This paper presents a sensitivity analysis of battery degradation in hybrid nanogrids and its implications for optimizing operational parameters. This work focuses on three key control parameters: Minimum State of the Charge, Maximum State of the Charge, and Charge Rate. We evaluate the influence of these parameters on battery degradation by adjusting them within practical ranges, using historical data from an existing nanogrid, and performing simulation via Matlab/Simulink. The sensitivity analysis shows that there can be up to a thirty two percent increase in battery degradation when using setups derived from a prior optimization study compared to those based on the references of the manufacturer. Moreover, we compare our results with previous optimization and manufacturer recommendations, providing insights into long-term cumulative damage for the whole seasons of a year. This highlights the critical importance of considering degradation in the nanogrid energy management.

International audience

Electric mobility is growing steadily, mainly driven by the need to decarbonise the transportation sector. This trend is even faster considering electric bus fleets. Challenges arise regarding a better integration between the fleet charging process and the grid. This research introduces a bi-level optimisation model that explores the use of dynamic time-differentiated pricing schemes for electric bus network operation. The retailer first defines the prices for each predefined period, and the public transportation operator reacts by scheduling the charging events and vehicle operation in the face of prices and energy requirements. We developed a hybrid approach, combining a metaheuristic and an exact solver, to maximise the retailer's profits subject to the minimisation of the bus system operational costs. A study is presented using real-world data from a small electric bus fleet in a medium-size Portuguese town.

Since a proton exchange membrane (PEM) fuel cell (FC) has time-varying characteristics, its online characteristics estimation (voltage, power, internal resistance, etc.) is becoming a key step in designing an energy management strategy (EMS) for hybrid FC vehicles. In this respect, this paper proposes a new method based on Lyapunov adaptation law to estimate the linear and nonlinear parameters of a renowned PEMFC model in the literature. Unlike most of similar estimators, the suggested approach determines the maximum current, which is a nonlinear parameter, online while guaranteeing the system closed-loop stability. This parameter is normally assumed to be constant while it changes through time owing to degradation and operating conditions variation. This alteration makes the model imprecise while extracting some important characteristics, such as maximum power and polarization curve. Therefore, it needs to be regularly updated along with other parameters. To demonstrate the capability of the suggested method, a detailed comparison is provided with the well-known extended Kalman filter (EKF) as an attested nonlinear estimator. Moreover, to highlight the effectiveness of the nonlinearity consideration, a comparison with KF is performed where the nonlinear parameter is considered constant. The performed experiments on a 500-W PEMFC show that the proposed method can be over twice as accurate as EKF and KF concerning the estimation of maximum power and current while its runtime is nearly half of them.

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.

Often groups of cells in parallel are assumed to behave as one single larger unit, with homogeneous properties. Although this assumption may seem correct , it is partly false because it neglects the impact of cell to cell current variations and the self balancing currents on pack characteristics. Modeling small cells into one larger unit hinders the possibility of studying the effects of internal resistance or state-of- charge variations between cells, on current distribution and heat generation. A better understanding of the subtle interactions that usually goes unwhitnessed inside a pack is necessary to design better battery systems. This article presents a simple electro-thermal battery model based on experimental data of electrical and thermal properties of single cells., useful for design purposes or reliability assesment. The model integrates state-of -the-art cell submodels, linked together via thermal and electrical networks. Model parameters such as the entropic coefficient, internal resistance correction and open circuit voltage are interpolated using experimental lookup tables. Simulation results of a full discharge, a full recharge and an interrupted discharge are presented, as examples.

In this paper, an innovative control method for a hybrid source electric vehicle (EV) is presented. Since the usual and practical way to measure currents consists in using sensors based on Hall effect, we present a technique to significantly reduce the noise generated by these sensors. Firstly, the model to build such controller and the variables involved is discussed. Then, to have a stable system and a controller easy to setup, a method to solve the control problem using a linear-quadratic regulator (LQR) is proposed. An Extended Kalman Filter (EKF) that uses the sensors and the converter model is exploited to obtain almost noise-free currents. The LQR and the EKF are validated separately, using an overall model implemented in Matlab/Simulink. The results obtained reveal a good behaviour by the controllers and an almost noise-free environment after the addition of the EKF. Further, the global performance is also validated using a standard driving cycle.

Electric vehicles (EVs) have been gaining prominence in the future of sustainable transportation. Advances in energy management systems have shown that EVs with hybrid electric storage systems might be important in situations with frequent speed variations as in urban traffic, attaining more efficient powertrain energy application. Therefore, EVs energy management is an important issue with significant influence in EVs performance. EVs management systems provide important benefits such as reducing energy consumption, decreasing pollution and improving driving performance in the EVs perspective. In this paper, a promising EV dual source architecture, with supercapacitors and batteries is proposed in order to satisfy the EVs energy requirements. Considering several EVs power scenarios, an energy management system based on fuzzy logic control strategy was developed. Thus, for a future reduced scale implementation on laboratory workbench, a good energy trade contribution from the two involved sources is required, improving the EVs efficiency and the performance of the overall system.

This paper presents a unified energy management strategy for small urban electric vehicles based on a hybrid energy storage system. This energy management strategy employs an integrated rule-based meta-heuristic approach that governs the power distribution in the vehicle powertrain. The proposed approach evaluates the optimal power distribution of the batteries and supercapacitors according to the power demand requirements. The multi-level energy management strategy has been validated using Matlab/Simulink®simulations for two normalized urban driving cycles. The simulation results indicate that the unified energy management strategy is effective to control the energy system components to work in their higher efficiency region and to satisfy the kinetic performance.

This paper presents a fault-tolerant modular control architecture for an electrical vehicle (VEIL) equipped with x-by-wire sub-systems. The proposed architecture is based on COTS components and includes steer-by-wire, brake-by-wire and accelerate-by-wire safety critical functions. The communication infrastructure is based on the FTT-CAN protocol, which provides the joint scheduling of message and tasks, according to a holistic approach.