Investigation on the Energy Management Strategy for Hydraulic Hybrid Wheel Loaders

Energy management strategies for a hydraulic hybrid wheel loader are studied in this paper. The architecture of the hydraulic hybrid wheel loader is first presented and the differences of the powertrain and the energy management system between on-road vehicles and wheel loaders are identified. Unlike the on-road vehicles where the engine only powers the drivetrain, the engine in a wheel loader powers both the drivetrain and the working hydraulic system. In a non-hybrid wheel loader, the two sub-systems interfere with each other since they share the same engine shaft. By using a power split drivetrain, it not only allows for optimal engine operation and regenerative braking, but also eliminates interferences between driving and working functions, which improve the productivity, fuel efficiency and operability of the wheel loader. An energy management strategy (EMS) based on dynamic programming (DP) is designed to optimize the operation of both the power split drivetrain and the working hydraulic system. A short loading cycle is selected as the duty cycle. The EMS based on DP is compared with a rule-based strategy through simulation.

Cost-Effective Energy Management for Hybrid Electric Heavy-Duty Truck Including Battery Aging

Battery temperature has large impact on battery power capability and battery life time. In Hybrid Electric Heavy-duty trucks (HEVs), the high-voltage battery is normally equipped with an active Battery Thermal Management System (BTMS) guaranteeing a desired battery life time. Since the BTMS can consume a substantial amount of energy, this paper aims at integrating the Energy Management Strategy (EMS) and BTMS to minimize the overall operational cost of the truck (considering diesel fuel cost and battery life time cost). The proposed on-line strategy makes use of the Equivalent Consumption Minimization Strategy (ECMS) along with a physics-based approach to optimize both the power split (between the Internal Combustion Engine (ICE) and the Motor Generator (MG)) and the BTMS’s operation. The strategy also utilizes a quasi-static battery cycle-life model taking into account the effects of battery power and battery temperature on the battery capacity loss. Simulation results present an appropriate strategy for EMS and BTMS integration, and demonstrate the trade-off between the total vehicle fuel consumption and the battery life time.

Proportional Navigation (PN) Based Tracking of Ground Targets by Quadrotor UAVs

This paper addresses the problem of controlling a rotary wing Unmanned Aerial Vehicle (UAV) tracking a target moving on ground. The target tracking problem by UAVs has received much attention recently and several techniques have been developed in literature most of which have been applied to fixed wing aircrafts. The use of quadrotor UAVs, the subject of this paper, for target tracking presents several challenges especially for highly maneuvering targets since the development of time-optimal controller (required if target is maneuvering fast) for quadrotor UAVs is extremely difficult due to highly non-linear dynamics. The primary contribution of this paper is the development of a proportional navigation (PN) based method and its implementation on quad-rotor UAVs to track moving ground target. The PN techniques are known to be time-optimal in nature and have been used in literature for developing guidance systems for missiles. There are several types of guidance laws that come within the broad umbrella of the PN method. The paper compares the performance of these guidance laws for their application on quadrotors and chooses the one that performs the best. Furthermore, to apply this method for target tracking instead of the traditional objective of target interception, a switching strategy has also been designed. The method has been compared with respect to the commonly used Proportional Derivative (PD) method for target tracking. The experiments and numerical simulations performed using maneuvering targets show that the proposed tracking method not only carries out effective tracking but also results into smaller oscillations and errors when compared to the widely used PD tracking method.

Development of Energy Management Strategies for Heavy Mobile Machinery

This paper introduces a method for developing energy management strategies (EMS) for task-oriented heavy mobile machinery. The case application is a hybrid underground mining loader but the method is also used for a diesel-electric and electric loader. Depending on the optimization target, the sequence for optimal power-split between the engine-generator (gen-set) and battery is defined. The minimization of the energy consumption and maximization of the operating efficiency are used as the optimization targets. The developed method is based on dynamic programming simulations which generate the optimal power-split for the evaluation of the control parameters. The simulation results showed that there are no significant differences between the two optimization targets in terms of the control sequence of the hybrid loader. The major difference was observed in the battery charging power which was much lower in the case of the minimization of the energy consumption.

