P2C2: Peer-to-Peer Car Charging

Prabuddha Chakraborty, Robert C. Parker, Tamzidul Hoque, Jonathan Cruz, and Swarup BhuniaDepartment of Electrical & Computer Engineering

University of Florida, Gainesville, FL, USA

Abstract— With the rising concerns of fossil fueldepletion and impact of Internal Combustion Engine(ICE) vehicles on our climate, the transportationindustry is observing a rapid proliferation of ElectricVehicles (EVs). However, long-distance travel withEV is not possible yet without making multiple haltsat EV charging stations. Many remote regions donot have charging stations, and even if they arepresent, it can take several hours to recharge thebattery. Conversely, ICE vehicle fueling stations aremuch more prevalent, and re-fueling takes a couple ofminutes. These facts have deterred many from movingto EVs. Existing solutions to these problems, such asbuilding more charging stations, increasing batterycapacity, and road-charging have not been provenefficient so far. In this paper, we propose Peer-to-Peer Car Charging (P2C2), a highly scalable noveltechnique for charging EVs on the go with minimalcost overhead. We allow EVs to share charge amongeach other based on the instructions from a cloud-based control system. The control system assigns andguides EVs for charge sharing. We also introduceMobile Charging Stations (MoCS), which are highbattery capacity vehicles that are used to replenishthe overall charge in the vehicle networks.We haveimplemented P2C2 and integrated it with the trafficsimulator, SUMO. We observe promising results withup to 65% reduction in the number of EV halts withup to 24.4% reduction in required battery capacitywithout any extra halts.

I. Introduction

Electric vehicles have existed for a while, but havenever enjoyed mainstream adoption. Now, with the needto reduce our carbon footprint and companies like Tesla,Nissan, and Chevrolet in the picture, the electric vehiclehas become more appealing and affordable. Nevertheless,the adoption of EVs remains slow, mainly due to con-sumer concerns regarding battery life, battery range, andlimited access to charging stations [1]. Inefficient charg-ing cycles or complete discharge of a battery reduces itslife, making it imprudent to travel the full range providedby the battery without any recharging in the middle [2].Even though major cities in developed countries havecharging stations, the amount is still unable to supporta large EV population. Charging stations in remoteregions are few and far between. Most of the existingcharging stations are Level-2 (220V) which require longwaiting periods to charge a vehicle. Level-3 chargingstations or DC fast chargers (DCFC) (440V) are a fasteralternative; however, they are limited and very expensiveto build[3]. With these concerns in mind, researchers havebeen looking into several potential solutions. Andwariet al. surveyed innovations in EV battery technologies[1], but concluded that the battery range and chargingtime remains the most critical barrier. Novel solutions

Fig. 1: P2C2 enabled charge sharing among EVs andMoCS-based charge distribution for charging on the go.

like charging via solar-powered roads are not applicableacross the geography [4].

In this paper, we propose a scalable peer-to-peer vehi-cle charging solution that is both low cost and easily toimplement with minimal changes to the EVs. As shownin Fig. 1, vehicles will share charge and sustain each otherto reach their respective destinations. A set of cloud-based schedulers decides charge providers and receivers.Based on the charge transaction and subsequent reroutedecisions, the cloud-based control system instructs theEVs to carry out the charge transfer operations. Withthis scheme in place, the total charge in the EV networkwill eventually spread out across all the EVs. However,even in a dynamic network with EVs entering and leav-ing, we observe through simulation, the total charge ofthe network will slowly deplete. To keep the EVs in astate of perpetual motion, we introduce Mobile ChargingStations (MoCS), to bring in a considerable amountof outside charge into the EV network. The EVs arethen responsible for the fine-grained distribution of theoutside charge deposited by the MoCS. We have devel-oped a scheduling algorithm that controls the chargetransactions and decides when and where to insert a newMoCS. We quantitatively analyze its effectiveness usingSUMO [5] as our traffic simulator. We demonstrate thatour algorithm is fast, scalable, and efficient in dealingwith battery-related problems present in modern EVs. Inparticular, we make the following major contributions:

1) We introduce a novel solution to address the electricvehicle charging issue by proposing an on-the-gopeer-to-peer charge sharing scheme.

