相关论文: Optimal Charging Method for Effective Li-ion Batte…
This paper presents findings on dynamic cell modeling for state-of-charge (SOC) estimation in an autonomous electric vehicle (AEV). The studied cells are Lithium-Ion Polymer-based with a nominal capacity of around 8Ah, optimized for…
The naive application of Reinforcement Learning algorithms to continuous control problems -- such as locomotion and manipulation -- often results in policies which rely on high-amplitude, high-frequency control signals, known colloquially…
This paper proposes a novel computationally efficient algorithm for optimal sizing of Battery Energy Storage Systems (BESS) considering renewable energy bidding strategies. Unlike existing two-stage methods, our algorithm enables the…
The rapid rise in electric vehicle (EV) adoption presents significant challenges in managing the vast number of retired EV batteries. Research indicates that second-life batteries (SLBs) from EVs typically retain considerable residual…
Battery degradation significantly impacts the reliability and efficiency of energy storage systems, particularly in electric vehicles and industrial applications. Predicting the remaining useful life (RUL) of lithium-ion batteries is…
The increased deployment of intermittent renewable energy generators opens up opportunities for grid-connected energy storage. Batteries offer significant flexibility but are relatively expensive at present. Battery lifetime is a key factor…
Silicon-containing lithium-ion batteries can exhibit capacity gain early in life, which makes forecasting future cell behavior difficult. We have observed these anomalous trends even in conditions where known mechanisms, such as overhang…
By informing accurate performance (e.g., capacity), health state management plays a significant role in safeguarding battery and its powered system. While most current approaches are primarily based on data-driven methods, lacking in-depth…
A growing interest in the study of aging related phenomena in lithium-ion batteries is propelled by the increasing utilization of energy storage systems in electric vehicles and in buildings as stationery energy accumulators paired with…
Autonomous vehicles with a self-evolving ability are expected to cope with unknown scenarios in the real-world environment. Take advantage of trial and error mechanism, reinforcement learning is able to self evolve by learning the optimal…
This paper presents an integrated control strategy for optimal fast charging and active thermal management of Lithium-ion batteries in extreme ambient temperatures, striking a balance between charging speed and battery health. A…
Lithium-ion batteries have found their way into myriad sectors of industry to drive electrification, decarbonization, and sustainability. A crucial aspect in ensuring their safe and optimal performance is monitoring their energy levels. In…
Accurately identifying the parameters of electrochemical models of li-ion battery (LiB) cells is a critical task for enhancing the fidelity and predictive ability. Traditional parameter identification methods often require extensive data…
Accurate state-of-charge (SOC) estimation is essential for optimizing battery performance, ensuring safety, and maximizing economic value. Conventional current and voltage measurements, however, have inherent limitations in fully inferring…
The proper disposal and repurposing of end-of-life electric vehicle batteries are critical for maximizing their environmental benefits. This study introduces a robust model predictive control (MPC) framework designed to optimize the battery…
This paper studies control-theory-enabled intelligent charging management for battery systems in electric vehicles (EVs). Charging is crucial for the battery performance and life as well as a contributory factor to a user's confidence in or…
Renewable energy is critical for combating climate change, whose first step is the storage of electricity generated from renewable energy sources. Li-ion batteries are a popular kind of storage units. Their continuous usage through…
Coverage Path Planning (CPP) is a fundamental capability for agricultural robots; however, existing solutions often overlook energy constraints, resulting in incomplete operations in large-scale or resource-limited environments. This paper…
Whereas in typical standardized tests batteries are almost exclusively loaded with constant current or relatively slowly changing cycles, real applications involve rapid load ripple, which do not contribute to the net energy. The trend to…
Early degradation prediction of lithium-ion batteries is crucial for ensuring safety and preventing unexpected failure in manufacturing and diagnostic processes. Long-term capacity trajectory predictions can fail due to cumulative errors…