scholarly journals Control and Powertrain Management for Multi-Autonomous Hybrid Vehicles

2019 ◽  
Vol 141 (7) ◽  
Author(s):  
Masood Ghasemi ◽  
Xingyong Song
Keyword(s):  
Real Time ◽  
Fuel Consumption ◽  
Autonomous Vehicles ◽  
Minimum Principle ◽  
Optimal Solution ◽  
Algebraic Equations ◽  
Computationally Efficient ◽  
Hybrid Powertrain ◽  
Fuel Consumption Rate ◽  
Optimal Approach

The need for less fuel consumption and the trend of higher level of autonomy together urge the power optimization in multihybrid autonomous vehicles. Both the multivehicle coordination control and the hybrid powertrain energy management should be optimized to maximize fuel savings. In this paper, we intend to have a computationally efficient framework to optimize them individually and then evaluate the overall control performance. The optimization is conducted in series. First is at the multivehicle system's level where the distributed locally optimal solution is given for vehicles with nonlinear dynamics. Second, the powertrain management optimization is conducted at the hybrid powertrain level. We provide an analytical formulation of the powertrain optimization for each hybrid vehicle by using Pontryagin's minimum principle (PMP). By approximating the optimal instantaneous fuel consumption rate as a polynomial of the engine speed, we can formulate the optimization problem into a set of algebraic equations, which enables the computationally efficient real-time implementation. To justify the applicability of the methodology in real-time, we give directions on numerical iterative solutions for these algebraic equations. The analysis on the stability of the method is shown through statistical analysis. Finally, further simulations are given to confirm the efficacy and the robustness of the proposed optimal approach. An off-road example is given in the simulation, although the framework developed can be applied to on-road scenario as well.

A Computationally Efficient Optimal Power Management for Power Split Hybrid Vehicle Based on Pontryagin’s Minimum Principle

2017 ◽  
Author(s):  
Masood Ghasemi ◽  
Xingyong Song
Keyword(s):  
Real Time ◽  
Fuel Consumption ◽  
Optimization Problem ◽  
Minimum Principle ◽  
Hybrid Vehicle ◽  
Algebraic Equations ◽  
Pontryagin’S Minimum Principle ◽  
Computationally Efficient ◽  
Power Split ◽  
Pontryagin's Minimum Principle

The need for less fuel consumption urges effective powertrain management optimization in hybrid vehicles. In this study, we consider the real time power optimization problem of a power split hybrid vehicle. Assuming that the power on demand at the driveline can be predicted and known for each driving cycle, the powertrain management and optimization are conducted at the hybrid powertrain system’s level in a computationally efficient fashion. Specifically, we provide an analytical formulation of the powertrain optimization for the hybrid vehicle by using the Pontryagin’s minimum principle (PMP). By approximating the optimal instantaneous fuel consumption rate as a polynomial of the engine speed, we can formulate the optimization problem into a set of algebraic equations. In order to justify the applicability of the methodology for real-time implementations, we give directions on numerical iterative solutions for these algebraic equations. The analysis on the stability of the method is shown through statistical analysis. Finally, further simulations are given to confirm the efficacy and the robustness of the proposed optimal approach.

Real-Time Velocity Optimization for Energy-Efficient Control of Connected and Automated Vehicles

2021 ◽  
Author(s):  
Shiying Dong ◽  
Bing Zhao Gao ◽  
Hong Chen ◽  
Yanjun Huang ◽  
Qifang Liu
Keyword(s):  
Real Time ◽  
Numerical Algorithm ◽  
Energy Efficient ◽  
Trajectory Optimization ◽  
Minimum Principle ◽  
Optimal Solution ◽  
Automated Vehicles ◽  
Pontryagin Minimum Principle ◽  
Optimal Inputs ◽  
Efficient Control

Abstract This paper presents a fast numerical algorithm for velocity optimization based on the Pontryagin' minimum principle (PMP). Considering the difficulties in the application of the PMP when state constraints exist, the penalty function approach is proposed to convert the state-constrained problem into an unconstrained one. Then this paper proposes an iterative numerical algorithm by using the explicit solution to find the optimal solution. The proposed numerical algorithm is applied to the velocity trajectory optimization for energy-efficient control of connected and automated vehicles (CAVs). Simulation results indicate that the algorithm can generate the optimal inputs in milliseconds, and a significant improvement in computational efficiency compared with traditional methods (a few seconds). Hardware in the Loop test for experimental validation is given to further verify the real-time performance of the proposed algorithm.

Vehicle Trajectory Specification in Presence of Traffic Lights with Known or Uncertain Switching Times

2020 ◽  
Vol 2674 (8) ◽  
pp. 53-66
Author(s):  
Panagiotis Typaldos ◽  
Ioanna Kalogianni ◽  
Kyriakos Simon Mountakis ◽  
Ioannis Papamichail ◽  
Markos Papageorgiou
Keyword(s):  
Optimal Control ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Real Time ◽  
Switching Time ◽  
Minimum Principle ◽  
Stochastic Dynamic ◽  
Traffic Lights ◽  
Switching Times ◽  
Optimal Approach

The main purpose of this work is to generate optimal trajectories for vehicles crossing a signalized junction, with traffic signals operated in either fixed-time or real-time (adaptive) mode. In the latter case, the next switching time is decided in real time based on the prevailing traffic conditions and is therefore uncertain in advance. The GLOSA (Green Light Optimal Speed Advisory) problem is addressed by using traffic lights information and calculating a trajectory and velocity profile for the vehicle based on the vehicle’s initial state (position and speed) and a fixed final destination state. At first, an appropriate optimal control problem is formulated and solved analytically via Pontryagin’s minimum principle (PMP) for the case of known switching times. Subsequently, for the case of real-time signals, availability of a time-window of possible signal switching times, along with the corresponding probability distribution, is assumed, and the problem is cast in the format of a stochastic optimal control problem and is solved numerically using stochastic dynamic programming (SDP) techniques. Application results, for various driving scenarios, of the deterministic approach, which considers the case of known switching times, and a comprehensive comparison of the stochastic GLOSA approach with a sub-optimal approach are presented. In particular, it is demonstrated that the proposed SDP approach achieves better average performance compared with the sub-optimal approach because of the better (probabilistic) information on the traffic light switching time.

scholarly journals Driving Mode Optimization for Hybrid Trucks Using Road and Traffic Preview Data

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5341
Author(s):  
Yutao Chen ◽  
Nazar Rozkvas ◽  
Mircea Lazar
Keyword(s):  
Fuel Consumption ◽  
Minimum Principle ◽  
Optimal Solution ◽  
Real World Data ◽  
Engine Torque ◽  
Driving Mode ◽  
Tuning Parameters ◽  
Controlled Switching ◽  
Warm Start ◽  
Optimal Driving

This paper proposes a predictive driver coaching (PDC) system for fuel economy driving for hybrid electric trucks using upcoming static map and dynamic traffic data. Unlike traditional methods that optimize over engine torque and brake to obtain a speed profile, we propose to optimize over driving modes of trucks to achieve a trade-off between fuel consumption and trip time. The optimal driving mode is provided to the driver as a coaching recommendation. To obtain the optimal solution, the truck dynamics are firstly modeled as a hybrid controlled switching dynamical system with autonomous subsystems and then a hybrid optimal control problem (HOCP) is formulated. The problem is solved using an algorithm based on discrete hybrid minimum principle. A warm-start strategy to reduce algorithmic iterations is used by employing a shrinking horizon strategy. In addition, an extensive analysis of the proposed algorithm is provided. We prove that the the coasting mode is never optimal given the truck configuration and and we provide a guideline for tuning parameters to maintain the optimal mode sequence. Finally, the algorithm is validated using real-world data from baseline driving tests using a DAF hybrid truck. Significant reduction in fuel consumption is achieved when the data is perfectly available.

scholarly journals Real-Time Approximate Equivalent Consumption Minimization Strategy Based on the Single-Shaft Parallel Hybrid Powertrain

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7919
Author(s):  
Penghui Qiang ◽  
Peng Wu ◽  
Tao Pan ◽  
Huaiquan Zang
Keyword(s):  
Real Time ◽  
Fuel Consumption ◽  
Hybrid Powertrain ◽  
Computational Load ◽  
Time Optimization ◽  
Equivalence Factor ◽  
Optimization Control ◽  
Parallel Hybrid ◽  
Equivalent Consumption Minimization Strategy ◽  
Real Time Optimization

Real-time energy management strategy (EMS) plays an important role in reducing fuel consumption and maintaining power for the hybrid electric vehicle. However, real-time optimization control is difficult to implement due to the computational load in an instantaneous moment. In this paper, an Approximate equivalent consumption minimization strategy (Approximate-ECMS) is presented for real-time optimization control based on single-shaft parallel hybrid powertrain. The quadratic fitting of the engine fuel consumption rate and the single-axle structure characteristics of the vehicle make the fitness function transformed into a cubic function based on ECMS for solving. The candidate solutions are thus obtained to distribute torque and the optimal distribution is got from the candidate solutions. The results show that the equivalent fuel consumption of Approximate-ECMS was 7.135 L/km by 17.55% improvement compared with Rule-ECMS in the New European Driving Cycle (NEDC). To compensate for the effect of the equivalence factor on fuel consumption, a hybrid dynamic particle swarm optimization-genetic algorithm (DPSO-GA) is used for the optimization of the equivalence factor by 9.9% improvement. The major contribution lies in that the Approximate-ECMS can reduce the computational load for real-time control and prove its effectiveness by comparing different strategies.

Determining Model Accuracy Requirements for Automotive Engine Coldstart Hydrocarbon Emissions Control

2012 ◽  
Vol 134 (5) ◽  
Author(s):  
Nasser L. Azad ◽  
Pannag R. Sanketi ◽  
J. Karl Hedrick
Keyword(s):  
Optimal Control ◽  
Sensitivity Analysis ◽  
Real Time ◽  
Minimum Principle ◽  
Optimal Solution ◽  
Control Performance ◽  
Automotive Engine ◽  
Engine Model ◽  
Time Optimal ◽  
Hc Emissions

In this work, a systematic method is introduced to determine the required accuracy of an automotive engine model used for real-time optimal control of coldstart hydrocarbon (HC) emissions. The engine model structure and development are briefly explained and the model predictions versus experimental results are presented. The control design problem is represented with a dynamic optimization formulation on the basis of the engine model and solved using the Pontryagin’s minimum principle (PMP). To relate the level of plant/model mismatch and the control performance degradation in practice, a sensitivity analysis using a computationally efficient method is employed. In this way, the sensitivities or the effects of small parameter variations on the optimal solution, which is the minimum of cumulative tailpipe HC emissions over the coldstart period, are calculated. There is a good agreement between the sensitivity analysis results and the experimental data. The sensitivities indicate the directions of the subsequent parameter estimation and model improvement tasks to enhance the control-relevant accuracy, and thus, the control performance. Furthermore, they provide some insights to simplify the engine model, which is critical for real-time implementation of the coldstart optimal control system.

scholarly journals Real-time Eco-Driving Control in Electrified Connected and Autonomous Vehicles Using Approximate Dynamic Programming

2021 ◽  
Author(s):  
Shreshta Rajakumar Deshpande ◽  
Shobhit Gupta ◽  
Abhishek Gupta ◽  
Marcello Canova
Keyword(s):  
Dynamic Programming ◽  
Real Time ◽  
Real World ◽  
Autonomous Vehicles ◽  
Energy Savings ◽  
Approximate Dynamic Programming ◽  
Connected Vehicle ◽  
Hybrid Powertrain ◽  
Embedded Controller ◽  
Hybrid Electric Powertrain

