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Most studies on supervisory controllers of hybrid electric vehicles consider only fuel economy in the objective function. Taking into consideration the importance of the energy storage system health and its impact on the vehicle’s functionality, cost, and warranty, recent studies have included battery degradation as the second objective function by proposing different energy management strategies and battery life estimation methods. In this paper, a rule-based supervisory controller is proposed that splits the torque demand based not only on fuel consumption, but also on the battery capacity fade using the concept of severity factor. For this aim, the severity factor is calculated at each time step of a driving cycle using a look-up table with three different inputs including c-rate, working temperature, and state of charge of the battery. The capacity loss of the battery is then calculated using a semi-empirical capacity fade model.

The components in a hybrid electric vehicle (HEV) powertrain include the battery pack, an internal combustion engine, and the electric machines such as motors and possibly a generator. These components generate a considerable amount of heat during driving cycles. A robust thermal management system with advanced controller, designed for temperature tracking, is required for vehicle safety and energy efficiency. In this study, a hybridized mid-size truck for military application is investigated. The paper examines the integration of advanced control algorithms to the cooling system featuring an electric-mechanical compressor, coolant pump and radiator fans. Mathematical models are developed to numerically describe the thermal behavior of these powertrain elements. A series of controllers are designed to effectively manage the battery pack, electric motors, and the internal combustion engine temperatures.

The Organic Rankine Cycle (ORC) has proven to be a promising technology for Waste Heat Recovery (WHR) systems in heavy duty diesel engine applications. However, due to the highly transient heat source, controlling the working fluid flow through the ORC system is a challenge for real time application. With advanced knowledge of the heat source dynamics, there is potential to enhance power optimization from the WHR system through predictive optimal control. This paper proposes a look-ahead control strategy to explore the potential of increased power recovery from a simulated WHR system. In the look-ahead control, the future vehicle speed is predicted utilizing road topography and V2V connectivity. The forecasted vehicle speed is utilized to predict the engine speed and torque, which facilitates estimation of the engine exhaust conditions used in the ORC control model.

Exhaust gas rebreathing is considered to be a practical enabler that could be used in HCCI production engines. Recent experimental work at the University of Michigan demonstrates that the combustion characteristics of an HCCI engine using large amounts of hot residual gas by rebreathing are very sensitive to engine thermal conditions. This computational study addresses HCCI engine operation with rebreathing, with emphasis on the effects of engine thermal conditions during transient periods. A 1-D cycle simulation with thermal networks is carried out under load and speed transitions. A knock integral auto-ignition model, a modified Woschni heat transfer model for HCCI engines and empirical correlations to define burn rate and combustion efficiency are incorporated into the engine cycle simulation model. The simulation results show very different engine behavior during the thermal transient periods compared with steady state.

Thermal barrier coatings (TBCs) have been of interest since the 1970s for application in internal combustion (IC) engines. Thin TBCs exhibit a temperature swing phenomenon wherein wall temperatures dynamically respond to the transient working-gas temperature throughout the engine cycle, thus reducing the temperature difference driving the heat transfer. Determining these varying wall temperatures is necessary to evaluate and study the effect of coatings on wall heat transfer. This study focuses on developing a 3D computational fluid dynamics (CFD)-finite element analysis (FEA) coupled simulation, or co-simulation, routine to determine the wall temperatures of a piston coated with a thin TBC layer subject to spark ignition combustion heat flux. A CONVERGE 3D-CFD model was used to simulate the combustion process in a single-cylinder, light-duty experimental spark ignition (SI) engine.

This paper investigates series plug-in hybrid electric vehicle (PHEV) behavior during one-day with synthesized representative one-day missions. The amounts of electric energy and fuel consumption are predicted to assess the PHEV impact on the grid with respect to the driving distance and different charging scenarios: (1) charging overnight, (2) charging whenever possible. The representative cycles are synthesized using the extracted information from the real-world driving data in Southeast Michigan gathered through the Field Operational Tests (FOT) conducted by the University of Michigan Transportation Research Institute (UMTRI). The real-world driving data include 4,409 trips covering 830 independent days and temporal distributions of departure and arrival times. The sample size is large enough to represent real-world driving.

