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Plug-in hybrid electric vehicle (PHEV) technologies have the potential for considerable petroleum consumption reductions, possibly at the expense of increased tailpipe emissions due to multiple “cold” start events and improper use of the engine for PHEV specific operation. PHEVs operate predominantly as electric vehicles (EVs) with intermittent assist from the engine during high power demands. As a consequence, the engine can be subjected to multiple cold start events. These cold start events may have a significant impact on the tailpipe emissions due to degraded catalyst performance and starting the engine under less than ideal conditions. On current hybrid electric vehicles (HEVs), the first cold start of the engine dictates whether or not the vehicle will pass federal emissions tests. PHEV operation compounds this problem due to infrequent, multiple engine cold starts.

New engine controls have been developed for the turbocharged Lexus NX200t to improve driving power by reducing engine torque output lag. Drive power management functions have been centralized in an integrated drive power control system. The newly developed controls minimize the potential reduction in drivability associated with the adoption of a turbocharged engine while improving fuel efficiency. General driveability issues commonly associated with a turbocharged engine include sudden increases in drive power due to the response lag of the turbocharger, and higher shifting frequencies if this response lag triggers a disturbed accelerator operation pattern by the driver. The developed technologies detect and control sudden increases in drive power to create the optimum drive power map, and reduce unnecessary shifts even if the driver's accelerator operation is disturbed.

A Machine Learning-Genetic Algorithm (ML-GA) approach was developed to virtually discover optimum designs using training data generated from multi-dimensional simulations. Machine learning (ML) presents a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. In the present work, a total of over 2000 sector-mesh computational fluid dynamics (CFD) simulations of a heavy-duty engine were performed. These were run concurrently on a supercomputer to reduce overall turnaround time. The engine being optimized was run on a low-octane (RON70) gasoline fuel under partially premixed compression ignition (PPCI) mode. A total of nine input parameters were varied, and the CFD simulation cases were generated by randomly sampling points from this nine-dimensional input space. These input parameters included fuel injection strategy, injector design, and various in-cylinder flow and thermodynamic conditions at intake valve closure (IVC).

In the present paper, we introduce a drivetrain system using an electromagnetic coupling for hybrid electric vehicles, and propose a new control concept of vibration torque interception. The electromagnetic coupling is an electric machine that is composed of a pair of rotors, and electromagnetic torque acts mutually between the rotors. In the drivetrain system, the electromagnetic coupling works as a torque transmission device with a rotational-speed-converting function. We demonstrate that, by using this control, the electromagnetic coupling also works as a damping device that intercepts the vibration torque of the internal combustion engine, while transmitting the smooth torque to its drive line. Using a model of a two-inertia resonance system, a control system is designed such that a transfer function representing input-to-output torque is shaped in the frequency domain.

Vehicle stability is maintained by proper interactions between the driver and vehicle stability control system. While driver describes the desired target path by commanding steering angle and acceleration/deceleration rates, vehicle stability controller tends to stabilize higher dynamics of the vehicle by correcting longitudinal, lateral, and roll accelerations. In this paper, a finite-horizon optimal solution to vehicle stability control is introduced in the presence of driver's dynamical decision making structure. The proposed concept is inspired by Nash strategy for exactly known systems with more than two players, in which driver, commanding steering wheel angle, and vehicle stability controller, applying compensated yaw moment through differential braking strategy, are defined as the dynamic players of the 2-player differential linear quadratic game.

As batteries become a major component of numerous advanced vehicles, significant efforts have been allocated towards characterizing and estimating battery energy capability over the lifetime of a vehicle. Currently, battery State of Charge (SOC) is one of the primary values used for this characterization; however SOC usage has several issues when implemented in Electric Vehicle (EV), Hybrid Electric Vehicle (HEV), and Plug-In Hybrid Electric Vehicle (PHEV) systems. One of the main issues with reporting battery SOC as a characterization of battery energy capability is that it only gives a percentage of the energy available to the operator. SOC does not accurately represent the true capability or capacity of the battery, and thus fails to account for the impact to capability with respect to battery size, age, and recent operational history.

This paper considers an application of reference governor (RG) to automotive diesel aftertreatment temperature control. Recently, regulations on vehicle emissions have become more stringent, and engine hardware and software are expected to be more complicated. It is getting more difficult to guarantee constraints in control systems as well as good control performance. Among model-based control methods that can directly treat constraints, this paper focuses on the RG, which has recently attracted a lot of attention as one method of model prediction-based control. In the RG, references in tracking control are modified based on future prediction so that the predicted outputs in a closed-loop system satisfy the constraints. This paper proposes an online RG algorithm, taking account of the real-time implementation on engine embedded controllers.

