Nasser L. Azad

An optimal power management strategy for power split plug-in hybrid electric vehicles

Model Predictive Control (MPC) can be an interesting concept for designing a power management strategy for Hybrid Electric Vehicles (HEVs) according to its capability of online optimisation by receiving current information from the powertrain and handling hard constraints on such problems. In this paper, a power management strategy for a power split plug-in HEV is proposed using the concept of MPC to evaluate the effectiveness of this method on minimising the fuel consumption of those vehicles. Also, the results are compared with dynamic programming.

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.

Determining Model Accuracy Requirements for Automotive Engine Coldstart Hydrocarbon Emissions Control

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.

Robust state feedback stabilization of articulated steer vehicles

In this work, a full-state feedback controller is designed to prevent the oscillatory instability or snaking behaviour of an articulated steer vehicle. To design the controller, first, a linearized model of the vehicle is developed and analyzed to identify the most important uncertain tire parameters with regard to the snaking mode. By using this linearized model, the equations of motion are represented in the form of a polytopic system, which depends affinely on the most important uncertain tire parameters. Then, by solving some linear matrix inequalities, both the Lyapunov and state feedback matrices for the robust stabilization of the vehicle are found. The performance of the resulting controller is evaluated by conducting several simulations based on the linearized model. To verify the results from the linearized model analysis, some simulations are also done by a virtual prototype of the vehicle in ADAMS. The results based on the linearized model are reasonably consistent with those from the simulations in ADAMS. They show that the controller can effectively stabilize the vehicle during the snaking mode in different driving conditions.

A survey of stability enhancement strategies for articulated steer vehicles

Some applications of Articulated Steer Vehicles (ASVs) result in instability problems, such as rollover, jackknifing and snaking. To find the direction of future investigations and developments for enhancing their stability, the lack of research in this area must be identified. In this work, for ASVs, the published literature on stability, proposed early warning devices as well as stability control systems is reviewed. The literature survey shows that the previous work on the subject of stability analysis and control of ASVs is limited and mainly concerned with the snaking instability. Therefore, more work is needed in predicting and preventing rollover and jackknifing.

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.

High-Fidelity Modeling of a Power-Split Plug-In Hybrid Electric Powertrain for Control Performance Evaluation

Plug-in hybrid electric vehicle (PHEV) development seems to be essential for a sustainable transportation system along with electric vehicles. An appropriate power management strategy for a PHEV determines how to blend the engine and the battery power in such a way that leads to significant fuel economy improvement and environmental footprint reduction. To evaluate and validate the controls design, software and hardware-in-the-loop (SIL/HIL) simulations are useful approaches, especially at the early stages of controls design. To conduct SIL/HIL tests, an accurate and relatively fast mathematical model of the real powertrain is required which solely contains the essential dynamics of the plant. In this paper, a physics-based model of a power-split plug-in powertrain is developed and implemented using MapleSim software. This model contains a chemistry-based lithium-ion battery pack, which can distinguish it from other models used in the literature, since the performance of a PHEV greatly depends on its battery. The symbolic computation power of MapleSim makes the model very suitable for real-time SIL/HIL tests.Copyright

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.