IU Indianapolis ScholarWorks :: Browsing by Author "Shen, Dan"

Browsing by Author "Shen, Dan"

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Assessing the Effectiveness of In-Vehicle Highway Back-of-Queue Alerting System
(The National Academies of Sciences, Engineering, and Medicine, 2021-01) Shen, Dan; Zhang, Zhengming; Ruan, Keyu; Tian, Renran; Li, Lingxi; Li, Feng; Chen, Yaobin; Sturdevant, Jim; Cox, Ed; Electrical and Computer Engineering, School of Engineering and Technology
This paper proposes an in-vehicle back-of-queue alerting system that is able to issue alerting messages to drivers on highways approaching traffic queues. A prototype system was implemented to deliver the in-vehicle alerting messages to drivers via an Android-based smartphone app. To assess its effectiveness, a set of test scenarios were designed and implemented on a state-of-the-art driving simulator. Subjects were recruited and their testing data was collected under two driver states (normal and distracted) and three alert types (no alerts, roadside alerts, and in-vehicle auditory alerts). The effectiveness was evaluated using three parameters of interest: 1) the minimum Time-to-Collision (mTTC), 2) the maximum deceleration, and 3) the maximum lateral acceleration. Statistical models were utilized to examine the usefulness and benefits of each alerting type. The results show that the in-vehicle auditory alert is the most effective way for delivering alerting messages to drivers. More specifically, it significantly increases the mTTC (30% longer than that of 'no warning') and decreases the maximum lateral acceleration (60% less than that of 'no warning'), which provides drivers with more reaction time and improves driving stability of their vehicles. The effects of driver distraction significantly decrease the efficiency of roadside traffic sign alert. More specifically, when the driver is distracted, the roadside traffic sign alert performs significantly worse in terms of mTTC compared with that of normal driving. This highlights the importance of the in-vehicle auditory alert when the driver is distracted.
Collision-Free Path Planning for Automated Vehicles Risk Assessment via Predictive Occupancy Map
(IEEE, 2020-11) Shen, Dan; Chen, Yaobin; Li, Lingxi; Chien, Stanley; Electrical and Computer Engineering, School of Engineering and Technology
Vehicle collision avoidance system (CAS) is a control system that can guide the vehicle into a collision-free safe region in the presence of other objects on road. Common CAS functions, such as forward-collision warning and automatic emergency braking, have recently been developed and equipped on production vehicles. However, these CASs focus on mitigating or avoiding potential crashes with the preceding cars and objects. They are not effective for crash scenarios with vehicles from the rear-end or lateral directions. This paper proposes a novel collision avoidance system that will provide the vehicle with all-around (360-degree) collision avoidance capability. A risk evaluation model is developed to calculate potential risk levels by considering surrounding vehicles (according to their relative positions, velocities, and accelerations) and using a predictive occupancy map (POM). By using the POM, the safest path with the minimum risk values is chosen from 12 acceleration-based trajectory directions. The global optimal trajectory is then planned using the optimal rapidly exploring random tree (RRT*) algorithm. The planned vehicle motion profile is generated as the reference for future control. Simulation results show that the developed POM-based CAS demonstrates effective operations to mitigate the potential crashes in both lateral and rear-end crash scenarios.
Combined Generation and Optimization of a Wind-Solar-Battery Power System
(Office of the Vice Chancellor for Research, 2014-04-11) Shen, Dan; Izadian, Afshin
Compared with other renewables, wind and solar have been established as proven future sources of energy, because of their environment-friendly, safe and cost-effective characteristics. However, there are some difficulties associated with combined utilization of solar and wind, e.g. intermittency of wind and of the solar radiation, and their variation do not match the time distribution of the demand. For this purpose, advanced network of multiple renewable energy systems with storage units have been proposed. Small-scale standalone combination of solar, wind and battery has been found effective in some independent power supply system. Proposed in this research on a standalone distributed hybrid power system which consists of solar power, wind power and battery storage. A control strategy is introduced to maximize the simultaneous energy harvesting from both renewable sources. The supervisory controller results in five contingencies considering the level of power generation available at each renewable energy source and the state of charge in the battery. Power converters interface the source with a common DC bus. The interfacing converter is controlled either as a current or a voltage source. A supervisory controller is proposed to accomplish the source type allocations and balance of energy in the operating contingencies. Simulation results demonstrate accurate operation of the controllers and functionality of the maximum power point tracking algorithm in each operating condition both for solar and for wind power.
