From simulation to real time control of an all electric bus : the ElLiSup project

Similar documents
Signal Hardware-In-the-Loop simulation of a Hybrid locomotive

Dr. Tony LETROUVE, Dr. Julien POUGET SNCF Innovation & Research Dep., MEGEVH network,

«electricity & Vehicles» PLATFORM

Experience the Hybrid Drive

Real-Time Simulation of A Modular Multilevel Converter Based Hybrid Energy Storage System

Regenerative Braking System for Series Hybrid Electric City Bus

The MathWorks Crossover to Model-Based Design

Real-Time Modelica Simulation on a Suse Linux Enterprise Real Time PC

Building Fast and Accurate Powertrain Models for System and Control Development

«OPTIMAL ENERGY MANAGEMENT BY EMR AND META-HEURISTIC APPROACH FOR MULTI-SOURCE ELECTRIC VEHICLES»

Modeling and Simulate Automotive Powertrain Systems

Prototypage rapide du contrôle d'un convertisseur de puissance DC-DC à haut rendement

Integration of EtherCAT in Advanced Test Systems Solutions and Challenges. Dr. Frank Schütte, Andreas Tenge, Dr. László Juhász dspace GmbH, Paderborn

VEHICLE DYNAMICS BASED ABS ECU TESTING ON A REAL-TIME HIL SIMULATOR

Bicycle Hardware in the Loop Simulator for Braking Dynamics Assistance System

Effect of Hybridization on the Performance of Fuel Cell Energy/Power Systems (FCEPS) for Unmanned Aerial Vehicle (UAV)

Low Carbon Vehicle Technology Program

Benefits of SiC MOSFET technology in powertrain inverter of a Formula E racing car

Dynamic Behaviour of a Fuel Cell with Ultra Capacitor Peak Power Assistance for a Light Vehicle

Tony LETROUVE L2EP, University Lille1, PSA Peugeot Citroën, MEGEVH network

Powertrain and Chassis Hardware-in-the- Loop (HIL) Simulation of Ford s Autonomous Vehicle Platform

Balancing operability and fuel efficiency in the truck and bus industry

Highly dynamic control of a test bench for highspeed train pantographs

development of hybrid electric vehicles

CELL VEHICLE» Graz University of Technology (Austria) April 2012

Switching Control for Smooth Mode Changes in Hybrid Electric Vehicles

SIL, HIL, and Vehicle Fuel Economy Analysis of a Pre- Transmission Parallel PHEV

Technologies for ToT & Power Electronics Test Facility. under NaMPET-II CDAC, Thiruvananthapuram

Evaluation of hybrid flash-charging stations for electric public transport

Enhancing Driving Dynamics whilst halving emissions: electric Dynamic Control of MIRA Hybrid 4WD Vehicle (H4V)

HIL and PHIL simulation examples in EPMLab Frédéric Colas INTERNATIONAL SUMMER SCHOOL HIL 16

DYNA4 Open Simulation Framework with Flexible Support for Your Work Processes and Modular Simulation Model Library

Model based development of Cruise Control for Mercedes-Benz Trucks

Energy Management Strategy Based on Frequency- Varying Filter for the Battery Supercapacitor Hybrid System of Electric Vehicles

EVREST: Electric Vehicle with Range Extender as a Sustainable Technology.

The design and implementation of a simulation platform for the running of high-speed trains based on High Level Architecture

Control System for a Diesel Generator and UPS

Optimal energy efficiency, vehicle stability and safety on the OpEneR EV with electrified front and rear axles

USING OF dspace DS1103 FOR ELECTRIC VEHICLE POWER CONSUMPTION MODELING

Developing a Methodology for Certifying Heavy Duty Hybrids based on HILS

Challenge A: A more and more energy efficient railway

Models everywhere: How a fully integrated model-based test environment can enable progress in the future

IN SPRINTS TOWARDS AUTONOMOUS DRIVING. BMW GROUP TECHNOLOGY WORKSHOPS. December 2017

«FAULT-OPERATION MODES OF A HIGHLY REDUNDANT MILITARY HEV»

Model-Based Design and Hardware-in-the-Loop Simulation for Clean Vehicles Bo Chen, Ph.D.

