Research Article
IoT enabled Electric Vehicle’s Battery Monitoring System
@INPROCEEDINGS{10.4108/eai.7-8-2017.152984, author={Mohammad Asaad and Furkan Ahmad and Mohammad Saad Alam and Yasser Rafat}, title={IoT enabled Electric Vehicle’s Battery Monitoring System}, proceedings={The 1st EAI International Conference on Smart Grid Assisted Internet of Things}, publisher={EAI}, proceedings_a={SGIOT}, year={2017}, month={8}, keywords={Internet of Things Battery Monitoring System MQTT SOC estimation.}, doi={10.4108/eai.7-8-2017.152984} }- Mohammad Asaad
Furkan Ahmad
Mohammad Saad Alam
Yasser Rafat
Year: 2017
IoT enabled Electric Vehicle’s Battery Monitoring System
SGIOT
EAI
DOI: 10.4108/eai.7-8-2017.152984
Abstract
The Internet of Things (IoT) technology has immense potential for application in improvement and development of Smart Grid. The rising number of distributed generation, aging of present grid infrastructure and appeal for the transformation of networks have sparked the interest in smart grid. The need for energy storage system primarily the electrical energy storage systems is growing as the prospects for their usage is becoming more compelling. Dynamic electrical energy storage system viz., Electric Vehicles (EVs) are relatively standard due to their excellent electrical properties and flexibility but the possibility of damage to their batteries is there in case of overcharging or deep discharging and their mass penetration profoundly impacts the grid. To circumvent the possibility of damage, EVs’ batteries need a precise state of charge estimation to increase their lifespan and to protect the equipment they power. Based on ease of implementation and less overall complexity, this paper proposes a real-time Battery Monitoring System (BMS) using coulomb counting method for SoC estimation and messaging based MQTT as the communication protocol. The proposed BMS is implemented on hardware platform using appropriate sensing technology, central processor, interfacing devices and the Node-RED environment. An optimization model aimed at maximizing the trade revenue for EVs’ aggregator is presented aimed at enabling the smart charging. [9] L. Lu, et.al., “A review on the key issues for lithium-ion battery management in electric vehicles,” Journal of Power Sources, vol. 226. pp. 272–288, 2013. [10] M. Charkhgard and M. Farrokhi, “State-of-charge estimation for lithium-ion batteries using neural networks and EKF,” IEEE Trans. Ind. Electron., vol. 57, no. 12, pp. 4178–4187, 2010. [11] G. Y. Y. Ding-xuan, “SOC estimation of Lithium-ion battery based on Kalman filter algorithm,” in 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE), 2013. [12] C. M. and Y. H. K.S. Ng, Y.F Huang, “An enhanced coulomb counting method for estimating state-of-charge and state-of-health of lead-acid batteries,” in 31st International Telecommunications Energy Conference (INTELEC 2009). [13] Y. M. Jeong, Y. K. Cho, J. H. Ahn, S. H. Ryu, and B. K. Lee, “Enhanced coulomb counting method with adaptive SOC reset time for estimating OCV,” in 2014 IEEE Energy Conversion Congress and Exposition, ECCE 2014, 2014, pp. 4313–4318. [14] K. S. Ng, et.al., “Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries,” Appl. Energy, vol. 86, no. 9, pp. 1506–1511, 2009. [15] Node-Red.: http://nodered.org/. [16] MQTT Publish & Subscribe.: http://www.hivemq.com/blog/mqtt-essentials-part2-publish-subscribe.
