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The adoption of lithium-ion batteries in vehicle electrification is fast growing due to high power and energy demand on hybrid and electric vehicles. However, the battery overall performance changes with time through the vehicle life. This paper investigates the electric vehicle battery cell aging under different usages. Battery cell experimental data including open circuit voltage and internal resistance is utilized to build a typical electric vehicle model in the AVL-Cruise platform. Four driving cycles (WLTP, UDDS, HWFET, and US06) with different ambient temperatures are simulated to acquire the battery cell terminal currents. These battery cell terminal current data are inputs to the MATLAB/Simulink battery aging model. Simulation results show that battery degrades quickly in high ambient temperatures. After 15,000 hours usage in 50 degrees Celsius ambient temperature, the usable cell capacity is reduced up to 25%.

As one of the core components of electric vehicle, the performance of power battery is largely determined by thermal management system. Air cooling is difficult to meet the heat dissipation requirements of high-power power batteries. Liquid cooling arrangement is complex and requires high sealing performance. Phase change materials will increase the mass of battery packs. Heat pipes have good heat conduction, temperature equalization performance and light weight, and it is an ideal cooling and heat dissipation technology with efficient cooling fins. In this paper, a thermal management system of power battery based on heat pipe and fin is proposed. The maximum temperature and wall temperature difference of power battery are reduced by heat pipe and fin heat dissipation. The influence of different fin spacing and heights on the thermal management system is studied, and then the fin spacing and height are optimized.

A lumped electro-thermal model for a battery module with 14 cells in series (14S1P), and with a cooling channel, is created by two-way coupling of an equivalent circuit model (ECM) and a linear time-invariant (LTI) method based thermal reduced order model (ROM). To create the ROM, a step response data in the form of temperature versus time curve is required. This data is obtained by running a transient full three-dimensional (3D) computational fluid dynamics (CFD) analysis for the full module. The thermal ROM accounts for the effect of the heat generated by the active cells, the joule heat generated in tabs and connectors, and the coolant inlet temperature. To create an ECM, data from hybrid pulse power characterization (HPPC) test is used. Such a lumped electro-thermal model for a battery module can run faster than a 3D CFD analysis and can be easily integrated in a system level model.

With the increasing concern on environmental pollution and CO2 emission all over the world, range-extended electrical vehicle (REEV) has gradually got more attention because it could avoid the mileage anxiety of the battery electrical vehicles (BEV) and get high energy efficiency. Nevertheless, NVH performance of internal combustion engine range extender (ICRE) is a critical problem that affects the driving experiences for REEV. In this paper, a two-cylinder PFI gasoline engine and a permanent magnet synchronous motor (PMSM) are coaxially mounted to run as an ICRE. The ICRE control system was established based on Compact RIO hardware and LabVIEW, who has the functions of the intake throttle PID closed-loop control, autonomous ICRE operation control, and speed PID closed-loop control. In this paper, the gasoline engine was first driven to the idle condition by PMSM in speed-control mode.

Nowadays, due to the internal combustion engine (ICE) industry’s orientation toward downsizing, modern efficient cooling systems with lower power consumption, small size, and high compactness are essential. To improve these items, applying precision cooling and boiling phenomenon are inevitable. Having an appropriate coolant flow velocity that leads to utilize only the advantages of boiling heat transfer has always been a challenge. Two experimental test rigs, one for modeling and accurate prediction of subcooled flow boiling and the other for measurement and validation of coolant velocity in a water jacket by particle image velocimetry (PIV) method, are set up. An accurate and robust empirical correlation for modeling of subcooled flow boiling that occurs in the water jacket is developed. Then, through a three-dimensional (3D) thermal analysis, the heat transfer parameters such as heat flux and temperature distribution of the ICE cylinder block and head are obtained numerically.

The design of better active materials for lithium-ion batteries (LIBs) is crucial to satisfy the increasing demand of high performance batteries for portable electronics and electric vehicles. Currently, the development of new active materials is driven by physical experimentation and the designer’s intuition and expertise. During the development process, the designer interprets the experimental data to decide the next composition of the active material to be tested. After several trial-and-error iterations of data analysis and testing, promising active materials are discovered but after long development times (months or even years) and the evaluation of a large number of experiments. Bayesian global optimization (BGO) is an appealing alternative for the design of active materials for LIBs. BGO is a gradient-free optimization methodology to solve design problems that involve expensive black-box functions. An example of a black-box function is the prediction of the cycle life of LIBs.