HK Equipment CZD Brake tester Max. weight Size Roller size Measure range Error Rated use condition: CCH Sideslip tester 10,000kg (15,000kg) 3990 x 840

Podium Engineering complete race cars, vehicle prototypes high performance hybrid/electric powertrain

SimMotor User Manual Small Engine Simulator and HIL V COPY RIGHTS ECOTRONS LLC All rights reserved

Design of HIL Test System for VCU of Pure Electric Vehicle

Predictive Control Strategies using Simulink

GRPE/HDH Engine-Base Emissions Regulation using HILS for Commercial Hybrid Vehicles JASIC

Low Carbon Technology Project Workstream 8 Vehicle Dynamics and Traction control for Maximum Energy Recovery

OPTIMORE - Optimised Modular Range Extender for every day customer usage AVL SCHRICK project summary

dspace GmbH Rathenaustr Paderborn Germany

Optimal Fuzzy Logic Energy Management Strategy of Hybrid Electric Locomotives

Application of PLC in automatic control system in the production of steel. FAN Zhechao, FENG Hongwei

Ming Cheng, Bo Chen, Michigan Technological University

The European Commission s science and knowledge service. Joint Research Centre. VECTO - Overview VECTO Workshop Ispra, November, 2018

Implementation of a Grid Connected Solar Inverter with Maximum Power Point Tracking

SIZING AND TECHNO-ECONOMIC ANALYSIS OF A GRID CONNECTED PHOTOVOLTAIC SYSTEM WITH HYBRID STORAGE

New propulsion systems for non-road applications and the impact on combustion engine operation

HOW TO USE H5000B TO RUN 5KW OF ENPHASE M250 OFF-GRID

II. HYBRID TEST TRAIN A.

for Critical Applications in Extreme Environments

Integrated Monitoring System Design of Hybrid Aircompressors

Preliminary Study on Quantitative Analysis of Steering System Using Hardware-in-the-Loop (HIL) Simulator

EVs and PHEVs environmental and technological evaluation in actual use

Model Based Design: Balancing Embedded Controls Development and System Simulation

Design of DC/DC Converters for 42V Automotive Applications

Hardware-In-the-Loop (HIL) Testbed for Evaluating Connected Vehicle Applications

12V / 48V Hybrid Vehicle Technology Steven Kowalec

Five Cool Things You Can Do With Powertrain Blockset The MathWorks, Inc. 1

Dr. Charles Kim. EcoCar Team 2 (R.E.V)

Alves, F.R.M. Henriques, R.M. Passos Fº, J.A. Gomes Jr., S. Borges, C.L.T. CEPEL UFJF UFJF CEPEL UFRJ

A Novel Proton Exchange Membrane Fuel Cell-Battery Partial Hybrid System Design for Unmanned Aerial Vehicle Application. Dr.

Incorporating Drivability Metrics into Optimal Energy Management Strategies for Hybrid Vehicles. Daniel Opila

A New Buck-Boost Converter for a Hybrid-Electric Drive Stand P. Mašek

A Methodology to Investigate the Dynamic Characteristics of ESP Hydraulic Units - Part II: Hardware-In-the-Loop Tests

ADVANCES IN INTELLIGENT VEHICLES

Modelling and Control of Ultracapacitor based Bidirectional DC-DC converter systems PhD Scholar : Saichand K

AUTOMOTIVE ELECTRIFICATION

Parameter Design and Tuning Tool for Electric Power Steering System

A Simple and Effective Hardware-in-the-Loop Simulation Platform for Urban Electric Vehicles

FEVE HYDROGEN TRAM. Daniel Sopeña Hydrogen Technologies Manager CIDAUT

Development of the Small-Capacity UPS SANUPS A11K Series

«EMR AND INVERSION-BASED CONTROL

MODELLING FOR ENERGY MANAGEMENT A SHIPYARD S PERSPECTIVE EDWARD SCIBERRAS & ERIK-JAN BOONEN

INTELLIGENT ENERGY MANAGEMENT IN A TWO POWER-BUS VEHICLE SYSTEM

Oshkosh Corporation MTVR On Board Vehicle Power Program Update. May 5, Built Strong. Building for the Future.

