Towards the Convergence of Robotics and Energy Systems

Research Vision

All future mobility platforms—including electric vehicles, mobile robots, and drones—must be able to perform their assigned tasks without a continuous power supply from external power lines. Once separated from chargers or power sources, every aspect of mobility performance, including operational time and functional capability, relies solely on the onboard energy storage system. Consequently, the successful completion of a given mission critically depends on how effectively and safely the limited energy can be managed and utilized.

RECL focuses on research in energy storage and management systems,
leveraging control engineering expertise to maximize the efficiency and safety of future mobility systems.

New level of Research by Converging Robotics & Energy System

As global interest in robotics continues to expand, the role and importance of robots are expected to grow across a wide spectrum of applications ranging from manufacturing and logistics to healthcare, mobility, and service industries. Unlike electric vehicles (EVs) or stationary energy storage systems (ESS), which typically operate under relatively well-defined and stable boundary conditions, robots must adapt to a wide spectrum of environmental conditions. These range from controlled in-lab environments to extremely low-temperature cold storage warehouses and hot, harsh outdoor conditions, all of which present significant thermal, mechanical, and operational challenges. Such diversity in operating conditions imposes unique requirements on the design and management of robotic systems, particularly regarding energy storage, control, and operational reliability. Therefore, optimizing energy systems and implementing effective management strategies are essential to ensure that limited onboard energy can be utilized both safely and efficiently, ultimately enabling the successful completion of robotic missions in real-world scenarios.

RECL is planning to integrate its accumulated expertise in robotics and energy systems to establish a new dimension of research. Mobile robotic platforms, once separated from external power sources, are entirely dependent on the performance of their onboard battery systems. However, since batteries are not an ideal energy source, their limitations can significantly degrade system performance under certain operating conditions. To overcome these constraints, RECL develops hybrid energy storage systems (HESS) that combine multiple energy sources with complementary characteristics. For instance, supercapacitors can be utilized to meet high power demands, while fuel cells can be applied to support long-duration, energy-intensive tasks. Through such integration, the performance and capabilities of mobile robots can be significantly enhanced.

The development of such advanced energy systems requires not only a deep understanding of robotic platforms, including their control, power consumption, and operational requirements, but also a comprehensive knowledge of battery systems, such as electrochemical characteristics, safety constraints, and management strategies. Bridging these two domains is essential for designing mobility systems that are both efficient and reliable under real-world conditions. By pursuing research that spans robotics and energy storage technologies, RECL aims to develop integrated solutions that enhance performance, ensure safety, and ultimately contribute to the sustainable development of future mobility.

This multidisciplinary approach defines the core vision of RECL.

Research on Energy Systems

RECL’s core research area is the control of energy systems. The lab’s work spans the entire spectrum of battery system management, starting from the development of advanced algorithms for estimating various battery cell states (SOX estimation), to the design of fault diagnosis techniques that ensure safe and reliable operation, and further to the implementation of cell balancing control strategies that optimize performance across individual cells.

One of RECL’s key strengths is its ability to conduct end-to-end research on battery systems, encompassing every stage from fundamental analysis of cell-level electrical and thermal characteristics to system-level design and validation—all within a single laboratory.

Specific Topics on On-board BMS Algorithms

Battery Cell Parameter Determination

To ensure the performance of onboard battery systems, it is essential to define the electrical and thermal parameters of battery cells through systematic experimentation.

RECL is equipped with in-house facilities and processes to characterize these parameters and is continuously conducting research and development on effective battery cell characterization methodologies.

Internal State Estimation of Batteries

The only measurable physical quantities obtainable from a battery cell through sensors are current, voltage, and temperature. A Battery Management System (BMS) must estimate key state indicators such as State of Charge (SOC) and State of Health (SOH)—quantities that cannot be directly measured—from these three measurable signals. Since SOC and SOH are internal states of the battery cell that are not directly observable, they must be estimated using state estimation techniques. The design of such state estimators is as crucial in control engineering as the design of controllers themselves.

Based on strong foundations in control engineering, RECL conducts research not only on effective state estimation algorithms for internal battery states but also on various equivalent circuit models for battery cells.

Fault-Tolerant Onboard BMS Algorithms

In addition to improving the accuracy and robustness of internal state estimation, developing a fault-tolerant system that enables fault detection, clasification, and maintaining proper operation is a crucial aspect of a BMS. Faults or defects within the battery system not only make internal state estimation more difficult but can also lead to overall system failure—or in severe cases, result in fires or explosions.

At RECL, we are developing advanced fault detection algorithms that utilize a deep understanding of battery systems and accurate state estimation techniques to identify and classify faults in internal components or defects in individual battery cells. In particular, for component fault detection, we are developing BMS architectures that support fault-tolerant operation, including sensorless algorithms and other backup strategies, allowing temporary operation even after a fault occurs.

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