Colorado State University EcoCAR 3 Final Technical Report

<div class="section abstract"><div class="htmlview paragraph">The amount of data communicated in automotive networks is increasing rapidly, leading to more complex systems alongside the rising number of Electronic Control Units (ECUs) in modern vehicles. As the number of ECUs has risen, Controller Area Network (CAN) communication bandwidth has been pushed to its limit. Existing solutions - such as using multiple CAN buses, sending multiple CAN frames or switching to another protocol - require a significant system-design effort and replacement of hardware and software. This has paved the way for CAN Flexible Data-rate (CAN FD), which extends the CAN standard and permits significantly higher data rates, enabling faster firmware upgrades. However, some changes must be made like: exchange the classical CAN microcontroller with a CAN FD microcontroller and also a CAN FD transceiver must be used.</div><div class="htmlview paragraph">This article will explore the benefits of CAN FD, how it compares with the classical CAN protocol, requirements for systems and how to introduce CAN FD into existing CAN networks.</div></div>

<div class="section abstract"><div class="htmlview paragraph">This paper presents a methodology to optimize the blending of charge-depleting (CD) and charge-sustaining (CS) modes in a multi-mode plug-in hybrid electric vehicle (PHEV). The objective of the optimization is to best utilize onboard energy for minimum overall energy consumption based on speed and elevation profile. The optimization reduces overall energy consumption when the selected route cannot be completely driven in all-electric mode. The optimization method splits drive cycles into constant distance segments and then uses a reduced-order model to sort the segments by the best use of battery energy vs. fuel energy. The PHEV used in this investigation is the Stellantis Pacifica. Results support energy savings up to 20% which depend on the route and initial battery State of Charge (SOC). Initial optimization takes 1 second for 38 km and 3 seconds for 154 km.</div></div>

<div class="section abstract"><div class="htmlview paragraph">Energy management plays a key role in achieving higher fuel economy for plug-in hybrid electric vehicle (PHEV) technology; the state of charge (SOC) profile of the battery during the entire driving trip determines the electric energy usage, thus determining the fuel consumed. The energy management algorithm should be designed to meet all driving scenarios while achieving the best possible fuel economy. The knowledge of the power requirement during a driving trip is necessary to achieve the best fuel economy results; performance of the energy management algorithm is closely related to the amount of information available in the form of road grade, velocity profiles, trip distance, weather characteristics and other exogenous factors. Intelligent transportation systems (ITS) allow vehicles to communicate with one another and the infrastructure to collect data about surrounding, and forecast the expected events, e.g., traffic condition, turns, road grade, and weather forecast. The ability to effectively interpret this traffic and weather data to estimate the power demand is important for the energy management and plays crucial role in the battery utilization.</div><div class="htmlview paragraph">This paper presents an important step towards ITS integration with energy management of PHEVs: the goal of this research is to determine the correlation (or heuristic relationship) between different road events, weather conditions and PHEV energy management performance. The first step of this study utilizes real world data collected from a plug-in Toyota Prius (after-market conversion kit Hymotion L5) to determine the correlations between events and velocity profile characteristics. The second step finds the impact of power profile characteristics on the performance of equivalent consumption minimization Strategy (ECMS) for PHEV energy management using a high fidelity, validated PHEV simulator. The goal of this study is to identify the impact factors and define qualitative impact on the energy management algorithm and vehicle fuel economy.</div></div>

