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The conversion design strategy, and emissions and performance results for a dedicated propane, vapour injected, 1995 Dodge Dakota truck are reported. Data is obtained from the University of Waterloo entry in the 1997 Propane Vehicle Challenge. A key feature of the design strategy is its focus on testing and emissions while preserving low engine speed power for drivability. Major changes to the Dakota truck included the following: installation of a custom shaped fuel tank, inclusion of a fuel temperature control module, addition of a vaporizer and a fuel delivery metering unit, installation of a custom vapour distribution manifold, addition of an equivalence ratio electronic controller, inclusion of a wide range oxygen sensor, addition of an exhaust gas recirculation cooler and installation of thermal insulation on the exhaust system. A competition provided natural gas catalyst was used.

Forecasting the motion of the leading vehicle is a critical task for connected autonomous vehicles as it provides an efficient way to model the leading-following vehicle behavior and analyze the interactions. In this study, a personalized time-series modeling approach for leading vehicle trajectory prediction considering different driving styles is proposed. The method enables a precise, personalized trajectory prediction for leading vehicles with limited inter-vehicle communication signals, such as vehicle speed, acceleration, space headway, and time headway of the front vehicles. Based on the learning nature of human beings that a human always tries to solve problems based on grouping and similar experience, three different driving styles are first recognized based on an unsupervised clustering with a Gaussian Mixture Model (GMM).

A real-time control-oriented mean value engine plant model that includes engine thermals and cold starts is developed for a Toyota Prius 2015 plug-in hybrid engine in Modelica and MapleSim and validated experimentally. The model consists of an engine block model, intake and exhaust manifold models, and a throttle model. An advantage of the engine block model is the ability to compute the frictional Mean Effective Pressure during engine cold starts from calculated air, oil, and coolant temperatures at various locations in the engine block. Traditionally, engine thermals are modelled utilizing thermal resistances and capacitors. The proposed model utilizes linear graph theory with terminal equations to study the topology of the different components that affect engine thermals, including engine head, liner, coolant, and oil sump.

Due to the high power and energy density and also relative safety, lithium ion batteries are receiving increasing acceptability in industrial applications especially in transportation systems with electric traction such as electric vehicles and hybrid electric vehicles. In this regard, to ensure performance reliability, accurate modeling of calendar life of such batteries is a necessity. In fact, potential failure of Li-ion battery packs remains a barrier to commercialization. Battery pack life is a critical feature to warranty and maintenance planning for hybrid vehicles, and will require adaptive control systems to account for the loss in vehicle range, and loss in battery charge and discharge efficiency. Failure not only results in large replacement costs, but also potential safety concerns such as overheating or short circuiting which may lead to fires.

Whole-body vibration (WBV) is associated with several adverse health and safety outcomes including low-back pain (LBP) and driver fatigue. The objective of this study was to evaluate the efficacy of three commercially-available air-suspension truck seats for reducing truck drivers’ exposures to WBV. Seventeen truck drivers operating over a standardized route were recruited for this study and three commercially-available air suspension seats were evaluated. The predominant, z-axis average weighted vibration (Aw) and Vibration Dose Values (VDV) were calculated and normalized to represent eight hours of truck operation. In addition, the Seat Effective Amplitude Transmissibility (SEAT), the ratio of the seat-measured vibration divided by the floor-measured vibration, was compared across the three seats. One seat had significantly higher on-road WBV exposures whereas there were no differences across seats in off-road WBV exposures.

Due to rising fuel prices and environmental concerns, Electric Vehicles (EVs) and Hybrid Electric Vehicles (HEVs) have been gaining market share as fuel-efficient, environmentally friendly alternatives. Lithium-ion batteries are commonly used in EV and HEV applications because of their high power and energy densities. During controls development of HEVs and EVs, hardware-in-the-loop simulations involving real-time battery models are commonly used to simulate a battery response in place of a real battery. One physics-based model which solves in real-time is the reduced-order battery model developed by Dao et al. [1], which is based on the isothermal model by Newman [2] incorporating concentrated solution theory and porous electrode theory [3]. The battery models must be accurate for effective control; however, if the battery parameters are unknown or change due to degradation, a method for estimating the battery parameters to update the model is required.

The drive to improve and optimize hybrid vehicle performance is increasing with the growth of the market. With this market growth, the automotive industry has recognized a need to train and educate the next generation of engineers in hybrid vehicle design. The University of Waterloo Alternative Fuels Team (UWAFT), as part of the EcoCAR 3 competition, has developed a control strategy for a novel parallel-split hybrid architecture. This architecture features an engine, transmission and two electric motors; one pre-transmission motor and one post-transmission motor. The control strategy operates these powertrain components in a series, parallel, and all electric power flow, switching between these strategies to optimize the energy efficiency of the vehicle. Control strategies for these three power flows are compared through optimization of efficiencies within the powertrain.

