scholarly journals Development and Real-Time Performance Evaluation of Energy Management Strategy for a Dynamic Positioning Hybrid Electric Marine Vessel

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1280
Author(s):  
Truong M. N. Bui ◽  
Truong Q. Dinh ◽  
James Marco ◽  
Chris Watts
Keyword(s):  
Performance Evaluation ◽  
Real Time ◽  
Energy Management ◽  
Power Flow ◽  
Control Strategies ◽  
Storage System ◽  
Dynamic Positioning ◽  
Energy Management Strategy ◽  
Time Performance ◽  
Hybrid Electric

Hybridisation of energy sources in marine vessels has been recognized as one of the feasible solutions to improve fuel economy and achieve global emission reduction targets in the maritime sector. However, the overall performance of a hybrid vessel system is strongly dependent on the efficiency of the energy management system (EMS) that regulates the power-flow amongst the propulsion sources and the energy storage system (ESS). This study develops a simple but production-feasible and efficient EMS for a dynamic positioning (DP) hybrid electric marine vessel (HEMV) and real-time experimental evaluation within a hardware-in-the-loop (HIL) simulation environment. To support the development and evaluation, map-based performance models of HEMVs’ key components are developed. Control logics that underpin the EMS are then designed and verified. Real-time performance evaluation to assess the performance and applicability of the proposed EMS is conducted, showing the improvement over those of the conventional control strategies. The comparison using key performance indicators (KPIs) demonstrates that the proposed EMS could achieve up to 4.8% fuel saving per voyage, while the overall system performance remains unchanged as compared to that of the conventional vessel.

scholarly journals A Real Time Energy Management Strategy for Plug-in Hybrid Electric Vehicles based on Optimal Control Theory

2014 ◽  
Vol 45 ◽  
pp. 949-958 ◽  
Author(s):  
Laura Tribioli ◽  
Michele Barbieri ◽  
Roberto Capata ◽  
Enrico Sciubba ◽  
Elio Jannelli ◽  
...  
Keyword(s):  
Optimal Control ◽  
Control Theory ◽  
Real Time ◽  
Energy Management ◽  
Electric Vehicles ◽  
Optimal Control Theory ◽  
Management Strategy ◽  
Hybrid Electric Vehicles ◽  
Energy Management Strategy ◽  
Hybrid Electric

scholarly journals Smart Energy Management for Series Hybrid Electric Vehicles Based on Driver Habits Recognition and Prediction

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2954
Author(s):  
Loïc Joud ◽  
Rui Da Silva ◽  
Daniela Chrenko ◽  
Alan Kéromnès ◽  
Luis Le Moyne
Keyword(s):  
Experimental Study ◽  
Dynamic Programming ◽  
Real Time ◽  
Energy Management ◽  
Control Strategy ◽  
Management Strategy ◽  
Energetic Efficiency ◽  
Energy Management Strategy ◽  
Common Cycles ◽  
Hybrid Electric

The objective of this work is to develop an optimal management strategy to improve the energetic efficiency of a hybrid electric vehicle. The strategy is built based on an extensive experimental study of mobility in order to allow trips recognition and prediction. For this experimental study, a dedicated autonomous acquisition system was developed. On working days, most trips are constrained and can be predicted with a high level of confidence. The database was built to assess the energy and power needed based on a static model for three types of cars. It was found that most trips could be covered by a 10 kWh battery. Regarding the optimization strategy, a novel real time capable energy management approach based on dynamic vehicle model was created using Energetic Macroscopic Representation. This real time capable energy management strategy is done by a combination of cycle prediction based on results obtained during the experimental study. The optimal control strategy for common cycles based on dynamic programming is available in the database. When a common cycle is detected, the pre-determined optimum strategy is applied to the similar upcoming cycle. If the real cycle differs from the reference cycle, the control strategy is adapted using quadratic programming. To assess the performance of the strategy, its resulting fuel consumption is compared to the global optimum calculated using dynamic programming and used as a reference; its optimality factor is above 98%.

