Verification oriented development of a scalable battery management system for lithium-ion batteries

Liu-Henke, Xiaobo; Scherler, Soeren; Jacobitz, Sven

doi:10.1109/ever.2017.7935868

Cited by 8 publications

(5 citation statements)

References 3 publications

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“…To meet this, the hardware architecture of the BMS for LIBs comprises six components, namely: cell monitoring (e.g., temperature, charge/discharge monitoring); passive/active cell balancing; current measurement; contactor and interlock control and monitoring; isolation monitoring; and communication interfaces to peripherals and the environment [13]. The software structure of the BMS consists of four functionalities to aid in state determination, power capability prediction, load balancing, and safety monitoring [2]. All of these tasks are run by the BMS controller, which also extracts high-level battery pack information from the individual cell within the pack, and serves as an interface between the battery pack and the vehicle system controller [48].…”

Section: Architecture Of the Bmsmentioning

confidence: 99%

“…The diagnosis contains functions to estimate and predict battery states. Therefore, information is used, on the one hand, to observe the safe operation of the battery while aging and, on the other hand, to perform complex algorithms, e.g., for a range estimation in EVs [2]. When a component suddenly fails and the system cannot perform its functions, maintenance actions are automatically carried out to restore the system to working order [56].…”

Section: Diagnosismentioning

confidence: 99%

“…Lithium-ion batteries (LIBs) are one of the most promising technologies due to advantages like high efficiency, lower volume, small weight, temperature sensitivity, and maintenance [1][2][3]. They are the most ideal power source of the twenty-first century and have a bright future in many applications, such as portable electronic devices, electric vehicles (EVs) [4], aerospace systems, and power storage for renewable energy sources, like solar and wind turbines.…”

Section: Introductionmentioning

confidence: 99%

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Review on Health Management System for Lithium-Ion Batteries of Electric Vehicles

2018

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The battery is the most ideal power source of the twenty-first century, and has a bright future in many applications, such as portable consumer electronics, electric vehicles (EVs), military and aerospace systems, and power storage for renewable energy sources, because of its many advantages that make it the most promising technology. EVs are viewed as one of the novel solutions to land transport systems, as they reduce overdependence on fossil energy. With the current growth of EVs, it calls for innovative ways of supplementing EVs power, as overdependence on electric power may add to expensive loads on the power grid. However lithium-ion batteries (LIBs) for EVs have high capacity, and large serial/parallel numbers, when coupled with problems like safety, durability, uniformity, and cost imposes limitations on the wide application of lithium-ion batteries in EVs. These LIBs face a major challenge of battery life, which research has shown can be extended by cell balancing. The common areas under which these batteries operate with safety and reliability require the effective control and management of battery health systems. A great deal of research is being carried out to see that this technology does not lead to failure in the applications, as its failure may lead to catastrophes or lessen performance. This paper, through an analytical review of the literature, gives a brief introduction to battery management system (BMS), opportunities, and challenges, and provides a future research agenda on battery health management. With issues raised in this review paper, further exploration is essential.

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Section: Architecture Of the Bmsmentioning

confidence: 99%

Section: Diagnosismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Review on Health Management System for Lithium-Ion Batteries of Electric Vehicles

2018

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“…[1] According to the Global Electric Vehicle Outlook 2018, the global automotive market will successfully deploy 117.6 million electric vehicles. [2] The primary issue in the development of electric vehicles is the power source problem, [3] and lithium-ion batteries stand out in the electric vehicle market by virtue of their high energy density, [4] low self-discharge rate, [5] and long cycle life. [6] However, lithium-ion batteries (LiBs) performance is bound to degrade gradually due to calendar aging and cyclic aging effects.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network‐Long Short‐Term Memory‐Based State of Health Estimation for Li‐Ion Batteries under Multiple Working Conditions

Feng,

Song,

Lin

et al. 2023

Energy Tech

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The state of health (SOH) for lithium‐ion batteries is an important indicator to ensure the safety and reliability of battery energy storage systems. Aiming at the difficulty of accurately estimating the SOH of lithium‐ion batteries under different working conditions, this article proposes a method based on a hybrid convolutional neural network‐long short‐term memory (CNN‐LSTM) model. First, the battery health indicators and capacity data under different operating conditions are extracted from the public dataset to form a new dataset. