Powertrain Control System Research Articles

Optimization of Power Control for Autonomous Hybrid Electric Vehicles with Flexible Power Demand

Abstract Technology advancement for on-road vehicles has gained significant momentum in the past decades, particularly in the field of vehicle automation and powertrain electrification. The optimization of powertrain controls for autonomous vehicles typically involves a separated consideration of the vehicle's external dynamics and powertrain dynamics, with one key aspect often overlooked. This aspect, known as flexible power demand, recognizes that the powertrain control system does not necessarily have to precisely match the power requested by the vehicle motion controller at all times. Leveraging this feature can lead to control designs achieving improved fuel economy by adding an extra degree of freedom to the powertrain control while maintaining safety and drive comfort. The present research investigates the use of an Approximate Dynamic Programming (ADP) approach to develop a powertrain controller, which takes into account the flexibility in power demand within the ADP framework. The concept of reachable sets is incorporated into the ADP framework to ensure safety, improve ride comfort, and enhance the accuracy of the optimization solution. The formulation is based on an autonomous hybrid electric vehicle (HEV), while the methodology can also be applied to other types of vehicles. It is also found that necessary customization of the ADP algorithm is needed for this particular control problem to prevent convergence issues. Finally, a case study is presented to evaluate the effectiveness of flexible power demand, as addressed by the ADP method. The experiment demonstrates a 14.1% improvement in fuel economy compared to a scenario without flexible power demand.

Systematic hyperparameter selection in Machine Learning-based engine control to minimize calibration effort

For automotive powertrain control systems, the calibration effort is exploding due to growing system complexity and increasingly strict legal requirements for greenhouse gas and real-world pollutant emissions. These powertrain systems are characterized by their highly dynamic operation, so transient performance is key. Currently applied control methods require tuning of an increasing number of look-up tables and of parameters in the applied models. Especially for transient control this state-of-the-art calibration process is unsystematic and requires a large development effort. Also, embedding models in a controller can set challenging requirements to production control hardware. In this work, we assess the potential of Machine Learning to dramatically reduce the calibration effort in transient air path control development. This is not only done for the existing benchmark controller, but also for a new preview controller. In order to efficiently realize preview, a strategy is proposed where the existing reference signal is shifted in time. These reference signals are then modeled as a function of engine torque demand using a Long Short-Term Memory (LSTM) neural network, which can capture the dynamic input–output relationship. A multi-objective optimization problem is defined to systematically select hyperparameters that optimize the trade-off between model accuracy, system performance, calibration effort and computational requirements. This problem is solved using an exhaustive search approach. The control system performance is validated over a transient driving cycle. For the LSTM-based controllers, the proposed calibration approach achieves a significant reduction of 71% in the control calibration effort compared to the benchmark process. The expert effort and turbocharger experiments used in calibrating transient compensation maps in physics-based feedforward controller are replaced by little simulation time and parametrization effort in ML-based controller, which requires significantly less expert effort and system knowledge compared to benchmark process. The best trade-off between multi-objective cost terms is achieved with one layer and 32 cells LSTM neural network for both non-preview and preview control. For non-preview control, a comparable control system performance is achieved with the LSTM-based controller, while 5% reduction in cumulative NOx emissions and similar fuel consumption is achieved with preview controller.

Adaptive policy learning for data-driven powertrain control with eco-driving

Modern powertrain control design practices rely on model-based approaches accompanied by costly calibrations in order to meet ever stringent energy use and emissions targets specified for standardized drive cycles. These practices struggle to capture the complexity and uncertainty of real-world driving. However, the deluge of operational data now available with connected vehicle technology presents opportunities to foster data-driven control design methods that adapt the powertrain control systems to their field use conditions. While the most attractive of these methods is reinforcement learning (RL), it is rarely directly applicable in physical applications due to its challenges of learning efficiency (sample complexity) and guaranteeing safety. In this paper, we propose and evaluate an adaptive policy learning (APL) framework that leverages existing source policies shipped with vehicles to accelerate initial learning while recovering the asymptotic performance of a reinforcement learning-based powertrain control agent trained from scratch. We present a critique of related residual policy learning approaches and detail our algorithmic implementations for two versions of the proposed framework. We find that the APL powertrain control agents offer in the order of 10% fuel economy improvement over a default powertrain controller of a commercial vehicle without compromising driver accommodation metrics. We demonstrate that the APL frameworks offer a viable approach towards potentially applying RL for real-world scenarios by addressing its learning efficiency issues.

