Wireless charging and shared autonomous battery electric vehicles (W+SABEV): synergies that accelerate sustainable mobility and greenhouse gas emission reduction

Abstract

Emerging technologies play important roles in shaping future mobility systems and impacting sustainability performance of the transportation sector in major economies, such as the United States of America (USA) and China. This study applies a life cycle framework to demonstrate and evaluate the synergies of the following four emerging transportation system technologies both qualitatively and quantitatively: (1) wireless charging; (2) shared mobility services; (3) autonomous driving; and (4) battery electric vehicles (BEV). The new concept of a wireless charging and shared autonomous battery electric vehicle (W+SABEV) system is introduced and modeled. First, an analytical framework is presented to assess the pros and cons of the W+SABEV system vs. a conventional plug-in charging BEV system, adhering to the principles of sustainable mobility and highlighting the impacts and dynamics of the disruptive technologies on the key parameters that define sustainable mobility. Second, a quantitative analysis presents the synergies of the four technologies by modeling a W+SABEV system and demonstrates that the combination of the four technologies can shorten the payback time of greenhouse gas (GHG) emission burdens for infrastructure and vehicles. Compared to a plug-in charging BEV system, a W+SABEV system pays back the additional GHG emission burdens of wireless charging infrastructure deployment within 5 years if the wireless charging utility factor (ratio of en route charging time vs. trip time) is above 19%.

Appendix

This Appendix provides detailed model equations and calculations. To compare the system performance of the base case of a plug-in charging BEV fleet vs. a W+SABEV fleet, it is assumed that both systems will provide mobility services for a fixed total passenger travel per day, i.e., Φ = 12500 passengers per day, as shown in Eq. (1). Φ is a product of average number of passengers per trip (${\overline{n}}_{\mathrm{passenger}}$ for the base case and ${{\overline{n}}^{\prime}}_{\mathrm{passenger}}$ for the W+SABEV), average number of trips per vehicle per day (${\overline{n}}_{\mathrm{trip}}$ for the base case and ${{\overline{n}}^{\prime}}_{\mathrm{trip}}$ for the W+SABEV), and number of vehicles (n_veh for the base case and ${n}_{\mathrm{veh}}^{\prime }$ for the W+SABEV). The average mileage per vehicle per day ($\overline{L}$ for the base case and ${\overline{L}}^{\prime }$ for the W+SABEV) can be calculated using Eq. (2) to Eq. (5) with the average miles per trip (${\overline{d}}_{\mathrm{trip}}$ = 10 miles/trip), miles of detour per passenger (${\overline{d}}_{\mathrm{passenger}}$ = 1 mile/passenger), smart routing (eco-driving) factor θ, range extension factor β, and ridership factor κ.

$$ \varPhi ={\overline{n}}_{\mathrm{passenger}}{\overline{n}}_{\mathrm{trip}}{n}_{\mathrm{veh}}={{\overline{n}}^{\prime}}_{\mathrm{passenger}}{{\overline{n}}^{\prime}}_{\mathrm{trip}}{n}_{\mathrm{veh}}^{\prime } $$

(1)

$$ \overline{L}=\frac{\varPhi }{{\overline{n}}_{\mathrm{passenger}}{n}_{\mathrm{veh}}}\left({\overline{d}}_{\mathrm{trip}}+{\overline{d}}_{\mathrm{passenger}}{\overline{n}}_{\mathrm{passenger}}\right) $$

(2)

$$ \beta =\frac{{\overline{L}}^{\prime }}{\overline{L}} $$

(3)

$$ \kappa =\frac{{{\overline{n}}^{\prime}}_{\mathrm{passenger}}}{{\overline{n}}_{\mathrm{passenger}}} $$

(4)

$$ {\overline{L}}^{\prime }=\frac{\varPhi }{{\overline{n}}_{passenger}^{\prime }{n}_{veh}^{\prime }}\left({\overline{d}}_{trip}+{\overline{d}}_{passenger}{\overline{n}}_{passenger}^{\prime}\right)\theta $$

(5)