Battery-Ultracapacitor based Hybrid Energy System for Standalone power supply and Hybrid Electric Vehicles - Part I: Simulation and Economic Analysis

The Modeling and Simulation of DC Traction Power Supply Network for Urban Rail Transit Based on Simulink

SIMULATION FOR TESTING

Generator Efficiency Optimization at Remote Sites

Powertrain & Thermal Systems

Design & Development of Regenerative Braking System at Rear Axle

MORSE: MOdel-based Real-time Systems Engineering. Reducing physical testing in the calibration of diagnostic and driveabilty features

AC Induction Motor Controller with VCL

SiC for emobility applications

OPENSTEERING PLATFORM

Lead Acid Batteries Modeling and Performance Analysis of BESS in Distributed Generation

Transcription:

1 Summer school HIL 2016 September 1&2, 2016 From simulation to real time control of an all electric bus : the ElLiSup project B. Jeanneret, R. Trigui, D. Ndiaye IFSTTAR Site de Bron Laboratoire Transports et Environnement

Outline Brief presentation of the project Software In the Loop (Plant and Controller are both simulated) Processor In the Loop (Plant simulated/controller on final processor) Hardware In the Loop (Plant is part of real component/controller on proc.) Rapid Control Prototyping (Plant is vehicle/controller on proc.) 2

ElLiSup project A project supported by ADEME with the following partners: 3

4 Project objectives Purpose : Electromobility for public transport. Series hybrid bus 12m long - 3 km ZEV All electric bus with a dual energy storage system composed of batteries and supercapacitors 12m long 8 km ZEV Fast charging system with catenary at the end of the line (up to 250 kw ) Véhicule tout électrique avec recharge rapide en fin de ligne Supercapacités Boîtier de dérivation Refroidissement traction Batteries IFSTTAR contributions: Battery caracterization & selection due to this specific usage Convertisseur 230V/24V 4 Onduleurs traction Modeling and energy management development of the dual system (batteries & supercapacitors) Compresseur d air Groupe de chauffage autonome 4 Moteurs Realization of the prototype supervisor

The vehicle A bus with 4 axles : 3 steering axles, 2 driven axles Small wheels : 17 inches (small diameter for increased interior space) 4 electric motors of 50 kw each located in the wheel 4 batteries packs of 80 kw each 1 supercapacitor pack of 80 kw DC/AC convertors (380V et 24 V) for vehicle s auxiliairies (power steering, air compressor, fans ) A fast charging system with catenary 5

Electrical Architecture 6

Main development steps for the supervisor 7

8 Step 1: Model in the loop (MIL) Objective : energy sharing between battery and supercapacitor Backward models (from the wheels to the energy sources) are used to find optimal solutions regarding objective functions A priori knowledge of the vehicle mission Dynamic programming, Pontryaguin minimum principle Forward models are developped to find sub-optimal solutions applicable in real time

Examples of solution studied in this step 3 levels for control and energy management strategy Level 1 : SOC regulation : Power demand function of SOC of each branch CVS power: Level 2 : Loss minimization by adapting voltage level as a function of vehicle speed Selection of active axle Level 3 : Sharing power between battery and supercapacitor Static look up tables (default strategy) Dynamic control in order to minimize battery RMS currrent Minimize P CVS J i P t res f I * 1 ( soc soc 2 batt dt i ) / 4 t 0 9

Couple 50 10 0 Example of optimization of level 2 somme des pertes / somme des pertes max 10 Mapping of gains/losses between one and two axles Minimizing losses by adapting DC bus voltage as a function of speed 800 600 400 10 0 50-10 -10 0 Torque area for two axles (4 EM) 1 0.95 400 500 600 700 200 0-200 10 50 50 10 10 0 10 0 0 10 50 50 50 50 10 0 0.9 0.85-400 -600-10 0-10 0.8-800 0.75 0 2000 4000 6000 8000 10000 12000 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 Regime Regime Both strategy can be cumulated: This leads to a reduction of 2 to 4% of battery energy depending on the cycle

Example of level 3 optimization MIL1 : backward - dynamic programming 11 IbatRMS=75,1 A

