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The analysis of nickel metal hydride (Ni-MH) battery performance is very important for automotive researchers and manufacturers. The performance of a battery can be described as a direct consequence of various chemical and physical phenomena taking place inside the container. In this paper, a physics-based model of a Ni-MH battery will be presented. To analyze its performance, the efficiency of the battery is chosen as the performance measure, which is defined as the ratio of the energy output from the battery and the energy input to the battery while charging. Parametric sensitivity analysis will be used to generate sensitivity information for the state variables of the model. The generated information will be used to showcase how sensitivity information can be used to identify unique model behavior and how it can be used to optimize the capacity of the battery. The results will be validated using a finite difference formulation.

The performance, life cycle cost, and safety of electric and hybrid electric vehicles (EVs and HEVs) depend strongly on their energy storage system. Advanced batteries such as lithium-ion (Li-ion) polymer batteries are quite viable options for storing energy in EVs and HEVs. In addition, thermal management is essential for achieving the desired performance and life cycle from a particular battery. Therefore, to design a thermal management system, a designer must study the thermal characteristics of batteries. The thermal characteristics that are needed include the surface temperature distribution, heat flux, and the heat generation from batteries under various charge/discharge profiles. Therefore, in the first part of the research, surface temperature distribution from a lithium-ion pouch cell (20Ah capacity) is studied under different discharge rates of 1C, 2C, 3C, and 4C.

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).

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.

A predictive model of a specific vehicle was modeled in the system-level physical modeling tool, MapleSim, for performance and fuel consumption prediction of a full vehicle powertrain, driving a multi-body chassis model with tire models. The project also includes investigation into overall fuel efficiency and effect on vehicle handling for different drive cycles. The goals of this project were to investigate: 1) the relationships between the forces at tire/road interfaces during various drive cycles and the fuel efficiency of a vehicle, and 2) the interaction between the powertrain and the chassis of the vehicle. To accomplish these goals, a complete vehicle model was created in the lumped-parameter physical modeling tool, MapleSim. A great deal of effort has gone into using real parameters and to assure that some mathematical rigour has been employed in its development.

Motorized shoulder belt tensioning is an occupant protection technology that has promise to reduce automotive crash injuries. The objective of this study was to model the response of a diverse forward-leaning occupant population (6-year-old child, 5th female, 50th male, 95th male) to shoulder belt tensioning during straight line pre-crash braking. The lumped mass model was based on experimental volunteer data for motorized shoulder belt tensioning gathered in a previous quasistatic study. The three dimensional model incorporated the biomechanical properties of the occupant populations, a motorized shoulder belt tensioner (DC motor and controller) and shoulder belt webbing models. Model validation was achieved against the volunteer experiments for angular torso position, torso velocity and shoulder belt moment applied to the torso.

In this work, a new air hybrid engine is introduced in which two throttles are used to manage the engine load in three modes of operation i.e. braking, air motor, and conventional mode. The concept includes an air tank to store pressurized air during braking and rather than a fully variable valve timing (VVT) system, two throttles are utilized. Use of throttles can significantly reduce the complexity of air hybrid engines. The valves need three fixed timing schedules for the three modes of operation. To study this concept, for each mode, the results of engine simulations using GT-Power software are used to generate the operating maps. These maps show the maximum braking torque as well as maximum air motor torque in terms of air tank pressure and engine speed. Moreover, the resulting maps indicate the operating conditions under which each mode is more effective. Based on these maps, a power management strategy is developed to achieve improved fuel economy.

Robust lane marking detection remains a challenge, particularly in temperate climates where markings degrade rapidly due to winter conditions and snow removal efforts. In previous work, dynamic Bayesian networks with heuristic features were used with the feature distributions trained using semi-supervised expectation maximization, which greatly reduced sensitivity to initialization. This work has been extended in three important respects. First, the tracking formulation used in previous work has been corrected to prevent false positives in situations where only poor RANSAC hypotheses were generated. Second, the null hypothesis is reformulated to guarantee that detected hypotheses satisfy a minimum likelihood. Third, the computational requirements have been greatly reduced by computing an upper bound on the marginal likelihood of all part hypotheses upon generation and rejecting parts with an upper bound less likely than the null hypothesis.

