scholarly journals Multi-Level Energy Management for Hybrid Electric Vehicles—Part I

Vehicles ◽  
2019 ◽  
Vol 1 (1) ◽  
pp. 3-40 ◽  
Author(s):  
Vital van Reeven ◽  
Theo Hofman
Keyword(s):  
Energy Management ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Level Energy ◽  
Conventional Vehicle ◽  
Decision Variables ◽  
Multi Level ◽  
Hybrid Electric ◽  
System States ◽  
Battery Energy

The fuel economy of a hybrid electric vehicle (HEV) is improved, by taking the energy relevant system states into account in the energy management system (EMS). With an increasing number of states and decision variables, energy optimizing algorithms in the EMS can be prohibitive for real-time implementation. In part I of this work, a model-based, multi-level approach is taken to subdivide the original (large) optimization problem into computational efficient sub-problems, based on optimal control techniques using a preview. The resulting EMS solves the problem of power-split between engine and motor/generator, mode and gear switching including switching costs, with battery energy constraints. The superior energy efficiency of the multi-level EMS is simulated on a representative heavy duty drive cycle, where it saves 7.0% fuel, compared to a conventional vehicle, where the baseline EMS for the HEV saves 5.8%. In part II, real-world validation of the EMS is performed.

scholarly journals Multi-Level Energy Management—Part II: Implementation and Validation

Vehicles ◽  
2019 ◽  
Vol 1 (1) ◽  
pp. 41-56 ◽  
Author(s):  
Vital van Reeven ◽  
Theo Hofman
Keyword(s):  
Energy Management ◽  
Hybrid Electric Vehicle ◽  
Fuel Efficiency ◽  
Real Life ◽  
Level Energy ◽  
Fuel Saving ◽  
Energy Management Systems ◽  
Multi Level ◽  
Hybrid Electric ◽  
Map Data

In hybrid electric vehicles, energy management systems (EMS) using optimization show superior fuel efficiency compared to rule-based strategies. However, little research shows its real-life applicability. In Part II of this work, the multi-level, model-predictive EMS from Part I is implemented on a heavy-duty parallel hybrid electric vehicle, using GPS and map data as preview. The power split, hybrid mode, and gear selection, including switching costs, are optimized in real time, thereby proving the feasibility of optimal control techniques for hybrid driveline control. Functional validation of the EMS on a test track confirm the fuel-saving mechanism as simulated in Part I. In addition to a fuel saving of 36%, the EMS also improves the drivability, by reducing the amount of open driveline events.

Hybrid Electric Vehicle Energy Management and Control Based on Battery Energy Balance

2013 ◽  
Vol 273 ◽  
pp. 764-767
Author(s):  
Bin Yan ◽  
Yan Qing Hu ◽  
Ting Yan ◽  
Pei Pei Ma ◽  
Lin Yang
Keyword(s):  
Energy Balance ◽  
Electric Vehicle ◽  
Energy Management ◽  
Fuel Economy ◽  
Power Efficiency ◽  
Hybrid Electric Vehicle ◽  
Real Time Control ◽  
Energy Management Strategy ◽  
Hybrid Electric ◽  
Battery Energy

Hybrid electric vehicle has better power and economy than conventional vehicle attributed to power efficiency range is optimized by battery energy. So making battery energy balance not only can ensure hybrid power system operate normally, but also is the key role in meeting vehicle drivability and improving fuel economy effectively. This paper analyze of the regenerating and using of battery energy. Real-time control and global optimization is used to adjust energy management strategy, the adaptive control strategy also introduced to making energy power balance on the basis of maximum fuel economy in the driving cycle.

scholarly journals Optimization of the fuel economy and emissions for plug in hybrid electric recreational boat energy management strategy using genetic algorithm

2019 ◽  
Vol 10 (2) ◽  
pp. 792
Author(s):  
Jabar Siti Norbakyah ◽  
Abdul Rahman Salisa
Keyword(s):  
Genetic Algorithm ◽  
Energy Management ◽  
Fuel Economy ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Energy Management Strategy ◽  
Conventional Vehicle ◽  
Cycle Simulation ◽  
Water Transportation ◽  
Hybrid Electric

<span>Today, the transportation sector has undergone a change from conventional vehicle to hybrid electric vehicle especially land-based with the aim to reduce fuel consumption and emissions. However, water transportation is also one of the contributors of excessive use of fuel and emissions. Therefore, water transport needs changes as it has been done on land transport, especially cars. In this paper, plug in hybrid electric recreational boat (PHERB) is introduced. PHERB is a special model because in PHERB powertrain configuration, it only needed one EM compared to existing configuration with energy management strategy (EMS).  In this work, the optimal EMS for PHERB are presented via genetic algorithm (GA). To estimate the fuel economy and emissions, the model of PHERB is employed numerically in the MATLAB/SIMULINK environment with a special EMS using Kuala Terengganu (KT) river driving cycle. Simulation result of PHERB optimization using GA improve to 15% for KT river driving cycles without violating the PHERB performance.</span>

scholarly journals Intelligent Control of Parallel Hybrid Electric Vehicles

1999 ◽  
Author(s):  
Bradley Glenn ◽  
Gregory Washington ◽  
Giorgio Rizzoni
Keyword(s):  
Fuzzy Logic ◽  
Electric Vehicles ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Fuzzy Logic Controller ◽  
Hybrid Electric Vehicles ◽  
Conventional Vehicle ◽  
Parallel Hybrid ◽  
Hybrid Electric ◽  
Reduce Emissions

