Preview Horizon Research Articles

Predictive Energy Management Strategy for Hybrid Electric Air-Ground Vehicle Considering Battery Thermal Dynamics

Hybrid electric air-ground vehicles (HEAGVs), which can run on the land and fly in the air, are considered a promising future transportation. The operation of HEAGVs, accompanied by high energy consumption, could lead to increasing battery temperature, which may affect the lifespan of the battery. To make the battery last longer and improve energy efficiency, an effective energy management strategy (EMS) is necessary for the operation of HEAGVs. In this regard, this paper proposes a predictive EMS based on model predictive control (MPC). Firstly, speed information is obtained by intelligent network technology to achieve a prediction of power demand, and then the state of charge (SOC) reference trajectory is planned. Secondly, a Pontryagin’s minimum principle-based model predictive control (PMP-MPC) framework is proposed, including battery thermal dynamics. Under the framework, fuel efficiency is improved by reducing the temperature of the battery. Finally, the proposed method is compared to PMP, dynamic programming (DP), and rule-based (RB) methods. The effect of different preview horizon sizes on fuel economy and battery temperature is analyzed. Verification results under two driving cycles indicate that compared with the rule-based method, the proposed method improves fuel economy by 5.14% and 5.2% and decreases the temperature by 5.9% and 4.9%, respectively. The results demonstrate that the proposed PMP-MPC method can effectively improve fuel economy and reduce temperature.

A trip distance adaptive real-time optimal energy management strategy for a plug-in hybrid vehicle integrated driving condition prediction

To achieve better fuel economy for plug-in hybrid electric vehicles (PHEVs), this paper proposes a novel improved adaptive equivalent consumption minimization strategy (A-ECMS) integrated driving condition prediction using artificial neural network (ANN) combined the least-squares with forgetting factors. Firstly, the ANN method and the least-square with forgetting factors are used to predict the velocity of the vehicle and the slope of the road. Then a trip adaptive ECMS is proposed which the equivalent factor (EF) is adjusted in real-time according to the remaining distance. Furthermore, the driving condition prediction technology is integrated into A-ECMS to decrease fuel consumption further. Besides, the impact of different preview horizon lengths on fuel consumption is analyzed. Finally, a simulation study is conducted for applying the proposed strategy to a practical trip path in the Fuzhou road network. Simulation results show that, compared with CD-CS, the A-ECMS combined with driving condition prediction can achieve better fuel economy with a fuel consumption reduction by 12.1%, which effectively improves the fuel economy of the PHEV.

Inverse Resource Rational Based Stochastic Driver Behavior Model

Human drivers have limited and time-varying cognitive resources when making decisions in real-world traffic scenarios, which often leads to unique and stochastic behaviors that can not be explained by perfect rationality assumption, a widely accepted premise in modeling driving behaviors that presume drivers rationally make decisions to maximize their own rewards under all circumstances. To explicitly address this disadvantage, this study presents a novel driver behavior model that aims to capture the resource rationality and stochasticity of the human driver's behaviors in realistic longitudinal driving scenarios. The resource rationality principle can provide a theoretic framework to better understand the human cognition processes by modeling human's internal cognitive mechanisms as utility maximization subject to cognitive resource limitations, which can be represented as finite and time-varying preview horizons in the context of driving. An inverse resource rational-based stochastic inverse reinforcement learning approach (IRR-SIRL) is proposed to learn a distribution of the planning horizon and cost function of the human driver with a given series of human demonstrations. A nonlinear model predictive control (NMPC) with a time-varying horizon approach is used to generate driver-specific trajectories by using the learned distributions of the planning horizon and the cost function of the driver. The simulation experiments are carried out using human demonstrations gathered from the driver-in-the-loop driving simulator. The results reveal that the proposed inverse resource rational-based stochastic driver model can address the resource rationality and stochasticity of human driving behaviors in a variety of realistic longitudinal driving scenarios.

