With the rapid proliferation of electric vehicles, the safety and reliability of lithium-ion batteries have become critical concerns. Effective anomaly detection is essential for ensuring safe battery operation. However, as battery systems and operating scenarios become increasingly complex, battery fault diagnosis and maintenance require stronger cross-domain adaptability and human-AI collaboration. Traditional fault detection and diagnosis methods are usually designed for specific scenarios and predefined workflows, making them less effective in complex real-world applications. To address the scarcity of open-source battery fault report corpora and the lack of unified maintenance knowledge representation, this study proposes a descriptive text modeling approach for battery signal reports. Monitoring signals, statistical features, anomaly records, and state assessment results are transformed into structured and readable natural language descriptions, forming a language corpus for battery health diagnosis and maintenance. Based on this corpus, we propose VBFDD-Agent, a vehicle battery fault detection and diagnosis agent for automotive-grade battery systems. VBFDD-Agent integrates d
Early prediction of battery cycle life is essential for improving battery design, manufacturing and deployment. However, despite encouraging progress with machine learning, battery life prediction remains constrained by scarce data and pronounced heterogeneity across battery chemistries, specifications, formation protocols and operating conditions. Although transfer learning has been widely explored to alleviate these challenges, its effectiveness is limited by the absence of a foundation model that can integrate heterogeneous battery life data and provide broadly useful knowledge for target-scenario specialization. Here we introduce the pretrained battery transformer (PBT), a foundation model for battery life prediction that incorporates battery-knowledge-encoded mixture-of-experts layers to learn from scarce and heterogeneous lifetime data. PBT is first pretrained on 13 lithium-ion battery datasets to yield a general PBT that encodes comprehensive battery lifetime knowledge, and is then adapted through transfer learning into specialized PBT models for target scenarios. Across 15 datasets covering 977 batteries and 528 sets of aging conditions from lithium-ion, sodium-ion and zinc
Quadrotor endurance is ultimately limited by battery behavior, yet most energy aware planning treats the battery as a simple energy reservoir and overlooks how flight motions induce dynamic current loads that accelerate battery degradation. This work presents an end to end framework for motion aware battery health assessment in quadrotors. We first design a wide range current sensing module to capture motion specific current profiles during real flights, preserving transient features. In parallel, a high fidelity battery model is calibrated using reference performance tests and a metaheuristic based on a degradation coupled electrochemical model.By simulating measured flight loads in the calibrated model, we systematically resolve how different flight motions translate into degradation modes loss of lithium inventory and loss of active material as well as internal side reactions. The results demonstrate that even when two flight profiles consume the same average energy, their transient load structures can drive different degradation pathways, emphasizing the need for motion-aware battery management that balances efficiency with battery degradation.
The distribution relationship of quantum battery capacity is investigated. First, it is proved that for two-qubit X-states, the sum of the subsystem battery capacities does not exceed the total system's battery capacity, and the conditions are provided under which they are equal. Then define the difference between the total system's and subsystems'battery capacities as the residual battery capacity (RBC) and show that this can be divided into coherent and incoherent components. Furthermore, it is observed that this capacity monogamy relation for quantum batteries extends to general n-qubit X states and any n-qubit X state's battery capacity distribution can be optimized to achieve capacity gain through an appropriate global unitary evolution. Specifically, for general three-qubit X states, stronger distributive relations are derived for battery capacity. Quantum batteries are believed to hold significant potential for outperforming classical counterparts in the future. These findings contribute to the development and enhancement of quantum battery theory.
