Papers by Angelo Bonfitto
Designing a Real-Time Implementable Optimal Adaptive Cruise Control for Improving Battery Health and Energy Consumption in EVs through V2V Communication
Energies, Apr 23, 2024
Ultracapacitors in Light Duty Hybrid Electric Vehicle Energy Storage Systems: Technical Impact and Economic Perspectives
Fuzzy Logic vs Equivalent Consumption Minimization Strategy for Energy Management in P2 Hybrid Electric Vehicles
This paper presents a comparison between a Fuzzy Logic and an Equivalent Consumption Minimization... more This paper presents a comparison between a Fuzzy Logic and an Equivalent Consumption Minimization Strategy for the energy management of a Hybrid Electric Vehicle in P2 configuration, i.e. with the secondary energy converter located downstream the clutch. The design of the two methods is conducted aiming to minimize the fuel consumption. Although the adopted strategies are not charge sustaining, an additional goal of the techniques is to obtain a net energy extracted from the battery over a driving cycle that is not far from zero. The presented simulation results are obtained in the case of two homologation driving cycles, namely NEDC and WLTP. The objective of the study is to demonstrate that a non-optimal rule-based method can achieve a performance that is equivalent to a model-based optimal analytical approach.
A Model Predictive Control Strategy for Lateral and Longitudinal Dynamics in Autonomous Driving
This paper presents a controller dedicated to the lateral and longitudinal vehicle dynamics contr... more This paper presents a controller dedicated to the lateral and longitudinal vehicle dynamics control for autonomous driving. The proposed strategy exploits a Model Predictive Control strategy to perform lateral guidance and speed regulation. To this end, the algorithm controls the steering angle and the throttle and brake pedals for minimizing the vehicle’s lateral deviation and relative yaw angle with respect to the reference trajectory, while the vehicle speed is controlled to drive at the maximum acceptable longitudinal speed considering the adherence and legal speed limits. The technique exploits data computed by a simulated camera mounted on the top of the vehicle while moving in different driving scenarios. The longitudinal control strategy is based on a reference speed generator, which computes the maximum speed considering the road geometry and lateral motion of the vehicle at the same time. The proposed controller is tested in highway, interurban and urban driving scenarios to check the performance of the proposed method in different driving environments.
Localization Method for Autonomous Vehicles with Sensor Fusion Using Extended and Unscented Kalman Filters
SAE technical paper series, Sep 15, 2021
A LIDAR-Based Clustering Technique for Obstacles and Lane Boundaries Detection in Assisted and Autonomous Driving
This paper presents a clustering technique for the detection of the obstacles and lane boundaries... more This paper presents a clustering technique for the detection of the obstacles and lane boundaries on a road. The algorithm consists of two nested clustering stages. The first stage is based on hierarchical clustering, and the second on k-means clustering. The method exploits a preliminary ground-plane filtering algorithm to process the raw LIDAR point cloud, that is based on the semantic segmentation of point clouds. The clustering algorithm estimates the position of the obstacles that define the race track. Once the race track is sensed, the lane boundaries are detected. The method is validated experimentally on a four-wheel drive electric vehicle participating in the Formula SAE events. The validation environment is structured with traffic cones to define the race track.
Energies, Jan 2, 2023
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Local Trajectory Planning for Autonomous Racing Vehicles Based on the Rapidly-Exploring Random Tree Algorithm
This paper presents a local trajectory planning method based on the Rapidly-exploring Random Tree... more This paper presents a local trajectory planning method based on the Rapidly-exploring Random Tree (RRT) algorithm using Dubins curves for autonomous racing vehicles. The purpose of the investigated method is the real-time computation of a trajectory that could be feasible in autonomous driving. The vehicle is considered as a three Degree-of-Freedom bicycle model and a Model Predictive Control (MPC) algorithm is implemented to control the lateral and longitudinal vehicle dynamics. The trajectory planning algorithm exploits a perception pipeline using a LiDAR sensor that is mounted onto the front wing of the racing vehicle. The MPC computes the acceleration/ deceleration command and the front wheel steering angle to follow the predicted trajectory. The trajectory and control algorithms are tested on real data acquisition performed on-board the vehicle. For validation purposes, the vehicle is driven autonomously during different maneuvers performed in the racing environment that is structured with traffic cones. The feasibility of the algorithm is evaluated in terms of error with respect to the planned trajectory, tracking velocity and maximum longitudinal acceleration. The effectiveness of the method is also evaluated with respect to command signals for the steering and acceleration actuators featured by the retained racing vehicle. The results demonstrate that the trajectory is well-tracked and the signals are compatible with the actuator constraints.