Abstract Currently Hybrid Electric Vehicles (HEV) are being considered as an alternative to conventional automobiles in order to improve efficiency and reduce emissions. To demonstrate the potential of an advanced control strategy for HEV’s, a fuzzy logic control strategy has been developed and implemented in simulation in the National Renewable Energy Laboratory’s simulator Advisor (version 2.0.2). The Fuzzy Logic Controller (FLC) utilizes the electric motor in a parallel hybrid electric vehicle (HEV) to force the ICE (66KW Volkswagen TDI) to operate at or near its peak point of efficiency or at or near its best fuel economy. Results with advisor show that the vehicle with the Fuzzy Logic Controller can achieve (56) mpg in the city, while maintaining a state of charge of .68 for the battery pack, compared to (43) mpg for a conventional vehicle. This scheme has also brought to light various rules of thumb for the design and operation of HEV’s.

Fuzzy Logic vs Equivalent Consumption Minimization Strategy for Energy Management in P2 Hybrid Electric Vehicles

2020 ◽  
Author(s):  
Hadi Rahmeh ◽  
Angelo Bonfitto ◽  
Sanjarbek Ruzimov
Keyword(s):  
Fuzzy Logic ◽  
Energy Management ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Net Energy ◽  
Energy Converter ◽  
Driving Cycles ◽  
Hybrid Electric ◽  
Equivalent Consumption Minimization Strategy

Abstract This paper presents a comparison between a Fuzzy Logic and an Equivalent Consumption Minimization Strategy for the energy management of a Hybrid Electric Vehicle in P2 configuration, i.e. with the secondary energy converter located downstream the clutch. The design of the two methods is conducted aiming to minimize the fuel consumption. Although the adopted strategies are not charge sustaining, an additional goal of the techniques is to obtain a net energy extracted from the battery over a driving cycle that is not far from zero. The presented simulation results are obtained in the case of two homologation driving cycles, namely NEDC and WLTP. The objective of the study is to demonstrate that a non-optimal rule-based method can achieve a performance that is equivalent to a model-based optimal analytical approach.

Driving shaft lifetime enhancement by hybrid electrification

2018 ◽  
Vol 233 (3) ◽  
pp. 572-584
Author(s):  
Volkan Sezer
Keyword(s):  
Fuel Consumption ◽  
Electric Vehicle ◽  
Power Management ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Driving Cycle ◽  
Conventional Vehicle ◽  
Management Algorithm ◽  
Hybrid Electric

As a classical definition, the main aim of hybrid electric vehicle technology is to decrease the fuel consumption and emissions with the assistance of its power management algorithm. However, hybrid electric vehicles could also be optimized for fatigue minimization of the driving shaft to enhance its lifetime. To the best of our knowledge, there are no studies on hybrid electric vehicles regarding this concept. In this study, we model a conventional vehicle, convert it into hybrid electric vehicle in simulation environment, and optimize the power management algorithm by considering its driving shaft lifetime enhancement. The optimization is done by redesigning one of the previous equivalent cost minimization strategy studies, which includes a new state of charge sustaining approach. In this work, we reformulate the solution considering the assumptive torque–cycle life curve of the driving shaft instead of fuel consumption or emissions. Longitudinal vehicle model is prepared for simulations and the performance of the new strategy is compared with the conventional vehicle under the real driving cycle data. The results demonstrate a significant enhancement potential of 26% in driving shaft’s lifetime. Finally, we show the additional electric motor’s optimum torque tracking performance under a real driving cycle using the experimental testbed.

scholarly journals Real-Time Energy Management of Parallel Hybrid Electric Vehicles Using Linear Quadratic Regulation

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5538
Author(s):  
Bảo-Huy Nguyễn ◽  
João Pedro F. Trovão ◽  
Ronan German ◽  
Alain Bouscayrol
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Engine Fuel ◽  
Linear Quadratic ◽  
Loop Control ◽  
Parallel Hybrid ◽  
Linear Quadratic Regulation ◽  
Hybrid Electric

Optimization-based methods are of interest for developing energy management strategies due to their high performance for hybrid electric vehicles. However, these methods are often complicated and may require strong computational efforts, which can prevent them from real-world applications. This paper proposes a novel real-time optimization-based torque distribution strategy for a parallel hybrid truck. The strategy aims to minimize the engine fuel consumption while ensuring battery charge-sustaining by using linear quadratic regulation in a closed-loop control scheme. Furthermore, by reformulating the problem, the obtained strategy does not require the information of the engine efficiency map like the previous works in literature. The obtained strategy is simple, straightforward, and therefore easy to be implemented in real-time platforms. The proposed method is evaluated via simulation by comparison to dynamic programming as a benchmark. Furthermore, the real-time ability of the proposed strategy is experimentally validated by using power hardware-in-the-loop simulation.

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