Parameterizing high-fidelity "digital twins" of batteries is a critical yet challenging inverse problem that hinders the pace of battery innovation. Prevailing methods formulate this as a black-box optimization (BBO) task, employing algorithms that are sample-inefficient and blind to the underlying physics. In this work, we introduce a new paradigm that reframes the inverse problem as a reasoning task, and present Battery-Sim-Agent, the first framework to deploy a Large Language Model (LLM) agent in a closed loop with a high-fidelity battery simulator. The agent mimics a human scientist's workflow: it interprets rich, multi-modal feedback from the simulator, forms physically-grounded hypotheses to explain discrepancies, and proposes structured parameter updates. On a systematically constructed benchmark suite spanning diverse battery chemistries, operating conditions, and difficulty levels, our agent significantly outperforms strong BBO baselines like Bayesian optimization in identifying accurate parameters. We further demonstrate the framework's capability in complex long-horizon degradation fitting tasks and validate its practical applicability on real-world battery datasets. Our
Battery swapping as a business model for battery energy storage (BES) has great potential in future integrated low-carbon energy and transportation systems. However, frequent battery swapping will inevitably accelerate battery degradation and shorten the battery life accordingly. To model the tradeoff of BES use between energy and transportation applications coupled by battery swapping, we develop a life-cycle decision model that coordinates battery charging and swapping. This model is derived based on an improved intertemporal decision framework, in which the optimal marginal degradation cost (MDC) of BES is determined to maximize the BES benefit across time and application. The proposed framework and model are applied to manage a battery swapping station that simultaneously provides battery swapping services to electric vehicle customers and provides flexibility service to the power grid, including energy arbitrage and reserve. The case study shows that while the end of the physical life of BES occurs faster with battery swapping, the economic life becomes considerably longer. The results also reveal that the optimal MDC depends on the battery values in each application, and we a
The reduced state of a small system strongly coupled to a charger in thermal equilibrium may be athermal and used as a small battery once disconnected. By harnessing the battery-charger correlations, the battery's extractable energy can increase above the ergotropy. We introduce a protocol that uses a quantum system as a memory that measures the charger and leaves the battery intact in its charged state. Using the information gained from the measurement, the daemonic ergotropy of the battery is extracted. Then the battery is reconnected to the charger, thermalizing and charging it. However, the memory should return to its initial standard state to close the thermodynamic cycle. Thus, on the one hand, the work cost of the cycle is the sum of the disconnecting and reconnecting battery-charger work plus the measurement and erasure work. On the other hand, the extracted energy is the daemonic ergotropy of the battery plus the ergotropy of the memory. The ratio of these quantities defines the efficiency of the cycle. The protocol is exemplified by a modified transverse spin 1/2 Ising chain, one spin functioning as the battery and the others as the charger. The memory is another auxiliar
We propose and investigate the performance of a hybrid quantum battery, the so-called Kerr quantum battery, which consists of two interacting quantum oscillators, i.e., the charger is a harmonic oscillator and the battery is an anharmonic oscillator involving the Kerr nonlinearity. Such a setup creates nonuniform spacing between energy levels of the quantum oscillator that increases with the energy level. We find that the Kerr quantum battery can store more energy than the qubit battery and reaches maximum stored energy faster than the harmonic oscillator battery. In particular, the average charging power of the Kerr quantum battery is larger than the qubit battery. Furthermore, most of the stored energy in the Kerr quantum battery can be extracted for work. All of the properties of the Kerr quantum battery are controlled by the strength of nonlinearity, in which the enhancement of the nonlinearity transforms the battery from a harmonic oscillator to a qubit.
Battery life estimation is critical for optimizing battery performance and guaranteeing minimal degradation for better efficiency and reliability of battery-powered systems. The existing methods to predict the Remaining Useful Life(RUL) of Lithium-ion Batteries (LiBs) neglect the relational dependencies of the battery parameters to model the nonlinear degradation trajectories. We present the Battery GraphNets framework that jointly learns to incorporate a discrete dependency graph structure between battery parameters to capture the complex interactions and the graph-learning algorithm to model the intrinsic battery degradation for RUL prognosis. The proposed method outperforms several popular methods by a significant margin on publicly available battery datasets and achieves SOTA performance. We report the ablation studies to support the efficacy of our approach.
Battery recycling is a critical process for minimizing environmental harm and resource waste for used batteries. However, it is challenging, largely because sorting batteries is costly and hardly automated to group batteries based on battery types. In this paper, we introduce a machine learning-based approach for battery-type classification and address the daunting problem of data scarcity for the application. We propose BatSort which applies transfer learning to utilize the existing knowledge optimized with large-scale datasets and customizes ResNet to be specialized for classifying battery types. We collected our in-house battery-type dataset of small-scale to guide the knowledge transfer as a case study and evaluate the system performance. We conducted an experimental study and the results show that BatSort can achieve outstanding accuracy of 92.1% on average and up to 96.2% and the performance is stable for battery-type classification. Our solution helps realize fast and automated battery sorting with minimized cost and can be transferred to related industry applications with insufficient data.
