Plug-in hybrid electric vehicle emissions impacts on control strategy and fuel economy

Plug-in hybrid electric vehicle (PHEV) technologies have the potential for considerable petroleum consumption reductions, at the expense of increased tailpipe emissions due to multiple cold start events and improper use of the engine for PHEV specific operation. PHEVs operate predominantly as electric vehicles (EVs) with intermittent assist from the engine during high power demands. As a consequence, the engine can be subjected to multiple cold start events. These cold start events have a significant impact on the tailpipe emissions due to degraded catalyst performance and starting the engine under less than ideal conditions. On current hybrid electric vehicles (HEVs), the first cold start of the engine dictates whether or not the vehicle will pass federal emissions tests. PHEV operation compounds this problem due to infrequent, multiple engine cold starts.The dissertation research focuses on the design of a vehicle supervisory control system for a pre-transmission parallel PHEV powertrain architecture. Energy management strategies are evaluated and implemented in a virtual environment for preliminary assessment of petroleum displacement benefits and rudimentary drivability issues. This baseline vehicle supervisory control strategy, developed as a result of this assessment, is implemented and tested on actual hardware in a controlled laboratory environment over a baseline test cycle. Engine cold start events are aggressively addressed in the development of this control system, which lead to enhanced pre-warming and energy-based engine warming algorithms that provide substantial reductions in tailpipe emissions over the baseline supervisory control strategy.The flexibility of the PHEV powertrain allows for decreased emissions during any engine starting event through powertrain torque shaping algorithms that eliminate high engine torque transients during these periods. The results of the dissertation research show that PHEVs do have the potential for substantial reductions in fuel consumption, while remaining environmentally friendly. Tailpipe emissions from a representative PHEV test platform have been reduced to acceptable levels through the development and refinement of vehicle supervisory control methods only. Impacts on fuel consumption are minimal for the emissions reduction techniques that are implemented, while in some cases, substantial fuel consumption reductions are observed

Modelling and Co-simulation of hybrid vehicles: A thermal management perspective

Thermal management plays a vital role in the modern vehicle design and delivery. It enables the thermal analysis and optimisation of energy distribution to improve performance, increase efficiency and reduce emissions. Due to the complexity of the overall vehicle system, it is necessary to use a combination of simulation tools. Therefore, the co-simulation is at the centre of the design and analysis of electric, hybrid vehicles. For a holistic vehicle simulation to be realized, the simulation environment must support many physical domains. In this paper, a wide variety of system designs for modelling vehicle thermal performance are reviewed, providing an overview of necessary considerations for developing a cost-effective tool to evaluate fuel consumption and emissions across dynamic drive-cycles and under a range of weather conditions. The virtual models reviewed in this paper provide tools for component-level, system-level and control design, analysis, and optimisation. This paper concerns the latest techniques for an overall vehicle model development and software integration of multi-domain subsystems from a thermal management view and discusses the challenges presented for future studies