In this paper, a degradation testing of a lithium-ion battery used for an electric vehicle (EV) is performed and the capacity fade is measured over 400 cycles. For this, a 200 Ah LiFePO4 battery cell is tested under ambient temperature conditions with charge-discharge cycles at rate of 1C (constant current). Additionally, individual cell characterization is conducted using a C/25 (0.8A) charge-discharge cycle and hybrid pulse power characterization (HPPC). Later, the Thevenin battery model was constructed in MATLAB along with an empirical degradation model and validated in terms of voltage for all cycles. It is also found that the presented model closely estimated the profiles observed in the experimental data. Data collected from the experimental results showed that a capacity fade occurred over the 400 cycles and the discharge capacity at the end of 400th cycle is found to be 137.73 Ah. The error between model/experiments is found to be less than 3.5% for all cycles.

In this paper, gear ratios of a two-speed transmission system are optimized for an electric passenger car. Quasi static system models, including the vehicle model, the motor, the battery, the transmission system, and drive cycles are established in MATLAB/Simulink at first. Specifically, since the regenerative braking capability of the motor is affected by the SoC of battery and motors torque limitation in real time, the dynamical variation of the regenerative brake efficiency is considered in this study. To obtain the optimal gear ratios, iterations are carried out through Nelder-Mead algorithm under constraints in MATLAB/Simulink. During the optimization process, the motor efficiency is observed along with the drive cycle, and the gear shift strategy is determined based on the vehicle velocity and acceleration demand. Simulation results show that the electric motor works in a relative high efficiency range during the whole drive cycle.

The vision for the future automotive chassis is to interconnect the lateral, longitudinal, and vertical dynamics by separately controlling driving, braking, steering, and damping of each individual wheel. A major advantage of all wheel drive electric vehicles with four in-wheel motors is the possibility to control the torque and speed at each wheel independently. This paper proposes a traction controller for such a vehicle. It estimates the road's adhesion potential at each wheel and adjusts each motor voltage, such that the longitudinal slip is kept in an optimal range. For development and validation, a full vehicle model is designed in ADAMS/View software, in co-simulation with motor and control elements, modeled in MATLAB/Simulink.

The recent increase in oil prices and environmental concerns have attracted various research efforts on hybrid electric vehicles (HEVs) which provide promising alternatives to conventional engine-powered vehicles with better fuel economy and fewer emissions. To speed up the design and prototyping processes of new HEVs, a method that automatically generates mathematics equations governing the vehicle system response in an optimized symbolic form is desirable. To achieve this goal, we employed MapleSimTM, a new physical modeling tool developed by Maplesoft Inc., to develop the multi-domain model of a series-HEV, utilizing the symbolic computing algorithms of Maple software package to generate an optimized set of governing equations. The HEV model consists of a mean-value internal combustion engine (ICE), a chemistry-based Ni-MH battery pack, and a multibody vehicle model. Simulations are then used to demonstrate the performance of the developed HEV system.

Ride performance characteristics of a road vehicle involving different suspension system layouts are investigated. The suspension layouts consist of conventional rectangular 4-wheel, novel diamond-shaped 4-wheel, triangular 3-wheel and inverse-triangular 3-wheel. A generalized full-vehicle model integrating different suspension system layouts is formulated. The fundamental suspension properties are compared in terms of bounce-, roll- and pitch-mode. The ride dynamic responses and power consumption characteristics are explored under two measured road roughness excitations and a range of vehicle speeds. The results demonstrate that the novel diamond-shaped suspension system layout could yield significantly enhanced vehicle ride performance in an energy-saving manner.

Strict environmental regulations are driving the automotive industry toward electric vehicles as they offer zero emissions. A key component in electric vehicles is the electric motor, where the stator and rotor are manufactured from stacks of thin electrical steel sheets. The electrical steel sheets can be cut in different ways, and the cutting methods may significantly affect the fatigue strength of the component. It is important to understand the effect of the cutting processes on the fatigue properties of electrical steel to ensure there is no premature failure of the electric motor resulting from an improper cutting process. This investigation compared the effect of three different edge preparation methods (stamping, CNC machining, and waterjet cutting) on the fatigue performance of 0.27mm thick electrical steel sheets. To investigate the effect of the edge finish on fatigue behavior, surface roughness was measured for these different samples.