Propulsion system control algorithms covering the functional needs of xEV propulsion (‘x’ donates P0-P4 configurations) systems are presented in this paper. The scope and foundation are based on generic well-established HEV controller architectures. However, unlike conventional HEV (series, parallel and power split) powertrains, the next generation of integrated electric propulsion configurations will utilize a single micro controller that supports multiple control functions ranging from the electric machines, inverters, actuators, clutch solenoids, coolant pumps, etc. This presents a unique challenge to architect control algorithms within the AUTOSAR framework while satisfying the complex timing requirements of motor/generator-inverter (MGi) control and increased interface definitions between software components to realize functional integration between the higher level propulsion system and its sub-systems.

Low-temperature activity is an important requirement for automotive catalysts. In particular, most of the tailpipe emissions occur right after the engine starts (cold emissions). These emissions can be effectively reduced by using a trap material such as zeolite for hydrocarbon (HC) adsorption [1, 2, 3, 4, 5, 6, 7, 8, 9]. However, using zeolite as a trap material in automotive catalyst is limited due to its low durability under hydrothermal aging conditions. That is the reason why zeolites can be often used for diesel engines which usually run at lower temperature than the gasoline engines during entire mode driving. In most cases, zeolites need to be placed away from large thermal loads in order to take advantage of their adsorption abilities. In general, the thermal endurance of close-coupled catalysts for gasoline powered vehicles proceeds at about 1000 °C in the presence of water.

For the upcoming norms of BSVI, it is very important to keep the balance of emission and fuel economy. In these paper different concepts for exhaust gas temperature management will be analyzed and compared. In transient and steady conditions with medium and low load, the effects of active control strategies on exhaust thermal management were studied at the test bench, which include E waste gate intake throttle valve opening, injection advance angle, injection pressure and post injection. The comparison study was factors impacting the fuel economy and temperature management along with to meet WHSC & WHTC emission. The DOE was done to understand the best suitable match with the above function to achieve the optimized fuel economy and BSVI legislative requirement. Different test where carried with 0-100% of opening of intake throttle valve, E waste full open and late post injection to understand the thermal management of engine in part and full load.

Abstract: This paper presents an experimental study on engine performance & emission based on Taguchi method and grey relational analysis for optimization of six input parameters and their five levels. Combined effect of input parameters viz. compression ratio, injection pressure, injection-nozzle geometry, additive, fuel fraction and EGR in controlling BSFC and NOxas the response variables in CI engine fueled with Mangifera Indica biodiesel blends was investigated. Number of experiments was reduced by employing Taguchi's L25orthogonal array. The signal-to-noise (S/N) ratio and grey relational analysis techniques were used for data analysis. The combination of six input parameters was obtained for optimized engine performance and emission. The optimal combination of input parameters so obtained was further confirmed through experiments. The injection nozzle geometry was the most influencing parameter.

In the automotive industry, multiple prototypes are used for vehicle development purposes. These prototypes are typically put through rigorous testing, both under accelerated and real world conditions, to ensure that all the problems related to design, manufacturing, process etc. are identified and solved before it reaches the hands of the customer. One of the challenges faced in testing, is the low repeatability of the real world tests. This may be predominantly due to changes in the test conditions over a period of time like road, traffic, climate etc. Estimating the repeatability of a real world test has been difficult due to the complex and multiple parameters that are usually involved in a vehicle level test and the time correlation between different runs of a real world test does not exist. In such a scenario, the popular and the well-known univariate correlation methods do not yield the best results.

The Deep Orange framework is an integral part of the graduate automotive engineering education at Clemson University International Center for Automotive Research (CU-ICAR). The initiative was developed to immerse students into the world of an OEM. For the 6th generation of Deep Orange, the goal was to develop an urban utility/activity vehicle for the year 2020. The objective of this paper is to explain the interior concept that offers a flexible interior utility/activity space for Generation Z (Gen Z) users. AutoPacific data were first examined to define personas on the basis of their demographics and psychographics. The resulting market research, benchmarking, and brand essence studies were then converted to consumer needs and wants, to establish technical specifications, which formed the foundation of the Unique Selling Points (USPs) of the concept.

With continuous pressure for reducing vehicle development time and cost, without compromising on system reliability, it is imperative to move from Road to Lab to Model (RLM) based development. Every OEM is currently using virtual environment to complete functional checks of systems during development. The method of developing control functions and calibration in virtual environment brings repeatability and reproducibility which typically is challenging in real world testing. This process is cost effective and optimizes the time for development and brings high level of system maturity before testing it in the vehicle. This paper focuses on defining a front-loading approach for setting up of virtual test environment. Development of virtual test environment and its validation with respect to real vehicle data will be discussed, with focus on vehicle plant model and driver model.

