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Improving vehicle response through advanced knowledge of traffic behavior can lead to large improvements in energy consumption for the single isolated vehicle. This energy savings across multiple vehicles can even be larger if they travel together as a cohort in harmonization. Additionally, if the vehicles have enough information about their immediate path of travel, and other vehicles’ in that path (and their respective critical forward-looking information), they can safely drive close enough to each other to share aerodynamic load. These energy savings can be upwards of multiple percentage points, and are dependent on several criteria. This analysis looks at criteria that contributes to energy savings for a cohort of vehicles in synchronous motion, as well as describes a study that allows for better understanding of the potential benefits of different types of cohorted vehicles in different platoon arrangements.

A Machine Learning (ML) approach is presented to correlate in-cylinder images of early flame kernel development within a spark-ignited (SI) gasoline engine to early-, mid-, and late-stage flame propagation. The objective of this study was to train machine learning models to analyze the relevance of flame surface features on subsequent burn rates. Ultimately, an approach of this nature can be generalized to flame images from a variety of sources. The prediction of combustion phasing was formulated as a regression problem to train predictive models to supplement observations of early flame kernel growth. High-speed images were captured from an optically accessible SI engine for 357 cycles under pre-mixed operation. A subset of these images was used to train three models: a linear regression model, a deep Convolutional Neural Network (CNN) based on the InceptionV3 architecture and a CNN built with assisted learning on the VGG19 architecture.

The AUTomotive Open System ARchitecture (AUTOSAR) Development Partnership has published early 2008 the specifications Release 3.0 [1], with a prime focus on the overall architecture, basic software, run time environment, communication stacks and methodology. Heavy developments have taken place in the OEM and supplier community to deliver AUTOSAR loaded cars on the streets starting 2008 [2]. The 2008 achievements have been: Improving the specifications in order to secure the exploitation for body, chassis and powertrain applications Adding major features: safety related functionalities, OBD II and Telematics application interfaces.

As the automotive industry quickly moves towards hybridized and electrified vehicles, the optimal integration of power electronics in these vehicles will have a significant impact not only on the cost, performance, reliability, and durability; but ultimately on customer acceptance and market success of these technologies. If properly executed with the right cost, performance, reliability and durability, then both the industry and the consumer will benefit. It is because of these interdependencies that the pace and scale of success, will hinge on effective collaboration. This collaboration will be built around the convergence of automotive and industrial technology. Where real time embedded controls mixes with high power and voltage levels. The industry has already seen several successful collaborations adapting power electronics to the automotive space in target vehicles.

Hybrid high voltage battery pack is not only heavy mass but also large in dimension. It interacts with the vehicle through the battery tray. Thus the battery tray is a critical element of the battery pack that interfaces between the battery and the vehicle, including the performances of safety/crash, NVH (modal), and durability. The tray is the largest and strongest structure in the battery pack holding the battery sections and other components including the battery disconnect unit (BDU) and other units that are not negligible in mass. This paper describes the mass optimization work done on one of the hybrid batteries using CAE simulation. This was a multidisciplinary optimization project, in which modal performance and fatigue damage were accessed through CAE analysis at both the battery pack level, and at the vehicle level.

A high voltage battery is an essential part of hybrid electric vehicles (HEVs). It is imperative to precisely estimate the state of charge (SOC) and state of health (SOH) of battery in real time to maintain reliable vehicle operating conditions. This paper presents a method of estimating SOC and SOH through the incorporation of current integration, voltage translation, and Ah-throughput. SOC estimation utilizing current integration is inadequate due to the accumulation of errors over the period of usage. Thus voltage translation of SOC is applied to rectify current integration method which improves the accuracy of estimation. Voltage translation data is obtained by subjecting the battery to hybrid pulse power characterization (HPPC) test. The Battery State of Health was determined by semi-empirical model combined with accumulated Ah-throughput method. Battery state of charge was employed as an input to estimate damages accumulated to battery aging through a real-time model.

This paper describes the design of a multivariable, constrained Model Predictive Control (MPC) system for torque tracking in turbocharged gasoline engines scheduled for production by General Motors starting in calendar year 2018. The control system has been conceived and co-developed by General Motors and ODYS. The control approach consists of a set of linear MPC controllers scheduled in real time based on engine operating conditions. For each MPC controller, a linear model is obtained by system identification with data collected from engines. The control system coordinates throttle, wastegate, intake and exhaust cams in real time to track a desired engine torque profile, based on measurements and estimates of engine torque and intake manifold pressure.

