Research Topics

We have founded the electric-Transport, Energy Storage and Conversion (e-TESC) laboratory in 2015, an intensively multidisciplinary expertise in electrified vehicles (EV) have been developed to respond to Canadian and international research and development standards. e-TESC Lab has a large critical mass developed by Professors, Research Engineers, Postdoctoral fellows, PhD, MSC and undergraduate students to share and contribute to new knowledge in 5 emergent topics.

Energy and power management of multiple energy sources

  • Innovative techniques using this multi-level management architecture (strategic and tactical planning levels) to increase the efficiency of EVs are developed at e-TESC Lab. This original architecture, combined with meta-heuristic techniques, enables power-sharing decisions in computational times compatible with real-time implementation [RI5]. This contribution introduced an innovative use of mathematical tools for intelligent multi-source management as presented in [RI12].
  • The developed real-time strategy significantly increases the performance and efficiency (+ 8.5% of available energy at the end of an ARTEMIS driving cycle) and simultaneously guarantees full performance of the vehicle in normal operation as validated in [RI9].
  • In a collaborative framework with Arts et Métiers Paris Tech [RI17], we have proposed a non-uniform sampling method for model predictive control (MPC) for energy management of semi-active hybrid energy storage system. The sampling is distributed from small to large sampling time to achieve long horizon with precise prediction to effectively reduce the stress in the battery pack.
  • Recently, in collaboration with L2EP, we have extended my studies to propose an adaption of Pontryagin’s minimum principle (PMP) with close-form analytical solution for easy implementation in real vehicles [RI28]. This contribution reaches a reduction of 50% in battery current RMS value for a real-world driving cycle that powerfully increase the battery lifetime.

Extensions of these contributions are now under development for hybrid parallel and hybrid series electric vehicles as we have proposed in [RI23] [RI29].

Design and state-of-charge estimation of high-performance battery pack

  • A new methodology to design new generation of battery packs, based on cylindrical Li-ion cells, was developed inside e-TESC Lab and mechanical design is presented in open access. This contribution was been structural for the success of the Capstone Project prototypes: EMUS 56, EMUS 64, Hertz and EMUS‑Phoenix. For instance, EMUS 56 was ranked first at the eMotoRacing Varsity Challenge in 2016 and 2017. These consecutive and important achievements were based on the novelty of this battery pack design contribution.
  • Thereafter, we extend this new design-method framework to reach an automatic design tool based on a constraint Satisfaction Problem and Constrained Optimization Problem [RI48] methods. This new framework helps engineers to understand the influence of design decisions from the early stages of the engineering process, guiding the design in the multi-domain optimization problem. This tool is now used for several battery industrial designs; a demonstration of research transfer to industry.
  • As a validation of this design method contribution, a full design passive hybrid energy storage system (Li-ion battery and Li-ion capacitors) for high-performance electric vehicle has been published in [RI33]. The passive hybrid topology resulting of this design-methodology reduces by 15% the battery current RMS increasing Li-ion cells lifetime for the same maximum volume and weight.
  • A novel state-of-charge (SoC) estimation algorithm based on disturbance observer theory has been developed [RI36]. The performance of this algorithm has been successfully compared to other algorithms presented in the literature like extended Kalman filter and commercial products. Simulation results and validation by hardware-in-the-loop (HIL) establish the great potential of this novel estimation algorithm. This new algorithm is perfectly adapted for full implementation into automotive industry microcontrollers with the same accuracy than up-to-date commercial estimators.

Variable inductor for electric vehicle power electronics converters

In electric vehicles field, power electronics converters are essentially needed to drive the motors, charging systems and couple multiple sources. e-TESC Lab is working on the development of variable inductor concept and switching frequency increase capability to improve power electronics converters efficiency.

