Search Results

NOx and PM are the critical emissions to meet the legislation limits for diesel engines. Often a value for these emissions is needed online for on-board diagnostics, engine control, exhaust aftertreatment control, model-based controller design or model-in-the-loop simulations. Besides the obvious method of measuring these emissions, a sensible alternative is to estimate them with virtual sensors. A lot of literature can be found presenting different modeling approaches for NOx emissions. Some are very close to the physics and the chemical reactions taking place inside the combustion chamber, others are only given by adapting general functions to measurement data. Hence, generally speaking, there is not a certain method which is seen as the solution for modeling emissions. Finding the best model approach is not straightforward and depends on the model application, the available measurement channels and the available data set for calibration.

The present paper investigates possible enhancement of ESP performance associated with the use of smart tires. In particular a novel control logic based on a direct feedback on the longitudinal forces developed by the four tires is considered. The control logic was developed using a simulation tool including a 14 dofs vehicle model and a smart tires emulator. Performance of the control strategy was evaluated in a series of handling maneuvers. The same maneuvers were performed on a HiL test bench interfacing the same vehicle model with a production ESP ECU. Results of the two logics were analyzed and compared.

This paper analyzes the control of the series-parallel powersplit used in the 2001 Michigan Tech FutureTruck. An electronic throttle controller is implemented and a new control algorithm is proposed and tested. A vehicle simulation has been created in MATLAB and the control algorithm implemented within the simulation. A program written in C has also been created that implements the control algorithm in the test vehicle. The results from both the simulation and test vehicle are presented and discussed and show a 15% increase in fuel economy. With the increase in fuel economy, and through the use of the original exhaust after treatment, lower emissions are also expected.

Electronic Stability Program (ESP) and Antilock Braking System (ABS) are nowadays a standard equipment for passenger cars. ESP increases vehicle safety by applying differential braking torque to the wheels while cornering, thus it extends the area of intervention of ABS which prevents the wheels from being locked up in emergency braking, especially on low friction road surfaces, allowing the driver to maintain steering control of the vehicle, to avoid obstacles and to reduce vehicle stopping distance on most road surfaces. This paper describes a flexible mechatronic test bench for ESP/ABS Electronic Control Unit (ECU) based on Hardware-In-the-Loop (HIL) simulation technique. It consists of a passenger car hydraulic braking system (from master cylinder to brake calipers), with the ESP/ABS ECU integrated and a flexible real-time platform, which simulates vehicle dynamics.

Active controls for braking dynamics are widely investigated in literature [1]-[8] as one of the way to improve vehicle safety and avoid collisions. Active systems commonly mounted on passenger cars like ABS/EBD, have achieved a high level of robustness towards possible changes in the tires' characteristics due to multiple causes such as: under-inflation, wear and also replacement of tires with new ones different from the first equipment series. Although these electronic control systems have been designed to be robust and no case-sensitive to such variations in tire conditions, a further improvement of their performance could be achieved by means of a continuous adaptive control.

The evolution of automotive engines calls for the design of electronic control systems optimizing the engine performance in terms of reduced fuel consumption and pollutant emissions. However, the opportunities provided by modern engines have not yet completely exploited, since the adopted control strategies are still largely developed in a very heuristic way and rely on a number of SISO (Single Input Single Output) designs. On the contrary, the strong coupling between the available actuators calls for a MIMO (Multi Input Multi Output) control design approach. To this regard, the availability of reliable dynamic engine models plays an important role in the design of engine control and diagnostic systems, allowing for a significant reduction of the development times and costs. This paper presents a control-oriented model of the air-path system of today's gasoline internal combustion engines.

