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REVIEW 3 major objections 2 minor 32 references

Coupled Routing and Charge Schedule Optimization of Electrified Delivery Truck Fleets: Feasibility Analyses

T0 review · 3 major / 2 minor · reviewed 2026-06-28 · grok-4.3

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Pith's one-line read An optimal battery pack capacity exists for electrified delivery truck fleets due to trade-offs in cost, lifespan, weight and energy consumption.

desk verdict The paper applies standard coupled routing and charging optimization to Class 7-8 trucks, finds an optimal battery capacity from cost-weight-energy trade-offs, and notes viability at low penetration with cheap depot power, but both results rest on the simulation model's accuracy and fast-charging availability. read the letter →

arxiv 2606.00792 v1 pith:6EYF5G3B submitted 2026-05-30 math.OC

Yared Tadesse Wendimagegnehu , Beshah Ayalew , Andrej Ivanco , Habtamu Hailemichael This is my paper
classification math.OC
keywords electrifiedfleetsroutingoptimizationchargeschedulingbatterycapacityenergyconsumptionmodelingdeliverytruckscostfeasibilityfleetpenetration
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper couples routing decisions with charge scheduling to assess the cost feasibility of electrifying delivery truck fleets in urban areas. It builds a data-driven energy consumption model from powertrain simulations for Class 7-8 trucks. Analyses reveal an optimal battery size arising from opposing effects of larger packs on purchase cost, longevity, added weight, and consumption. The results also indicate that falling battery prices combined with low depot electricity rates can make electrification economical even when only a small fraction of the fleet is electric.

What carries the argument

Coupled routing and charge scheduling optimization that minimizes total amortized daily cost subject to battery range and charging time constraints, informed by a data-driven energy consumption model.

What would settle it

Observing that real Class 7-8 trucks consume substantially more or less energy than predicted by the model on representative urban delivery cycles, or that depot fast charging is unavailable, would undermine the identified optimal capacity and cost viability conclusions.

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Extended reading notes

Core claim

Using coupled optimization of routes and charging with a simulation-based energy model, the work identifies an optimal battery capacity for current technology and demonstrates that fleet electrification can reach cost parity with diesel operations at low penetration levels when depot electricity is inexpensive.

Load-bearing premise

The analyses assume that fast charging infrastructure is available at the depot and that the data-driven energy model from powertrain simulations accurately reflects real-world truck performance on the drive cycles considered.

Editorial extensions

If this is right

  • Larger battery packs increase upfront costs and vehicle weight, raising energy use and reducing lifespan benefits beyond a certain point.
  • Declining battery costs and access to cheap depot electricity improve the economics of partial fleet electrification.
  • Availability of fast charging at the depot is essential for maintaining operational feasibility.
  • The optimal capacity depends on the specific drive cycles and energy density of the batteries used.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Extending the model to include variable electricity prices throughout the day could reveal further scheduling benefits.
  • The findings suggest potential for microgrid integration to enhance viability without full fleet conversion.
  • Validation against real-world operational data would test whether the simulated energy model holds.
  • Similar optimization frameworks might apply to other heavy vehicle sectors like buses or construction equipment.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 2 minor

Summary. The manuscript develops a data-driven energy consumption model for Class 7-8 electric and diesel trucks from powertrain simulations on multiple drive cycles, then embeds this model in a coupled routing-plus-charge-scheduling optimization. It sweeps battery pack capacity, penetration level, battery cost, and depot electricity price to compute amortized daily fleet cost, concluding that an optimal pack capacity exists due to opposing effects on capital cost, cycle life, weight, and consumption, and that electrification becomes viable at low penetration under improving cost trends and reduced depot electricity prices (e.g., via microgrids).

Significance. If the consumption surface and optimization are shown to be robust, the work supplies concrete, quantitative guidance on battery sizing and depot-charging strategies that logistics operators can use to evaluate electrification pathways, particularly under realistic urban delivery constraints.

major comments (3)
  1. [Energy consumption model (abstract and §3)] The headline claim of an optimal battery pack capacity rests on the functional dependence of kWh/mi on pack mass that is produced by the data-driven powertrain model. No equation, fitted surface, or validation metric (R², cross-validation error, or comparison to real Class 7-8 data) is supplied in the abstract or described in sufficient detail to allow independent reproduction or sensitivity testing of the reported optimum.
  2. [Feasibility analyses (abstract and §4)] The cost-viability conclusions at low penetration levels presuppose the availability of depot fast charging. The manuscript does not report any sensitivity analysis or alternative runs in which fast charging is unavailable or limited, yet removal of this assumption would shift the cost curves and could eliminate or relocate the reported optimum.
  3. [Optimization formulation (abstract and §2)] The optimization formulation itself is not stated (objective, decision variables, constraints on routing, charging windows, or battery state-of-charge). Without the mathematical program or its solution method, it is impossible to assess whether the reported cost numbers are produced by a correctly solved model or by an artifact of the chosen solver/approximation.
minor comments (2)
  1. [Abstract] The abstract contains a subject-verb agreement error: 'limitations and … costs … imposes' should read 'impose'.
  2. [Energy consumption model] Drive-cycle names, number of cycles, and the precise Class 7-8 vehicle parameters used to generate the simulation data should be listed in a table for reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their thorough review and constructive feedback on our manuscript. We address each of the major comments below and outline the revisions we plan to make.

read point-by-point responses

  1. Referee: [Energy consumption model (abstract and §3)] The headline claim of an optimal battery pack capacity rests on the functional dependence of kWh/mi on pack mass that is produced by the data-driven powertrain model. No equation, fitted surface, or validation metric (R², cross-validation error, or comparison to real Class 7-8 data) is supplied in the abstract or described in sufficient detail to allow independent reproduction or sensitivity testing of the reported optimum.

