1,707 research outputs found
Advancing vehicle electrification: control strategies for hybrid electric vehicles and battery management algorithms for state estimation
No full textL'abstract è presente nell'allegato / the abstract is in the attachmen
Sleek dual Extended Kalman Filter for Battery State of Charge and State of Health Estimation in Electric Vehicle Applications
No full textAccurate battery state estimation is crucial for the performance, safety, and durability of electric vehicle (EV) battery management systems (BMS). The model-based dual extended Kalman filter (DEKF) has been widely used for concurrent state of charge (SOC) and state of health (SOH) estimation. However, tuning the process and measurement covariance matrices of the DEKF is challenging and typically done through a trial and error process. In this work, a sleek version of the standard DEKF is formulated relying on a second order equivalent circuit battery model (ECM) to estimate the SOC and SOH of EV batteries. The proposed sleek DEKF estimates the capacity fading of the battery. The main advantage of the proposed formulation is the significant reduction in tuning effort. On the other hand, to account for the non-negligible resistance increase over battery lifespan, the ohmic resistance is here formulated as a function of the state of charge and available capacity. Finally, the effectiveness of the proposed method is demonstrated over laboratory data reproducing real world driving scenarios. The results show that the proposed DEKF obtains high accuracy, comparable to the standard DEKF
States Estimation for Parallel-Connected Battery Module: A Moving Horizon Approach
No full textIn this work, a moving horizon estimation (MHE)-based method is developed for estimating battery cells state in parallel-connected modules. Unlike conventional approaches, the proposed method acknowledges the impact of cell-to-cell (CtC) variations and heterogeneity propagation on module performance. A nonlinear observability analysis is performed to assess the feasibility of reconstructing individual cell states from module voltage and current measurements, considering interconnection resistance, state of charge (SOC)-dependent parameters, and different numbers of cells. The results indicate that states are distinguishable when the interconnection resistance is not null, and observability improves as the number of cells in parallel decreases. To the best of our knowledge, this is the first application of MHE in the context of battery modules, validated with real-world battery data. In contrast with conventional estimation methods, this study leverages MHE’s ability to handle equality constraints, allowing for the solution of Kirchhoff’s laws without complicating the module dynamics, maintaining the estimation accuracy. The proposed estimation algorithm demonstrates robustness against measurement noise and model uncertainties, with a maximum SOC error below 2.65%. Furthermore, the MHE results are compared against two widely used observers, the extended Kalman filter (EKF) and unscented Kalman filter (UKF), showing consistently higher estimation accuracy across all experimental conditions
Acceleration control strategy for Battery Electric Vehicle based on Deep Reinforcement Learning in V2V driving
Full text linkThe transportation sector is seeing the flourishing of one of the most interesting technologies, autonomous driving (AD). In particular, Cooperative Adaptive Cruise Control (CACC) systems ensure higher levels both of safety and comfort, enhancing at the same time the reduction of energy consumption. In this framework a real-time velocity planner for a Battery Electric Vehicle, based on a Deep Reinforcement Learning algorithm called Deep Deterministic Policy Gradient (DDPG), has been developed, aiming at maximizing energy savings, and improving comfort, thanks to the exchange of information on distance, speed and acceleration through the exploitation of vehicle-to-vehicle technology (V2V). The aforementioned DDPG algorithm relies on a multi-objective reward function that is adaptive to different driving cycles. The simulation results show how the agent can obtain good results on standard cycles, such as WLTP, UDDS and AUDC, and on real-world driving cycles. Moreover, it displays great adaptability to driving cycles different from the training one
Online Temperature-aware Equivalent Consumption Minimization Strategy for Mild Hybrid Electric Powertrains
Full text linkIn this work, an online energy management strategy for mild hybrid electric vehicles is developed to minimize the fuel consumption while simultaneously preventing battery overheating. Since mild hybrids are typically equipped with a passively cooled battery pack, the energy management strategy design needs to keep the battery temperature below an upper limit, preventing accelerated aging and thermal runaway. To address this issue, the equivalent consumption minimization strategy (ECMS) approach is extended to develop a real-time capable controller, termed thermal ECMS (Th-ECMS), that is sensitive to the thermal dynamics of the battery and that can enforce constraints on its temperature. The rationale for our formulation is based on Pontryagin's minimum principle from optimal control theory. The online Th-ECMS is developed on the basis of the offline version of Th-ECMS, introduced in a previous work. Exploiting the a priori knowledge of the driving mission, the offline Th-ECMS calibrates the equivalence factors and obtains the optimal solution, which is compared with the globally optimal dynamic programming solution. This offline calibration method is run on a large number of driving missions and the collected data is used to train a feed-forward neural network that estimates optimal equivalence factors as functions of the battery state of charge, battery temperature, and distance yet to travel. The trained network is then used to populate two look-up tables mapping the equivalence factors, and implementable on the vehicle electronic control unit. Finally, the online Th-ECMS obtains the equivalence factors through the look-up tables in real-time. The online strategy was tested in four different driving missions, achieving a fuel economy remarkably similar to the optimal solution and successfully avoiding battery overheating
