Tag Archives: Fuzzy Logic

Analyzing Li-Ion battery health

September 3, 2026 08:14 ,

Haixu Yang, Fengwei Liang, Kerui Li, Jichao Hong, Jinghan Zhang, Xiaohui Chen, State of health estimation and remaining useful life prediction of Li-ion battery oriented to real-world electric vehicles: A comprehensive evaluation methodology, Engineering Applications of Artificial Intelligence, Volume 181, Part 1, 2026, 10.1016/j.engappai.2026.115311.

State of health estimation is essential to ensure the battery’s reliability, safety and economy. It is an integral part of the battery management system. Estimation often faces the problems of a lone indicator and ineffective real-vehicle application. The use of a lone indicator leads to biased and inaccurate state estimation. The complexity of real-world driving scenarios renders the method impractical and inaccurate. Here, we propose a comprehensive evaluation method oriented to real-world electric vehicles. Capacity, ohmic internal resistance, cell voltage inconsistency, and battery pack temperature inconsistency are considered as four indicators of battery degradation. For each indicator, a state of health estimation value can be obtained. A fuzzy matrix is used to fuse the four indicators for a comprehensive evaluation. Also, the fuzzy matrix gives the weights of different health indicators as 0.1988, 0.2463, 0.3183, and 0.2366. The inconsistency of cell voltages carries the highest weighting among these indicators, exerting a more significant influence than the other three. In terms of artificial intelligence, this paper proposes a complete set of data processing, feature computation, and fusion estimation methods for battery health state. In engineering applications, the metrics for health state assessment are greatly enriched and the degree of accuracy is significantly improved. The method breaks through the limitations of the traditional evaluation based on a singular indicator, achieving a multi-dimensional comprehensive and objective evaluation of automotive batteries.

Posted in: Electronics , Tagged: ,

Improving the adaptation of RL to robots with different parameters through Fuzzy

May 2, 2025 08:42 ,

A. G. Haddad, M. B. Mohiuddin, I. Boiko and Y. Zweiri, Fuzzy Ensembles of Reinforcement Learning Policies for Systems With Variable Parameters, IEEE Robotics and Automation Letters, vol. 10, no. 6, pp. 5361-5368, June 2025 10.1109/LRA.2025.3559833.

This paper presents a novel approach to improving the generalization capabilities of reinforcement learning (RL) agents for robotic systems with varying physical parameters. We propose the Fuzzy Ensemble of RL policies (FERL), which enhances performance in environments where system parameters differ from those encountered during training. The FERL method selectively fuses aligned policies, determining their collective decision based on fuzzy memberships tailored to the current parameters of the system. Unlike traditional centralized training approaches that rely on shared experiences for policy updates, FERL allows for independent agent training, facilitating efficient parallelization. The effectiveness of FERL is demonstrated through extensive experiments, including a real-world trajectory tracking application in a quadrotor slung-load system. Our method improves the success rates by up to 15.6% across various simulated systems with variable parameters compared to the existing benchmarks of domain randomization and robust adaptive ensemble adversary RL. In the real-world experiments, our method achieves a 30% reduction in 3D position RMSE compared to individual RL policies. The results underscores FERL robustness and applicability to real robotic systems.

Posted in: Applications of reinforcement learning to robots , Tagged: , ,

using fuzzy Petri nets for mobile robot navigation

February 28, 2018 08:35 ,

Seung-yun Kim, Yilin Yang, A self-navigating robot using Fuzzy Petri nets, Robotics and Autonomous Systems, Volume 101, 2018, Pages 153-165, DOI: 10.1016/j.robot.2017.11.008.

Petri nets (PNs) are capable of modeling nearly any conceivable system and can provide a better understanding of the idealized action sequence in which to most effectively describe or execute said system through their powerful analytical capabilities. However, because real world instances are rarely as consistent and ideal as simulated models, basic PN modeling and simulation properties may be insufficient in practical application. We remedy this through specialization in Fuzzy Petri nets (FPNs). Fuzzy logic is incorporated to better model a self-navigating robot algorithm, thanks to its versatile multi-valued logic reasoning. By using FPNs, it is possible to simulate, assess, and communicate the process and reasoning of the navigational algorithm and apply it to real world programming. In this paper, we propose a series of specific fuzzy algorithms intended to be implemented in concert on a mobile robot platform in order to optimize the sequence of actions needed for a given task, primarily the navigation of an unknown maze. A set of varied maze configurations were developed and simulated as PN and FPN models, providing a testing environment to examine the efficiency of several methodologies. Five methods, including an original proposal in this paper, were compared across 30,000 simulations, evaluating in particular performance in processing cost in time. Our experiments concluded with results suggesting a very competitive task completion time at a considerable fraction in processing cost compared to the closest performing alternatives.

Posted in: Robot motion planning , Tagged: ,