2) We formalize a complete framework to enable elec-tric vehicles for sharing charges guided by a cloud-based control system.

3) We introduce the concept of mobile charging sta-tions, that seamlessly fit into our framework.

4) We propose an algorithm for charge transactionscheduling and MoCS insertion that also controls theEVs for optimal rerouting and charge sharing.

5) We quantitatively analyze the effectiveness of our

solution with an extensive simulation in SUMO [5].

II. Motivation and BackgroundA. Impact of charging issues in EV adoption

1) Limited range: The limited battery capacity re-stricts long-distance driving in EVs. Even with enoughcharging stations, the travel time is impacted due tofrequent, long halts for charging. The price per kilowatt-hour of lithium-ion battery is reducing at a meager rate,making it difficult to increase the battery capacity of EVswithout a drastic price increase [1]. High-end EVs suchas Tesla Model S and Model X with maximum range of300 to 370 miles, suffer from high charging times. Evenwith a 220V charging station, it takes about 10 hours fora full charge [6]. Although 440V stations can reduce thecharging time, the amount of charging stations requiredto support a large EV fleet will be enormous.

2) Limited stationary charging stations: The overallnumber of stationary charging stations are few comparedto ICE refueling stations and mostly limited to urbanareas. High-end EVs will suffer long charging time fromlevel-1 or level-2 stations. DCFC (Level-3) stations arevery few, making it infeasible to sustain a big EV Fleet.Creating large number of DCFC station is financiallyinfeasible as each charging unit costs $10,000-$40,000 [3].

3) Battery life: Most of the modern high-end EVsare using Lithium-ion batteries [7]. Complete dischargingand charging, or inefficient charging cycles cause theLithium-ion batteries to age at an accelerated rate [8].Hence, a long-distance drive without recharging the EVis undesirable for the battery.

B. Existing solutions to address charging Issues1) Better access to fast charging stations: A brute

force solution to the battery range and charging problemis to build a high concentration of very high speed (Level-3) charging stations to allow fast charging anywhere inthe world. However, dense and uniformly placed Level-3stations costing $100,000 each is not feasible. Further-more, the local power grids must be able to handle thelarge amount of power that must be transferred in a shortamount of time for these stations [9].

2) Improving battery capacity: While improving thebattery capacity is undoubtedly helpful, it could sig-nificantly increase the price of the EV [1]. Besides, itdoes not solve the core problem of having to stop at adesignated station to recharge.

3) Charging from road: Charging from the road isan exciting solution to the core problem. However, thesolar panels fitted road in Normandy, France producedonly 80,000kWh in 2018 and around 40,000kWh by theend of July 2019 due to its inherent dependency overthe weather [4]. Converting every road in the worldinto electric/solar road is a big financial undertaking,rendering the solution infeasible.

III. Peer-to-Peer Charging MethodologyA. System overview

To allow efficient charge sharing, we design a cloud-based control system containing a charge transactionscheduling unit, a rerouting unit, and a database forstoring the information from EVs. The EVs will interactwith each other and the control system, as shown in

Fig.2(a). The control system (1) instructs some EVs toshare charge with some other EVs, (2) reroutes specificEVs to bring charge providers and receivers together,(3) speed lock EVs to allow seamless charge sharing, (4)detaches a charge provider/receiver for overall networkcharge optimization. To allow the charge scheduler tooperate, the EVs send information to the control systemperiodically. An example EV-to-EV synchronization forcharge sharing is shown in Fig. 2(b) and Fig. 2(c). A bigroad system can be divided into sections having separatecontrol systems doing the micromanagement. The overallframework will have a global control system that will helpin seamless transfer of EVs from one region to another.

Sharing charge between EVs can distribute the totalcharge in the network among all the entities. But weobserve through simulation in Fig. 6(c) that withoutan outside-the-network charge source, the network willexperience a slow overall charge decay increasing the per-centage of EV halts. To avoid this problem, we introduceMobile Charging Stations (MoCS) which introduces ahigh volume of charge into the network. Fig.1, showsan MoCS charging a set of EVs in a lane. To identifycharge deprived regions, the control unit maintains acharge distribution map that is updated at a regularinterval. MoCS are inserted in charge deprived regionsif the constraints permit.B. Scheduler optimization goals

While computing the reroute, the charge transaction,and the MoCS insertion schedules, the scheduler takesinto account certain factors. In our embodiment of thescheduler, we consider the following optimization goals:

1) Maximize effective charge usage by analyzingthe charge distribution map.

2) Minimize charging station halts by sustaininglow battery vehicles.

3) Minimize travel time of all EVs by limiting thenumber of rerouting.

4) Maximize battery life by taking into account thedepth of discharge of each EV.

5) Prioritize MoCS as provider over normal EVs.The final decision of the scheduler is a function of all

the optimization parameters, where each parameter canbe weighted differently depending on which goals the userwishes to prioritize. Note that the optimization goals wepropose are by no means exhaustive and more goals canbe added to the scheduler depending on the scenario.C. Scheduling Algorithms

The core algorithm for P2C2 scheduling is presentedin Algo. 1. The scheduler takes in the charge distributionmap (Charge Dist Map) as input and generates a listof instructions (Instruction List) to be followed by theEVs, MoCS, and MoCS depots. The scheduler acts asan intelligent decision function. We design a methodfind critical evs for identifying the EVs in the networkwith critical battery capacity using the charge distribu-tion map maintained in the cloud control system. In line3, we use this method to generate the critical EV list(Crit EV s List). The method find prov ev is designedto identify the best provider EV (Prov EV ) for a givencritical EV from all nearby EVs within a user-specifiedrange. This method uses a greedy search algorithm basedon a linear weighted function of all the optimization

Fig. 2: (a)A system view of P2C2 showing the interaction between the control system and EVs. The control system islocated in the cloud facilitates EV-to-EV charge sharing. (b)The paired EVs are being guided by the control systemto move closer and come on the same lane. (c)The donor EV is sustaining the EV with critical battery condition.

Algorithm 1 P2C2 Context-Aware MoCS Scheduler1: procedure generate Schedule(Charge Dist Map)2: Instruction List = ∅ � Initialized to empty set.3: Crit EV s List = find critical evs(Charge Dist Map)4: i = 05: while i < length(Crit EV s List) do6: P rov EV = find prov ev(Crit EV s List[i])7: inst = gen charge tran inst(P rov EV, Crit EV s List[i])8: Instruction List.append(inst)9: i = i + 1

10: Charge DR List = find charge dr(Charge Dist Map)11: i = 012: while i < length(Charge DR List) do13: ins pt = find best mocs ins pt(Charge DR List[i])14: mocs ins num = find MoCS num(Charge DR List[i])15: MoCS Inst = gen mocs ins inst(ins pt, mocs ins num)16: Instruction List.append(MoCS Inst)17: i = i + 118: return Instruction List

goals mentioned earlier. In line 7, we generate the chargetransaction instruction (inst) required to facilitate thecharge transfer. The instruction (inst) is appended to theInstruction List in line 8. The instructions are targetedtowards helping the EVs to come nearby and speedlock. We define a method find charge dr which finds allthe charge deprived regions in the network using linearsearch. In line 10, the method find charge dr finds theregions in the road system with a high density of criticalEVs. We define two methods find best mocs ins pt andfind MoCS num to find out the best MoCS insertionpoint and amount of MoCS that should be spawnedto deal with a particular charge deprived region, re-spectively. The MoCS insertion point (ins pt) is se-lected based on the predicted trajectory of the lowbattery charge EVs such that the MoCS can easilyconverge with them. The number of MoCS to be inserted(mocs ins num) is based on the severity (number ofcritical EVs) of the charge deprived region and theMoCS quota remaining. The function gen mocs ins instgenerates the instruction (MoCS Inst) specifying theamount of MoCS and MoCS insertion location to be sentto the MoCS depot. The complete Instruction List is re-turned in line 18 from the GENERATE SCHEDULEmethod. The instructions generated are sent to the re-spective MoCS depots and EVs. For the purpose of our

Fig. 3: A physical embodiment of EV-to-EV on-the-gocharging mechanism.

simulation, we modify SUMO to emulate MoCS depotsand the whole EV network.D. Car-to-Car charging mechanism