Robust Predictive Energy Management of Connected Power-split Hybrid Electric Vehicles Using Dynamic Traffic Data

2021 ◽  
Author(s):  
Seyedeh Mahsa Sotoudeh ◽  
Baisravan HomChaudhuri
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Driving Cycle ◽  
Traffic Data ◽  
Energy Management Strategy ◽  
Short Term ◽  
Low Level ◽  
Hybrid Electric

Abstract This research focuses on the predictive energy management of connected human-driven hybrid electric vehicles (HEV) to improve their fuel efficiency while robustly satisfying system constraints. We propose a hierarchical control framework that effectively exploits long-term and short-term decision-making benefits by integrating real-time traffic data into the energy management strategy. A pseudospectral optimal controller with discounted cost is utilized at the high-level to find an approximate optimal solution for the entire driving cycle. At the low-level, a Long Short-Term Memory neural network is developed for higher quality driving cycle (velocity) predictions over the low-level's short horizons. Tube-based model predictive controller is then used at the low-level to ensure constraint satisfaction in the presence of driving cycle prediction errors. Simulation results over real-world driving cycles show an improvement in fuel economy for the proposed controller that is real-time applicable and robust to the driving cycle's uncertainty.

scholarly journals Energy Management of Fuel Cell Vehicles Based on Model Prediction Control Using Radial Basis Functions

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Weiwei Xin ◽  
Weiguang Zheng ◽  
Jirong Qin ◽  
Shangjun Wei ◽  
Chunyu Ji
Keyword(s):  
Fuel Cell ◽  
Real Time ◽  
Energy Management ◽  
Model Prediction ◽  
Management Strategy ◽  
Energy Management Strategy ◽  
Time Performance ◽  
Prediction Horizon ◽  
Model Prediction Control ◽  
Prediction Control

Energy management strategies can improve fuel cell hybrid electric vehicles’ dynamic and fuel economy, and the strategies based on model prediction control show great advantages in optimizing the power split effect and in real time. In this paper, the influence of prediction horizon on prediction error, fuel consumption, and real time was studied in detail. The framework of energy management strategy was proposed in terms of the model prediction control theory. The radial basis function neural network was presented as the predictor to obtain the short-term velocity in the future. A dynamic programming algorithm was applied to obtain optimized control laws in the prediction horizon. Considering the onboard controller’s real-time performance, we established a simple fuel cell vehicle mathematical model for simulation. Different prediction horizons were adopted on UDDS and HWFET to test the influence on prediction and energy management strategy. Simulation results showed the strategy performed well in fuel economy and real-time performance, and the prediction horizon of around 20 s was appropriate for this strategy.

Hardware-in-the-loop test for the design of a hybrid electric bus control system

2015 ◽  
Vol 229 (15) ◽  
pp. 2760-2773 ◽  
Author(s):  
Jun Wang ◽  
Qing-nian Wang ◽  
Peng-yu Wang
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Management Strategy ◽  
Hybrid Electric Vehicles ◽  
Hardware In The Loop ◽  
Energy Management Strategy ◽  
Engine Torque ◽  
Driving Cycles ◽  
Battery State Of Charge ◽  
Hybrid Electric

Hybrid electric vehicles present a promising approach to reduce fuel consumption and carbon dioxide emissions. The core technology of hybrid electric vehicles is an energy management strategy to distribute torque between the engine and the electric motor. This study presents an optimized energy management strategy based on real-time control. The operation platform of the control system is based on the dSPACE/simulator, which is a commercial hardware closed-loop system. First, an energy management strategy is built by using an empirical analysis method. To reduce fuel consumption further and to maintain the balance of the battery state of charge, dynamic programming is introduced to achieve the best fuel economy. Optimal gear shifting and engine torque control rules are then extracted into a rule-based control algorithm. Meanwhile, genetic algorithm is introduced to optimize the mode transition rules and the engine torque under parallel mode through an iterative method by defining a cost function over specific driving cycles. Second, a driving cycle recognition algorithm is built to obtain the optimization result over different driving cycles. The real vehicle model is verified by using a hardware-in-the-loop simulator in a virtual forward-facing simulation environment. The energy management strategy uses a code generation technology in the TTC200 controller to achieve vehicle real-time communication. Simulation results demonstrate that the real-time energy management strategy can coordinate the overall hybrid electric powertrain system to optimize fuel economy over different driving cycles and to maintain the battery state of charge.

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