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This work presents the development, validation and use of a SIMULINK integrated vehicle system simulation composed of engine, driveline and vehicle dynamics modules. The engine model links the appropriate number of single-cylinder modules, featuring thermodynamic models of the in-cylinder processes with transient capabilities to ensure high fidelity predictions. A detailed fuel injection control module is also included. The engine is coupled to the driveline, which consists of the torque converter, transmission, differential and prop shaft and drive shafts. An enhanced version of the point mass model is used to account for vehicle dynamics in the longitudinal and heave directions. A vehicle speed controller replaces the operator and allows the feed-forward simulation to follow a prescribed vehicle speed schedule.

The number of control actuators available on spark-ignition engines is rapidly increasing to meet demand for improved fuel economy and reduced exhaust emissions. The added complexity greatly complicates control strategy development because there can be a wide range of potential actuator settings at each engine operating condition, and map-based actuator calibration becomes challenging as the number of control degrees of freedom expand significantly. Many engine actuators, such as variable valve actuation and flow control valves, directly influence in-cylinder combustion through changes in gas exchange, mixture preparation, and charge motion. The addition of these types of actuators makes it difficult to predict the influences of individual actuator positioning on in-cylinder combustion without substantial experimental complexity.

This study investigates the impact of transient engine operation on the emissions formed during a tip-in procedure. A medium-duty production V-8 diesel engine is used to conduct experiments in which the rate of pedal position change is varied. Highly-dynamic emissions instrumentation is implemented to provide real-time measurement of NOx and particulate. Engine subsystems are analyzed to understand their role in emissions formation. As the rate of pedal position change increases, the emissions of NOx and particulates are affected dramatically. An instantaneous load increase was found to produce peak NOx values 1.8 times higher and peak particulate concentrations an order of magnitude above levels corresponding to a five-second ramp-up. The results provide insight into relationship between driver aggressiveness and diesel emissions applicable to development of drive-by-wire systems. In addition, they provide direct guidance for devising low-emission strategies for hybrid vehicles.

A comprehensive quasi-dimensional computer simulation of the spark-ignition (SI) engine was used to explore part-load, fuel economy benefits of the Variable Stroke Engine (VSE) compared to the conventional throttled engine. First it was shown that varying stroke can replace conventional throttling to control engine load, without changing the engine characteristics. Subsequently, the effects of varying stroke on turbulence, burn rate, heat transfer, and pumping and friction losses were revealed. Finally these relationships were used to explain the behavior of the VSE as stroke is reduced. Under part load operation, it was shown that the VSE concept can improve brake specific fuel consumption by 18% to 21% for speeds ranging from 1500 to 3000 rpm. Further, at part load, NOx was reduced by up to 33%. Overall, this study provides insight into changes in processes within and outside the combustion chamber that cause the benefits and limitations of the VSE concept.

This work studies the complex interactions resulting from the application and control of Exhaust Gas Recirculation (EGR) on a production heavy-duty diesel engine system, and its effectiveness in reducing NOx emissions. The coupling between EGR, the Variable Geometry Turbocharger (VGT) and the EGR cooler critically affects boost pressure, air/fuel ratio (A/F), combustion efficiency and pumping work. It is shown that EGR provides an effective means for reducing flame temperatures and NOx emissions, particularly under low A/F ratio conditions. However, engine thermal efficiency tends to decrease with EGR as a result of decreasing indicated work and increasing pumping work. Combustion deterioration is predominant at higher load, low speed and low boost conditions, due to a significant decrease of A/F ratio with increasing EGR.

Drive cycles are a core piece of vehicle development testing methodology. The control and calibration of the vehicle is often tuned over drive cycles as they are the best representation of the real-world driving the vehicle will see during deployment. To obtain general performance numerous drive cycles must be generated to ensure final control and calibration avoids overfitting to the specifics of a single drive cycle. When real-world driving cycles are difficult to acquire methods can be used to create statistically similar synthetic drive cycles to avoid the overfitting problem. This subject has been well addressed within the passenger vehicle domain but must be expanded upon for utilization with tracked off-road vehicles. Development of hybrid tracked vehicles has increased this need further. This study shows that turning dynamics have significant influence on the vehicle power demand and on the power demand on each individual track.