<div class="section abstract"><div class="htmlview paragraph">The automotive industry continues to develop new powertrain and vehicle technologies aimed at reducing overall vehicle-level fuel consumption. While the use of electrified propulsion systems is expected to play an increasingly important role in helping OEMs meet fleet CO<sub>2</sub> reduction targets, hybridized propulsion solutions will continue to play a vital role in the electrification strategy of vehicle manufacturers.</div><div class="htmlview paragraph">Plug-in hybrid electric vehicles (PHEV) and range extender vehicles (REx) come with unique NVH challenges due to their different possible operation modes. First, the paper outlines different driveline and vehicle architectures for PHEV and REx. Given the multiple general architectures, as well as operation modes which typically accompany these vehicles, NVH characterizations and noise source-path analysis can be more complicated than conventional vehicles.</div><div class="htmlview paragraph">In the following steps, typical NVH related challenges are highlighted and potential solutions for NVH optimization are discussed. While the overall noise levels are low in electric mode, the NVH behavior of electrified vehicles can be objectionable due to the presence of tonal noise coming from electric machines and geartrain components. Additionally, road and wind noise shares can be relatively high during mid/high vehicle speed operation.</div><div class="htmlview paragraph">The switch-over from pure electric drive to operation with the combustion engine introduces transient NVH challenges, such as engine start and hybrid architecture dependent drivetrain torque disturbances. Downsizing and boosting of modern combustion engines can increase the combustion related excitation and hence requires detailed attention during vehicle NVH integration. Further, operation strategy of the combustion engine during operation must be refined for pleasant NVH while not compromising fuel economy of the vehicle. The NVH assessment of PHEV drivetrains require evaluations under multiple operating conditions for identification and characterization of the various issues which may be experienced by the driver. Examples from case studies are provided to illustrate the NVH challenges and solutions.</div></div>

<div class="section abstract"><div class="htmlview paragraph">In-vehicle communication among different vehicle electronic controller units (ECU) to run several applications (I.e. to propel the vehicle or In-vehicle Infotainment), CAN (Controller Area Network) is most frequently used. Given the proprietary nature and lack of standardization in CAN configurations, which are often not disclosed by manufacturers, the process of CAN reverse engineering becomes highly complex and cumbersome. Additionally, the scarcity of publicly accessible data on electric vehicles, coupled with the rapid technological advancements in this domain, has resulted in the absence of a standardized and automated methodology for reverse engineering the CAN. This process is further complicated by the diverse CAN configurations implemented by various Original Equipment Manufacturers (OEMs).</div><div class="htmlview paragraph">This paper presents a manual approach to reverse engineer the series CAN configuration of an electric vehicle, considering no vehicle information is available to testing engineers. To execute reverse engineering, the CAN data log is to be taken from the OBD-II port by systematically identifying and mapping the CAN with various ECUs interfaced with that CAN line. Driver actions and continuous data logged from the OBD-II port are cross-referenced with CAN data to determine the byte order of signals and message frames. The signals derived from one vehicle use scenario (driving) are then validated against another scenario (charging) to ensure consistency and accuracy.</div></div>

<div class="section abstract"><div class="htmlview paragraph">Excellent energy consumption performance of a plug-in hybrid electric vehicle (PHEV) is usually attributed to its hybrid drive mode. However, the factors including vehicle performance, driver behavior and traffic status have been shown to cause unsatisfactory performance. This phenomenon leads to a necessity of study on energy consumption control strategies under real-world driving conditions. This paper proposes a new approach for energy management optimization of plug-in hybrid electric vehicles based on real-world driving data for two purposes. One is for improving the energy consumption of PHEV under real-world driving conditions and the other is for reducing the computational complexity of optimization methods in simulation models. In this process, the paper collected real-world driving record data from 180 drivers within 6 months. Then the principal component analysis (PCA) was employed to extract and define the hidden factors from the initial real-world driving data. The K-means clustering method was employed to evaluate the sensitivity of new factors to the energy consumption of PHEV, in which the sensitivity was defined through the Pearson correlation coefficient and covariance. Moreover, an optimal energy management strategy based on the sensitivity results and constrained optimization functions was used to optimize the speed-curve of PHEV. Furthermore, the 100 simulation tests based on MATLAB/Simulink platform were carried out to validate the feasibility of the proposed approach. The results indicate that 60% of the factors extracted from this paper show a strong sensitivity to the energy consumption of PHEV and the optimization method costs less computational time, leading to a better energy consumption performance. The proposed methodology is valuable for the research on real-time energy management of electric vehicles and speed control of intelligence vehicles under real-world road conditions.</div></div>

