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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.

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 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.

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.

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.

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.

This paper investigates the opportunities for improving truck fuel economy through the use of a Fuel Cell Auxiliary Power Unit (FC APU) for silent watch, as well as for powering electrified engine accessories during driving. The particular vehicle selected as the platform for this study is a prototype of the Family of Medium Tactical Vehicles (FMTV) capable of carrying a 5 ton payload. Peak stand-by power requirements for on-board power are determined from the projected future digitized battlefield vehicle requirements. Strategic selection of electrified engine accessories enables engine shutdowns when the vehicle is stopped, thus providing additional fuel savings. Proton Exchange Membrane (PEM) fuel cell is integrated with a partial oxidation reformer in order to allow the use of the same fuel (JP8) as for the propulsion diesel engine.

The target cascading methodology is applied to the conceptual design of an advanced heavy tactical truck. Two levels are defined: an integrated truck model is represented at the top (vehicle) level and four independent suspension arms are represented at the lower (system) level. Necessary analysis models are developed, and design problems are formulated and solved iteratively at both levels. Hence, vehicle design variables and system specifications are determined in a consistent manner. Two different target sets and two different propulsion systems are considered. Trade-offs between conflicting targets are identified. It is demonstrated that target cascading can be useful in avoiding costly design iterations late in the product development process.

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.

This paper describes a test cell setup for concurrent running of a real engine and a vehicle system simulation, and its use for evaluating engine performance when integrated with a conventional and a hybrid electric driveline/vehicle. This engine-in-the-loop (EIL) system uses fast instruments and emission analyzers to investigate how critical in-vehicle transients affect engine system response and transient emissions. Main enablers of the work include the highly dynamic AC electric dynamometer with the accompanying computerized control system and the computationally efficient simulation of the driveline/vehicle system. The latter is developed through systematic energy-based proper modeling that tailors the virtual model to capture critical powertrain transients while running in real time. Coupling the real engine with the virtual driveline/vehicle offers a chance to easily modify vehicle parameters, and even study two different powertrain configurations.

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.

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.

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.

Refinements were made to a post-processing technique, termed the Thermal Stratification Analysis (TSA), that couples the mass fraction burned data to ignition timing predictions from the autoignition integral to calculate an apparent temperature distribution from an experimental HCCI data point. Specifically, the analysis is expanded to include all of the mass in the cylinder by fitting the unburned mass with an exponential function, characteristic of the wall-affected region. The analysis-derived temperature distributions are then validated in two ways. First, the output data from CFD simulations are processed with the Thermal Stratification Analysis and the calculated temperature distributions are compared to the known CFD distributions.

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.

The fidelity of the hybrid electric vehicle simulation is increased with the integration of a computationally-efficient finite-element based electric machine model, in order to address optimization of component design for system level goals. In-wheel electric motors are considered because of the off-road military application which differs significantly from commercial HEV applications. Optimization framework is setup by coupling the vehicle simulation to the constrained optimization solver. Utilizing the increased design flexibility afforded by the model, the solver is able to reshape the electric machine's efficiency map to better match the vehicle operation points. As the result, the favorable design of the e-machine is selected to improve vehicle fuel economy and reduce cost, while satisfying performance constraints.