International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Energy-Efficient Management Algorithm for Electric Vehicles |
|---|---|
| ABSTRACT | Electric vehicles (EVs) rely heavily on robust powermanagement strategies to achieve optimal energy efficiency and extended driving range. Variations in driving behaviour, auxiliary loads and road conditions introduce complexities that significantly affect power consumption and battery health. This paper presents a hybrid energy-efficient management algorithm that integrates driving-mode classification with a rule-based supervisory controller. The system dynamically regulates power flow between the traction motor, battery, ultracapacitor and regenerative braking subsystem based on real-time driving conditions. A comprehensive MATLAB/Simulink EV powertrain model is developed and validated using a 900-second urban driving cycle. Results demonstrate improved State-of-Charge (SOC) retention, reduced battery peak current loads, smoother transient response and enhanced regenerative braking capture compared to a fixed-rule baseline. The expanded modelling, analysis and results presented here establish a clear pathway toward practical implementation in embedded EV controllers. |
| AUTHOR | LOKESH T R, M S MAYUR, MAHESH, MALLIKARJUN, RANGANATHA Department of Electrical and Electronics Engineering, Dr. Ambedkar Institute of Technology, Bengaluru, India |
| VOLUME | 181 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1402026 |
| pdf/26_Energy-Efficient Management Algorithm for Electric Vehicles.pdf | |
| KEYWORDS | |
| References | [1] S. Rehman et al., “Energy Estimation for EVs,” IEEE Access, 2021. [2] J. Lee and B. Park, “Energy Management via Deep Learning,” IEEE T-ITS, 2020. [3] H. Zhang et al., “Driving Behaviour Analysis,” IEEE Access, 2020. [4] P. Kumar et al., “Rule-Based EV EMS,” IEEE TTE, 2020. [5] Y. Lin et al., “Driving Style Classification,” IEEE THMS, 2018. [6] A. Di Napoli et al., “Optimal Energy Management in EVs,” RSE Reviews, 2018. [7] S. Onori et al., Hybrid Electric Vehicles, Springer, 2016. [8] M. Ehsani et al., Modern Electric Vehicles, CRC Press, 2004. [9] A. Khaligh and Z. Li, “Battery and Ultracapacitor Systems for EVs,” IEEE TVT, 2010. |
