The goal of this work is to develop easily generalized models of heavy duty truck engine maps that allow for approximate comparisons of engine performance, thus enabling fuel efficient matching of engines to a set of corresponding loads and routes. This is achieved by applying dimensional analysis to create a uniformly applicable, dimensionless Brake Specific Fuel Consumption (BSFC) map that fits the behavior of a wide range of diesel engines. A commonality between maps was found to occur when engine data is scaled by specific dimensional parameters that target data consistency among the primary operating points across engines. This common map highlights observable trends in engine performance based on the influence of these same parameters being scaled across engines. The resulting dimensionless engine map fits the minimum BSFC regions of four diesel engines to within 2.5 percent.
{"title":"Diesel Engine Characterization and Performance Scaling via Brake Specific Fuel Consumption Map Dimensional Analysis","authors":"E. Pelletier, S. Brennan","doi":"10.1115/dscc2019-9110","DOIUrl":"https://doi.org/10.1115/dscc2019-9110","url":null,"abstract":"\u0000 The goal of this work is to develop easily generalized models of heavy duty truck engine maps that allow for approximate comparisons of engine performance, thus enabling fuel efficient matching of engines to a set of corresponding loads and routes. This is achieved by applying dimensional analysis to create a uniformly applicable, dimensionless Brake Specific Fuel Consumption (BSFC) map that fits the behavior of a wide range of diesel engines. A commonality between maps was found to occur when engine data is scaled by specific dimensional parameters that target data consistency among the primary operating points across engines. This common map highlights observable trends in engine performance based on the influence of these same parameters being scaled across engines. The resulting dimensionless engine map fits the minimum BSFC regions of four diesel engines to within 2.5 percent.","PeriodicalId":41412,"journal":{"name":"Mechatronic Systems and Control","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2019-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79610485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Christian Earnhardt, Ben Groelke, John Borek, C. Vermillion
With pairs or groups of heterogeneous vehicles (with different masses, aerodynamic coefficients, etc.), collaborative platooning can be advantageous in some scenarios due to aerodynamic drag reduction, while being detrimental in other scenarios due to mismatches in vehicle properties. This paper introduces two controllers capable of alternating between independent vehicle velocity trajectory optimization (VTO) and a collaborative platooning/VTO approach based on the aggregate fuel savings of all vehicles within the platoon. The first uses the difference in mass between the vehicles within a platoon and the upcoming road grade to decide whether platooning will be economically advantageous, relying on a support vector classification algorithm to make the switching decision. The second runs both independent VTO and collaborative VTO/platooning in parallel, making a decision based on which method predicts the least amount of fuel consumption over an upcoming stretch of highway. The performance of these techniques was evaluated using a medium-fidelity Simulink model of a heavy-duty truck. Results show a 5.1% to 14.1% decrease in fuel consumption for the following vehicle of a platoon as compared to a baseline controller not platooning, where the exact fuel consumption improvement depends on the desired following distance. These results were also compared to a baseline that platooned over the entire route, providing evidence that there are situations where disengaging from a platoon is beneficial in the presence of heterogeneity.
