Effects of time horizon on Model Predictive Control for Hybrid Electric Vehicles

© 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.. One of the challenges in Model Predictive Control (MPC) for Hybrid Electric Vehicles (HEVs), is real time implementation, Bo-Ah et al. (2012). Computation time can be reduced by limiting the time horizon of the prediction. Limiting the time horizon results in sub optimal control, but may yield nearly optimal control if the time horizon is chosen appropriately. This paper investigates the sensitivity of MPC to predicted horizon length with regard to Fuel Economy (FE). The results show that predicting Driver\u27s Desired Power (DDP) for the next 10 seconds on the highway and 20 seconds in the city, is sufficient for MPC to perform close to the Globally Optimized Controller (GOC). In other words: Regarding fuel economy optimization on the highway, knowing DDP for the next 10 seconds is almost equivalent to knowing the DDP for the whole trip

Wavelet-based burst system model change detection

A new scheme for detecting system burst changes is developed based on the continuous wavelet transform (CWT). The system is concisely represented by its time-frequency representation (TFR), the ratio of the CWTs of the output and the input. The TFR is first estimated, assuming that no system changes occur during a time period. A chi-square test is then executed to test the TFR estimate\u27s match to the data. System model changes are detected at the time samples when the assumption that the system is time invariant is broken. Simulations verify the capability of the proposed algorithm to detect burst system changes

Continuous wavelet based linear time-varying system identification

A systematic framework is developed to address the parametric linear time-varying system identification problem, using the continuous wavelet transform (CWT). The system is modeled by a differential equation with unknown parameters and identified via the timefrequency representation, the ratio of the CWTs of the output and the input. The efficient execution of this system identification algorithm requires selecting appropriate scales from the wavelet transform. Scale selection can be formulated as an optimization problem: find a set of scales that contains the most information about the system\u27s dynamics and has high signal to noise ratio. In addition, selecting scales that have a minimum amount of redundant information is desirable. Three candidate selection metrics are presented that address these criteria and are based on an analytic investigation of the wavelet transform\u27s probabilistic structure. Finally, a non-linear least squares algorithm, coupled with a scale selection algorithm, is presented to identify the system model. Simulations and experiments verify this algorithm\u27s capability of tracking different types of model variation. © 2010 Elsevier B.V

Prediction of vehicle velocity for model predictive control

© 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.. In model predictive control, knowledge about the future trajectories of the set points or disturbances is used to optimize the overall system performance, Camacho and Bordons (2007). For hybrid electric vehicles, by predicting the future Driver\u27s Desired Velocity (DDV), fuel economy, or emissions can be improved, Debert et al. (2010). For predicting DDV, different approaches have been suggested, for example, artificial neural networks, Fotouhi et al. (2011), statistical methods, or methods based on GPS and Geographical Information Systems(GIS), Keulen et al. (2009). In this work, some of these approaches are introduced and autoregressive methods with GPS/GIS information are evaluated

Nonlinear correlation for estimating the motion of multiple objects in image sequences

A nonlinear correlation algorithm is proposed for estimating the motion of objects from an image pair. This algorithm requires no a priori information on the number, size, or shape of the moving objects and does not require feature extraction or segmentation of either image. The algorithm directly yields information on the number of moving objects, the motion of the objects, and the size of the objects. Additional processing can be performed to yield the centroid of the objects in either frame. The utility of the resulting algorithm is demonstrated by application to a pair of example image sequences. © 2001 Optical Society of America

Estimation of the ECMS Equivalent Factor Bounds for Hybrid Electric Vehicles

© 1993-2012 IEEE. The strategy for energy management (EM) of a hybrid electric vehicle (HEV) has a considerable impact on the vehicle fuel economy. One well-known EM strategy is the equivalent consumption minimization strategy (ECMS) that is a form of Pontryagin\u27s minimum principle (PMP). PMP proves under certain conditions that ECMS yields the maximum fuel economy. However, even if the required conditions are met, the optimal value of the costate still has to be estimated. Many approaches have been suggested for estimating the optimal value of the costate, or the equivalent factor for using battery power in the ECMS cost function. Instead of direct estimation of ECMS optimal equivalent factor, this brief derives estimations for the upper and lower bounds of the optimal equivalent factor. The derived bounds are functions of the HEV configuration and independent of the drivecycle, verified by simulation results. The knowledge about these bounds can be employed in designing new types of adaptive ECMSs (A-ECMSs). To demonstrate the application of the bounds, this brief introduces a new A-ECMS. Finally, the simulation results are presented comparing the fuel economy of the introduced A-ECMS with the fuel economies of an existing A-ECMS and global optimal controller

Tilt estimation in moderate-to-strong scintillation

Adaptive optics systems are being applied in ever more challenging environments, for example, the projection of lasers over long horizontal paths through the atmosphere. These long atmospheric paths corrupt the signal received from the beacon and typically yield highly scintillated received wave fronts. Tilt estimation for controlling the fast steering mirror in these systems is complicated by the presence of branch points in the scintillated received wave fronts. In particular, correlation between the tilt and the projected beam’s centroid error at the target has been observed in horizontal laser beam projection experiments. The presence of this correlation indicates that better tracking performance should be achievable. We compare the performance of four estimation schemes applied to tilt estimation in a horizontal laser projection system. It is demonstrated that all four schemes underestimate the tilt required to return the laser beam to a target in highly scintillated environments. A method of correcting this tilt is presented, and the expected performance improvement is quantified. © 2001 Optical Society of America

Catch energy saving opportunity in charge-depletion mode, a real-time controller for plug-in hybrid electric vehicles

© 2018 IEEE. The energy management of plug-in hybrid electric vehicles (HEVs) is commonly divided into two modes: charge-depletion mode and charge-sustaining mode. This paper presents the optimal adaption law for any type of adaptive energy consumption minimization strategy (ECMS) in charge-depletion mode for plug-in HEVs. To present the optimal law, a particular adaptive ECMS is selected, known as catch energy saving opportunity (CESO). CESO has previously been introduced for series and parallel HEVs in charge-sustaining mode. Here, by introducing the optimal adaption law, CESO strategy is expanded to charge-depletion mode for plug-in HEVs

A New Real-Time Optimal Energy Management Strategy for Parallel Hybrid Electric Vehicles

© 1993-2012 IEEE. Based on the equivalent consumption minimization strategy (ECMS), a novel real-time energy management (EM) strategy for parallel hybrid electric vehicles (HEVs) is introduced. Given the full trajectory of the driver demanded power, the ECMS optimal equivalent factor \lambda ^{∗} can be determined. For causal EM strategies, the entire drivecycle is not known in advance. Thus, adaptive ECMS (A-ECMS) was introduced, which sets the time-varying equivalent factor \lambda as an estimate of \lambda ^{∗}. The proposed EM strategy is an A-ECMS. This EM strategy is designed to catch energy-saving opportunities (CESOs) during the trip, and thus, it is named ECMS-CESO. Since ECMS-CESO eliminates the calculations used for predicting the vehicle velocity and performing horizon optimization, it is easy to implement and fast for real-time applications. Simulation results show that ECMS-CESO yields fuel economy (FE) close to the maximum FE. Compared with an A-ECMS, the proposed strategy improves FE by 7%