Ardalan Vahidi

Predictive energy management of a power-split hybrid electric vehicle

In this paper, a Model Predictive Control (MPC) strategy is developed for the first time to solve the optimal energy management problem of power-split hybrid electric vehicles. A power-split hybrid combines the advantages of series and parallel hybrids by utilizing two electric machines and a combustion engine. Because of its many modes of operation, modeling a power-split configuration is complex and devising a near-optimal power management strategy is quite challenging. To systematically improve the fuel economy of a power-split hybrid, we formulate the power management problem as a nonlinear optimization problem. The nonlinear powertrain model and the constraints are linearized at each sample time and a receding horizon linear MPC strategy is employed to determine the power split ratio based on the updated model. Simulation results over multiple driving cycles indicate better fuel economy over conventional strategies can be achieved. In addition the proposed algorithm is causal and has the potential for real-time implementation.

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

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.

Recursive least squares with forgetting for online estimation of vehicle mass and road grade: theory and experiments

Good estimates of vehicle mass and road grade are important in automation of heavy duty vehicles, vehicle following manoeuvres or traditional powertrain control schemes. Recursive least square (RLS) with multiple forgetting factors accounts for different rates of change for different parameters and thus, enables simultaneous estimation of the time-varying grade and the piece-wise constant mass. An ad hoc modification of the update law for the gain in the RLS scheme is proposed and used in simulation and experiments. We demonstrate that the proposed scheme estimates mass within 5% of its actual value and tracks grade with good accuracy provided that inputs are persistently exciting. The experimental setups, signals, their source and their accuracy are discussed. Issues like lack of persistent excitations in certain parts of the run or difficulties of parameter tracking during gear shift are explained and suggestions to bypass these problems are made.

Predictive Cruise Control: Utilizing Upcoming Traffic Signal Information for Improving Fuel Economy and Reducing Trip Time

This brief proposes the use of upcoming traffic signal information within the vehicles adaptive cruise control system to reduce idle time at stop lights and fuel consumption. To achieve this goal an optimization-based control algorithm is formulated that uses short range radar and traffic signal information predictively to schedule an optimum velocity trajectory for the vehicle. The control objectives are: timely arrival at green light with minimal use of braking, maintaining safe distance between vehicles, and cruising at or near set speed. Three example simulation case studies are presented to demonstrate the potential impact on fuel economy, emission levels, and trip time.

Current Management in a Hybrid Fuel Cell Power System: A Model-Predictive Control Approach

The problem of oxygen starvation in fuel cells coupled with air compressor saturation limits, is addressed in this paper. We propose using a hybrid configuration, in which a bank of ultracapacitors supplements the polymer electrolyte membrane fuel cell during fast current transients. Our objective is to avoid fuel cell oxygen starvation, prevent air compressor surge and choke, and simultaneously match an arbitrary level of current demand. We formulate the distribution of current demand between the fuel cell and the bank of ultracapacitors in a model predictive control framework, which can handle multiple constraints of the hybrid system. Simulation results show that reactant deficit during sudden increase in stack current is reduced from 50% in stand-alone architecture to less than 1% in the hybrid configuration. In addition, the explicit constraint handling capability of the current management scheme prevents compressor surge and choke and maintains the state-of-charge of the ultracapacitor within feasible bounds

Role of Terrain Preview in Energy Management of Hybrid Electric Vehicles

Energy-management strategy plays a critical role in high fuel economy that modern hybrid electric vehicles can achieve, yet a lack of information about future driving conditions is one of the limitations of fulfilling the maximum fuel economy potential of hybrid vehicles. Today, with wider deployment of vehicle telematic technologies, prediction of future driving conditions, e.g., road grade, is becoming more realistic. This paper evaluates the potential gain in fuel economy if road grade information is integrated into the energy management of hybrid vehicles. Real-world road geometry information is utilized in power-management decisions by using both dynamic programming (DP) and a standard equivalent consumption minimization strategy (ECMS). At the same time, two baseline control strategies with no future information are developed and validated for comparison purposes. Simulation results show that road terrain preview enables fuel savings. The level of improvement depends on the cruising speed, control strategy, road profile, and the size of the battery.

Route Preview in Energy Management of Plug-in Hybrid Vehicles

This brief evaluates the use of terrain, vehicle speed, and trip distance preview to increase the fuel economy of plug-in hybrid vehicles. Access to future information is classified into full, partial, or no future information and for each case an energy management strategy with the potential for a real-time implementation is proposed. With full knowledge of future driving conditions, dynamic programming (DP) provides a best-achievable benchmark. A partial preview level has access to future trip terrain and requires velocity estimation. Equivalent consumption minimization strategy (ECMS) is deployed as an instantaneous real-time minimization strategy with parameters adjusted by estimated future driving conditions and obtained either from DP or from a backward solution of ECMS. To reduce the requirement for future velocity and detailed terrain information, another partial preview level only assumes known trip distance to the next charging station and elevation changes (if available). In this level, the parameter of the real-time ECMS is estimated based on the remaining trip distance, the batterys state-of-charge, and elevation changes if included. The results are evaluated against cases with no preview. Results from a number of simulation case studies indicate that the fuel economy can be substantially enhanced with only partial preview.

Predictive Control of Voltage and Current in a Fuel Cell–Ultracapacitor Hybrid

This paper presents a system integration and control strategy for managing power transients on a Nexa polymer electrolyte membrane fuel cell (FC) with the assistance of an ultracapacitor (UC) module. The two degrees of freedom provided by the use of two dc/dc converters enable the independent low-level control of dc bus voltage and the current split between the FC and UC. The supervisory-level control objectives are to respond to rapid variations in load while minimizing damaging fluctuations in FC current and maintaining the UC charge (or voltage) within allowable bounds. The use of a model predictive control approach which optimally balances the distribution of power between the FC and UC while satisfying the constraints is shown to be an effective method for meeting the supervisory-level objectives. The results are confirmed in experiments.

Ultracapacitor Assisted Powertrains: Modeling, Control, Sizing, and the Impact on Fuel Economy

This paper considers modeling and energy management control problems for an automotive powertrain augmented with an ultracapacitor and an induction motor. The ultracapacitor-supplied motor assists the engine during periods of high power demand. The ultracapacitor may be recharged via regeneration during braking and by the engine during periods of low power demand. A reduced-order model and a detailed simulation model of the powertrain are created for control design and evaluation of fuel economy, respectively. A heuristic rule-based controller is used for testing the impact of different component combinations on fuel economy. After a suitable combination of engine, motor, and ultracapacitor sizes has been determined, a model predictive control strategy is created for power management which achieves better fuel economy than the rule-based approach. Various component sizing and control strategies tested consistently indicate a potential for 5% to 15% improvement in fuel economy in city driving with the proposed mild hybrid powertrain. This order of improvement to fuel economy was confirmed by deterministic dynamic programming which finds the best possible fuel economy.

Simultaneous mass and time-varying grade estimation for heavy-duty vehicles

In this paper, two different approaches are proposed for simultaneous mass and grade estimation for heavy-duty vehicles. In the first method, an observer is used which can estimate mass and time-varying grade given their feasible range. The second approach is a recursive time-varying least square method with forgetting. Inclusion of multiple forgetting factors makes the algorithm suitable for simultaneous estimation of a constant and a fast time-varying parameter. Accurate mass estimation and good tracking of time-varying grade is demonstrated in simulations.