In traditional automotive electronic design, software update has been a component oriented, manual process rather than a systematic designed in capability suitable for automation. In recent days as software content in vehicles grow, the need to update software in vehicles more frequently is becoming a necessity. Moreover, additional attributes for software updates, for example timely delivery of security related update for vehicles, desire to add features using software update, control cost of software updates, etc., requires a system engineered design rather than a component oriented approach. As the automobile domain utilizes various means of mobility (Combustion Engine, Hybrid, Battery, etc.) and various functional domains (Infotainment, Safety, Mobility, Telematics, ADAS (Advance Driving Assist service), Autonomous, etc.), to control the overall cost of future software update for such a diverse environment, it is beneficial to introduce automation in the software update process.

The performance of a High Voltage (HV) battery in an advanced propulsion application is often highly dependent on the customer controlled climate settings of the vehicle system. Cooling of the battery requires an understanding of the environment in which it is expected to operate. Results from testing on various air-cooled systems will be discussed to portray the interactions of the battery system design and the climate control system design. The following topics (in relation to battery cooling) will be discussed: climate control system temperature settings; climate-control system fan settings; climate control system recirculation mode and outside air mode settings; venting of the battery thermal system exhaust air; and the battery location and mounting.

Multi-core systems are promising a cost-effective solution for (1) advanced vehicle features requiring dramatically more software and hence an order of magnitude more processing power, (2) redundancy and mixed-IP, mixed-ASIL isolation required for ISO 26262 functional safety, and (3) integration of previously separate ECUs and evolving embedded software business models requiring separation of different software parts. In this context, designing, optimizing and verifying the mapping and scheduling of software functions onto multiple processing cores becomes key. This paper describes several multi-core task design and scheduling design options, including function-to-task mapping, task-to-core allocation (both static and dynamic), and associated scheduling policies such as rate-monotonic, criticality-aware priority assignment, period transformation, hierarchical partition scheduling, and dynamic global scheduling.

Designing power electronics to operate in harsh vehicle environments while meeting packaging requirements such as mass, volume, and power density, creates several challenges for their mechanical design. In this work, we concentrate on the power inverter module (PIM) which converts high voltage (HV) DC voltage power from the HV battery to AC power to drive the motor. The PIM main components are the power module, gate drive and the bulk capacitor. The sizing and selection of the bulk capacitor and power module depend on performance criteria and drive profiles in addition to operating temperatures. In this work, we share the main challenges of packaging components within the inverter. We then discuss best practices to ensure a robust mechanical design which meets inverter durability and reliability targets for an electric vehicle application. The main challenges discussed are bulk capacitor thermals, sealing, and Silicon Carbide (SiC) packaging.

In the automotive embedded system domain, the measurements from vehicle and Hardware-In-Loop are currently evaluated against the testcases, either manually or via automation scripts. These evaluations are localized; they evaluate a limited number of signals for a particular measurement without considering system-level behavior. This results in defect leakage. This study aims to develop a tool that can notify anomalies at the signal level in a new measurement without referring to the testcases, considering a more significant number of system-level signals, thereby significantly reducing the defect leakage. The tool learns important features and patterns of each maneuver from many historical measurements using deep learning techniques. We tried two CNN (convolution neural network) models. The first one is a specially designed CNN that does this maneuver classification and class-specific feature extraction.

Vehicle electrification is driven globally due to the increased concerns on carbon emissions. But the challenges in customer acceptance remains esp. in relation to vehicle costs. Virtual simulations can help in cutting down product development cost and enable faster launch of new vehicles. An early stage system model based design iterations can help in cutting down the product development costs and building more robust products. In the current paper, we develop and analyze a battery pack system model for early phase design. We extend a previously developed system model to include critical physics like sub-component level multiphysics for electrical joint integrity. Also, we demonstrate an integration of 3D FEM & system model for improving the accuracy of joint temperature predictions during charging and/or discharging. A typical High Voltage (HV) battery system comprises of battery modules (Li-ion cells, cooling channels, structural frames, interconnect boards) and HV bus bars.