Despite significant progress toward application of Li-ion batteries in electric vehicles, there are still major concerns about the range of electric vehicles and battery life. Depending on the climate of the region where the vehicle is in use, auxiliary loads could also play a significant role on the battery performance and durability. In this paper, the effect of air conditioning (AC) load on the electric range and Li-ion battery life is evaluated. For this purpose, a thermodynamic model for the vehicle cabin is developed and integrated to a battery model. The thermodynamic model takes the ambient conditions, solar load, and the vehicle drive cycle as inputs and calculates the instantaneous cabin temperature and humidity. The battery model, which represents a Li-on battery pack installed on a fully electrified Ford Escape 2009, consists of a voltage source in series with a lump resistance, a thermal sub-model, and a degradation sub-model to predict the battery capacity fade.

A major challenge in the development of the next generation electric and hybrid electric vehicle (EV and HEV) technology is the control and management of heat generation and operating temperatures. Vehicle performance, reliability and ultimately consumer market adoption are integrally dependent on successful battery thermal management designs. In addition to this, crucial to thermal modeling is accurate thermo-physical property input. Therefore, to design a thermal management system and for thermal modeling, a designer must study the thermal characteristics of batteries. This work presents a purely experimental thermal characterization of thermo-physical properties of a lithium-ion battery utilizing a promising electrode material, LiFePO4, in a prismatic pouch configuration. In this research, the thermal resistance and corresponding thermal conductivity of prismatic battery materials is evaluated.

The automobile industry has been undergoing a transition from fossil fuels to a low emission platform due to stricter environmental policies and energy security considerations. Electric vehicles, powered by lithium-ion batteries, have started to attain a noticeable market share recently due to their stable performance and maturity as a technology. However, electric vehicles continue to suffer from two disadvantages that have limited widespread adoption: charging time and energy density. To mitigate these challenges, vehicle Original Equipment Manufacturers (OEMs) have developed different vehicle architectures to extend the vehicle range. This work seeks to compare various powertrains, including: combined power battery electric vehicles (BEV) (zinc-air and lithium-ion battery), zero emission fuel cell vehicles (FCV)), conventional gasoline powered vehicles (baseline internal combustion vehicle), and ICE engine extended range hybrid electric vehicle.

Electrical steels are silicon alloyed steels that possess great magnetic properties, making them the ideal material choice for the stator and rotor cores of electric motors. They are typically comprised of laminated stacks of thin electrical steel sheets. An electric motor can reach high temperatures under a heavy load, and it is important to understand the combined effect of temperature and load on the electrical steel’s performance to ensure the long life and safety of electric vehicles. This study investigated the fatigue strength and failure behavior of a 0.27mm thick electrical steel sheet, where the samples were prepared by a stamping process. Stress-control fatigue tests were performed at both room temperature and 150°C. The S-N curve indicated a decrease in the fatigue strength of the samples at the elevated temperature compared to the room temperature by 15-25 MPa in the LCF and HCF regimes, respectively.

Electrical steel, also known as silicon steel, is a ferromagnetic material that is often used in electric vehicles (EVs) for stator and rotor applications. Since the design and manufacturing of rotors require the use of laminated thin electrical steel sheets, the fatigue characterization of these single sheets is of interest. In this study, a 0.27mm thick non-oriented electrical steel sheet was tested under cyclic loading in the load-controlled mode with the load ratio R = 0.1 at room temperature. The specimens were prepared using the Computer Numerical Control (CNC) machining method. The Smith-Watson-Topper mean stress correction was used to find the equivalent fully reversed stress-life (S-N) curve. The Basquin equation was used to describe the fatigue strength of the electrical steel and the fatigue parameters were extracted. Furthermore, a design curve with a reliability of 90% and a confidence level of 90% was generated using Owen’s Tolerance Limit method.

Decision-making of lane-change for autonomous vehicles faces challenges due to the behavioral differences among human drivers in dynamic traffic environments. To enhance the performances of autonomous vehicles, this paper proposes a game theoretic decision-making method that considers the diverse Social Value Orientations (SVO) of drivers. To begin with, trajectory features are extracted from the NGSIM dataset, followed by the application of Inverse Reinforcement Learning (IRL) to determine the reward preferences exhibited by drivers with distinct Social Value Orientation (SVO) during their decision-making process. Subsequently, a reward function is formulated, considering the factors of safety, efficiency, and comfort. To tackle the challenges associated with interaction, a Stackelberg game model is employed.

Within the last decade, the automotive industry has made major progress toward the electrification of drive trains and application of electrochemical power sources. Among available storage solutions, Li-ion batteries are considered as the most attractive and are set to be used in the next generation of hybrid and electric vehicles. This is due to their superiority in energy density, power density, and low self-discharge and high cycle life compared to other chemistries. However, there are some limitations associated with Li-ion battery; among them is the operating temperature range. Any deviation from a narrow temperature range may result in low overall performance and potential degradation of the cells. In this paper, impact of ambient temperature on the A123 Li-ion batteries performance is investigated. A123 cells have been tested under constant charge-discharge cycles, hybrid pulse power characterization (HPPC) tests and also standard drive cycle tests.