Esa Tuviala
Esa Tuviala has a total of 12 co-authors. Here are the top co-authors by number of shared publications:
- Altti Meriläinen
Lappeenranta-Lahti University of Technology LUT
3 shared publications - Jero Ahola
School of Energy Systems, Lappeenranta-Lahti University of Technology (LUT), Lappeenranta, Finland
3 shared publications - Pertti Kauranen
School of Energy Systems, LUT University, Lappeenranta, Finland
3 shared publications - Teemu Hiltunen3 shared publications
- Tuomo Lindh
LUT School of Energy Systems
2 shared publications - Veikka Riipinen2 shared publications
Latest Publications
- Open Access
- 12 October 2026
- Published by Elsevier in SSRN Electronic Journal
Weather-aware energy prediction for battery electric trucks from fleet telematics and road weather data
In regional freight operation, route, payload, and charging plans for battery electric trucks depend on trip energy. In Nordic winters this demand varies with temperature and road-surface state, which vehicle energy models omit by treating auxiliary demand as constant and rolling resistance as independent of weather. This study predicts the trip-level energy consumption of a battery electric truck in commercial service in Finland and Sweden from 18 months of telematics, matched along the route to road-weather analyses of temperature, water film, and surface snow. A calibrated longitudinal-dynamics model is combined with a gradient-boosting correction and evaluated on held-out trips, months, and an unseen winter. The physics residual rises from slightly below zero above 15◦C to 50 kWh/100km below -15◦C, each 1◦C drop adding 1.17 kWh/100km, and neither a constant-power auxiliary load nor a load-proportional rolling loss reproduces it alone. A 0.15 mm water film adds 8.4 kWh/100km and snow cover 6.8 kWh/100km plus 7.8 kWh/100km per centimeter, independently of temperature, so consumption peaks near 0◦C. On unseen trips thecorrection raises the R2 of total consumption from 0.40 to 0.87 and recovers 53% of the residual variance, 41% for an unseen winter, twice what a temperature-dependent auxiliary load achieves. A wet, snowy day at -20◦C shortens the range of a fully loaded truck from about 260 km to 185–195 km. A diesel truck analyzed as a reference shows that fleet fuel-level signals are too coarse for such prediction.
- 12 October 2026
- Published by Elsevier in SSRN Electronic Journal
Open AccessWeather-aware energy prediction for battery electric trucks from fleet telematics and road weather data
In regional freight operation, route, payload, and charging plans for battery electric trucks depend on trip energy. In Nordic winters this demand varies with temperature and road-surface state, which vehicle energy models omit by treating auxiliary demand as constant and rolling resistance as independent of weather. This study predicts the trip-level energy consumption of a battery electric truck in commercial service in Finland and Sweden from 18 months of telematics, matched along the route to road-weather analyses of temperature, water film, and surface snow. A calibrated longitudinal-dynamics model is combined with a gradient-boosting correction and evaluated on held-out trips, months, and an unseen winter. The physics residual rises from slightly below zero above 15◦C to 50 kWh/100km below -15◦C, each 1◦C drop adding 1.17 kWh/100km, and neither a constant-power auxiliary load nor a load-proportional rolling loss reproduces it alone. A 0.15 mm water film adds 8.4 kWh/100km and snow cover 6.8 kWh/100km plus 7.8 kWh/100km per centimeter, independently of temperature, so consumption peaks near 0◦C. On unseen trips thecorrection raises the R2 of total consumption from 0.40 to 0.87 and recovers 53% of the residual variance, 41% for an unseen winter, twice what a temperature-dependent auxiliary load achieves. A wet, snowy day at -20◦C shortens the range of a fully loaded truck from about 260 km to 185–195 km. A diesel truck analyzed as a reference shows that fleet fuel-level signals are too coarse for such prediction.
- Open Access
- 17 September 2026
- Published by Elsevier in Applied Energy
- Vol. 427
Cost comparison of low- and zero-emission heavy-duty vehicles: a case route analysis
- 17 September 2026
- Published by Elsevier in Applied Energy
- Vol. 427
Open AccessCost comparison of low- and zero-emission heavy-duty vehicles: a case route analysis
- Open Access
- 7 October 2025
- Published by Elsevier in Sustainable Futures
- Vol. 10
Multi-layer optimization model to simulate the levelized cost of driving of battery-swapping heavy-duty vehicles
- 7 October 2025
- Published by Elsevier in Sustainable Futures
- Vol. 10
Open AccessMulti-layer optimization model to simulate the levelized cost of driving of battery-swapping heavy-duty vehicles