The amount of data collected in Electric Vehicles has been growing fast because we have many more sensors, higher bandwidth communication systems, and cheaper memory to monitor and measure real-time driving range related data and store the data on the vehicles, in connected clouds, etc. This massive amount of data can have different levels of accuracy, resolutions, and relevance in unstructured ways. Big Data technologies have been emerging to address huge, diverse and unstructured data to substantially improve the overall system performance. With proper use of Big Data concepts and techniques, the remaining driving range estimation of the vehicle can be substantially improved.
The range estimation needs the incorporation and synchronization of all standard, real-time and historical data. Usually, the standard and historical data provides an initial prediction of the driving range; and the real-time data updates the estimation during the driving. However, under different conditions, some data are more relevant than others for the range estimation. This data can be historical, standard, or real-time depending on different situations. The big data analytics helps us identify the relevant data and discover its correlation to the remaining driving range estimation.
Publication
[1] H. Rahimi-Eichi and M.-Y. Chow, “Big-Data Framework for Electric Vehicle Range Estimation,” presented at the 40th Annual Conference of the IEEE Industrial Electronics Society (IECON2014), IEEE, Dallas, TX , 2014.
[2] Z. Cheng, M. Chow, D. Jung and J. Jeon, “A big data based deep learning approach for vehicle speed prediction,” 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE), Edinburgh, 2017, pp. 389-394, doi: 10.1109/ISIE.2017.8001278.
[3] D. Jung, M. Chow, Z. Cheng, and J. Jeon, “Method and apparatus for estimating driving information,“US10215579B2, 2019.
The First Principle Based Four Dimensional Battery Degradation Model (4DM) is computer simulation model for battery dynamics studies under different degradation and operating conditions. The 4DM is designed based on the physics of operation of the battery, i.e., the actual components such as anode, cathode, electrolyte, separator and current collector, are used to construct the model. This particular approach is used to bridge the gap between material science, electrochemical and electrical engineering.
The 4DM, because of the design, is capable of simulating:
different battery chemistries,
batteries of different capacities,
progressive component degradation,
different operating conditions – C-rates, temperatures, depth of discharge, partial charging and discharging effects,
component degradation over time.
The 4DM provides a platform to study the sensitivity of the battery’s rate of change of voltage and capacity with respect to the degradation of different physical and electrochemical components. This feature/capability of the 4DM enables users to better understand the impact of different operating conditions on the degradation of their battery and determine appropriate use cases for their batteries to prolong the remaining useful life.
The 4DM has an intuitive user-interface that assists the user to perform different tests on the model under different operating conditions. The user interface is designed to be simple, yet intuitive and capable of providing the user with sufficient options to understand the working of the 4DM with access to the core back-end tool with all the features.
Real-time estimation of the state of charge (SOC) of the battery is a crucial need in the growing fields of plug-in hybrid electric vehicles and smart grid applications. The SOC estimation accuracy depends on the accuracy of the model used to describe the characteristics of the battery. To accurately estimate the SOC of the battery, a Co-Estimation algorithm is proposed. The Co-Estimation algorithm is developed based on a resistance–capacitance (RC)-equivalent circuit model to model the battery dynamics. Considering the parameters of the battery model are functions of the SOC, C-rate, temperature, and aging, the Co-Estimation algorithm adopts an adaptive online parameter-identification algorithm to identify and update the model’s parameters as they change. We also deployed a piecewise linearized mapping of the VOC–SOC curve along with continuously updating the parameters to accurately represent all of the battery’s static and dynamic characteristics. Using this adaptive structure, we design an observer based on the updating model to estimate the SOC as one of the states of the battery model.
More than 10,000 sensors and detectors are usually adopted in a typical nuclear power plant (NPP) unit for measurements and controls of nuclear and non-nuclear processes, radiation monitoring, and other special applications including vibration measurements, hydrogen concentration monitoring, water conductivity and boric acid concentration measurement, or failed fuel detection, etc. (IAEA, 1999). Among a wide range of different sensing technologies, piezoelectric sensing has been increasingly used in temperature measurement, vibration monitoring, pressure and flow rate measurement, hydrogen concentration measurement, detection of water level and structural defects (Korsah, et al., 2009), largely due to the nature of compact assembly of piezoelectric devices and free from electromagnetic interference. However, harsh environments (high temperatures, high radiations, etc.) in nuclear reactors and fuel cycle systems greatly challenge the existing NPP sensing technology. Specifically, existing piezoelectric (including acoustic sensing) sensing techniques are mostly limited to applications at temperatures up to <250°C, by their lack of radiation resistance, their lack of embed-ability because of the wired electric power supply and communication, and their unknown long-term performance behavior under harsh environments.
In this project, we develop 1) high temperature (> 600°C) embedded/integrated sensors (HiTEIS) for wireless monitoring of temperature, vibration, water level, pressure and structural integrity; 2) investigate remote acoustic charging of HiTEISs using laser ultrasound; 3) implement nuclear environment compatible secured remote communication for HiTEISs; 4) verify the HiTEIS technology in reactor and fuel cycle environments.
Kelola interaksi pelanggan secara efisien lewat sistem CRM MTP di KAYARAYA, yang dirancang untuk menyatukan data dan strategi dalam satu dasbor cerdas.