Determining Human Control Intent Using Inverse LQR Solutions

In this paper, we have developed a method for determining the control intention in human subjects during a prescribed motion task. Our method is based on the solution to the inverse LQR problem, which can be stated as: does a given controller K describe the solution to a time-invariant LQR problem, and if so, what weights Q and R produce K as the optimal solution? We describe an efficient Linear Matrix Inequality (LMI) method for determining a solution to the general case of this inverse LQR problem when both the weighting matrices Q and R are unknown. Additionally, we propose a gradient-based, least-squares minimization method that can be applied to approximate a solution in cases when the LMIs are infeasible. We develop a model for an upright seated-balance task which will be suitable for identification of human control intent once experimental data is available.

Development of a Master-Slave Robotic System for MRI-Guided Intracardiac Interventions

Restricted space inside the magnetic resonance imaging (MRI) scanner bore prevents surgeons to directly interact with the patient during MRI-guided procedures. This motivates the development of a robotic system that can act as an interface during those interventions. In this paper, we present a master-slave robotic system as a solution to the aforedescribed issue. The proposed system consists of a commercial PHANTOM device (product of The Sensable Technologies) as the master robot and an MRI-compatible patient-mounted parallel platform (that we name ROBOCATH) designed to serve as the slave mechanism inside the scanner bore. We present in this paper the design principles for the platform, as well as the PID control design for the system. We use our experimental setup to evaluate the performance of the system by examining the effectiveness of the slave platform in tracking the reference trajectories generated by the master robot.

Maximum Power Point Tracking of Multi-String Photovoltaic Array via Simultaneous Perturbation Stochastic Approximation

This paper presents a Maximum Power Point Tracking (MPPT) strategy for multi-string photovoltaic (PV) systems using the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm. The multi-string PV system considered is a decentralized control configuration, controlling the voltage reference to each PV module but based on the feedback of the total power at the DC bus. This requires only one pair of voltage and current measurements. The MPPT control problem for such topology of multi-string PV systems features a high input dimension, which can dramatically slow down the searching process for the real-time optimization process involved. The SPSA algorithm is considered in this study due to its remarkable capability of fast convergence for high dimensional search problems endorsed by various applications recently. Simulation study is performed for an 8-string PV system, and experimental study is performed for a 4-string PV system. Good performances are observed for both simulation and experimental results.

System Level Dynamic Modeling Framework Being Developed at Clemson University’s Wind Turbine Drivetrain Testing Facility

This paper describes the system level, dynamic modeling and simulation strategy being developed at the Wind Turbine Drivetrain Testing Facility (WTDTF) at Clemson University’s Restoration Institute in North Charleston, SC, USA. An extensible framework that allows various workflows has been constructed and used to conduct preliminary analysis of one of the facility’s test benches. The framework dictates that component and subsystem models be developed according to a list of identified needs and modeled in software best suited for the particular task. Models are then integrated according to the desired execution target. This approach allows for compartmentalized model development which is well suited for collaborative work. The framework has been applied to one of the test benches and has allowed researches to begin characterizing its behavior in the time and frequency domain.

Battery State of Health and Charge Estimation Using Polynomial Chaos Theory

In this effort, we use the generalized Polynomial Chaos theory (gPC) for the real-time state and parameter estimation of electrochemical batteries. We use an equivalent circuit battery model, comprising two states and five parameters, and formulate the online parameter estimation problem using battery current and voltage measurements. Using a combination of the conventional recursive gradient-based search algorithm and gPC framework, we propose a novel battery parameter estimation strategy capable of estimating both battery state-of-charge (SOC) and parameters related to battery health, e.g., battery charge capacity, internal resistance, and relaxation time constant. Using a combination of experimental tests and numerical simulations, we examine and demonstrate the effectiveness of the proposed battery estimation method.

Vehicle Stability Control in Anti-Lock Braking System on Split-U Road Surface Considering Driver in the Loop

With the introduction and development of Anti-lock Braking System in modern vehicles, remarkable progress in brake efficiency and brake stability has been achieved. However, it is a significant challenge to deal with the control law in certain critical situations, especially on split-μ road surface. In low vehicle velocity, as some standards and regulations specified, the stability in such situation is comparably easy to be achieved. But with the vehicle velocity increasing, the driver behavior contributes a large impact on the trajectory maintenance and easily causes sympathetic vibration of the vehicle because of the unexpected synchronization between the driver input and control law output, which could be very dangerous. This paper presents the research work in vehicle stability control when Anti-lock Braking System is activated at split-μ road surface. The principal contribution of this work is that the driver behavior is taken into account and the control law is tuned to adapt to this situation, which effectively maintains the stability of the vehicle without compromising the brake efficiency.