Today's advanced technology engines have a high content of electronic actuation requiring sophisticated real-time embedded software sensing and control. To enable research on such engines, a system with a flexible engine control unit (ECU) that can be rapidly configured and programmed is desired. Such a system is being used in the Advanced Internal Combustion Engine (AICE) Laboratories at Michigan Tech University (MTU) for research on a multi-cylinder spark-ignited gasoline, a high pressure common rail diesel and a single cylinder alternative fuels research engine. The system combines a production ECU with a software development system utilizing Mathworks Simulink/Stateflow © modeling tools. The interface in the Simulink modeling environment includes a library of modeling and interface blocks to the production Operating System (OS), Low Level Drivers (LLD) and CAN-based calibration tool.

This paper presents the development of a reduced-order powertrain model for energy and SOC estimation of a multi-mode plug-in hybrid electric vehicle using only vehicle speed profile and route elevation as inputs. Such a model is intended to overcome the computational inefficiencies of higher fidelity powertrain and vehicle models in short and long horizon energy optimization efforts such as Coordinated Adaptive Cruise Control (CACC), Eco Approach and Departure (EcoAND), Eco Routing, and PHEV mode blending. The reduced-order powertrain model enables Connected and Automated Vehicles (CAVs) to utilize the onboard sensor and connected data to quickly react and plan their maneuvers to highly dynamic road conditions with minimal computational resources.

The Electronic Control Unit (ECU) of an Electric Power Steering (EPS) system is a core device to decide how much assistance an electric motor applies on a steering wheel. The EPS ECU plays an important role in EPS systems. The effectiveness of an ECU needs to be thoroughly tested before mass production. Hardware-in-the-loop simulation provides an efficient way for the development and testing of embedded controllers. This paper focuses on the development of a HiL system for testing EPS controllers. The hardware of the HiL system employs a dSPACE HiL simulator. The EPS plant model is an integrated model consisting of a Vehicle Dynamics model of the dSPACE Automotive Simulation Model (ASM) and the Nexteer Steering model. The paper presents the design of an EPS HiL system, the simulation of sensors and actuators, the functions of the ASM Vehicle Dynamics model, and the integration method of the ASM Vehicle Dynamics model with a Steering model.

The proliferation of small silicon micro-chips has led to a large assortment of low-cost transducers for data acquisition. Production vehicles on average exploit more than 60 on board sensors, and that number is projected to increase beyond 200 per vehicle by 2020. Such a large increase in sensors is leading the fourth industrial revolution of connectivity and autonomy. One major downfall to installing many sensors is compromises in their accuracy and processing power due to cost limitations for high volume production. The same common errors in data acquisition such as sampling, quantization, and multiplexing on the CAN bus must be accounted for when utilizing an entire array of vehicle sensors. A huge advantage of onboard sensors is the ability to calculate vehicle parameters during a daily drive cycle to update ECU calibration factors in real time. One such parameter is driveline damping, which changes with gear state and drive mode. A damping value is desired for every gear state.

To improve torque management algorithms for drivability, the powertrain controller must be able to compensate for the nonlinear dynamics of the driveline. In particular, the presence of backlash in the transmission and drive shafts excites sharp torque fluctuations during tip-in or tip-out transients, leading to a deterioration of the vehicle drivability and NVH. This paper proposes a model-based estimator that predicts the wheel torque in an automotive drivetrain, accounting for the effects of backlash and drive shaft flexibility. The starting point of this work is a control-oriented model of the transmission and vehicle drivetrain dynamics that predicts the wheel torque during tip-in and tip-out transients at fixed gear. The estimator is based upon a switching structure that combines a Kalman Filter and an open-loop prediction based on the developed model.

A robust optimization approach has been applied to the design of a piezoelectric brake. The force generated by the piezoelectric actuator is transmitted to the pad shoe through a lever. The optimal design of the lever is crucial for obtaining the desired performance of the brake. Increasing the stiffness and reducing the mass of the lever is the key problem for such kind of mechatronic brake. A trade off between mass and stiffness of the lever must be obtained. Multi-objective programming (MOP) has been applied in order to achieve the best compromise. In addition to MOP, the optimal robust design method has been applied to perform the optimal design not only by considering the performance of the system (the stiffness and mass of the lever) but also by taking into account the robustness (the sensitivity to the uncertain system parameters).