Predictive energy management for plug-in hybrid electric vehicles considering electric motor thermal dynamics

Energy management is essential for improving the fuel economy of plug-in hybrid electric vehicles (PHEVs). Some existing efforts have focused on optimizing fuel consumption and battery degradation, but without adequately considering the onboard electric motor's (EM) thermal dynamics. To address this research gap, this paper proposes a predictive energy management strategy considering EM thermal control. Specifically, we make three main contributions that distinguish our study from the existing studies. First, we design four velocity predictors based on the artificial neural network (ANN) and examine their prediction accuracy and computational efficiency. Second, we present a Pontryagin's Minimum Principle-based model predictive control (PMP-MPC) framework that includes EM thermal dynamics. The framework minimizes the operating costs while ensuring that the EM temperature is less than the limit value. Finally, we analyze and compare the effects of different reference temperature thresholds and preview horizon sizes on the fuel economy and EM temperature. The results demonstrate that the proposed PMP-MPC approach can effectively control the EM temperature rise and realize online applications with high computational efficiency.

Preview Reference Governors: A constraint management technique for systems with preview information

This paper presents a constraint management strategy based on Scalar Reference Governors (SRG) to enforce output, state, and control constraints while taking into account the preview information of the reference signal. The strategy, referred to as the Preview Reference Governor (PRG), can outperform SRG while maintaining the highly-attractive computational benefits of SRG. However, as it is shown, the performance of PRG may suffer if large preview horizons are used. An extension of PRG, referred to as Multi-horizon PRG, is proposed to remedy this issue. Quantitative comparisons between SRG, PRG, and Multi-horizon PRG on a one-link robot arm example are presented to illustrate their performance and computation time. Finally, novel extensions of PRG are presented to handle systems with uncertain preview information, systems with disturbance preview, and systems with multiple reference signals.

Novel dual‐layer‐oriented strategy for fully automated vehicles’ lane‐keeping system

A novel concept of a dual-layer-oriented control strategy for fully automated vehicles’ lane-keeping system is proposed which consists of an inner controller with a modified preview driver model and an outer cooperative copilot controller. With designed weighting function of displacement error based on a hyperbolic tangent and adjustable preview horizon according to road geometry, the inner controller is supposed to track the lane centreline precisely and efficiently through optimal control framework. The outer controller is specially designed for situations where the vehicle may run out of the lane and cause a collision. Only when the vehicle is at high risk of lane-crossing, the outer controller is activated to guide the vehicle back to lane centreline by exerting proper steering command, which is calculated by solving a model predictive control-based constrained optimisation problem with a designed quadratic cost function. Finally, simulation tests based on CarSim-Matlab joint platform are carried out to verify the proposed strategy. Results demonstrate that the modified preview driver model is able to improve path following performance and the dual-layer-oriented control strategy with less computational burden can effectively prevent the vehicle from crossing lane boundaries.

Eco-Driving of Connected and Automated Vehicle With Preceding Driver Behavior Prediction

Abstract The development of vehicle connectivity and autonomy in the ground transportation sector is not only able to enhance traffic safety and driving comfort as well as fuel economy. This study presents a receding-horizon optimization-based control strategy integrated with the preceding vehicle speed prediction model to achieve an eco-driving strategy for connected and automated vehicles (CAVs). In the real traffic scenario where the CAV follows the preceding vehicle on the road, a gated recurrent unit (GRU) network is used to predict the behavior of the preceding vehicle by utilizing the historical inter-vehicle information collected through on-board sensors. Then, a nonlinear model predictive control (NMPC) algorithm is adopted for CAV to minimize the accumulated fuel consumption within the preview horizon. The NMPC approach solves the fuel-optimal speed profile of the CAV, considering a predicted short-term speed preview of the preceding vehicle. With the awareness of the preview speed conditions, the fuel consumption of the CAV is reduced by avoiding unnecessary braking and acceleration, especially during transient traffic conditions. The Pareto front framework is used to examine a trade-off between the vehicle speed prediction accuracy, computational burden, and the fuel consumption of the CAV in the proposed GRU-NMPC design. To analyze the effectiveness of the GRU-NMPC design, adaptive cruise control with constant time headway policy (ACC-CTH) is adopted as a benchmark control design. Comparison results show significant fuel economy improvement of the proposed design and expose possible fuel benefits from vehicle autonomy and sensor fusion technology.

