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Assessing the cost-effectiveness of electric trucks in Indian food supply chains

  • Arush Singh , Atul J. Patil ORCID logo EMAIL logo , Ramesh N. Sharma ORCID logo and Raj K. Jarial ORCID logo
Published/Copyright: March 22, 2024
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International Journal of Emerging Electric Power Systems
From the journal International Journal of Emerging Electric Power Systems Volume 26 Issue 2

Abstract

Fostering green sustainable transportation in the Indian food supply chain with the adoption of electric trucks (ETs) is seemingly a promising alternative. However, before widescale adoption, a thorough cost-benefit analysis is essential for making an apt decision towards transitioning to ETs. Utilizing data of 13 ETs, a total cost of ownership (TCO) model has been developed that outlines the economic benefits of ETs by evaluating the impact of factors like energy consumption, charging strategy, annual driven distances, route optimization, etc. With acquisition costs of ETs constituting 50–55 % of TCO the study outlines that ETs have significantly lower energy costs of 6–12 % (50–58 % for IC-engine trucks) and maintenance costs of 6–8% (13–18 % for IC-engine trucks), thereby reducing the overall TCO. Subsequently, a fleet-level analysis of ET adoption in a food supply chain comprising of four heavy-duty trucks and 16 medium-duty trucks has been performed with emphasis on assessment of supply chain-specific parameters like battery-mass penalty, level of utilization, etc. Overall, this study provides insight for fleet managers, policymakers and industry leaders to promote sustainable through an extended use of ET in the present scenario for the food supply chain.

Keywords: electric trucks (ET); food supply chain (FSC); cost benetfit analysis; Monte Carlo
Corresponding author: Atul J. Patil, Electrical Engineering Department, National Institute of Technology Hamirpur, Hamirpur 177005, HP, India, E-mail:

  1. Research ethics: We affirm that all information provided regarding research ethics is true to the best of our belief.

  2. Author contributions: Arush Singh conceived the research idea, designed the study, conducted data analysis, and drafted the manuscript. Atul J. Patil collected and analyzed data, contributed to the interpretation of results, and revised the manuscript critically for important intellectual content. Ram N. Sharma provided expertise in the field of cost-effectiveness analysis, contributed to the study design, and revised the manuscript for intellectual content. Raj K. Jarial contributed to the literature review, data interpretation, and manuscript revision.

  3. Competing interests: The authors state no conflict of interest.

  4. Research funding: The authors express sincere gratitude to Entellifarm Private Limited, an esteemed agtech startup based in India (DIPP128577), for their invaluable contribution to this research. Their generous sharing of information, insightful perspectives, and practical implications regarding the adoption of electric trucks in the food supply chain have significantly enriched our study. We deeply appreciate their support and collaboration, which have been instrumental in shaping the outcomes of this research endeavor.

  5. Data availability: Not applicable.

Appendix A. Key to abbreviations

TCOF = Total cost of ownership in a food supply chain, ETa = Acquisition cost of an ET, ETb = Cost of acquisition of ET’s battery, EVSE = Cost of electric vehicle supply equipment/cost of charging equipment, TS = Cost of tariffs and subsidies, SV = Salvage cost at the end of operational life, EC = Energy cost, MC = Maintenance cost, IC = Insurance cost, MT = Motor taxes, n = time period of ownership (in years),

r = discount rate (%), V Resale,B = Resale value of a truck at the end of its life (in $), C Purchase,B = Purchase cost of a truck (in $),

S = Salvage value of the ET,

P = Original cost of ET, i = Depreciation rate,

y = Number of years, EC(ICET) = Estimated energy cost for an ICET, FC(FUZZY) = Fuel consumption (in liters),		 AKTt = Age of the truck at the time of resale (in years), BWM = Total useful life of the truck (in years), V End,B = Salvage value of the truck at the end of its useful life (in $), EVSE(charging) = Cost of electric vehicle supply equipment for charging infrastructure, EVSE(swapping) = Cost of electric vehicle supply equipment for swapping infrastructure,

Ftract = Tractive force required to move the vehicle, m = Mass of the vehicle,

V = velocity of the vehicle, t = Time of motion,

dV/dt = Rate of change of velocity with respect to time (acceleration),

ρ = Density of the air,

C_d = Drag coefficient of the vehicle,

A_f = Frontal area of the vehicle,

G = Acceleration due to gravity,

C_rr = Rolling resistance coefficient of the tires,

θ = Slope angle of the road surface, CF(ICET) = Cost of fuel,

M k  = kth manufacturing unit, r i  = ith regional distribution centre,

l j  = local distribution point,

Appendix B
Figure B1: Overall structure of the TCOF computation model.
Figure B1:

Overall structure of the TCOF computation model.