Cooperative Adaptive Cruise Control Based on Reinforcement Learning for Heavy-Duty BEVs
Full text linkAdvanced driver assistance systems (ADAS) are playing an increasingly important role in supporting the driver to create safer and more efficient driving conditions. Among all ADAS, adaptive cruise control (ACC) is a system that provides consistent aid, especially in highway mobility, guaranteeing safety by minimizing the possible risk of collision due to variations in the speed of the vehicle in front, automatically adjusting the vehicle velocity and maintaining the correct spacing. Theoretically, this type of system also makes it possible to optimize road throughput, increasing
its capacity and reducing traffic congestion. However, it was found in practice that the current generation of ACC systems does not guarantee the so-called string stability of a vehicle platoon and can therefore lead to an actual decrease in traffic capacity. To overcome these issues, new cooperative adaptive cruise control (CACC) systems are being proposed that exploit vehicle-to-vehicle (V2V) connectivity, which can provide
additional safety and robustness guarantees and introduce the possibility of concretely improving traffic flow stability
Battery Electric Vehicle Control Strategy for String Stability based on Deep Reinforcement Learning in V2V Driving
Full text linkThis works presents a Reinforcement Learning (RL) agent to implement a Cooperative Adaptive Cruise Control (CACC) system that simultaneously enhances energy efficiency and comfort, while also ensuring string stability. CACC systems are a new generation of ACC which systems rely on the communication of the so-called egovehicle with other vehicles and infrastructure using V2V and/ or V2X connectivity. This enables the availability of robust information about the environment thanks to the exchange of information, rather than their estimation or enabling some redundancy of data. CACC systems have the potential to overcome one typical issue that arises with regular ACC, that is the lack of string stability. String stability is the ability of the ACC of a vehicle to avoid unnecessary fluctuations in speed that can cause traffic jams, dampening these oscillations along the vehicle string rather than amplifying them. In this work, a real-time ACC for a Battery Electric Vehicle, based on a Deep Reinforcement Learning algorithm called Deep Deterministic Policy Gradient (DDPG), has been developed, aiming at maximizing energy savings, and improving comfort, thanks to the exchange of information on distance, speed and acceleration through the exploitation of vehicle-to-vehicle technology (V2V). The aforementioned DDPG algorithm is also designed in order to achieve the string stability. It relies on a multi-objective reward function that is adaptive to different driving cycles. The simulation results show how the agent can obtain energy savings up to 11% comparing the first following vehicle and the Lead on standard cycles and good adaptability to driving cycles different from the training one
Battery temperature aware equivalent consumption minimization strategy for mild hybrid electric vehicle powertrains
Full text linkAn energy management strategy for mild hybrids that prevents battery overheating is introduced in this digest. Energy management strategy design for mild hybrids requires particular care to prevent overheating of the battery pack as they typically do not have an active cooling system. To tackle this issue, we extend the well-known equivalent consumption minimization strategy approach to develop a real-time capable fuel-optimal controller that is sensitive to the battery’s thermal dynamics and that can enforce constraints on its temperature. The rationale for our formulation is developed using Pontryagin’s minimum principle from optimal control theory. The same principle is also used to design an off-line numerical procedure for the energy management strategy’s calibration. The effectiveness of the procedure is corroborated by numerical experiments on two different drive cycles, whose results are also compared with the solution obtained with a dynamic programming algorithm. Several peculiar aspects of our solution procedure, such as the method used to incorporate state constraints and the approximate boundary value problem solution method using a particle swarm optimization algorithm, are also detailed and discussed. The proposed controller is computationally light-weight and can be readily extended to on-line control provided that a suitable co-state selection procedure is employed, based on the data collected by using our calibration method on a large number of driving missions
TUTELA DEL LAVORO E LIBERTA' D'IMPRESA NEI PROCESSI DI ESTERNALIZZAZIONE
No full textL’elaborato analizza le conseguenze lavoristiche della successione fra imprenditori, muovendo da una ricognizione delle varie tipologie di esternalizzazione con le relative esigenze e principali criticità.
