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Route-Optimized Energy Management of Connected and Automated Multi-Mode Plug-In Hybrid Electric Vehicle Using Dynamic Programming

Michigan Technological University-Neeraj Rama, Huanqing Wang, Joshua Orlando, Darrell Robinette, Bo Chen
Published 2019-04-02 by SAE International in United States
This paper presents a methodology to optimize the blending of charge-depleting (CD) and charge-sustaining (CS) modes in a multi-mode plug-in hybrid electric vehicle (PHEV) that reduces overall energy consumption when the selected route cannot be completely driven in all-electric mode. The PHEV used in this investigation is the second-generation Chevrolet Volt and as many as four instrumented vehicles were utilized simultaneously on road to acquire validation data. The optimization method used is dynamic programming (DP) paired with a reduced-order powertrain model to enable onboard embedded controller compatibility and computational efficiency in optimally blending CD, CS modes over the entire drive route. The objective of the optimizer is to enable future Connected and Automated Vehicles (CAVs) to best utilize onboard energy for minimum overall energy consumption based on speed and elevation profile information from Intelligent Transportation Systems (ITS), Internet of Things (IoT), High-definition Mapping, and onboard sensing technologies. Emphasis is placed on runtime minimization to quickly react and plan an optimal mode scheme in highly dynamic road conditions with minimal computational resources. On-road performance of the…
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Model-Based Analysis of V2G Impact on Battery Degradation

Michigan Technological University-Luting Wang, Bo Chen
Published 2017-03-28 by SAE International in United States
Vehicle-to-Grid (V2G) service has a potential to improve the reliability and stability of the electrical grid due to the ability of providing bi-directional power flow from/to the grid. However, frequent charging/discharging may impact the battery lifetime. This paper presents the analysis of battery degradation in three scenarios. In the first scenario, different battery capacities are considered. In the second scenario, the battery degradation with various depth of discharge (DOD) are studied. In the third scenario, the capacity loss due to different charging regime are compared. The charging/discharging of plug-in electric vehicles (PEVs) are simulated in a single-phase microgrid system integrated with a photovoltaics (PV) farm, an energy storage system (ESS), and ten electric vehicle service equipment (EVSE). The battery degradation model is an energy throughput model, which is developed based on the Arrhenius equation and a power law relationship between time and capacity fading. The simulation results show that V2G service potentially increases the PEV battery degradation. With the same DOD, higher battery capacity can increase the degradation degree. For a specific PEV battery, the…
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The Model Integration and Hardware-in-the-Loop (HiL) Simulation Design for the Analysis of a Power-Split Hybrid Electric Vehicle with Electrochemical Battery Model

SAE International Journal of Passenger Cars - Electronic and Electrical Systems

Michigan Technological University-Ming Cheng, Bo Chen
  • Journal Article
  • 2017-01-0001
Published 2017-03-28 by SAE International in United States
This paper studies the hardware-in-the-loop (HiL) design of a power-split hybrid electric vehicle (HEV) for the research of HEV lithiumion battery aging. In this paper, an electrochemical model of a lithium-ion battery pack with the characteristics of battery aging is built and integrated into the vehicle model of Autonomie® software from Argonne National Laboratory. The vehicle model, together with the electrochemical battery model, is designed to run in a dSPACE real-time simulator while the powertrain power distribution is managed by a dSPACE MicroAutoBoxII hardware controller. The control interface is designed using dSPACE ControlDesk to monitor the real-time simulation results. The HiL simulation results with the performance of vehicle dynamics and the thermal aging of the battery are presented and analyzed. The research laid a solid foundation for the future work on HiL design of HEV control with the consideration of battery aging and the component-in-the-loop study of the lithium ion battery running in real-time.
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Nonlinear Model Predictive Control of a Power-Split Hybrid Electric Vehicle with Electrochemical Battery Model

