Real-Time Implementation of Optimal Energy Management in Hybrid Electric Vehicles: Globally Optimal Control of Acceleration Events

2020 ◽  
Vol 142 (8) ◽  
Author(s):  
Zachary D. Asher ◽  
David A. Trinko ◽  
Joshua D. Payne ◽  
Benjamin M. Geller ◽  
Thomas H. Bradley
Keyword(s):  
Optimal Control ◽  
Real Time ◽  
Fuel Economy ◽  
Control Strategy ◽  
Limited Information ◽  
Optimal Control Strategy ◽  
Time Optimization ◽  
Vehicle Operation ◽  
Operation Knowledge ◽  
Real Time Optimization

Abstract Widely published research shows that significant fuel economy improvements through optimal control of a vehicle powertrain are possible if the future vehicle velocity is known and real-time optimization calculations can be performed. In this research, however, we seek to advance the field of optimal powertrain control by limiting future vehicle operation knowledge and using no real-time optimization calculations. We have realized optimal control of acceleration events (AEs) in real-time by studying optimal control trends across 384 real world drive cycles and deriving an optimal control strategy for specific acceleration event categories using dynamic programming (DP). This optimal control strategy is then applied to all other acceleration events in its category, as well as separate standard and custom drive cycles using a look-up table. Fuel economy improvements of 2% average for acceleration events and 3.9% for an independent drive cycle were observed when compared to our rigorously validated 2010 Toyota Prius model. Our conclusion is that optimal control can be implemented in real-time using standard vehicle controllers assuming extremely limited information about future vehicle operation is known such as an approximate starting and ending velocity for an acceleration event.

Real-Time Optimal Power Management for Hybrid Electric Vehicle Based on Prediction of Trip Information

2013 ◽  
Vol 321-324 ◽  
pp. 1539-1547 ◽  
Author(s):  
Li Cun Fang ◽  
Gang Xu ◽  
Tian Li Li ◽  
Ke Min Zhu
Keyword(s):  
Optimal Control ◽  
Real Time ◽  
Power Management ◽  
Fuel Economy ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Driving Cycle ◽  
Moving Window ◽  
Optimal Control Strategy ◽  
Time Optimal

Power management of hybrid electric vehicle (HEV) is an important operational factor for HEV to enhance fuel economy and reduce emissions. Optimal control for HEV requires the knowledge of entire driving cycle and elevation profile to obtain the optimal control strategy over fixed driving cycle. In this paper, the traffic knowledge extracted from intelligent transportation systems (ITSs),global positioning systems (GPSs) and geographical information systems (GISs) is used for predicting the knowledge of the future driving cycle, and the real-time optimal control strategy based on dynamic programming in a moving window is investigated in order to minimize fuel consumption. A simulation study was conducted for two driving cycles, and the results showed significant improvement in fuel economy compared with a rule-based control. Furthermore, the results showed that the distance of the moving window has obvious effect on the fuel economy.

Characterizing Optimal Control Strategy Parameters for Improving Fuel Economy of Hybrid Electric Vehicles in Velocity Planning

2012 ◽  
Author(s):  
Jinling Wang ◽  
Wen F. Lu
Keyword(s):  
Optimal Control ◽  
Real Time ◽  
Fuel Economy ◽  
Control Strategy ◽  
Hybrid Electric Vehicles ◽  
Optimal Control Strategy ◽  
Velocity Planning ◽  
Optimal Value ◽  
Polluting Emissions ◽  
Strategy Parameters

Modern traffic prediction technologies enable real-time velocity planning of vehicles for less fuel consumption and polluting emissions by reducing the frequency of acceleration/deceleration, idle time, the number of stop, and variation of vehicle speeds. The fuel economy could be further improved if the optimal control strategy parameter could be used in the real-time velocity planning. However, it is difficult to find the optimal value of the control strategy parameter in this real-time velocity planning of vehicles. This paper aims to develop an advising system for control strategy parameters of HEVs in velocity planning. With this aim, the characteristics of the optimal control strategy parameters for various velocity profiles obtained from predictive velocity planning are studied in a parallel HEV. The optimal control strategy parameters with the effect of the average speed, stop frequency, and the traveling distance are investigated. The observed characteristics of the optimal parameters are obtained and can be used in the advising system to improve fuel economy in real-time velocity planning of HEVs.

