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Optimization techniques are applied to improve the continuing challenges associated with the design of green energy devices, contributing in shaping sustainable processes.

BMW, Alenia Aermacchi, Ford, and other leading companies and research institutions rely on the modeFRONTIER design optimization platform to develop lighter products. 

modeFRONTIER streamlines the HE design process and pinpoints the best configuration in terms of performance, efficiency and sizing. 

Hybrid Electric Motors Optimization using modeFRONTIER

Global demand for higher fuel efficiency and decreased vehicle emissions has triggered the development of pure electric (EV) and hybrid electric (HEV) vehicles. According to Gartner, the majority of automakers will launch hybrid vehicles during the next five years to maintain competitive positions in a rapidly changing environment. Where the hybrids market is forecasted to grow at a compound annual growth rate of 6%, the PEVs segment alone (combined plug-in hybrid and battery electric) will grow at a rate of 39% between 2012 and 20201.

Top industry players have chosen ESTECO technology to effectively set up design tasks associated with EV/HEV systems and to quickly identify the right engineering strategies to cope with highly constrained design problems. 

Design Challenges

Beside the battery pack, the motor represents a major cost and most automotive OEMs have decided to invest in developing their own motor hardware and are facing design challenges to meet specific requirements of the advanced electronically-controlled systems included in the EVs design. The increasing dependency on electrical components and overall complexity, coupled with shorter design cycles, require design teams to constantly improve their design methods for mechatronic integration. Simulation processes of such sophisticated systems require a large amount of time and computational resources. Automating the search for optimal configurations by exploiting the optimization and discipline integration capabilities of modeFRONTIER brings about significant advantages as workloads are rationalized.   

The majority of traction motors currently used in HEV/EV drivetrains are Interior Permanent Magnet (IPM) synchronous machines, but also Induction Machines (IMs) and Switched-Reluctance Machines (SRMs) are considered. These systems require to be re-calibrated for HEV/EV applications and automotive R&D departments typically study hundreds of thousands of design alternatives for motor magnetics addressing structural and thermal considerations along with electromagnetic design constraints.

modeFRONTIER helps manage the strong non-linearity of this design optimization process and identify the best components and mechatronics integration to ensure energy efficiency, safety and reliability of hybrid systems.

modeFRONTIER allows leading HEV/EV manufacturers to:

  • Identify optimal configuration and size of HEV / EV powertrains and components (motors, controllers, and energy management)
  • Understand and enhance multi-disciplinary behavior at the system level – mechanical, electrical, thermal, and magnetics
  • Develop optimal control strategies, power management, torque/speed coupling, and vehicle dynamics
  • Enhance system safety and reliability owing to the Robust Design Optimization tool MORDO 

[1] Pike Research, 2012: “Electric Vehicle Market Forecasts” 

A few examples

The paper proposes an analytical methodology that uses empirical based models and CFD simulations to efficiently evaluate design alternatives in the conversion of a diesel engine to either CNG dedicated or dual fuel engines.

The Series Hydraulic/Electric Synergy System (SHESS) has the potential to improve the overall energy efficiency of HEVs— a current limitation. In this paper, designers optimize energy performance and develop a method that simultaneously maintains reliability, contains maintenance costs, avoids energy loss, and maximizes regeneration and driving energy.

This paper builds on the previous published works in which the authors had employed nonlinear model predictive control for the (sub)optimal power management of a power-split hybrid electric vehicle (HEV). In addition to the battery’s state of charge, in this work we include the effect of inertial powertrain dynamics in the control-oriented model that are usually ignored because of their fast dynamics.

​Lithium-ion batteries have proven higher energy density and longer life cycles than secondary batteries, yet HEV/EV manufacturers are held back due to thermal limitations. This paper presents a coupled experimental/computational method to simultaneously determine multiple thermal parameters of large laminated lithium-ion batteries.

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