Search Results

This paper presents a low-cost path for extending the range of small urban pure electric vehicles by hydraulic hybridization. Energy management strategies are investigated to improve the electric range, component efficiencies, as well as battery usable capacity. As a starting point, a rule-based control strategy is derived by analysis of synergistic effects of lead-acid batteries, high efficient operating region of DC motor and the hydraulic pump/motor. Then, Dynamic Programming (DP) is used as a benchmark to find the optimal control trajectories for DC motor and Hydraulic Pump/Motor. Implementable rules are derived by studying the optimal control trajectories from DP. With new improved rules implemented, simulation results show electric range improvement due to increased battery usable capacity and higher average DC motor operating efficiency. Presenter Xianke Lin

Global warming is the driver for introduction of CO2 and fuel consumption legislation worldwide. Indian truck manufacturers are facing the introduction of Indian fuel efficiency norms. In the European Union the CO2 emission monitoring phase of the most relevant truck classes was completed in June 2020 by usage of the Vehicle Energy Consumption Calculation TOol VECTO. Indian rule makers are currently considering an adaptation of VECTO for the usage in India, too. Indian truck market has always been very cost sensitive. Introduction of Bharat Stage VI Phase I has already led to a significant cost increase for emission compliance. Therefore, it will be of vital importance to keep the additional product costs for achievement of future fuel consumption legislation as low as possible as long as the real-world operation will not be promoted by the government.

The battery of a vehicle with an electrified powertrain (Hybrid Electric Vehicle or Battery Electric Vehicle), is required to operate with highly dynamic power outputs, both for charging and discharging operation. Consequently, the battery current varies within an extensive range during operation and the battery temperature also changes. In some cases, the relationship between the current flow and the change in the electrical energy stored seems to be affected by inefficiencies, in literature described as current losses, and nonlinearities, typically associated with the complex chemical and physical processes taking place in the battery. When calculating the vehicle electrical energy consumption over a trip, the change in the electrical energy stored at vehicle-level has to be taken into account. This quantity, what we could call the vehicle electricity balance, is typically obtained through a time-based integration of the battery current of all the vehicle batteries during operation.

The high level of electric power available on a Hybrid Electric Vehicle (HEV) enables the introduction of electrical auxiliaries in addition or in substitution to the ones currently available on a conventional powertrain. Among these auxiliaries, electric Superchargers (eSC) for the improvement of the vehicle performance or electrically heated catalysts for the reduction of the light-off time of the after-treatment may dramatically affect the Energy Management System (EMS) of an HEV. Moreover, since these devices are only fluid-dynamically, but not mechanically, linked to the powertrain, they are traditionally neglected in the optimization of the powersplit between internal combustion engine and electric machines by the EMS. The aim of the current work is the development of an EMS that is able to consider in real time the overall electric energy consumption of the entire powertrain.

The sustainable use of energy and the reduction of pollutant emissions are main concerns of the automotive industry. In this context, Hybrid Electric Vehicles (HEVs) offer significant improvements in the efficiency of the propulsion system and allow advanced strategies to reduce pollutant and noise emissions. The paper presents the results of a simulation study that addresses the minimization of fuel consumption, NOx emissions and combustion noise of a medium-size passenger car. Such a vehicle has a parallel-hybrid diesel powertrain with a high-voltage belt alternator starter. The simulation reproduces real-driver behavior through a dynamic modeling approach and actuates an automatic power split between the Internal Combustion Engine (ICE) and the Electric Machine (EM). Typical characteristics of parallel hybrid technologies, such as Stop&Start, regenerative braking and electric power assistance, are implemented via an operating strategy that is based on the reduction of total losses.