Appendix C
Figure C1: Virtual teardown analysis of the prices (in $) of various components of an ET in 2020, estimated reductions in 2020–25, 2025–30 and projected prices in 2030 [13].
Figure C1:

Virtual teardown analysis of the prices (in $) of various components of an ET in 2020, estimated reductions in 2020–25, 2025–30 and projected prices in 2030 [13].

Appendix D
Table D1:

Cost breakdown of a typical Charging station [49].

S. No Parameter 150 kW DC fast charger 3–5 chargers per site 350 kW DC fast charger

3–5 chargers per site
1 Labor $11,760 $16,240
2 Material cost $16,380 $22,620
3 Cost of permits $105 $145
4 Taxes $67 $92
Total $28,312 $39,037

Appendix E

Table E1:

Cost breakdown of a typical battery swap station [43].

S. No Parameter Swap station cost
1 Swap station cost $1,000,000
2 Swap-ready ET modification $20,000
3 Battery cost $700,000
4 Average battery size 900 kWh
5 Replacement time (in hours) 0.015
6 Charging rate (per hour) 150 kW
7 Battery replacement cost 0.170$/kWh for used energy
0.029$/kWh for unused energy
Appendix F
Figure F1: Variation of fuel consumption with payload for urban, regional, and long-haul cycles.
Figure F1:

Variation of fuel consumption with payload for urban, regional, and long-haul cycles.

Appendix G
Table G1:

Relationship between energy efficiency ratio and vehicle type [43].

Vehicle type & class Average speed (km/h) Diesel (l/km) Electric (kWh/km) Electric (kmple) EER (Cal)
HDT (class 8) 10.62 0.7142 1.31 7.7775 5.5
15.28 0.6756 1.31 7.65 5.1
20.43 0.6060 1.12 9.0525 5.4
30.73 0.6211 1.49 6.5875 4.1
37.65 0.4807 1.30 7.6075 3.7
43.45 0.5617 1.43 6.9275 3.9
55.36 1.3888 4.35 2.295 3.2
61.15 0.3144 9.32 11.05 3.7
80.78 0.4291 1.24 8.16 3.5
MDT (class 5) 19.79 0.2475 0.43 22.23 5.5
22.53 0.2012 0.43 23.88 4.8
LDT (class 2) 24.11 0.1017 0.25 50.14 5.1
18.12 0.0982 0.19 51.98 5.5
Appendix H
Figure H1: Overall structure of transport of goods in an electric truck-based food supply chain.
Figure H1:

Overall structure of transport of goods in an electric truck-based food supply chain.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/ijeeps-2023-0221).

Received: 2023-06-23
Accepted: 2024-03-08
Published Online: 2024-03-22

© 2024 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

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  2. Review
  3. Coupling energy management of power systems with energy hubs through TSO-DSO coordination: a review
  4. Research Articles
  5. Quantitative impact assessment of transmission congestion and demand side management on electricity producers’ market power
  6. A hybrid step-up converter for PV integration with wide input variation acceptability: comprehensive performance and reliability assessment
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https://doi.org/10.1515/ijeeps-2023-0221
Keywords for this article
electric trucks (ET); food supply chain (FSC); cost benetfit analysis; Monte Carlo

Articles in the same Issue

  1. Frontmatter
  2. Review
  3. Coupling energy management of power systems with energy hubs through TSO-DSO coordination: a review
  4. Research Articles
  5. Quantitative impact assessment of transmission congestion and demand side management on electricity producers’ market power
  6. A hybrid step-up converter for PV integration with wide input variation acceptability: comprehensive performance and reliability assessment
  7. Distributed self-healing control of single-phase grounding fault in neutral point non-effective grounding system
  8. Transmission line tower inclination measurement method based on three-dimensional laser scanning and inter frame difference
  9. Assessing the cost-effectiveness of electric trucks in Indian food supply chains
  10. A differential amplitude variation based pilot relaying scheme for microgrid integrated distribution system
  11. Active cooling of a photovoltaic module in hot-ambient temperatures: theory versus experiment
  12. Multi-stage voltage sag frequency evaluation based on process immunity in the distribution network
  13. A new triple voltage gain seven level switched capacitor-based inverter with minimum voltage stress
  14. The planning method of new energy distribution network in plateau area based on local accommodation
  15. An experiment-based comparison of different cooling methods for photovoltaic modules
  16. Simulation and experimental analysis of dynamic thermal rise relaxation characteristics for dry-type distribution transformer
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