OEMs these days are focusing on front loading the activities to Virtual Test Environment (VTE) based development owing to high development cost and complexity in achieving repeatability during testing phase of vehicle development,. This process not only helps in reducing the cost and time but also helps in increasing the maturity and confidence level of the developed system before actual prototype is built. In the past, extensive research has happened for increasing the fidelity of VTE by improving plant model efficacy which involves powertrain and other vehicle systems. On the other hand, improving the precision of driver model which is one of the most complex nonlinear systems of virtual environment still remains a challenge. It is apparent that various drivers show different behavior in real world for a given drive profile. While modelling a driver for a VTE, the real world driver attributes are seldom considered.

This research is an attempt to investigate the significance of Value Stream Mapping (VSM) in the lean transformation of manufacturing units (largely automotive) and then apply the same in a tool room. It is an essential tool used to interpret both material and information flow in a system. The tool room under study specializes in production of a large variety of high precision tools for the automotive industry. A product family is chosen to map and analyze various stages of its production process, starting from the raw material (R/M) to the finished goods’ (F/G) stage. VSM is then implemented in the tool room to correctly identify wastes and thus improvement areas to bridge gaps between current and future states. Both current and future state maps are drafted along with usage of other lean tools to justify its implementation in a small setup like tool room.

As the design of engines advances and continues to push the capabilities of current hardware closer to their durability limits, more accurate and reliable analysis is necessary to ensure that designs are robust. This research evaluates a method of conjugate heat transfer analysis for a diesel engine that combines the combustion CFD, Engine FEA, and cooling jacket CFD with the aim of getting more accurate heat loss predictions and a more accurate temperature distribution in the engine than with current analysis methods. A 15.0 L Cummins ISX heavy duty engine operating at 1250 RPM and 15 bar BMEP load is selected for this work. Spray combustion computational fluid dynamics (CFD) simulations are performed for the diesel engine and the results are validated with experimental data. Finite Element Analysis (FEA) simulations were performed in a separate software platform.

Preparation of air-fuel mixture and its stratification, plays the key role to determine the combustion and emission characteristics in a gasoline direct injection (GDI) engine working in stratified conditions. The mixture stratification is mainly influenced by the in-cylinder flow structure, which mainly relies upon engine geometry i.e. cylinder head, intake port configuration, piston profile etc. Hence in the present analysis, authors have attempted to comprehend the effect of cylinder head geometry on the mixture stratification, combustion and emission characteristics of a GDI engine. The computational fluid dynamics (CFD) analysis is carried out on a single-cylinder, naturally-aspirated four-stroke GDI engine having a pentroof shaped cylinder head. The analysis is carried out at four pentroof angles (PA) viz., 80 (base case), 140, 200 and 250 with the axis of the cylinder.

Field tests were conducted on a late full sized sedan with the HVAC unit operating in both Recirculation and OSA modes to monitor build-up of the CO2 concentration inside the cabin and its influence on occupant’s fatigue and alertness. These tests were conducted during 2015 summer on interstate highways with test durations ranging from 4 to 7 hours. During the above tests, fatigue or tiredness of the occupants (including CO2 levels) was monitored and recorded at 30 min intervals. Based on this investigation it is determined that the measured cabin concentration levels reaches ASHRAE (Standard 62-1999) specified magnitudes (greater than 700 ppm over ambient levels) with three occupants in the vehicle. Further, the occupants did show fatigue when the HVAC unit was operated in recirculation mode in excess of 5 hours. Further details have been presented in the paper.

This paper addresses R1234yf A/C system performance impacted by condenser airflow passage blockages of nonhotspot and hotspot objects. With the modern vehicle design trend, more and more chances exist in blocking condenser airflow passages by objects such as TOC (transmission oil cooler) or fine grills etc. These objects create hotspots and narrowed airflow passages to the condenser and result in A/C performance degradation. It is important to understand the specific area of the condenser which is most impacted by a blockage so this area can be avoided in the design/packaging of front end components. In addition, it is important to understand the magnitude of performance loss associated with the specific areas of blockage. As a result of this understanding, optimal design locations for these blockages (including hotspots and grilles) can be proposed in order to mitigate the impact on A/C cooling performance.

In the winter, windshield glass fogging must be prevented through the intake of outdoor air into a vehicle. However, the corresponding energy loss via the ventilation system cannot be ignored. In the present study, the defogging pattern on the windshield is evaluated and the water vapor transportation in the flow field in the vehicle is analyzed in order to investigate the ventilation load by means of a numerical simulation. Some examined cases involve new outlet positions. Additionally, a new, energy-saving air supply method for defogging, with so-called “double-layer ventilator”, is proposed. In this method, one air jet layer is obtained via a conventional defogging opening in the vicinity of the windshield, supplying an outdoor air intake. The other jet consists of recirculated air that covers the outdoor air, preventing it from mixing with the surrounding air.