Abstract Connected and Automated Vehicles (CAVs), particularly those with a hybrid electric powertrain, have the potential to significantly improve vehicle energy savings in real-world driving conditions. In particular, the Eco-Driving problem seeks to design optimal speed and power usage profiles based on available information from connectivity and advanced mapping features to minimize the fuel consumption over an itinerary. This paper presents a hierarchical multi-layer Model Predictive Control (MPC) approach for improving the fuel economy of a 48V mild-hybrid powertrain in a connected vehicle environment. Approximate Dynamic Programming (ADP) is used to solve the Receding Horizon Optimal Control Problem (RHOCP), where the terminal cost for the RHOCP is approximated as the base-policy obtained from the long-term optimization. The controller was tested virtually (with deterministic and Monte Carlo simulation) across multiple real-world routes, demonstrating energy savings of more than 20%. The controller was then deployed on a test vehicle equipped with a rapid prototyping embedded controller. In-vehicle testing confirm the energy savings obtained in simulation and demonstrate the real-time ability of the controller.

Online optimization of Pontryagin’s minimum principle for a series hydraulic hybrid wheel loader

2021 ◽  
pp. 095440702110398
Author(s):  
Qi Zhang ◽  
Feng Wang ◽  
Bing Xu ◽  
Zongxuan Sun
Keyword(s):  
Dynamic Programming ◽  
Fuel Consumption ◽  
Minimum Principle ◽  
Pontryagin’S Minimum Principle ◽  
Energy Management Strategy ◽  
Hybrid Powertrain ◽  
Rule Based ◽  
Wheel Loader ◽  
Hydraulic Hybrid ◽  
Pontryagin's Minimum Principle

The hydraulic hybrid powertrain has great potential for reducing fuel consumption and emission of off-road vehicles. The energy management strategy is the key to hybrid powertrain and currently there are many well-developed strategies. Of which the Pontryagin’s minimum principle is of research interest since it is a global optimization method while less computational burden than dynamic programming. However, it requires full cycle information to calculate co-state value in the principle, making it not implementable. Therefore in this study an implementable Pontryagin’s minimum principle is proposed for a series hybrid wheel loader, where the optimal co-state value in the principle is trained through repetitive wheel loader duty cycle. The Pontryagin’s minimum principle formulations of hybrid wheel loader are developed. The online co-state training algorithm is presented. A dynamic simulation model of hybrid wheel loader is developed. The fuel consumption of hybrid wheel loader with proposed strategy is compared with dynamic programming strategy and rule-based strategy in wheel loader long and short loading cycles. Results show the fuel consumption with proposed strategy is close to dynamic programming result and is lower than rule-based strategy. Finally, the influence of pressure level of hybrid powertrain on vehicle fuel consumption is studied.

Simulation and Performance Analysis of Tilted Time Window and Support Vector Machine Based Learning Object Ranking Method

2020 ◽  
Vol 13 (2) ◽  
pp. 153-164
Author(s):  
Narina Thakur ◽  
Deepti Mehrotra ◽  
Abhay Bansal ◽  
Manju Bala
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Time Window ◽  
Optimal Solution ◽  
Similarity Score ◽  
Learning Objects ◽  
Retrieval Algorithm ◽  
Support Vector ◽  
Computationally Efficient ◽  
User Query

Objective: Since the adequacy of Learning Objects (LO) is a dynamic concept and changes in its use, needs and evolution, it is important to consider the importance of LO in terms of time to assess its relevance as the main objective of the proposed research. Another goal is to increase the classification accuracy and precision. Methods: With existing IR and ranking algorithms, MAP optimization either does not lead to a comprehensively optimal solution or is expensive and time - consuming. Nevertheless, Support Vector Machine learning competently leads to a globally optimal solution. SVM is a powerful classifier method with its high classification accuracy and the Tilted time window based model is computationally efficient. Results: This paper proposes and implements the LO ranking and retrieval algorithm based on the Tilted Time window and the Support Vector Machine, which uses the merit of both methods. The proposed model is implemented for the NCBI dataset and MAT Lab. Conclusion: The experiments have been carried out on the NCBI dataset, and LO weights are assigned to be relevant and non - relevant for a given user query according to the Tilted Time series and the Cosine similarity score. Results showed that the model proposed has much better accuracy.

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