Toyota has developed a new belt-type continuously variable transmission (CVT) for 1.5-liter compact vehicles. To improve both driveability and fuel economy over previous CVTs, pressure management was adopted as the shift control method. The new shift control system was designed using a model-based control method which uses a two-degree-of-freedom system composed of feedback and feedforward controls. Smooth shifting in all the target shift speed regions was realized by combining a feedback loop that considers the output limit of the pulley thrust into the feedforward controller. Furthermore, shift response was improved while maintaining or even improving stability. This paper describes the details of this shift control system.

One seminal question that faces a vehicle's driver (either human or computer) is predicting the capability of the vehicle as it encounters upcoming terrain. A Performance Margin (PM) is defined in this work as the ratio of the required tractive effort to the available tractive effort for the front and rear respectively. This simple definition stems from and incorporates many traditional handling metrics and is robust in its scope of applicability. The PM is implemented in an Intervention Strategy demonstrating its use to avoid situations in which the vehicle exceeds its handling capabilities. Results from a design case study are presented to show the potential efficacy of developing a PM-based control system.

The application of Model-Based Development (MBD) techniques for automotive control system and software development have become standard processes due to the potential for reduced development time and improved specification quality. In order to improve development productivity even further, it is imperative to introduce a systematic Verification and Validation (V&V) process to further minimize development time and human resources while ensuring control specification quality when developing large complex systems. Traditional methods for validating control specifications have been limited by control specification scale, structure and complexity as well as computational limitations restricting their application within a systematic model-based V&V process. In order to address these issues, Toyota developed Hierarchical Accumulative Validation (HAV) for systematically validating functionally structured executable control specifications.

TOYOTA has developed the iQ with a 1.3L engine for the Scion brand in USA. Due to the importance of fun-to-drive factor for the Scion brand image, a responsive driving performance is required even with compact packaging and a small engine. In addition, because of the recent attention to global-warming and energy issues on a global scale, development of vehicles with high fuel economy is one of the most important issues for a car manufacturer. Therefore, it is necessary for a vehicle to have both high driving performance and fuel economy. TOYOTA has adopted the CVT-i as the transmission for this purpose. The following were achieved by adopting the CVT-i as the transmission for the iQ(1.3L). 1 Responsive driving performance with shift changes without a time lag. 2 Compact transmission for efficient vehicle packaging 3 Class-leading fuel economy performance. Moreover, it was developed with adjustments for the US market by improving the shift schedule for a linear acceleration feel.

Routing quality always dominates the top 20% of in vehicle- navigation customer complaints. In vehicle navigation routing engines do not customize results based on customer behavior. For example, some users prefer the quickest route while some prefer direct routes. This is because in vehicle navigation systems are traditionally embedded systems. Toyota announced that new model vehicles in JP, CN, US will be connected with routing function switching from the embedded device to the cloud in which there are plenty of probe data uploaded from the vehicles. Probe data makes it possible to analyze user preferences and customize routing profile for users. This paper describes a method to analyze the user preferences from the probe data uploaded to the cloud. The method includes data collection, the analysis model of route scoring and user profiling. Furthermore, the evaluation of the model will be introduced at the end of the paper.

Automobile manufactures need to adopt new technologies to meet global CO2 (carbon dioxide) emission regulations and better fuel efficiency demands from customers. Also, the production cost should be as low as possible for an affordable vehicle. Therefore, it is advantageous for OEMs to develop fuel efficient technologies which can be controlled by software without additional hardware costs. The coasting control is a fuel efficiency improvement technology that can be implemented by the change of vehicle software only. The coasting control is a technology that reduces the driving resistance (Deceleration) when the driver releases the gas pedal. This technology leads to reducing the energy required for the vehicle to drive and results in improving the real-world fuel economy. In an internal combustion engine (ICE) vehicle, the coasting state is achieved by changing the gear to neutral, and the effect has been discussed and clarified by many previous studies.

This experimental work focuses on defining the detailed morphology of secondary emission products derived from the combustion of cerium (Ce) fuel-borne catalyst (FBC) doped with diesel fuel. Cerium is often used to promote the oxidation of diesel particulates collected in diesel aftertreatment systems, such as diesel particulate filters (DPFs). However, it is suspected that the secondary products could be emitted from the vehicle tailpipe without being effectively filtered by the aftertreatment systems. In this work, these secondary emissions were identified by means of a high-resolution transmission electron microscope (TEM), and their properties were examined in terms of morphology and chemistry. In preparation for fuel doping, a cerium-based aliphatic organic compound solution was mixed with a low-sulfur (110 ppm) diesel fuel at 50 ppm in terms of weight concentration.