The average BEV battery capacity for the base case c_B is defined by Eq. (6), where e is the average energy consumption rate of a battery electric vehicle (0.33 kWh/mile) and ε is the state of charge window (60%), i.e., the operating capacity relative to the nameplate capacity. The autonomous driving device consumes additional energy e_AV(kWh/mile), which is assumed to be a proportion (μ = 10%) of e, as shown in Eq. (7). The time of en route charging ${\overline{T}}_{\mathrm{SWPT}}$ (hours of charging per vehicle per day) at SWPT charging spots can be calculated by multiplying the utility factor k_SWPT (probability of encountering a SWPT charging spot), number of stops at traffic intersections per day $\frac{{\overline{L}}^{\prime }}{{\overline{d}}_{\mathrm{stop}}}$ (where ${\overline{d}}_{\mathrm{stop}}$ is the average distance between stops), and average stoppage time at a traffic intersection (${\overline{t}}_{\mathrm{SWPT}}$), as shown in Eq. (8). Similarly, the time of en route charging ${\overline{T}}_{\mathrm{DWPT}}$ (hours of charging per vehicle per day) on DWPT charging lanes can be calculated by multiplying the utility factor k_DWPT (probability of driving on a DWPT lane) and total vehicle operating time per day $\frac{{\overline{L}}^{\prime }}{\overline{v}}$ (where $\overline{v}$ is average vehicle speed), as shown in Eq. (9). The average battery capacity for a vehicle in the W+SABEV fleet ${c}_{\mathrm{B}}^{{\prime\prime} }$ can be calculated using Eq. (10) to Eq. (13). The base case battery capacity c_B needs to be adjusted by taking into account the following factors: (1) those en route charging (${\eta}_{\mathrm{SWPT}}{\eta}_{\mathrm{B}}P{\overline{T}}_{\mathrm{SWPT}}$ and ${\eta}_{\mathrm{DWPT}}{\eta}_{\mathrm{B}}P{\overline{T}}_{\mathrm{DWPT}}$) can downsize the battery; (2) vehicle weight change resulting from battery weight change, equipment weight change, and passengers’ weight change; and (3) energy consumption rate change due to vehicle weight change, smart driving, and additional energy consumption by autonomous driving device. η_SWPT is the SWPT grid-to-battery charging efficiency, η_B is the battery charge/discharge efficiency (90%), P is the actual power rate (30 kW), η_DWPT is the grid-to-battery DWPT charging efficiency, c_{B, min} is the assumed minimum battery capacity (20 kWh), ρ is 0.156 kWh/kg of lithium ion battery of a Tesla Model S, ${\overline{\varDelta}}_e$ is the average change in energy consumption rate (kWh/mile),w_person is the average weight of a person (79 kg),w_OW is the weight of an onboard wireless charger (16 kg), w_AV is the weight of autonomous driving device (16–22 kg) (Gawron et al. 2018),w_veh is the weight of a base case vehicle (1595 kg), ϕ is the lightweighting correlation (6% energy consumption reduction per 10% vehicle weight reduction) (Bi et al. 2015), λ is a factor characterizing the energy saving due to smart routing (eco-driving) and better speed control of autonomous vehicles and is assumed to equal θ, e^′ is the energy consumption rate (kWh/mile) for a W+SABEV, ${c}_{\mathrm{B}}^{\prime }$ is the intermediate calculated battery capacity for a W+SABEV, and ${c}_{\mathrm{B}}^{{\prime\prime} }$ is the final calculated battery capacity of a W+SABEV.

$$ {c}_{\mathrm{B}}=\frac{\overline{L}\cdot e}{\varepsilon } $$

(6)

$$ {e}_{\mathrm{AV}}=e\cdot \mu $$

(7)

$$ {\overline{T}}_{\mathrm{SWPT}}={k}_{\mathrm{SWPT}}\cdot \frac{{\overline{L}}^{\prime }}{{\overline{d}}_{\mathrm{stop}}}\cdot {\overline{t}}_{\mathrm{SWPT}} $$

(8)

$$ {\overline{T}}_{\mathrm{DWPT}}={k}_{\mathrm{DWPT}}\cdot \frac{{\overline{L}}^{\prime }}{\overline{v}} $$

(9)

$$ {c}_B^{\prime }=\max \left\{\frac{\left({\overline{L}}^{\prime}\cdot \left(e+{e}_{\mathrm{AV}}\right)-{\eta}_{\mathrm{SWPT}}{\eta}_BP{\overline{T}}_{\mathrm{SWPT}}-{\eta}_{\mathrm{DWPT}}{\eta}_BP{\overline{T}}_{\mathrm{DWPT}}\right)}{\varepsilon },{c}_{B,\min}\right\} $$

(10)

$$ {\overline{\varDelta}}_e=\frac{\left({c}_B-{c}_B^{\prime}\right)/\rho +\left({w}_{\mathrm{person}}{\overline{n}}_{\mathrm{passenger}}+{w}_{\mathrm{person}}\right)-\left({w}_{\mathrm{person}}{{\overline{n}}^{\prime}}_{\mathrm{passenger}}+{w}_{\mathrm{OW}}+{w}_{\mathrm{AV}}\right)}{w_{\mathrm{veh}}}\cdot \phi \cdot e $$