A parametric study to improve first firing cycle emissions of a gasoline direct injection engine during cold start

A parametric study was carried out for the first firing cycle of a 4-cylinder, 2.0-liter, turbocharged gasoline direct injection (GDI) engine. The primary goal was to see how changes in the fuel injection parameters would affect the GDI engine combustion and emissions for the first four combustion events that constitute the first firing cycle. Experimental studies were carried out with a custom-designed powertrain control system to measure the HC emissions and pressure development for the first firing cycle. The quantitative experimental results were accompanied by simulations of the detailed temporal and spatial fuel concentration profiles using Converge CFD engine simulation software. An alternative calculation method was used to calculate the average combustion equivalence ratio for each of the four cylinders. This method showed that the majority of the cold start HC emissions during the first firing cycle was unburned gasoline and its possible decomposition products, which did not contribute significantly to the combustion and heat release. For the same amount of fuel injected into a cylinder, increased fuel rail pressure resulted in better evaporation and combustion, while slightly increasing the HC emissions during the cold start process. A multiple injection strategy was studied that split the fuel delivery between the intake stroke and the compression stroke with either one or two injections in each of those strokes (two or four injections total). The quadruple injection strategy led to better first cycle combustion, with higher engine IMEP and lower HC emissions. This resulted from a richer fuel mixture in the region near the spark plug due to better fuel evaporation and a better spatial fuel distribution. While increasing fuel rail pressure with either injection strategy failed to significantly lower the HC emissions given the same amount of injected fuel mass, higher rail pressure with the quadruple injection strategy resulted in higher IMEP for the same amount of injected fuel; this may provide the possibility to reduce the total fuel injection mass which may have benefits for both fuel consumption and emissions.

Co-Optimization Scheme for the Powertrain and Exhaust Emission Control System of Hybrid Electric Vehicles Using Future Speed Prediction

Hybrid electric vehicles (HEVs) have been an effective solution for improved vehicle fuel efficiency and reduced emission pollution. However, the optimization design for HEVs is complicated due to the presence of nonlinear dynamics and complicated integration of the HEV systems. Moreover, there is a trade-off between fuel optimization and emission reduction. In this paper, a co-optimization scheme is proposed to optimize fuel efficiency for HEVs. The proposed optimization scheme uses obtainable future speed prediction as the basis to optimally tune control parameters for the existing powertrain control system. Moreover, the ramp-up time of the catalyst temperature to reach its light-off level in the exhaust emission system is also considered as an additional optimization constraint to reduce emission. At first, the Toyota Prius Hybrid Simulink model which is an integrated model for a powertrain and exhaust emission system is validated using real data from a number of real driving cycle scenarios. Then, to simplify the formulation of the proposed algorithm, the model employed for the optimization for both powertrain and exhaust emission systems is represented by a set of equivalent neural network (NN) models, which are learned using the data generated from the well-validated Toyota Prius Hybrid Simulink model. Using the NN model, a co-optimization algorithm is established that provides an optimal tuning of some fuel-sensitive powertrain control parameters using future speed prediction, leading to a novel co-optimization algorithm, achieving on average a further 9.22% fuel savings for the Toyota Prius Hybrid Simulink model.

