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Electrification is the well-accepted solution to address carbon emissions and modernize vehicle controls. Batteries play a critical in the journey of electrification and modernization with battery voltage prediction as the foundation for safe and efficient operation. Due to its strong dependency on prior information, battery voltage was estimated with recurrent neural network methods in the recent literatures exploring a variety of deep learning techniques to estimate battery behaviors. In these studies, standard recurrent neural networks, gated recurrent units, and long-short term memory are popular neural network architectures under review. However, in most cases, each neural network architecture is individually assessed and therefore the knowledge about comparative study among three neural network architecture is limited. In addition, many literatures only studied either the dynamic voltage response or the voltage relaxation.

Computer programs and models are playing an increasing role in simulating vehicle crashworthiness, dynamic, and fuel efficiency. To maximize the effectiveness of these models, the validity and predictive capabilities of these models need to be assessed quantitatively. For a successful implementation of Computer Aided Engineering (CAE) models as an integrated part of the current vehicle development process, it is necessary to develop objective validation metric that has the desirable metric properties to quantify the discrepancy between multiple tests and simulation results. However, most of the outputs of dynamic systems are multiple functional responses, such as time history series. This calls for the development of an objective metric that can evaluate the differences of the multiple time histories as well as the key features under uncertainty.

As vehicle fuel economy continues to grow in importance, the ability to accurately measure the level of efficiency on all driveline components is required. A standardized test procedure enables manufacturers and suppliers to measure component losses consistently and provides data to make comparisons. In addition, the procedure offers a reliable process to assess enablers for efficiency improvements. Previous published studies have outlined the development of a comprehensive test procedure to measure transfer case speed-dependent parasitic losses at key speed, load, and environmental conditions. This paper will take the same basic approach for the Power Transfer Units (PTUs) used on Front Wheel Drive (FWD) based All Wheel Drive (AWD) vehicles. Factors included in the assessment include single and multi-stage PTUs, fluid levels, break-in process, and temperature effects.

Hybrid, plug-in hybrid, and electric vehicles have enthusiastically embraced rechargeable Li-ion batteries as their primary/supplemental power source of choice. Because the state of charge (SoC) of a battery indicates available remaining energy, the battery management system of these vehicles must estimate the SoC accurately. To estimate the SoC of Li-ion batteries, we derive a normalized state-space model based on Li-ion electrochemistry and apply a Bayesian algorithm. The Bayesian algorithm is obtained by modifying Potter's squareroot filter and named the Potter SoC tracker (PST) in this paper. We test the PST in challenging test cases including high-rate charge/discharge cycles with outlier cell voltage measurements. The simulation results reveal that the PST can estimate the SoC with accuracy above 95% without experiencing divergence.

We present research in progress to develop and implement a transportable instrumentation package (TIP) to collect driver data in a vehicle. The overall objective of the project is to investigate the symbiotic relationship between humans and their vehicles. We first describe the state-of-art technologies to build the components of TIP that meet the criteria of ease of installation, minimal interference with driving, and sufficient signals to monitor driver state and condition. This method is a viable alternative to current practice which is to first develop a fully instrumented test vehicle, often at great expense, and use it to collect data from each participant as he/she drives a prescribed route. Another practice, as for example currently being used in the SHRP-2 naturalistic driving study, is to install the appropriate instrumentation for data collection in each individual's vehicle, often requiring several hours.

Rare earths are a group of elements whose availability has been of concern due to monopolistic supply conditions and environmentally unsustainable mining practices. To evaluate the risks of rare earths availability to automakers, a first step is to determine raw material content and value in vehicles. This task is challenging because rare earth elements are used in small quantities, in a large number of components, and by suppliers far upstream in the supply chain. For this work, data on rare earth content reported by vehicle parts suppliers was assessed to estimate the rare earth usage of a typical conventional gasoline engine midsize sedan and a full hybrid sedan. Parts were selected from a large set of reported parts to build a hypothetical typical mid-size sedan. Estimates of rare earth content for vehicles with alternative powertrain and battery technologies were made based on the available parts' data.

This paper reports a 2010 study undertaken to determine generic acceleration pulses for testing and evaluating advanced batteries for application in electric passenger vehicles. These were based on characterizing vehicle acceleration time histories from standard laboratory vehicle crash tests. Crash tested passenger vehicles in the United States vehicle fleet of the model years 2005-2009 were used. The crash test data were gathered from the following test modes and sources: 1 Frontal rigid flat barrier test at 35 mph (NHTSA NCAP) 2 Frontal 40% offset deformable barrier test at 40 mph (IIHS) 3 Side moving deformable barrier test at 38 mph (NHTSA side NCAP) 4 Side oblique pole test at 20 mph (US FMVSS 214/NHTSA side NCAP) 5 Rear 70% offset moving deformable barrier impact at 50 mph (US FMVSS 301). The accelerometers used were from locations in the vehicle where deformation is minor or non-existent, so that the acceleration represents the “rigid-body” motion of the vehicle.

The SAE Fuel Cell Vehicle (FCV) Safety Working Group has been addressing FCV safety for over 11 years. In the past couple of years, significant attention has been directed toward a revision to the standard for vehicular hydrogen systems, SAE J2579(1). In addition to streamlining test methodologies for verification of Compressed Hydrogen Storage Systems (CHSSs) as discussed last year,(2) the working group has been considering the effect of vehicle fires, with the major focus on a small or localized fire that could damage the container in the CHSS and allow a burst before the Pressure Relief Device (PRD) can activate and safely vent the compressed hydrogen stored from the container.

The automotive industry is investigating the change of electrical system voltage in a vehicle from the present 14 volt (12V battery) to 42 volt (36V battery) to integrate new electrical and electronic features. These new features require more amperes, thicker wires, large power devices, and eventually higher cost. The existing 14V system is very difficult to sustain so much content because of constraints of performance, efficiency, cost, packaging space, and manufacture-ability. This paper discusses foreseeable needs moving to a higher voltage, and reasons of 42V selection. It explores benefits and drawbacks when the voltage is changed from 14V to 42V in the areas of wire harness, power electronics, smart switching, power supply, etc. Finally, two typical 42/14V dual voltage architectures are presented for a likely 42V transition scenario.

A fuel cell (FC) powerplant is an electrochemical engine that converts fuel and an oxidant electrochemically into electric energy, water and other chemical byproducts. When hydrogen is used as the fuel, the only products of the electrochemical reactions are water and electric power. Other conventional and advanced powerplants for transportation, such as the internal combustion (IC) engine, the Diesel engine and others, are thermal combustion engines. The theoretical or thermodynamic efficiency of a fuel cell or electrochemical engine is much higher than the thermodynamic efficiency of a heat engine. The practical efficiency of a fuel cell is highest at partial load, whereas the practical efficiency of a heat engine is highest at maximum power. A survey is presented of the different fuel cell types and their characteristics. The proton-exchange-membrane (PEM) fuel cell is shown to be the best available fuel cell for transportation applications.

This paper discusses the growing use of electronics to provide improved fuel economy and control of engine emissions. The advantages of electronic engine controls are outlined, transducers utilized in a 1980 EEC III CFI application are described, and potential future expansion of electronic engine control is discussed.

The purpose of this paper is to project the performance and economic objectives of over-the-road powerplants in the decade of the 1970’s. The influencing factors for this projection are trends in: intercity ton miles of freight, size and weight legislation, the interstate highway system maximum legal speed laws, and operating costs of interstate carriers. These factors set the stage and establish the horizon for over-the-road vehicles of tomorrow.