Amir Taghavipour

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.

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.

Design and Evaluation of a Real-time Fuel-optimal Control System for Series Hybrid Electric Vehicles

Razavian, R. S., Taghavipour, A., Azad, N. L., & McPhee, J. (2012). Design and evaluation of a real-time fuel-optimal control system for series hybrid electric vehicles. International Journal of Electric and Hybrid Vehicles, 4(3), 260. Final version published by Inderscience Publishers, and available at: https://doi.org/10.1504/IJEHV.2012.050501

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

Design and evaluation of a predictive powertrain control system for a plug-in hybrid electric vehicle to improve the fuel economy and the emissions:

In this article, a power management scheme for a plug-in power-split hybrid electric vehicle is designed on the basis of the model predictive control concept of charge depletion plus charge sustenance strategy and the blended-mode strategy. The commands of model predictive control are applied to the powertrain components through appropriate low-level controllers: standard proportional–integral controllers for electric machines, and sliding-mode controllers for engine torque control. Minimization of the engine emissions is a key factor for designing the engine’s low-level controller. Applying this control scheme to a validated high-fidelity model of a plug-in hybrid electric vehicle, developed in the MapleSim environment with a chemistry-based Lithium-ion battery model, results in considerable improvements in the fuel economy and the emissions performance.

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 hybrid switching predictive controller with proportional integral derivative gains and GMDH neural representation of automotive engines for coldstart emission reductions

In this research, a high-performance predictive controller is developed for automotive coldstart emission reductions. The proposed control scheme combines a hybrid switching predictive controller (HSPC) with proportional integral derivative (PID) gains to simultaneously minimize the cumulative hydrocarbon emissions (HCcum) and the control input variations for a given engine during the coldstart operation. It is essential to use a sufficiently accurate surrogate meta-representation of the real engine within this model-based controller to predict the states of the plant and impart proper control commands to the system. The existing studies in the research literature have clearly demonstrated that automotive engines have a highly transient nonlinear behavior during coldstart periods and different disturbances can affect their operations. To cope with the mentioned difficulties, several coldstart experiments are performed to capture a comprehensive database for the considered engine. Thereafter, a powerful knowledge-based black-box meta-modeling tool, known as group method data handling (GMDH), is adopted to have a neural representation of the engines coldstart behavior. As a real-time controller, the proposed PID-based HSPC requires a fast and robust solver to calculate the gains of PID in a computationally efficient manner. Here, a multivariate quadratic fit-sectioning algorithm (MQFSA) is proposed to deterministically determine the control commands. Other than the considered online optimizer, a powerful chaos-enhanced evolutionary algorithm (CEA) is used to heuristically optimize the prediction horizon (HP) and control commands horizon (HU) to achieve the best results. It is demonstrated that using such an optimizer, instead of trial-and-errors, to heuristically set the control and plant prediction horizon lengths is an effective strategy. Finally, several comparative studies are conducted to further indicate the efficacy of the proposed PID-based HSPC for the automotive coldstart control problem.

Multi-parametric energy management system with reduced computational complexity for plug-in hybrid electric vehicles

Due to the limited computational capabilities of commercial control hardware, the implementation of model-based optimal control approaches remains a challenging problem. Among the model-based approaches, model predictive control (MPC) is infamous for its cumbersome computational cost especially for designing a hybrid vehicle powertrain energy management system (EMS). To resolve this issue, two multi-parametric model predictive EMSs for a plug-in hybrid electric vehicle (PHEV) are introduced, by considering the limited memory size of a control hardware. One of the EMSs is designed based on an improved control-oriented model that is derived by using the control-relevant parameter estimation (CRPE) approach. The results of simulation using Autonomie software shows significant fuel saving by using these EMSs compared to a baseline controller, while maintaining real-time capabilities.

Nonlinear Power Balance Control of a SPA hydraulic hybrid truck

An integrated procedure for mathematical modeling and power control balance for a SPA (Split Parallel Architecture) hydraulic hybrid vehicle is presented in this paper. Dynamic mathematical model of the powertrain is constructed firstly, which includes five modules: diesel engine, traction motor, starter, loading pump and accumulator. Based on the mathematic model of control modes a stability analysis is done. For some control modes a Sliding Mode Control is designed, which uses full-states closed-loop feedback. The paper finally illustrates and discusses the results of simulation. The results show that the performance and stability of control modes are proper and a power control strategy can be designed to reduce the consumption even more than the presented control.