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• A probabilistic model for energy estimation with Bayesian machine learning is pro- posed. • A two stage time-dependent EVRP with chance-constrained partial recharging is pre- sented. • Realistic simulations and experiments with data from electric vehicles are performed. • Results indicate high accuracy, reliability and potential for energy savings. Routing electric commercial vehicles requires taking into account their limited driving range, which is affected by several uncertain factors such as traffic conditions. This paper presents the time-dependent Electric Vehicle Routing Problem with Chance-Constraints (EVRP-CC) and partial recharging. The routing method is divided into two stages, where the first finds the best paths and the second optimizes the routes. A probabilistic Bayesian machine learning approach is proposed for predicting the expected energy consumption and variance for the road links, paths and routes. Hence it is possible to consider the uncertainty in energy demand by planning charging within a confidence interval. The energy estimation is validated with data from electric buses driving a public transport route in Gothenburg-Sweden as well as with realistic simulations for 24 hours traffic in the city of Luxembourg connected to a high fidelity vehicle model. Routing solutions are compared with a deterministic formulation of the problem similar to the ones found in the literature. The results indicate high accuracy for the energy prediction as well as energy savings and more reliability for the routes. [ABSTRACT FROM AUTHOR]

Highlights • An energy model is shown including topography, speed, acceleration and powertrain efficiency. • A method to calculate energy cost coefficients for the road network is proposed. • The cost coefficients are incorporated into the vehicle routing problem. • A two-stage approach is presented, integrating path finding with route planning. • Results show that energy and time estimation are more precise than existing methods. Abstract When planning routes for fleets of electric commercial vehicles, it is necessary to precisely predict the energy required to drive and plan for charging whenever needed, in order to manage their driving range limitations. Although there are several energy estimation models available in the literature, so far integration with Vehicle Routing Problems has been limited and without demonstrated accuracy. This paper introduces the Two-stage Electric Vehicle Routing Problem (2sEVRP) that incorporates improved energy consumption estimation by considering detailed topography and speed profiles. First, a method to calculate energy cost coefficients for the road network is outlined. Since the driving cycle is unknown, the model generates an approximation based on a linear function of mass, as the latter is only determined while routing. These coefficients embed information about topography, speed, powertrain efficiency and the effect of acceleration and braking at traffic lights and intersections. Secondly, an integrated two-stage approach is described, which finds the best paths between pairs of destinations and then finds the best routes including battery and time-window constraints. Energy consumption is used as objective function including payload and auxiliary systems. The road cost coefficients are aggregated to generate the path cost coefficients that are used in the routing problem. In this way it is possible to get a proper approximation of the complete driving cycle for the routes and accurate energy consumption estimation. Lastly, numerical experiments are shown based on the road network from Gothenburg-Sweden. Energy estimation is compared with real consumption data from an all-electric bus from a public transport route and with high-fidelity vehicle simulations. Routing experiments focus on trucks for urban distribution of goods. The results indicate that time and energy estimations are significantly more precise than existing methods. Consequently the planned routes are expected to be feasible in terms of energy demand and that charging stops are properly included when necessary. [ABSTRACT FROM AUTHOR]

Residential buildings account for almost a quarter of the total energy use in Sweden and building owners are, therefore, under pressure from policy makers to improve the energy performance of their buildings. Building portfolio owners (BPOs) generally face multiple barriers in energy efficiency investments such as financial constraints and lack of knowledge of the current state when planning energy efficiency measures. This paper presents a method for cost-optimal scheduling of maintenance and retrofit measures on a portfolio level by drawing on research on building stock modeling and maintenance retrofit planning. The method uses a building stock modeling approach to model costs, energy and greenhouse gas emissions (GHG)of a building portfolio and combines this with a method for optimal maintenance and retrofit scheduling in order to forecast and optimize the timing of measures on a building portfolio level. This enables the integrated long-term planning on retrofit investments and reduction of energy demand and GHG emissions for a portfolio of existing buildings. The application to the building portfolio of the municipal housing company of Gothenburg showed that by optimizing the maintenance and retrofit plans, ambitious retrofit measures can be introduced in the majority of the buildings with a positive effect on the service-life cycle costs. Moreover, the method is easily transferable to other building portfolios in Sweden as it builds up on nationally available data sets but is ideally complemented and verified using inspection data and existing maintenance plans of the BPOs in future applications. • This paper presents a method for cost-optimal planning of maintenance and retrofit measures in portfolio level. • The application of the method showed that retrofit measures can be applied in most buildings with a reduction of EAC. • Through optimization, the total EAC of the retrofit scenario can be lowered by 15% compared to a reinstatement scenario. • By applying an ambitious retrofit scenario, the average energy demand can be lowered from 120 to 65 kWh/m2 year by 2050. • The method is easily transferable to other building portfolios in Sweden as it builds up on national data sets. [ABSTRACT FROM AUTHOR]

Building developers are continuously seeking solutions to increase saleable/rentable floor area and thus the profitability of investments, especially in large/dense cities where the real estate/rental values are high and shortage of available land results in smaller building footprints. Application of passive energy efficiency measures (e.g., thick insulation in walls) not only affects the life cycle sustainability of buildings, but also the floor area and its profitability. This can affect the decisions made on the choice of measures when aiming to improve sustainability. In line with limited studies in this context, a case study is presented here in which multi-objective optimization was used to explore the impact of various passive energy efficiency measures on the life cycle sustainability when accounting for the profitability of the floor area. The building case was a high-rise apartment based on a standardized building concept situated in different locations in Sweden, namely Vindeln, Gothenburg, and Stockholm. The findings indicated that, regardless of the location, use of (1) thick cellulose coating for the roof, and (2) moderately thick expanded polystyrene for the floor, were necessary to improve the life cycle sustainability. However, the optimal wall insulation was dependent on the location; in locations with high real estate values, the scope for using thick and conventional insulations (mineral wool/cellulose) was limited due to the significant economic loss caused by floor area reductions. In general, the optimization identified optimal solutions that could save up to 1410.7 GJ energy, 23 tonnes CO2e, and 248.4 TEUR cost from a life cycle perspective relative to the building's initial design. [ABSTRACT FROM AUTHOR]