Electric Vehicles and Infrastructure

State of the evidence

The evidence establishes a strong link between driver behavior and energy consumption 2, and demonstrates the technical feasibility of dynamic wireless charging to reduce battery weight 1,4. Grid stability is a critical concern as large-scale EV integration creates new load peaks 10, while consumer acceptance of V2G is driven by specific psychological and behavioral factors 5. There is a documented decline in the cost of EVs and PHEVs due to learning rates, though the speed of price parity varies by vehicle type 7.

Key findings

• Dynamic wireless charging allows for smaller, lighter batteries by providing power while the vehicle is in motion 1,4. 1 4
• Driver behavior and driving style significantly impact energy consumption, state-of-charge usage, and overall range 2. 2
• Uncontrolled large-scale EV charging threatens grid stability and increases greenhouse gas emissions if the grid remains fossil-fuel dependent 10. 10
• The cost of electric cars and PHEVs is declining at different learning rates, with electric cars showing a faster decline in price differentials relative to conventional cars 7. 7
• PHEV energy consumption is highly sensitive to daily vehicle miles traveled (DVMT), which follows a gamma distribution 9. 9
• V2G acceptance is influenced by factors aligned with the Theory of Planned Behaviour, including user perceptions and motivations 5. 5

Where the evidence disagrees

• While dynamic charging reduces battery size requirements 1, the infrastructure cost and grid load management 3,10 create a trade-off between vehicle-level efficiency and system-level cost.

Open opportunities

What the research supports that no idea in the catalog takes yet.

• V2G Incentive Optimizer for Consumers

  P4 identifies that consumer acceptance of V2G is a major hurdle; a tool that calculates personalized financial/environmental incentives based on user behavior could bridge this gap.

• Dynamic Charging Route Optimizer for Battery-Constrained EVs

  P0 and P3 establish that dynamic charging allows for smaller batteries. A navigation tool that routes vehicles specifically through wireless charging segments to avoid 'range anxiety' with small batteries is missing.

• PHEV DVMT-Based Energy Budgeting Tool

  P8 validates the gamma distribution of daily vehicle miles traveled (DVMT). A tool for fleet managers to predict energy costs based on this distribution would be highly accurate.

• Driver-Style Energy Coaching Interface

  P1 proves driving style significantly affects range. A real-time feedback system that coaches drivers to adopt energy-efficient styles based on their specific vehicle's SOC could extend range.

References

1. 1.Economic Analysis of the Dynamic Charging Electric VehicleSeungmin Jeong, Young Jae Jang, Dongsuk Kum · 2015 · 370 citations
2. 2.Impact of driving characteristics on electric vehicle energy consumption and rangeChris Bingham, Chris Walsh, Steve Carroll · 2012 · 215 citations
3. 3.Optimal Traffic-Power Flow in Urban Electrified Transportation NetworksWei Wei, Shengwei Mei, Lei Wu et al. · 2016 · 277 citations
4. 4.Survey of the operation and system study on wireless charging electric vehicle systemsYoung Jae Jang · 2018 · 250 citations
5. 5.Factors influencing consumer acceptance of vehicle-to-grid by electric vehicle drivers in the NetherlandsKoen van Heuveln, Rishabh Ghotge, Jan Anne Annema et al. · 2021 · 143 citations
6. 6.Real-Time Energy Management for Diesel Heavy Duty Hybrid Electric VehiclesDezong Zhao, Richard Stobart, Guangyu Dong et al. · 2014 · 104 citations
7. 7.Fully electric and plug-in hybrid cars - An analysis of learning rates, user costs, and costs for mitigating CO2 and air pollutant emissionsMartin Weiss, Andreas Zerfass, Eckard Helmers · 2018 · 113 citations
8. 8.Electric vehicles survey and a multifunctional artificial neural network for predicting energy consumption in all-electric vehiclesBukola Peter Adedeji · 2023 · 73 citations
9. 9.Estimation of Energy Use by Plug-In Hybrid Electric VehiclesZhenhong Lin, Jing Dong, Changzheng Liu et al. · 2012 · 78 citations
10. 10.Energy Management and Optimization of Large-Scale Electric Vehicle Charging on the GridRaymond O. Kene, Thomas O. Olwal · 2023 · 49 citations