Electric Vehicle Health Monitoring with Electric Vehicle Range Prediction and Route Planning

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Jayapradha Jayaram
J Chetan
Barun Nayak

Abstract

The automotive industry is experiencing a revolutionary wave due to the rapid spread of electric vehicles (EVs), which is paving the way for a fundamental and long-lasting revolution in the way we approach transportation. The global movement to reduce greenhouse gas emissions and lessen the environmental impact of traditional internal combustion engine vehicles has seen a significant boost in the popularity of electric vehicles as people come together to support environmentally conscious and sustainable mobility solutions. But the ecology surrounding electric vehicles must continue to flourish if the particular problems that EVs present are to be successfully addressed. Chief among these are the formidable foes of range anxiety and battery health management. Range anxiety is a real issue felt by many potential EV owners worry about becoming stuck because their battery has run out before reaching their destination. This psychological barrier is very noticeable and makes present and future EV owners doubtful. In addition, the longevity and health of EV batteries are essential to their continued effectiveness and affordability. The driving range and operating efficiency of the vehicle are directly affected by the gradual degradation of the battery due to several factors like aging, charging patterns, and temperature. This research presents an integrative and holistic approach to address these pressing issues, enhancing and elevating the whole EV ownership experience by combining Electric Vehicle Health Monitoring (EVHM) with Electric Vehicle Range Prediction (EVRP) and Route Planning (EVRP). Combining these three essential elements creates an all-encompassing plan created to not only lessen these enormous obstacles but also accelerate the switch to electric vehicles by giving consumers the knowledge and assurance they require for a smooth, eco-friendly, and sustainable mobility in the future.

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References

M. Wei, M. Ye, J. B. Li, Q. Wang and X. Xu, "State of Charge Estimation of Lithium-Ion Batteries Using LSTM and NARX Neural Networks," in IEEE Access, vol. 8, pp. 189236-189245, 2020, doi: 10.1109/ACCESS.2020.3031340.

L. Thibault, G. De Nunzio and A. Sciarretta, "A Unified Approach for Electric Vehicles Range Maximization via Eco-Routing, Eco-Driving, and Energy Consumption Prediction," in IEEE Transactions on Intelligent Vehicles, vol. 3, no. 4, pp. 463-475, Dec. 2018, doi: 10.1109/TIV.2018.2873922.

C. -H. Lee and C. -H. Wu, "A Novel Big Data Modeling Method for Improving Driving Range Estimation of EVs," in IEEE Access, vol. 3, pp. 1980-1993, 2015, doi: 10.1109/ACCESS.2015.2492923.

M. Elmahallawy, T. Elfouly, A. Alouani and A. M. Massoud, "A Comprehensive Review of Lithium-Ion Batteries Modeling, and State of Health and Remaining Useful Lifetime Prediction," in IEEE Access, vol. 10, pp. 119040-119070, 2022, doi: 10.1109/ACCESS.2022.3221137.

C. Vidal, P. Malysz, P. Kollmeyer and A. Emadi, "Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art," in IEEE Access, vol. 8, pp. 52796-52814, 2020, doi: 10.1109/ACCESS.2020.2980961.

N. Yang, H. Hofmann, J. Sun and Z. Song, "Remaining Useful Life Prediction of Lithium-ion Batteries with Limited Degradation History Using Random Forest," in IEEE Transactions on Transportation Electrification, vol.69, pp.14765-14779, Dec.2020. doi: 10.1109/TTE.2023.3323976.

Y. E. Ekici, O. Akdag, A. A. Aydin and T. Karadag, "A Novel Energy Consumption Prediction Model of Electric Buses Using Real-Time Big Data From Route, Environment, and Vehicle Parameters," in IEEE Access, vol. 11, pp. 104305-104322, 2023, doi: 10.1109/ACCESS.2023.3316362.

Q. Geng, Z. Liu, B. Li, C. Zhao and Z. Deng, "Long-Short Term Memory-Based Heuristic Adaptive Time-Span Strategy for Vehicle Speed Prediction," in IEEE Access, vol. 11, pp. 65559-65568, 2023, doi: 10.1109/ACCESS.2023.3289197.

M. A. Hannan, M. S. H. Lipu, A. Hussain, M. H. Saad and A. Ayob, "Neural Network Approach for Estimating State of Charge of Lithium-Ion Battery Using Backtracking Search Algorithm," in IEEE Access, vol. 6, pp. 10069-10079, 2018, doi: 10.1109/ACCESS.2018.2797976.

H. Lu, C. Shao, B. Hu, K. Xie, C. Li and Y. Sun, "En-Route Electric Vehicles Charging Navigation Considering the Traffic-Flow-Dependent Energy Consumption," in IEEE Transactions on Industrial Informatics, vol. 18, no. 11, pp. 8160-8171, Nov. 2022, doi: 10.1109/TII.2021.3131735.

