1,721,146 research outputs found
Measuring Urban Sidewalk Practicability: A Sidewalk Robot Feasibility Index
Full text linkAutonomous parcel delivery is attracting a lot of interest. Terrestrial delivery drones travel at lower speeds, are smaller and lighter than passenger cars. These features make them an ideal and valuable first step and experimental sandbox toward fully autonomous vehicles. To be useful, however, small wheeled drones need to operate on parts of the roads that are reserved to pedestrians. This is a challenge by itself. Pedestrian areas are less structured than road and abide by looser rules. The best route for a delivery drone may not be the shortest path; other aspects need to be accounted for that make a route more or less practical for the specific features of the vehicle. This paper introduces a quantitative analysis of these specific issues. The paper proposes a quantitative index that asses a route practicability for a small terrestrial drone. It combines different aspects that account for sidewalk width, sidewalk surface condition, route length and the number of driveways and crosswalks present on the way. We provide the mathematical definition of the index, and use our wheeled drone prototype to show how it can be used to classify and chose the best routes among a selection. Although the index is designed for autonomous drones, given the specific dynamic features of the drone, it can also be employed as is to quantify the accessibility of different routes for disabled people
Flexible Pricing Strategies in Electric Free-Floating Bicycle Sharing
Full text linkBike sharing is an important tool to reduce congestion and pollution in urban areas. Electrically Power Assisted Bicycles (EPAC's) make cycling possible also for sedentary people. Standard EPAC's are difficultly integrable into a free-floating sharing system because the battery pack requires frequent recharging. This paper studies the challenges, opportunities and solutions of implementing a free-floating bike sharing system based on electric bicycles. The analysis revolves around the charge sustaining paradigm. The idea of charge sustaining leverages the metabolic efficiency gaps to reduce the overall physical effort required without determining a net discharge of the battery. Already validated in private bicycles, the idea needs to be modified and adapted to the challenges of a shared fleet. The paper analyzes two approaches to the fleet level energy management and assistance control of a fleet of charge sustaining bicycles. Specifically, we compare a fixed price approach against a flexible pricing approach where the user can select the cost based on the pedaling effort they are willing to exercise. A simulation framework (calibrated on data collected during a large trial in Milan, Italy) assesses the operational costs and revenues of the two approaches quantifying how they depend on the design and environmental parameters. We provide and validate a lower bound in terms of usage rate that guarantees economic sustainability, additionally showing that a flexible pricing strategy can lower this bound and grant more degrees of freedom to the users
Active adaptive battery aging management for electric vehicles
Full text linkThe battery pack accounts for a large share of an electric vehicle cost. In this context, making sure that the battery pack life matches the lifetime of the vehicle is critical. The present work proposes a battery aging management framework which is capable of controlling the battery capacity degradation while guaranteeing acceptable vehicle performance in terms of driving range, recharge time, and drivability. The strategy acts on the maximum battery current, and on the depth of discharge. The formalization of the battery management issue leads to a multi-objective, multi-input optimization problem for which we propose an online solution. The algorithm, given the current battery residual capacity and a prediction of the driver's behavior, iteratively selects the best control variables over a suitable control discretization step. We show that the best aging strategy depends on the driving style. The strategy is thus made adaptive by including a self-learnt, Markov-chain-based driving style model in the optimization routine. Extensive simulations demonstrate the advantages of the proposed strategy against a trivial strategy and an offline benchmark policy over a life of 200 000 (km)
Closed-loop battery aging management for electric vehicles
Full text linkIn this work, a closed-loop battery aging management strategy for electric vehicles is proposed. The aging management strategy, following the model predictive control rationale, optimizes aging and vehicle performance online. The proposed formulation is based on a closed-loop term which aims at tracking a user defined aging profile. A thorough simulation study validates the approach and verifies its robustness against model uncertainties and anomalous aging phenomena
Model-Based Estimation of Lithium Concentrations and Temperature in Batteries Using Soft-Constrained Dual Unscented Kalman Filtering
Full text linkThis brief proposes an electrochemical model-based estimator of the Lithium-ion (Li-ion) concentration and temperature of a Li-ion cell. The use of the electrochemical approach allows for the estimation of the spatial distribution of lithium concentration and temperature. The estimation is based on a soft-constrained dual unscented Kalman filter (DUKF) designed on the pseudo-2-D model of a Li-ion cell. The dual structure, along with parallelization, reduces the computational complexity, whereas the soft-constraint improves convergence. A simulation analysis validates the approach showing bulk state of charge (SoC) estimation error lower than 1.5%, solid-phase lithium concentration estimation errors of less than 4%, and temperature estimation errors within 0.2 °C from the true value in any point of the cell
