1,721,051 research outputs found
A Method for the Combined Estimation of Battery State of Charge and State of Health Based on Artificial Neural Networks
Full text linkThis paper proposes a method for the combined estimation of the state of charge (SOC) and state of health (SOH) of batteries in hybrid and full electric vehicles. The technique is based on a set of five artificial neural networks that are used to tackle a regression and a classification task. In the method, the estimation of the SOC relies on the identification of the ageing of the battery and the estimation of the SOH depends on the behavior of the SOC in a recursive closed-loop. The networks are designed by means of training datasets collected during the experimental characterizations conducted in a laboratory environment. The lithium battery pack adopted during the study is designed to supply and store energy in a mild hybrid electric vehicle. The validation of the estimation method is performed by using real driving profiles acquired on-board of a vehicle. The obtained accuracy of the combined SOC and SOH estimator is around 97%, in line with the industrial requirements in the automotive sector. The promising results in terms of accuracy encourage to deepen the experimental validation with a deployment on a vehicle battery management system
Cluster-based route planning for electric vehicles travel time optimization
No full textPrivate mobility electrification is slowed down by technical limitations, such as the low autonomy
of electric vehicles (EVs) compared to internal combustion engine vehicles (ICEVs). As a consequence,
accurate planning of the route is needed before a travel with an EV begins. Routing algorithms
are of crucial importance to identify the route which allows to minimize total travel time,
reducing the drawbacks of battery’s limited energy density. The complexity of the problem and
the size of road networks considered for this task imply computational times which are not in line
with users’ needs. The method proposed in this article employs clustering and pruning techniques
to speed up planning by downsizing the network analyzed during route planning. By reducing
the computational cost, it is possible to apply Dijkstra algorithm, which provides an exact minimization
of total travel time
A Study on Combined Lithium and Sodium-Ion Hybrid Energy Storage Systems in Battery Electric Vehicles
No full textWith an end-goal of carbon neutrality set by many nations across the world, various industries face the challenging task of incorporating novel technologies in their products while remaining economically sustainable. This is particularly evident in the automotive sector, where diversifying the product portfolio of many Original Equipment Manufacturers to meet carbon emission targets is a primary goal, investing in research ranging from hydrogen fuel propulsion to energy storage systems. It is in this latter area in which this paper provides an insightful analysis in the coupling of two rechargeable energy storage systems based on different chemical technologies. The work herein evaluates a hybrid energy storage system for a subcompact crossover sport utility vehicle that includes a lithium-ion (LIB) and sodium-ion battery (NaIB) pack, with varying divisions of the total energy between the two. The aim is to exploit the NaIB pack to the greatest possible degree such that the longevity of the LIB pack is extended, given that NaIBs are more forgiving in their response to high C-rates. The division is performed on the basis of two methodologies: 1) maintaining a constant mass of the combined battery pack and 2) maintaining a constant energy of the hybrid energy storage system. The analysis considers two energy management strategies that are both deterministic rule-based, the first termed as a Load Follower and the second as a Range Extender. The analysis of a selection of energy division and energy management strategies shows that a hybrid battery system comprising of 70% of energy provided by a LIB and 30% by a NaIB coupled with a Range Extender energy management provides the best trade-off between C-rate reduction, range penalization and cost reduction. Such a solution allows for up to 46% reduction in LIB C-rate and 7.6% reduction in cost with respect to the vehicle mounted with the baseline LIB
Electric Bus Fleet Management Considering Vehicle-to-Grid Interaction and Solar Energy Production
No full textEnvironment and health concerns are driving an evergrowing number of large cities to introduce restriction to the circulation of vehicles powered by fossil fuels. Such ordinances inevitably involve public transportation as well, driving an increase in the number of electric bus fleets and their sizes. In planning operation of mass transit, minimization of downtime is a key objective. The large batteries required for mass transportation vehicles and the short time allowed for recharging imply a large power request, which significantly affects the surrounding distribution network. Unidirectional vehicle-to-grid integration of electric bus fleets is helped by the precise time scheduling, giving an advantage over private vehicles integration. The high power required for their recharging operation makes them a suitable storage option to absorb peak solar production during afternoon hours, to reduce the energy wasted because of photovoltaic curtailment. The integration of bus recharging planning with time-dependant photovoltaic production represents an important element for the optimization of electricity production from renewable sources. This article presents a novel methodology which integrates vehicle simulation in scheduling algorithms to optimally integrate bus recharging with electricity generation through non-programmable renewable sources. This approach allows to reduce curtailment by 27.8%, cutting the share of fossil-generated electricity by 27.5%
