One of the main technological stumbling blocks in the field of environmentally friendly vehicles is related to the energy storage system. It is in this regard that car manufacturers are mobilizing to improve battery technologies and to accurately predict their behavior. The work proposed in this paper deals with the advanced electro-thermal modeling of a hybrid energy storage system integrating Lithium-ion batteries and supercapacitors. The objective is to allow the aging aspects of the components of this system to be taken into account. The development of a model including the electro-thermal behaviors makes it possible to evaluate the progressive degradation of the performance of the hybrid energy storage system. The characterization of both components constituting the hybrid system is carried out via a hybrid Particle Swarm–Nelder–Mead (PSO–NM) optimization algorithm using the experimental data of an urban electric vehicle. The obtained results show the good performance of the developed model and confirm the feasibility of our approach. The use of the PSO–NM optimization algorithm facilitated the identification of the parameters of the developed model with high efficiency, as the error observed is less than 3%. The advanced model associated with an adapted sizing method can be used in many cases to compare energy management strategies in electric vehicle applications.

Combining a high power source like a supercapacitor with a Li-ion battery for electric vehicle applications results in good performance improvements, highly efficient, long lifetime, lightweight design and relatively modest cost of the overall source. A hybrid energy storage system controlled by a smart energy management strategy can play a key role in the design and development of multi-source electric vehicles. In this work, an optimal energy management strategy based on particle swarm optimization incorporating Nelder-Mead simplex method is proposed. The goal of the proposed strategy is to minimize the battery power stress and improves its lifetime. This is achieved by coupling a rule-based method based on the knowledge of the battery and supercapacitor efficiency operating with a hybrid Particle Swarm–Nelder–Mead (PSO–NM) optimization algorithm. This latter approach is proposed to optimize the control parameters of the rule-based energy management strategy, once the off-line optimization algorithm is over, the control method can be implemented on-line. The obtained results demonstrate significant lifetime enhancements for Li-Ion battery, an increase of up to 20% as compared to the mono-source based on regular single battery.

In order to democratize electric vehicles, automakers have to give a solution to reduce the Total Cost of Ownership (TCO) and bring it better than thermal vehicles one. This paper presents results of works on costs improvement of a 3.5t light-duty commercial electric vehicle. TCO (Total Cost of Ownership) was separated into investment costs and operating costs. The batteries of an electric vehicle were the most expensive element of the powertrain, which can represent until 45% of the vehicle price. What's more, contrary to the life of thermal vehicles, electric energy storage systems have a limited life around 5 years, highly dependent on conditions of use. Thus, study of batteries represents a capital step toward TCO improvement. Inspired by literature, battery aging model is proposed and identified by the calendar and cycling aging tests. A multi-physical model of the battery, including aging, was identified and incorporated inside the energetic vehicle model. Charging strategies were developed and adapted to operate according to the influence of the conditions of use. These models and strategies are integrated in a software tool. Taking into account the intended use of the vehicle, this tool, first, designates the most suitable battery solution dimensions, and then, associates the charging strategy to the requirements of use, in order to ensure the best battery life or the lowest TCO.

Quadrotor unmanned aerial vehicles have a limited quantity of embedded energy. To preserve and guaranty the success of the UAV mission, we should manage energy consumption during the mission. In this study, we introduce an optimization algorithm to minimize the consumed energy in the flying vehicle mission under windy conditions. In order to calculate the energy consumed by the quadrotor, we present a power loss model, where the energy is formulated as a function of rotor speed and acceleration. Then, we formulate the energy minimization problem as an optimal control problem and solve this problem in order to calculate minimum energy for a point-to-point quadrotor mission under windy conditions. In order to highlight the proposed optimization approach, we compare energy consumption obtained by optimization algorithm with an adaptive control approach in simulation experiment.

Currently, batteries and supercapacitors play a vital role as energy storage systems in industrial applications, particularly in electric vehicles. Electric vehicles benefit from the high energy density of lithium batteries as well as the high power density of supercapacitors. Hence, a robust and efficient energy management system is required to coordinate energy flows between these two storage systems, ensuring road safety. In this study, we develop a novel rule-based strategy called “Continuous Regulation with Dynamic Battery Power Limiting” to establish robust control between the lithium-ion battery and the supercapacitor. A comparative analysis is conducted to evaluate the performance of this proposed approach in comparison to conventional methods. The results show that this approach significantly enhances driving comfort and prevents depletion of the main energy source, resulting in a gain of nearly 30% compared to a lithium-ion battery electric vehicle. Additionally, this new rules-based strategy ensures that the supercapacitor is charged at the end of each drive cycle.