{"title":"Fused Model Predictive Control Techniques for Strategic Platooning Amongst Heterogeneous Pairs of Heavy-Duty Trucks","authors":"Christian Earnhardt, Ben Groelke, John Borek, C. Vermillion","doi":"10.1115/dscc2019-9071","DOIUrl":"https://doi.org/10.1115/dscc2019-9071","url":null,"abstract":"\u0000 With pairs or groups of heterogeneous vehicles (with different masses, aerodynamic coefficients, etc.), collaborative platooning can be advantageous in some scenarios due to aerodynamic drag reduction, while being detrimental in other scenarios due to mismatches in vehicle properties. This paper introduces two controllers capable of alternating between independent vehicle velocity trajectory optimization (VTO) and a collaborative platooning/VTO approach based on the aggregate fuel savings of all vehicles within the platoon. The first uses the difference in mass between the vehicles within a platoon and the upcoming road grade to decide whether platooning will be economically advantageous, relying on a support vector classification algorithm to make the switching decision. The second runs both independent VTO and collaborative VTO/platooning in parallel, making a decision based on which method predicts the least amount of fuel consumption over an upcoming stretch of highway. The performance of these techniques was evaluated using a medium-fidelity Simulink model of a heavy-duty truck. Results show a 5.1% to 14.1% decrease in fuel consumption for the following vehicle of a platoon as compared to a baseline controller not platooning, where the exact fuel consumption improvement depends on the desired following distance. These results were also compared to a baseline that platooned over the entire route, providing evidence that there are situations where disengaging from a platoon is beneficial in the presence of heterogeneity.","PeriodicalId":41412,"journal":{"name":"Mechatronic Systems and Control","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2019-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89785092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Manuel Lanchares, I. Kolmanovsky, A. Girard, Denise M. Rizzo
Reference governors are add-on control schemes that modify the reference commands, if it becomes necessary, in order to avoid constraint violations. To implement a reference governor, explicit knowledge of a model of the system and its constraints is typically required. In this paper, a reference governor which does not require an explicit model of the system or constraints is presented. It constructs an approximation of the maximal output admissible set, as the system operates, using online neural network learning. This approximation is used to modify the reference command in order to satisfy the constraints. The potential of the algorithm is demonstrated through simulations for an electric vehicle and an agile positioning system.
{"title":"Reference Governors Based on Online Learning of Maximal Output Admissible Set","authors":"Manuel Lanchares, I. Kolmanovsky, A. Girard, Denise M. Rizzo","doi":"10.1115/dscc2019-8950","DOIUrl":"https://doi.org/10.1115/dscc2019-8950","url":null,"abstract":"\u0000 Reference governors are add-on control schemes that modify the reference commands, if it becomes necessary, in order to avoid constraint violations. To implement a reference governor, explicit knowledge of a model of the system and its constraints is typically required. In this paper, a reference governor which does not require an explicit model of the system or constraints is presented. It constructs an approximation of the maximal output admissible set, as the system operates, using online neural network learning. This approximation is used to modify the reference command in order to satisfy the constraints. The potential of the algorithm is demonstrated through simulations for an electric vehicle and an agile positioning system.","PeriodicalId":41412,"journal":{"name":"Mechatronic Systems and Control","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2019-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90061259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This work deals with the voltage response of parametric resonance of electrostatically actuated microelectromechanical (MEMS) circular plates under hard excitations. Method of Multiple Scales (MMS) and Reduced Order Model (ROM) method using two modes of vibration are used to predict the voltage-amplitude response of the MEMS circular plates. ROM is solved using AUTO 07p, a software package for continuation and bifurcation. MMS used in this paper has one term in the electrostatic force being considered significant. This is the way MMS is used to model hard excitations. MMS shows results similar to those of ROM at lower amplitudes and lower voltages. The differences between the two methods, MMS and ROM, are significant in high amplitudes for all voltages, and the differences are significant in all amplitudes for larger voltages. Significant differences can be noted in the effect of different parameters such as the detuning frequency and damping on the voltage response. ROM AUTO 07p is calibrated using ROM time responses in which the ROM is solved using the solver ode15s in Matlab.
