An instantaneous optimization strategy based on efficiency maps for internal combustion engine/battery hybrid vehicles

Gökce, Kürşad; Özdemir, Ayhan

doi:10.1016/j.enconman.2014.02.034

Cited by 50 publications

(19 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…P fcs and a veh are the two control variables of this control problem. The goal of this control problem is to find the two optimal control variables to make the control system, expressed in (6) The performance measure to be minimized here is also the total fuel consumption considering the constraints in (7). The application process of the PMP for this subsection is similar to that for the previous subsection.…”

Section: The Proposed Energy Management Approachmentioning

confidence: 98%

“…Up to now, several types of energy management strategies have been investigated for hybrid vehicles including heuristic concept-based energy management strategies [1][2][3] and optimal control theory-based energy management strategies [4][5][6][7]. These energy management strategies were usually evaluated on some specific vehicle driving cycles.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An energy management approach of hybrid vehicles using traffic preview information for energy saving

Zheng

et al. 2015

Energy Conversion and Management

View full text Add to dashboard Cite

Section: The Proposed Energy Management Approachmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

An energy management approach of hybrid vehicles using traffic preview information for energy saving

Zheng

et al. 2015

Energy Conversion and Management

View full text Add to dashboard Cite

“…The existing EMS methods can be generally classified into the following three categories: (1) Rule-based EMS, such as the thermostatic strategy, the load following strategy, and electric assist strategy [2,3]. These methods rely heavily on the results of extensive experimental trials and human expertise without the a priori knowledge of the driving conditions [4].…”

Section: Introductionmentioning

confidence: 99%

Energy Management Strategy for a Hybrid Electric Vehicle Based on Deep Reinforcement Learning

Liu

et al. 2018

Applied Sciences

212

View full text Add to dashboard Cite

An energy management strategy (EMS) is important for hybrid electric vehicles (HEVs) since it plays a decisive role on the performance of the vehicle. However, the variation of future driving conditions deeply influences the effectiveness of the EMS. Most existing EMS methods simply follow predefined rules that are not adaptive to different driving conditions online. Therefore, it is useful that the EMS can learn from the environment or driving cycle. In this paper, a deep reinforcement learning (DRL)-based EMS is designed such that it can learn to select actions directly from the states without any prediction or predefined rules. Furthermore, a DRL-based online learning architecture is presented. It is significant for applying the DRL algorithm in HEV energy management under different driving conditions. Simulation experiments have been conducted using MATLAB and Advanced Vehicle Simulator (ADVISOR) co-simulation. Experimental results validate the effectiveness of the DRL-based EMS compared with the rule-based EMS in terms of fuel economy. The online learning architecture is also proved to be effective. The proposed method ensures the optimality, as well as real-time applicability, in HEVs.

show abstract

“…The fuel savings are influenced by the driving patterns that the vehicles are exposed to (Saxena et al, 2014). Some cycles are predicted beforehand for different application (Wang et al, 2016) or used in a model predictive control to get a better overall efficiency of HEV system (Gökce and Ozdemir, 2014;Li et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Regardless of the driving cycles, most of the time for a HEV system, the internal combustion engine (ICE) operating points are controlled as close as possible to the optimal operating points (OOP) or emissions at a particular engine speed known as the optimal torque region (Wirasingha and Emadi, 2011;Hu et al, 2013;Gökce and Ozdemir, 2014;Hou et al, 2014;Wu et al, 2016). In ICE, the fuel efficiency is not more than 30% where 60-70% of the energy is wasted in form of heat making development of waste heat energy recovery technologies like thermoelectric generators, six-stroke cycle ICE, turbochargers, and Rankine Cycle attractive and promising (Horrein et al, 2015;Zhou et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Fuel consumption evaluation of a hybrid electric car over aggressive cycles for thermal engine optimization

Asus

2018

Int. j. adv. appl. sci.

View full text Add to dashboard Cite

This paper investigates fuel consumption of a thermal engine in a hybrid electric vehicle system over aggressive cycles in order to optimize its energy consumption. The model of the system is first developed using data obtained from experiments over two aggressive driving cycles and then used to validate the model and further used as a platform to test other control strategies. The car's actual control strategy operates on high torque region of the engine to sustain battery charge and caused high fuel consumption. Based on previous researches, there are two optimal control strategies that can be implemented for a series hybrid electric vehicle system; the dynamic programming control method and the optimal torque control method. Result analysis shows that the operations of the engine are different between the two control methods; it is concentrated on high speed region for the dynamic programming control method and at the middle of the engine map for the optimal torque control method.

show abstract

An instantaneous optimization strategy based on efficiency maps for internal combustion engine/battery hybrid vehicles

Cited by 50 publications

References 31 publications

An energy management approach of hybrid vehicles using traffic preview information for energy saving

An energy management approach of hybrid vehicles using traffic preview information for energy saving

Energy Management Strategy for a Hybrid Electric Vehicle Based on Deep Reinforcement Learning

Fuel consumption evaluation of a hybrid electric car over aggressive cycles for thermal engine optimization

Contact Info

Product

Resources

About