Planning of In-motion Electric Vehicle Charging on Freeways

By Ahmed Mohamed, Andrew Meintz, and Kevin Walkowicz

The proper system planning (design) of in-motion charging for electric vehicles has the potential to allow the vehicles to realize charge-sustaining operation (unlimited driving range and zero downtime) at minimum overall cost. There is a tradeoff between system power and road coverage. Low-power system requires significant length of roads to be electrified, which increases the infrastructure cost. A high-power system requires powerful components, which might be infeasible due to technology limitations.

The electric vehicle (EV) market is growing quickly because of the benefits they offer in terms of safety, convenience, fuel economy, operating cost, maintenance and emissions [1]. However, the main challenges for EV adoption is the limited driving range and long refueling time. Today’s light-duty EVs are targeting a driving range of 150-300 miles and a refueling time that ranges from less than 30 minutes to almost a full day depending on the battery and charger technologies [2]. One way to overcome these challenges is to install a large onboard battery—to reach a target mileage— and a stationary (over-night) charging capability that realizes a target refueling time. This stationary charger can be conductive [ac level 1 (L1), ac level 2 (L2) or dc fast charger (DCFC)] or wireless [based on inductive power transfer (IPT) or capacitive power transfer (CPT) technology], as indicated in Fig. 1 [3]. The large battery increases vehicle’s capital and operating costs and requires extreme fast charger for a reasonable recharge time. Another solution is to deploy in-route charging such that an EV can charge during its transit stops (quasi-dynamic charging at traffic signals and/or intersections) or even while movement (in-motion charging) [4]. Both dynamic and quasi-dynamic technologies can be either conductive or wireless, as described in Fig. 1. This solution has the potential to dramatically extend driving range (essentially to infinity), permit use of a smaller on-board battery (consequently reducing the vehicle’s cost, size, and energy consumption), and theoretically eliminate recharge downtime (through realizing continuous charge-sustaining operation) [5]. Quasi-dynamic charging shows a perfect fit for secondary roadways due to the availability of transient stops and low-speed driving areas. In this case, a vehicle will be able to recover enough energy with low charging infrastructure capability (power and coverage). However, on freeways (highways, interstates, etc.) with continuous high-speed driving, only in-motion charging can fit there [4]. In-motion charging is a technology that allows an EV to charge its battery during movement by electrifying the roadway segments, either by using overhead power lines with pantograph, conductive rails with sliding contacts, or wireless charging technology, as depicted in Fig. 1 [6].

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Fig. 1: EV charging technologies.

Planning of In-motion Charging System

Regardless the road electrification technology, a proper design of dynamic charging system may allow EVs to realize charge-sustaining operation (unlimited range and zero downtime) using small on-board battery. In addition, it can significantly reduce the capital infrastructure cost associated with power converters, materials, structure work, installation, etc., and vehicle’s cost associated with battery size and number of vehicles in the fleet due to increasing the operating hours.

Theoretically, the supplied energy by an in-motion charger is function of charger power, electrified segment length, and driving speed. For a certain vehicle’s needs (energy and speed), a high-power, low-coverage charger or a low-power, high-coverage system can be installed. The former shows high unit cost, small number of units, high battery cost and low construction cost. The latter experiences the opposite. Therefore, the proper planning for the system is crucial to decide the appropriate power level, road coverage [ratio of electrified segments length and total road length (δ)], battery capacity, and location and length of electrified segments. The main objectives for the system design are to allow EVs to realize charge-sustaining operation at minimum overall cost. Therefore, a linear energy estimation model for charge-sustaining operation is developed, considering vehicle’s dynamics and charger power, as shown in Fig. 2. In this case, the vehicle’s battery sustains a fixed energy level with a narrow SOC operating window (ΔSOC=10-20%). The model involves vehicle’s kWh/mile, driving speed, battery capacity (Qb), length of electrified segment [LE (mile)], and charger power (Pc).

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Fig. 2: Linear energy model for charge-sustaining operation.

A Case of Study

Among the different in-motion charging technologies, dynamic wireless charging shows more advantages over others: 1) it is interoperable so that the same system can be used with different vehicles, 2) it is automatic and simple in operation, 3) it doesn’t have visual impact, and 4) it is safer due to the removal of exposed energized conductors and mechanical connection. A planning analysis of dynamic wireless charging supporting EV system is achieved by solving the charge-sustaining model in Fig. 1 for passenger cars on highways, as the most vehicle mile traveled in the U.S. [7]. The average vehicle kWh/mile and driving speed are estimated using the real-world data available at the National Renewable Energy Laboratory (NREL) [8].