    Authors: We agree that providing the explicit form of the energy consumption model and its validation would enhance the manuscript's reproducibility. The data-driven model was developed from powertrain simulations on multiple drive cycles, and the dependence on pack mass arises from the simulated energy consumption data. In the revised version, we will include the fitted equation or surface, along with R² values and any available comparisons to real-world Class 7-8 truck data in Section 3. revision: yes

  2. Referee: [Feasibility analyses (abstract and §4)] The cost-viability conclusions at low penetration levels presuppose the availability of depot fast charging. The manuscript does not report any sensitivity analysis or alternative runs in which fast charging is unavailable or limited, yet removal of this assumption would shift the cost curves and could eliminate or relocate the reported optimum.

    Authors: The analyses in the manuscript are conducted under the assumption of fast charging availability at the depot, as stated. We recognize that this is a key assumption, and without it, the viability at low penetration may change. We will add a sensitivity analysis or discussion section exploring scenarios with limited or no fast charging to address this concern. revision: yes

  3. Referee: [Optimization formulation (abstract and §2)] The optimization formulation itself is not stated (objective, decision variables, constraints on routing, charging windows, or battery state-of-charge). Without the mathematical program or its solution method, it is impossible to assess whether the reported cost numbers are produced by a correctly solved model or by an artifact of the chosen solver/approximation.

    Authors: Section 2 of the manuscript presents the coupled routing and charge scheduling optimization problem. The objective is to minimize the amortized daily total cost, with decision variables including routes, charging schedules, and battery state-of-charge over time. Constraints include routing feasibility, charging time windows, and SOC limits. We will revise to explicitly state the full mathematical program, including the objective function, variables, and constraints, to improve clarity and allow assessment of the solution method. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity; results from external simulations and optimization

full rationale

The paper constructs an energy consumption model from detailed powertrain simulations on drive cycles, then feeds it into a coupled routing and charge-scheduling optimization to sweep battery capacities and costs. No equations, fitted predictions, or self-citations are shown that reduce the reported optimal capacity or viability conclusions to quantities defined by the authors' own prior parameters. The derivation remains self-contained against the external simulation data and stated assumptions about depot fast charging.

Assumptions & free parameters 2 free parameters · 2 assumptions · 0 invented entities

Abstract-only review yields limited visibility into parameters and assumptions; the ledger below captures the main elements inferable from the provided text.

free parameters (2)
  • battery pack capacity
    Treated as a variable whose value is optimized or swept to balance cost, weight, lifespan, and energy use; specific tested values not stated in abstract.
  • electricity price at depot
    Varied to include reduced rates from renewable microgrids; concrete price points not provided.
assumptions (2)
  • domain assumption Powertrain simulation data accurately represent real-world energy consumption of Class 7-8 trucks on the drive cycles considered.
    Used to generate the energy model that drives the routing and scheduling optimization.
  • domain assumption Fast charging is available at the depot for the fleet.
    Explicitly stated as a consideration in the cost analyses.

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Cite this review

Pith. "Pith review of Coupled Routing and Charge Schedule Optimization of Electrified Delivery Truck Fleets: Feasibility Analyses." pith.science (2026). https://pith.science/paper/6EYF5G3B

@misc{pith2026260600792,
  author       = {Pith},
  title        = {Pith review of: Coupled Routing and Charge Schedule Optimization of Electrified Delivery Truck Fleets: Feasibility Analyses},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6EYF5G3B}},
  note         = {Machine review of arXiv:2606.00792}
}
read the original abstract

Electrifying truck fleets has the potential to improve energy efficiency and reduce carbon emissions from the freight transportation sector. However, the range limitations and substantial capital costs with current battery technologies imposes constraints that challenge the overall cost feasibility of electrifying fleets for logistics companies. In this paper, we investigate the coupled routing and charge scheduling optimization of a delivery fleet serving a large urban area as one approach to discovering feasible pathways. To this end, we first build an improved energy consumption model for a Class 7-8 electric and diesel truck using a data-driven approach of generating energy consumption data from detailed powertrain simulations on numerous drive cycles. We then conduct several analyses on the impact of battery pack capacity, cost, and electricity prices on the amortized daily total cost of fleet electrification at different penetration levels, considering availability of fast charging at the depot. Findings indicate that at typical energy density of current battery technology, there is an optimal battery pack capacity that results from the contradicting effects of increasing pack capacity on cost, life span, weight and energy consumption. It is also observed that with currently improving trends in battery pack costs and availability of reduced electricity prices at the depot, such as with renewable microgrids, fleet electrification can become viable even at low levels of penetration.

Figures

Figures reproduced from arXiv: 2606.00792 by the authors.

Figure 6
Figure 6. Operational cost per mile of serving a 150 customer with 10 trucks at battery pack price of $240/kWh and fuel price of $3.86/gal. Overall, these results show that making delivery fleet electrification competitive requires lower electricity prices to even take advantage of lower battery pack prices, which are trending in the right direction in the marketplace [32]. An equivalent interpretation is that coupled routing… view at source ↗
Figure 10
Figure 10. Total daily mileage of the fleet to serve the 79 customers (battery pack price of $240/kWh, fuel price of $3.65/gal and electricity price of $0.075/kWh). Summary/Conclusions The electrification of local delivery trucks has a clear potential for improving the energy efficiency and climate impact of freight transportation. This paper analyzes the total amortized daily cost of using electrified fleets for large-scale d… view at source ↗

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Works this paper leans on

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