Mahyar Vajedi

Ecological Adaptive Cruise Controller for Plug-In Hybrid Electric Vehicles Using Nonlinear Model Predictive Control

Plug-in hybrid electric vehicles (PHEVs) are promising options for future transportation. Having two sources of energy enables them to offer better fuel economy and fewer emissions. Significant research has been done to take advantage of future route information to enhance vehicle performance. In this paper, an ecological adaptive cruise controller (Eco-ACC) is used to improve both fuel economy and safety of the Toyota Prius Plug-in Hybrid. Recently, an emerging trend in the research has been to improve the adaptive cruise controller. However, the majority of research to date has focused on driving safety, and only rare reports in the literature substantiate the applicability of such systems for PHEVs. Here, we demonstrate that using an Eco-ACC system can simultaneously improve total energy costs and vehicle safety. The developed controller is equipped with an onboard sensor that captures upcoming trip data to optimally adjust the speed of PHEVs. The nonlinear model predictive control technique (NMPC) is used to optimally control vehicle speed. To prepare the NMPC controller for real-time applications, a fast and efficient control-oriented model is developed. The authenticity of the model is validated using a high-fidelity Autonomie-based model. To evaluate the designed controller, the global optimum solution for cruise control problem is found using Pontryagins minimum principle (PMP). To explore the efficacy of the controller, PID and linear MPC controllers are also applied to the same problem. Simulations are conducted for different driving scenarios such as driving over a hill and car following. These simulations demonstrate that NMPC improves the total energy cost up to 19%.

A robust safety-oriented autonomous cruise control scheme for electric vehicles based on model predictive control and online sequential extreme learning machine with a hyper-level fault tolerance-based supervisor

Abstract In this investigation, an advanced modeling method, called online sequential extreme learning machine with a hyper-level fault tolerance-based supervisor (OSELM–FTS), is utilized to develop a robust safety-oriented autonomous cruise control based on the model predictive control (MPC) technique. The resulting MPC-based cruise controller is used to improve the driving safety and reduce the energy consumption of an electric vehicle (EV). The structural flexibility of OSELM–FTS allows us to not only improve the operating features of the EV, but also develop an intelligent supervisor which can detect any operating fault and send proper commands for the adaption of the MPC controller. This introduces a degree of robustness to the devised controller, as OSELM–FTS automatically detects and filters any operating faults which may undermine the performance of the MPC controller. To ascertain the veracity of the devised controller, three well-known MPC formulations, i.e. linear MPC (LMPC) and nonlinear MPC (NMPC) and diagonal recurrent neural network MPC (DRNN-MPC), are applied to the baseline EV and their performances are compared with OSELM–FTS-MPC. To further elaborate on the computational advantages of OSELM, a well-known chunk-by-chunk incremental machine learning approach, namely selective negative correlation learning (SNCL), is taken into account. The results of the comparative study indicate that OSELM–FTS-MPC is a very promising control scheme and can be reliably used for safety-oriented autonomous cruise control of the EVs.

Multi-objective component sizing of a power-split plug-in hybrid electric vehicle powertrain using Pareto-based natural optimization machines

The urgent need to meet increasingly tight environmental regulations and new fuel economy requirements has motivated system science researchers and automotive engineers to take advantage of emerging computational techniques to further advance hybrid electric vehicle and plug-in hybrid electric vehicle (PHEV) designs. In particular, research has focused on vehicle powertrain system design optimization, to reduce the fuel consumption and total energy cost while improving the vehicles driving performance. In this work, two different natural optimization machines, namely the synchronous self-learning Pareto strategy and the elitism non-dominated sorting genetic algorithm, are implemented for component sizing of a specific power-split PHEV platform with a Toyota plug-in Prius as the baseline vehicle. To do this, a high-fidelity model of the Toyota plug-in Prius is employed for the numerical experiments using the Autonomie simulation software. Based on the simulation results, it is demonstrated that Pareto-based algorithms can successfully optimize the design parameters of the vehicle powertrain.

Intelligent power management of plug–in hybrid electric vehicles, part II: real–time route based power management

PHEVs are considered as a viable solution to address environmental concerns facing the global automotive industry. In this paper, we design a new real–time route–based power management system for PHEVs based on the adaptive equivalent consumption minimisation strategy (A–ECMS). The designed controller takes advantage of preview trip information to achieve even higher efficiencies. To evaluate the designed system, this controller was assigned to a high fidelity model of Toyota Prius Plug–in Hybrid built in Autonomie. The results are compared against those of several existing power management systems, including: charge depleting charge sustaining (CDCS), manual CDCS, and rule–based strategies as well as Pontryagins minimum principle (PMP). While PMP efficiency is slightly higher than the new controller, the real–time power management system proved more reliable in terms of the computational time. It is shown that route based power management strategy improved the fuel economy up to 11% compared to the rule–based controller within Autonomie.