本文研究了硬激励下静电驱动微机电(MEMS)圆板参数共振的电压响应。采用多尺度法(MMS)和降阶模型法(ROM)两种振动模态对MEMS圆板的电压幅值响应进行了预测。采用auto07p -一个连续分岔软件包对ROM进行求解。本文使用的MMS在静电力中有一项被认为是有效的。这就是MMS用来模拟硬激发的方法。在较低的振幅和较低的电压下，MMS显示出与ROM相似的结果。MMS和ROM两种方法之间的差异在所有电压下的高幅值上都是显著的，在较大电压下的所有幅值上的差异都是显著的。不同参数(如失谐频率和阻尼)对电压响应的影响有显著差异。ROM AUTO 07p使用ROM时间响应进行校准，其中使用Matlab中的求解器ode15s对ROM进行求解。
{"title":"Voltage Response of Parametric Resonance of MEMS Circular Plates Under Hard Excitations","authors":"Julio Beatriz, D. Caruntu","doi":"10.1115/dscc2019-9059","DOIUrl":"https://doi.org/10.1115/dscc2019-9059","url":null,"abstract":"\u0000 This work deals with the voltage response of parametric resonance of electrostatically actuated microelectromechanical (MEMS) circular plates under hard excitations. Method of Multiple Scales (MMS) and Reduced Order Model (ROM) method using two modes of vibration are used to predict the voltage-amplitude response of the MEMS circular plates. ROM is solved using AUTO 07p, a software package for continuation and bifurcation. MMS used in this paper has one term in the electrostatic force being considered significant. This is the way MMS is used to model hard excitations. MMS shows results similar to those of ROM at lower amplitudes and lower voltages. The differences between the two methods, MMS and ROM, are significant in high amplitudes for all voltages, and the differences are significant in all amplitudes for larger voltages. Significant differences can be noted in the effect of different parameters such as the detuning frequency and damping on the voltage response. ROM AUTO 07p is calibrated using ROM time responses in which the ROM is solved using the solver ode15s in Matlab.","PeriodicalId":41412,"journal":{"name":"Mechatronic Systems and Control","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2019-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87333802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Diesel engines are becoming increasingly complex to control and calibrate with the desire of improving fuel economy and reducing emissions (NOx and Soot) due to global warming and energy usage. With ever increased control features, it is becoming more and more difficult to calibrate engine control parameters using the traditional engine mapping based methods due to unreasonable calibration time required. Therefore, this research focuses on the problem of performing engine calibration within a limited budget by efficiently optimizing three control parameters: namely variable geometry turbocharger (VGT) position, exhaust gas recirculation (EGR) valve position, and start of injection (SOI). Engine performance in terms of fuel consumption (BSFC) and emissions (NOX) are considered as objective function here with the constraint on boost pressure and engine load (BMEP). Since the engine calibration process requires a large number of high-fidelity evaluations, surrogate modeling methods are used to perform calibration quickly with a significantly reduced computational budget. Kriging metamodeling is used for this work with Expected Improvement (EI) as acquisition function. Results show more than 60% decrease in computational cost with results close to actual near Pareto optimal set.
{"title":"Engine Calibration Optimization Based on its Surrogate Models","authors":"Anuj Pal, Yan Wang, Ling Zhu, G. Zhu","doi":"10.1115/dscc2019-8984","DOIUrl":"https://doi.org/10.1115/dscc2019-8984","url":null,"abstract":"\u0000 Diesel engines are becoming increasingly complex to control and calibrate with the desire of improving fuel economy and reducing emissions (NOx and Soot) due to global warming and energy usage. With ever increased control features, it is becoming more and more difficult to calibrate engine control parameters using the traditional engine mapping based methods due to unreasonable calibration time required. Therefore, this research focuses on the problem of performing engine calibration within a limited budget by efficiently optimizing three control parameters: namely variable geometry turbocharger (VGT) position, exhaust gas recirculation (EGR) valve position, and start of injection (SOI). Engine performance in terms of fuel consumption (BSFC) and emissions (NOX) are considered as objective function here with the constraint on boost pressure and engine load (BMEP). Since the engine calibration process requires a large number of high-fidelity evaluations, surrogate modeling methods are used to perform calibration quickly with a significantly reduced computational budget. Kriging metamodeling is used for this work with Expected Improvement (EI) as acquisition function. Results show more than 60% decrease in computational cost with results close to actual near Pareto optimal set.","PeriodicalId":41412,"journal":{"name":"Mechatronic Systems and Control","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2019-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77227148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiang He, Jake A. Steiner, Joseph R. Bourne, K. Leang
This paper presents a multi-vehicle chemical-plume mapping process that incorporates onboard wind speed and direction estimation. A Gaussian plume model is exploited to develop the kernel for extrapolating the measured data. Compared to the uni- or bi-variate kernels, the proposed kernel uses the estimated wind information to refine the chemical concentration prediction downwind of the source. This new approach, compared to previous mapping methods, relies on fewer parameters and provides 30% reduction in the mapping mean-squared error. Simulation and experimental results are presented to validate the approach. Specifically, outdoor flight tests show three aerial robots with chemical sensing capabilities mapping a real propane gas leak to demonstrate feasibility of the approach.
{"title":"Gaussian-Based Kernel for Multi-Agent Aerial Chemical-Plume Mapping","authors":"Xiang He, Jake A. Steiner, Joseph R. Bourne, K. Leang","doi":"10.1115/dscc2019-9027","DOIUrl":"https://doi.org/10.1115/dscc2019-9027","url":null,"abstract":"\u0000 This paper presents a multi-vehicle chemical-plume mapping process that incorporates onboard wind speed and direction estimation. A Gaussian plume model is exploited to develop the kernel for extrapolating the measured data. Compared to the uni- or bi-variate kernels, the proposed kernel uses the estimated wind information to refine the chemical concentration prediction downwind of the source. This new approach, compared to previous mapping methods, relies on fewer parameters and provides 30% reduction in the mapping mean-squared error. Simulation and experimental results are presented to validate the approach. Specifically, outdoor flight tests show three aerial robots with chemical sensing capabilities mapping a real propane gas leak to demonstrate feasibility of the approach.","PeriodicalId":41412,"journal":{"name":"Mechatronic Systems and Control","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2019-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82460025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper describes a modular 2-DOF serial robotic system and accompanying experiments that have been developed to instruct robotics students in the fundamentals of dynamic force control. In prior work, we used this same robot to showcase and compare the performance of a variety of textbook techniques for dynamic motion control (i.e. fast/accurate trajectory tracking using dynamic model-based and robust control techniques). In this paper we now add a low-cost 3D-printed 2-DOF force sensor to this modular robot and demonstrate a variety of force control techniques for use when the robot is in physical contact with the environment. These include stiffness control, impedance control, admittance control, and hybrid position/force control. Each of these various force control schemes can be first simulated and then experimentally implemented using a MATLAB/Simulink real-time interface. The two-degrees of freedom are just enough to demonstrate how the manipulator Jacobian can be used to implement directional impedances in operational space, and to demonstrate how hybrid control can implement position and force control in different axes. This paper will describe the 2-DOF robot system including the custom force sensor, illustrate the various force control methods that can be implemented, and demonstrate sample results from these experiments.
{"title":"Educational Force Control Using a Modular 2-DOF Serial Robot Manipulator and Low-Cost 2-DOF Force Sensor","authors":"S. Mascaro","doi":"10.1115/dscc2019-9245","DOIUrl":"https://doi.org/10.1115/dscc2019-9245","url":null,"abstract":"\u0000 This paper describes a modular 2-DOF serial robotic system and accompanying experiments that have been developed to instruct robotics students in the fundamentals of dynamic force control.\u0000 In prior work, we used this same robot to showcase and compare the performance of a variety of textbook techniques for dynamic motion control (i.e. fast/accurate trajectory tracking using dynamic model-based and robust control techniques).\u0000 In this paper we now add a low-cost 3D-printed 2-DOF force sensor to this modular robot and demonstrate a variety of force control techniques for use when the robot is in physical contact with the environment. These include stiffness control, impedance control, admittance control, and hybrid position/force control. Each of these various force control schemes can be first simulated and then experimentally implemented using a MATLAB/Simulink real-time interface. The two-degrees of freedom are just enough to demonstrate how the manipulator Jacobian can be used to implement directional impedances in operational space, and to demonstrate how hybrid control can implement position and force control in different axes.\u0000 This paper will describe the 2-DOF robot system including the custom force sensor, illustrate the various force control methods that can be implemented, and demonstrate sample results from these experiments.","PeriodicalId":41412,"journal":{"name":"Mechatronic Systems and Control","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2019-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80289633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ashkan Pourkand, Christopher White, Naghmeh Zamani, David I. Grow
This paper explores the viability of neural-network-based classification of ground surface for vehicles. By classifying road surface in near real-time, improvements in vehicle performance (e.g. braking and cornering) may be possible. Classification performance for many combinations of feature encoding and neural network types are compared. The vehicle used here was an Audi “S3” with a magnetic suspension system on the Sport mode. An NI CompactRIO (or cDAQ) module was used to record from a lowing the cDAQ to communicate with the PCB 352C03 one-axis accelerometer. The accelerometer was firmly attached to the windshield of the car. This work focuses on the classification of four road surfaces (asphalt, dirt, concrete, and sand), though larger target sets were also considered. The most accurate method involved a MATLAB feature extraction package with a back-propagation neural network, yielding an overall accuracy of 97%. Lessons learned from this wide exploration of options may extend to other related classification problems.
