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Cooperative Mandatory Lane Change for Connected Vehicles on Signalized Intersection Roads

Clemson University-Zhiyuan Du, Bin Xu, Pierluigi Pisu
  • Technical Paper
  • 2020-01-0889
To be published on 2020-04-14 by SAE International in United States
This paper presents a hierarchical control architecture to coordinate a group of connected vehicles on signalized intersection roads, where vehicles are allowed to change lane to follow a prescribed path. The proposed hierarchical control strategy consists of two control levels: a high level controller at the intersection and a decentralized low level controller in each car. In the hierarchical control architecture, the centralized intersection controller estimates the target velocity for each approaching connected vehicle to avoid red light stop based on the signal phase and timing (SPAT) information. Each connected vehicle as a decentralized controller utilizes model predictive control (MPC) to track the target velocity in a fuel efficient manner. The main objective in this paper is to consider mandatory lane changes. As in the realistic scenarios, vehicles are not required to drive in single lane. More specifically, they more likely change their lanes prior to signals. Hence, the vehicle decentralized controllers must prepare to cooperate with the vehicle that has a mandatory lane change request (host vehicle). The cooperative mandatory lane change is accomplished…
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Optimization of Energy Management Strategy for Range-Extended Electric Vehicle Using Reinforcement Learning and Neural Network

Nipun Mittal, Aditya Pundlikrao Bhagat, Shubham Bhide, Bharadwaj Acharya, Bin Xu, Chris Paredis
  • Technical Paper
  • 2020-01-1190
To be published on 2020-04-14 by SAE International in United States
A Range-Extended Electric Vehicle (REEV) uses battery as the primary energy source and engine as the secondary source to extend the total range of the vehicle. Deep Orange 11 program at Clemson University is proposing a REEV for solving the mobility needs in the year 2040. Designing the Energy Management System (EMS) of such a vehicle is a critical aspect of the problem statement of this program to improve the vehicle economy and bring down the cost of operation of the vehicle. This paper proposes a reinforcement learning based algorithm for designing the EMS of such a vehicle. Q-learning is a model-free algorithm which seeks to improve the cumulative reward by finding the best policy over the course of operation. A rule-based strategy is first used to establish a baseline model of engine operation during the operation of vehicle over an EPA drive-cycle (FHDS). The Q-learning strategy is then deployed which learns over the baseline strategy as the vehicle travels over the drive cycle and improves the fuel economy of the vehicle. A high-fidelity vehicle…
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Machine Learning Based Optimal Energy Storage Devices Selection Assistance for Vehicle Propulsion Systems

Clemson University-Bin Xu
Stanford University-Simona Onori
  • Technical Paper
  • 2020-01-0748
To be published on 2020-04-14 by SAE International in United States
This study investigates the vehicle propulsion system energy storage devices selection. In recent years, powertrain electrification has been popular in all kinds of vehicles such as commercial vehicles and military utility vehicles. Energy storage devices are necessary for all levels of electrification. However, due to the large number of available energy storage devices (e.g. chemistry, size, energy density, and power density), and various class of vehicles (e.g. weight, range, acceleration, operating road environment), the energy storage devices selection process requires tremendous work if using traditional method. This study aims to assist the energy storage devices selection using the data sets collected from existing vehicles that equipped with energy storage devices. Machine Learning models are used to extract the relationship between the vehicles and the corresponding energy storage devices. After the training, the Machine Learning models can predict the ideal energy storage devices given the target vehicles design parameters as the inputs. The predicted ideal energy storage devices can be treated as the initial design and modification can be made based on the validation results. With…
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A Look-Ahead Model Predictive Optimal Control Strategy of a Waste Heat Recovery-Organic Rankine Cycle for Automotive Application