This paper presents the development of an electrochemical aging model of LiFePO4-Graphite battery based on single particle (SP) model. Solid electrolyte interphase (SEI) growth is considered as the aging mechanism. It is intended to provide both sufficient fidelity and computational efficiency required for integration within the HEV power management optimization framework. The model enables assessment of the battery aging rate by considering instantaneous lithium ion surface concentration rather than average concentration, thus enhancing the fidelity of predictions. In addition, an approximate analytical method is applied to speed up the calculation while preserving required accuracy. Next, this aging model are illustrated two applications. First is hybrid electric powertrain system model integration and simulation.

The thermal conditions of an engine structure, in particular the wall temperatures, have been shown to have a great effect on the HCCI engine combustion timing and burn rates through wall heat transfer, especially during transient operations. This study addresses the effects of thermal inertia on combustion in an HCCI engine. In this study, the control of combustion timing in an HCCI engine is achieved by modulating the residual gas fraction (RGF) while considering the wall temperatures. A multi-cylinder engine simulation with detailed geometry is carried out using a 1-D system model (GT-Power®) that is linked with Simulink®. The model includes a finite element wall temperature solver and is enhanced with original HCCI combustion and heat transfer models. Initially, the required residual gas fraction for optimal BSFC is determined for steady-state operation. The model is then used to derive a map of the sensitivity of optimal residual gas fraction to wall temperature excursions.

This paper proposes a framework to perform design optimization of a series PHEV and investigates the impact of using real-world driving inputs on final design. Real-World driving is characterized from a database of naturalistic driving generated in Field Operational Tests. The procedure utilizes Markov chains to generate synthetic drive cycles representative of real-world driving. Subsequently, PHEV optimization is performed in two steps. First the optimal battery and motor sizes to most efficiently achieve a desired All Electric Range (AER) are determined. A synthetic cycle representative of driving over a given range is used for function evaluations. Then, the optimal engine size is obtained by considering fuel economy in the charge sustaining (CS) mode. The higher power/energy demands of real-world cycles lead to PHEV designs with substantially larger batteries and engines than those developed using repetitions of the federal urban cycle (UDDS).

Variable geometry turbines (VGT) are of particular interest to advanced diesel powertrains for future conventional trucks, since they can dramatically improve system transient response to sudden changes in speed and load, characteristic of automotive applications. VGT systems are also viewed as the key enabler for the application of the EGR system for reduction of heavy-duty diesel emissions. This paper applies an artificial neural network methodology to VGT modeling in order to enable representation of the VGT characteristics for any blade (nozzle) position. Following validation of the ANN model of the baseline, fixed geometry turbine, the VGT model is integrated with the diesel engine system. The latter is linked to the driveline and the vehicle dynamics module to form a complete, high-fidelity vehicle simulation.

Opposed-piston 2-stroke (OP-2S) engines have the potential to achieve higher thermal efficiency than a typical diesel engine. However, the uniflow scavenging process is difficult to control over a wide range of speeds and loads. Scavenging performance is highly sensitive to pressure dynamics, port timings, and port design. This study proposes an analysis of the effects of port vane angle on the scavenging performance of an opposed-piston 2-stroke engine via simulation. A CFD model of a three-cylinder opposed-piston 2-stroke was developed and validated against experimental data collected by Achates Power Inc. One of the three cylinders was then isolated in a new model and simulated using cycle-averaged and cylinder-averaged initial/boundary conditions. This isolated cylinder model was used to efficiently sweep port angles from 12 degrees to 29 degrees at different pressure ratios.

