Title: Smart Energy Management and Monitoring System for Electric Vehicles with IoT Integration
Abstract: Chapter 3 Smart Energy Management and Monitoring System for Electric Vehicles with IoT Integration Challa Krishna Rao, Challa Krishna Rao Department of Electrical and Electronics Engineering, Aditya Institute of Technology and Management, Tekkali, A.P., India Dept. of Electrical Engineering, Parala Maharaja Engineering College, Berhampur, Odisha, IndiaSearch for more papers by this authorSarat Kumar Sahoo, Sarat Kumar Sahoo Dept. of Electrical Engineering, Parala Maharaja Engineering College, Berhampur, Odisha, IndiaSearch for more papers by this authorFranco Fernando Yanine, Franco Fernando Yanine School of Engineering of Universidad, Finis Terrae, Providencia, Santiago, ChileSearch for more papers by this author Challa Krishna Rao, Challa Krishna Rao Department of Electrical and Electronics Engineering, Aditya Institute of Technology and Management, Tekkali, A.P., India Dept. of Electrical Engineering, Parala Maharaja Engineering College, Berhampur, Odisha, IndiaSearch for more papers by this authorSarat Kumar Sahoo, Sarat Kumar Sahoo Dept. of Electrical Engineering, Parala Maharaja Engineering College, Berhampur, Odisha, IndiaSearch for more papers by this authorFranco Fernando Yanine, Franco Fernando Yanine School of Engineering of Universidad, Finis Terrae, Providencia, Santiago, ChileSearch for more papers by this author Book Editor(s):Krishan Arora, Krishan AroraSearch for more papers by this authorSuman Lata Tripathi, Suman Lata TripathiSearch for more papers by this authorHimanshu Sharma, Himanshu SharmaSearch for more papers by this author First published: 17 April 2024 https://doi.org/10.1002/9781394205097.ch3 AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onEmailFacebookTwitterLinkedInRedditWechat Summary Urban areas are searching for solutions to this issue as environmental deterioration and air pollution levels rise. Reducing vehicle pollution is one of the most important approaches to managing air pollution in urban areas. The creative move in this area is represented by electric vehicles (EVs). Electric cars are high-tech devices that rely on a ton of data to work at their best. The usage of electric cars must increase because they are the most advanced form of transportation currently available. To increase battery life and safeguard the associated components that they power, electric vehicle batteries must accurately evaluate the level of charge because they can be harmed by overcharging or over-discharging. This article covers a low-cost, real-time, Internet of Things (IoT)–based, and user-friendly technology for controlling and monitoring the batteries in electric cars. It displays crucial details about the battery's health, such as battery capacity and how it is being charged and discharged right now. Real-time display and updating of these data are possible. The proposed system is implemented using an ESP32 microcontroller, the Blynk mobile app, and the Blynk IoT platform. Among the performance, measurements are those for tracking speed, acceleration, mileage, battery management, charging, issue alarms, and preventative maintenance systems. The monitoring of these electric cars is significantly impacted by IoT. References Abd Wahab , M.H. , Mohamad Anuar , I.N. , Ambar , R. , Baharum , A. , Shanta , S. , Sulaiman , M.S. , et al . ( 2018 ) IoT-Based Battery Monitoring System for Electric Vehicle . Int. J. Eng. Technol. , 7 , 505 - 510 . Google Scholar Rao , S.S. and Rangaswamy , D. ( 2021 ) Power Quality Mitigation and Transient Analysis in AC/DC Hybrid Microgrid for Electric Vehicle Charging . Indones. J. Electr. Eng. Comput. Sci. , 24 , 1315 - 1322 . Google Scholar Arora , K. ; Kumar , A. ; Kamboj , V.K. ; Prashar , D. ; Shrestha , B. ; Joshi , G.P. Impact of Renewable Energy Sources into Multi Area Multi-Source Load Frequency Control of Interrelated Power System . Mathematics , 2021 , 9 , 186 . 