{"title":"Surface Recognition for Cars: A Comprehensive Approach for Neural Networks","authors":"Ashkan Pourkand, Christopher White, Naghmeh Zamani, David I. Grow","doi":"10.1115/dscc2019-9148","DOIUrl":"https://doi.org/10.1115/dscc2019-9148","url":null,"abstract":"\u0000 This paper explores the viability of neural-network-based classification of ground surface for vehicles. By classifying road surface in near real-time, improvements in vehicle performance (e.g. braking and cornering) may be possible. Classification performance for many combinations of feature encoding and neural network types are compared. The vehicle used here was an Audi “S3” with a magnetic suspension system on the Sport mode. An NI CompactRIO (or cDAQ) module was used to record from a lowing the cDAQ to communicate with the PCB 352C03 one-axis accelerometer. The accelerometer was firmly attached to the windshield of the car. This work focuses on the classification of four road surfaces (asphalt, dirt, concrete, and sand), though larger target sets were also considered. The most accurate method involved a MATLAB feature extraction package with a back-propagation neural network, yielding an overall accuracy of 97%. Lessons learned from this wide exploration of options may extend to other related classification problems.","PeriodicalId":41412,"journal":{"name":"Mechatronic Systems and Control","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2019-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82792960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xin Wang, Amir Khameneian, P. Dice, Bo Chen, M. Shahbakhti, J. Naber, Chad Archer, Qiuping Qu, C. Glugla, G. Huberts
In homogeneous spark-ignition (SI) engines, ignition timing is used to control the combustion phasing (crank angle of fifty percent of fuel burned, CA50), which affects fuel economy, engine torque output, and emissions. This paper presents a model-based adaptive ignition timing prediction strategy using a control-oriented dynamic combustion model for real-time closed-loop combustion phasing control. The combustion model predicts the burn duration from ignition timing to CA50 (ΔθIGN-CA50) at Intake Valve Closing (IVC) for the upcoming cycle based on current engine operating conditions, including variable valve timing, predicted ignition timing, air-fuel ratio, engine speed, and engine load. To maintain the accuracy of combustion model and ignition timing prediction during the engine lifetime, a Recursive-Least-Square (RLS) with Variable Forgetting Factor (VFF) based adaptation algorithm is developed to handle both short term (operating-point-dependent) and long term (engine aging) model errors. Due to short term model errors and stochastic characteristics of cycle-to-cycle combustion variations, large model errors may occur during severe transient operating conditions (tip-in/tip-out), which can result in wrong adjustments and excessive adaptations. Since on-road SI engines are always operating in transient conditions, the ‘Heavy Transient Detection’ algorithm is developed to avoid fault adaptation and assist the adaptation algorithm to be stable. On-road vehicle testing data is used to evaluate the performance of the entire model-based adaptive burn duration and ignition timing prediction algorithm. With only 64 calibration points, a mean ignition timing prediction error of 0.2 Crank Angle Degree (CAD) and average iteration number of 2 shows the capability of adaptive ignition timing prediction, a significant reduction of calibration efforts, and potential of real-time application of the developed adaptive ignition timing prediction algorithm.
{"title":"Control-Oriented Model-Based Burn Duration and Ignition Timing Prediction With Recursive-Least-Square Adaptation for Closed-Loop Combustion Phasing Control of a Spark Ignition Engine","authors":"Xin Wang, Amir Khameneian, P. Dice, Bo Chen, M. Shahbakhti, J. Naber, Chad Archer, Qiuping Qu, C. Glugla, G. Huberts","doi":"10.1115/dscc2019-9073","DOIUrl":"https://doi.org/10.1115/dscc2019-9073","url":null,"abstract":"\u0000 In homogeneous spark-ignition (SI) engines, ignition timing is used to control the combustion phasing (crank angle of fifty percent of fuel burned, CA50), which affects fuel economy, engine torque output, and emissions. This paper presents a model-based adaptive ignition timing prediction strategy using a control-oriented dynamic combustion model for real-time closed-loop combustion phasing control. The combustion model predicts the burn duration from ignition timing to CA50 (ΔθIGN-CA50) at Intake Valve Closing (IVC) for the upcoming cycle based on current engine operating conditions, including variable valve timing, predicted ignition timing, air-fuel ratio, engine speed, and engine load. To maintain the accuracy of combustion model and ignition timing prediction during the engine lifetime, a Recursive-Least-Square (RLS) with Variable Forgetting Factor (VFF) based adaptation algorithm is developed to handle both short term (operating-point-dependent) and long term (engine aging) model errors. Due to short term model errors and stochastic characteristics of cycle-to-cycle combustion variations, large model errors may occur during severe transient operating conditions (tip-in/tip-out), which can result in wrong adjustments and excessive adaptations. Since on-road SI engines are always operating in transient conditions, the ‘Heavy Transient Detection’ algorithm is developed to avoid fault adaptation and assist the adaptation algorithm to be stable. On-road vehicle testing data is used to evaluate the performance of the entire model-based adaptive burn duration and ignition timing prediction algorithm. With only 64 calibration points, a mean ignition timing prediction error of 0.2 Crank Angle Degree (CAD) and average iteration number of 2 shows the capability of adaptive ignition timing prediction, a significant reduction of calibration efforts, and potential of real-time application of the developed adaptive ignition timing prediction algorithm.","PeriodicalId":41412,"journal":{"name":"Mechatronic Systems and Control","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2019-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81655422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sidharth Jangra, C. Chung, Qingzhi Lai, Xinfan Lin
Plug-in electric vehicle (PEV) is emerging as one of the most viable choices for the transportation sector to reduce fossil fuel consumption and CO2 emission. As the most critical component of PEV, battery systems require intensive management and diagnostics to ensure safety, efficiency, and endurance. Most existing studies focus on battery management when PEVs are under operation while none has explored battery maintenance during overnight parking, which accounts for a majority of the time (> 12 hours per day). Battery states during this period significantly affect the lifetime due to the side reactions that occur even when the battery is not in use. The process occurs at an accelerated rate when the battery energy level or temperature is too high or too low. In this paper, we propose to utilize an existing infrastructure available to PEV owners, the home charging unit, for intelligent battery maintenance during overnight parking. We will design an optimal charging profile that would charge the battery to a specific level while maintaining its states at optimal conditions to minimize degradation due to side reactions over the whole overnight period. Optimal charging profiles are created for different ambient temperatures and at different stages of battery life to investigate various scenarios. To demonstrate the effectiveness of the designed optimal charging profiles, the total capacity loss overnight is compared with those of three standard charging profiles.
{"title":"Optimal Maintenance of Electric Vehicle Battery System Through Overnight Home Charging","authors":"Sidharth Jangra, C. Chung, Qingzhi Lai, Xinfan Lin","doi":"10.1115/dscc2019-9004","DOIUrl":"https://doi.org/10.1115/dscc2019-9004","url":null,"abstract":"\u0000 Plug-in electric vehicle (PEV) is emerging as one of the most viable choices for the transportation sector to reduce fossil fuel consumption and CO2 emission. As the most critical component of PEV, battery systems require intensive management and diagnostics to ensure safety, efficiency, and endurance. Most existing studies focus on battery management when PEVs are under operation while none has explored battery maintenance during overnight parking, which accounts for a majority of the time (> 12 hours per day). Battery states during this period significantly affect the lifetime due to the side reactions that occur even when the battery is not in use. The process occurs at an accelerated rate when the battery energy level or temperature is too high or too low. In this paper, we propose to utilize an existing infrastructure available to PEV owners, the home charging unit, for intelligent battery maintenance during overnight parking. We will design an optimal charging profile that would charge the battery to a specific level while maintaining its states at optimal conditions to minimize degradation due to side reactions over the whole overnight period. Optimal charging profiles are created for different ambient temperatures and at different stages of battery life to investigate various scenarios. To demonstrate the effectiveness of the designed optimal charging profiles, the total capacity loss overnight is compared with those of three standard charging profiles.","PeriodicalId":41412,"journal":{"name":"Mechatronic Systems and Control","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2019-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80411478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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