Predictive Control for NOx Emission Reductions in Diesel Engine Vehicle Platoon Application

This paper presents a predictive control design for diesel vehicle platoons, such as medium- and heavy-duty truck platoons, to achieve lower NOx emissions. By incorporating vehicle longitudinal dynamics control and powertrain estimation with aftertreatment system control, an enhanced emission performance can be attained for the entire platoon considering different operating conditions for individual vehicles. The proposed method utilizes exhaust gas predictive information within a preview horizon to generate optimal control actions such that the tradeoff between NOx reduction efficiency and NH3 slip in the aftertreatment systems can be well-managed. Performance improvement is demonstrated through simulation studies based on an experimental validated powertrain and aftertreatment system model.

Model predictive energy management for plug-in hybrid electric vehicles considering optimal battery depth of discharge

When developing an energy management strategy (EMS) including a battery aging model for plug-in hybrid electric vehicles, the trade-off between the energy consumption cost (ECC) and the equivalent battery life loss cost (EBLLC) should be considered to minimize the total cost of both and improve the life cycle value. Unlike EMSs with a lower State of Charge (SOC) boundary value given in advance, this paper proposes a model predictive control of EMS based on an optimal battery depth of discharge (DOD) for a minimum sum of ECC and EBLLC. First, the optimal DOD is identified using Pontryagin's Minimum Principle and shooting method. Then a reference SOC is constructed with the optimal DOD, and a model predictive controller (MPC) in which the conflict between the ECC and EBLC is optimized in a moving horizon is implemented. The proposed EMS is examined by real-world driving cycles under different preview horizons, and the results indicate that MPCs with a battery aging model lower the total cost by 1.65%, 1.29% and 1.38%, respectively, for three preview horizons (5, 10 and 15 s) under a city bus route of about 70 km, compared to those unaware of battery aging. Meanwhile, global optimization algorithms like the dynamic programming and Pontryagin's Minimum Principle, as well as a rule-based method, are compared with the predictive controller, in terms of computational expense and accuracy.

Pontryagin’s Minimum Principle based model predictive control of energy management for a plug-in hybrid electric bus

To improve computational efficiency of energy management strategies for plug-in hybrid electric vehicles (PHEVs), this paper proposes a stochastic model predictive controller (MPC) based on Pontryagin’s Minimum Principle (PMP), which differs from widely used dynamic programming (DP)-based predictive methods. First, short-time speed forecasting is achieved using a Markov chain model, based on real-world driving cycles. The PMP- and DP-based MPCs are compared under four preview horizons (5 s, 10 s, 15 s and 20 s), and the results show that the computational time of the DP-MPC is almost four times of that in the PMP-MPC. Moreover, the influence of predication horizon length on computational time and energy consumption is examined. Given a preview horizon of 5 s, the PMP-MPC holds a total energy consumption cost of 7.80 USD and computational time per second of 0.0130 s. When the preview horizon increases to 20 s, the total cost is 7.77 USD with the computational time per second increasing to 0.0502 s. Finally, DP, PMP, and rule-based strategies are contrasted to the PMP-MPC method, further demonstrating the promising performance and computational efficiency of the proposed methodology.

Predictive vehicle-following power management for plug-in hybrid electric vehicles

This paper presents a new integrated model predictive control (IMPC) method that combines power management and adaptive velocity control during vehicle-following scenarios in reality, for a plug-in hybrid electric vehicle (PHEV). Innovatively, the IMPC is able to plan the battery state of charge (SOC) and vehicular velocity trajectories, in order to improve fue economy and driving safety. To assess the performance of the IMPC, a comparison is performed with common charge-depleting and charge-sustaining (CDCS) and DP-based energy management strategies, where an improved full velocity difference model (IFVDM) is incorporated to simulate vehicle-following behavior. These solutions are examined using a real-world driving cycle. The results reveal an enormous potential of flexibly tuning the inter-vehicle distance to increase fuel economy. This is distinct from the rigid vehicle-following behavior of the IFVDM just for driving safety. Moreover, the proposed IMPC can ensure the battery charge depletion at the end of the trip. The quantitative results witness that the total cost of the IMPC with a preview horizon of 3s can be reduced by 17.9% and 36.1% for a 70 km city-bus route, compared to IFVDM-based DP and CDCS counterparts, respectively. In addition, the effect of the preview-horizon length on both fuel economy and computational time is examined.