10.3390/math9020186 Google Scholar Dost , P. , Spichartz , P. and Sourkounis , C. ( 2015 ) Charging Behaviour of Users Utilising Battery Electric Vehicles and Extended Range Electric Vehicles within the Scope of a Field Test . 2015 International Conference on Renewable Energy Research and Applications (ICRERA), Palermo, 22-25 November , 2015 , 1162 - 1167 . 10.1109/ICRERA.2015.7418592 Google Scholar Hannan , M.A. , Hoque , M.M. , Hussain , A. , Yusof , Y. and Ker , P.J. ( 2018 ) State-of-the-Art and Energy Management System of Lithium-Ion Batteries in Electric Vehicle Applications: Issues and Recommendations . IEEE Access , 6 , 19362 - 19378 . 10.1109/ACCESS.2018.2817655 Web of Science®Google Scholar Koko Friansa , Irsyad Nashirul Haq , Bening Maria Santi , Deddy Kurniadi , Edi Leksono , Brian Yuliarto , Development of Battery Monitoring System in Smart Microgrid Based on Internet of Things (IoT) , Procedia Eng. , Vol. 170 , pp. 482 - 487 ( 2017 ). 10.1016/j.proeng.2017.03.077 Google Scholar W. Iqbal , H. Abbas , M. Daneshmand , B. Rauf and Y. A. Bangash , An In-Depth Analysis of IoT Security Requirements, Challenges, and Their Countermeasures via Software-Defined Security , in: IEEE Internet Things J. , vol. 7 , no. 10 , pp. 10250 - 10276 , Oct. 2020 . 10.1109/JIOT.2020.2997651 Web of Science®Google Scholar D. N. Tsyani , A. Kurniasari and C. Hudaya , Battery Monitoring System with LoRa Technology , 2018 3rd International Conference on Information Technology, Information System and Electrical Engineering (ICITISEE) , 2018 , pp. 125 - 129 . 10.1109/ICITISEE.2018.8720952 Google Scholar Alireza Zourmand , Chan Wai Hung , Andrew Lai Kun Hing and Mohammad AbdulRehman , Internet of Things (IoT) using LoRa Technology , IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS 2019) , 29 June 2019, Selangor , Malaysia , pp 324 - 330 Google Scholar Arora , K. ; Kumar , A. ; Kamboj , V.K. ; Prashar , D. ; Jha , S. ; Shrestha , B. ; Joshi , G.P. Optimization Methodologies and Testing on Standard Benchmark Functions of Load Frequency Control for Interconnected Multi Area Power System in Smart Grids . Mathematics , 2020 , 8 , 980 . 10.3390/math8060980 Google Scholar C. K. Rao , S. K. Sahoo and F. F. Yanine , Demand Response for Renewable Generation in an IoT based Intelligent Smart Energy Management System , 2021 Innovations in Power and Advanced Computing Technologies (i-PACT) , Kuala Lumpur, Malaysia , 2021 , pp. 1 - 7 , 10.1109/i-PACT52855.2021.9696781 Google Scholar Ramadan , H.S. , Becherif , M. and Claude , F. ( 2017 ) Energy Management Improvement of Hybrid Electric Vehicles via Combined GPS/Rule-Based Methodology . IEEE Trans. Autom. Sci. Eng. , 14 , 586 - 597 . 10.1109/TASE.2017.2650146 Web of Science®Google Scholar A. Lavric and V. Popa , Internet of Things and LoRa ™ Low-Power Wide-Area Networks: A survey , 2017 International Symposium on Signals, Circuits and Systems (ISSCS) , Iasi , Romania , 2017 , pp. 1 - 5 . 10.1109/ISSCS.2017.8034915 Google Scholar C. Choi , J. Jeong , I. Lee and W. Park , LoRa based renewable energy monitoring system with open IoT platform , 2018 International Conference on Electronics, Information, and Communication (ICEIC) , 2018 , pp. 1 - 2 . Google Scholar V. K, R. K. Nema and A. Ojha , Various Types of Wireless Battery Management System in Ev , 2020 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) , 2020 , pp. 1 - 5 . Google Scholar Gholizadeh , M. and Salmasi , F.R. ( 2014 ) Estimation of State of Charge, Unknown Nonlinearities, and State of Health of a Lithium-Ion Battery Based on a Comprehensive Unobservable Model . IEEE Trans. Ind. Electron. , 61 , 1335 - 1344 . 