Effect of a Nu Vinci type CVT based Energy Efficient Cruise Control on an Electric Vehicle's Energy Consumption
No full textThis work investigated the effect of a Nu Vinci type Continuous Variable Transmission (CVT) based Energy Efficient Cruise Control (EECC) on an Electric Vehicle's (EV's) energy consumption. Unlike petrol and diesel vehicles, almost all EVs have a fixed gear ratio. Although it reduces the capital cost, it may not offer optimal energy efficiency and may increase the recharge costs. While accelerating and cruising, it may cause an EV to spend more energy than needed. While braking, it may cause underutilisation of the regenerative braking potential. In this work, an EECC was designed to operate the EV's power train close to its peak efficiency region by controlling the CVT ratio and Electric Machine (EM) torque. The EECC exploits data from the lead vehicle and employs a constrained linear Model Predictive Control (MPC) with a novel problem formulation that reduces the computational complexity. The proposed EECC was tested in a MATLAB simulation environment for different drive cycles. The results show that compared to a baseline EV with a fixed gear ratio and an Adaptive Cruise Control, the proposed system can reduce an EV's energy consumption in urban drive cycles by 16.6%
Experimental Validation of a Nonlinear Slip Control for 4-Wheel Drive Full Electric Vehicles
No full textElectric vehicles are becoming widely adopted; besides environmental advantages, electric power trains present peculiar characteristics that pave the way for reconsidering classical vehicle dynamics control (e.g. ABS, TC) and improving their performance. In this paper, we propose an ABS/TC system for full electric vehicles with 4-wheel drive. The regulator is derived from a nonlinear brake-based slip control. We modelled the wheel dynamics with an augmented single-corner model that includes the transmission, a crucial element. The controller has been tuned in simulation on the identified grey-box model and validated with an instrumented vehicle on ice and snow. It shows good performance relieving the driver of limiting the slips. The wheels are kept controlled and the magnitude of the average acceleration is increased with respect to professional driver performance
Socially Aware Local Planning: a Dynamic Window-based Approach
No full textAs the interest in mobile robots navigation on public sidewalks increases, so does the attention given to local planners capable of navigating around humans in a socially acceptable way. This paper presents a socially aware version of the Dynamic Window Approach planner. The DWA is augmented with an additional cost function, which predicts pedestrian trajectories using the Social Forces Model. Scoring of the robot control action is achieved by weighting the disturbance the robot causes to pedestrians. The approach is validated in a simulation environment with realistic pedestrian motions, showing superior performance with respect to the original DWA as well as to a distance-based scoring method
A mixed sideslip yaw rate stability controller for over-actuated vehicles
Full text linkElectronic stability control (ESC) has become a fundamental safety feature for passenger cars. Commonly employed ESCs are based on differential braking. Nevertheless, electric vehicles' growth, particularly those featuring an over-actuated configuration with individual wheel motors, allows for maintaining driveability without slowing down the vehicle. Standard control strategies are based on yaw rate tracking. The reference signal is model-based and needs precise knowledge of the friction coefficient. To increase the system robustness, more sophisticated approaches that include vehicle sideslip are introduced. Still, it is unclear how the two signals have to be weighted, and rarely proposed controllers have been experimentally validated. In this paper, we present a mixed sideslip and yaw rate stability controller. The mixed approach allows to address the control design as a single-input single-output problem simplifying the tuning process. Furthermore, we explain the rationale behind the choice of the weighting parameter. Eventually, the proposed ESC is validated following EU regulation in simulation and with an experimental vehicle on dry asphalt and snow. The results obtained in all the performed tests demonstrate that the proposed control strategy is robust and effective. The mixed approach is able to halve the sideslip in critical conditions with respect to a pure yaw rate approach
Hybrid Kinematic-Dynamic Sideslip and Friction Estimation
Full text linkVehicle sideslip and tyre/road friction are crucial variables for advanced vehicle stability control systems. Estimation is required since direct measurement through sensors is costly and unreliable. In this paper, we develop and validate a sideslip estimator robust to unknown road grip conditions. Particularly, the paper addresses the problem of rapid tyre/road friction adaptation when sudden road condition variations happen. The algorithm is based on a hybrid kinematic-dynamic closed-loop observer augmented with a tyre/road friction classifier that reinitializes the states of the estimator when a change of friction is detected. Extensive experiments on a four wheel drive electric vehicle carried out on different roads quantitatively validate the approach. The architecture guarantees accurate estimation on dry and wet asphalt and snow terrain with a maximum sideslip estimation error lower than 1.5 deg. The classifier correctly recognizes 87% of the friction changes; wrongly classifies 2% of the friction changes while it is unable to detect the change in 11% of the cases. The missed detections are due to the fact that the algorithm requires a certain level of vehicle excitation to detect a change of friction. The average classification time is 1.6 s. The tests also indicate the advantages of the friction classifiers on the sideslip estimation error
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