The global energy situation, the dependence of the transportation sector on fossil fuels, and a need for rapid response to the global warming challenge, provide a strong impetus for development of fuel efficient vehicle propulsion. The task is particularly challenging in the case of trucks due to severe weight/size constraints. Hybridization is the only approach offering significant breakthroughs in near and mid-term. In particular, the series configuration decouples the engine from the wheels and allows full flexibility in controlling the engine operation, while the hydraulic energy conversion and storage provides exceptional power density and efficiency. The challenge stems from a relatively low energy density of the hydraulic accumulator, and this provides part of the motivation for a simulation-based approach to development of the system power management. The vehicle is based on the HMMWV platform, a 4×4 off-road truck weighing 5112 kg.

This paper proposes a simulation based methodology to assess plug-in hybrid vehicle (PHEV) behavior over 24-hour periods. Several representative 24-hour missions comprise naturalistic cycle data and information about vehicle resting time. The data were acquired during Filed Operational Tests (FOT) of a fleet of passenger vehicles carried out by the University of Michigan Transportation Research Institute (UMTRI) for safety research. Then, PHEV behavior is investigated using a simulation with two different charging scenarios: (1) Charging overnight; (2) Charging whenever possible. Charging/discharging patterns of the battery as well as trends of charge depleting (CD) and charge sustaining (CS) modes at each scenario were assessed. Series PHEV simulation is generated using Powertrain System Analysis Toolkit (PSAT) developed by Argonne National Laboratory (ANL) and in-house Matlab codes.

The global energy situation, the dependence of the transportation sector on fossil fuels, and a need for rapid response to the global warming challenge, provide a strong impetus for development of fuel efficient vehicle propulsion. The task is particularly challenging in the case of trucks due to severe weight/size constraints. Hybridization is the only approach offering significant breakthroughs in near and mid-term. In particular, the series configuration decouples the engine from the wheels and allows full flexibility in controlling the engine operation, while the hydraulic energy conversion and storage provides exceptional power density and efficiency. The challenge stems from a relatively low energy density of the hydraulic accumulator, and this provides part of the motivation for a simulation-based approach to development of the system power management. The vehicle is a 4×4 truck weighing 5112 kg and intended for both on- and off-road use.

A supervisory controller strategy for a hybrid vehicle coordinates the operation of the two power sources onboard of a vehicle to maximize objectives like fuel economy. In the past, various control strategies have been developed using heuristics as well as optimal control theory. The Stochastic Dynamic Programming (SDP) has been previously applied to determine implementable optimal control policies for discrete time dynamic systems whose states evolve according to given transition probabilities. However, the approach is constrained by the curse of dimensionality, i.e. an exponential increase in computational effort with increase in system state space, faced by dynamic programming based algorithms. This paper proposes a novel approach capable of overcoming the curse of dimensionality and solving policy optimization for a system with very large design state space.

Impact of driving patterns on fuel economy is significant in hybrid electric vehicles (HEVs). Driving patterns affect propulsion and braking power requirement of vehicles, and they play an essential role in HEV design and control optimization. Driving pattern conscious adaptive strategy can lead to further fuel economy improvement under real-world driving. This paper proposes a real-time driving pattern recognition algorithm for supervisory control under real-world conditions. The proposed algorithm uses reference real-world driving patterns parameterized from a set of representative driving cycles. The reference cycle set consists of five synthetic representative cycles following the real-world driving distance distribution in the US Midwestern region. Then, statistical approaches are used to develop pattern recognition algorithm. Driving patterns are characterized with four parameters evaluated from the driving cycle velocity profiles.

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.

A quasi-dimensional computer simulation of the turbocharged spark-ignition engine has been developed in order to study system performance as various design parameters and operating conditions are varied. The simulation is of the “filling and emptying” type. Quasi-steady flow models of the compressor, intercooler, manifolds, turbine, wastegate, and ducting are coupled with a multi-cylinder engine model where each cylinder undergoes the same thermodynamic cycle. A turbulent entrainment model of the combustion process is used, thus allowing for studies of the effects of various combustion chamber shapes and turbulence parameters on cylinder pressure, temperature, NOx emissions and overall engine performance. Valve open areas are determined either based on user supplied valve lift data or using polydyne-generated cam profiles which allow for variable valve timing studies.

This paper presents a new way to evaluate vehicle speed profile aggressiveness, quantify it from the perspective of the rapid speed fluctuations, and assess its impact on vehicle fuel economy. The speed fluctuation can be divided into two portions: the large-scale low frequency speed trace which follows the ongoing traffic and road characteristics, and the small-scale rapid speed fluctuations normally related to the driver's experience, style and ability to anticipate future events. The latter represent to some extent the driver aggressiveness and it is well known to affect the vehicle energy consumption and component duty cycles. Therefore, the rapid speed fluctuations are the focus of this paper. Driving data collected with the GPS devices are widely adopted for study of real-world fuel economy, or the impact on electrified vehicle range and component duty cycles.

