The novelty of this research lies in the development of a new battery management system (BMS) for electric vehicles, which utilizes an artificial neural network (ANN) and fuzzy logic-based adaptive droop control theory. This innovative approach offers several advantages over traditional BMS systems, such as decentralized control architecture
Customer ServiceConsequently, new energy vehicles have experienced a rapid growth in recent years [1]. However, the battery technology of new energy vehicles requires further optimization. At present, the life of power batteries is generally between 5 and 8 years; thus, energy storage batteries used in the early stages of new energy vehicle popularity now
Customer ServiceAccurate online estimation of the state of charge (SOC) and state of energy (SOE) of lithium-ion batteries are essential for efficient and reliable energy management of
Customer ServiceCurrently, various deep learning algorithms are crucial for advancing BTMS in new energy electric vehicles, facilitating the prediction of batteries'' thermal and electrical properties as well as overall system performance. This paper delineates the foundational principles and practical applications of prevalent deep learning techniques in
Customer ServiceAbstract: This research aims to enhance the precision of evaluating the state of new energy vehicle (NEV) batteries using deep learning techniques. A deep learning architecture, incorporating Long Short-Term Memory (LSTM) units with an attention mechanism, has been developed. Through comparative analysis, the LSTM model with attention has been
Customer ServiceBattery management system (BMS) plays a significant role to improve battery lifespan. This review explores the intelligent algorithms for state estimation of BMS. The
Customer ServiceBattery management system (BMS) plays a significant role to improve battery lifespan. This review explores the intelligent algorithms for state estimation of BMS. The thermal management, fault diagnosis and battery equalization are investigated. Various key issues and challenges related to battery and algorithms are identified.
Customer ServiceThis book systematically introduces the core algorithms of battery management systems for electric vehicles, provides a detailed introduction and comprehensive description of model-based state estimation methods and includes their
Customer ServiceAs countries are vigorously developing new energy vehicle technology, electric vehicle range and driving performance has been greatly improved by the electric vehicle power system (battery) caused by a series of problems but restricts the development of electric vehicles, with the national subsidies for new energy vehicles regression, China''s new energy vehicle
Customer ServiceLithium-ion batteries (LIBs) with relatively high energy density and power density are considered an important energy source for new energy vehicles (NEVs). However, LIBs are highly sensitive to temperature, which makes their thermal management challenging. Developing a high-performance battery thermal management system (BTMS) is crucial for the battery to
Customer ServiceAs the new energy industry continues to progress, the health management of power batteries has become the key to ensuring the performance and safety of automobiles. Therefore, accurately predicting battery capacity decline is particularly important. A battery capacity degradation prediction model combining unscented particle filtering, particle swarm
Customer ServiceAbstract: In recent years, with the emergence of a new round of scientific and technological revolution and industrial transformation, the new energy vehicle industry has entered a stage of accelerated development. After years of continuous efforts, China''s new energy vehicle industry has significantly improved its technical level, the industrial system has been gradually
Customer ServiceThe study focuses on the comprehensive testing of power batteries for new energy vehicles. Firstly, a life decline prediction model for LB is constructed using PSO. The
Customer ServiceThis book systematically introduces readers to the core algorithms of battery management system (BMS) for electric vehicles. These algorithms cover most of the technical bottlenecks encountered in BMS applications, including battery system modeling, state of charge (SOC) and state of health (SOH) estimation, state of power (SOP) estimation, remaining
Customer ServiceCurrently, various deep learning algorithms are crucial for advancing BTMS in new energy electric vehicles, facilitating the prediction of batteries'' thermal and electrical properties as well as overall system
Customer ServiceElectric vehicle (EV) battery technology is at the forefront of the shift towards sustainable transportation. However, maximising the environmental and economic benefits of electric vehicles depends on advances in battery life cycle management. This comprehensive review analyses trends, techniques, and challenges across EV battery development, capacity
Customer ServiceThe battery system is the technical bottleneck of new energy vehicles, and the battery management technology is the core and key to ensure the high efficiency, safety, and long life of the battery system.
