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DOI: 10.3390/en13215538

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.

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.

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®.

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.

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.

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. Its performances 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.

Electric and hybrid vehicles are among the keys to solve the problems of global warming and exhausted fossil fuel resources in transportation sector. Due to the limits of energy sources and energy converters in terms of power and energy, hybridizations are of interest for future electrified vehicles. Two typical hybridizations are studied in this thesis: • hybrid energy storage subsystem combining batteries and supercapacitors (SCs); and • hybrid traction subsystem combining internal combustion engine and electric drive. Such combined energy sources and converters must be handled by energy management strategies (EMSs). In which, optimization-based methods are of interest due to their high performance. Nonetheless, these methods are often complicated and computation consuming which can be difficult to be realized in real-world applications. The objective of this thesis is to develop simple but effective real-time optimization-based EMSs for an electric car and a parallel hybrid truck supplied by batteries and SCs. The complexities of the studied system are tackled by using Energetic Macroscopic Representation (EMR) which helps to conduct reduced models for energy management at the supervisory level. Optimal control theory is then applied to these reduced models to accomplish real-time EMSs. These strategies are simple due to the suitable model reductions but systematic and high-performance due to the optimization-based methods. The performances of the proposed strategies are verified via simulations by comparing with off-line optimal benchmark deduced by dynamic programming. Moreover, real-time capabilities of these novel EMSs are validated via experiments by using reduced-scale power hardware-in-the-loop simulation. The results confirm the advantages of the proposed strategies developed by the unified approach in the thesis.