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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.

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.

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.

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 Three Way Catalyst (TWC) is an effective pollutant conversion system widely used in current production vehicles to satisfy emissions regulations. A TWC’s conversion efficiency degrades over time due to chemical and/or thermal mechanisms causing the catalyst to age. This reduction in conversion efficiency must be accounted for to ensure full useful life emissions compliance. This paper presents an experimental study of the aging impact on TWC performance. Four TWCs differentiated by their age, given in terms of miles driven, were tested. It is shown that the dynamics of oxygen storage are substantially affected by aging of the TWC. A previously developed physics-based oxygen storage model [1] is subsequently used to incorporate the effect of aging on the total Oxygen Storage Capacity (OSC). Parameter identification results for the different age catalysts show that total oxygen storage capacity decreases substantially with aging and is insensitive to operating conditions.

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.

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.

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.

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 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.

This paper introduces a Hydraulic Linear Engine (HLE) concept and describes a model to simulate instantaneous engine behavior. The United States Environmental Protection Agency has developed an HLE prototype as an evolution of their previous six-cylinder, four-stroke, free-piston engine (FPE) hardware. The HLE design extracts work hydraulically, in a fashion identical to the initial FPE, and is intended for use in a series hydraulic hybrid vehicle. Unlike the FPE, however, the HLE utilizes a crank for improved timing control and increased robustness. Preliminary experimental results show significant speed fluctuations and cylinder imbalance that require careful controls design. This paper also introduces a model of the HLE that exhibits similar behavior, making it an indispensible tool for controls design. Further, the model's behavior is evaluated over a range of operating conditions currently unobtainable by the experimental setup.

This paper investigates the development of a deaeration device to remove nitrogen from the hydraulic fluid in hydraulic hybrid vehicles (HHVs). HHVs, which use accumulators to store and recycle energy, can significantly reduce vehicle emissions in urban delivery vehicles. In accumulators, nitrogen behind a piston cylinder or inside a bladder pressurizes an incompressible fluid. The permeation of the nitrogen through the rubber bladder into the hydraulic fluid limits the efficiency and reliability of the HHV system, since the pressure drop in the hydraulic fluid can in turn cause cavitation on pump components and excessive noise in the system. The nitrogen bubbles within the hydraulic fluid may be removed through the employment of commercial bubble eliminators if the bubbles are larger than a certain threshold. However, gas is also dissolved within the hydraulic fluid; therefore, novel design is necessary for effective deaeration in the fluid HHV circuit.

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.

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.

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.

The majority of a refuse truck collection cycle consists of frequent Stop and Go events while moving from one household to another. The nature of this driving mission creates the opportunity to reduce fuel consumption by capturing and re-using the kinetic energy normally wasted during braking. This paper includes the evaluation of the brake energy available for regeneration from the conventional drivetrain; the description of the impact of the vehicle variable mass and auxiliary loads; a model validation over a real-world duty cycle; and the potential for an increase in fuel efficiency through hybridization of the drivetrain. The Hydraulic Hybrid (HH) technology is selected since it has a large power density.

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).

The paper describes the approach, addresses integration challenges and discusses capabilities of the Hybrid Powertrain-in-the-Loop (H-PIL) facility for the series/hydrostatic hydraulic hybrid system. We describe the simulation of the open-loop and closed-loop hydraulic hybrid systems in H-PIL and its use for concurrent engineering and development of advanced supervisory strategies. The configuration of the hydraulic-hybrid system and details of the hydraulic circuit developed for the H-PIL integration are presented. Next, software and hardware interfaces between the real components and virtual systems are developed, and special attention is given to linking component-level controllers and system-level supervisory control. The H-PIL setup allows imposing realistic dynamic loads on hydraulic pump/motors and accumulator based on vehicle driving schedule.

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 simulation study explores the potential synergy between the HCCI engine system and three hybrid electric vehicle (HEV) configurations, and proposes the supervisory control strategy that maximizes the benefits of combining these two technologies. HCCI operation significantly improves fuel efficiency at part load, while hybridization aims to reduce low load/low speed operation. Therefore, a key question arises: are the effects of these two technologies additive or overlapping? The HEV configurations include two parallel hybrids with varying degrees of electrification, e.g. with a 5kW integrated starter/motor (“Mild”) and with a 10 kW electric machine (“Medium”), and a power-split hybrid. The engine is a dual-mode, SI-HCCI system and the engine map reflects the impact of HCCI on brake specific fuel consumption.