Operational Considerations for Active Electromagnetic Suspension Systems
The use of active suspensions has been rekindled by increasingly stringent comfort requirements i... more The use of active suspensions has been rekindled by increasingly stringent comfort requirements in light of the diffusion of electric and safe-driving vehicles. In particular, electromagnetic solutions can guarantee intrinsic reversibility and regenerative operation, which would allow for energy harvesting and/or reuse on active quadrants. However, the use of non-ideal actuators has an impact in the regenerative and active capabilities of any active damping solution. To address this shortcoming, this work aims at validating optimal active operation. Hence, a linear quadratic regulator is used to tune a linear optimal control strategy in a quarter-car model. This plant is equipped first with an ideal actuator. Later, this device is enhanced with multiple non-ideal features: inertial and friction contributions of the moving parts, compliance of the parts that exchange forces with the suspension and the resistive load of the motor windings. Details regarding comfort, handling, are compared. Then, the realistic actuator model is compared for comfort- and handling-oriented control tasks. For both strategies, operational aspects like dynamic performance, control calibration, suspension duty cycle, damping force-speed characteristics and power absorption are discussed. It is demonstrated that a significant part of the control effort is used to mitigate non-ideal features of the realistic actuator.
Assessment of State of Charge Estimation Methods Based on Neural Networks and Support Vector Machine for Lithium-Ion Batteries Used in Vehicular Applications
The State of Charge (SOC) estimation in Lithium-ion batteries is a challenging task that is curre... more The State of Charge (SOC) estimation in Lithium-ion batteries is a challenging task that is currently assessed with different methods in a vast variety of applications. This paper presents the design and assessment of two SOC estimation methods, based on Artificial Neural Networks (ANNs) and Support Vector Machine (SVM) algorithms for Lithium-ion batteries used in vehicular applications. The paper validates the two proposed approaches with experimental data collected during a laboratory test campaign. The obtained results are compared in terms of estimation accuracy, proving the feasibility of the considered algorithms. Moreover, the paper describes the retained software architectures and the design procedure related to the two proposed techniques based on Artificial Intelligence (AI). In detail, the retained Lithium-ion battery is a 21.6V 3.3Ah battery pack that is used as an energy module for vehicular applications. The considered battery module is numerically modeled with a 2nd order RC equivalent Thevenin model to collect a sufficient amount of data for the algorithms design phase. The model parameters are identified with a grey-box approach based on a non-linear least squares algorithm designed to accurately estimate the battery SOC with both the ANN-based and SVM-based methods. Specifically, the resulting mean prediction error is always below 2.5% and 3.5% for the ANN-based and SVM-based algorithms, respectively.
Damping Strategies on a Horizontal Rotor Supported by Electrodynamic Bearings
IEEE Transactions on Transportation Electrification
This article investigates the economic viability of replacing the high-voltage battery pack of a ... more This article investigates the economic viability of replacing the high-voltage battery pack of a power-split hybrid electric vehicle (HEV) and a plug-in hybrid electric vehicle (PHEV) by estimating the impact of battery aging on the fuel economy, drivability capability, and electric range. The HEV is modeled first, an optimal energy management strategy based on dynamic programming is then implemented, and experimental characterization data for the battery cell is presented. The batteries are tested to a heavily aged state, with up to an 84% loss of capacity. The battery pack payback period is estimated by assessing the vehicle operative costs in terms of fuel and electricity as obtained through numerical simulations as a function of battery aging. Replacing the battery pack at the conventional end-of-life limit of 80% residual capacity is suggested not to be convenient from an economic standpoint for both the HEV and the PHEV. On the other hand, acceptable payback periods (i.e., 2-5 years) can be achieved for the battery pack when being replaced at 20% to 40% residual capacity. The proposed methodology can be implemented to advise an HEV or PHEV user regarding the benefit of replacing the battery pack due to excessive aging.