L’indagine si concentra in primo luogo sul trasferimento d’azienda, esaminando la normativa e la giurisprudenza europee per passare poi alla disciplina di diritto interno, alle procedure sindacali e a uno specifico focus sul trasferimento delle aziende in crisi.
Successivamente l’autore si sofferma sull’appalto, prendendone in particolare considerazione gli indici di genuinità, i criteri di distinzione dalla somministrazione illecita di manodopera e la tutela delle maestranze in caso di avvicendamento fra imprese.
Da ultimo, la ricerca approfondisce le c.d. “clausole sociali”, sia di prima che di seconda generazione, valutandone la compatibilità con il diritto eurounitario e con la costituzione nonché riflettendo sui possibili rimedi in caso di loro violazione.The author analyzes the labour consequences of the succession between entrepreneurs, starting from a recognition of the various types of outsourcing with the related needs and main critical issues.
The survey focuses primarily on the transfer of businesses, examining European legislation and case-law and then moving on to internal legislation, trade union procedures and a specific focus on the transfer of companies in crisis.
The author then dwells on the contract, taking into account in particular the indications of authenticity, the criteria of distinction from the illicit administration of labour and the protection of workers in the event of turnover between companies.
Finally, the research deepens the "social clauses", both first and second generation, assessing their compatibility with European law and with the constitution and reflecting on possible remedies in case of their violation
Associations of Battery Cell Voltage Consistency with Driving Behavior of Real-world Electric Vehicles
Full text linkFor proposing an adaptive-threshold-based method for detecting battery voltage inconsistency fault, this
study explored the associations between driving behavior and voltage consistency between cells (VCC) at a
microscopic level, by performing a naturalistic driving experiment on real-world electric vehicles (EVs). The
running process of EVs is divided into four kinds of micro-segments A, B, C, D through the driver’s pedal actions.
Focusing on these segments, Pearson correlation coefficients (PCCs) between driving behavior parameters (DBPs)
and voltage variation coefficient between cells (VVCC) are calculated, the impact patterns of DBPs to VVCC are
analyzed by accumulated local effects (ALE) plots obtained from random forest (RF) modeling. The results show
that the maximum PCC is reached by average accelerator pedal stroke with 0.724 for segments A, and by average
speed with 0.789, 0.554, and 0.553 for the other three segments. The four RF models show a high accuracy of VVCC
prediction with goodness of fit over 0.919, and the ALE plots demonstrate the impact patterns are positive-nonlinear
overall. The maximum VVCC growing rates are reached by average accelerator pedal stroke for segments A
(48.09%), and average speed for other segments (55.70%, 29.01%, and 23.68% for segments B, C, and D,
respectively). These results imply a strong connection between driving behavior and battery voltage consistency,
which could be effectively captured to provide crucial inputs and interpretation methods for modeling voltage
consistency prediction during EVs running. Hence, this work lays the foundation for the development of battery
voltage fault detection algorithms considering different driving states
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