The relationship between the system key design parameters (δ, Pc and Qb), is described in Figs. 3-5. At a fixed battery capacity (Qb=30 kWh), the system road coverage is inversely proportional to its power level (Fig. 3), which means a high-power system requires less road coverage and less construction and materials, but more expensive units. The variation of the road coverage with the battery capacity for a fixed charging power (Pc=100 kW) is presented in Fig. 4, which shows a linear proportional relationship. Increasing Qb requires more road coverage to compensate for the increase in the vehicle’s energy consumption. The three design parameters are analyzed in Fig. 5, which shows that increasing Pc and reducing Qb leads to the least road coverage and infrastructure cost in consequence. However, this system configuration requires a battery that can handle very high charging rate, which might be infeasible.

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Fig. 3: Road coverage vs. charging power @ Qb=30kWh

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Fig. 4: Road coverage vs. battery capacity @ Pc=100 kW.

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Fig. 5: Road coverage, charging power and battery capacity.


This article introduced the different charging technologies for EVs, including stationary, dynamic and quasi-dynamic. It highlighted the challenges of system design of dynamic charging using a linear energy model for charge-sustaining operating. Planning of dynamic wireless charging for passenger cars on highways is investigated, as a case study. The outcomes show that a low-power system requires high roadway coverage, which increases the infrastructure cost. A high-power system can significantly reduce the road coverage but requires powerful components (converters, coils, and battery), which might be infeasible due to technology limitations.


  1. J. Clairand, J. Rodríguez-García, and C. Álvarez-Bel, “Smart Charging for Electric Vehicle Aggregators Considering Users’ Preferences,” IEEE Access, vol. 6, pp. 54624–54635, 2018, doi: 10.1109/ACCESS.2018.2872725.
  2. Clean Cities U.S. Departmnet of Energy, “Plug-In Electric Vehicle Handbook for Fleet Managers,” Clean Cities U.S. Departmnet of Energy, Apr. 2012.
  3. D. Kettles, “Electric Vehicle Charging Technology Analysis and Standards,” Florida Solar Energy Center, FSEC-CR-1996-15, Feb. 2015.
  4. A. A. S. Mohamed, C. R. Lashway, and O. Mohammed, “Modeling and Feasibility Analysis of Quasi-Dynamic WPT System for EV Applications,” IEEE Transactions on Transportation Electrification, vol. 3, no. 2, pp. 343–353, Jun. 2017, doi: 10.1109/TTE.2017.2682111.
  5. A. A. S. Mohamed, L. Zhu, A. Meintz, and E. Wood, “Planning Optimization for Inductively Charged On-demand Automated Electric Shuttles Project at Greenville, South Carolina,” IEEE Transactions on Industry Applications, pp. 1–1, 2019, doi: 10.1109/TIA.2019.2958566.
  6. J. Suul and G. Guidi, “Technology for dynamic on-road power transfer to electric vehicles,” 2018.
  7. “Annual Vehicle Distance Traveled in Miles and Related Data - 2000 - Table VM-1.” [Online]. Available: [Accessed: 30-Jan-2019].
  8. “About the Transportation Secure Data Center | Transportation Secure Data Center | NREL.” [Online]. [Accessed: 30-Jan-2019].



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Ahmed A S Mohamed

Ahmed A S Mohamed received his B.Sc. and M.Sc. degrees in Electrical Engineering from the Electrical Power and Machines Department, ZU, Egypt, in 2008 and 2012, respectively. He finished his Ph.D. degree in Electrical Engineering at Florida International University (FIU), Miami, FL, USA, in December 2017.

Dr. Mohamed is currently a Research Engineer in the Advanced Vehicles & Charging Infrastructure Group at the National Renewable Energy Laboratory (NREL), Golden, CO, USA. From 2008 to 2013, Dr. Mohamed served as a faculty member at ZU, Egypt. His research focus on wireless power transfer systems, power electronics, transportation electrification, as well as PV power systems. Dr. Mohamed is a Senior IEEE member and he was a recipient of the Outstanding Doctoral Student Award in fall 2017 from FIU.

kevin walkowicz1

Kevin Walkowicz manages the Advanced Vehicle and Charging Infrastructure Group within NREL’s Center for Integrated Mobility Sciences at the National Renewable Energy Laboratory. He leads efforts to develop and implement innovative research approaches to reduce transportation energy consumption and increase the use of domestic renewable resources in the transportation industry. His work focuses on conducting laboratory and in-use research, characterizing data and developing analysis, simulation & visualization tools. His work will ultimately help to assess the impacts and opportunities for improved efficiency of advanced technology medium and heavy-duty vehicle + infrastructure technology. While at NREL, a primary focus of his has been on understanding vehicle operations, assessing electric vehicle efficiency and characterizing fueling and recharging operations through duty cycle analysis. Prior to joining NREL he worked at General Motors Corporation leading emission control development projects. He holds a B.S.M.E from Lawrence Technological University and a M.S. in engineering from Rensselaer Polytechnic Institute.

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