Plug-in hybrid electric vehicles (PHEVs) offer an immediate solution for emissions reduction and fuel displacement within the current infrastructure. Targeting PHEV powertrain optimization, a plethora of energy management strategies (EMSs) have been proposed. Although these algorithms present various levels of complexity and accuracy, they find a limitation in terms of availability of future trip information, which generally prevents exploitation of the full PHEV potential in real-life cycles. This paper presents a comprehensive analysis of EMS evolution toward blended mode (BM) and optimal control, providing a thorough survey of the latest progress in optimization-based algorithms. This is performed in the context of connected vehicles and highlights certain contributions that intelligent transportation systems (ITSs), traffic information, and cloud computing can provide to enhance PHEV energy management. The study is culminated with an analysis of future trends in terms of optimization algorithm development, optimization criteria, PHEV integration in the smart grid, and vehicles as part of the fleet.

Energy Management in Plug-in Hybrid Electric Vehicles: Recent Progress and a Connected Vehicles Perspective

Rule based energy management strategy for a series–parallel plug-in hybrid electric bus optimized by dynamic programming

The performance and practicality of predictive energy management in hybrid electric vehicles (HEVs) are highly dependent on the forecast of future vehicular velocities, both in terms of accuracy and computational efficiency. In this brief, we provide a comprehensive comparative analysis of three velocity prediction strategies, applied within a model predictive control framework. The prediction process is performed over each receding horizon, and the predicted velocities are utilized for fuel economy optimization of a power-split HEV. We assume that no telemetry or on-board sensor information is available for the controller, and the actual future driving profile is completely unknown. Basic principles of exponentially varying, stochastic Markov chain, and neural network-based velocity prediction approaches are described. Their sensitivity to tuning parameters is analyzed, and the prediction precision, computational cost, and resultant vehicular fuel economy are compared.

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Velocity Predictors for Predictive Energy Management in Hybrid Electric Vehicles

Faced with environmental issues caused by fossil fuel burning in the industrial and transportation sectors, innovations towards cleaner solutions to replace the ever diminishing fossil fuels have been the focus of not only researchers but governments all around the world. The hybrid electric vehicle (HEV) technology is the result of the desire to have vehicles with a better fuel economy and lower tailpipe emissions to meet the requirements of environmental policies as well as to absorb the impact of rising fuel prices. The objectives are met by combining a conventional internal combustion engine (ICE) with one or more electric motors powered by a battery pack that can be charged using an on-board generator and the regenerative braking technology to power the transmission. The challenge is to develop an efficient energy management strategy (EMS) to satisfy the objectives while not having a reduced vehicle performance. In this paper, EMSs that are proposed and developed in the recent years are revisited and reviewed. Additionally, the Plug-in HEV is discussed in a new perspective from the EMS point of view. The through-the-road (TtR) HEV with in-wheel motors (IWM) is a fairly new concept in the HEV design that features less complicated configuration with reduced hardware requirements and lower cost. Recent research findings are evaluated throughout this paper leading to a hypothetical TtR HEV materialization. A thorough discussion is made encompassing the advantages and disadvantages of the concept, its performance compared to conventional HEVs and the way forward.

A review on hybrid electric vehicles architecture and energy management strategies

Model predictive control power management strategies for HEVs: A review

A power-split hybrid electric vehicle (HEV) combines the advantages of both series and parallel hybrid vehicle architectures by utilizing a planetary gear set to split and combine the power produced by electric machines and a combustion engine. Because of the different modes of operation, devising a near optimal energy management strategy is quite challenging and essential for these vehicles. To improve the fuel economy of a power-split HEV, we first formulate the energy management problem as a nonlinear and constrained optimal control problem. Then two different cost functions are defined and model predictive control (MPC) strategies are utilized to obtain the power split between the combustion engine and electrical machines and the system operating points at each sample time. Simulation results on a closed-loop high-fidelity model of a power-split HEV over multiple standard drive cycles and with different controllers are presented. The results of a nonlinear MPC strategy show a noticeable improvement in fuel economy with respect to those of an available controller in the commercial Powertrain System Analysis Toolkit (PSAT) software and the other proposed methodology by the authors based on a linear time-varying MPC.