Example of level 3 optimization MIL1 : backward - Pontryaguin Minimum Principle 12 IbatRMS=75,7 A Minimize at each time step : H ( E uc, P uc ) I 2 batt ( P uc ) p P 0 uc ( E uc, P uc )

Example of level 3 optimization MIL2 : backward - Simplified calculation based on PMP IbatRMS=78,2 A UC power : P uc 2 U P res bat 2 p U bat 2 2 13

Example of level 3 optimization MIL3 : forward model Simplified calculation based on PMP 14 IbatRMS=74,8 A Choose an initial value for lagrange parameter, p Add a regulator to stabilize UC level of energy

Step 2 : Progressive integration of components 15 A transition between Processor in the loop (PIL) to Hardware in the loop (HIL) At the beginning of this step, the model can even be compiled in the hardware The real components are progressively suppressed from the simulation model and integrated in the project An intensive use of test bench 2 examples: Step 2.1 : Integration of the driver in the loop Step 2.2 : Testing the application in an engine test bench

Functions tested: Forward and reverse speed Recovery braking modes Anti move back 16 Step 2.1 : Driver in the loop test Development of a framework (MODYVES project) to connect any kind of input (driver input) to any kind of output (vehicle model) Python code Application running on windows Windows timers «Soft» real time application Use of SDL library (G27) Peak or Systec usb adapter

17 A parenthesis : jitter response for this «soft» RT Modyves framework Jitter response for the Modyves framework and two theoretical period of 100Hz and 1 khz (~1 mn) Intel Core i7 3610QM 2.3 GHz Windows Seven Mean dt = 0,01003 Max = 0,053 Min = 0,00999 Mean dt = 0,001002 Max = 0,0027 Min = 0,0009999 According to the pc characteristics, deviation from theoretical frequency could be important, but still far from human time response

18 Step 2.2 : HIL test on engine test bench Rotronics bench

19 Step 2.2 : HIL test on engine test bench 80 70 60 rec1_095.mat vitesse (km/h) Position accélérateur (%) Position frein analogique (%) 50 40 30 20 10 0-10 0 20 40 60 80 100 120 140 160 180 200

Puissances en kw Step 2.2 : HIL test on engine test bench 40 30 20 rec1_095.mat CVS1 CVS2 CVS3 ME4 10 0-10 -20 10-30 0 20 40 60 0 80 100 120 140 160 180 200-10 -20 75 80 85 20

21 Step 3 : Control Prototyping with the final supervisor Coded in Simulink (~6000 elementary simulink blocks) with a many Stateflow charts on a dspace micro-autobox Single tasking/single rate, loop frequency =1 khz Four CAN network (Vehicle, EM, BMS and DC/DC converter, auxiliaries). For each critical frame, Rx time is scheduled in order to detect a default in the communication between ECU. ~20 analog or digitial inputs/outputs Wired Safety Lines between the supervisor and the electric machines, in redondance with a CAN based safety Line.

Structure of the supervisor 22 Each state of the diagram is associated with meta blocks which outputs the appropriate command Pre conditioning Drive Fast charge Slow charge Emergency stop Two main data bus are consolidated for inputs and outputs Pre stop Stop

Comparisons between measure and simulation on SORT2 cycle 23

Electrical power of one motor 24

DC/DC converters power 25 Some difficulties to stabilize the different converters power Each DC/DC ECU has its own low level control

26 Conclusion Electric bus with complex architecture has been designed Different levels of control were studied A progressive methodology of controller design is adopted : Simulation approach (from simple to more realistic models) Processor in the loop Hardware in the loop This approach allows to built optimal control for energy management and supervisor Prototyping hardware makes the debugging phase more easy, but it s not an industrial solution C2000 cards from TI have been successfully tested with simulink applications and adapted to our needs (2 CAN, 16 ADC, 16 DI, 5 DIO, 4 PWM, 2 DAC) Modyves framework wants to be as generic as possible in order to connect any kind of inputs (example: the driver) to any kind of outputs.

Thanks for your attention Ifsttar Contacts : Bruno.jeanneret@ifsttar.fr www.ifsttar.fr 27