Customer ServiceThe study focuses on the comprehensive testing of power batteries for new energy vehicles. Firstly, a life decline prediction model for LB is constructed using PSO. The batteries are tested from the perspective of battery health. Next, to address the shortcomings of PSO, the UPF algorithm is introduced to improve PSO. Finally, an SVR model is
Customer ServiceElectric vehicle (EV) battery technology is at the forefront of the shift towards sustainable transportation. However, maximising the environmental and economic benefits of electric vehicles depends on advances in battery life
Customer ServiceThis book systematically introduces the core algorithms of battery management systems for electric vehicles, provides a detailed introduction and comprehensive description of model-based state estimation methods and includes their background, as well as their experimental and hardware development.
Customer ServiceThis study focuses on the battery life prediction of new energy vehicles (NEV), and proposes and optimizes an algorithm based on deep learning (DL) to improve t
Customer ServiceThe lithium-ion battery (LIB) has become the primary power source for new-energy electric vehicles, and accurately predicting the state-of-health (SOH) of LIBs is of crucial significance for ensuring the stable operation
Customer Service[1] Li Y K and Li Z B 2019 Current situation, problems and suggestions on the recycling of power batteries for new energy vehicles in China Resource Recycling. J 08 32-37. Google Scholar [2] Yuan B 2019 Study on power battery scrap and recovery strategy Automotive Abstracts. J 11 58-62. Google Scholar [3] Liu J S 2019 Research on improving the utilization and recovery
Customer ServiceThe novelty of this research lies in the development of a new battery management system (BMS) for electric vehicles, which utilizes an artificial neural network (ANN) and fuzzy logic-based adaptive droop control theory.
Customer ServiceAbstract: This research aims to enhance the precision of evaluating the state of new energy vehicle (NEV) batteries using deep learning techniques. A deep learning architecture,
Customer ServiceThis study focuses on the battery life prediction of new energy vehicles (NEV), and proposes and optimizes an algorithm based on deep learning (DL) to improve t
Customer ServiceAccurate online estimation of the state of charge (SOC) and state of energy (SOE) of lithium-ion batteries are essential for efficient and reliable energy management of new energy...
Customer ServiceHowever, the battery technology of new energy vehicles requires further optimization. At present, the life of power batteries is generally between 5 and 8 years; thus, energy storage batteries used in the early stages of new energy vehicle popularity now require recycling or scrapping. These waste batteries contain a variety of heavy metals and toxic electrolytes as well as other
Customer ServiceIn conclusion, the research described in the study establishes a solid platform for creative battery management strategies for electric vehicles that make use of artificial neural networks and adaptive droop control.
Globally, the research on battery technology in electric vehicle applications is advancing tremendously to address the carbon emissions and global warming issues. The effectiveness of electric vehicles depends on the accurate assessment of key parameters as well as proper functionality and diagnosis of the battery storage system.
In the prediction of battery properties and the design of BTMS, GAN can be utilized to expand sparse datasets, addressing the challenges of data acquisition and the issue of data uniformity. This approach can enhance the learning capacity and robustness of predictive models.
With the rapid development of artificial intelligence (AI) technology in recent years, deep learning (DL), as one of the hottest research trends in the field of AI, has developed swiftly , and its application in the field of thermal management for new energy vehicle batteries is increasing.
Battery management system of a droop-controlled electric vehicle. A gradual transition to fuel-cell hybrid electric vehicles (FCHEVs) is necessary to help solve the problems arising from dependence on fossil fuels. It is common to add energy storage systems (ESS) on fuel cell vehicles.
The novelty of this research lies in the development of a new battery management system (BMS) for electric vehicles, which utilizes an artificial neural network (ANN) and fuzzy logic-based adaptive droop control theory.
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