Data Collection and Processing Methods for the Evaluation of Vehicle Road Departure Detection Systems
(IEEE, 2018) Shen, Dan; Yi, Qiang; Li, Lingxi; Chien, Stanley; Chen, Yaobin; Sherony, Rini; Mechanical and Energy Engineering, School of Engineering and Technology
Road departure detection systems (RDDSs) for avoiding/mitigating road departure crashes have been developed and included on some production vehicles in recent years. In order to support and provide a standardized and objective performance evaluation of RDDSs, this paper describes the development of the data acquisition and data post-processing systems for testing RDDSs. Seven parameters are used to describe road departure test scenarios. The overall structure and specific components of data collection system and data post-processing system for evaluating vehicle RDDSs is devised and presented. Experimental results showed sensing system and data post-processing system could capture all needed signals and display vehicle motion profile from the testing vehicle accurately. Test track testing under different scenarios demonstrates the effective operations of the proposed data collection system.
Distributed Nonlinear Model Predictive Control for Heterogeneous Vehicle Platoons Under Uncertainty
(IEEE Xplore, 2021-09) Shen, Dan; Yin, Jianhua; Du, Xiaoping; Li, Lingxi; Electrical and Computer Engineering, School of Engineering and Technology
This paper presents a novel distributed nonlinear model predictive control (DNMPC) for minimizing velocity tracking and spacing errors in heterogeneous vehicle platoon under uncertainty. The vehicle longitudinal dynamics and information flow in the platoon are established and analyzed. The algorithm of DNMPC with robustness and reliability considerations at each vehicle (or node) is developed based on the leading vehicle and reference information from nodes in its neighboring set. Together with the physical constraints on the control input, the nonlinear constraints on vehicle longitudinal dynamics, the terminal constraints on states, and the reliability constraints on both input and output, the objective function is defined to optimize the control accuracy and efficiency by penalizing the tracking errors between the predicted outputs and desirable outputs of the same node and neighboring nodes, respectively. Meanwhile, the robust design optimization model also minimizes the expected quality loss which consists of the mean and standard deviation of node inputs and outputs. The simulation results also demonstrate the accuracy and effectiveness of the proposed approach under two different traffic scenarios.
Distributed Stochastic Model Predictive Control With Taguchi’s Robustness for Vehicle Platooning
(IEEE, 2022-02-03) Yin, Jianhua; Shen, Dan; Du, Xiaoping; Li, Lingxi; Mechanical and Energy Engineering, School of Engineering and Technology
Vehicle platooning for highway driving has many benefits, such as lowering fuel consumption, improving traffic safety, and reducing traffic congestion. However, its performance could be undermined due to uncertainty. This work proposes a new control method that combines distributed stochastic model predictive control with Taguchi’s robustness (TR-DSMPC) for vehicle platooning. The proposed method inherits the advantages of both Taguchi’s robustness (maximizing the mean performance and minimizing the performance variation due to uncertainty) and stochastic model predictive control (ensuring a specific reliability level). Taguchi’s robustness is achieved by introducing a variation term in the control objective to bring a trade-off between mean performance and its variation. TR-DSMPC propagates uncertainty via an approximation method: First-Order Second Moment, which is far more efficient than Monte Carlo-based methods. The uncertainty is considered from two perspectives, time-independent uncertainty by random variables and time-dependent uncertainty by stochastic processes. We compare the proposed method with two other MPC-based methods in terms of safety (spacing error) and efficiency (relative velocity). The results indicate that our proposed method can effectively reduce the performance variation and maintain the mean performance.
Extremum Power Seeking Control of A Hybrid Wind-Solar-Storage DC Power System
(Office of the Vice Chancellor for Research, 2015-04-17) Shen, Dan; Izadian, Afshin
Recently, there have been concerns on global climate deterioration and its related harmful effect such as environmental pollution and sustainable development problems. As a solution, clean renewable resources have been given increasing interest. Compared with other new energy technologies, wind and solar have been set up as proven future sources of energy because of their environment-friendly, abundant and cost-effective utilization characteristics. Harnessing these two energies for electric power generation is the area of aiming at quality and reliability in the electricity delivery. However, there are some difficulties associated with wind and solar in power system, e.g. intermittency of wind or solar and instability of the load. Accordingly, photovoltaic (PV) arrays, wind turbines and batteries are used to feed a dc or ac bus connected to the load, as well as the utility grid, constituting the so-called micro-grid. Micro-grids operate in both standalone and grid connected modes. The wind and solar sources can compensate each other and their simultaneous intermittency is complemented by the use of an energy storage device. This research presents a combined power system with a common dc bus which contains solar power, wind power, battery storage and a constant dc load (CDL). In wind system, the AC-DC rectifier is controlled by a maximum power point tracker (MPPT) at first stage and the voltage regulation is accomplished through a boost converter by utilizing an adaptive voltage controller. In the solar system, two cascaded boost converters are controlled through a sliding mode controller (SMC) to regulate the power flow to the load. A supervisory control strategy is also introduced to maximize the simultaneous energy harvesting from both renewable sources and balance the energy between the sources, battery and the load. According to the level of power generation available at each renewable energy source and the state of charge in the battery, the controller results in four contingencies. Simulation results show accurate operation of the supervisory controller and functionality of the maximum power point tracking algorithm for solar and for wind power.
