A Scoping Review on Artificial Intelligence-Based Control Strategies for Regenerative Braking Optimisation

Okache Alfred Adoga, Ogbeche Lawrence Ogbudu

Abstract


Global patronage of electric vehicles is increasing year by year. Amidst the adoption of this clean means of mobility lies the underlying issue of energy utilisation. The lower energy density of most battery storage systems results in limited driving range, and the prolonged charging time, among other factors, is a constraint impeding the full adoption of electric vehicles. This review seeks to investigate the various artificial intelligence control strategies currently implemented in the regenerative braking system of electric vehicles and provide insights for future study in the field. The study was conducted using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) framework, and the research questions were formulated using the Population-Concept-Context (PCC) framework, as these guided the development of inclusion criteria and the literature search strategy. The study was conducted after a rigorous search of 2 databases (IEEE Xplore and Google Scholar), and the search was narrowed down to 9 articles through the identification and screening of various studies. Findings from the study identified 5 AI/ML control strategies applied in the regenerative braking system of electric vehicles, as the performance of electric vehicles is generally improved through the use of artificial intelligence optimisation strategies in the braking torque, predicting and managing the energy storage in real time, and adapting braking strategies, respectively. Future review should prioritise standardised benchmarking protocols as well as validating their findings using hardware-in-the-loop for real-world applications. 


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References


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