(11)

$$ {e}^{\prime }=\lambda \cdot \left(e-{\overline{\varDelta}}_e\right) $$

(12)

$$ {c}_B^{{\prime\prime} }=\max \left\{\frac{{\overline{L}}^{\prime}\cdot \left({e}^{\prime }+{e}_{\mathrm{AV}}\right)-{\eta}_{\mathrm{SWPT}}{\eta}_BP{\overline{T}}_{\mathrm{SWPT}}-{\eta}_{\mathrm{DWPT}}{\eta}_BP{\overline{T}}_{\mathrm{DWPT}}}{\varepsilon },{c}_{B,\min}\right\} $$

(13)

Finally, the breakeven time τ (days) is calculated by letting f_net = f_burdens − f_savings = 0 and solving Eq. (14) to Eq. (19). f_net,f_burdens, and f_savings are the cumulative net emissions (kg CO₂-eq), additional burdens of W+SABEV infrastructure and device (kg CO₂-eq), and additional savings of W+SABEV (kg CO₂-eq), respectively.n_SWPT is number of traffic intersections with SWPT and ζ_DWPT is the total mileage of DWPT lanes. σ_{SWPT, base} (453 kg CO₂-eq), σ_{DWPT, base} (1,184,000 kg CO₂-eq/lane-mile), and σ_{OW, base} (103 kg CO₂-eq) are the burdens of a 6 kW (P_base) wireless charger (Bi et al. 2015) and σ_AV (1300 kg CO₂-eq) is the burden of an autonomous vehicle device (Gawron et al. 2018). $\overline{l}$ is the actual average length of SWPT at a traffic intersection and l_base is the base length (10 m). s_V is the savings from fleet size reduction (kg CO₂-eq), s_B is the battery savings (kg CO₂-eq), and s_E is the electricity savings (kg CO₂-eq). σ_veh is the vehicle production and manufacturing burden without battery (7000 kg CO₂-eq) (Kim et al. 2016) and σ_B is the battery production and manufacturing burden (32.64 kg CO₂-eq/kWh) (Bi et al. 2015). η_PC is the plug-in charging efficiency from grid-to-battery (90%). ${\overline{\sigma}}_{\mathrm{E},\mathrm{night}}$ and ${\overline{\sigma}}_{\mathrm{E},\mathrm{day}}$ are average nighttime and daytime electricity emission intensities (kg CO₂-eq/kWh), respectively (U.S. EPA 2016).

$$ {f}_{\mathrm{net}}={f}_{\mathrm{burdens}}-{f}_{\mathrm{savings}} $$

(14)

$$ {f}_{\mathrm{burdens}}={n}_{\mathrm{SWPT}}{\sigma}_{\mathrm{SWPT},\mathrm{base}}\frac{P}{P_{\mathrm{base}}}\frac{\overline{l}}{l_{\mathrm{base}}}+{\zeta}_{\mathrm{DWPT}}{\sigma}_{\mathrm{DWPT},\mathrm{base}}\frac{P}{P_{\mathrm{base}}}+{n}_{\mathrm{veh}}^{\prime }{\sigma}_{\mathrm{OW},\mathrm{base}}\frac{P}{P_{\mathrm{base}}}+{n}_{\mathrm{veh}}^{\prime }{\sigma}_{\mathrm{AV}} $$

(15)

$$ {f}_{\mathrm{savings}}={s}_V+{s}_B+{s}_E\cdot \tau $$

(16)

$$ {s}_V={n}_{\mathrm{veh}}{\sigma}_{\mathrm{veh}}-{n}_{\mathrm{veh}}^{\prime }{\sigma}_{\mathrm{veh}} $$

(17)

$$ {s}_B={n}_{\mathrm{veh}}{c}_B{\sigma}_B-{n}_{\mathrm{veh}}^{\prime }{c}_B^{{\prime\prime} }{\sigma}_B $$

(18)

$$ {s}_E={n}_{\mathrm{veh}}\frac{\overline{L}\cdot e}{\eta_{PC}{\eta}_B}\cdot {\overline{\sigma}}_{\mathrm{E},\mathrm{night}}-{n}_{\mathrm{veh}}^{\prime}\left[P\left({\overline{T}}_{\mathrm{SWPT}}+{\overline{T}}_{\mathrm{DWPT}}\right)\cdot {\overline{\sigma}}_{\mathrm{E},\mathrm{day}}+\frac{{\overline{L}}^{\prime}\cdot \left({e}^{\prime }+{e}_{\mathrm{AV}}\right)-{\eta}_{\mathrm{SWPT}}{\eta}_BP{\overline{T}}_{\mathrm{SWPT}}-{\eta}_{\mathrm{DWPT}}{\eta}_BP{\overline{T}}_{\mathrm{DWPT}}}{\eta_{\mathrm{SWPT}}{\eta}_B}\cdot {\overline{\sigma}}_{\mathrm{E},\mathrm{night}}\right] $$

(19)