Mercedes Benz R&D North America-Lei Feng
Michigan Technological University-Ming Cheng, Bo Chen
Published 2017-03-28 by SAE International in United States
This paper studies the nonlinear model predictive control for a power-split Hybrid Electric Vehicle (HEV) power management system to improve the fuel economy. In this paper, a physics-based battery model is built and integrated with a base HEV model from Autonomie®, a powertrain and vehicle model architecture and development software from Argonne National Laboratory. The original equivalent circuit battery model from the software has been replaced by a single particle electrochemical lithium ion battery model. A predictive model that predicts the driver’s power request, the battery state of charge (SOC) and the engine fuel consumption is studied and used for the nonlinear model predictive controller (NMPC). A dedicated NMPC algorithm and its solver are developed and validated with the integrated HEV model. The performance of the NMPC algorithm is compared with that of a rule-based controller. This study provides a sound basis for the further study of stochastic MPC and NMPC for the HEV power management with the consideration of battery aging and thermal performance.
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Integration of OpenADR with Node-RED for Demand Response Load Control Using Internet of Things Approach

Argonne National Laboratory-Jason Harper
Michigan Technological University-Piyush Aggarwal, Bo Chen
Published 2017-03-28 by SAE International in United States
The increased market share of electric vehicles and renewable energy resources have raised concerns about their impact on the current electrical distribution grid. To achieve sustainable and stable power distribution, a lot of effort has been made to implement smart grids. This paper addresses Demand Response (DR) load control in a smart grid using Internet of Things (IoT) technology. A smart grid is a networked electrical grid which includes a variety of components and sub-systems, including renewable energy resources, controllable loads, smart meters, and automation devices. An IoT approach is a good fit for the control and energy management of smart grids. Although there are various commercial systems available for smart grid control, the systems based on open sources are limited. In this study, we adopt an open source development platform named Node-RED to integrate DR capabilities in a smart grid for DR load control. The DR system employs the OpenADR standard. To enable an OpenADR Virtual Top Node (VTN) to communicate with Node-RED nodes, an OpenADR Node-RED node has been developed to translate and…
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Grid-Tied Single-Phase Bi-Directional PEV Charging/Discharging Control

SAE International Journal of Passenger Cars - Electronic and Electrical Systems

Michigan Technological Univ-Luting Wang, Chong Cao, Bo Chen
  • Journal Article
  • 2016-01-0159
Published 2016-04-05 by SAE International in United States
This paper studies the bi-directional power flow control between Plug-in Electric Vehicles (PEVs) and an electrical grid. A grid-tied charging system that enables both Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) charging/discharging is modeled using SimPowerSystems in Matlab/Simulink environment. A bi-directional AC-DC converter and a bi-directional DC-DC buck-boost converter are integrated to charge and discharge PEV batteries. For AC-DC converter control, Predictive Current Control (PCC) strategy is employed to enable grid current to reach a reference current after one modulation period. In addition, Phase Lock Loop (PLL) and a band-stop filter are designed to lock the grid voltage phase and reduce harmonics. Bi-directional power flow is realized by controlling the mode of the DC-DC converter. Simulation tests are conducted to evaluate the performance of this bi-directional charging system. The simulation results show that the integrated PCC, PLL, and band-stop filter can achieve fast dynamic response, low Total Harmonics Distortion (THD) of grid voltage and current, and unity power factor.
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Model Integration and Hardware-in-the-Loop (HiL) Simulation Design for the Testing of Electric Power Steering Controllers

Michigan Technological University-Chuanliangzi Liu, Bo Chen, Ming Cheng
Nexteer Automotive-Anthony Champagne, Keyur Patel
Published 2016-04-05 by SAE International in United States
The Electronic Control Unit (ECU) of an Electric Power Steering (EPS) system is a core device to decide how much assistance an electric motor applies on a steering wheel. The EPS ECU plays an important role in EPS systems. The effectiveness of an ECU needs to be thoroughly tested before mass production. Hardware-in-the-loop simulation provides an efficient way for the development and testing of embedded controllers. This paper focuses on the development of a HiL system for testing EPS controllers. The hardware of the HiL system employs a dSPACE HiL simulator. The EPS plant model is an integrated model consisting of a Vehicle Dynamics model of the dSPACE Automotive Simulation Model (ASM) and the Nexteer Steering model. The paper presents the design of an EPS HiL system, the simulation of sensors and actuators, the functions of the ASM Vehicle Dynamics model, and the integration method of the ASM Vehicle Dynamics model with a Steering model. The offline simulation of the integrated model is performed and the results for different driving maneuvers are presented.
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Predictive Control of a Power-Split HEV with Fuel Consumption and SOC Estimation

