This work proposes a multi-domain modelling methodology to support the design of new battery packs for automotive applications. The methodology allows electro-thermal evaluation of different spatial arrangements of the storage cells by exploiting the implementation of numerical and geometrical battery pack models. Concerning the case study on Li-NMC battery technology, the study has completed the electro-thermal characterization of the storage cells starting from the collected experimental data, considering both the thermal interactions among cells and the effects of the state of health. This work also investigates the effects of forced air-cooling systems focusing on battery pack hot spots and temperature distributions. The results show a good fit between numerical models and data obtained from single-cell experiments. The virtual linking of geometric and numerical lumped-parameter models proved to be effective in rapid battery pack prototyping for electric vehicles, helping designers and manufacturers find suitable solutions for specific automotive applications.
from 1992 to date has been the study of the structure/function relationship of both native and recombinant proteins from terrestrial and marine organisms.
In this paper some preliminary experimental results on a Zebra battery based propulsion system for urban bus applications are presented. The tests were carried out using a laboratory 1:1 scale test bench, composed by a 65 kW electric drive, specifically designed for urban bus applications, supplied by two 20 kWh Zebra batteries connected in parallel. The electric power train was tested on a laboratory bench, connected through a fixed ratio gear box to a 100 kW regenerative electric brake provided with speed and torque controls, in order to evaluate the propulsion system performance in steady state operative conditions. The obtained preliminary experimental results were utilized to implement a Matlab-Simulink model of urban bus, which might be powered by the same electric propulsion system studied. Thanks to this model it was possible to evaluate the dynamic behavior of the urban bus, working on standard driving cycles, taking into account the resistant forces represented by proper vehicle/road/aerodynamic parameters. An evaluation of the expected real vehicle driving range was also estimated in different road conditions.
<div class="section abstract"><div class="htmlview paragraph">The use of electric propulsion systems for road vehicle applications is widely recognized as one of the most feasible solutions for sustainable mobility. On the other hand, improvement, and optimization of battery technologies remain challenging technical bottlenecks to be addressed. In particular, the design of proper packaging and heat dissipation structures can greatly support obtaining robust, high energy and power density battery packs. In this regard, this paper presents an experimental analysis of a metal foam-based frame used for the support and cooling of a small battery pack composed of 18650 cylindrical cells. The considered frame is manufactured in Al 6082 alloy according to the lost-PLA replication method. With a double extruder 3D printer it is possible to make polymer-based samples of the lost model. Through CAD modeling, different geometries can be replicated in order to get PLA samples. PLA foams are inserted into a plaster mix, and successively the polymer is thermally burned. The final step consists of the gravity casting of the Al-alloy in the plaster form, obtaining the metal foam-based frame with the same geometry as the 3D-printed PLA foam. The electro-thermal behavior of the cells is investigated with a laboratory test bench in natural convection conditions, with and without the metal foam support to highlight its effect. Specific thermal stress analyses have been performed through charging/discharging pulsed current profiles. Thermal imaging is used as non-contact diagnostics, to detect battery pack and frame temperature without interfering with the heating process. Experimental results highlight the advantages of using the considered metal foam in terms of temperature gradient for the battery pack under investigation. These advantages, combined with the structural characteristics of the metal frame, provide useful insight for future improvements.</div></div>
<div class="section abstract"><div class="htmlview paragraph">The aim of this paper is to analyze the effects of different driving styles and patterns onboard battery packs (BPs) supplying electric vehicles. The analysis is carried out by using real urban driving cycles, acquired through vehicle On Board Diagnostic Port (OBDP), and a Matlab-Simulink scaled BP model, in which lithium BP has been parametrized and validated through specific experimental tests. The results have been mainly focused on the evaluations of BP State of Health (SoH) and capacity fading decreasing during its lifetime at several critical conditions. In particular, these evaluations have allowed critical driving and environmental operative conditions to be identified and highlighted. The obtained results provide useful information for both producers of Battery Electric Vehicle (BEV) Energy Storage Systems (ESS) in the design stage, and for artificial intelligence driver support systems, mainly focused on extending overall vehicle life.</div></div>
The paper deals with the model based systems engineering (MBSE) approach, focused on the designing process of propulsion systems for road electric vehicles. In particular, the paper adopts multi-domain Modelling, in accordance with a top-down approach. The process, in fact, starts from the main requirement analysis of the road electric vehicle which is considered as reference. Then, a wide range of parameters, related to the characteristics of propulsion system components and resistance forces, are evaluated to build a parametric model of the propulsion system running on a road. In this way, a procedure for the evaluation of vehicle performance is accomplished within the developed simulation environment. Therefore, the procedure allows all the requirements to be satisfied, under different operative conditions, through an iterative procedure of verification for the imposed parameters. The tested operative conditions are represented in this paper by standard driving cycles, expressed in terms of vehicle speed and autonomy requirement.
