The automobile industry is moving towards electrification of Vehicle to remove the exhaust gas emissions. A project was undertaken to develop Electric Vehicle control system from concept to vehicle trials in less than a year. The complete development cycle of an electronic controller required to be compressed to prepare mule electric vehicle within timeline. Agile methodology has been used for this project instead of waterfall as other control systems were in developing stage; system requirements were changing frequently. This paper presents the electric vehicle control unit development with agile methodology using model based development (MBD) in MATLAB and Simulink environment. The project flow consists of major phases like design of electrical architecture, system requirements specification, selection and setting up the simulation platform, EVCU strategy development, testing on Model in Loop (MIL)/ Hardware in Loop (HIL), vehicle trials. The electrical architecture of EV has been designed in Simulink for depicting the interfaces of EVCU with different sensors, battery management subsystem (BMS), and charging subsystem including both AC and DC, motor subsystem, relays and actuators. The strategies…

Since WLTP introduction in Europe, Exhaust Emission standards are based also on real driving conditions. The tuning and calibration work for Engine-out Emissions and Exhaust After-treatment Systems must therefore include all driving conditions in real life use of the vehicle. This includes temperature conditions, altitude, vehicle load and driving style. Consequently, the workload, cost and duration for the engine and after treatment system calibration activities, based on physical tests as today, are no more compatible with realistic development targets. The purpose of the methodology described in this paper is to replace chassis dyno vehicle tests by Hardware in the Loop, using the Engine Electronic Control Unit as physical part. The vehicle, driver, engine, gearbox are all modeled by 0D/1D simulation running in real time. The methodology used to build the simulation models is described. A Design of Experiment Approach based mainly on steady state engine testing is used to build the engine-out emission model. A very extensive work has been done to validate the method, by comparing results from the model with vehicle chassis dyno…

Faced with the need to reduce development time and cost in view of additional system complexity driven by ever more stringent emission regulations, the Hardware-in-the-Loop (HiL) simulation increasingly proves itself to be an advantageous tool not only in automotive companies but also in the off-road engine industry. The approach offers the possibility to analyze new engine control systems with fewer expensive engine dynamometer experiments and test drives. Thus, development cycles can be shortened and development costs reduced. This paper presents the development of an Internal Combustion Engine (ICE) and the correspondent Exhaust Aftertreatment System (EAS) model, its deployment on a HiL system and its application to pre-calibrate the engine for different vehicle cycles. A zero-dimensional mean value approach was chosen to guarantee adequate real-time factors for the coupling between the models and the Engine Control Unit (ECU). The main components of the airpath were parametrized according to data provided by the supplier and measurements. The combustion and NOx Engine-Out emissions were modeled using AVL-CRUISEMTM/MoBEOTM libraries, combining physical and empirical approaches, to reach an optimal trade-off…

Automotive Applications of Hardware-in-the-Loop (HIL) Simulation shines a light on HIL simulation testing methodology commonly used in the automotive industry for conventional, electrification and autonomy applications and can serve as an introductory resource for college students looking to join the automotive industry or experienced technical professionals who need a deeper understanding on what is HIL simulation, what are its benefits and how can it be used in their respective organizations.

It is considered that slow convergence speed and large calculation amount of commonly used adaptive algorithm in the active control system for vehicle interior noise yield noise reduction performance and hardware requirements problems. In this paper, a 4-channel active control of vehicle interior noise based on adaptive notch filter is established, and road test is carried out to test and analyze the performance of the control system. Firstly, the general mathematic model of the multi-channel active control system based on adaptive notch filter is established. The computational complexity of the algorithm is analyzed and compared with that of the FXLMS algorithm. Secondly, a hardware-in-the-loop test bench based on multi-channel adaptive notch filter is set up, to measure the noise reduction performance of ANC system under various working conditions. Finally, the typical test conditions are designed to test the system noise reduction performance, and the road test is carried out and result is analyzed.

Increasing complexity of automotive systems along with growing safety and performance requirements, is causing development cycle costs to swell. A common solution is to use a Model-Based Design (MBD) approach, particularly using x-In the Loop (xIL) simulation methods for Validation and Verification (V&V). MBD allows efficient workflow from offline control design using high-fidelity models to real time V&V using Hardware-in-the-Loop (HIL) simulations. It is very challenging to reduce the complex non-linear high-fidelity models to real-time capable models for HIL simulation. Current literature does not provide a standard approach for obtaining the HIL-capable reduced model for complex non-linear systems. In this paper we present an approach to perform model reduction in light of HIL-level requirements. The approach is presented using an example of a 10-speed automatic transmission. The system constitutes three subsystems - the hydraulic network, mechanical gearbox, and torque converter. In the first step, a high-fidelity model for each subsystem is built up from the component level using one-dimensional mechanics and zero-dimensional hydraulic fluid flow. Secondly, the model is reduced gradually to meet the real-time…

The Electric Pump (E-Pump) is a critical component in the hybrid transmission system. The E-Pump provides flow to maintain a stable line pressure when the engine is in an off state. The main applications of the E-Pump are Park Pawl engagement and disengagement, engine start-stop operation and shadow shifting. A Systems Engineering Approach was followed to develop a medium fidelity plant model for the E-Pump. The developed model was initially tested and validated in the Model in-the loop (MIL) environment. After initial validation, the model was integrated into the overall vehicle model which was then tested on the Software in-the loop (SIL) and Hardware in-the loop (HIL) environments. The model was validated across different platforms and several operating conditions. The basic applications of the E-Pump such as park pawl actuation, engine starting and shadow shifting were validated. The model was later validated using the data, which was acquired on a prototype vehicle run on a dynamometer. The goal was to develop a common model, which could be used across different simulation platforms such as MIL,…

This paper presents a HiL test bench specifically designed for closed-loop testing of the monocular-vision based ADAS sensors, whereby the animated pictures of the virtual scene is calibrated and projected onto a 120-degree circular screen, such that the camera sensor installed has the same vision as the observation of the real-world scene. A high-fidelity AEBs model is established and deployed in the real-time target of the HiL system, making intervention decisions based on the instance-level detection information transmitted from the physical sensor. By referring to the 2018 edition of the C-NCAP testing protocol, the HiL tests of the rear-end collision scenarios is performed to investigate the performance and characteristics of the longitudinal-motion sensing of the sensor sample under test. By comparing and analyzing the differences between the measured data and the ground truth, the sensor’s visual-perceptual capabilities with respect to accuracy and delays are evaluated in the presence of emergency braking of the subject and the leading vehicles. Finally, the matching and optimization solution is offered for performance enhancement of the camera-only AEBs using this…