Quantum harmonic oscillator (QHO) battery models have been studied with significant importance in the recent past because these batteries are experimentally realizable and have high ergotropy and capacity to store more than one quanta of energy. QHO battery models are reinvestigated here to answer a set of fundamental questions: Do such models have any benefit? Is unbounded charging possible? Does the use of a catalyst system enhance the energy transfer to quantum batteries? These questions are answered both numerically and analytically by considering a model that allows a laser to shine on a QHO charger that interacts with a QHO battery. In contrast to some of the existing works, the obtained answers are mostly negative. Specifically, in the present work, the laser frequency is tuned with the frequency of the global charger-battery system, which is affected by the interaction between QHOs. It is reported that for a fixed laser field amplitude $\textit{F}$, the battery can store more energy when tuned with the frequency of the global charger-battery system compared to energy stored by tuning the laser frequency with local frequencies of the charger and battery. The charging process
Battery health diagnostics today rely overwhelmingly on electrochemical signals measured at the cell terminals. A parallel literature has shown that magnetic sensing can resolve information that terminal-only measurements miss, but method development is limited by the absence, to the best of our knowledge, of public battery magnetic-measurement datasets paired with degradation labels. We release MagBridge-Battery v1.0, a synthetic dataset of 6,760 magnetic-field signatures that bridges real magnetic morphology from the Mohammadi-Jerschow Open Science Framework (OSF) archive with state-of-health (SOH) labels from the PulseBat dataset. The release contains 5,600 PulseBat-conditioned grounded samples, 600 synthetic sensor-anomaly samples derived from clean parents, and 560 low-voltage Regime-B extrapolation samples. A cell-disjoint, parent-child-leakage-free primary benchmark split is verified to contain zero overlapping cells, zero cross-split parent-child pairs, and zero sample-ID overlap. We define three primary benchmark tasks: SOH regression, second-life classification, and anomaly detection, plus an auxiliary anomaly-subtype classification task. A controlled label-shuffle ablati
Batteries are pivotal for transitioning to a climate-friendly future, leading to a surge in battery research. Scopus (Elsevier) lists 14,388 papers that mention "lithium-ion battery" in 2023 alone, making it infeasible for individuals to keep up. This paper discusses strategies based on structured, semantic, and linked data to manage this information overload. Structured data follows a predefined, machine-readable format; semantic data includes metadata for context; linked data references other semantic data, forming a web of interconnected information. We use a battery-related ontology, BattINFO to standardise terms and enable automated data extraction and analysis. Our methodology integrates full-text search and machine-readable data, enhancing data retrieval and battery testing. We aim to unify commercial cell information and develop tools for the battery community such as manufacturer-independent cycling procedure descriptions and external memory for Large Language Models. Although only a first step, this approach significantly accelerates battery research and digitalizes battery testing, inviting community participation for continuous improvement. We provide the structured dat
Shared electric vehicles (SEVs) have emerged as a promising solution to contribute to sustainable urban mobility. However, ensuring the efficient operation and effective battery management of SEV systems remains a complex challenge. This challenge stems from factors such as slow plug-in charging, the potential role of SEVs in balancing grid load pressure, and the optimization of SEV operations to ensure their economic viability. To tackle these challenges, this paper introduces an integrated strategy for optimizing various aspects of SEV systems, encompassing strategies like Vehicle-to-Grid (V2G), Battery-to-Grid (B2G), and battery swapping. This approach is built on a space-time-energy network model that facilitates the optimization of battery charging and discharging scheduling, SEV operations like relocations and battery swapping, battery swapping station selection and the number of batteries. The objective of this approach is to maximize profits while addressing operational constraints and the complexities of energy management within SEV systems. Given the substantial complexity that arises with large-problem scales, the paper introduces a column generation-based heuristic algo
Battery management system plays an important role for modern battery-powered application such as Electric vehicles, portable electronic equipment and storage for renewable energy sources. It also increases the life-cycle of the battery, battery state and efficiency. Monitoring the state of charge of the battery is a crucial factor for battery management system. This paper deals with monitoring the state of charge of the battery along with temperature, current for Solar panel fitted with battery for residential application. Microcontroller is used for controlling purpose, analog sensors are used for sensing the parameters of voltage, current. The information of the battery is given with tabular form and shown in photograph. Battery parameters are displayed with the LCD screen.