A COMPREHENSIVE METHODOLOGY for the OPTIMIZATION of the OPERATING STRATEGY of HYBRID ELECTRIC VEHICLES

The sustainable exploitation of energy and reduction of pollutant emissions are main concerns in our society. Driven by more stringent international standards, automobile manufacturers are developing new technologies such as the Hybrid Electric Vehicles (HEVs). These innovative systems combine the main benefits of traditional Internal Combustion Engines (ICEs) with those of Battery Electric Vehicles (BEVs), while overcoming their main drawbacks. HEVs can offer significant improvements in the efficiency of the propulsion system, but they also lead to higher complexities in the design and in the control. In order to exploit all the expected advantages, a dedicated optimization of the Hybrid Operating Strategy (HOS) is required. In this framework, simulation plays a key role in identifying the optimal HOS, where the primary design targets are the fuel economy, emission reduction and improvement in the vehicle performance (including acceleration, driving range, operational flexibility and noise). With such a perspective, a simulation study was performed involving the implementation, in Matlab environment, of zero-dimensional models of a Series Hybrid Electric Vehicle (SHEV) and a Parallel Hybrid Electric Vehicle (PHEV). As far as the hybrid operating strategy is concerned, three different approaches were investigated: _ A novel Benchmark Optimizer (BO), that determines the best possible operating strategy for the selected target, mission profile and powertrain design. The single solution is characterized by a vector, in which every scalar independently defines the mechanical power of the electric machine, for the PHEV, or the engine speed, for the SHEV, at each time step of the selected driving cycle _ A real-time optimizer based on the Minimization of the Total system Losses (TLM). It involves a vector-approach, in order to select, at each time step, the power split that guarantees the minimum system losses. It requires a reduced number of calibration parameters and, therefore, is computationally fast and adequate to work in real-world applications. Based on this technique, two different methodologies concerning the engine component are considered: the Total engine losses (TLM TOT) and the Recoverable (with respect to the optimal operating point) engine losses (TLM REC) _ A real-time optimizer based on the Total Load Switch Thresholds. It switches the operating mode depending on the load and speed signals. It involves a scalar-approach and requires a reduced number of calibration parameters. It is by far the method that requires the least computational effort In all the three cases, the numerical optimizer is based on Genetic Algorithm (GA) techniques. GAs are inspired by the mechanism of natural selection, in which better individuals are likely to be the winners in a competing environment. It is a statistical approach able to solve optimization problems whose objective function is non-continuous, non-differentiable, stochastic and highly non-linear. The study analyses the optimization of the well-to-wheel CO2 emissions of a Parallel and a Series Hybrid Electric Vehicles along the New European Driving Cycle (NEDC) and the Artemis Driving Cycles. In the case of the only compression ignition engine, also NOx emissions were considered as optimization criteria along the NED

MODELING OF THERMAL DYNAMICS IN CHEVROLET VOLT GEN II HYBRID ELECTRIC VEHICLE FOR INTEGRATED POWERTRAIN AND HVAC OPTIMAL OPERATION THROUGH CONNECTIVITY

Integrated thermal energy management across system level components in electric vehicles (EVs) and hybrid electric vehicles (HEVs) is currently an under explored space. The proliferation of connected vehicles and accompanying infrastructure in recent years provides additional motivation to explore opportunities in optimizing thermal energy management in EVs and HEVs with the help of this newly available connected vehicle data. This thesis aims to examine and analyze the potential to mitigate vehicle energy consumption and extend usable driving range through optimal control strategies which exploit physical system dynamics via controls integration of vehicle subsystems. In this study, data-driven and physics-based models for heating, ventilation and air-conditioning (HVAC) are developed and utilized along with the vehicle dynamics and powertrain (VD\&PT) models for a GM Chevrolet Volt hybrid electric vehicle to enable co-optimization of HVAC and VD\&PT systems of HEVs. The information available in connected vehicles like driver schedules, trip duration and ambient conditions is leveraged along with the vehicle system dynamics to predict operating conditions of the vehicle under study. All this information is utilized to optimize the operation of an integrated HVAC and VD\&PT system in a hybrid electric vehicle to achieve the goal of reduced energy consumption. For achieving the goals outlined for this thesis, an integrated HVAC and VD\&PT model is developed and the various components, sub-systems and systems are validated against real world test data. Then, integrated relationships relevant to the thermal dynamics of an HEV are established. These relationships comprise the combined operational characteristics of the internal combustion (IC) engine coolant and the cabin electric heater for cabin heating, coordinated controls of IC engine using engine coolant and catalyst temperatures for cabin thermal conditioning in cold ambient conditions and the combined operational characteristics of the air-conditioning compressor for conditioning both cabin and high-voltage battery in an HEV. Next, these sub-system and system relationships are used to evaluate potential energy savings in cabin heating and cooling when vehicle\u27s operating schedule is known. Finally, an optimization study is conducted to establish an energy efficient control strategy which maximizes the HVAC energy efficiency whilst maintaining occupant comfort levels according to ASHRAE standards, all while improving the usable range of the vehicle relative to its baseline calibration. The mean energy savings in overall vehicle energy consumption using an integrated HVAC - Powertrain control strategy and a coordinated thermal management strategy proposed in this work are $3$\% and $14$\% respectively

