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From a research tool for the energy management of hybrid vehicles to a commercial software tool: the example of HOT

May 2017

The transport sector is responsible for 20-25% of global greenhouse gas emissions and it is the leading cause of air pollution in major towns and cities.

Reducing these emissions requires new solutions based on clean, carbon-free vehicles.

Electrification and hybridization are among the most relevant solutions to address these new challenges, but require the development of increasingly complex powertrains and subsystems.

Consequently, the industry needs access to design and dimensioning methods and tools capable of achieving the best compromises between emissions/fuel consumption and driving performance.

Optimizing the performances of hybrid powertrains requires the simulation of energy management strategy between the two sources: the fuel and the electricity stored in the battery. IFPEN's control, signal and system research teams have been working for the past ten years or so on simulation algorithms, applying, in particular, optimal control techniques aimed at minimizing fuel consumption and/or pollutant emissions, by acting on the degrees of freedom offered by hybridization.

Subsequently, while working on the online implementation of these onboard control strategies for real vehicles, IFPEN has developed so-called offline optimization algorithms, which calculate the best energy management for a given powertrain architecture, for a given driving profile or cycle. These algorithms are based on the Pontryagin’s Minimum Principle (PMP) and an iterative method for the calculation of the Lagrange multiplier, or "adjoint state", associated with the only dynamic variable retained, battery charge.

These "offline" algorithms lie at the heart of a tool developed at IFPEN called the Hybrid Optimization Tool (HOT). This tool is based on a parametric definition of the powertrain components but also on the architecture and configuration, which is what makes it so innovative. HOT had initially been designed as a working tool for IFPEN's researchers [1-2]. Recently [3], its characteristics caught the attention of Siemens PLM Software, an industrial partner of IFPEN for more than ten years, within the context of the development of specialized libraries (IFP-Drive, IFP-Engine, IFP-Exhaust and Electric Storage) dedicated to powertrains and integrated in the LMS Imagine.Lab Amesim™ multiphysical modeling environment. In 2016, the collaboration of IFPEN's engine and vehicle modeling teams with Siemens PLM Software led to the integration of HOT in LMS Amesim's IFP-Drive library.

In terms of choosing and optimizing a hybrid vehicle for a given usage, IFP-Drive's users will now be able to:

  • select an architecture (conventional, pure electric, parallel hybrid, series, series-parallel, etc.),
  • configure the components (IC engine, electric motor, battery, transmission, etc.),
  • choose a driving cycle (speed and altitude versus time),
  • launch energy management optimization computation,
  • visualize results (pairing trajectories, powers, etc. during the cycle, fuel consumption, etc.).

                                             Click on image to expand

This process can be operated on loop to further optimize a few of the system's parameters.

Reference text on methods:
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), 2013
>> DOI:10.1007/978-3-642-35913-2

Scientific contact: Antonio Sciarretta

Video >>


[1] Chasse A, Sciarretta A (2011), Supervisory control of hybrid powertrains: an experimental benchmark of offline optimization and online energy management, Control Engineering Practice, vol. 19, no. 11, p. 1253-1265.
>> DOI: 10.1016/j.conengprac.2011.04.008

[2] Chasse A, Pognant-Gros P, Sciarretta A (2009), Online implementation of an optimal supervisory control for a parallel hybrid powertrain, SAE Int. J. Engines, vol. 2, no. 1, p. 1630-1638, paper 2009-01-1868,
>> DOI: 10.4271/2009-01-1868 (first published in Proc. of the SAE Int. Powertrains, Fuels and Lubricants Meeting, Florence, Italy, 15-17 Jun. 2009).

[3] Sciarretta A, Dabadie JC, Font G (2015), Automatic Model-Based Generation of Optimal Energy Management Strategies for Hybrid Powertrains, Proc. of the SIA Powertrain Conf., Versailles, France, 27-28 May 2015.

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