We envision a safe, insulated, and firm telescopic armcarrying the charging cable. After two EVs lock speedand are in range for charge sharing, they will extend theircharging arms, as shown in Fig. 3. The arms heads willcontain the charging ports, and they will latch togetherusing either magnetic pads or other means. The armsand the overall charging operation will be coordinatedby the respective arm controllers of each EV. This is justone possible realization of the charge transfer mechanism.The entire charging operation can be safely orchestratedif the EVs involved follow a certain predefined proto-col. For autonomous/semi-autonomous EVs, the pairingmechanism can be further streamlined. Wireless chargingis also possible in the future.E. Battery chemistry

Battery to battery charging required for charge sharingis feasible and being actively explored. Products like[10] allow EVs to share charge. Efforts are also beingmade towards faster-charging batteries. In particular,lithium plating-free charging allows quick recharge atall temperatures without sacrificing the durability ofbattery cells [11]. Emerging battery technologies such asthe aluminum dual-ion battery have been shown to haveimpressive charging rates and high energy density [12].IV. Simulation Results Peer-to-Peer ChargingA. Simulation Setup & Fundamental Observations

To analyze the effectiveness of our cloud control systemand the scheduling algorithm, we use an open-sourcetraffic simulator, SUMO (Simulation of Urban Mobil-ity) [5] and integrate the P2C2 scheduler with it. Wemade modifications to SUMO to support peer-to-peer carcharging and MoCS. The P2C2 scheduler communicateswith SUMO periodically to gather traffic information and

send instructions. We use a 240 km highway to test ourmethod. We run each simulation instance for 5 hours inreal-time. We ensure that each EV travels at least 50km. Each EV weighs 2109 kg with a battery capacityof 75 kWh. Unless otherwise mentioned, the EVs andMoCS enter the simulation with full charge. The weightof each MoCS is 11793 kgs which is the gross vehicleweight rating for a class 6 truck [13]. Each MoCS carries850 kWh charge and are battery powered themselves. Weobserve the effect of other parameters such as (1) MoCS-to-EV charge transfer rate, (2) amount of MoCS in thenetwork, and (3) battery capacity reduction of the EVsin later sections.

We test most of our observations on three differenttraffic scenarios. The internal parameters defining eachof these scenarios are as follows:

1) Light Traffic: Initially 500 EVs are inserted with anew EV entering the simulation every 4 seconds. Atotal of 5000 EVs will be inserted over 5 hours.

2) Medium Traffic: Initial traffic of 1000 EVs with anew EV entering the simulation every 3 seconds. Atotal of 7000 EVs will be inserted over 5 hours.

3) High Traffic: Initially 2000 EVs are inserted witha new EV entering the simulation every 2 seconds.A total of 11000 EVs will be inserted over 5 hours.

We use a charging rate of 1kW/min for simulationbased on a realistic EV-to-EV charging estimate pro-vided in [10]. We consider an EV to be halted when itscharge reaches zero. All charge transfer is carried outwith 95% efficiency (i.e., 5% loss during transfer).

Fig. 4 illustrates the overall charge distribution in thehighway. Each point on the plot indicates the averagecharge of vehicles in the region. In the charge distributionmap shown, we can observe a potential charge deprivedregion. A few MoCS will probably be inserted in theregion depending on the scheduler’s decision.

In Fig. 5, we can see the battery charge trend for6 sampled EVs (red) on the left and 2 sampled MoCS(blue) on the right from the network. The EVs generallyexperience an initial drop in the battery charge beforethey are assigned another EV as a provider. After thatpoint, most of the EVs maintain a particular battery leveland continue to move perpetually. The purpose of MoCSis to deposit a huge amount of charge in the networkquickly; hence, they constantly lose charge as can be seenfrom the blue plots.

B. Effect of MoCS Charge Transfer Rate on HaltWe observe the effect of different MoCS-to-EV charge

transfer rate on the percentage of EV halts. 1x charge

Fig. 4: The charge distribution map maintained by thecloud application at a particular time instance.

rate is 1kWh per minute based on [10]. Note that we onlychange the charge transfer rate between an MoCS and anEV. The EV-to-EV charge transfer rate remains 1kWhper minute throughout the experiment. In Fig. 6(a), weobserve that the percentage of halts for all the three traf-fic scenarios decreases as we increase the MoCS chargetransfer rate. If fast charge transfer batteries can be usedin the EVs/MoCS, then the effectiveness of P2C2 willbe increased. P2C2 charging scheme appears to be moreeffective in denser traffic scenarios. As can be seen inFig. 6(a), the percentage of halts for high traffic is least.With more EVs in the network, less amount of reroutingis needed, and an EV with a critical battery state can bequickly assigned to a provider EV which is close by.