Enabling Rapid State of Health Offline Estimation of a 48V Lithium-Ion Battery Pack
2022 IEEE Vehicle Power and Propulsion Conference (VPPC)
Adaptive LQR Control for a Rear-Wheel Steering Battery Electric Vehicle
2022 IEEE Vehicle Power and Propulsion Conference (VPPC)
Crankshaft Decoupling Effects on Fuel Economy in HEV-P0
The increasingly stringent emission regulations have been one of the driving forces to explore mo... more The increasingly stringent emission regulations have been one of the driving forces to explore more sustainable vehicle powertrains. On the one hand, carmakers are finding alternatives to meet these regulations; on the other hand, they are trying to improve smart features, such as chassis dynamics, passenger comfort, and, ride safety. The P0 architecture in a hybrid electric vehicle is a cost-effective layout due to its easy adaptation to the existing conventional vehicle architecture. In a P0 architecture, fuel consumption and emissions are increased when idling the engine during the stops to fulfill cabin air conditioning with the compressor. Thus, optimal energy management during these events would lead to an improvement in vehicle fuel economy. To this end, this work presents a novel configuration in a hybrid electric vehicle by installing an electromagnetic clutch between the internal combustion engine and the belt drive system. The objective of the study is to quantify the benefits of driving the air conditioning compressor with an electric machine under vehicle idle/stop conditions. It is demonstrated that the proposed solution improves the fuel economy as it shuts down the engine and decouples the engine resisting torque during stops. The results presented are simulated in the Worldwide harmonized Light vehicle Test Procedure homologation driving cycle and US06.
Comparison of two control strategies for range extender hybrid electric vehicles
2022 International Symposium on Electromobility (ISEM)
Artificial Intelligence Based State of Health Estimation With Short-Term Current Profile in Lead-Acid Batteries for Heavy-Duty Vehicles
Volume 1: 24th International Conference on Advanced Vehicle Technologies (AVT)
The State of Health (SOH) estimation for automotive batteries is currently assessed with differen... more The State of Health (SOH) estimation for automotive batteries is currently assessed with different techniques which may involve long testing procedure or require costly hardware to be implemented. This paper aims at contributing to this domain by exploiting the response of a lead-acid battery with respect to a short-term current profile using an Artificial Neural Network (ANN) classifier for SOH estimation. The method is applicable onboard the vehicle and no additional instrumentation is required on the retained vehicle. The design and validation of a SOH method with a short-term current profile using Artificial Intelligence (AI) in lead-acid batteries, which are commonly used in heavy-duty vehicles for cranking and cabin systems, are presented. The paper validates the considered approach with experimental data, which are representative of actual vehicle operations. In detail, the paper describes the retained hardware and software architectures and the design procedure related to th...
A LIDAR-Based Clustering Technique for Obstacles and Lane Boundaries Detection in Assisted and Autonomous Driving
Volume 4: 22nd International Conference on Advanced Vehicle Technologies (AVT), 2020
This paper presents a clustering technique for the detection of the obstacles and lane boundaries... more This paper presents a clustering technique for the detection of the obstacles and lane boundaries on a road. The algorithm consists of two nested clustering stages. The first stage is based on hierarchical clustering, and the second on k-means clustering. The method exploits a preliminary ground-plane filtering algorithm to process the raw LIDAR point cloud, that is based on the semantic segmentation of point clouds. The clustering algorithm estimates the position of the obstacles that define the race track. Once the race track is sensed, the lane boundaries are detected. The method is validated experimentally on a four-wheel drive electric vehicle participating in the Formula SAE events. The validation environment is structured with traffic cones to define the race track.
External disturbance rejection for compressors on active magnetic bearings
This paper presents the experimental results of a feedforward control strategy applied to a centr... more This paper presents the experimental results of a feedforward control strategy applied to a centrifugal compressor to reject disturbance coming from ground motion. The compressor is used for refrigeration tasks in public transport, it has a power of 30 kW with nominal speed of 51000 rpm and is levitated magnetically by means of cylindrical Active Magnetic Bearings (AMB). The proposed control strategy acts in combination with a classical decentralized control, it is implemented on a prototype of the compressor equipped with a substitute impeller without compression and is validated by means of acceleration tests simulating ground motion. The obtained results represents the basis for the future development of the proposed control strategy that will combined to an unbalance compensation action to minimize the effects of surge and stall of the compressor. The paper illustrates the architecture of the machine, the control strategy and the experimental results conducted in laboratory envi...
Rotor on cone-shaped active magnetic bearings with three-phases power drivers
This paper presents the modeling, the control design and the experimental results obtained on a r... more This paper presents the modeling, the control design and the experimental results obtained on a rotor suspended by means of cone-shaped active magnetic bearings controlled by an innovative three-phases drive technique. The machine reproduces a turbo-compressor group of a conditioning unit used in high performance jet aircrafts. The conical geometry of magnetic bearings allows to perform a compact design of actuation stage which is composed of only four pairs of electromagnets instead of five of conventional cylindrical solution, resulting of great interest for the application in small machines. The modeling phases of the system are illustrated along with the drive technique and control design procedure. The mechanical subsystem has been reproduced starting from the finite element (FE) model, reduced to its first bending modes. The electromechanical interaction has been modeled considering each electromagnet as a two-port element (electrical and mechanical) exerting a force expressed...
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Papers by Angelo Bonfitto