The Impact of HVAC on the Development of Autonomous and Electric Vehicle Concepts

Automation and electrification are changing vehicles and mobility. Whereas electrification is mainly changing the powertrain, automation enables the rethinking of the vehicle and its applications. The actual driving range is an important requirement for the design of automated and electric vehicles, especially if they are part of a fleet. To size the battery accordingly, not only the consumption of the powertrain has to be estimated, but also that of the auxiliary users. Heating Ventilation and Air Conditioning (HVAC) is one of the biggest auxiliary consumers. Thus, a variable HVAC model for vehicles with electric powertrain was developed to estimate the consumption depending on vehicle size and weather scenario. After integrating the model into a tool for autonomous and electric vehicle concept development, various vehicle concepts were simulated in different weather scenarios and driving cycles with the HVAC consumption considered for battery sizing. The results indicate that the battery must be resized significantly depending on the weather scenario to achieve the same driving ranges. Furthermore, the percentage of HVAC consumption is in some cases higher than that of the powertrain for urban driving cycles, due to lower average speeds. Thus, the HVAC and its energy demand should especially be considered in the development of autonomous and electric vehicles that are primarily used in cities.

Research into the Impacts of Driving Cycles and Load Weight on the Operation of a Light Commercial Electric Vehicle

The European Parliament has adopted Directive 2019/1161 on the promotion of environmentally friendly and energy-efficient road transport vehicles, which also defines the obligations and forms of support for the procurement of environmentally friendly vehicles in urban logistics. The increase in the number of shipments delivered within e-commerce, which is also the result of the COVID-19 pandemic, requires a transition to a sustainable logistics system. New research questions are being raised in the preparation of new projects for the introduction of small electric commercial vehicles in particular. One of the main research questions about deployment itself is whether light commercial electric vehicles are able to fully replace conventionally powered vehicles. What operating conditions are optimal for the operation of them? How does load weight affect the energy efficiency of operating a light commercial electric vehicle? The authors decided to carry out research into the impacts of weight and the nature of a driving cycle under laboratory conditions to eliminate all external factors that could distort individual measurements and their results. In order to simulate driving cycles, an urban driving cycle was designed on the basis of the course of speed, acceleration, deceleration and slope conditions of roads in the selected regional city of Žilina (Slovakia). In the case of the operation of an electrically powered light commercial vehicle, the impact of load weight on the range of the vehicle is low, and is below the level of the theoretical maximum range of the vehicle in urban logistics applications. The operation of electrically powered vehicles in hilly terrains with relatively longer gradients and steeper slopes increases electricity consumption and, thereby, reduces their range.

Influence of the Final Ratio on the Consumption of an Electric Vehicle under Conditions of Standardized Driving Cycles

Electric vehicles must improve their electric drive system efficiency and effectively use their limited energy to become a viable means of transportation. As such, these technologies have undergone substantial improvements from their initial conception. More efficient powertrains, together with improved storage technologies, have enabled more extended autonomy. However, from an engineering perspective, these systems are still a key area of research and optimization. This work presents a powertrain optimization methodology, developing energy savings and improving the performance of the electric vehicle by focusing on the differential. The proposed methodology includes a study of the dynamics of the electric vehicle and the generation of a mathematical model that represents it. By simulating the vehicle and varying the final ratio of the differential, a significant optimization for energy savings is obtained by developing a standardized driving cycle. In this case, NEDC, WLTC-2, and WLTC-3 test cycles are used. The results show that a short ratio improves performance, even if this implies a larger torque from the prime mover. Depending on the operating cycle used, an energy-saving between 3% and 8% was registered. An extended energy autonomy and an increment in the life-cycle of the batteries are expected in real driving scenarios.

Modeling research of city bus fuel consumption for different driving cycles

Simulation studies can be used to determine the fuel consumption and carbon dioxide emissions of city buses. The operating conditions of such vehicles are characterised by a very high variability of vehicle speed due to the large number of stops along the route of the bus. During vehicle testing, driving cycles are used to replicate the real-world conditions and to achieve repeatable test conditions. Such a driving cycle is a profile of speed represented as a function of time or as a function of distance. The speed profile over time can be an advantageous determinant, based on laboratory tests, for estimating fuel consumption and pollutant emissions of city buses. The research subject of this paper was the simulation of bus driving under simulated urban traffic conditions, carried out by means of the VECTO software. VECTO is a tool designed to perform the calculations of fuel consumption and carbon dioxide emissions of vehicles. It enables to model the powertrain of trucks and buses and to carry out simulations on various routes defined by driving cycles. The test object was a mega class bus, equipped with a 225 kW engine. The bus has three axles, including the rear drive axle. The scope of research included four cycles: urban, interurban, urbandelivery and interurban. Each of these was analysed in terms of speed and road gradient. The aim of this work was to perform a simulation study of the effect of the vehicle traffic conditions on the amount of CO2 emitted and fuel consumption. The obtained results were analysed.