Energy security and climate change challenges provide a strong impetus for investigating Electric Vehicle (EV) concepts. EVs link two major infrastructures, the transportation and the electric power grid. This provides a chance to bring other sources of energy into transportation, displace petroleum and, with the right mix of power generation sources, reduce CO₂ emissions. The main obstacles for introducing a large numbers of EVs are cost, battery weight, and vehicle range. Battery health is also a factor, both directly and indirectly, by introducing limits on depth of discharge. This paper considers a low-cost path for extending the range of a small urban EV by integrating a parallel hydraulic system for harvesting and reusing braking energy. The idea behind the concept is to avoid replacement of lead-acid or small Li-Ion batteries with a very expensive Li-Ion pack, and instead use a low-cost hydraulic system to achieve comparable range improvements.

A naturally-aspirated, Miller cycle, Spark-Ignition (SI) engine that controls output with variable intake valve closure is compared to a conventionally-throttled engine using computer simulation. Based on First and Second Law analyses, the two load control strategies are compared in detail through one thermodynamic cycle at light load conditions and over a wide range of loads at 2000 rpm. The Miller Cycle engine can use late intake valve closure (LIVC) to control indicated output down to 35% of the maximum, but requires supplemental throttling at lighter loads. The First Law analysis shows that the Miller cycle increases indicated thermal efficiency at light loads by as much as 6.3%, primarily due to reductions in pumping and compression work while heat transfer losses are comparable.

Electrification and hybridization have great potential for improving fuel economy and reducing visual signature or soot emissions in military vehicles. Specific challenges related to military applications include severe duty cycles, large and uncertain energy flows through the system and high thermal loads. A novel supervisory control strategy is proposed to simultaneously mitigate severe engine transients and to reduce high electric current in the battery without oversizing the battery. The described objectives are accomplished by splitting the propulsion power demand through filtering in the frequency domain. The engine covers only low frequency power demand profile while the battery covers high frequency components. In the proposed strategy, the separation filter is systematically designed to identify different frequency components with the consideration of fuel consumption, aggressive engine transients, and battery electric loads.

Electrification and hybridization show great potential for improving fuel economy and reducing emission in heavy-duty vehicles. However, high battery cost is unavoidable due to the requirement for large batteries capable of providing high electric power for propulsion. The battery size and cost can be reduced with advanced battery control strategies ensuring safe and robust operation covering infrequent extreme conditions. In this paper, the impact of such a battery control strategy on battery sizing and fuel economy is investigated under various military and heavy-duty driving cycles. The control strategy uses estimated Li-ion concentration information in the electrodes to prevent battery over-charging and over-discharging under aggressive driving conditions. Excessive battery operation is moderated by adjusting allowable battery power limits through the feedback of electrode-averaged Li-ion concentration estimated by an extended Kalman filter (EKF).

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.

A series hydraulic hybrid concept (SHHV) has been explored as a potential pathway to an ultra-efficient city vehicle. Intended markets would be congested metropolitan areas, particularly in developing countries. The target fuel economy was ~100 mpg or 2.4 l/100km in city driving. Such an ambitious target requires multiple measures, i.e. low mass, favorable aerodynamics and ultra-efficient powertrain. The series hydraulic hybrid powertrain has been designed and analyzed for the selected light and aerodynamic platform with the expectation that (i) series configuration will maximize opportunities for regeneration and optimization of engine operation, (ii) inherent high power density of hydraulic propulsion and storage components will yield small, low-cost components, and (iii) high efficiency and high power limits for accumulator charging/discharging will enable very effective regeneration.

Thermal barrier coatings with low conductivity and low heat capacity have been shown to improve the performance of homogeneous charge compression ignition (HCCI) engines. These coatings improve the combustion process by reducing heat transfer during the hot portion of the engine cycle without the penalty thicker coatings typically have on volumetric efficiency. Computational fluid dynamic simulations with conjugate heat transfer between the in-cylinder fluid and solid piston of a single cylinder HCCI engine with exhaust valve rebreathing are carried out to further understand the impacts of these coatings on the combustion process. For the HCCI engine studied with exhaust valve rebreathing, it is shown that simulations needed to be run for multiple engine cycles for the results to converge given how sensitive the rebreathing process is to the residual gas state.

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 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.