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The combination of batteries and supercapacitors is promising in electric vehicles context to minimize battery aging. Such a system needs an energy management strategy (EMS) that distributes energy in real-time for real driving cycles. Pontryagin’s minimum principle (PMP) is widely used in adaptive forms to develop real-time optimization-based EMSs thanks to its analytical approach. This methodology leads to an off-line optimal solution which requires an extra adaptive mechanism for real-time applications. In this paper, a simplification of the PMP method is proposed to avoid the adaptation mechanism in real-time. This new EMS is compared to well-known conventional strategies by simulation. Furthermore, experimental results are provided to assess the real-time operation of the proposed EMS. Simulation and experimental results prove the advantages of the proposed approach by a reduction up to 50% of the batteries rms current on a real-world driving cycle compared to a battery-only EV.

Parallel hybrid electric vehicles (HEVs) are suitable for heavy-duty applications like trucks. Yet there is an issue of this power configuration that the electrical drives have to work with many fluctuations which can degrade the batteries. Supercapacitors (SCs) can be added to save the batteries life-time. However, it also adds more losses to the system which can increase the engine fuel consumption. This paper aims to study the impact of SCs subsystem on fuel consumption and batteries life-time of a hybrid truck. Energetic Macroscopic Representation (EMR) is used to deal with the complexity of the studied system. A fair comparison is achieved by using dynamic programming due to its global optimal solutions. Simulation results figure out that the SCs subsystem causes 14% decrease of the maximum optimal saved fuel but can reduce up to 49% of batteries rms current for a specific hybrid truck. A positive effect of using hybrid energy storage subsystems for HEVs can be therefore concluded.

This study deals with the driving range evolution of an EV as a function of the cumulated hours of operations. The studied vehicle is a three-wheel electric vehicle (e-TESC 3W platform) developed and instrumented at the University of Sherbrooke. The system model is built and organized using the Energetic Macroscopic Representation. A current cycle is applied with a programmable source on one cell to emulate the driving current constraints. The cell model parameters are characterized periodically. Then, the EV simulation is performed with the battery parameters corresponding to different cumulated driving hours. It is shown that the driving ranges is reduced by about 12% after 200 h of operations.

Hybrid electric vehicles (HEVs) can be supplied by hybrid energy storage systems (H-ESSs). Such kind of system needs to be handled by complex energy management strategies (EMSs) considering several objectives. Besides, to evaluate such EMSs, an optimal benchmark could be developed. Dynamic programming (DP) is suitable for that purpose because of its ability to obtain the optimal solution. However, it is non-trivial to address a multi-objective energy management problem by using DP because of the complexity of the system and computational issues. This paper develops a DP-based optimal EMS for a hybrid truck supplied by battery/supercapacitor H-ESS using a bi-level optimization approach. This approach decomposes the EMS into optimal sub-strategies regarding the structure of the studied system. The validation of the optimal strategy is demonstrated by simulation results.

Battery operation is influenced by the temperature. Thus, thermal modelling is an important aspect for battery electric vehicles (EVs). Generally the electro-thermal parameters are identified for one single cell. In EVs the battery is divided into several modules (groups of cells side by side). The objective of this paper is to quantify the accuracy by using the electro-thermal parameters of one single cell to model the behavior of a module. The temperature of a cell inside a module and the voltage of this module are recorded during a real driving cycle in an instrumented commercial EV. Considering the module as a group of identical single cells leads to an error of 7% for module voltage and 4.8% on the self-heating.

Fuel consumption is a critical issue of hybrid vehicles, especially the heavy-duty ones. Studies on energy saving ability considering different power and energy capabilities of the electrical components are therefore of interest. This paper aims to compare the maximal fuel saving of the hybrid truck with different hybridizations. Modeling, control, and energy management of the system are carried out using Energetic Macroscopic Representation formalism. Dynamic programming is employed as a global optimization-based strategy for energy management of the vehicle. An 8.5-ton parallel hybrid truck primarily driven by a 147-kW internal combustion engine is under study. Simulation results point out a preferred hybridization option by using a 120-kW electrical machine supplied by a 19.4-kWh Li-ion battery pack. A respective 21.6% reduction of fuel consumption is reported for USA FTP Highway driving cycle.

Hybrid energy storage systems (H-ESSs), as hybridizations of batteries and supercapacitors (SCs), need to be handled by appropriate energy management systems (EMSs). Smoothness of battery current to reduce their degradation are normally the aim of the EMSs. The evolutions of SC voltage however also need to be addressed. Various approaches for handling SC voltage while managing battery current can be found in the literature. Most of the time, their limitations are achieved directly or indirectly in the EMS. This paper aims to develop a merging control scheme to include the SC control outside the EMS. This control scheme based on the inversion principle of the Energetic Macroscopic Representation is proposed for a battery/SC subsystem. Two control schemes are thus systematically deduced and then merged. The battery current reference is generated regarding their aging behavior. Meanwhile, the SC voltage reference is deduced concerning the required SCs stored energy as a function of the vehicle velocity. The proposed approach is validated by simulation in Matlab/Simulink®.

Energy management strategies are mandatory for hybrid energy storage systems in applications for electric and hybrid vehicles. Optimization–based real–time strategies are of interest since they are straightforward to optimize performance criteria. The available methods are often the combinations of adaptive mechanisms with solutions deduced by optimal control techniques such as Pontryagin’s minimum principle (PMP). This paper proposes an approach to develop a real–time strategy for battery/supercapacitor systems based on PMP and Energetic Macroscopic Representation without any need of adaptive mechanism for the co-state variable. Performances of the proposed strategy are validated by simulation.

Using supercapacitors (SCs) to configure the hybrid energy storage system (H-ESS) to extend battery life-time is promising for electric vehicles (EVs). Energy management strategy (EMS) is mandatory for such systems. Most of previous works develop improved EMS with the only one objective, normally on battery life-time. However, at least the costs on SCs operation also need to be taken into account. Generally, a methodology to develop multi-objective EMS is demanded. This paper presents a procedure to develop multi-objective EMS of H-ESS for an EV. SCs system losses are considered as the second cost function. Dynamic programming is used to give the benchmark in order to evaluate the performance of the other strategies. Simulation results validate the effectiveness of the developed off-line EMS. The dynamic programming generates a Pareto front to be used in order to compare with other on-line EMS.

In electric vehicles, supercapacitors (SC) are commonly used to perform a hybrid energy storage system (H-ESS). This approach, correctly coordinated and designed, can extend battery lifetime by limiting the battery current stress. SC are often coupled to DC/DC converters. To use the maximum energy stored in SC, high variability of the SC voltage is mandatory. Several time, the associated converter voltage ratio becomes too high and can produce instability of the system. Thus, at each time there is a minimal value for the SC voltage. This article proposes a dynamical limitation method for the SC voltage. The dynamic method is compared with a traditional constant limitation technique. Both are tested under the urban part of New European Driving Cycle with slope. The results point out that the dynamical limitation can ensure a better stability of the system regardless environment disturbance.

Nowadays energy storage is the keypoint of the development of electrical vehicles (EV) for charging time, autonomy and cost reasons. The main energy storage system for electrical vehicles is the battery and can cost up to one third of the vehicle price. Nevertheless the battery has limited power features and can be damaged by EV peak power requirements. Thus higher power density energy storage systems (such as supercapacitors) are used in combination with batteries (multisource EV). A reduced-scale EV demonstrator has been developed to present the concept of multi-source EV to high school and undergraduate students. The aim of this demonstrator is to highlight the interest of research on electric vehicle to attract students in this research topic.