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Energy Consumption and Driving Range Estimation of Catenary-Free Light Rail Vehicles Using an Integrated Model: Case Study of Khon Kaen Red Line Transit System

IMPACT SIGNAL75/100
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Information from the abstract

Battery-powered catenary-free light rail vehicles (CF-LRVs) are increasingly adopted as sustainable urban transport solutions, offering advantages such as the elimination of overhead catenary infrastructure, reduced visual intrusion, and enhanced operational flexibility. However, the maximum driving range achievable per charge remains a critical challenge for system design and operation. This range is influenced by factors including route profile, train dynamics, tractive effort, running resistance, traction system efficiency, battery state of charge (SOC), auxiliary power demand, and regenerative braking capability. This study presents an integrated mathematical model for estimating the energy consumption and maximum driving range of a CF-LRV. The model incorporates train dynamics, tractive effort, battery modeling, SOC, regenerative braking, and energy consumption into a unified analytical framework. It is applied to the Khon Kaen Province Red Line Light Rail Transit system in Thailand, with simulations conducted using realistic operating conditions such as station spacing, speed profiles, route gradients, passenger loading, and auxiliary power requirements. Results show that the system achieves a specific energy consumption of 1.90–2.30 kWh/km. Under AW1 loading condition, regenerative braking recovers 31.50% of the traction and auxiliary energy, resulting in a 31.17% increase in driving range compared with the non-regenerative case. With an onboard battery capacity of 240 kWh, the vehicle can travel up to 82.59 km on a single charge. The proposed model provides practical applications in battery sizing, operational planning, and energy management of CF-LRVs, thereby supporting the development of efficient and sustainable battery-powered urban rail systems.

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Why this record is monitored

This record has an Impact Signal of 75/100 based on recency, source, collaboration, and bibliographic signals. It prioritizes monitoring and is not a judgment of research quality.

Related topics: Railway Systems and Energy Efficiency · Railway Engineering and Dynamics · Electric and Hybrid Vehicle Technologies

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Thai researcher and institutional participation

Paiwan Kerdtuad · Kwanjai Nachaiyaphum · Kunjana Chaiamarit · Thanatchai Kulworawanichpong · Rajamangala University of Technology Isan, Khon Kaen Campus · Suranaree University of Technology

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Data limitations

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