Fuzzy energy management strategy for a hybrid electric vehicle based on driving cycle recognition

Wu, Jian; Zhang, C. H.; Cui, Naxin

doi:10.1007/s12239-012-0119-z

Cited by 87 publications

(49 citation statements)

References 11 publications

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“…However, we mainly focus on the adjustment of PI parameters instead of optimizing membership function in this paper. Thus, we select the Triangular Membership function that is commonly used [31,32]. The range of inputs and outputs is determined as (−0.02, 0.02) and (−1.8, 1.8), respectively, after completing the testing.…”

Section: Adjust the Efmentioning

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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Section: Adjust the Efmentioning

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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“…The objective function was defined so as to minimize the vehicle engine fuel consumption and emissions with regard to vehicle dynamic requirements. In [21], a fuzzy energy management system (EMS) based on driving cycle recognition was proposed to improve the fuel economy of PHEV. The energy management system can recognize the driving cycle based on learning vector quantization.…”

Section: Figurementioning

confidence: 99%

“…By considering the effect of the driving cycle on the EMS, a fuzzy EMS based on driving cycle recognition has been proposed to improve the fuel economy of a parallel hybrid electric vehicle. The EMS was composed of driving cycle recognition and a fuzzy torque distribution controller, which optimized simultaneously by using particle swarm optimization [21].…”

Section: Figurementioning

confidence: 99%

Investigation of energy usage and emissions on plug-in and hybrid electric vehicle

2016

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Preliminary noteOne of the major issues in automotive industry is to develop hybrid electric vehicles (HEVs) as the prime candidate for improving fuel efficiency and emissions reduction. This study addresses the impact of an actual drive pattern and control strategy on the conventional and plug-in hybrid electric vehicles (PHEV). In the first step the development procedure of driving cycle for real condition is proposed. Then effects of driving pattern and energy management strategy as the main factors, which strongly affect the fuel consumption and emission of pollutants, are investigated for HEVs and PHEVs. For this purpose fuzzy logic controller which is optimized with genetic algorithm is developed for energy management system. Then vehicle performance is simulated in ADVISOR. Simulation results demonstrate the effectiveness of the approach for reducing the fuel consumption and emissions. Furthermore, simulation results indicate that PHEVs in comparison to HEVs improve fuel efficiency and reduce emissions in real world driving cycle. Keywords: emission; fuel consumption; fuzzy controller; genetic algorithm; hybrid vehicle; plug-in hybrid vehicleIstraživanje potrošnje energije i ispuštanja štetnih plinova kod plug-in i hibridnih električnih vozila Prethodno priopćenje Jedan od osnovnih zadataka u automobilskoj industriji je razvoj hibridnih električnih vozila (HEVs) kao primarnog kandidata za poboljšanje učinkovitosti goriva i smanjenje ispušnih plinova. Ovo se istraživanje bavi učinkom aktualnog modela pogona i upravljanja na konvencionalna i plug-in hibridna električna vozila (PHEV). Najprije se predlaže postupak razvoja pogonskog ciklusa za stvarne uvjete. Tada se za HEVs i PHEVs istražuju učinci pogonskog modela i načina upravljanja potrošnjom energije kao glavnih čimbenika koji bitno djeluju na potrošnju goriva i ispuštanje plinova koji zagađuju okolinu. U tu je svrhu za sustav upravljanja energijom razvijen upravljač neizrazite logike optimiziran genetskim algoritmom. Tada je rad vozila simuliran u ADVISOR-u. Rezultati simulacije pokazuju učinkovitost pristupa kojim se smanjuje potrošnja goriva i količina ispušnih plinova. Nadalje, ti rezultati pokazuju da PHEVs u usporedbi s HEVs poboljšavaju učinkovitost goriva i smanjuju količinu ispušnih plinova u stvarnom pogonu. Ključne riječi: genetički algoritam; hibridno vozilo; ispuštanje plinova; plug-in hibridno vozilo; potrošnja goriva; upravljač neizrazite logike

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“…[1][2][3][4][5] In the process of starting, speeding up, slowing down, and braking, hydraulic hybrid power system can stably charge/ discharge energy at a higher power density. It makes engineering vehicles with such a system widely used.…”

Section: Introductionmentioning

confidence: 99%

“…Fuzzy energy management strategy (EMS) controls the hybrid power train with fuzzy logic; it has good real-time performance and strong robustness. 4,16 However, fuzzy logic and controller are designed by expertise. It cannot find the optimal solution.…”

Section: Introductionmentioning

confidence: 99%

Working cycle identification–based braking control strategy and its application for hydraulic hybrid loader

Lin

Zhao

et al. 2018

Advances in Mechanical Engineering

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Due to the introduction of multiple subsystems, it is very difficult for a hydraulic hybrid loader to improve its fuel economy with traditional braking control strategy and classic optimization algorithm. To deal with this problem, a combined braking control strategy based on fuzzy control system was proposed. In this strategy, the current working step and working cycle were first recognized with sensor signals. And the optimal control rules for different working cycles were optimized by genetic algorithm off line. After doing that, the obtained control rules were used to control the braking process and the energy distribution process of loaders. Simulation and experimental results show that the control strategy can make full use of the braking energy and can improve the recovery rate of it under the guarantee of safety and stability.

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Fuzzy energy management strategy for a hybrid electric vehicle based on driving cycle recognition

Cited by 87 publications

References 11 publications

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

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

Investigation of energy usage and emissions on plug-in and hybrid electric vehicle

Working cycle identification–based braking control strategy and its application for hydraulic hybrid loader

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