Modelling minimum-time manoeuvering with global optimisation of local receding horizon control

In this paper, we explore the notion that a human driver uses a receding horizon model predictive control (MPC) scheme for minimum-time manoeuvering. However, MPC is an inherently sub-optimal control scheme because not all future information is incorporated into its finite preview horizon. In many practical applications, this sub-optimality is tolerated as the solution is sufficiently close to optimal. However, it is known that professional drivers have the ability to learn driving circuits and exploit its features to minimise their global manoeuvering time. In this paper, we will model their process with a cascaded optimisation structure. Therein, the inner-loop features a local MPC scheme tasked with finding the control inputs that achieve a blended objective of minimising time and maximising velocity in each preview horizon/distance. The outer loop of this cascaded structure computes the best set of weights for the two components of the local objectives in order to minimise the global manoeuvering time. The proposed cascaded optimisation and control approach is compared against a straight-forward fixed-cost time optimal MPC applied to minimum-time manoeuvering over two well-known race courses. The paper also includes an extended literature review and details of the computational formulation of the model approach.

Preview Horizon Analysis for Vehicle Rollover Prevention Using the Zero-Moment Point

This research estimates the minimum preview time needed to prevent untripped wheel lift events by analyzing simple maneuvers that are idealizations of a human driver's response in collision avoidance situations. To predict a vehicle's future rollover propensity, the zero-moment point (ZMP) metric is applied to projected vehicle trajectories. Comparing different amounts of preview, the results show that short-range predictions, ranging from 0.1 s to 0.7 s, are sufficient to prevent nearly all dynamics-induced rollovers in typical highway curves. These results are useful to determine the minimum preview horizons, with respect to rollover, that may be necessary for more advanced vehicle control methods, such as model predictive control (MPC).

Modified Preview Control for a Wireless Tracking Control System With Packet Loss

In this paper, a modified preview control technique is proposed to compensate packet loss in a wireless tracking control system, where future reference signals over a finite horizon can be previewed. In order to utilize future reference information for the controller design, the system model is augmented with a reference generator whose states are the future reference signals. As a response to the packet loss that occurs in the wireless network, the preview control technique is modified by employing Bernoulli variables to represent packet loss in both controller-actuator and sensor-controller channels. The Bernoulli packet loss model, along with tracking errors and control inputs, is included in a quadratic cost function, and the optimal controller gain that minimizes the cost function is obtained by dynamic programming. A modified Kalman filter considering packet loss is utilized for full-state estimation and state feedback control. The choice of preview horizon is discussed and the performance of the proposed controller is verified by simulation and experimental results.

Real-Time Pricing Control on Generation-Side: Optimal Demand-Tracking Model and Information Fusion Estimation Solver

This paper develops the information fusion based pricing control (IFPC) scheme for the generation-side in the liberalized wholesale electricity market. The market mechanism is described by a feedback system, in which the independent system operator (ISO) interacts with generation companies (GenCOs) via proposing and responding to real-time nodal prices. The linear constrained quadratic optimal tracking problem is formulated; and the solution (nodal price sequence) is repeatedly computed by fusing the recently available information with backward manner and updated over the receding preview horizon. So that the total supply dynamically tracks the demand, the price is stationarized and each GenCO gains the maximal profit for the given price. Numeric results on a 118-bus network validate IFPC's effectiveness and flexibility on incorporating various line rating constraints as well as wind power fluctuation uncertainties. Comparative study demonstrates that IFPC outperforms the negotiated predictive dispatch but elapsing heavier computations. The irrational behavior analysis verifies the convergence to equilibrium; and speculation behavior of GenCOs in the real market is also discussed. Along with the rapid development of smart grid, IFPC is practically enabled by the information and communication technology (ICT); its significance and potentials for renewable energy sources (RES) integration would be realized and exploited.

Design and Vehicle Implementation of Preview Active Suspension Controllers

Active suspensions influencing the vertical dynamics of vehicles enable the improvement of ride comfort and handling characteristics compared to vehicles with passive suspensions. If the road height profile in front of the car is measured using vehicle sensors, more efficient control strategies can be applied. In this brief, two model predictive approaches for preview active suspension controllers are proposed and compared to the well-known optimal preview control approach. The considered active suspension consists of low bandwidth actuators in series with the spring and passive dampers. Equations of a linear full-car model are derived. The first model predictive controller optimizes the actuator displacements on a nonequidistant grid over the preview horizon. The second controller optimizes trajectories for heave, pitch, and roll of the vehicle body over the preview horizon using a quadratic program. Therefore, a model inversion is presented. The trajectories are formulated as a summation of nonequidistant B-spline basis functions and constraints on the input are incorporated in trajectory generation via the inverse vehicle model. Simulation results are presented and vehicle implementation using recorded road height data is conducted.

