Technological advancements in batteries have paved new opportunities for energy storage technology, particularly in the transportation sector, including electric vehicles. In electric vehicles, optimizing battery performance in the context of safety and efficiency is crucial for users and manufacturers. However, accurately predicting battery degradation is challenging due to complex physical properties. Many parameters contribute to battery degradation, such as temperature variations, charging and discharging rates, number of cycles, internal resistance, depth of discharge, and electrode materials. It is crucial to investigate these variables in-depth to develop techniques that minimize battery degradation and maximize battery life. This paper proposes a digital twin technology as an adaptive modeling approach to follow the actual model in dynamic operating conditions and, eventually, minimize capacity degradation. This paper considers experimental data as an actual model, and a mathematical model using an electrical equivalent circuit is the basis of the battery model. The curve fitting has been applied using an error minimization technique to validate the adaptive model performance with the actual data. Further, the adaptive model performance has been calculated through sensitivity analysis. Simulation results demonstrate the effectiveness of the adaptive model in calculating degradation, enabling an achievable strategy for increasing battery life.

This article develops a stochastic programming framework for multiagent systems, where task decomposition, assignment, and scheduling problems are simultaneously optimized. The framework can be applied to heterogeneous mobile robot teams with distributed subtasks. Examples include pandemic robotic service coordination, explore and rescue, and delivery systems with heterogeneous vehicles. Owing to their inherent flexibility and robustness, multiagent systems are applied in a growing range of real-world problems that involve heterogeneous tasks and uncertain information. Most previous works assume one fixed way to decompose a task into roles that can later be assigned to the agents. This assumption is not valid for a complex task where the roles can vary and multiple decomposition structures exist. Meanwhile, it is unclear how uncertainties in task requirements and agent capabilities can be systematically quantified and optimized under a multiagent system setting. A representation for complex tasks is proposed: agent capabilities are represented as a vector of random distributions, and task requirements are verified by a generalizable binary function. The conditional value at risk is chosen as a metric in the objective function to generate robust plans. An efficient algorithm is described to solve the model, and the whole framework is evaluated in two different practical test cases: capture-the-flag and robotic service coordination during a pandemic (e.g., COVID-19). Results demonstrate that the framework is generalizable, is scalable up to 140 agents and 40 tasks for the example test cases, and provides low-cost plans that ensure a high probability of success.

For robot swarms operating on complex missions in an uncertain environment, it is important that the decision-making algorithm considers both heterogeneity and uncertainty. This paper presents a stochastic programming framework for the vehicle routing problem with stochastic travel energy costs and heterogeneous vehicles and tasks. We represent the heterogeneity as linear constraints, estimate the uncertain energy cost through Gaussian process regression, formulate this stochasticity as chance constraints or stochastic recourse costs, and then solve the stochastic programs using branch and cut algorithms to minimize the expected energy cost. The performance and practicality are demonstrated through extensive computational experiments and a practical test case.

Army ground vehicles often operate in extremely severe environmental and battlefield conditions. Condition Based Maintenance (CBM) allows maintenance to be performed based on evidence of need provided by reliability modeling and/or other enabling technologies, thus reducing maintenance costs and increasing vehicle availability. A fuzzy model is developed to diagnose the axle fatigue of light trucks. The extraction of the fuzzy rules is based upon expert knowledge and a linear damage model. Training data will be used to modify the membership functions and the fuzzy If-Then rules to improve the quality of the fuzzy model for fault diagnostics. The improvement of the fuzzy model will be carried out using re-clustering operation and membership function optimization.

Army ground vehicles often operate in extremely severe environmental and battlefield conditions. There are challenges for the reliability of the military ground vehicle fleet, which need to be addressed. Condition Based Maintenance (CBM) allows maintenance to be performed based on evidence of need provided by reliability modeling and/or other enabling technologies, thus reducing maintenance costs and increasing vehicle availability. The architecture of the Intelligent Vehicle Health Management System (IVHMS) for light trucks is presented. A fuzzy model is developed to diagnose the axle fatigue of the vehicle. The extraction of the fuzzy rules is based upon expert knowledge and a linear damage model. Training data will be used to modify the membership functions and the fuzzy If-Then rules to improve the quality of the fuzzy model for fault diagnostics. The improvement of the fuzzy model will be carried out using re-clustering operation and membership function optimization.