Effect of noise factors in energy management of series plug-in hybrid electric vehicles

It has been demonstrated that charge depletion (CD) energy management strategies are more efficient choices for energy management of plug-in hybrid electric vehicles (PHEVs). The knowledge of drive cycle as a priori can improve the performance of CD energy management in PHEVs. However, there are many noise factors which affect both drivetrain power demand and vehicle performance even in identical drive cycles. In this research, the effect of each noise factor is investigated by introducing the concept of power cycle instead of drive cycle for a journey. Based on the nature of the noise factors, a practical solution for developing a power-cycle library is introduced. Investigating the predicted power cycle, an energy management strategy is developed which considers the influence of temperature noise factor on engine performance. The effect of different environmental and geographic conditions, driver behavior, aging of battery and other components are considered. Simulation results for a modelled series PHEV similar to GM Volt show that the suggested energy management strategy based on the driver power cycle library improves both vehicle fuel economy and battery health by reducing battery load and temperature.<br /

Powertrain Systems for Net-Zero Transport

The transport sector continues to shift towards alternative powertrains, particularly with the UK Government’s announcement to end the sale of petrol and diesel passenger cars by 2030 and increasing support for alternatives. Despite this announcement, the internal combustion continues to play a significant role both in the passenger car market through the use of hybrids and sustainable low carbon fuels, as well as a key role in other sectors such as heavy-duty vehicles and off-highway applications across the globe. Building on the industry-leading IC Engines conference, the 2021 Powertrain Systems for Net-Zero Transport conference (7-8 December 2021, London, UK) focussed on the internal combustion engine’s role in Net-Zero transport as well as covered developments in the wide range of propulsion systems available (electric, fuel cell, sustainable fuels etc) and their associated powertrains. To achieve the net-zero transport across the globe, the life-cycle analysis of future powertrain and energy was also discussed. Powertrain Systems for Net-Zero Transport provided a forum for engine, fuels, e-machine, fuel cell and powertrain experts to look closely at developments in powertrain technology required, to meet the demands of the net-zero future and global competition in all sectors of the road transportation, off-highway and stationary power industries

DEVELOPMENT OF THE ECOCAR 3 PROPOSAL AND GUIDELINES FOR MODELING AND DESIGN IN YEAR ONE OF ECOCAR 3

This report summarizes the work done for the Vehicle Powertrain Modeling and Design Problem Proposal portion of the EcoCAR3 proposal as specified in the Request for Proposal from Argonne National Laboratory. The results of the modeling exercises presented in the proposal showed that: An average conventional vehicle powered by a combustion engine could not meet the energy consumption target when the engine was sized to meet the acceleration target, due the relatively low thermal efficiency of the spark ignition engine. A battery electric vehicle could not meet the required range target of 320 km while keeping the vehicle weight below the gross vehicle weight rating of 2000 kg. This was due to the low energy density of the batteries which necessitated a large, and heavy, battery pack to provide enough energy to meet the range target. A series hybrid electric vehicle has the potential to meet the acceleration and energy consumption parameters when the components are optimally sized. A parallel hybrid electric vehicle has less energy conversion losses than a series hybrid electric vehicle which results in greater overall efficiency, lower energy consumption, and less emissions. For EcoCAR3, Michigan Tech proposes to develop a plug-in parallel hybrid vehicle (PPHEV) powered by a small Diesel engine operating on B20 Bio-Diesel fuel. This architecture was chosen over other options due to its compact design, lower cost, and its ability to provide performance levels and energy efficiency that meet or exceed the design targets. While this powertrain configuration requires a more complex control system and strategy than others, the student engineering team at Michigan Tech has significant recent experience with this architecture and has confidence that it will perform well in the events planned for the EcoCAR3 competition