Scope of this work is the analysis of the energy consumed by lubricating gear pumps for automotive applications during a driving cycle. This paper presents the lumped parameter simulation model of gerotor lubricating pumps and the comparison between numerical outcomes and experimental results. The model evaluates the power required to drive the pump and the cumulative energy consumed in the driving cycle. The influence of temperature variations on leakage flows, viscous friction torque and lubricating circuit permeability is taken into account. The simulation model has been validated by means of a test rig for hydraulic pumps able to reproduce the typical speed, temperature and load profiles during a NEDC driving cycle. Experimental tests, performed on a crankshaft mounted pump for diesel engines, have confirmed a good matching with the simulation model predictions in terms of instantaneous quantities and overall energy consumption.

A Multi-Objective Optimization (MOO) problem concerning the thermal control problem of Multifunctional Structures (MFSs) is here addressed. In particular the use of Multi-Objective algorithms from an optimization tool and Self-Organizing Maps (SOM) is proposed for the identification of the optimal topological distribution of the heating components for a multifunctional test panel, the Advanced Bread Board (ABB). MFSs are components that conduct many functions within a single piece of hardware, shading the clearly defined boundaries that identify traditional subsystems. Generally speaking, MFSs have already proved to be a disrupting technology, especially in aeronautics and space application fields. The case study exploited in this paper refers to a demonstrator breadboard called ABB. ABB belongs to a particular subset of an extensive family of MFS, that is, of thermo-structural panels with distributed electronics and a health monitoring network.

Given the growing concern for environmental issues, the automotive industry is working more deeply on the development of innovative technologies that reduce gas emissions and fuel consumption. Many car manufacturers have identified hybrid electric vehicles (HEV) and fuel cell vehicles as the most promising solutions alternatives. IDRApegasus is a fuel cell hydrogen vehicle developed at the Politecnico of Turin. It participated at the Shell Eco-marathon Europe in Rotterdam (Netherlands) from 17-19 May 2012, a competition for low energy consumption vehicles and also an educational project that joins the value of sustainable development with a vehicle that will use the smallest amount of fuel and produce the lowest emissions possible.

An experimental investigation was performed on a turbocharged spark-ignition 4-cylinder production engine fuelled with natural gas and with two blends of natural gas and hydrogen (15% and 25% in volume of H₂). The engine was purposely designed to give optimal performance when running on CNG. The first part of the experimental campaign was carried out at MBT timing under stoichiometric conditions: load sweeps at constant engine speed and speed sweeps at constant load were performed. Afterwards, spark advance sweeps and relative air/fuel ratio sweeps were acquired at constant engine speed and load. The three fuels were compared in terms of performance (fuel conversion efficiency, brake specific fuel consumption, brake specific energy consumption and indicated mean effective pressure) and brake specific emissions (THC, NOx, CO).

Nowadays, the 48 V vehicle architecture seems to be the perfect bridge between the 12 V system and the costly High Voltage (HV) electrification towards the crucial goal of CO2 and pollutants emissions reduction in combination with enhanced performance. However, this approach leads to an increased complexity in the interaction between different sub-systems targeting the optimization of the Energy Management System (EMS). Therefore, it becomes essential to perform a preliminary hardware assessment, exploring the interactions between the different components and quantifying the cost vs benefit trade-off. To this purpose, an integrated experimental/numerical methodology has been adopted: a comprehensive map-based Hybrid Electric Vehicle (HEV) model has been built, allowing the simulation of a variety of hybrid architectures, including both HV and 48 V systems.

In order to meet current and future emission and CO2 targets, an efficient vehicle thermal management system is one of the key factors in conventional as well as in electrified powertrains. Global vehicle simulation is already a well-established tool to support the vehicle development process. In contrast to conventional vehicles, electrified powertrains offer an additional challenge to the thermal conditioning: the durability of E-components is not only influenced by temperature peaks but also by the duration and amplitude of temperature swings as well as temperature gradients within the components during their lifetime. Keeping all components always at the preferred lowest temperature level to avoid ageing under any conditions (driving, parking, etc.) will result in very high energy consumption which is in contradiction to the efficiency targets.

