Development of equivalent fuel consumption minimization strategy for hybrid electric vehicles

Park, J.; Park, J. H.

doi:10.1007/s12239-012-0084-6

Cited by 59 publications

(27 citation statements)

References 9 publications

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“…Rules optimization can be applied for two conceptual purposes: find optimal fixed formulation of the rules that mitigates the control sensitivity to driving conditions change (quantitatively or qualitatively) [86][87][88] or to find optimal control parameters related to every driving condition separately [25,44]. In ECMS and PMP, the equivalent factors are crucial control parameters to be optimized offline as a balance-fixed or case-based values [54,89].…”

Section: Insight Into Optimal Real-time Methodsmentioning

confidence: 99%

“…Due to the fact that equivalence factors estimation directly influences the performance of ECMS, this point is further investigated from two perspectives: finding optimal static values or dynamic update of the estimated value [11]. The former is a typical static optimization problem that can be solved using DP or GA [54]. The latter can be classified into three subcategories: First, utilizing a correction term for the offline optimized factor using feedback control [55].…”

Section: Ecmsmentioning

confidence: 99%

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Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions

2018

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Abstract:In light of increasing alerts about limited energy sources and environment degradation, it has become essential to search for alternatives to thermal engine-based vehicles which are a major source of air pollution and fossil fuel depletion. Hybrid electric vehicles (HEVs), encompassing multiple energy sources, are a short-term solution that meets the performance requirements and contributes to fuel saving and emission reduction aims. Power management methods such as regulating efficient energy flow to the vehicle propulsion, are core technologies of HEVs. Intelligent power management methods, capable of acquiring optimal power handling, accommodating system inaccuracies, and suiting real-time applications can significantly improve the powertrain efficiency at different operating conditions. Rule-based methods are simply structured and easily implementable in real-time; however, a limited optimality in power handling decisions can be achieved. Optimization-based methods are more capable of achieving this optimality at the price of augmented computational load. In the last few years, these optimization-based methods have been under development to suit real-time application using more predictive, recognitive, and artificial intelligence tools. This paper presents a review-based discussion about these new trends in real-time optimal power management methods. More focus is given to the adaptation tools used to boost methods optimality in real-time. The contribution of this work can be identified in two points: First, to provide researchers and scholars with an overview of different power management methods. Second, to point out the state-of-the-art trends in real-time optimal methods and to highlight promising approaches for future development.

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Section: Insight Into Optimal Real-time Methodsmentioning

confidence: 99%

Section: Ecmsmentioning

confidence: 99%

Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions

2018

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“…Normally, the transmission model can be formulated using Equations (20) and (21) when the gear is completely engaged…”

Section: Transmission Modelmentioning

confidence: 99%

A Supervisory Control Algorithm of Hybrid Electric Vehicle Based on Adaptive Equivalent Consumption Minimization Strategy with Fuzzy PI

Zhang

Liu

et al. 2016

Energies

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This paper presents a new energy management system based on equivalent consumption minimization strategy (ECMS) for hybrid electric vehicles. The aim is to enhance fuel economy and impose state of charge (SoC) charge-sustainability. First, the relationship between the equivalent factor (EF) of ECMS and the co-state of pontryagin's minimum principle (PMP) is derived. Second, a new method of implementing the adaptation law using fuzzy proportional plus integral (PI) controller is developed to adjust EF for ECMS in real-time. This adaptation law is more robust than one with constant EF due to the variation of EF as well as driving cycle. Finally, simulations for two driving cycles using ECMS are conducted as opposed to the commonly used rule-based (RB) control strategy, indicating that the proposed adaptation law can provide a promising blend in terms of fuel economy and charge-sustainability. The results confirm that ECMS with Fuzzy PI adaptation law is more robust than ECMS with constant EF as well as PI adaptation law and it achieves significant improvements compared with RB in terms of fuel economy, which is enhanced by 4.44% and 14.7% for china city bus cycle and economic commission of Europe (ECE) cycle, respectively.

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“…Therefore, energy is consumed by both of these power sources. The equivalent FC of the PHEV can be calculated [31]. However, for a long run, the di erence between the nal and initial SOCs ( SOC) has a negligible e ect on the total energy consumption compared to the vehicle FC.…”

Section: Energy Consumption Versus Fuel Consumptionmentioning

confidence: 99%

A modified control strategy for parallel hybrid electric vehicles equipped with continuously variable transmission

2016

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Abstract. This paper aims to establish a control strategy for parallel hybrid electric vehicles equipped with full-toroidal Continuously Variable Transmission (CVT). First, the advantages of CVT are elucidated. Afterwards, a modi ed control strategy on the base of Baseline static Control Strategy (BCS) is proposed. Employing this strategy, in some moments, the engine operates in its fuel-optimal point to decrease the vehicle Fuel Consumption (FC). It is demonstrated that the modi cations in BCS are applicable using a CVT as the power train. In order to investigate the implemented modi cation, an optimization on the proposed control strategy and BCS in SC03 driving cycle is accomplished and then, the optimized control strategies are compared. It will be demonstrated that the proposed method is superior to BCS in terms of FC in SC03 driving cycle. Finally, in order to examine generality of the comparison, the optimized control strategies are compared in other driving cycles. It is revealed that the optimized state of the proposed control strategy is advantageous in these cycles.

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Development of equivalent fuel consumption minimization strategy for hybrid electric vehicles

Cited by 59 publications

References 9 publications

Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions

Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions

A Supervisory Control Algorithm of Hybrid Electric Vehicle Based on Adaptive Equivalent Consumption Minimization Strategy with Fuzzy PI

A modified control strategy for parallel hybrid electric vehicles equipped with continuously variable transmission

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