Computational Intelligence for Energy and Mechatronic Systems

This book takes as its anchor the problem that energy storage has become one of the defining constraints of modern mechatronic design, and that managing it is a distributed problem rather than a circuit problem. A large lithium-ion pack is a population of cells whose states diverge over service life, and the weakest cell limits the whole. This book surrounds this central problem with the computational and mathematical methods that coordinated, uncertain systems require. The opening chapter develops agent-based control for active balancing, moving from cell fundamentals and balancing topologies to a multi-agent consensus framework in which each cell or module is represented by an agent that negotiates charge redistribution with its neighbours. The formulation is attractive precisely because it scales: consensus requires no central controller with global knowledge of every cell, which is what makes it viable for the packs now standard in vehicles and grid storage. The remaining chapters extend the theme of distributed decision-making under uncertainty. One formulates an emotion-modulated Markov decision process for cooperative robotic systems, incorporating persistence and reciprocal coupling between agents and establishing local stability through Jacobian eigenvalues, with the coupling variables shown to map onto trust and cooperation willingness in distributed robotic control. Two further chapters supply mathematical foundations: relative prime domination in fuzzy graphs applied to relay node placement in intelligent transportation systems and Pythagorean fuzzy ideals in lattice ordered semigroups as a structural framework for mechatronic decision-making under imprecise information.