10.1109/TIE.2013.2259779 Web of Science®Google Scholar Sun , F. and Xiong , R. ( 2015 ) A Novel Dual-Scale Cell State-of-Charge Estimation Approach for Series-Connected Battery Pack Used in Electric Vehicles . J. Power Sources , 274 , 582 - 594 . 10.1016/j.jpowsour.2014.10.119 CASWeb of Science®Google Scholar Jiya , I.N. , Gurusinghe , N. and Gouws , R. ( 2019 ) Hybridisation of Battery, Supercapacitor and Hybrid Capacitor for Load Applications with High Crest Factors: A Case Study of Electric Vehicles . Indones. J. Electr. Eng. Comput. Sci. , 16 , 614 - 622 . Google Scholar P Singh , K Arora , UC Rathore , Control Strategies for Improvement of Power Quality in Grid Connected Variable Speed WECS with DFIG – An Overview , J. Phys. Conf. Ser. , 2022 . 10.1088/1742-6596/2327/1/012008 PubMedGoogle Scholar Mahadik , Y. and Vadirajacharya , K. (2019) Battery Life Enhancement in a Hybrid Electrical Energy Storage System Using a Multi-Source Inverter . World Electr. Veh. J. , 2019 , 10 ( 2 ), 17 . 10.3390/wevj10020017 Google Scholar Rao , C. K. , Sahoo , S. K. , Balamurugan , M. , & Yanine , F. F. ( 2021 ). Design of Smart Socket for Monitoring of IoT-Based Intelligent Smart Energy Management System . In: Lect. Notes Electr. Eng ., (pp. 503 – 518 ). Springer Singapore . Google Scholar Rusimamto , P.W. , Endryansyah , E. , Anifah , L. , Harimurti , R. and Anistyasari , Y. ( 2021 ) Implementation of Arduino Pro Mini and ESP32 Cam for Temperature Monitoring on Automatic Thermogun IoT-Based . Indones. J. Electr. Eng. Comput. Sci. , 23 , 1366 - 1375 . Google Scholar Mohammed , E.A. , Al-Allaf , A.F. and Altamer , B.R. ( 2020 ) I oT-Based Monitoring and Management Power Sub-Station of the University of Mosul . IOP Conf. Ser.: Mater. Sci. Eng . Google Scholar Tehrani , Y.H. and Atarodi , S.M. ( 2019 ) Design & Implementation of a High Precision & High Dynamic Range Power Consumption Measurement System for Smart Energy IoT Applications . Measurement , 146 , 458 - 466 . 10.1016/j.measurement.2019.06.037 Google Scholar Nasser , A. , Mohammed , E. and Ali , A. ( 2022 ) Smart Energy Management of Wind/PV/Battery Renewable Energy Sources Based on IoT . Proceedings of 2nd International Multi-Disciplinary Conference Theme: Integrated Sciences and Technologies , Sakarya , 7 - 9 September 2021, 355-370. 10.4108/eai.7-9-2021.2314946 Google Scholar M. Bassoli , V. Bianchi , I. De Munari , and P. Ciampolini , An IoT approach for an AAL Wi-Fi-based monitoring system , IEEE Trans. Instrum. Meas. , vol. 66 , no. 12 , Dec. 2017 . 10.1109/TIM.2017.2753458 Google Scholar A. Pötsch , A. Berger , and A. Springer , Efficient analysis of power consumption behaviour of embedded wireless IoT systems , in: Proc. 2017 IEEE Int. Instrum. Meas. Technol. Conf. , May 2017 . Google Scholar F. Mohammadi , G.-A. Nazri , and M. Saif , Modeling and simulation of hybrid electric vehicle using MATLAB/Simulink , in: Proc. 2019 Int. Conf. Power Generation Syst. Renewable Energy Technol ., Aug. 2019 . 10.1109/PGSRET.2019.8882686 Google Scholar H. S. V. S. Kumar Nunna , S. Battula , S. Doolla , and D. Srinivasan , Energy management in smart distribution systems with vehicle-to-grid integrated microgrids , IEEE Trans. Smart Grid , vol. 8 , no. 5 , Sep. 2018 . Google Scholar M. Rizzi , A. Depari , P. Ferrari , A. Flammini , S. Rinaldi , and E. Sisinni , Synchronization uncertainty versus power efficiency in LoRaWAN networks , IEEE Trans. Instrum. Meas ., vol. 68 , no. 4 , Apr. 2019 . 10.1109/TIM.2018.2859639 Google Scholar Rao , C. K. , Sahoo , S. K. , & Yanine , F. F. ( 2022 ). Forecasting Electric Power Generation in Photovoltaic Power Systems for Smart Energy Management . In: 2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP) . Google Scholar Spinelli , G.M. , Gottesman , Z.L. and Deenik , J. ( 2019 ) A Low-Cost Arduino-Based Datalogger with Cellular Modem and FTP Communication for Irrigation Water Use Monitoring to Enable Access to CropManage . HardwareX , 6 , e00066 . 