Sensorless active magnetic dampers for the control of rotors
No full textThe aim of this paper is to present self-sensing Active Magnetic Dampers (AMDs) for the vibration control of rotating machines and evaluate their performance and advantages with respect to standard sensed solutions. The technique is implemented on a rotor reproducing the typical dynamic behaviour of an aero-engine gas turbine shaft. The proposed technique is based on a Luenberger observer that estimates the mechanical states of the system. The observed states are fed back in closed-loop to introduce damping into the system and to reduce the vibrations during critical speed crossing. The rotordynamic and electromechanical modeling is illustrated taking into account the anisotropy of rotor elastic supports. The control design is described along with a sensitivity analysis on the most critical model parameters and a study of electromagnet nonlinear effects on the closed loop behaviour. The importance of the inherent collocation in the self-sensing configuration during control design is discussed analysing modal shapes and sensor/actuator transfer functions. A phase of experimental identification of actuator parameters is performed on the open-loop system response to improve the reliability of the model. The effectiveness of the proposed method is evaluated experimentally by measuring unbalance response in open and closed-loop configuration showing a reduction of displacement during critical speed crossing from 0.35 mm to 0.04 mm. Furthermore, a classical AMD realized with the use of position sensors is implemented on the same rotor. The results obtained with sensed and self-sensing controls are compared to show the good quality of the damping performance reached with the proposed self-sensing technique
Aggressive Driving Behavior Detection Using Integrated I-DBSCAN and LSTM Neural Network
No full textLately, a significant rise in traffic fatalities linked to aggressive driving behaviors has been noted, accentuating the imperative need for research in this domain. Hence, the detection of aggressive driving is increasingly advocated as a strategy not only to alert drivers about their perilous behaviors but also to potentially diminish the incidence of accidents. In parallel, the widespread adoption of driving simulators in the automotive sector has unveiled their profound advantages, especially in terms of repeatability in a controlled environment, helping researchers significantly reduce time and cost of development. Furthermore, driving simulators also provide a safe platform for testing new technologies and evaluating driver behavior in various scenarios. The article presents a method for identifying aggressive driving by analyzing vehicle dynamics data (such as speed, acceleration, and steering angle) collected from simulations in an urban setting using the software SCANeRTMStudio. The algorithm employs Iterative Density-based spatial clustering of applications with noise, an unsupervised learning technique, to cluster aggressive driving maneuvers with sub-classification in terms of comfort and safety. Further, this labeled data is used to train a Bayesian optimization-based long short-term memory neural network, a pattern recognition model for the detection of driving behavior. The research findings confirm the model’s notable ability to recognize aggressive driving behaviors, as indicated by the confusion matrix and a F-score of 0.869, showing great potential to enhance road safety
Self-Powered Eddy Current Damper for Rotordynamic Applications
No full textThe vibration control of rotors is often performed using elastomeric or fluid dampers together with rolling element or hydrodynamic type bearings. Electromagnetic dampers seem a valid alternative to conventional solutions and also to active magnetic bearings (AMBs) because their simpler architecture, size and, if of transformer type, also for the absence of power electronics, position sensors, and any fast feedback loop. However, transformer eddy current dampers require a constant voltage power supply than can be provided by an embedded generator to reduce cost and improve the reliability. The present paper proposes a self-powered damper to fulfill these requirements. A three-phase permanent magnet electric generator (connected to the rotating shaft) generates the required power for the damping device. The generator is connected to the damping circuit by means of tuned impedance and a three-phase rectifie
Ultracapacitors in Light Duty Hybrid Electric Vehicle Energy Storage Systems: Technical Impact and Economic Perspectives
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