<div class="section abstract"><div class="htmlview paragraph">Honda has developed the 2018 model CLARITY PLUG-IN HYBRID. Honda’s new plug-in hybrid is a midsize sedan and shares a body platform with the CLARITY FUEL CELL and the CLARITY ELECTRIC. The vehicle’s electric powertrain boosts driving performance as an electric vehicle (EV) over Honda’s previous plug-in hybrid.</div><div class="htmlview paragraph">The CLARITY PLUG-IN HYBRID’s electric powertrain consists of a traction motor and generator built into the transmission, a Power Control Unit (PCU) positioned above the transmission, an Intelligent Power Unit (IPU) fitted under the floor, and an onboard charger fitted below the rear trunk.</div><div class="htmlview paragraph">The PCU integrates an inverter that drives the traction motor, an inverter that drives the generator, and a DC-DC converter to boost battery voltage (referred to as a “Voltage Control Unit (VCU)” below). The VCU employs an interleaved circuit configuration and a coupled inductor and realizes approximately three times the rated continuous power and approximately three times the power density of a standard unit employed in a hybrid vehicle.</div><div class="htmlview paragraph">The IPU contains 17 kWh high-capacity battery modules and a 12 V DC-DC converter. A coolant-cooling method has been adopted to respond to the increased power of the battery, boosting cooling performance. The battery modules are positioned below the front and rear seats, and the high-voltage wiring is positioned between the modules, in the center of the vehicle. This layout has made it possible to fit the IPU below the floor of the vehicle, helping to enable the realization of a spacious and comfortable cabin seating five passengers, as well as a useful amount of trunk space.</div><div class="htmlview paragraph">The use of a high-power VCU and a high-capacity IPU has made it possible to realize EV operation in the high-speed range with no need to start the engine, giving the vehicle an adequate all-electric range (AER) for everyday use. During hybrid operation, increasing both the VCU power and the battery power helps ensure quietness while also allowing the engine to be downsized from 2.0 to 1.5 liters. The developed electric powertrain realizes a long AER of 47 miles and extremely quiet hybrid operation with a high level of fuel economy at 42 mpg.</div></div>

<div class="section abstract"><div class="htmlview paragraph">A highly efficient two-motor plug-in hybrid system is developed to satisfy the global demands of CO<sub>2</sub> reduction. This system switches three operation modes, what is called “EV Drive”, “Hybrid Drive” and “Engine Drive”, to maximize fuel efficiency according to the driving condition of the vehicle. Practical plug-in EV (Electric Vehicle) capability is also realized by adding a high-power on-board charger and a high capacity Li-ion battery to the original system.</div><div class="htmlview paragraph">The outlines of the system components including a newly developed Atkinson cycle engine, a highly efficient electric coupled CVT (Continuously Variable Transmission) with built-in motor and generator, an integrated PCU (Power Control Unit) and an exclusive battery for plug-in HEV (Hybrid Electric Vehicle) are described in this paper.</div><div class="htmlview paragraph">In addition to the switching of three driving modes and the efficiency improvement of each device, cooperative control of the hybrid system is introduced.</div><div class="htmlview paragraph">By installing this system in 2014 Accord plug-in hybrid, superior fuel economy is achieved with values of 46 MPG in CS (Charge Sustaining) mode and 115 MPGe (Miles Per Gallon equivalent) in CD (Charge Depleting) mode. 13miles of AER (All Electric Range) is also achieved while maintaining sufficient driving performance as a mid-size sedan.</div><div class="htmlview paragraph">MPGe is a unit of fuel economy for the alternate fuel vehicles such as plug-in hybrid or electric vehicles. The ratings are calculated by converting used electric energy into equivalent amount of gasoline.</div></div>