In-Vehicle Test Results for Advanced Propulsion and Vehicle System Controls Using Connected and Automated Vehicle Information

<div class="section abstract"><div class="htmlview paragraph">A key enabler to maximizing the benefits from advanced powertrain technologies is to adopt a systems integration approach and develop optimized controls that consider the propulsion system and vehicle as a whole. This approach becomes essential when incorporating Advanced Driver Assistance Systems (ADAS) and communication technologies, which can provide information on future driving conditions. This may enable the powertrain control system to further improve the vehicle performance and energy efficiency, shifting from an instantaneous optimization of energy consumption to a predictive and “look-ahead” optimization. Benefits from this approach can be realized at all levels of electrification, from conventional combustion engines to hybrid propulsion systems and full electric vehicles, and at all levels of vehicle automation.</div><div class="htmlview paragraph">This paper documents an extensive simulation and experimental campaign that provides a systematic quantification of fuel economy and mobility benefits resulting from leveraging Vehicle-to-Everything (V2X) connectivity, longitudinal (Level 1+) automation, and advanced cylinder deactivation to improve the propulsion system and vehicle efficiency. A Vehicle Dynamics and Powertrain (VD&PT) optimization framework, developed jointly by The Ohio State University and BorgWarner, utilizes advanced route information available from the navigation system and GPS, and Vehicle-to-Infrastructure (V2I) communication when available to minimize the cumulative fuel consumption over a driver-selected route.</div><div class="htmlview paragraph">To assess the benefits of these technologies in real-world conditions, a Monte Carlo simulation framework was developed to quantify the impact of Signal Phase and Timing (SPaT), variability in traffic conditions, and behavior of different drivers on the fuel consumption and vehicle travel time. Further, verification through in-vehicle testing was conducted by reconstructing a subset of these scenarios on a proving ground.</div></div>

Anti-Windup Recursive Least Squares Method for Adaptive Lookup Tables with Application to Automotive Powertrain Control Systems

Anti-Windup Recursive Least Squares Method for Adaptive Lookup Tables with Application to Automotive Powertrain Control Systems

Real-Time Model Predictive Powertrain Control for a Connected Plug-In Hybrid Electric Vehicle

The continued development of connected and automated vehicle technologies presents the opportunity to utilize these technologies for vehicle energy management. Leveraging this connectivity among vehicles and infrastructure allows a powertrain controller to be predictive and forward-looking. This paper presents a real-time predictive powertrain control strategy for a Plug-in Hybrid Electric Vehicle (PHEV) in a connected vehicle environment. This work focuses on the optimal energy management of a multi-mode PHEV based on predicted future velocity, power demand, and road conditions. The powertrain control system in the vehicle utilizes vehicle connectivity to a cloud-based server in order to obtain future driving conditions. For predictive powertrain control, a Nonlinear Model Predictive Controller (NMPC) is developed to make torque-split decisions within each operating mode of the vehicle. The torque-split among two electric machines and one combustion engine is determined such that fuel consumption is minimized while battery SOC and vehicle velocity targets are met. The controller has been extensively tested in simulation across multiple real-world driving cycles where energy savings in the range of 1 to 4% have been demonstrated. The developed controller has also been deployed and tested in real-time on a test vehicle equipped with a rapid prototyping embedded controller. Real-time in-vehicle testing confirmed the energy savings observed in simulation and demonstrated the ability of the developed controller to be effective in a real-time environment.

Optimal Energy Speed Profile of Medium-Speed Maglev Trains Integrating the Power Supply System and Train Control System

As a new type of transportation serving the suburban passengers, the medium-speed maglev (MSM) is gradually becoming the focus of scholars. This paper addressess the speed profile optimization problem for MSM train operations by integrating the power supply system and train control system under various constraints. Unlike the model for optimizing the mechanical energy of the train, this approach aims at the lowest energy consumption of the substation for the MSM system. First, a speed profile optimization model for the MSM train is built by combining the mathematical control model of the long stator synchronous linear motor and dynamic equation of train, in which the stator current is the control variable. Next, a dedicated dynamic programming approach is proposed to solve the optimization model. The results of the numerical experiments show that the proposed model outperforms the model that only considers the train mechanical energy, and the energy consumption is reduced by 10.3% and 6.5% in the two case studies, respectively. Furthermore, the relationship between energy consumption and travel time is analyzed to reflect the optimal results of the proposed model limited to different fixed travel time.

