Energy Uncertainty Analysis of Electric Buses

Uncertainty in operation factors, such as the weather and driving behavior, makes it difficult to accurately predict the energy consumption of electric buses. As the consumption varies, the dimensioning of the battery capacity and charging systems is challenging and requires a dedicated decision-making process. To investigate the impact of uncertainty, six electric buses were measured in three routes with an Internet of Things (IoT) system from February 2016 to December 2017 in southern Finland in real operation conditions. The measurement results were thoroughly analyzed and the operation factors that caused variation in the energy consumption and internal resistance of the battery were studied in detail. The average energy consumption was 0.78 kWh/km and the consumption varied by more than 1 kWh/km between trips. Furthermore, consumption was 15% lower on a suburban route than on city routes. The energy consumption was mostly influenced by the ambient temperature, driving behavior, and route characteristics. The internal resistance varied mainly as a result of changes in the battery temperature and charging current. The energy consumption was predicted with above 75% accuracy with a linear model. The operation factors were correlated and a novel second-order normalization method was introduced to improve the interpretation of the results. The presented models and analyses can be integrated to powertrain and charging system design, as well as schedule planning.Peer reviewe

Kirja-arvosteluja – Book reviews

Brent Miles: Heroic Saga and Classical Epic in Medieval Ireland. Studies in Celtic History 30. Woodbridge: Boydell & Brewer Ltd. 2011.Lisa M. Bitel: Landscape with Two Saints: How Genovefa of Paris and Brigit of Kildare Built Christianity in Barbarian Europe. Oxford: Oxford University Press, 2009.David Jenkins: ’Holy, Holier, Holiest’: The Sacred Topography of the Early Medieval Irish Church. Studia Traditionis Theologiae 4. Turnhout: Brepols, 2010.Theresa C. Oakley: Lifting the Veil: a New Study of the Sheela-na-gigs of Britain and Ireland. BAR British Series 495. Oxford: Archaeopress. 2009.Wooding, J.M., R. Aist, T.O. Clancy & T. O’Loughlin (eds.) Adomnán of Iona: Theologian, Lawmaker, Peacemaker. Dublin: Four Courts Press, 2010

Statistical model of electric bus energy consumption

Dieselbussien pienhiukkaspäästöt aiheuttavat merkittäviä terveyshaittoja kaupungeissa, joissa haitalliset pienhiukkaset purkautuvat hengityskorkeudelle ja jäävät leijumaan rakennusten väliin. Dieselbussien korvaaminen lähipäästöttömällä akkusähköbussilla vähentää ilmansaasteongelmaa. Kilpailukykyisen sähköbussijärjestelmän suunnittelu ja optimointi edellyttävät tarkkaa mallia bussin kulutuksen vaihtelusta ja siihen vaikuttavista tekijöistä. Tässä työssä esitetään reaaliaikaiseen liikenne- ja säätietoon perustuva tilastollinen kulutuksen ennustemalli. Espoossa linjalla 11 koekäytettävän sähköbussin IoT-järjestelmä (Internet of things, suom. esineiden internet) mittasi 14 viikon ajanjaksolla akun parametreja ja ajoneuvon sijaintia sekä nopeutta. Mittaukset siirtyivät reaaliajassa palvelimelle, josta ne olivat ladattavissa pilvipalvelun kautta. Liikennetilanne määritettiin matkapuhelimien sijaintitiedoista, jotka ladattiin Google Maps API -rajapinnasta. Mittauksiin sovitettiin lineaarinen monimuuttujaregressiomalli. Keskimääräinen energiankulutus reitillä oli 0,77 kWh/km ja suurin kulutus yli kak-sinkertainen verrattuna pienimpään. Lämpötila ja liikenne selittivät kulutuksen vaihtelua tilastollisesti merkitsevästi. Lämpötila selitti kulutuksen vaihtelusta 28 prosenttia, mutta liikenne vain neljäsosan tästä. Lämpötilan noustessa yhden asteen kulutus laski keskimäärin 12,5 Wh/km. Minuutin positiivinen muutos liikennemäärään perustuvassa matka-ajan odotusarvossa liittyi 28,3 Wh/km kasvuun kulutuksessa. Työn tulokset osoittavat, että avoimen datan palvelujen reaaliaikaista liikenne- ja lämpötiladataa voidaan hyödyntää sähköbussin kulutuksen ennustamiseen. Malli on yleistettävissä muille reiteille lämpötilan osalta, jonka keskivirhe oli vain 1,9 Wh/km ja joka ei ollut riippuvainen reitin nopeusprofiilista. Liikennemäärän kulutusvaikutukseen liittyvä epävarmuus edellyttää mallin kalibrointia reittikohtaisilla mittauksilla.Particle emissions from internal combustion engine buses cause significant health problems in cities, where noxious particle stay trapped between buildings and are inhaled by people. Battery-powered electric buses can solve the pollution problem. However, engineering an economically competitive electric bus system constitutes a challenge as having an accurate understanding of the variation in bus energy consumption is required. A predictive energy consumption model for an electric bus, based on real-time traffic and weather data, is introduced in this thesis. Battery current and voltage, as well as vehicle velocity, were measured for 14 weeks from a battery electric bus operating in Espoo route 11. The bus IoT-system (Internet of Things) transferred the measurements in real time to a cloud server. Real-time mobile phone location data was utilised to determine congestion levels. A multivariable linear regression model was fitted to the data. Mean energy consumption was 770 Wh/km and maximum value more than twice the minimum. Both traffic and temperature were statistically significant variables in the regression model. Temperature explained 28% of the variance in consumption but traffic only one fourth of this. A one degree increase in temperature was related to 12,5 Wh/km decrease in consumption, while a one minute increase in expected travel time yielded a 28,3 Wh/km increase. Results indicate that traffic and temperature data may be successfully utilised to predict electric bus energy consumption. The model can be generalized to other routes with respect to temperature, which had small error and was not dependent on route velocity profile. However, the uncertainty related to the impact of traffic level to energy consumption indicates that the model needs to be calibrated with route specific traffic data

Marine vessel powertrain design optimization: Multiperiod modeling considering retrofits and alternative fuels

Over the coming decades, maritime transportation will transition from fossil hydrocarbon fuels to hydrogen, ammonia, and synthetic hydrocarbon fuels produced using renewable electricity as the primary energy source. In this context, a shipowner needs to identify a cost-efficient plan for the adoption of alternative fuels and onboard energy conversion system retrofits. This paper presents a multiperiod decision model for the selection of energy system components under increasingly stringent CO2 emissions regulations and cost forecasts over a multidecade planning horizon. The model considers the choice of newbuild architecture, timing of retrofits, component sizes, and allocation of fuels to converters with the objective of minimizing total cost of ownership (TCO). The decision problem is formulated as a discrete time multiperiod mixed-integer linear program. The application of the model is numerically illustrated for a Baltic Sea roll-on/roll-off ferry. The main findings are: (i) modifying the energy system with retrofits obtains 43% lower TCO compared to fuel switching alone; (ii) batteries contribute to 23% lower TCO; (iii) optimal component installation period can be shorter than their maximum lifetime; (iv) running an engine with hydrogen is favored over fuel cells and (v) hybrid propulsion is the key future-proofing design choice for short sea vessels.Peer reviewe