Journal of Electronic Materials, Год журнала: 2024, Номер 53(12), С. 7334 - 7354
Опубликована: Окт. 7, 2024
Язык: Английский
Journal of Energy Storage, Год журнала: 2024, Номер 86, С. 111354 - 111354
Опубликована: Март 22, 2024
Язык: Английский
Процитировано
16
Energies, Год журнала: 2025, Номер 18(5), С. 1041 - 1041
Опубликована: Фев. 21, 2025
This review explores recent advancements in electric vehicles (EVs), focusing on the transformative role of artificial intelligence (AI) battery management systems (BMSs) and system control technologies. While EVs are integral to sustainable transportation, challenges remain optimising longevity, energy efficiency, safety. AI-driven techniques—such as machine learning (ML), neural networks (NNs), reinforcement (RL)—enhance state health (SOH) charge (SOC) predictions, well temperature regulation, offering superior accuracy over traditional methods. Additionally, AI-powered frameworks optimise distribution, regenerative braking, power allocation under varying driving conditions. Deep RL enables adaptive, self-learning capabilities that improve efficiency extend life, even dynamic environments. also examines integration Internet Things (IoT) big data analytics EV systems, enabling predictive maintenance fleet-level optimisation. By analysing these advancements, this paper highlights AI’s pivotal shaping next-generation, energy-efficient EVs.
Язык: Английский
Процитировано
3
IEEE Access, Год журнала: 2024, Номер 12, С. 43984 - 43999
Опубликована: Янв. 1, 2024
This paper presents a comprehensive survey of machine learning, deep and digital twin technology methods for predicting managing the battery state health in electric vehicles. Battery estimation is essential optimizing usage, performance, safety, cost-effectiveness Estimating complex undertaking due to its dependency on multiple factors. These factors include characteristics such as type, chemistry, size, temperature, current, voltage, impedance, cycle number, driving pattern. There are drawbacks traditional methods, experimental model-based approaches, terms accuracy, complexity, expense, viability real-time applications. By employing variety algorithms discover nonlinear dynamic link between parameters health, data-driven techniques like technologies can get beyond these restrictions. Data-driven also incorporate physics domain knowledge improve explainability interpretability results. reviews latest advancements challenges using management The discusses future directions opportunities further research development this field. scope spans publications from year 2021 2023.
Язык: Английский
Процитировано
11
eTransportation, Год журнала: 2025, Номер unknown, С. 100417 - 100417
Опубликована: Март 1, 2025
Язык: Английский
Процитировано
1
Sensors, Год журнала: 2025, Номер 25(3), С. 749 - 749
Опубликована: Янв. 26, 2025
This paper presents a time-series point-to-point generative adversarial network (TS-p2pGAN) for synthesizing realistic electric vehicle (EV) driving data. The model accurately generates four critical operational parameters—battery state of charge (SOC), battery voltage, mechanical acceleration, and torque—as multivariate Evaluation on 70 real-world trips from an open dataset reveals the model’s exceptional accuracy in estimating SOC values, particularly under complex stop-and-restart scenarios across diverse initial levels. delivers high accuracy, with root mean square error (RMSE), absolute (MAE), dynamic time warping (DTW) consistently below 3%, 1.5%, 2.0%, respectively. Qualitative analysis using principal component (PCA) t-distributed stochastic neighbor embedding (t-SNE) demonstrates ability to preserve both feature distributions temporal dynamics original data augmentation framework offers significant potential advancing EV technology, digital energy management lithium-ion batteries (LIBs), autonomous comfort system development.
Язык: Английский
Процитировано
0
Micromachines, Год журнала: 2025, Номер 16(3), С. 301 - 301
Опубликована: Март 4, 2025
Technological advances have allowed various systems to be developed on a small scale [...]
Язык: Английский
Процитировано
0
Advances in computer and electrical engineering book series, Год журнала: 2025, Номер unknown, С. 1 - 32
Опубликована: Янв. 17, 2025
This comprehensive review explores the transformative impact of digital technologies on sustainability and quality control across diverse industries. The study aims to elucidate how such as Internet Things (IoT), artificial intelligence (AI), blockchain, data analytics, twins are leveraged optimize processes enhance operational efficiencies while ensuring adherence stringent standards. Methodology: A systematic analysis current literature empirical studies was conducted synthesize key findings insights. covers a wide range sectors including manufacturing, healthcare, transportation, energy, retail, finance, agriculture, construction, education, telecommunications, Results: reveals that play pivotal role in revolutionizing industry practices by improving supply chain transparency, optimizing logistics, enhancing patient care, enabling smart energy management, fostering sustainable agricultural practices.
Язык: Английский
Процитировано
0
Energy Reports, Год журнала: 2025, Номер 13, С. 4459 - 4476
Опубликована: Апрель 14, 2025
Язык: Английский
Процитировано
0
Energy Technology, Год журнала: 2025, Номер unknown
Опубликована: Апрель 24, 2025
Electric vehicles (EVs) are pivotal in the global transition toward sustainable transportation with lithium‐ion batteries and battery management systems (BMS) play critical roles safety, efficiency, reliability. This review synthesizes advancements technologies BMS functionalities, highlighting challenges such as thermal management, state estimation, cell balancing, fault diagnosis. It explores emerging chemistries including solid‐state sodium‐ion batteries, regulation techniques, preheating strategies, recycling methods, second‐life applications, advanced energy recovery examined for their potential to enhance performance lifecycle sustainability. The integration of cutting‐edge technologies, artificial intelligence, digital twins, cloud computing, blockchain, Internet Things, data analytics, is revolutionizing capabilities. These enable high‐precision monitoring, predictive optimized enabling EVs into complex networks through vehicle‐to‐grid, vehicle‐to‐home, smart grid systems. By synthesizing current research identifying gaps, this paper guides development EV technologies. underscores significant contributions integrating clarifies extensive benefits advancements. methodologies findings study poised sustainability setting a benchmark future field.
Язык: Английский
Процитировано
0
Energies, Год журнала: 2024, Номер 17(11), С. 2503 - 2503
Опубликована: Май 23, 2024
As the adoption of distributed energy resources (DERs) grows, future electricity distribution systems is confronted with significant challenges. These challenges arise from transformation consumers into prosumers and resulting increased system complexity, leading to more pressure on grids. To address this a Digital Twin framework designed simulate DERs within grids effectively. This structured around four key modules: DERs, grid, management system, consumers. It incorporates communication interface facilitate interactions among these modules includes considerations for grid topologies demand-side configurations. The allows exploration various DER rates capacities. validation involves case studies two Danish scenarios incorporating rooftop photovoltaic (PV) systems, batteries, electric vehicles, considering different combinations technologies. findings demonstrate framework’s ability depict states PV battery 10 min resolution over periods ranging day decade.
Язык: Английский
Процитировано
2