A comparative analysis of route-based power management strategies for real-time application in plug-in hybrid electric vehicles

Plug-in hybrid electric vehicles (PHEVs) are promising alternatives for sustainable transportation. Because of their embedded battery pack, they can significantly enhance the fuel economy compared to conventional vehicle. Improving the power management strategy can exploit the full potential of a PHEV powertrain and reduce the fuel consumption considerably. In this study, we compare two outstanding optimal route-based control approaches: model predictive control (MPC) and adaptive equivalent consumption minimization strategy (A-ECMS), for different levels of trip information. The controllers are fine-tuned for the 2013 Toyota Prius plug-in hybrid and implemented as a high-fidelity model in Autonomie software. To evaluate the designed control systems, the rule-based controller of Autonomie is considered as a benchmark power management system. The result of simulations show that MPC and A-ECMS lead to an approximately equal fuel economy, and they can improve the fuel consumption of this PHEV up to 10% in comparison to the benchmark controller. Both strategies can be implemented in real-time although A-ECMS is 15% faster than MPC.

Traction-Motor Power Ratio and Speed Trajectory Optimization for Power Split PHEVs Using Route Information

Plug-in hybrid electric vehicles (PHEVs) are a promising option for future of transportation. They suggest better fuel economy and less emission compared to conventional HEVs. In this work, a method to find the optimum traction-motor power ratio (TMPR) and speed trajectory for a power-split PHEV is proposed in order to minimize the fuel consumption. The traveling path is divided into several segments. Each segment consists of acceleration, constant speed, and deceleration sections. Also, the route information, such as travel distance, traffic data, the maximum permissible speed, and road grade are known in each segment.The results of simulation show a considerable reduction in the fuel consumption for different energy management strategies; up to 8% in CDCS, 12.9% in manual CDCS, and 18.2% in blended strategy, using the proposed optimum TMPR and speed trajectory.Copyright

Predictive Power Management Strategy for a PHEV Based on Different Levels of Trip Information

Abstract Increasing restrictions on emissions and fuel consumption of internal combustion engines and availability of fast processors have resulted in an increased interest in using model predictive control (MPC) for vehicle powertrain control applications. This approach is capable of fast and near-optimal control of hybrid electric vehicle powertrains. In this article, different MPC power management strategies for a plug-in hybrid electric vehicle are evaluated, with different levels of trip information available to the controller, based on fuel economy.

A cost-effective radar system for automotive powertrain control applications

This paper describes a new and unique merger of radar technology to automotive powertrain control systems, in particular, with the goal of increasing fuel efficiency in hybrid electric vehicles. The power management system can make more intelligent decisions impacting fuel consumption and emissions if it has knowledge of objects surrounding the vehicle. Furthermore, this paper proposes a scheme for creating a cost effective radar sensor. Discussed in detail is how such a system capable of providing the necessary information for powertrain control applications may be designed specifically to minimize cost and complexity. Finally, an evaluation and prototyping platform consisting of both hardware and software is described, which allows for evaluation and verification of the design tradeoffs. The same platform will also provide the future possibility of the powertrain control algorithm verification on the road as a later step of this research.

Implementation of a fast non-dominated sorting firefly algorithm and a vehicle simulation model for multi-objective component sizing of a power-split PHEV powertrain: a comparative numerical study

In the current investigation, the authors take advantage of a well-known emerging swarm intelligence-based metaheuristic method, i.e., firefly algorithm (FA), to cope with a tedious automotive optimisation problem, known as component sizing. As far as the authors are concerned, the presented research can be considered as one of the rare archived reports which substantiate the applicability and efficacy of metaheuristics for the component sizing of power-split plug-in hybrid electric vehicle (PHEV) powertrains. Here, the authors take one further step and formulate a complex multiobjective optimisation problem to clearly investigate the potentials of metaheuristics. It is worth pointing out that most of the existing classical optimisation approaches are unable to successfully solve a multiobjective component sizing problem, and are often trapped into local minimums and offer local Pareto solutions. Moreover, through a numerical comparative study, the superiority of the proposed fast non-dominated sorting firefly algorithm (FNSFA) over the non-dominated sorting genetic algorithm (NSGA-II) is demonstrated. The outcomes of this research encourage automotive engineers to take advantage of nature-based optimisers, e.g., FNSFA, for component sizing problems.

Real-Time Trip Planning Module Development and Evaluation

This chapter presents the Trip Planning module as a part of the devised energy-optimal controller. This module takes advantage of long-range trip data to optimize SOC profiles.