10.1016/j.ohx.2019.e00066 Google Scholar Ambrož , M. ( 2017 ) Raspberry Pi as a Low-Cost Data Acquisition System for Human Powered Vehicles . Measurement , 100 , 7 - 18 . 10.1016/j.measurement.2016.12.037 Google Scholar Shinde , V.R. , Tasgaonkar , P.P. and Garg , R.D. ( 2018 ) Environment Monitoring System through Internet of Things (IOT) . 2018 International Conference on Information, Communication, Engineering and Technology (ICICET) , Pune , 29 - 31 August, 2018, 1-4. 10.1109/ICICET.2018.8533835 Google Scholar Sung , W.-T. and Hsiao , S.-J. ( 2020 ) the Application of Thermal Comfort Control Based on Smart House System of IoT. Measurement . Google Scholar Salih , T.A. and Noori , M.S. ( 2020 ) U sing LoRa Technology to Monitor and Control Sensors in the Greenhouse . IOP Conf. Ser.: Mater. Sci. Eng . Google Scholar Rao , C. K. , Sahoo , S. K. , Balamurugan , M. , Satapathy , S. R. , Patnaik , A. , & Yanine , F. F. ( 2020 ). Applications of Sensors in Solar Energy Systems . In: 2020 International Conference on Renewable Energy Integration into Smart Grids: A Multidisciplinary Approach to Technology Modelling and Simulation (ICREISG) . IEEE . 10.1109/ICREISG49226.2020.9174190 Google Scholar Babiuch , M. , Foltynek , P. and Smutny , P. ( 2019 ) Using the ESP32 Microcontroller for Data Processing . 2019 20th International Carpathian Control Conference , Krakow- Wieliczka , 26 - 29 May 2019, 1-6. 10.1109/CarpathianCC.2019.8765944 Google Scholar Kurniawan , A. ( 2019 ) Internet of Things Projects with ESP32: Build Exciting and Powerful IoT Projects Using the All-New Espressif ESP32 . Packt Publishing Ltd. , Birmingham . Google Scholar Texas Instruments ( 2011 ) INA219 Zerø-Drift, Bidirectional Current/Power Monitor with I2C TM Interface. No . September, 2011. Google Scholar Lambert , J. , Monahan , R. and Casey , K. ( 2021 ) Power Consumption Profiling of a Lightweight Development Board: Sensing with the INA219 and Teensy 4.0 Microcontrollers . Electronics , 10 , Article No. 775. 10.3390/electronics10070775 Google Scholar Behera , S.K. ( 2019 ) Use of Ina219 Sensor for Locating . Int. J. Res. Anal. Rev. (IJRAR) , 6 , 1 - 4 . Google Scholar Schwunk , S. ; Armbruster , N. ; Straub , S. ; Kehl , J. ; Vetter , M. Particle filter for state of charge and state of health estimation for lithium–iron phosphate batteries . J. Power Sources 2013 , 239 , 705 – 710 . 10.1016/j.jpowsour.2012.10.058 CASWeb of Science®Google Scholar Tulsyan , A. ; Tsai , Y. ; Gopaluni , R.B. ; Braatz , R.D. State-of-charge estimation in lithium-ion batteries: A particle filter approach . J. Power Sources , 2016 , 331 , 208 – 223 . 10.1016/j.jpowsour.2016.08.113 CASWeb of Science®Google Scholar He , Y. ; Liu , X. ; Zhang , C. ; Chen , Z. A new model for state-of-charge (SoC) estimation for high-power li-ion batteries . Appl. Energy , 2013 , 101 , 808 – 814 . 10.1016/j.apenergy.2012.08.031 CASWeb of Science®Google Scholar Burgos-Mellado , C. ; Orchard , M.E. ; Kazerani , M. ; Cárdenas , R. ; Sáez , D. Particle-filtering-based estimation of maximum available power state in lithium-ion batteries . Appl. Energy , 2016 , 161 , 349 – 363 . 10.1016/j.apenergy.2015.09.092 CASWeb of Science®Google Scholar Zhou , D. ; Zhang , K. ; Ravey , A. ; Gao , F. ; Miraoui , A. Online estimation of lithium polymer batteries state-of-charge using particle filter-based data fusion with multimodal's approach . IEEE Trans. Ind. Appl. , 2016 , 52 , 2582 – 2595 . 10.1109/TIA.2016.2524438 Web of Science®Google Scholar Du , Q. ; Han , Q. ; Zhang , Y. ; Liu , Z. ; Tian , S. ; Zhang , Z. Adopting combined strategies to make state of charge (SoC) estimation for practical use . J. Renew. Sustain. Energy , 2018 , 10 , 034102 . 