<div class="section abstract"><div class="htmlview paragraph">The Environmental Protection Agency’s Advanced Light-Duty Powertrain and Hybrid Analysis (ALPHA) modeling tool was initially created to simulate the Greenhouse Gas emissions from light-duty vehicles. ALPHA is used to predict tailpipe CO<sub>2</sub> emissions and energy consumption from advanced automotive technologies. ALPHA is a physics-based, forward-looking vehicle computer simulation tool capable of analyzing various vehicle types with different powertrain technologies while replicating realistic vehicle behavior. ALPHA version 3.0 is the current version of the MATLAB/Simulink based software.</div><div class="htmlview paragraph">Key changes made for ALPHA v3.0 include the addition of new light- and medium-duty vehicle models to support simulation of electrified vehicle architectures (hybrid, plug-in hybrid, and battery electric vehicles) aligning with the automotive industry transition towards electrified fleets. Each electrified vehicle model was tuned to replicate operational behavior of components (such as engine, transmission, electric motor, and propulsion battery, if available) as observed in actual vehicle test data over EPA city, highway and US06 regulatory drive cycles.</div><div class="htmlview paragraph">This paper focuses on the new electrified vehicle architectures and models, the input data used to represent these new technologies, and the comparisons of ALPHA’s simulation results to the corresponding vehicle chassis tests and EPA certification data. As a final comparison, the new models are used to simulate variant vehicles that are similar, but not identical, to the base vehicle used to validate each model. The validation of these new models enables EPA to use ALPHA v3.0 to estimate fleet-wide CO<sub>2</sub> emissions and energy consumption for different types of new and emerging light- and medium-duty vehicles.</div></div>

<div class="section abstract"><div class="htmlview paragraph">The objective of this study was to evaluate the fuel saving potential of various hybrid powertrain architectures for medium and heavy duty vehicles. The relative benefit of each powertrain was analyzed, and the observed fuel savings was explained in terms of operational efficiency gains, regenerative braking benefits from powertrain electrification and differences in vehicle curb weight. Vehicles designed for various purposes, namely urban delivery, utility, transit, refuse, drayage, regional and long haul were included in this work. Fuel consumption was measured in regulatory cycles and various real world representative cycles.</div><div class="htmlview paragraph">A diesel-powered conventional powertrain variant was first developed for each case, based on vehicle technical specifications for each type of truck. Autonomie, a simulation tool developed by Argonne National Laboratory, was used for carrying out the vehicle modeling, sizing and fuel economy evaluation. Performance based sizing rules implemented in Autonomie were used to determine the component sizes for the hybridized concept trucks. In addition to the conventional baseline, a 48V start-stop system, parallel pre-transmission system and a series plug in hybrid system were considered in this work.</div><div class="htmlview paragraph">This study shows that not all trucks can utilize engine downsizing as part of hybridization strategy. Hybrid trucks designed to match conventional vehicle performance in all functional requirements, will require engines that are comparably sized as the conventional counterparts. Plug in hybrids can have downsized engines and still meet performance goals, as the larger battery packs can be used to assist engine for a longer period of time. Depending on the drive cycle, the observed fuel economy for the hybrid powertrains will vary. It could be comparable to that of baseline vehicle in highway driving while resulting in over 30% fuel savings in more transient drive cycles.</div></div>

<div class="section abstract"><div class="htmlview paragraph">Reducing emissions and the carbon footprint of our society have become imperatives requiring the automotive industry to adapt and develop technologies to strive for a cleaner sustainable transport system and for sustainable economic prosperity. Electrified hybrid electric vehicles (HEV), plug-in hybrid electric vehicles (PHEV) and range extender powertrains provide potential solutions for reducing emissions, but they present challenges in terms of thermal management. A key requirement for meeting these challenges is accurately to predict the thermal loading and temperatures of an internal combustion engine (ICE) quickly under multiple full-load and part-load conditions.</div><div class="htmlview paragraph">Computational Fluid Dynamics (CFD) and thermal survey database methods are used to derive thermal loading of the engine structure and are well understood but typically only used at full-load conditions. These methods are limited in terms of computational overhead with CFD and the validity of the database when wishing to look at part-load conditions to build up a thermal map of the engine for transient solutions.</div><div class="htmlview paragraph">This paper presents a new analytical Finite Element (FE) based tool for calculating thermal boundary conditions of all the components of the power cylinder using physical models and semi-empirical correlations, without the need for CFD analysis, and which overcomes the limitation of database methods which are not predictive. An iterative solution is used that captures all the heat paths in detail and where each solution runs quickly enough to allow different design variants to be assessed for multiple operating conditions. The calculated temperatures and the corresponding deformations can then be used to predict piston secondary motion and ring dynamics and to better predict friction, wear and oil consumption of the power cylinder and the structural durability of the components.</div><div class="htmlview paragraph">The toolset is applied to a 2.0 liter 4-cylinder Gasoline Turbocharged Direct Injection (GTDI) engine and validated with measurements on the piston, cylinder head and cylinder bore for different full and part-load conditions.</div></div>