Hybrid Wind-Solar-Storage Energy Harvesting Systems
(2016) Shen, Dan; Rizkalla, Maher; Izadian, Afshin; Li, Lingxi; King, Brian
With the increasing demand of economy and environmental pollutions, more and more renewable energy systems with clean sources appear and have attracted attention of systems involving solar power, wind power and hybrid new energy powers[1]. However, there are some difficulties associated with combined utilization of solar and wind, such as their intermittent behavior and their peak hours mismatch in generation and consumption[1]. For this purpose, advanced network of a variety of renewable energy systems along with controllable load and storage units have been introduced[1-3]. This thesis proposes some configurations of hybrid energy harvesting systems, including wind-wind-storage DC power system with BOOST converters, solar-solar-storage DC power system with cascade BOOST converters, wind-solar-storage DC power system with BOOST converter and cascade BOOST converter, and wind-solar DC power system with SEPIC converter and BOOST converter. The models of all kinds of systems are built in Matlab/Simulink and the mathematical state-space models of combined renewable energy systems are also established. Several MPPT control strategies are introduced and designed to maximize the simultaneous power capturing from wind and solar, such as Perturb & Observe (P&O) algorithm for solar and wind, Tip Speed Ratio (TSR) control and Power Signal Feedback (PSF) control for wind, and Sliding Mode Extremum Seeking Control (SM-ESC) for wind and solar systems[4]. The control effects of some of these MPPT methods are also compared and analyzed. The supervisory control strategies corresponding to each configurations are also discussed and implemented to maximize the simultaneous energy harvesting from both renewable sources and balance the energy between the sources, battery and the load[2]. Different contingencies are considered and categorized according to the power generation available at each renewable source and the state of charge in the battery[2]. Applying the system architectures and control methods in the proposed hybrid new energy systems is a novel and significant attempt, which can be more general in the practical applications. Simulation results demonstrate accurate operation of the supervisory controller and functionality of the maximum power point tracking algorithm in each operating condition both for solar and for wind power[3]
Implementation and Performance Evaluation of In-vehicle Highway Back-of-Queue Alerting System Using the Driving Simulator
(IEEE Xplore, 2021-09) Zhang, Zhengming; Shen, Dan; Tian, Renran; Li, Lingxi; Chen, Yaobin; Sturdevant, Jim; Cox, Ed; Electrical and Computer Engineering, School of Engineering and Technology
This paper proposes a prototype in-vehicle highway back-of-queue alerting system that is based on an Android-based smartphone app, which is capable of delivering warning information to on-road drivers approaching traffic queues. To evaluate the effectiveness of this alerting system, subjects were recruited to participate in the designed test scenarios on a driving simulator. The test scenarios include three warning types (no alerts, roadside alerts, and in-vehicle auditory alerts), three driver states (normal, distracted, and drowsy), and two weather conditions (sunny and foggy). Driver responses related to vehicle dynamics data were collected and analyzed. The results indicate that on average, the drowsy state decreases the minimum time-to-collision by 1.6 seconds compared to the normal state. In-vehicle auditory alerts can effectively increase the driving safety across different combinations of situations (driver states and weather conditions), while roadside alerts perform better than no alerts.
Modeling and Control of A Combined Wind-Solar Microgrid
(IEEE, 2014) Shen, Dan; Izadian, Afshin; Department of Engineering Technology, School of Engineering and Technology
This paper introduces a standalone hybrid power generation system consisting of solar and wind power sources and a DC load. A supervisory control unit, designed to execute maximum power point tracking (MPPT), is introduced to maximize the simultaneous energy harvesting from overall power generation under different climatic conditions. Two contingencies are considered and categorized according to the power generation from each energy source, and the load requirement. Simulation results demonstrate effectiveness of the controllers and functionality of the maximum power point tracking algorithm in each operating condition for both solar and wind power sources.

Browsing by Author "Shen, Dan"

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