This paper presents a low-cost path for extending the range of small urban pure electric vehicles by hydraulic hybridization. Energy management strategies are investigated to improve the electric range, component efficiencies, as well as battery usable capacity. As a starting point, a rule-based control strategy is derived by analysis of synergistic effects of lead-acid batteries, high efficient operating region of DC motor and the hydraulic pump/motor. Then, Dynamic Programming (DP) is used as a benchmark to find the optimal control trajectories for DC motor and Hydraulic Pump/Motor. Implementable rules are derived by studying the optimal control trajectories from DP. With new improved rules implemented, simulation results show electric range improvement due to increased battery usable capacity and higher average DC motor operating efficiency.

This paper develops a methodology to optimize the supervisory controller for a heavy-duty series hybrid electric vehicle, with consideration of battery aging and cooling loss. Electrochemistrybased battery aging model is integrated into vehicle model. The side reaction, reductive electrolyte decomposition, is modeled to determine battery aging rate, and the thermal effect on this reaction rate is considered by Arrhenius Law. The resulting capacity and power fading is included in the system-level study. Sensitivity analysis shows that battery aging could cause fuel economy loss by 5.9%, and increasing temperature could improve fuel economy at any given state-of-health, while accelerating battery aging. Stochastic dynamic programming algorithm is applied to a modeled system to handle the tradeoff between two objectives: maximizing fuel economy and minimizing battery aging.

This paper proposes a methodology to develop a nonlinear model predictive control (NMPC) of a dual-independent variable valve timing (di-VVT) engine using discretized nonlinear engine models. In multiple-input-multiple-output (MIMO) systems, model based control methodologies are critical for realizing the full potential of complex hardware. Fast and accurate control oriented models (COM) that capture combustion physics, actuator and system dynamics are prerequisites for developing NMPC. We propose a multi-scale simulation approach to generate the non-linear combustion model, where the high-fidelity engine cycle simulation is utilized to characterize effects of turbulence, air-to-fuel ratio, residual fraction, and nitrogen oxide (NOx) emissions. The input-to-output relations are subsequently captured with artificial neural networks (ANNs). Manifold and actuator dynamics are discretized to reduce computation efforts.

Commercial vehicles transport the majority of the inland freight in US and a significant number of passengers. They are large fuel consumers as they operate a large number of hours per day, pulling heavy loads. The increasing fuel price and the Green House Gas emission regulation have provided a strong impetus for new technologies capable of improving the commercial vehicle fuel economy. Among others, optimized powertrain control can improve the vehicle fuel economy, particularly if it is based on accurate information about the instantaneous load demand. Furthermore, model-based online vehicle parameter estimator is critical for implementation of an adaptive vehicle controller. While vehicle mass estimation has been successfully demonstrated, rolling resistance and aerodynamic drag estimation has not been fully explored yet. This paper examines this problem using model-based approach with a supervisory data extraction scheme.

An integrated vehicle system simulation has been developed to take advantage of advances in physical process and component models, flexibility of graphical programming environments (such as MATLAB-SIMULINK), and ever increasing capabilities of engineering workstations. A comprehensive, transient model of the multi-cylinder engine is linked with models of the torque converter, transmission, transfer case and differentials. The engine model is based on linking the appropriate number of single-cylinder modules, with the latter being thermodynamic models of the in-cylinder processes with built-in physical sub-models and transient capabilities to ensure high fidelity predictions. Either point mass or multi-body vehicle dynamics models can be coupled with the powertrain module to produce the ground vehicle simulation.

A hybrid electric vehicle simulation tool (HE-VESIM) has been developed at the Automotive Research Center of the University of Michigan to study the fuel economy potential of hybrid military/civilian trucks. In this paper, the fundamental architecture of the feed-forward parallel hybrid-electric vehicle system is described, together with dynamic equations and basic features of sub-system modules. Two vehicle-level power management control algorithms are assessed, a rule-based algorithm, which mainly explores engine efficiency in an intuitive manner, and a dynamic-programming optimization algorithm. Simulation results over the urban driving cycle demonstrate the potential of the selected hybrid system to significantly improve vehicle fuel economy, the improvement being greater when the dynamic-programming power management algorithm is applied.