scholarly journals Development of an adaptive neuro‐fuzzy inference system–based equivalent consumption minimisation strategy to improve fuel economy in hybrid electric vehicles

2021 ◽  
Author(s):  
Krishna Veer Singh ◽  
Hari Om Bansal ◽  
Dheerendra Singh
Keyword(s):  
Electric Vehicles ◽  
Fuel Economy ◽  
Fuzzy Inference System ◽  
Hybrid Electric Vehicles ◽  
Fuzzy Inference ◽  
Inference System ◽  
Improve Fuel Economy ◽  
Neuro Fuzzy ◽  
Hybrid Electric

An intelligent predictive controller for power and battery management in plug-in hybrid electric vehicles

2021 ◽  
pp. 1-18
Author(s):  
Mojtaba Hassanzadeh ◽  
Zahra Rahmani
Keyword(s):  
Real Time ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Fuzzy Inference ◽  
Battery Management ◽  
State Variables ◽  
Energy Management Strategy ◽  
Inference System ◽  
The Real ◽  
Hybrid Electric

Abstract This paper presents a novel real-time energy management strategy (EMS) for plug-in hybrid electric vehicles (PHEVs), which combines the adaptive neuro-fuzzy inference system (ANFIS) and the model predictive control (MPC). A two-objective EMS with two state variables is defined by integrating the battery aging and fuel economy in the objective function. First, the dynamic programming (DP) approach is applied offline to obtain the globally optimal solutions. Then a real-time predictive EMS is proposed, in which DP carries out a moving-horizon optimization. Contrary to the charge-sustaining HEVs, the optimal trajectory of the battery state-of-charge (SOC) in PHEVs does not fluctuate around a constant level. Thus, determining the desired value of SOC for the real-time moving-horizon optimization is a challenging issue. Unlike the EMSs with a pre-determined reference for SOC, a trained ANFIS model constructs the real-time sub-optimal SOC trajectory in advance. Finally, the effectiveness of the proposed approach is shown through simulation. The proposed EMS is examined over multiple real-time driving cycles, and the results indicate that the total cost is increased compared to those unaware of battery aging. The real-time EMS is then compared to different approaches. While suboptimal, the proposed EMS is real-time implementable, and the results are found to be close enough to those of optimal controller, compared to the two other tested approaches.

Aplikasi Peramalan Kebutuhan Beban Listrik Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

2017 ◽  
Vol 3 (1) ◽  
pp. 36-48
Author(s):  
Erwan Ahmad Ardiansyah ◽  
Rina Mardiati ◽  
Afaf Fadhil
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Prakiraan atau peramalan beban listrik dibutuhkan dalam menentukan jumlah listrik yang dihasilkan. Ini menentukan  agar tidak terjadi beban berlebih yang menyebabkan pemborosan atau kekurangan beban listrik yang mengakibatkan krisis listrik di konsumen. Oleh karena itu di butuhkan prakiraan atau peramalan yang tepat untuk menghasilkan energi listrik. Teknologi softcomputing dapat digunakan  sebagai metode alternatif untuk prediksi beban litrik jangka pendek salah satunya dengan metode  Adaptive Neuro Fuzzy Inference System pada penelitian tugas akhir ini. Data yang di dapat untuk mendukung penelitian ini adalah data dari APD PLN JAWA BARAT yang berisikan laporan data beban puncak bulanan penyulang area gardu induk majalaya dari januari 2011 sampai desember 2014 sebagai data acuan dan data aktual januari-desember 2015. Data kemudian dilatih menggunakan metode ANFIS pada software MATLAB versi b2010. Dari data hasil pelatihan data ANFIS kemudian dilakukan perbandingan dengan data aktual dan data metode regresi meliputi perbandingan anfis-aktual, regresi-aktual dan perbandingan anfis-regresi-aktual. Dari perbandingan disimpulkan bahwa data metode anfis lebih mendekati data aktual dengan rata-rata 1,4%, menunjukan prediksi ANFIS dapat menjadi referensi untuk peramalan beban listrik dimasa depan.

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