In order to meet current and future emission and CO2 targets, an efficient vehicle thermal management system is one of the key factors in conventional as well as in electrified powertrains. Furthermore the increasing number of vehicle configurations leads to a high variability and degrees of freedom in possible system designs and the control thereof, which can only be handled by a comprehensive tool chain of vehicle system simulation and a generic control system architecture. The required model must comprise all relevant systems of the vehicle (control functionality, cooling system, lubrication system, engine, drive train, HV components etc.). For proper prediction with respect to energy consumption all interactions and interdependencies of those systems have to be taken into consideration, i.e. all energy fluxes (mechanical, hydraulically, electrical, thermal) have to be exchanged among the system boundaries accordingly.

Governments worldwide are taking actions aiming to achieve a sustainable transportation system that can comprise of minimal pollutant and GHG emissions. Particular attention is given to the real-world emissions, i.e. to the emissions achieved in the real driving conditions, outside of a controlled testing environment. In this framework, interest in vehicle fleet electrification is rapidly growing, as it is seen as a way to simultaneously reduce pollutant and GHG emissions, while on the other hand OEMs are facing a significant increase in the number of tests which are needed to calibrate this new generation of electrified powertrains over a variety of different driving scenarios.

The Vehicle Energy Consumption calculation Tool (VECTO) is used in Europe for calculating standardised energy consumption and CO2 emissions from Heavy-Duty Trucks (HDTs) for certification purposes. The tool requires detailed vehicle technical specifications and a series of component efficiency maps, which are difficult to retrieve for those that are outside of the manufacturing industry. In the context of quantifying HDT CO2 emissions, the Joint Research Centre (JRC) of the European Commission received VECTO simulation data of the 2016 vehicle fleet from the vehicle manufacturers. In previous work, this simulation data has been normalised to compensate for differences and issues in the quality of the input data used to run the simulations. This work, which is a continuation of the previous exercise, focuses on the deeper meaning of the data received to understand the factors contributing to energy and fuel consumption.

The gap between the officially reported CO2 values and the actual performance of the vehicle on the road is continuously increasing. Numerous studies are showing differences between the official values and the real-world measurements of more than 40% in average, with further increases year by year. The fuel consumption of passenger cars are determined as part of the vehicle certification according to Euro 6 via carbon mass balance using exhaust gas measurement. By introducing the new world harmonized driving cycle (WLTC) in September 2017, which is addressing a more realistic speed profile or traffic conditions, the gap between the certification and road test is expected to be reduced in half. Additionally the EU Commission plans to monitor vehicles more closely. From 2020, devices for recording fuel and energy consumption will become mandatory in all passenger cars and light commercial vehicles, reflecting the average real world CO2 emissions.

Range anxiety in current battery electric vehicles is a challenging problem, especially for commercial vehicles with heavy payloads. Therefore, the development of electrified propulsion systems with multiple power sources, such as fuel cells, is an active area of research. Optimal speed planning and energy management, referred to as eco-driving, can substantially reduce the energy consumption of commercial vehicles, regardless of the powertrain architecture. Eco-driving controllers can leverage look-ahead route information such as road grade, speed limits, and signalized intersections to perform velocity profile smoothing, resulting in reduced energy consumption. This study presents a comprehensive analysis of the performance of an eco-driving controller for fuel cell electric trucks in a real-world scenario, considering a route from a distribution center to the associated supermarket.

In the face of growing concerns about environmental sustainability and urban congestion, the integration of eco-driving strategies has emerged as a pivotal solution in the field of the urban transportation sector. This study explores the potential benefits of a CAV functioning as a virtual eco-driving controller in an urban traffic scenario with a group of following human-driven vehicles. A computationally inexpensive and realistic powertrain model and energy management system of the Chrysler Pacifica PHEV are developed with the field experiment data and integrated into a forward-looking vehicle simulator to implement and validate an eco-driving speed planning and energy management strategy assuming longitudinal automation. The eco-driving algorithm determines the optimal vehicle speed profile and energy management strategy.