iSpace Robots

Intelligent Space (iSpace) is a relatively new concept to effectively use distributed sensors, actuators, robots, computing processors, and information technology over communication networks. iSpace is a large scale Mechatronics System by integrating sensors, actuators, and control algorithms in a communication system using knowledge from various engineering disciplines such as automation, control, hardware and software design, image processing, communication and networking.



Collaborative Distributed Energy Management Systems (CoDEMS)


Typically, the distributed energy resources (DER) are controlled by the utility distribution management system (DMS) or DER management system (DERMS). If hosted by microgrid, the microgrid energy management system (MG-EMS) will be added between the DMS/DERMS and DERs. This type of top-down hierarchical control chain is heavily constrained by the communication latency, quality, bandwidth, and availability. These systems are not positioned to embrace the DER boom and will be a bottle-neck for undergoing DER integration. The solution to the scalability is decentralization. Current academic and industry efforts are made to push control to the “edge”, namely on on-site DERs. With built-in edge autonomy in DERs, they can seamlessly work together and the system becomes more scalable. Another downside of the conventional centralized control scheme is the lack of resilience against natural and man-made disasters. The typical industry practice for resilience is by adding redundant central controllers. However, this redundancy is expensive yet cannot rapidly restore electric service in parallel. Therefore, the distributed control technologies have attracted significant academic and industry attention in recent years. Our lab has been developing distributed EMS, called Collaborative Distributed Energy Management Systems (CoDEMS), since 2008.



[1]Z. Cheng, J. Duan, and M.-Y. Chow, “To Centralize or to Distribute: That Is the Question: A Comparison of Advanced Microgrid Management Systems,” EEE Ind. Electron. Mag., vol. 12, no. 1, pp. 6–24, Mar. 2018, doi: 10.1109/MIE.2018.2789926.

[2]N. Rahbari-Asr, Y. Zhang, and M.-Y. Chow, “Consensus-based distributed scheduling for cooperative operation of distributed energy resources and storage devices in smart grids,” IET Generation, Transmission & Distribution, vol. 10, no. 5, pp. 1268–1277, Apr. 2016, doi: 10.1049/iet-gtd.2015.0159.

[3]Y. Zhang, N. Rahbari-Asr, J. Duan, and M.-Y. Chow, “Day-Ahead Smart Grid Cooperative Distributed Energy Scheduling With Renewable and Storage Integration,” IEEE Trans. Sustain. Energy, vol. 7, no. 4, pp. 1739–1748, Oct. 2016, doi: 10.1109/TSTE.2016.2581167.


Resilient Energy Magament in ugrid Simulator (REMμS)



The microgrid is envisioned to be the building block of the future smart grid, for its abilities to host distributed energy resources, to improve grid reliability, and to enhance system resiliency. One of the most studied research topics of the microgrid is the distributed microgrid energy management system. However, the algorithm prototyping and hardware validation still remain great challenges at the current stage. Our lab has been developing a highly scalable, customizable, and low-cost DC microgrid testbed framework that enables fast distributed MG-EMS prototyping and provides proof-of-concept validation.



[1]Cheng and M. Chow, “The Development and Application of a DC Microgrid Testbed for Distributed Microgrid Energy Management System,” IECON 2018 – 44th Annual Conference of the IEEE Industrial Electronics Society, Washington, DC, 2018, pp. 300-305, doi: 10.1109/IECON.2018.8591816.


First Principle Based Four Dimensional Battery Degradation Model (4DM)

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.


Co-Estimation Algorithm



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.