Linear quadratic game and non-cooperative predictive methods for potential application to modelling driver–AFS interactive steering control

This paper is concerned with the modelling of strategic interactions between the human driver and the vehicle active front steering (AFS) controller in a path-following task where the two controllers hold different target paths. The work is aimed at extending the use of mathematical models in representing driver steering behaviour in complicated driving situations. Two game theoretic approaches, namely linear quadratic game and non-cooperative model predictive control (non-cooperative MPC), are used for developing the driver–AFS interactive steering control model. For each approach, the open-loop Nash steering control solution is derived; the influences of the path-following weights, preview and control horizons, driver time delay and arm neuromuscular system (NMS) dynamics are investigated, and the CPU time consumed is recorded. It is found that the two approaches give identical time histories as well as control gains, while the non-cooperative MPC method uses much less CPU time. Specifically, it is observed that the introduction of weight on the integral of vehicle lateral displacement error helps to eliminate the steady-state path-following error; the increase in preview horizon and NMS natural frequency and the decline in time delay and NMS damping ratio improve the path-following accuracy.

A Framework for Discrete-Time H2 Preview Control

The purpose of this paper is to provide a set of synthesis and design tools for a wide class of H2 preview control systems. A generic preview design problem, which features both previewable and nonpreviewable disturbances, is embedded in a standard generalized regulator framework. Preview regulation is accomplished by a two-degrees-of-freedom output-feedback controller. A number of theoretical issues are studied, including the efficient solution of the standard H2 full-information Riccati equation and the efficient evaluation of the full-information preview gain matrices. The full-information problem is then extended to include the efficient implementation of the output-feedback controller. The synthesis of feedforward controllers with preview is analyzed as a special case—this problem is of interest to designers who wish to introduce preview as a separate part of a system design. The way in which preview reduces the H2-norm of the closed-loop system is analyzed in detail. Closed-loop norm reduction formulas provide a systematic way of establishing how much preview is required to solve a particular problem, and determine when extending the preview horizon will not produce worthwhile benefits. The paper concludes with a summary of the main features of preview control, as well as some controller design insights. New application examples are introduced by reference.

Optimal control to modelling motorcycle rider steering: local versus global coordinate systems in rider preview

The response of a motorcycle is heavily dependent on the rider’s control actions, and consequently a means of replicating the rider’s behaviour provides an important extension to motorcycle dynamics. The primary objective here is to develop effective path-following simulations and to understand how riders control motorcycles. Optimal control theory is applied to the tracking of roadway by a motorcycle, using a non-linear motorcycle model operating in free control by steering torque input. A path-following controller with road preview is designed by minimising tracking errors and control effort. Tight controls with high weightings on performance and loose controls with high weightings on control power are defined. Special attention is paid to the modelling of multipoint preview in local and global coordinate systems. The controller model is simulated over a standard single lane-change manoeuvre. It is argued that the local coordinates point of view is more representative of the way that a human rider operates and interprets information. The simulations suggest that for accurate path following, using optimal control, the problem must be solved by the local coordinates approach in order to achieve accurate results with short preview horizons. Furthermore, some weaknesses of the optimal control approach are highlighted here.

Predictive and linear quadratic methods for potential application to modelling driver steering control

A brief review of the literature reveals that both predictive control theory and linear quadratic (LQ) control theory have been used to design path-following controllers with preview, but it is not clear how the controllers compare. This article derives optimal linear preview controllers using the two approaches starting from a common state-space description of the vehicle dynamics. The transformation of the controllers from ground-fixed axes to vehicle-fixed axes is discussed. The influences of preview horizon, control horizon and cost function are investigated. For the case of long preview and long control horizons, it is found that the predictive and LQ approaches give identical controllers. The results in this article provide a basis for identifying human steering behaviour from measured data.