Joao P. Trovao

A Real-Time Energy Management Architecture for Multisource Electric Vehicles

This paper presents an energy management architecture for small urban electric vehicles based on hybrid energy sources and its real-time implementation. The energy management strategy uses an integrated rule-based metaheuristic approach to obtain solutions for sharing energy and power between two sources with different characteristics, namely, one with high specific energy and another with high specific power. A comprehensive real-time architecture for the energy management system is presented considering different management levels. The proposed approach determines an optimized real-time energy sharing between the sources without prior knowledge of the power demand profile. The multilevel energy management strategy has been validated using power-level reduced-scale hardware-in-the-loop simulations for a normalized urban driving cycle. The experimental results show the effectiveness of a real-time implementation based on particle swarm optimization supported by a set of rules restricting the search space. This strategy is effective in controlling the energy sources to work in their higher efficiency region and in satisfying the dynamic performance of the vehicle.

Simulation model and road tests comparative results of a small urban electric vehicle

Electric vehicles (EVs), due to its lack of local gas emissions, silent driving and much higher efficiency than internal combustion engine vehicles, are fundamental for the worlds sustainable mobility. In EVs projects, computer modeling and simulation can be used to reduce the expense and length of their design cycle by testing configurations and energy management strategies before starting the prototype construction. Accordingly, this paper presents the modeling and simulation of a small urban EV based on the conversion of an internal combustion engine vehicle in a fully EV, called VEIL. The ISEC electric vehicle VEIL project is shortly presented, along with the VEIL dynamic model. A real test circuit at ISEC campus is modeled and the results of simulation are compared with experimental results in order to validate the simulation model.

A Simulated Annealing Approach for Optimal Power Source Management in a Small EV

This paper presents a novel energy management strategy for a hybridized power source small urban electric vehicle (EV). First, an analysis of load requirements for typical urban driving cycles is presented. Thereafter, the energy and power management issues are addressed for a multisource EV to define an improved management architecture. A dynamically restricted search space strategy coupled with a simulated annealing technique is exploited to accomplish the global optimization of the energy management system (EMS). The control of the dc/dc converter operations based on this EMS is also presented. The multiple sources have been simulated using an overall model implemented in Matlab/Simulink. A reduced-scale prototype has been built to analyze the performance of the energy management strategy. The results obtained show that energy management has been enhanced leading to an increase of the vehicle performance with reduced size embarked sources.

Electric vehicles chargers characterization: Load demand and harmonic distortion

This paper presents electric vehicles chargers power quality characterization. The difference between domestic charge for small urban electric vehicle and industrial charge for forklift is addressed. Full charges of lead-acid and NiMH batteries are monitored by recent power analyser, allowing the study of power demands and the voltage and current waveform distortion of the considered charger. The data of the voltage and current waveforms is then used to study and analyse the best solution to mitigate the principal power quality problems. For this, a set of methods and techniques to attenuate the harmonics amplitude by network structural alteration is presented and discussed. The load levelling control concept has been introduced as an alternative option to structural alteration. This comprehensive and effective EV charging control system will respond to the undesirable increases in peak energy demand and power quality degradation, and avoid that the electrical energy network might not be prepared to respond to these requests.

Comparative study of different electric machines in the powertrain of a small electric vehicle

Electric vehicles (EVs), due to its lack of local gas emissions, silent driving and much higher efficiency than internal combustion engine vehicles, are fundamental for the worlds sustainable mobility. The efficiency and response of the powertrain, namely of the electric motor (EM), are very important aspects on the correct sizing and project of an electric (or hybrid) vehicle. The mobility problem and the importance of EVs and Hybrid Electric Vehicles (HEVs) to sustainable mobility are introduced, followed by a short presentation of the most important EMs for EVs. The ISEC electric vehicle VEIL project is shortly presented, along with the VEIL dynamic model and some results of the powertrain response. The study methodology is presented together with a comparative analysis of the use of different types of powertrains and EMs for different drive cycles for typical EVs utilization.

DC link control for multiple energy sources in electric vehicles

In this paper, a detailed description of a control architecture for managing the DC link control of EVs with multiple energy sources is presented. The proposed topology allows the control of the power flow among supercapacitors and batteries, while ensuring the regulation of the DC link voltage, thanks to a cascade of voltage and current linear controllers. A simple analytical study is provided to illustrate the tuning guidelines for the current and voltage, based on proportional + integral controllers. A prototype system has been designed and built in reduced scale hardware to analyze the performance of the proposed control system. The experimental results are in accordance with the simulations and demonstrated the effectiveness of the proposed control technique.

Adaptive Energy Management System Based on a Real-Time Model Predictive Control With Nonuniform Sampling Time for Multiple Energy Storage Electric Vehicle

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.

An automated energy management system in a smart grid context

The ongoing transformation of electric grids into smart grids provides the technological basis to implement demand-sensitive pricing strategies aimed at using the electric power infrastructure more efficiently. These strategies, also designated by demand response [1], already proved to be effective in altering patterns of electricity usage [2-6], and create benefits not only for end users (by lowering their electricity bill without degrading comfort levels), but also for the utilities (by managing the peak, flattening the aggregate demand curve, and meeting supply with demand) and the environment (by avoiding, or delaying, building new generation units and other network infrastructures). In fact, demand-sensitive pricing of electricity is expected to become the standard pricing mechanism in smart grids [3, 7, 8] and is considered essential to accelerate the deployment of variable renewable generation while maintaining electric system security and reliability at least cost [9].

Effectiveness of Supercapacitors in Pure Electric Vehicles Using a Hybrid Metaheuristic Approach

In this paper, a novel energy management system (EMS) for a hybrid power source in a small urban electric vehicle (EV) is presented. The problem is divided in three layers, according to the real-time needs of the system. The long-term management layer dynamically restricts the search space based on a set of rules. The short-term management layer implements the optimization strategy based on a hybrid metaheuristic approach combining particle swarm optimization (PSO) and simulated annealing (SA). The solutions to the optimal power sharing problem are used to generate the power references for the lower layer, i.e., a high-speed dc-dc controller. This controller is developed using a linear-quadratic regulator (LQR). A reduced-scale prototype has been built to analyze the performance of the energy management strategy. The results show the effectiveness of this multilayer EMS allowing to fulfill the requested performance with better source usage and much lower installed capacities.

Multiple energy sources monitoring system for electric vehicle

Electric vehicles (EVs) are fundamental components for a sustainable mobility. Unless a major technological breakthrough is achieved in energy storage and battery technology, EVs future will pass by multiple energy sources hybridisation, to benefit from the best characteristics of the energy sources available. In this paper, the energy and the mobility problems are introduced as well as the relevance of EVs in advanced and sustainable transportation. The EVs energy management issue is addressed together with the need for multiple energy sources hybridisation. Finally, a detailed description of the multiple energy sources monitoring system, particularly of its sensor data acquisition network, developed by the authors for their EV prototype, is presented along with collected data drawn from tests to one of the EV battery banks.