Auburn University-Mark Hoffman
Clemson University-Dhruvang Rathod, Bin Xu, Adamu Yebi, Ardalan Vahidi, Zoran Filipi
Published 2019-04-02 by SAE International in United States
The Organic Rankine Cycle (ORC) has proven to be a promising technology for Waste Heat Recovery (WHR) systems in heavy duty diesel engine applications. However, due to the highly transient heat source, controlling the working fluid flow through the ORC system is a challenge for real time application. With advanced knowledge of the heat source dynamics, there is potential to enhance power optimization from the WHR system through predictive optimal control. This paper proposes a look-ahead control strategy to explore the potential of increased power recovery from a simulated WHR system. In the look-ahead control, the future vehicle speed is predicted utilizing road topography and V2V connectivity. The forecasted vehicle speed is utilized to predict the engine speed and torque, which facilitates estimation of the engine exhaust conditions used in the ORC control model. In the simulation study, a reference tracking controller is designed based on the Model Predictive Control (MPC) methodology. Two variants of Non-linear MPC (NMPC) are evaluated: an NMPC with look-ahead exhaust conditions and a baseline NMPC without knowledge of future exhaust…
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Real-Time Reinforcement Learning Optimized Energy Management for a 48V Mild Hybrid Electric Vehicle

Clemson University-Bin Xu, Farzam Malmir, Dhruvang Rathod, Zoran Filipi
Published 2019-04-02 by SAE International in United States
Energy management of hybrid vehicle has been a widely researched area. Strategies like dynamic programming (DP), equivalent consumption minimization strategy (ECMS), Pontryagin’s minimum principle (PMP) are well analyzed in literatures. However, the adaptive optimization work is still lacking, especially for reinforcement learning (RL). In this paper, Q-learning, as one of the model-free reinforcement learning method, is implemented in a mid-size 48V mild parallel hybrid electric vehicle (HEV) framework to optimize the fuel economy. Different from other RL work in HEV, this paper only considers vehicle speed and vehicle torque demand as the Q-learning states. SOC is not included for the reduction of state dimension. This paper focuses on showing that the EMS with non-SOC state vectors are capable of controlling the vehicle and outputting satisfactory results. Electric motor torque demand is chosen as action. In the reward function definition, the fuel consumption contains engine fuel consumption and equivalent battery fuel consumption. The Q-learning table is executed over Worldwide Harmonized Light Vehicle driving cycle. The learning process shows fast convergence and fuel economy improvement of 5%.…
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A Heuristic Supervisory Controller for a 48V Hybrid Electric Vehicle Considering Fuel Economy and Battery Aging

Clemson University-Farzam Malmir, Bin Xu, Zoran Filipi
Published 2019-01-15 by SAE International in United States
Most studies on supervisory controllers of hybrid electric vehicles consider only fuel economy in the objective function. Taking into consideration the importance of the energy storage system health and its impact on the vehicle’s functionality, cost, and warranty, recent studies have included battery degradation as the second objective function by proposing different energy management strategies and battery life estimation methods. In this paper, a rule-based supervisory controller is proposed that splits the torque demand based not only on fuel consumption, but also on the battery capacity fade using the concept of severity factor. For this aim, the severity factor is calculated at each time step of a driving cycle using a look-up table with three different inputs including c-rate, working temperature, and state of charge of the battery. The capacity loss of the battery is then calculated using a semi-empirical capacity fade model. Eventually, the fuel economy, and capacity loss as two of the most important objectives are compared with and without implementing the proposed controller. In the comparative study, four customized driving cycles are…
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Transient Power Optimization of an Organic Rankine Cycle Waste Heat Recovery System for Heavy-Duty Diesel Engine Applications

