Acceleration and Regenerative Braking Energy of Electric Trains: A Review for Energy-Management Decision Making

FISCHER, Szabolcs (2026) Acceleration and Regenerative Braking Energy of Electric Trains: A Review for Energy-Management Decision Making. DECISION MAKING ADVANCES, 4 (1). pp. 228-256. ISSN 2956-2384

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Abstract

Optimizing traction energy is central to decarbonizing electrified railways, where two quantities dominate a train's electricity balance: energy consumed during acceleration and recovered during regenerative (recuperative) braking. Both are based on calculations or measurements, yet inconsistent definitions, metering boundaries, and reporting conventions make published values difficult to compare for energy-management decision-making. This paper reviews the possibilities for calculating and measuring electric locomotives and multiple units across 100 publications. A two-axis framework crosses energy type with analytical task, cross-cut by alternating versus direct-current supply and locomotive-hauled versus distributed-traction trains. For acceleration, it covers the multiplicative correction-factor method, effective mass and running resistance formulations, traction-measuring cars, onboard logging, optical character recognition, inertial sensors, and machine learning. For regeneration, it contrasts analytical, simulation-based, and data-driven calculations with onboard, wayside, and hybrid measurements, network receptivity, and usable energy. Measured energy depends strongly on sampling rate and metering boundary; wayside measurements underestimate recovery on direct-current systems; multiple units recover more efficiently than locomotive-hauled trains; and driving style and timetable structure dominate. Representative magnitudes recur: acceleration-energy correction factors of about 1.3 (range ≈1.25–1.5), regenerative-chain efficiencies of roughly 0.70–0.85, and recoverable braking energy of 10–30% of traction energy. The chief obstacle to comparability and reliable decision-making is methodological rather than technological; high-frequency, position-synchronized measurement, multi-train simulation, train-specific efficiency data, and harmonized metrics define best practice.

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