  • In collaboration with IT Coimbra, we have established a method that is instrumental to optimize the core size of power inductors used in bidirectional dc-dc converters for efficient coupling of multi-source [RI10]. This approach has significant merits to control current ripple and enhance the power inductors current capability with large reduction of the magnetic components as demonstrated in [RI19].
  • Reduction of passive components is also pursued by the switching frequency increase, and to do so the new switching devices based on Gallium nitride (GaN) high-electron-mobility transistor (HEMT) are under study with some industrial partners. In this sense, I have contributed to a new analytical method to determine the parasitic inductance values for GaN-HEMT applications [RI27].

Motor drive for high performance electric vehicles

  • e-TESC Lab have contributed to use the concept of motor constant power speed ratio (CPSR) to improve the design approach of hybrid excitation synchronous motor (HESM) for efficiency enhancement. In comparison with the original permanent magnet synchronous motor (PMSM), the developed approach gets 4.1% efficiency improvement and 16% decrease in rated values of drivetrain elements [RI26].
  • Based on Hybridization Ratio (HR) concept, we have developed a two-level methodology to optimize the design of Hybrid Excitation Synchronous Machine (HESM) for a given Electric Vehicle (EV) over an arbitrary-selected driving cycle. We are looking at a huge analysis problem of finding an optimal HR between the two excitation sources, namely, Permanent Magnet (PM) and Wound Excitation (WE). To find the optimal HR, the HR is scanned from 0 to 1, or from pure WE to pure PM excitation. For each HR, the motor is optimally designed at the component-level, its cost is minimized, and its global efficiency over the selected driving cycle is calculated. Then at the system-level, the global efficiencies associated to each HR are compared to find the optimal HR. The complexity of the design optimization at the component-level is addressed by Non-dominated Sorting Genetic Algorithm II (NSGA-II) [R44]. To make a compromise between accuracy and speed of calculations, a non-linear 3D dynamic Magnetic Equivalent Circuit (MEC) model is developed and evaluated by commercial Finite Element Analysis (FEA) software. Following the proposed methodology, the final HESM design can achieve up to 18.65 % higher global efficiency than pure Wound Excitation (WE), and 15.8 % higher than pure a Permanent Magnet (PM) excitation [R41].
  • e-TESC Lab have also participated in a major contribution regarding PMSM with Halbach array design to optimize the performance of the motor. This contribution was fundamental to minimize iron losses at cruising speed and then its energy consumption of E-Volve motor prototype. E-Volve took 1st place of all three America-Europe and Asia race of 2015 Shell Eco-marathon challenges (Urban Vehicle Category) with an energy consumption record of 3.0 Wh/km (2011-Nissan Leaf consumes 212 Wh/km).

Modeling and control of multiphysics systems

The electric vehicle, as a multiphysics system, can be globally simulated to accelerate testing and validation time, implement all operations and scenarios that are difficult or impossible to recreate with a real system, and conduct initial studies under controlled environments. This kind of simulation involves multiple simultaneous physical phenomena (chemical, electrical, thermal, magnetic and kinetic). Our main contributions in this topic are:

  • At battery level, a co-simulation approach of an electric three-wheel roadster battery pack using 1D-3D co-simulation model that considers the interaction of electric (1D) and thermal fields (3D). This co-simulation suggests a benchmark contribution for designed battery pack validation, and it is largely used for industrial projects; a good research transfer to industry.
  • At system level, based on previous dynamic EV model, hybrid energy storage system models were highlighted in my research work with a global multi-source EV model using Energetic Macroscopic Representation (EMR) [RI21] in order to define effective control layers (higher dynamics) reliable with the dynamics of the vehicles. We have contributed to develop electro-thermal models by introducing coupled effect of temperature in cylindrical battery cells using EMR, regarding the enhancement of the vehicle range estimation [RI49]. These models were central for the success of the energy management Topic. All these models have been integrated into a complete EV simulator [RI16] [RI18].
  • Using this EV simulator, I have contributed in collaboration with Univ. Florida to an improved cruise controller tuned by hybrid meta-heuristic algorithm and experimentally validated on real vehicle platform with maximum overshoot less than 10% for hardest accelerations on slippery roads is available [RI30].