SAE International Journal of Alternative Powertrains

BorgWarner Inc.-Xiaobing Liu, John Shutty, Paul Anschel
Clemson-ICAR-Bin Xu, Adamu Yebi, Simona Onori, Zoran Filipi, Mark Hoffman
  • Journal Article
  • 2017-01-0133
Published 2017-03-28 by SAE International in United States
This paper presents the transient power optimization of an organic Rankine cycle waste heat recovery (ORC-WHR) system operating on a heavy-duty diesel (HDD). The optimization process is carried on an experimentally validated, physics-based, high fidelity ORC-WHR model, which consists of parallel tail pipe and EGR evaporators, a high pressure working fluid pump, a turbine expander, etc. Three different ORC-WHR mixed vapor temperature (MVT) operational strategies are evaluated to optimize the ORC system net power: (i) constant MVT; (ii) constant superheat temperature; (iii) fuzzy logic superheat temperature based on waste power level. Transient engine conditions are considered in the optimization. Optimization results reveal that adaptation of the vapor temperature setpoint based on evaporation pressure strategy (ii) provides 1.1% mean net power (MNP) improvement relative to a fixed setpoint strategy (i). The highest net power is produced by setpoint strategy (iii), which exhibited a 2.1% improvement compared strategy (i), revealing importance of utilizing engine conditions during reference trajectory generation. These results serve as the benchmark for the ORC system net power optimal control.
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Physics-Based Modeling and Transient Validation of an Organic Rankine Cycle Waste Heat Recovery System for a Heavy-Duty Diesel Engine

BorgWarner Automotive-John Shutty
BorgWarner Inc-Xiaobing Liu
Published 2016-04-05 by SAE International in United States
This paper presents an Organic Rankine Cycle (ORC) system model for heavy-duty diesel (HDD) applications. The dynamic, physics-based model includes: heat exchangers for parallel exhaust and EGR circuits, compressible vapor working fluid, distribution and flow control valves, a high pressure pump, and a reservoir. A finite volume method is used to model the evaporator, and a pressure drop model is included to improve the accuracy of predictions. Experimental results obtained on a prototype ORC system are used for model calibration and validation. Comparison of predicted and measured values under steady-state conditions is pursued first, followed by the analysis of selected transient events. Validation reveals the model’s ability to track real-world temperature and pressure dynamics of the ORC system. Therefore, this modeling framework is suitable for future system design studies, optimization of ORC power generation, and as a basis for development of control-oriented ORC models.
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G-G Diagram Generation Based on Phase Plane Method and Experimental Validation for FSAE Race Car

Beijing Institute of Technology-Jun Ni, Jibin Hu, Xueyuan Li, Bin Xu, Junjie Zhou
Published 2016-04-05 by SAE International in United States
In order to discuss the limit handling performance of a FSAE race car, a method to generate the G-G diagram was proposed based on phase plane concept. The simulated G-G diagram was validated by experiments with an electric FSAE race car. In section 1, a nonlinear 7 DOFs dynamic model of a certain electric FSAE race car was built. The tire mechanical properties were described by Magic Formula, and the tire test data was provided by FSAE TTC. In section 2, firstly the steady-state yaw rate response was discussed in different vehicle speed and lateral acceleration based on the simulations. Then the method to generate the G-G diagram based on phase plane concept was proposed, and the simulated G-G diagram of a certain FSAE race car was obtained. In section 3, the testbed FSAE race car was described, including the important apparatuses used in the experiments. Based on the race track experiment, the G-G diagram of the race car was obtained. The comparison between simulated and actual G-G diagram shows that, the phase plane method…
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Numerical Investigation of Air Purifier Affecting Ultrafine Particle Transport in Vehicle Cabins

Tongji Univ.-Pengyi Cui, Bin Xu
Published 2014-04-01 by SAE International in United States
Air purifier has been prevalently used in the passenger vehicle cabins to reduce in-cabin UltraFine Particle (UFP) concentration. In this study, Computational Fluid Dynamics (CFD) was applied to simulate the in-cabin UFP transport and distribution under different ventilation modes with different characteristics of the air purifier. Ventilation settings, air purifier settings, and air purifier location were identified as the important factors determining the in-cabin UFP distribution and transport. Downward ventilation airflow direction and smaller ventilation air velocity can be considered by the drivers for a lower in-cabin UFP concentration. Upward airflow direction from the air purifier's inlet and larger air velocity were recommended since it led up to 50% in-cabin UFP reduction. Air purifier installed at middle ceiling of the cabin develops the most efficient airflow for UFP removal. Explicit relationships between in-cabin UFP distribution and the air purifier settings were presented as a reference to facilitate cabin air purifier design for more efficient in-cabin UFP removal.
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