An Edge Computing Framework for Powertrain Control System Optimization of Intelligent and Connected Vehicles Based on Curiosity-Driven Deep Reinforcement Learning

For the ongoing revolution in developing intelligent and connected vehicles (ICVs), there is a lack of research for powertrain control systems using the latest artificial intelligence and vehicle-to-everything technology that have already been widely adopted in the autonomous driving systems. In this context, recent development of deep reinforcement learning (DRL) and one of the latest computing frameworks are coupled to facilitate an onboard-based intelligent powertrain control. Taking the boost control of a diesel engine equipped with variable geometry turbocharger as an example, the results show that the final control behavior indicated by the cumulated rewards is improved by 50.43% and the learning efficiency is improved by 74.29% for the proposed curiosity-driven DRL algorithm, compared with the same structure DRL algorithm with classic random exploration policy. In addition, unlike most of the DRL-based powertrain optimization algorithms, which have only been applied to single-machine architecture, this work manages the proposed DRL algorithm in parallel and, more importantly, from an edge computing perspective. This, in addition to greatly speeding up the algorithm training, can also realize a good balance of control accuracy and generality depending upon the selected training scenario. Moreover, unlike most of the cloud computing frameworks, which require low network latency, the proposed architecture can achieve a similar final control performance even if good network communication is not allowed. Compared with other existing powertrain control methods, the proposed algorithm is able to approximate a global powertrain control optimization autonomously in a connected manner, making it attractive to current ICVs with advanced automated driving and traditional powertrain control.

Robust Oscillation Suppression Control of Electrified Powertrain System Considering Mechanical-Electric-Network Effects

This paper works on the oscillation of electrified powertrain system in an integrated manner where combined mechanical-electric-network effects are all taking into consideration, and a robust oscillation controller is proposed to suppress the effects-caused oscillation and to maintain the system stability. An integrated model is developed in which driving motor, drivetrain and communication network are all included. Thus, torque ripples in the driving motor, nonlinear gear backlash as well as driveshaft flexibility in the drivetrain and network-induced delays that may cause powertrain system oscillation can be all considered. In order to dealing with the coupling effects of network-induced delays and event-driven manner of the controller nodes, a delay-free discrete model is further built via polytopic inclusion approach and system augmentation technique. An energy-to-peak performance based robust controller is proposed to ensure the torsional oscillation damping as well as vehicle speed tracking performance. During backlash mode, as the driving motor and the load are decoupled, a sliding mode compensator is further adopted to restrain the torsional oscillation. The stability of electrified powertrain control system is ensured by using Lyapunov theory, and the controller gain is obtained by solving a set of linear matrix inequalities (LMIs). Comparative simulation tests are carried out by using Matlab/Simulink in which a delicate controller area network (CAN) model is developed via SimEvent. The combined mechanical-electric-networked effects on torsional oscillation are demonstrated during the simulation tests, while the performance of the proposed controller is well verified.

Design of a test rig in support of e-clutch dynamics characterisation, modelling, and control

E-clutch systems (also known as clutch-by-wire systems) have emerged recently as solutions that allow the powertrain control system to operate the clutch, while the driving procedures remain the same as with the regular manual transmissions (MTs). These systems offer possibilities of improved fuel economy and driving comfort over MT systems, while the driver maintains the existing driving habits. This paper presents the design of a test rig developed for a dry e-clutch equipped with a hydrostatic actuator mechanism driven by a spindle-based electric servomotor. The primary purpose of the test rig is to provide a basis for the experimental characterisation of both static and dynamic behaviours of the e-clutch, as well as to facilitate e-clutch model validation and control strategy verification. The use of the test rig is illustrated by presenting recordings of basic e-clutch static curves and time responses. These recordings are employed to validate axial dynamics and thermal dynamics models of an e-clutch, which are presented in the paper, as well.