Recently, the development of braking assistance system has largely benefit the safety of both driver and pedestrians. A robust prediction and detection of driver braking intention will enable driving assistance system response to traffic situation correctly and improve the driving experience of intelligent vehicles. In this paper, two types unsupervised clustering methods are used to build a driver braking intention predictor. Unsupervised machine learning algorithms has been widely used in clustering and pattern mining in previous researches. The proposed unsupervised learning algorithms can accurately recognize the braking maneuver based on vehicle data captured with CAN bus. The braking maneuver along with other driving maneuvers such as normal driving will be clustered and the results from different algorithms which are K-means and Gaussian mixture model (GMM) will be compared.

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.

Lithium-ion batteries, which are nowadays common in laptops, cell phones, toys, and other portable electronic devices, are also viewed as a most promising advanced technology for electric and hybrid electric vehicles (EVs and HEVs), but battery manufacturers and automakers must understand the performance of these batteries when they are scaled up to the large sizes needed for the propulsion of the vehicle. In addition, accurate thermo-physical property input is crucial to thermal modeling. Therefore, a designer must study the thermal characteristics of batteries for improvement in the design of a thermal management system and also for thermal modeling. This work presents a purely experimental thermal characterization in terms of measurement of the temperature gradient and temperature response of a lithium-ion battery utilizing a promising electrode material, LiFePO4, in a prismatic pouch configuration.

Dynamic modelling of the contact between the tires of automobiles and the road surface is crucial for accurate and effective vehicle dynamic simulation and the development of various driving controllers. Furthermore, an accurate prediction of the rolling resistance is needed for powertrain controllers and controllers designed to reduce fuel consumption and engine emissions. Existing models of tires include physics-based analytical models, finite element based models, black box models, and data driven empirical models. The main issue with these approaches is that none of these models offer the balance between accuracy of simulation and computational cost that is required for the model-based development cycle. To address this issue, we present a volumetric approach to model the forces/moments between the tire and the road for vehicle dynamic simulations.

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 development of a comprehensive battery simulator is essential for future improvements in the durability, performance and service life of lithium-ion batteries. Although simulations can never replace actual experimental data, they can still be used to provide valuable insights into the performance of the battery, especially under different operating conditions. In addition, a single-cell model can be easily extended to the pack level and can be used in the optimization of a battery pack. The first step in building a simulator is to create a model that can effectively capture both the voltage response and thermal behavior of the battery. Since these effects are coupled together, creating a robust simulator requires modeling both components. This paper will develop a battery simulator, where the entire battery model will be composed of four smaller submodels: a heat generation model, a thermal model, a battery parameter model and a voltage response model.

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.

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.

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.

Supervisory control strategies for a hybrid fuel cell powertrain are developed and simulated using Simulink models and the Powertrain Systems Analysis Toolkit (PSAT). The control strategy selects the power splitting ratio between a 65kW Hydrogenics fuel cell power module and a 70kW Cobasys Nickel Metal Hydride (NiMH) battery pack. Simple control algorithms targeting a battery pack State of Charge (SOC), or maximizing the instantaneous powertrain efficiency are initially considered and analyzed. A comprehensive control strategy optimizing powertrain efficiency, vehicle performance, emissions, and long-term reliability is then developed and simulated. The simulated vehicle using the comprehensive control strategy with reliability considerations exhibits a 21% mileage improvement as compared to a simple rule-based control algorithm.

A fuel cell-battery hybrid powertrain SUV vehicle is designed using an optimized model-based design process. Powertrain and fuel storage components selected include a 65 kW Polymer Electrolyte Membrane Fuel Cell (PEMFC) power module, two 67 kW electric traction motors, a 35 MPa compressed hydrogen storage tank, a 70 kW nickel metal hydride battery pack, and a University of Waterloo in-house DC/DC converter design. Hardware control uses two controllers, a main supervisory controller and a subsystem controller in addition to any embedded component control modules. Two key innovations of this work include the hybrid control strategy and the DC/DC converter. The final powertrain characteristics are expected to meet a set of Vehicle Technical Specifications (VTS).

In this paper, a new solution method is presented to study the effect of wave propagation in engine manifolds, which includes solving one-dimensional models for compressible flow of air. Velocity, pressure, and density profiles are found by solving a system of non-linear Partial Differential Equations (PDEs) in space and time derived from Euler’s equations. The 1D model includes frictional losses, area change, and heat transfer. The solution is traditionally found by utilizing the Method of Characteristics and applying finite difference solutions to the resulting system of ordinary differential equations (ODEs) over a discretized grid. In this work, orthogonal collocation is used to solve the system of ODEs that is defined along the characteristic curves. Orthogonal polynomials are utilized to approximate velocity, pressure, sound speed, and the characteristic curves along which the system of PDEs reduce to a system of ODEs.