Implementing an Adaptive Algorithm for Hybrid EVs: Recognising Driving Patterns with Artificial Intelligence

• Published in: E3S web of conferences Vol. 540; p. 2020
• Main Authors: Sikka, Rishi, Chaudhary, Hetan, Meena, Mansingh, Nachappa, M.N.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Les Ulis EDP Sciences 01.01.2024
• Subjects: Adaptability; Adaptive algorithms; Algorithms; Artificial intelligence; Data reduction; Electric vehicles; Energy efficiency; Energy management; Fuel consumption; Fuel economy; Hybrid electric vehicles; Learning vector quantization networks; Neural networks; Pattern recognition; Principal components analysis
• ISSN: 2267-1242; 2555-0403; 2267-1242
• DOI: 10.1051/e3sconf/202454002020

This review article delves into the enhancement of fuel efficiency in hybrid electric vehicles (HEVs) through the use of adaptive algorithms for precise driving pattern recognition. The review explores studies that delve into two distinct methodologies. Firstly, a method utilising a Learning Vector Quantisation neural network is highlighted, which analyses six standard driving cycles. By employing micro-trip extraction and Principal Component Analysis, this method ensures a comprehensive training sample, subsequently simplifying the model and reducing data convergence time. Simulations reveal a significant reduction in sampling duration whilst maintaining satisfactory accuracy, leading to an 8% improvement in fuel economy when paired with a parallel hybrid vehicle model. Additionally, the article examines the Neural Network Fuzzy Energy Management Strategy (NNF-EMS), designed to address the adaptability constraints of traditional energy management strategies. Through neural network learning and parameter analysis, the NNF-EMS showcases enhanced adaptability and practicality across diverse driving cycles, underscoring the potential of artificial intelligence in HEV algorithm development..