Field-Based Reliability and Battery Lifetime Assessment of Autonomous-Range Trolleybuses

This study presents an empirical fleet-level assessment of 110 autonomous-range trolleybuses using anonymized records collected over 12 months. The dataset comprises 40,150 vehicle-day operating records, 40,150 energy records, 3960 pack-month SOH records, and 584 maintenance, failure, and downtime events. Outcomes are reported in absolute units: RUB/km for LCC, kg CO2-eq/km for ELC, events per 100,000 km, and downtime hours per 10,000 km. Autonomous operation accounted for 24.5% of mileage. Average net energy consumption was 1.520 kWh/km, whereas mode-distributed gross energy was 1.521 kWh/km in contact-supply mode and 1.752 kWh/km in autonomous mode. The daily-energy model achieved a full-sample fit of R2 = 0.860 and MAPE = 8.119%. Validation of vehicle-grouped data using the generated dataset showed R2 = 0.842 and MAPE = 8.74%. Mean SOH decreased from 89.98% to 85.94%, accompanied by higher internal resistance. In the central 6.5-year scenario, diagnostic-gated strategy B2 reduced estimated LCC from 29.52 to 26.16 RUB/km. The event-weighted control effect by RPN decreased from 125.4 to 80.4 (35.9%). Baseline ELC decreased only from 0.6646 to 0.6594 kg CO2-eq/km because operational electricity dominated the total. The contribution is an observation-linked framework that integrates vehicle-day operation, pack-month diagnostics, and event-level maintenance data to compare cost, emissions, and risk under explicit battery-eligibility and service-coverage constraints. The novelty is therefore the empirical, observation-level coupling and joint calibration of existing energy, battery-condition, life-cycle, and reliability methods within one auditable fleet workflow, rather than the introduction of a new standalone degradation or reliability model.

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Publication Details

Journal
World Electric Vehicle Journal
Published
2026-07-22
DOI
https://doi.org/10.3390/wevj17070377
Primary Topic
Electric Vehicles and Infrastructure
Type
article
Field-Weighted Citation Impact
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article

Field-Based Reliability and Battery Lifetime Assessment of Autonomous-Range Trolleybuses

Tatyana Panfilova, M. A. Modina, Nikita V. Martyushev, A S Govorkov et al.
World Electric Vehicle Journal
Electric Vehicles and Infrastructure
article

Field-Based Reliability and Battery Lifetime Assessment of Autonomous-Range Trolleybuses

Tatyana Panfilova, M. A. Modina, Nikita V. Martyushev, A S Govorkov, В С Тынченко, Vitaliy Gladkikh, Boris V. Malozyomov, Valeriya V. Tynchenko
article en

Abstract

This study presents an empirical fleet-level assessment of 110 autonomous-range trolleybuses using anonymized records collected over 12 months. The dataset comprises 40,150 vehicle-day operating records, 40,150 energy records, 3960 pack-month SOH records, and 584 maintenance, failure, and downtime events. Outcomes are reported in absolute units: RUB/km for LCC, kg CO2-eq/km for ELC, events per 100,000 km, and downtime hours per 10,000 km. Autonomous operation accounted for 24.5% of mileage. Average net energy consumption was 1.520 kWh/km, whereas mode-distributed gross energy was 1.521 kWh/km in contact-supply mode and 1.752 kWh/km in autonomous mode. The daily-energy model achieved a full-sample fit of R2 = 0.860 and MAPE = 8.119%. Validation of vehicle-grouped data using the generated dataset showed R2 = 0.842 and MAPE = 8.74%. Mean SOH decreased from 89.98% to 85.94%, accompanied by higher internal resistance. In the central 6.5-year scenario, diagnostic-gated strategy B2 reduced estimated LCC from 29.52 to 26.16 RUB/km. The event-weighted control effect by RPN decreased from 125.4 to 80.4 (35.9%). Baseline ELC decreased only from 0.6646 to 0.6594 kg CO2-eq/km because operational electricity dominated the total. The contribution is an observation-linked framework that integrates vehicle-day operation, pack-month diagnostics, and event-level maintenance data to compare cost, emissions, and risk under explicit battery-eligibility and service-coverage constraints. The novelty is therefore the empirical, observation-level coupling and joint calibration of existing energy, battery-condition, life-cycle, and reliability methods within one auditable fleet workflow, rather than the introduction of a new standalone degradation or reliability model.

World Electric Vehicle JournalVol. 17(7)
Bauman Moscow State Technical University (RU), National Research Tomsk State University (RU), Siberian State Technological University (RU), Tomsk Polytechnic University (RU), Moscow State University of Civil Engineering (RU), Irkutsk National Research Technical University (RU), Admiral Ushakov State Maritime University (RU), Siberian Federal University (RU), Novosibirsk State Technical University (RU)
Affordable and clean energy
Openalex Percentile: Top 15%
Electric Vehicles and Infrastructure
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