Review of Methods for Improving the Energy Efficiency of Electrified Ground Transport by Optimizing Battery Consumption

Martyushev, Nikita V.; Malozyomov, Boris V.; Khalikov, Ilham H.; Kukartsev, Viktor Alekseevich; Kukartsev, Vladislav Viktorovich; Tynchenko, Vadim Sergeevich; Tynchenko, Yadviga Aleksandrovna; Qi, Mengxu · 2023 · DOAJ

DOI: 10.3390/en16020729

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Summary

This review paper addresses the critical need to improve the energy efficiency and extend the service life of electrified ground transport, particularly electric vehicles (EVs) and hybrid electric vehicles (HEVs). The motivation stems from the high cost of lithium-ion batteries, which constitute approximately 45% of an EV’s total cost, and their susceptibility to degradation under uneven loads, high cyclicality, and varying temperatures. The authors aim to systematize existing methods for optimizing battery consumption and increasing energy storage resources, identifying the most effective approaches for implementation. The study employs a comprehensive literature review of research and review papers published primarily between 2015 and 2023. The authors developed a classification framework to group methods by physical objects (e.g., batteries, traction drives) and operational processes (e.g., regenerative braking, energy-efficient consumption). This structured approach allowed for a comparative analysis of qualitative and quantitative efficiency indicators across different driving cycles and vehicle types, including personal, public, and freight transport. The findings categorize efficiency improvements into five main areas: regenerative braking, energy-efficient battery consumption, energy-saving technologies, energy storage device optimization, and traction drive efficiency. Specific results highlight that electromechanical flywheels can increase the braking energy recovery factor by up to 40.61% and reduce battery current change ranges by 43.16%. Dynamic programming-based energy management strategies were found to reduce battery degradation time by approximately 15% and lower total ownership costs by 11%. Additionally, optimizing traction and heating systems in hybrid powertrains can reduce energy consumption by up to 7% and extend battery life by another 15%. Other notable findings include a 17% energy saving on sloped roads using deep reinforcement learning and model predictive control, and a 10% reduction in fuel cell vehicle consumption through optimized power-splitting algorithms. The significance of this work lies in its conclusion that there is no universal solution for improving energy efficiency; instead, method selection must be based on thorough feasibility studies tailored to specific vehicle types and operating conditions. The paper emphasizes the importance of integrating qualitative assessments with quantitative data to facilitate better comparative analysis in future research. By synthesizing diverse strategies—from regenerative braking logic to advanced algorithmic control—the review provides a roadmap for developers to enhance the economic and technological viability of electric transport systems.

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