Search Results

Autonomous vehicles at various stages will impact the future of transportation by improving reliability, comfort and safety of the passengers. In this paper, for an existing experimental vehicle, fitted with various sensors and actuators typically required by autonomous vehicles, a basic level-1 autonomous controller for braking and throttle actuations is proposed. This controller is primarily developed for stop-and-go scenarios along with the additional functionalities of automatic cruise control (ACC) and automatic emergency braking (AEB). Since the rigorous testing of autonomous vehicle in actual roads can be time consuming, costly and having safety issues, a simulation test-bench based approach is considered to develop and test the controller. The controller, based on practical data is developed in simulation environment to primarily maintain safe distance from surrounding traffic objects while fulfilling requirements such as jerk levels, conditional braking, speed limits, etc.

Increasingly strict CO2 and emissions norms are pushing the automotive industry towards increasing adoption of Hybrid Electric Vehicle (HEV) technology. HEVs are complex hardware systems which are often controlled by software that is complex to maintain, time-consuming to calibrate, and not always guaranteed to deliver optimal fuel economy. Hence, coordinated, systematic control of different subsystems of HEV is an attractive proposition. In this paper, Model Predictive Control (MPC) and Equivalent Consumption Minimization Strategy (ECMS) based supervisory controllers have been developed to coordinate the power split between the two prime movers of an HEV – internal combustion engine and electric motor. A dynamical physics based HEV model has been developed for simulation of the system behavior. A cost function has been formulated to improve fuel economy and battery life.

The constant pressure on reducing fuel consumption and emissions in cost sensitive automotive markets has brought focus on retrofit HEV solutions. Through retrofit mechanism, existing conventional (solely engine powered) vehicles can be quickly converted into HEVs. However, the retrofit HEV, although cost effective, poses challenges in developing strategies to control the motor for a given fuel economy, emissions, drivability, battery life requirements and driver inputs. These challenges are primarily due to non-availability of calibration data from the OEMs. This paper focuses on the benefits and challenges with design, tuning and performance of MPC based supervisory controller against a conventional one for a retrofit HEV, using practical data in simulation environment. The inherent characteristics of MPC will lead to the choice of best possible inputs, while respecting the constraints.

Automobiles are getting more and more sophisticated with increased demand for more comfort and safety by customers. Due to this, the automotive Electronic Control Units (ECU) and the software applications running on these ECUs have become more complex and computationally more intensive. This has resulted in Original Equipment Manufacturers (OEMs) migrating to multicore platforms. Optimal usage of multicore platform necessitates the design of new scheduling algorithms. In the past decade, different approaches to implement hard real time scheduling in automotive domain have been proposed for single core as well as multicore architectures. We explore different scheduling techniques proposed so far which are relevant to automotive domain and also, provide a taxonomy of these scheduling algorithms, which will help the automotive design engineer to make an informed decision.

Model based approaches for engine fault diagnosis mostly address the faults external to cylinder since they predominantly use simplified averaged models which do not capture within cycle dynamics. Hence, by using an instantaneous engine model which distinctly characterizes the cylinder’s modes, the events occurring within the cycle can be captured. The events happening across various modes and the engine subsystems can be due to normal operation or faults whose symptoms can be seen as features. In this work, which involves detection and classification of faults occurring in cylinders, is carried out in simulation environment, where, a Kalman filter for state estimation incorporating a nominal instantaneous mode based engine model is considered. Using this estimator as base, faults occurring repetitively (every cycle) are addressed whose features are seen across relevant modes of a cycle.

Mean Value Engine Models, commonly used for model based fault diagnosis of SI engines, fail to capture the within-cycle dynamics of engines, often resulting in reduced fault sensitivity. This paper presents a new Hybrid Automata based modeling approach for characterizing the within-cycle dynamics of the thermo-fluidic processes in a Spark Ignition Gasoline Engine, targeted for use in model based fault diagnosis. Further, using a hybrid version of the Extended Kalman Filter (EKF), the states from the nonlinear hybrid automata based dynamic model are estimated and their results validated w.r.t standard industrial simulation software, AMESim. It is observed that due to the switching of within cycle engine dynamics, causing mode change, there is a corresponding change in model's structure which in turn can cause change in system's observability.