This paper introduces the cruise control system with distance control function, that is called Adaptive Cruise Control (ACC), that uses a scanning laser radar as a sensor to detect preceding vehicles. With the goal of increasing the driving convenience and comfort when compared to the conventional cruise control, lots of ACC systems have been proposed and developed. This paper presents ACC system using the scanning laser radar which was developed by Toyota, and describes the adaptation of the system specifications. This ACC system was able to greatly reduce the driver's work load, and increased the driver's convenience and comfort when operating the cruise controls system. In addition, we were able to design this system to be highly dependable and inexpensive and supply it to the market as a result of incorporating various ideas for improvements.

As part of an ongoing vehicle benchmarking effort at Argonne National Laboratory, four different power-split HEVs were tested on a chassis dynamometer to analyze their operational behavior and understand the control strategy and its relationship to the individual features of the vehicles tested. The controls that select the way in which engine operation matches best engine efficiency load points appears to have evolved From the Gen 1 to the Gen 2 Toyota Prius. The Ford Escape HEV and Lexus RX400h were also analyzed by using similar methods, although the data are not as extensive as those for the Prius hybrids. Whereas the Escape HEV appeared to operate in a manner similar to that of the Gen 1 Prius, the RX400h (with its relatively large engine) loads the engine with excess battery charge to keep it operating at higher power levels - apparently to improve overall efficiency.

Detailed diesel particulates morphology, nanostructures, fractal geometry, and nitrogen oxides (NOx) emissions were analyzed for five different test fuels in a 1.7-L, common-rail direct-injection diesel engine. The accurately formulated fuels permit the effects of sulfur, paraffins, aromatics, and naphthene concentrations to be determined. A novel thermophoretic sampling technique was used to collect particulates immediately after the exhaust valves. The morphology and nanostructures of particulate samples were examined, imaged with a high-resolution transmission electron microscope (HRTEM), and quantitatively analyzed with customized digital image processing/data acquisition systems. The results show that the particle sizes and the total mass of particulate matter (PM) emissions correlate most strongly with the fuels' aromatics and sulfur content.

Toyota Motor Corporation has already designed and developed vehicle brake control systems for relatively low speed off-road driving, such as Downhill Assist Control, Hill-start Assist Control and Active Traction Control. Though off-road utility is improved by virtue of these systems, in specific situations actual performance still depends on driving technique since the driver is required to control the accelerator pedal. Toyota has integrated these existing systems, and developed a new driving technology for off-road driving called “Crawl Control.” Crawl Control automatically modulates brake torque and drive torque to help keep the vehicle speed constant and slow. Unskilled drivers can thereby attain improved capabilities in places where high-level driving techniques are required. This system also reduces the effort required to control the accelerator and the brake pedal. This paper presents a new control algorithm for the realization of this Crawl Control system.

Restrictions on track testing, combined with advances in technology, have contributed to an increased dependence on sensors and data acquisition for diagnosing problems and improving performance in motorsports vehicles. This dependence has created a new set of challenges for race engineers to collect quality data from a vehicle at the track. Successful 7- or 8-post shaker rig testing is highly dependent on the quality of the data acquired at the track. An improperly configured data acquisition system can actually be worse than a faulty sensor. This paper highlights a few of the most common problems in motorsports data acquisition: aliasing and sample rate selection. The effects of these problems are described for typical suspension sensors such as accelerometers, shock potentiometers, load cells, and laser ride height sensors. An experimental case study is presented to explain the implications of these problems.

This paper covers the development of a closed loop transaxle synchronization algorithm which was a key deliverable in the control system design for the L3 Enigma, a Battery Dominant Hybrid Electric Vehicle. Background information is provided to help the reader understand the history that lead to this unique solution of the input and output shaft synchronizing that typically takes place in a manual vehicle transmission or transaxle when shifting into a gear from another or into a gear from neutral when at speed. The algorithm stability is discussed as it applies to system stability and how stability impacts the speed at which a shift can take place. Results are simulated in The MathWorks Simulink programming environment and show how traction motor technology can be used to efficiently solve what is often a machine design issue. The vehicle test bed to which this research is applied is a parallel biodiesel hybrid electric vehicle called the Enigma.