Experimental Evaluation of a Control Strategy for Real-Time Optimization of Low Temperature PEM Fuel Cell Stack

2009 ◽  
Author(s):  
Panini Kolavennu ◽  
Susanta K. Das ◽  
K. Joel Berry
Keyword(s):  
Fuel Cell ◽  
Real Time ◽  
Control Strategy ◽  
Pem Fuel Cell ◽  
Effective Control ◽  
Feed Forward ◽  
Fuel Cell Stack ◽  
Feed Forward Control ◽  
Time Optimization ◽  
Real Time Optimization

A robust control strategy which ensures optimum performance is crucial to proton exchange membrane (PEM) fuel cell development. In a PEM fuel cell stack, the primary control variables are the reactant’s stochiometric ratio, membrane’s relative humidity and operating pressure of the anode and cathode. In this study, a 5 kW (25-cell) PEM fuel cell stack is experimentally evaluated under various operating conditions. Using the extensive experimental data of voltage-current characteristics, a feed forward control strategy based on a 3D surface map of cathode pressure, current density and membrane humidity at different operating voltages is developed. The effectiveness of the feed forward control strategy is tested on the Green-light testing facility. To reduce the dependence on predetermined system parameters, real-time optimization based on extremum seeking algorithm is proposed to control the air flow rate into the cathode of the PEM fuel cell stack. The quantitative results obtained from the experiments show good potential towards achieving effective control of PEM fuel cell stack.

scholarly journals Experimental Comparison of Three Real-Time Optimization Strategies Applied to Renewable/ FC-Based Hybrid Power Systems Based on Load-Following Control

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3537 ◽  
Author(s):  
Nicu Bizon ◽  
Mihai Oproescu
Keyword(s):  
Power Systems ◽  
Real Time ◽  
Fuel Consumption ◽  
Fuel Economy ◽  
Experimental Comparison ◽  
Hybrid Power Systems ◽  
Hybrid Power ◽  
Load Following ◽  
Time Optimization ◽  
Real Time Optimization

Besides three different real-time optimization strategies analyzed for the Renewable/ Fuel Cell Hybrid Power Systems (REW/FC-HPS) based on load-following (LFW) control, a short but critical assessment of the Real-Time Optimization (RTO) strategies is presented in this paper. The advantage of power flow balance on the DC bus through the FC net power generated using the LFW control instead of using the batteries’ stack is highlighted in this study. As LFW control consequence, the battery operates in charge-sustained mode and many advantages can be exploited in practice such as: reducing the size of the battery and maintenance cost, canceling the monitoring condition of the battery state-of-charge etc. The optimization of three FC-HPSs topologies based on appropriate RTO strategy is performed here using indicators such as fuel economy, fuel consumption efficiency, and FC electrical efficiency. The challenging task to optimize operation of the FC-HPS under unknown profile of the load demand is approached using an optimization function based on linear mix of the FC net power and the fuel consumption through the weighting coefficients knet and kfuel. If optimum values are chosen, then a RTO switching strategy can improve even further the fuel economy over the entire range of load.

scholarly journals Optimized Fuel Economy Control of Power-Split Hybrid Electric Vehicle with Particle Swarm Optimization

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2278 ◽  
Author(s):  
Hsiu-Ying Hwang ◽  
Jia-Shiun Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Real Time ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Swarm Optimization ◽  
Time Optimization ◽  
Optimization Control ◽  
Power Split ◽  
Hybrid Electric ◽  
Real Time Optimization

This research focused on real-time optimization control to improve the fuel consumption of power-split hybrid electric vehicles. Particle swarm optimization (PSO) was implemented to reduce fuel consumption for real-time optimization control. The engine torque was design-variable to manage the energy distribution of dual energy sources. The AHS II power-split hybrid electric system was used as the powertrain system. The hybrid electric vehicle model was built using Matlab/Simulink. The simulation was performed according to US FTP-75 regulations. The PSO design objective was to minimize the equivalent fuel rate with the driving system still meeting the dynamic performance requirements. Through dynamic vehicle simulation and PSO, the required torque value for the whole drivetrain system and corresponding high-efficiency engine operating point can be found. With that, the two motor/generators (M/Gs) supplemented the rest required torques. The composite fuel economy of the PSO algorithm was 46.8 mpg, which is a 9.4% improvement over the base control model. The PSO control strategy could quickly converge and that feature makes PSO a good fit to be used in real-time control applications.

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