Y. Zhang, M. Li, Y. Chen, Y. -Y. Chiang and Y. Hua, "A Constraint-Based Routing and Charging Methodology for Battery Electric Vehicles With Deep Reinforcement Learning," in IEEE Transactions on Smart Grid, vol. 14, no. 3, pp. 2446-2459, May 2023, doi: 10.1109/TSG.2022.3214680.

S. Rahimifard, R. Ahmed and S. Habibi, "Interacting Multiple Model Strategy for Electric Vehicle Batteries State of Charge/Health/ Power Estimation," in IEEE Access, vol. 9, pp. 109875-109888, Aug 2021, doi: 10.1109/ACCESS.2021.3102607.

Q. Xue, S. Shen, G. Li, Y. Zhang, Z. Chen and Y. Liu, "Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Capacity Estimation and Box-Cox Transformation," in IEEE Transactions on Vehicular Technology, vol. 69, no. 12, pp. 14765-14779, Dec. 2020, doi: 10.1109/TVT.2020.3039553.

F. Morlock, B. Rolle, M. Bauer and O. Sawodny, "Forecasts of Electric Vehicle Energy Consumption Based on Characteristic Speed Profiles and Real-Time Traffic Data," in IEEE Transactions on Vehicular Technology, vol. 69, no. 2, pp. 1404-1418, Feb. 2020, doi: 10.1109/TVT.2019.2957536.

J. W. Pavlat and R. W. Diller, "An energy management system to improve electric vehicle range and performance," in IEEE Aerospace and Electronic Systems Magazine, vol. 8, no. 6, pp. 3-5, June 1993, doi: 10.1109/62.216890.

A. Pal, A. Bhattacharya and A. K. Chakraborty, "Planning of EV Charging Station With Distribution Network Expansion Considering Traffic Congestion and Uncertainties," in IEEE Transactions on Industry Applications, vol. 59, no. 3, pp. 3810-3825, May-June 2023, doi: 10.1109/TIA.2023.3237650.

X. Duan, Z. Hu, Y. Song, K. Strunz, Y. Cui and L. Liu, "Planning Strategy for an Electric Vehicle Fast Charging Service Provider in a Competitive Environment," in IEEE Transactions on Transportation Electrification, vol. 8, no. 3, pp. 3056-3067, Sept. 2022, doi: 10.1109/TTE.2022.3152387.

J. P. ORTIZ, G. P. AYABACA, A. R. CARDENAS, D. CABRERA and J. D. Valladolid, "Continual Reinforcement Learning Using Real-World Data for Intelligent Prediction of SOC Consumption in Electric Vehicles," in IEEE Latin America Transactions, vol. 20, no. 4, pp. 624-633, April 2022, doi: 10.1109/TLA.2022.9675468.

V. Quintero, C. Estevez, M. Orchard, A. Pérez, J. Y. Yu and X. Yu, "A Reliable and Simple Method to Estimate the Electric-Vehicle Battery State-of-Health," 2022 International Conference on Connected Vehicle and Expo (ICCVE), Lakeland, FL, USA, 2022, pp. 1-6, doi: 10.1109/ICCVE52871.2022.9743069.

D. Shen, D. Karbowski and A. Rousseau, "A Minimum Principle-Based Algorithm for Energy-Efficient Eco-Driving of Electric Vehicles in Various Traffic and Road Conditions," in IEEE Transactions on Intelligent Vehicles, vol. 5, no. 4, pp. 725-737, Dec. 2020, doi: 10.1109/TIV.2020.3011055.

K. Liu, Y. Shang, Q. Ouyang and W. D. Widanage, "A Data-Driven Approach With Uncertainty Quantification for Predicting Future Capacities and Remaining Useful Life of Lithium-ion Battery," in IEEE Transactions on Industrial Electronics, vol. 68, no. 4, pp. 3170-3180, April 2021, doi: 10.1109/TIE.2020.2973876.

E. A. Anaam, S-C. Haw, K-W. Ng, P. Naveen, R. Thabit, “Utilizing Fuzzy Algorithm for Understanding Emotional Intelligence on Individual Feedback”, in Journal of Informatics and Web Engineering, vol.2 No. 2, pp.273-283, September 2023, doi: 10.33093/jiwe.2023.2.2.19

Y. Lim, K-W. Ng, P. Naveen, S-C. Haw, “Emotion Recognition by Facial Expression and Voice: Review and Analysis”, in Journal of Informatics and Web Engineering, vol.1 No. 2, pp.45-54, September 2022.