Statistical Verification of Hyperproperties for Cyber-Physical Systems

Many important properties of cyber-physical systems (CPS) are defined upon the relationship between multiple executions simultaneously in continuous time. Examples include probabilistic fairness and sensitivity to modeling errors (i.e., parameters changes) for real-valued signals. These requirements can only be specified by hyperproperties . In this article, we focus on verifying probabilistic hyperproperties for CPS. To cover a wide range of modeling formalisms, we first propose a general model of probabilistic uncertain systems (PUSs) that unify commonly studied CPS models such as continuous-time Markov chains (CTMCs) and probabilistically parametrized Hybrid I/O Automata (P 2 HIOA). To formally specify hyperproperties, we propose a new temporal logic, hyper probabilistic signal temporal logic (HyperPSTL) that serves as a hyper and probabilistic version of the conventional signal temporal logic (STL). Considering the complexity of real-world systems that can be captured as PUSs, we adopt a statistical model checking (SMC) approach for their verification. We develop a new SMC technique based on the direct computation of significance levels of statistical assertions for HyperPSTL specifications, which requires no a priori knowledge on the indifference margin. Then, we introduce SMC algorithms for HyperPSTL specifications on the joint probabilistic distribution of multiple paths, as well as specifications with nested probabilistic operators quantifying different paths, which cannot be handled by existing SMC algorithms. Finally, we show the effectiveness of our SMC algorithms on CPS benchmarks with varying levels of complexity, including the Toyota Powertrain Control System.

An adaptive intelligent system to minimize energy use for a parallel hybrid electric vehicle

This paper elaborates on the synthesis of a neuro-fuzzy controller and also its application to hybrid vehicle powertrain control system. This technique builds a fuzzy inference system for which the parameters can be updated to achieve a preferred input–output mapping. The procedure of learning (guidelines on the parameter updates) is essentially composed of the gradient descent algorithm as well as the least square estimate method. The hybrid vehicle used in this study is the parallel hybrid vehicle. With its small engine, this type of hybrid vehicle is made for steady highway driving. In addition, it composes an electrical device that provides acceleration and deceleration along with a battery management system. A fuzzy logic inference system is used for energy administration. In which, the inference system states the guidelines on recording the nonlinearities in the mapping. The neuro-fuzzy controller is able to accomplish the task of an inverse model with an adaptive fuzzy logic controller. This paper will draw upon the growth and advancement in SOC estimation method and power management system of lithium-ion battery for future high-tech EV applications. It is shown that the intelligent management system proposed in this paper has a significant improve in performance to that of the other existing systems.

Analysis of New Energy Vehicle Dynamic System

As for the new energy vehicle, how to solve its core power problem is the bottleneck of research. In this paper, several power driving modes of new energy automobile engine are introduced in order to better apply to the actual road conditions. Motor drive system as an important part of the hybrid vehicles is an important object of study. Based on the hybrid electric vehicle power train control system for permanent magnet synchronous motor as the object of study. Can be applied to hybrid car compares the characteristics of traction motors, chose permanent magnet synchronous Motors as drive motors for hybrid vehicles. Building applications in hybrid cars in MATLAB/Simulink simulation model of permanent-magnet synchronous motor speed control system and analysis of simulation results.