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 cooptimization of both by updating the BESS size during the training of the bidding policy, leveraging an extended reinforcement learning (RL) framework inspired by advancements in embodied cognition. By integrating the Deep Recurrent Q-Network (DRQN) with a distributed RL framework, the proposed algorithm effectively manages uncertainties in renewable generation and market prices while enabling parallel computation for efficiently handling long-term data.
Battery aging is one of the major concerns for the pervasive devices such as smartphones, wearables and laptops. Current battery aging mitigation approaches only partially leverage the available options to prolong battery lifetime. In this regard, we claim that wireless crowd charging via network-wide smart charging protocols can provide a useful setting for applying battery aging mitigation. In this paper, for the first time in the state-of-the-art, we couple the two concepts and we design a fine-grained battery aging model in the context of wireless crowd charging, and two network-wide protocols to mitigate battery aging. Our approach directly challenges the related contemporary research paradigms by (i) taking into account important characteristic phenomena in the algorithmic modeling process related to fine-grained battery aging properties, (ii) deploying ubiquitous computing and network-wide protocols for battery aging mitigation, and (iii) fulfilling the user QoE expectations with respect to the enjoyment of a longer battery lifetime. Simulation-based results indicate that the proposed protocols are able to mitigate battery aging quickly in terms of nearly 46.74-60.87% less r
A quantum system which can store energy, and from which one can extract useful work, is known as a quantum battery. Such a device raises interesting issues surrounding how quantum physics can provide certain advantages in the charging, energy storage or discharging of the quantum battery as compared to their classical equivalents. However, the pernicious effect of dissipation degrades the performance of any realistic battery. Here we show how one can circumvent this problem of energy loss by proposing a quantum battery model which benefits from quantum squeezing. Namely, charging the battery quadratically with a short temporal pulse induces a hyperbolic enhancement in the stored energy, such that the dissipation present becomes essentially negligible in comparison. Furthermore, we show that when the driving is strong enough the useful work which can be extracted from the quantum battery, that is the ergotropy, is exactly equal to the stored energy. These impressive properties imply a highly efficient quantum energetic device with abundant amounts of ergotropy. Our theoretical results suggest a possible route to realizing high-performance quantum batteries, which could be realized i
The control of a battery thermal management system (BTMS) is essential for the thermal safety, energy efficiency, and durability of electric vehicles (EVs) in hot weather. To address the battery cooling optimization problem, this paper utilizes dynamic programming (DP) to develop an online rule-based control strategy. Firstly, an electrical-thermal-aging model of the $\rm LiFePO_4$ battery pack is established. A control-oriented onboard BTMS model is proposed and verified under different speed profiles and temperatures. Then in the DP framework, a cost function consisting of battery aging cost and cooling-induced electricity cost is minimized to obtain the optimal compressor power. By exacting three rules "fast cooling, slow cooling, and temperature-maintaining" from the DP result, a near-optimal rule-based cooling strategy, which uses as much regenerative energy as possible to cool the battery pack, is proposed for online execution. Simulation results show that the proposed online strategy can dramatically improve the driving economy and reduce battery degradation under diverse operation conditions, achieving less than a 3% difference in battery loss compared to the offline DP. Re
Battery diagnosis, prognosis and health management models play a critical role in the integration of battery systems in energy and mobility fields. However, large-scale deployment of these models is hindered by a myriad of challenges centered around data ownership, privacy, communication, and processing. State-of-the-art battery diagnosis and prognosis methods require centralized collection of data, which further aggravates these challenges. Here we propose a federated battery prognosis model, which distributes the processing of battery standard current-voltage-time-usage data in a privacy-preserving manner. Instead of exchanging raw standard current-voltage-time-usage data, our model communicates only the model parameters, thus reducing communication load and preserving data confidentiality. The proposed model offers a paradigm shift in battery health management through privacy-preserving distributed methods for battery data processing and remaining lifetime prediction.