C. Effect of Number of MoCS on Percentage of HaltTo observe the effect of the number of MoCS in the

network on the percentage of EV halts, we set the MoCS-to-EV charge transfer rate to 2x (2kWh per minute),and EV-to-EV charging rate to 1x (1kWh per minute)and vary the limit on the percentage of MoCS in thenetwork. The percentage of MoCS refers to the maximumallowable MoCS for every 100 EVs in the network. InFig. 6(c), we observe that as we increase the limit ofthe percentage of MoCS, the percentage of EV haltsdecreases. So a higher quantity of charge influx also helpsin halt reduction.

D. Charging Time Reduction AnalysisBased on the battery capacity of the cars used in the

simulation, it should take approximately 10 hours to fullycharge on the NEMA 14-50 plugs through a 240v outlet[6]. By multiplying the average time charging for eachhalt with the total number of halts from Table I, weobtain the total charge time for all traffic scenarios. Asshown in Table I, the total time spent for stationarycharging reduces significantly due to the charge sharingscheme proposed. The % of reduction for P2C2 is calcu-lated compared to the required charge time results forno P2C2 (without). We use MoCS-to-EV charging rateof 2x and 5% MoCS amount limit for obtaining the P2C2results in Table I.

E. Battery Capacity Reduction and MoCS TradeoffHigh capacity batteries in EVs lead to an increase in

EV weight and cost. In Fig. 6(b), we observe the effectof reducing the battery capacity of the EVs on the per-centage of halts for the medium-traffic scenario. We seethe percentage of faults increase as the battery capacityis reduced. There is a trade-off possibility between theamount of MoCS and the battery capacity of EVs. If theamount of MoCS inserted is within 15% of the total EVsin the network, we can reduce the battery capacity of allEVs by 24.4% and still achieve the same amount of haltscompared to not using the P2C2 scheme. Therefore, wecan reduce the battery capacities of all EVs and havemore MoCS running in the system. In future work, wewill look into incorporating cost in this trade-off.

V. ConclusionWe have presented a novel framework, called P2C2,

for charging EVs on the go to address the issue ofEV charging infrastructure. P2C2 relies on EV-to-EVcoordination as well as a cloud-based guidance system for

Fig. 5: Change of battery charge level over time for sampled EVs(red) and MoCS(blue) in the network.

Fig. 6: (a) The percentage of EV halts reduces as the MoCS-to-EV charge transfer rate increases. (b) The percentageof halt increases as we decrease the battery capacity. The halt percentage is less in presence of more percentage ofMoCS. (c) The percentage of EV halts reduces as the limit on the percentage of MoCS in the network is increased.TABLE I: The percentage of halt induced charging timereduction in different traffic scenarios.

Light Traffic Medium Traffic High TrafficBaseline P2C2 Baseline P2C2 Baseline P2C2

% of Halts 19.68 12.62 19.02 10.18 18.25 9.16Num of EVs 5000 5000 7000 7000 11000 11000

Halt Time (hrs.) 9840 6310 13314 7126 20075 10076Halt Time Cut % - 35.87 - 46.48 - 49.81

creating a real-time charge distribution map of the net-work of EVs and making informed decisions about chargetransactions. We incorporate the concept of MoCS - amobile charging vehicle with a large battery - that canbe dispatched to recharge a network of EVs. We havedeveloped a system, algorithm, and method to enableEV-to-EV charging. Using a popular traffic simulator,SUMO, we have realized the P2C2 framework with real-istic charging parameters and observed a reduction inthe number of halts, and reduction in battery capac-ity requirements of EVs (thus, leading to reduced costand weight). Future work will involve augmenting oursolution for a heterogeneous network of battery operatedentities, like drones and utility robots.

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