Operational Strategy of Hybrid Heavy-Duty Trucks by Utilizing a Genetic Algorithm to Optimize the Fuel Economy Multiobjective Criteria

An optimum operating strategy is needed for early amortization of expensive hybrid electric vehicle powertrain parts. Especially in the operation of long-haul trucks, fuel costs have a large impact on the total cost of ownership, which is the key entrepreneurial figure in the transportation business. Combined with route information, a predictive powertrain control (PPC) system increases the fuel-saving. In a MATLAB/Simulink model-based generic approach, the operating strategy and the PPC are optimized using a genetic algorithm. The tradeoff between minimizing the fuel consumption and simultaneously maximizing the vehicle speed and gradeability to decrease time-related fixed costs has to be solved. This leads to a multi-optimization problem. The operating strategy is developed for a parallel hybrid topology that includes the fuel-saving functions of regenerative braking, boosting, shifting the load point, and electric drive only operation. The methodology developed answers the search for an optimum control parameter setup combining the operational strategy and the PPC system in long-haul operations. This paper describes the model building, simulation, and optimization of a rule-based control strategy. The route profile and fuel consumption of an internal combustion engine long haul truck were measured in a real-life test run. The recorded data are used for model building and to validate the simulation tool. With an optimized parameter setup, fuel-saving of up to 11% were achieved in theory with acceptable vehicle speed and gradeability.

Ecological Driving System for Connected/Automated Vehicles Using a Two-Stage Control Hierarchy

To improve a vehicle's fuel efficiency when operating on roadways, this study develops an ecological driving system under the connected and automated vehicle (CAV) environment. The system includes three critical functions, including traffic state prediction, eco-driving speed control, and powertrain control implementation. According to the real-time traffic information obtained from vehicle-to-infrastructure and vehicle-to-vehicle communications, the embedded traffic state prediction model will estimate and predict the average speeds and densities of freeway subsections. With an objective of minimizing the fuel consumption, the eco-driving speed control function follows a two-stage hierarchical framework. The first stage, which is executed at the global level, aims to optimize the travel speed profile of the CAV over a certain time period. The second stage, local speed adaption, is designed to dynamically adjust the CAV's speed and make lane-changing decisions based on the local driving condition. The resulting control parameters will then be forwarded to the powertrain control system for implementations. To evaluate the proposed system, this study performs comprehensive numerical tests by using simulation models. This results confirm the effectiveness of the proposed system in reducing fuel consumption. Further comparisons with different models highlights the need to consider traffic state information in the first-stage optimization and lane-changing decision module in the local adaption function.

Teaching Reforms in Automotive Power train and Chassis Control System Experiment

The Automotive Power train and Chassis Control System Experiment is a supporting course for Automotive Power train Control System and Chassis Control System. This paper discusses the choice of experimental materials and how to vividly explain theoretical knowledge and conduct experiments.

Model Based Calibration

This article describes calibration of control systems for downsized boosted engines. Model-based design of powertrain and aftertreatment control systems includes plant modeling and controller synthesis. The article illustrates the typical work flow of the model-based calibration process for an engine. In the process, the first step is the design of experiment. The experiment design should cover a multisurface space. That guides the next step to collect data from an engine or powertrain at critical operating points. Once the data are collected, an engine system model is built along with its designed controller models, then the operation of the control systems and controller parameters are optimized or calibrated based on the plant models. With these initial values of the calibrations handy, one can either download the calibration into the production ECU or use a rapid prototyping controller to conduct a full validation or final fine tuning of the engine powertrain control system in test cells or on vehicles.

Numerically-aided Deductive Safety Proof for a Powertrain Control System

The use of deductive techniques, such as theorem provers, has several advantages in safety verification of hybrid systems. There is often a gap, however, between the type of assistance that a theorem prover requires to make progress on a proof task and the assistance that a system designer is able to provide. To address this deficiency we present an extension to the deductive verification framework of differential dynamic logic that allows the theorem prover KeYmaera to locally reason about behaviors by leveraging forward invariant sets provided by external methods, such as numerical techniques and designer insights. Our key contribution is a new inference rule, the forward invariant cut rule, introduced into the proof calculus of KeYmaera. We demonstrate the cut rule in action on an example involving an automotive powertrain control systems, in which we make use of a simulation-driven numerical technique to compute a local barrier function.