How engineers can choose and optimize their hybrid architectures for a specific application on different driving cycles? Architecture selection comes at an early stage of the development cycle and requires an efficient decision making tool. This tool must be ergonomic, require few parameters and provide a fast and easy workflow.
NB for LMS Amesim users: the step by step methodology is further detailed in the LMS Amesim Hybrid Optimization Tool solution.
Since the beginning of the industrial era, the worldwide CO2 emissions reached a critical point. Road transportation, representing 20% of those emissions, must reduce its fuel consumption and pollutant emissions.
Hybridization of vehicle, a key solution to meet new challenges? Over the past 10 years, a strong engagement from car manufacturers for hybrid vehicles can be seen on that way.
Thus, there is a need for car manufacturers and Original Equipment Manufacturers (OEMs) to have simple and efficient simulation tools to assist them, during conception phase, finding the best compromise between emissions, drivability and performance for different architectures. Furthermore, they need to take into account different subsystems (Stop&Start, air-conditioning, power steering) and for different driving cycles New European Driving Cycle (NEDC), Worldwide harmonized Light vehicles Test Procedures (WLTC), Real Driving Emissions (RDE).
Hybrid optimization tool, a LMS Imagine.Lab Amesim solution to support industrial actors to manage new performance
The Hybrid Optimization Tool (HOT), from LMS Imagine.Lab Amesim, proposes a solution to test, optimize and compare different hybrid architectures. Seven steps are necessary to start from a blank page to a running simulation.
The first three steps are dedicated to the architecture definition (up to 100 architectures), the component parameters (scalar, vectors, maps) and cycle definition. In early development phase, only macroscopic parameters or dimensions are available. Thanks to LMS Imagine.Lab IFP-Drive library approach, all the components can be setup easily.
Driving cycles are defined by four profiles: speed, gear ratio, altitude/slope, electric/torque request. A set of predefined cycles such as NEDC, Federal Test Procedure (FTP75), WLTC…can be loaded.
The fourth step is used to define the optimization criteria and penalty. Fuel consumption penalty can be defined for each engine start or gear change. The initial and final battery State Of Charge (SOC) are defined as optimization constraints.
The fifth step is dedicated to the post processing and visualization of results. Predefined energy summary and plots are available to ease the decision making.
Energy law optimization
In order to find the optimal energy management strategy, the degree of freedom used is the power split between the engine and the battery. The torque split is computed by minimizing the fuel consumption over a given driving cycle with a constraint on the final SOC. It is equivalent to minimizing a cost function which is the sum of the fuel consumption and the electric consumption weighted by a cycle-dependent “equivalence factor”.
After the optimization, a sketch can be generated. It will represent the overall architecture without the engine control unit (ECU). Engineers will need to setup their ECU and compare the simulation results with HOT optimal solution.
Vehicle speed, engine speed, torque, CO2, cumulative fuel consumption and SOC can be compared.
For each step, a “Generate sketch” button is available. It allows the tool to automatically:
in function of the selected architecture.
Thanks to the Hybrid Optimization Tool, engineers have an efficient workflow to see the pros/cons of one architecture or component against another. With the optimization algorithm, they will be able to find the best energy law between thermal and electric power sources. Finally, thanks to the save and generate sketch functionalities, it will be easier to transfer, to another person, study cases and to generate an identical sketch.
 L. Guzzella, A. Sciarretta: “Vehicle Propulsion Systems. Introduction to Modeling and Optimization”, 3rd edition, Springer-Verlag, Berlin Heidelberg, ISBN 978-3-642-35912-5 (Print), 978-642-35913-2 (Online), DOI:10.1007/978-3-642-35913-2, 2013
 A. Sciarretta, J.C. Dabadie, G. Font: “Automatic Model-Based Generation of Optimal Energy Management Strategies for Hybrid Powertrains”, SIA POWERTRAIN VERSAILLES 2015