence (not shown due to space limitation) has decreased from 1.8 for NoC scheme to 0.02 for the other charging schemes (NoS, OBS, TTS). Moreover, charging schemes have increased the Average Waiting Delays (not shown) with 57 seconds only over the original 3800 seconds in case of NoC scheme. 4500 4000 3500 3000 2500 2000 1500 1000 500 0 OBS Average Time to First Recharge: it shows the average time to the first time the vehicle’s energy falls below the energymin threshold. In our model, as described earlier, once the vehicle’s energy falls below energymin, it gets recharged to its full capacity instantly. No Strategy No Strategy increase the stop time without introducing long waiting time. 9.6 kW 2.5 Figure 4. Averagee Time to First Recharge for different charging schemes with different charging rates 2 1.5 Avg Remaining Energy(kWh) 1 0.5 Avg. Recharging Occurance 0.096 kW 0.96 kW TTS OBS No Strategy TTS OBS No Strategy TTS OBS No Strategy 0 4.8 kW Figure 2. Performance Comparison of different charging schemes with different charging rates Figure 5 shows the effect of different control strategies in comparison to NoS and NoC schemes over time when charging rate is 0.96kW. For NoC scheme, vehicles lose their energy faster and several of them reach to the point that they need to be recharged around the time of 1800 seconds, while several vehicles in the charging schemes recharge around 5400. The Average Recharging Occurrence (not shown) for all charging schemes is less than the NoC. In addition, as shown, OBS and TTS schemes have low fluctuations in their Average Remaining Energy with respect to NoS scheme. traffic at the intersection in the control strategy. That is part of our future work. 3.5 3 0.6 2.5 2 NOC 1.5 NOS 1 OBS 0.5 TTS 0.5 Avg. Recharging Occurance times for NOS 0.4 Avg. Recharging Occurance times for TLS 0.3 7200 6600 6000 5400 4800 4200 3600 3000 2400 1800 1200 600 0 0 0.2 0.1 time(seconds) Figure 5. Average Remaining Energy(kWh) with 0.96 kW charging rate scenario The Average Remaining Energy when the charging rate equals the consumption rate (9.6kW) is shown in Figure 6. In this scenario, all the charging schemes have provided to vehicles enough energy that exceeded the consumed energy during simulation. Therefore, the control strategies (OBS, TTS) could be optimized to avoid extra recharge and consequently reduce delays at intersections. Average Remaining Energy (kWh) 5 4.5 4 3.5 0 40 50 60 70 80 90 Percentage of intersection with charging capability 100 Figure 7. Effect of percentage of intersection with charging capabilty on Average Recharging Occurence TLS also behaves better than NoS in terms of Average Remaining Energy (Figure 8). Even though the difference is not significant, it shows that inclusion of knowledge about the topology of intersections with charging capability can provide more energy. This improvement happens with some delays especially at low percentage such as 40% as shown in Figure 9. This delay gets reduced significantly with large percentage such as 60%. 4.5 3 4 2.5 3.5 2 NOC NOS OBS TTS 1.5 1 0.5 3 2.5 2 Avg Remaining Energy(kWh)-NOS 1.5 0 0 900 1800 2700 3600 4500 5400 6300 7200 time(s) Figure 6. Average Remaining Energy(kWh) with 9.6 kW charging rate scenario Next, we investigate the presence of charging capability only at a percentage of intersections. Intersections with charging capabilities are selected uniformly. Average Recharging Occurrences are shown in Figure 7 for different percentage of intersections with charging capability. We compare Traffic light based Strategy (TLS) only with NoS method because the other methods do not include the knowledge of charging capability of intersections in their decision. As it is shown, when 40% to 70% of the intersections have a charging capability, TLS outperforms NoS significantly. Both converge when the number of intersections with charging capability is 70% or more. Vehicles in TLS scheme are forced to stop as much as they can in the traffic light with charging capability to have enough energy to go to the next traffic light with charging capability. This situation causes vehicles to wait for long times at intersections when the percentage of intersection with low charging capabilities. This situation can be solved with smart control strategies. For example, we could include the volume of 1 Avg Remaining Energy(kWh)-TLS 0.5 0 40 50 60 70 80 90 Percentage of intersection with charging capability 100 Figure 8. Effect of percentage of intersection with charging capability on Average Remaining Energy 3960 3940 Avg. Waiting time(s)- NoS 3920 Avg. Waiting time(s)-TLS 3900 