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Battery packs of electric vehicles are typically composed of lithium-ion batteries with aluminum and copper acting as cell terminals. These terminals are joined together in series by means of connector tabs to produce sufficient power and energy output. Such critical electrical and structural cell terminal connections involve several challenges when joining thin, highly reflective and dissimilar materials with widely differing thermo-mechanical properties. This may involve potential deformation during the joining process and the formation of brittle intermetallic compounds that reduce conductivity and deteriorate mechanical properties. Among various joining techniques, laser welding has demonstrated significant advantages, including the capability to produce joints with low electrical contact resistance and high mechanical strength, along with high precision required for delicate materials like aluminum and copper.

The characterization of sheet metals under in-plane uniaxial bending is challenging due to the aspect ratios involved that can cause buckling. Anti-buckling plates can be employed but require compensation for contact pressure and friction effects. Recently, a novel in-plane bending fixture was developed to allow for unconstrained sample rotation that does not require an anti-buckling device. The objective of the present study is to design the sample geometry for sheared edge fracture characterization under in-plane bending along with a methodology to resolve the strains exactly at the edge. A series of virtual experiments were conducted for a 1.0 mm thick model material with different hardening rates to identify the influence of gage section length, height, and the radius of the transition region on the bend ratio and potential for buckling. Two specimen geometries are proposed with one suited for constitutive characterization and the other for sheared edge fracture.

Vehicle weight reduction through the use of components made of magnesium alloys is an effective way to reduce carbon dioxide emission and improve fuel economy. In the design of these components, which are mostly under cyclic loading, notches are inevitably present. In this study, surface strain distribution and crack initiation sites in the notch region of AZ31B-H24 magnesium alloy notched specimens under uniaxial load are measured via digital image correlation. Predicted strains from finite element analysis using Abaqus and LS-DYNA material types 124 and 233 are then compared against the experimental measurements during quasi-static and cyclic loading. It is concluded that MAT_233, when calibrated using cyclic tensile and compressive stress-strain curves, is capable of predicting strain at the notch root. Finally, employing Smith-Watson-Topper model together with MAT_233 results, fatigue lives of the notched specimens are estimated and compared with experimental results.

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.

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.

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.

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.

Lithium-ion batteries (LIBs) are one of the best candidates as energy storage systems for automobile applications due to their high power and energy densities. However, durability in comparison to other battery chemistries continues to be a key factor in prevention of wide scale adoption by the automotive industry. In order to design more-durable, longer-life, batteries, reliable and predictive battery models are required. In this paper, an effective model for simulating full-size LIBs is employed that can predict the operating voltage of the cell and the distribution of variables such as electrochemical current generation and battery state of charge (SOC). This predictive ability is used to examine the effect of parameters such as current collector thickness and tab location for the purpose of reducing non-uniform voltage and current distribution in the cell. It is identified that reducing the non-uniformities can reduce the ageing effects and increase the battery durability.

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.

A compact sized vehicle that has a narrow track could solve problems caused by vehicle congestion and limited parking spaces in a mega city. Having a smaller footprint reduces the vehicle's total weight which would decrease overall vehicle power consumption. Also a smaller and narrower vehicle could travel easily through tight and congested roads that would speed up the traffic flow and hence decrease the overall traffic volume in urban areas. As an additional benefit of having a narrow track length, a driver can experience similar motorcycle riding experience without worrying about bad weather conditions since a driver sits in a weather protected cabin. However, reducing the vehicle's track causes instability in vehicle dynamics, which leads to higher possibility of rollovers if the vehicle is not controlled properly. A three wheel personal vehicle with an active tilting system is designed in MapleSim.

This paper seeks to identify the refrigeration load of a hybrid electric truck in order to find the demand power required by the energy management system. To meet this objective, in addition to the power consumption of the refrigerator, the vehicle mass needs to be estimated. The Recursive Least Squares (RLS) method with forgetting factors is applied for this estimation. As an example of the application of this parameter identification, the estimated parameters are fed to the energy control strategy of a parallel hybrid truck. The control system calculates the demand power at each instant based on estimated parameters. Then, it decides how much power should be provided by available energy sources to minimize the total energy consumption. The simulation results show that the parameter identification can estimate the vehicle mass and refrigeration load very well which is led to have fairly accurate power demand prediction.

The effect of stress triaxiality on failure strain in as-cast magnesium alloy AM60B is examined. Experiments using one uniaxial and two notched tensile geometries were used to study the effect of stress triaxiality on the quasi-static constitutive response of super vacuum die cast AM60B castings. For all tests, local strains, failure location and specimen elongation were tracked using two-dimensional digital image correlation (DIC) analysis. The uniaxial specimens were tested in two orthogonal directions to determine the anisotropy of the casting. Finite element models were developed to estimate effective plastic strain histories and stress state (triaxiality) as a function of notch severity. It was found that there is minimal, if any, anisotropy present in AM60B castings. Higher stress triaxiality levels caused increases in maximum stress and decreases in elongation and local effective plastic strain at failure.

In electrified vehicle applications, the heat generated of lithium-ion (Li-ion) cells may significantly affect the vehicle range and state of health (SOH) of the pack. Therefore, a major design task is creation of a battery thermal management system with suitable control and cooling strategies. To this end, the thermal behavior of Li-ion cells at various temperatures and operating conditions should be quantified. In this paper, two different commercial pouch cells for plug-in hybrid electric vehicles (PHEVs) are studied through comprehensive thermal performance tests. This study employs a fractional factorial design of experiments to reduce the number of tests required to characterize the behavior of fresh cells while minimizing the effects of ageing. At each test point, the effects of ambient temperature and charge/discharge rate on several types of cell efficiencies and surface heat generation are evaluated.

In this paper, initial results of Li-ion battery performance characterization through field tests are presented. A fully electrified Ford Escape that is equipped by three Li-ion battery packs (LiFeMnPO4) including an overall 20 modules in series is employed. The vehicle is in daily operation and data of driving including the powertrain and drive cycles as well as the charging data are being transferred through CAN bus to a data logger installed in the vehicle. A model of the vehicle is developed in the Powertrain System Analysis Toolkit (PSAT) software based on the available technical specification of the vehicle components. In this model, a simple resistive element in series with a voltage source represents the battery. Battery open circuit voltage (OCV) and internal resistance in charge and discharge mode are estimated as a function of the state of charge (SOC) from the collected test data.

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.

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 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.