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.

This paper proposes a comprehensive methodology for the design of a controllable electric vehicle charger capable of making the most of the interaction with an autonomous smart energy management system (EMS) in a residential setting. Autonomous EMSs aim achieving the potential benefits associated with energy exchanges between consumers and the grid, using bidirectional and power-controllable electric vehicle chargers. A suitable design for a controllable charger is presented, including the sizing of passive elements and controllers. This charger has been implemented using an experimental setup with a digital signal processor to validate its operation. The experimental results obtained foresee an adequate interaction between the proposed charger and a compatible autonomous EMS in a typical residential setting.

The performance of a dual energy storage electric vehicle system mainly depends on the quality of its power and energy managements. A real-time management strategy supported by a model predictive control (MPC) using the nonuniform sampling time concept is developed and fully addressed in this paper. First, the overall multiple energy storage powertrain model including its inner control layer is represented with the energetic macroscopic representation and used to introduce the energy strategy level. The model of the system with its inner control layer is translated into the state-space domain in order to develop an MPC approach. The management algorithm based on mixed short- and long-term predictions is compared to rule-based and constant sampling time MPC strategies in order to assess its performance and its ability to be used in a real vehicle. The real-time simulation results indicate that, compared to other strategies, the proposed MPC strategy can balance the power and the energy of the dual energy storage system more effectively, and reduce the stress on batteries. Moreover, battery and supercapacitor key variables are kept within safety limits, increasing the lifetime of the overall system.

Specific applications such as recreational vehicles require new developments with respect to their energy storage system (ESS). Despite some recent trends in battery development, the ratio between power and energy has not yet met the requirements of these specific kinds of vehicles. This paper presents the integration of a SuperCapacitors (SCs) pack in a three-wheel electric vehicle considering the energy- and power-split management strategy. An energy-management strategy based on a comprehensive fuzzy logic controller approach is fully addressed to increase the global efficiency and performance of the studied vehicle. The proposed control and management strategy ensures that the battery supplies the average portion of the power demand, while the energy level of the SCs is smartly handled. The proposed strategy is easily adaptable to other vehicles or different driving modes. The approach was validated with a power-level hardware-in-the-loop (HIL) platform for a reduced-scale hybrid dual ESS. This experimental test allows real-time verification of the proposed energy management and evaluates the ability to coordinate more efficiently the energy flow. The proposed approach enhances the battery lifetime by reducing the battery current root-mean-square (RMS) value by 12% compared to a battery-only architecture.