Hybrid electric vehicles and battery electric vehicles (BEV) use power electronics (PE) devices to convert between high voltage DC power of the battery and other formats of power. These PE components requires operation within certain temperature range, otherwise, overheating causes component as well as vehicle performance degradation. Therefore, a thermal management system is required for PE components. This paper focuses on the design and development of such a PE components thermal control system. The proposed control system is a distributed thermal control system in which all the PE components are placed in series within one cooling loop. The advantage of the proposed control system is its reduced system complexity, energy efficiency and flexibility to add future PE components. In addition, electric control unit (ECU) are utilized so that complex control algorithms can be implemented.

Modern automotive manufacturing and after-sale service environments require tailoring of configuration values or “calibrations” within the vehicle’s various electronic control units (ECUs) to that vehicle’s specific option content. Historically, ECU hardware and software limitations have led designers to implement calibratable values using opaque binary blocks tied directly to ECU internal software data structures. Such coupling between calibration data files and ECU software limits traceability and reuse across different software versions and ECU variants. However, more and more automotive ECUs are featuring fast microprocessors, large memories, and preemptive, multi-tasking operating systems that open opportunities to object-oriented approaches. This paper presents the CalDef system for automotive ECU calibration software architecture. CalDef uses XML database methods to define, develop, and deliver calibration values as objects in both manufacturing and service environments.

Reductions of hardware costs as well as implementations of new innovative functions are the main drivers of today's automotive electronics. Indeed more and more resources are spent on adapting existing solutions to different environments. At the same time, due to the increasing number of networked components, a level of complexity has been reached which is difficult to handle using traditional development processes. The automotive industry addresses this problem through a paradigm shift from a hardware-, component-driven to a requirement- and function-driven development process, and a stringent standardization of infrastructure elements. One central standardization initiative is the AUTomotive Open System ARchitecture (AUTOSAR). AUTOSAR was founded in 2003 by major OEMs and Tier1 suppliers and now includes a large number of automotive, electronics, semiconductor, hard- and software companies.

This paper presents recent advances in automotive microprocessor, operating system, and supporting software technology that supports regulatory and/or functional safety graphics within vehicle cockpit displays. These graphics include “virtual switches” that replace physical switches in the vehicle, as well as “virtual indicators” that replace physical indicator lights. We discuss the functional safety design process and impacts to software and hardware architecture as well as the software design methods to implement End-To-End [E2E] network protection between different ECUs and software processes. We also describe hardware monitoring requirements within the display panel, backlighting, and touch screen and examine an example system design to illustrate the concepts.

Third generation advanced high strength steels (AHSS) that rely on the transformation of austenite to martensite have gained growing interest for implementation into vehicle architectures. Previous studies have identified a dependency of the rate of austenite decomposition on the amount of strain and the associated strain path imposed on the sheet. The rate and amount of austenite transformation can impact the work hardening behavior and tensile properties. However, a deeper understanding of the impact on toughness, and thus crash performance, is not fully developed. In this study, the strain path and strain amounts were systematically controlled to understand the associated correlation to impact toughness in the end application condition (strained and baked). Impact toughness was evaluated using an instrumented Charpy machine with a single sheet v-notch sample configuration.

The front camera module is a fundamental component of a modern vehicle’s active safety architecture. The module supports many active safety features. Perception of the road environment, requests for driver notification or alert, and requests for vehicle actuation are among the camera software’s key functions. This paper presents a novel method of testing these functions virtually. First, the front camera module software is compiled and packaged in a Docker container capable of running on a standard Linux computer as a software in the loop (SiL). This container is then integrated with the active safety simulation tool that represents the vehicle plant model and allows modeling of test scenarios. Then the following simulation components form a closed loop: First, the active safety simulation tool generates a video data stream (VDS). Using an internet protocol, the tool sends the VDS to the camera SiL and other vehicle channels.

Battery Electric Vehicles (BEVs) are becoming more competitive day by day to achieve maximum peak power and energy requirement. This poses challenges to the design of Thermal Interface Material (TIM) which maintains the cell temperature and ensure retention of cell and prevent electrolyte leak under different crash loads. TIM can be in the form of adhesives, gels, gap fillers. In this paper, TIM is considered as structural, and requires design balance with respect to thermal and mechanical requirements. Improving structural strength of TIM will have negative impact on its thermal conductivity; hence due care needs to be taken to determine optimal strength that meets both structural and thermal performance. During various crash conditions, due to large inertial force of cell and module assembly, TIM is undertaking significant loads on tensile and shear directions. LS-DYNA® is used as simulation solver for performing crash loading conditions and evaluate structural integrity of TIM.