3880 3860 3840 3820 3800 3780 40 50 60 70 80 90 Percentage of intersection with charging capability 100 Figure 9. Effect of percentage of intersection with charging capability on Average Waiting Time The results demonstrate that the TLS performance could be improved if information like traffic flow and vehicles’ routes at intersections is utilized in the control strategy so TLS becomes more intelligent. V. DISCUSSION A. Improvement to TLS TLS method can be improved using smart method that tries to calculate the time needed for arriving more vehicles to the next enabled intersection rather than only the vehicle with the minimum level of battery. Vehicles will not have the same route and will arrive to the enabled intersection at different times and locations. Hence, this information could be taken into account to improve the performance of TLS. B. Coverage of Charging Zones In our simulation, we assumed all intersections have the same coverage charging area. However, each intersection could have different coverage area. Two scenarios could be investigated: (I) using the global optimum charging zone for all of the intersections based on different parameters like the flow of vehicles; (II) finding the local optimum charging zone for each intersection based on the flow of traffic at that intersection and also the flows at the adjacent intersections. Also, the dependency between this parameter and vehicles characteristics such as battery charging rate can be investigated. C. Intelligent Traffic Lights Many of the traffic lights use different intelligent control strategies (e.g. SCOOT) that depend on different statistics such as flow of traffic. We believe that these mechanisms can be used in combination with our methods. In fact, our strategies can be enhanced based on similar factors like flow of traffic or synchronization between close intersection traffic lights. We can make smarter decision at traffic lights if we know the incoming traffic of BEVs. D. Market Penetration BEVs will be presented to market gradually and some solutions can be adapted based on the percentage of BEVs to all the vehicles. For example, in OBS, instead of calculating the portion of BEVs with critical energy level to all vehicles, only to all BEVs should be considered. E. Higher Charging Rate Charging systems are advancing every day and higher charging rate will be feasible in near future. Presence of these charging systems provides two opportunities. First, delay time at intersections can be reduced and also number of intersection with charging capability can be reduced as well. Second, this can be used to provide scenarios of charging vehicle even when they drive in the highways. With fast charging systems, vehicle can be charged at the ramps before entering the highway and at exists after exiting the highways. F. Variation in Vehicles One important issue that should be addressed in future is the design of control strategies that take into consideration different characteristics of various vehicles. In practice, there will be a blend of vehicles with different charging rates, consumption rates, and battery capacities. These parameters should be taken into account when designing a strategy. For instance, when the majority of vehicles at an intersection have higher charging rate, stop time can be less than the scenario in which the vehicles have lower charging rate. Another example, in strategies like TTS, when the vehicle’s routes are estimated to calculate energy consumption, varieties of consumption rates should be taken into account. VI. CONCLUSION AND FUTURE WORKS We evaluated the effectiveness of exploiting frequent stops at traffic intersections in cities to charge battery electric vehicles. BEVs will be charged based on wireless charging technology. Performance of charging methods was evaluated through extensive simulation scenarios. In summary, charging methods showed to be very effective. With more collected information about traffic and vehicles, the control strategy schemes could be optimized and optimum result could be achieved. We believe that there are still some challenges to realize this idea. However, we generally believe that this idea has significant advantages and is valuable to further investigation. In future work, we will evaluate methods while making them more intelligent using different existing traffic intersection control strategies. VII. ACKNWOLDGEMENT Authors would like to thank Liang Chen on her efforts for the initial work in this research. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] C. 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