Alternative powertrains are rapidly increasing in popularity in city buses. Hence, it is vitally important to understand the factors affecting the performance of the powertrains in order to operate them on appropriate routes and as efficiently as possible. To that end, this paper presents an exhaustive driving cycle and passenger load sensitivity analysis for the most common city bus powertrain topologies. Three-thousand synthetic cycles were generated for a typical suburban bus route based on measured cycles and passenger numbers from the route. The cycles were simulated with six bus models: compressed natural gas, diesel, parallel hybrid, series hybrid, hydrogen fuel cell hybrid, and battery electric bus. Twenty reference cycles featuring various types of routes were simulated for comparison. Correlations between energy consumption and the various driving cycle parameters and passenger loads were examined. Further analysis was conducted with variance decomposition. Aggressiveness and stop frequency had the highest correlation with the consumption. The diesel bus was the most sensitive to aggressiveness. The parallel hybrid had a lower statistical dispersion of consumption than the series hybrid on the suburban route. On the varied routes, the opposite was true. The performance of the parallel hybrid powertrain deteriorated significantly on cycles with high aggressiveness and stop frequency. In general, the high correlation between aggressiveness and energy consumption implies that particular attention must be paid to limiting high-speed accelerations of city buses.

Previous driving cycle generation studies have focused on constructing a single cycle from a large data set. This paper takes the opposite approach by presenting a novel driving cycle synthetization method that can generate a large number of varying cycles and passenger numbers for a chosen bus route based on only a handful of measured cycles. The validity of the method was confirmed by comparing the statistical properties of synthesized cycles to those of measured cycles on a selected route. The method was applied to analyze the impact of driving cycle and passenger load variations on energy consumption variations of a battery electric bus on a suburban route in Espoo, Finland. Ten-thousand synthetic cycles were run with a validated electric bus simulation model, and the energy consumption during each cycle was recorded. The mean consumption was 0.914 kWh/km, the standard deviation was 0.043 kWh/km, and the range of the consumption variation was 0.331 kWh/km. The new cycle synthetization and consumption distribution forecasting methods are useful tools for public transport authority, route operators, and bus manufacturers, as they could be used in optimizing bus powertrain dimensions as well as bus line schedules.

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.

This study investigates the potential of improving the energy efficiency and reducing the lifecycle costs of electric city buses with multispeed gearboxes. A two-speed dual clutch gearbox and a continuously variable transmission were studied and compared to a reference fixed gear ratio powertrain. A novel two-level optimization model was introduced. The top level involves an exhaustive search algorithm and quasi-static vehicle dynamic model for optimizing the two-speed gearbox gear ratios, utilizing efficiency maps for the electric motor and the inverter. The second level is an integer programming model, which finds an optimal gear shifting policy subject to constraints on hysteresis and gear shifting induced losses. The model was applied with a standard driving cycle and additionally with three measured cycles acquired from a prototype battery electric city bus operating on a daily schedule on a suburban route in Espoo, Finland. The results showed that a two-speed gearbox reduced energy consumption by 2–3.2%, depending on the driving cycle characteristics. On the other hand, the continuously variable transmission was found to increase consumption by 1.9–4.0% due to large losses of the belt mechanism. It was concluded that the two-speed gearbox is a cost-effective investment for electric city buses characterized by operation profiles with frequent acceleration and braking events.

Transportation electrification is increasing and recently more focus has been directed on heavy vehicles and especially on city buses. Battery electric buses are inherently more energy efficient than diesel buses and the efficiency can be further increased by different methods. This paper evaluates the energy consumption reductions that are achievable with an aluminum chassis, low-drag body, low-rolling-resistance class C tires, heat pump, and predictive driving. A simulation model of a generic electric bus was developed in the Simulink software. Simulations were carried out on various types of driving cycles in cold (-10 °C) and warm conditions (20 °C). A novel nonlinear model predictive control problem formulation was created for minimizing the energy consumption of an electric bus. Using a heat pump instead of an electric heater provided the highest energy savings in the cold conditions with an average consumption reduction of 12.7%. The results indicated that a heat pump is particularly effective on low-speed bus routes. However, the class C tires and aluminum chassis provided higher energy savings than the heat pump in the warm conditions. The low-rolling-resistance tires achieved the most robust energy savings. The aluminum chassis reduced the energy consumption more than the class C tires, but the benefit of the lighter chassis was shown to also correlate strongly with the aggressiveness of the driving. The results showed that a low-drag body is a potential method for consumption reduction on high-speed bus routes. Predictive driving was found to reduce the average consumption by 9.5% at -10 °C when using 10-second prediction and control horizons.