MPC-Based Energy Management of a Power-Split Hybrid Electric Vehicle

In this paper, merits of using an ultracapacitor in combination with a battery in a power-split Hybrid Electric Vehicle (HEV) is analyzed. For this, an online optimization-based model predictive controller (MPC) is designed and a closed-loop model of the system is developed. Based on the definition of C-rate parameter which indicates the discharge intensity of the battery, a number of simulations over standard driving cycles are performed. Closed-loop simulations on a detailed model of the HEV show that adding the ultracapacitor to the ESS unit can reduce intensity of battery discharge, as indicated by the C-rate, noticeably.

Model predictive control of a power-split Hybrid Electric Vehicle with combined battery and ultracapacitor energy storage

An internal combustion engine (ICE) is a highly nonlinear dynamic and complex engineering system whose operation is constrained by operational limits, including emissions, noise, peak in-cylinder pressure, combustion stability, and actuator constraints. To optimize today’s ICEs, seven to ten control actuators and 10–20 feedback sensors are often used, depending on the engine applications and target emission regulations. This requires extensive engine experimentation to calibrate the engine control module (ECM), which is both cumbersome and costly. Despite these efforts, optimal operation, particularly during engine transients and to meet real driving emission (RDE) targets for broad engine speed and load conditions, has still not been obtained. Methods of model predictive control (MPC) have shown promising results for real-time multi-objective optimal control of constrained multi-variable nonlinear systems, including ICEs. This paper reviews the application of MPC for ICEs and analyzes the recent developments in MPC that can be utilized in ECMs. ICE control and calibration can be enhanced by taking advantage of the recent developments in the field of Artificial Intelligence (AI) in applying Machine Learning (ML) to large-scale engine data. Recent developments in the field of ML-MPC are investigated, and promising methods for ICE control applications are identified in this paper.

Model Predictive Control of Internal Combustion Engines: A Review and Future Directions

A battery management system for a rechargeable battery includes a battery monitor configured to acquire data regarding the rechargeable battery and a processor. The processor is configured to determine an initial state of charge of the battery based on the acquired data; determine a target state of charge for the battery; determine a plurality of charging solutions to achieve the target state of charge based on an optimization of one variable of the battery of a plurality of variables of the battery; narrow the plurality of charging solutions to charging solutions that meet an objective of each of the remaining plurality of variables of the battery; select the charging solution that corresponds with the fastest charge time for the battery from the charging solutions that meet the objective of each of the plurality of battery variables; and command a controller to regulate an amount of charge to the battery in accord with the selected charging solution.

Optimization-based predictive method for battery charging

In this paper, the development of a multivariable control system based on Model Predictive Control (MPC) is described and experimentally validated for a diesel engine with a complex dual-loop Exhaust Gas Recirculation (EGR) and Variable Geometry Turbocharger (VGT) air handling system. This air architecture is required to meet the stringent emissions and fuel economy requirements for this engine. The air system is a multi-input multi-output nonlinear system that makes the design and tuning of the controller challenging. In this paper, a switched linear MPC with feed-forward and feedback control architecture is developed for the system. The presented design process is systematic, modular, and model-based leading to a considerable reduction in the development and calibration time of the control system. A significant improvement is also observed with respect to a well-tuned feed-forward controller. The design process and the performance of the controller are demonstrated experimentally in the engine test cell using Matlab/SIMULINK software and dSPACE rapid prototyping tool.

Hoseinali Borhan

Papers

MPC-Based Energy Management of a Power-Split Hybrid Electric Vehicle

Model predictive control of a power-split Hybrid Electric Vehicle with combined battery and ultracapacitor energy storage

Model Predictive Control of Internal Combustion Engines: A Review and Future Directions

Optimization-based predictive method for battery charging

Air handling control of a diesel engine with a complex dual-loop EGR and VGT air system using MPC