Michigan Technological Univ.-Lei Feng, Ming Cheng, Bo Chen
Published 2015-04-14 by SAE International in United States
This paper studies model predictive control algorithm for Hybrid Electric Vehicle (HEV) energy management to improve HEV fuel economy. In this paper, Model Predictive Control (MPC), a predictive control method, is applied to improve the fuel economy of power-split HEV. A dedicated model predictive control method is developed to predict vehicle speed, battery state of charge (SOC), and engine fuel consumption. The power output from the engine, motor, and the mechanical brake will be adjusted to match driver's power request at the end of the prediction window while minimizing fuel consumption. The controller model is built on Matlab® MPC toolbox® and the simulations are based on MY04 Prius vehicle model using Autonomie®, a powertrain and fuel economy analysis software, developed by Argonne National Laboratory. The study compares the performance of MPC and conventional rule-base control methods. A parametric study is conducted to understand the influence of 3 weighting factors in MPC formulation on the performance of the vehicles. The conclusion provides a sound ground for further fuel consumption optimization and implementation of stochastic MPC.
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Simulation of Lithium Ion HEV Battery Aging Using Electrochemical Battery Model under Different Ambient Temperature Conditions

Michigan Technological University-Ming Cheng, Lei Feng, Bo Chen
Published 2015-04-14 by SAE International in United States
This paper investigates the aging performance of the lithium ion cobalt oxide battery pack of a single shaft parallel hybrid electric vehicle (HEV) under different ambient temperatures. Varying ambient temperature of HEVs results in different battery temperature and then leads to different aging performance of the battery pack. Battery aging is reflected in the increasing of battery internal resistance and the decreasing of battery capacity. In this paper, a single shaft parallel hybrid electric vehicle model is built by integrating Automotive Simulation Model (ASM) from dSPACE and AutoLion-ST battery model from ECPower to realize the co-simulation of HEV powertrain in the common MATLAB/Simulink platform. The battery model is a physics-based and thermally-coupled battery (TCB) model, which enables the investigation of battery capacity degradation and aging. Standard driving cycle with differing ambient temperatures is tested using developed HEV model. The variation of capacity fading rate under different temperatures is observed. Under extreme temperature conditions, it is found that the range of the voltage response of the battery pack is wider under lower temperatures indicating an increase…
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Stochastic Knock Detection, Control, Software Integration, and Evaluation on a V6 Spark-Ignition Engine under Steady-State Operation

Ford Motor Co.-Chris Glugla
Michigan Technological Univ.-Wei Luo, Bo Chen, Jeffrey Naber
Published 2014-04-01 by SAE International in United States
The ability to operate a spark-ignition (SI) engine near the knock limit provides a net reduction of engine fuel consumption. This work presents a real-time knock control system based on stochastic knock detection (SKD) algorithm. The real-time stochastic knock control (SKC) system is developed in MATLAB Simulink, and the SKC software is integrated with the production engine control strategy through ATI's No-Hooks. The SKC system collects the stochastic knock information and estimates the knock level based on the distribution of knock intensities fitting to a log-normal (LN) distribution. A desired knock level reference table is created under various engine speeds and loads, which allows the SKC to adapt to changing engine operating conditions. In SKC system, knock factor (KF) is an indicator of the knock intensity level. The KF is estimated by a weighted discrete FIR filter in real-time. Both offline simulation and engine dynamometer test results show that stochastic knock control with fixed length of finite impulse response (FIR) filter has slow and excessive retard issue when a significant knock event happens. To enhance…
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