<div class="section abstract"><div class="htmlview paragraph">Nowadays, a push towards decarbonisation to reduce the problem of the environmental pollution is increasingly pressing. In the current automotive context, a tendency among the cars manufacturer to consider the development of hybrid vehicles is growing. Indeed, thanks to the battery downsizing due to the addition of the range extender (REx), a hybrid electric vehicle (HEV) allows to overcome the limitations of pure electric vehicles (EV) such as the infrastructure which is linked to the battery charging process. Moreover, the performance of battery in terms of efficiency and operating limits are strictly related with the temperature of the battery pack and with the energy management strategy (EMS). The proposed work aims to analyse the performance of a Plug-In series hybrid vehicle (Plug-In HEV) depending on the temperature of battery pack and the EMS. The considered Plug-In HEV is equipped with a hydrogen-fuelled internal combustion engine that is used as REx. First, a lumped dynamic thermal model of is proposed to compute the performance of the battery pack depending on the operating temperature. Moreover, a Rule Based (RB) and a Pontryagin’s Minimum Principle (PMP) Energy Management Strategy (EMS) are considered. The results in terms of fuel consumption are discussed accounting for the battery lumped thermal dynamics. The effects of the battery pack thermal transients according to the EMS decisions are analysed. The relationship between the EMS metrics, the battery pack downsizing and the cooling mechanism of the battery pack is discussed. The authors will observe how the adoption of an optimal EMS can help to simplify the vehicle powertrain by allowing a battery pack downsizing and avoiding the use of an active cooling system of the battery pack.</div></div>

<div class="section abstract"><div class="htmlview paragraph">Conventional charging systems for electric and plug-in hybrid vehicles currently use cables to connect to the grid. This methodology creates several disadvantages, including tampering, risk, depreciation and non-value added user efforts. Loose or faulty cables may also create a safety issue. Wireless charging for electric vehicles delivers both a simple, reliable and safe charging process. The system enhances consumer adoption and promotes the integration of electric vehicles into the automotive market. Increased access to the grid enables a higher level of flexibility for storage management, increasing battery longevity.</div><div class="htmlview paragraph">The power class of 3.7kW or less is an optimal choice for global standardization and implementation, due to the readily available power installations for potential customers throughout the world. One of the key features for wireless battery chargers are the inexpensive system costs, reduced content and light weight, easing vehicle integration.</div><div class="htmlview paragraph">This paper demonstrates a wireless charging design with minimal component content. It includes a car pickup coil with 300 mm side length and low volume and mass 1.5 dm<sup>3</sup> power interface electronics. After an overview of its hardware requirements, power transfer and efficiency benefits are presented, providing the anticipated horizontal and lateral deviations.</div><div class="htmlview paragraph">An intense magnetic field is required to transfer the target power at low volumes between the transfer units. This field heats up any metal object over the transfer coil, similar to an induction oven. Consequently, the system should be powered down whenever a metal object is detected in this area. A Foreign Object Detection (FOD) design has been developed to continuously monitor the critical high field area. Device testing results are also provided.</div><div class="htmlview paragraph">Field characteristics are verified alongside the vehicle, ensuring system safety for living beings; compliant with all applicable standards reference limits which is more challenging than the basic limits [<span class="xref">13</span>].</div></div>

Microscopic series plug-in hybrid electric vehicle energy consumption model: Model development and validation

<div class="section abstract"><div class="htmlview paragraph">In the twenty-first century, the development of vehicles has been proceeding towards electronics, electric propulsion and system integration in 5 big trends. Environment, Safety, Market Change, Energy Security and Natural Resources. Especially, “Electric Propulsion of Vehicles” is rapidly accelerated for countermeasure of global warming. In this paper, we will propose the current status analysis for automotive high power supply voltage and classification for future view of HEV(Hybird Electric Vehicle). PHEV(Plug-in Hybrid Electric Vehicle) and EV(Electric Vehicle).</div></div>