This paper describes a novel Energy Management Strategy (EMS) for hybrid energy storage systems, when used to supply urban electric vehicles. A preliminary off-line procedure, based on nonlinear programming, is performed in order to optimize the battery current profile for fixed working cycles. Hence, a suitable control strategy, which is based on a constrained minimization problem, is tailored for real-time applications. This control strategy exploits the off-line solution of a proper isoperimetric problem and aims to dynamically optimize the battery durability by reducing peak charging/discharging current values. The main advantage of the analysed EMS consists in the easy on-board implementation through the use of one single parameter, which can be quickly identified through a simple off-line numerical procedure. The proposed strategy is evaluated in simulation environment, through the use of a Matlab-Simulink model, for the case study of an urban electric vehicle running on a ECE 15 driving cycles. Simulation results have confirmed the good performance of the above strategy in reducing the battery peak charging/discharging current through the proper management of the hybrid energy storage system.
Battery Thermal Management systems are a key component for modern electric vehicles. Many systems use advanced models for temperature prediction and for the optimal cooling or heating strategies. Thermo-electric characterizations of cells and battery packs are performed inside the research labs to properly tune the models before uploading them to the on-board control units. However, these data depict the storage system under brand new conditions. Throughout the vehicle life, the battery pack behavior can change because of several factors related to the surrounding environment and operating conditions. Moreover, in the case of battery swap strategies, that consist in the pack replacement, the temperature model would be totally unsuitable for the new installed one. Therefore, an on-board, online procedure for the evaluation and update of the battery thermal behavior could be needed. This work presents a method for the evaluation of the battery thermal parameters on-board, during real driving cycles using data that are available in the vehicle control unit. The main novelty of this work consists in the solution to combine and assist the temperature model in the vehicle control units with an optimization algorithm which does not increase the computational load and provides reliable thermal parameters estimations. To demonstrate the potential of this methodology, the evaluated parameters are used in a short-term temperature model suitable for control strategies for battery thermal management systems. Concerning the first part, an optimization procedure is run for different driving cycles, recorded using a GPS system on a real vehicle. Finite-difference method is used to identify the convective heat transfer coefficient and the specific heat capacity of a single cell that composes the battery pack in laboratory tests. Then, the reliability of the estimated thermal parameters is analyzed reducing the number of source records and using the remaining cycles for the validation. Four worst cases have been identified and used to check the performance of the model prediction. Considering a temperature measure tolerance of 6 %, up to 93.75 % of the estimated values is reliable. Finally, the thermal parameters are used in a short-term temperature model for control strategies. The results highlight the good performance of the model in the estimation of the on-board battery temperature during a real driving cycle, simulating the future heat generation on the basis of the current load demand of the previous time step. The temperature predictions of the short-term model have been also tested in the worst cases denoting a good reliability; the maximum error is + 5 % in overestimation and 3 % in underestimation. Temperature predictions would help in the feed-forward control of battery thermal management systems for a smooth and safe operation of future electric vehicles. Moreover, this solution can be adopted for even more complex cooling methods, enhancing the update of the battery pack characteristics also in case of deterioration or battery swap.