Real Driving Cycle-Based State of Charge Prediction for EV Batteries Using Deep Learning Methods

An accurate prediction of the State of Charge (SOC) of an Electric Vehicle (EV) battery is important when determining the driving range of an EV. However, the majority of the studies in this field have either been focused on the standard driving cycle (SDC) or the internal parameters of the battery itself to predict the SOC results. Due to the significant difference between the real driving cycle (RDC) and SDC, a proper method of predicting the SOC results with RDCs is required. In this paper, RDCs and deep learning methods are used to accurately estimate the SOC of an EV battery. RDC data for an actual driving route have been directly collected by an On-Board Diagnostics (OBD)-II dongle connected to the author’s vehicle. The Global Positioning System (GPS) data of the traffic lights en route are used to segment each instance of the driving cycles where the Dynamic Time Warping (DTW) algorithm is adopted, to obtain the most similar patterns among the driving cycles. Finally, the acceleration values are predicted from deep learning models, and the SOC trajectory for the next trip will be obtained by a Functional Mock-Up Interface (FMI)-based EV simulation environment where the predicted accelerations are fed into the simulation model by each time step. As a result of the experiments, it was confirmed that the Temporal Attention Long–Short-Term Memory (TA-LSTM) model predicts the SOC more accurately than others.

Effect of tire slip losses on the energy demand and fuel consumption of a light-duty vehicle

The contribution of the tire-road slip of traction wheels to the total resistance opposing the motion of a light-duty commercial vehicle has been investigated through the simulation of several homologation and custom driving cycles. The calculation of the contribution of the tire slip losses was based on the estimation of the longitudinal tire slip, by means of Pacejka’s MF5.2 tire model. In this work, the computational steps required to evaluate this contribution were implemented in a previously developed fuel consumption simulation tool. Simulations were performed under several vehicle loading conditions and tire inflation pressures on traction and non-traction wheels, and considering different tire-road adherence conditions, in order to obtain a characterization of the tire slip losses over a wide range of working conditions. An analysis of the results shows that, although the contribution of tire slip losses to the total vehicle energy demand and fuel consumption may be relevant – especially under low-load, low adherence conditions – the sensitivity of the average on-cycle vehicle energy/fuel consumption to changes in the tire inflation pressure is only affected slightly by tire slip losses. Therefore, tire slip losses can be neglected in practice, when the aim of a simulation is to optimize the tire pressure to achieve average vehicle working conditions over a driving cycle.

Performance, Emissions, and Efficiency of Biodiesel versus Hydrotreated Vegetable Oils (HVO), Considering Different Driving Cycles Sensitivity Analysis (NEDC and WLTP)

The use of biofuels in vehicles becomes more advantageous than the consumption of fossil fuels, mainly because it uses renewable sources of energy. Recently there are some concerns about biodiesel sources, and hydrotreated vegetable oils (HVO) appear as a possible advanced solution. To understand the effect that the implementation of the new and old European type-approval test cycles (NEDC e WLTP) has on the results of these fuels considering pollutant emissions and fuel consumption results, a EURO V vehicle was subject to these cycles and also to engine performance evaluation tests. For this analysis, the fuels considered were: B0 (pure diesel), B7 (7% of biodiesel), B15 (15% of biodiesel), B100 (pure biodiesel), and HVO15 (15% of HVO). The findings lead to the conclusion that completely replacing fossil fuels with biofuels is not the most cost-effective approach. No significant differences were observed considering the two homologation cycles, the oldest (NEDC) and the actual (WLTP) and the use of HVO also does not present any relevant differences concerning the fuel consumption differences to B0 (+0.58% NEDC and +0.05%WLTP), comparing well with biodiesel behavior (−1.74% NEDC and −0.69%WLTP for B7 and +1.48% NEDC and 1.89% WLTP for B15). Considering the power of the engine obtained with the fuels, the differences are almost negligible, revealing variations smaller than 2% for B7, B15, and HVO15.