10.1063/1.5024031 Google Scholar Xia , B. ; Sun , Z. ; Zhang , R. ; Lao , Z. A cubature particle filter algorithm to estimate the state of the charge of lithium-ion batteries based on a second-order equivalent circuit model . Energies , 2017 , 10 , 457 . 10.3390/en10040457 Web of Science®Google Scholar Li , B. ; Peng , K. ; Li , G. State-of-charge estimation for lithium-ion battery using the gauss-hermite particle filter technique . J. Renew. Sustain. Energy , 2018 , 10 , 014105 . 10.1063/1.5020028 Web of Science®Google Scholar Ye , M. ; Guo , H. ; Xiong , R. ; Yu , Q. A double-scale and adaptive particle filter-based online parameter and state of charge estimation method for lithium-ion batteries . Energy , 2018 , 144 , 789 – 799 . 10.1016/j.energy.2017.12.061 Web of Science®Google Scholar Hu , X. ; Sun , F. ; Zou , Y. ; Peng , H. Online estimation of an electric vehicle lithium-ion battery using recursive least squares with forgetting . In: Proceedings of the American Control Conference (ACC) , San Francisco, CA, USA , 29 June–1 July 2011 . Google Scholar Eddahech , A. ; Briat , O. ; Vinassa , J.-M. Adaptive voltage estimation for ev li-ion cell based on artificial neural networks state-of-charge meter . In: Proceedings of the 2012 IEEE International Symposium on Industrial Electronics (ISIE) , Hangzhou, China , 28 – 31 May 2012 . 10.1109/ISIE.2012.6237281 Google Scholar Lim , K. ; Bastawrous , H.A. ; Duong , V.-H. ; See , K.W. ; Zhang , P. ; Dou , S.X. Fading kalman filter-based real-time state of charge estimation in lifepo4 battery-powered electric vehicles . Appl. Energy , 2016 , 169 , 40 – 48 . 10.1016/j.apenergy.2016.01.096 Web of Science®Google Scholar Lotfi , N. ; Landers , R.G. ; Li , J. ; Park , J. Reduced-order electrochemical model-based SoC observer with output model uncertainty estimation . IEEE Trans. Control Syst. Technol. , 2017 , 25 , 1217 – 1230 . 10.1109/TCST.2016.2598764 Google Scholar Safwat , I.M. ; Li , W. ; Wu , X. A novel methodology for estimating state-of-charge of li-ion batteries using advanced parameters estimation . Energies , 2017 , 10 , 1751 . 10.3390/en10111751 Web of Science®Google Scholar Duong , V.-H. ; Bastawrous , H.A. ; See , K.W. Accurate approach to the temperature effect on state of charge estimation in the lifepo4 battery under dynamic load operation . Appl. Energy , 2017 , 204 , 560 – 571 . 10.1016/j.apenergy.2017.07.056 CASWeb of Science®Google Scholar Xia , B. ; Lao , Z. ; Zhang , R. ; Tian , Y. ; Chen , G. ; Sun , Z. ; Wang , W. ; Sun , W. ; Lai , Y. ; Wang , M. Online parameter identification and state of charge estimation of lithium-ion batteries based on forgetting factor recursive least squares and nonlinear kalman filter . Energies , 2018 , 11 , 3 . 10.3390/en11010003 Web of Science®Google Scholar Shen , P. ; Ouyang , M. ; Lu , L. ; Li , J. ; Feng , X. The co-estimation of state of charge, state of health, and state of function for lithium-ion batteries in electric vehicles . IEEE Trans. Veh. Technol. , 2018 , 67 , 92 – 103 . 10.1109/TVT.2017.2751613 Web of Science®Google Scholar Ali , M. ; Kamran , M. ; Kumar , P. ; Nengroo , S. ; Khan , M. ; Hussain , A. ; Kim , H.-J. An online data-driven model identification and adaptive state of charge estimation approach for lithium-ion-batteries using the lagrange multiplier method . Energies , 2018 , 11 , 2940 . 10.3390/en11112940 Web of Science®Google Scholar Zhang , C. ; Allafi , W. ; Dinh , Q. ; Ascencio , P. ; Marco , J. Online estimation of battery equivalent circuit model parameters and state of charge using decoupled least squares technique . Energy , 2018 , 142 , 678 – 688 . 10.1016/j.energy.2017.10.043 Web of Science®Google Scholar Electric Vehicle Design: Design, Simulation and Applications ReferencesRelatedInformation
Publication Year: 2024
Publication Date: 2024-04-17
Language: en
Type: other
Indexed In: ['crossref']
Access and Citation
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot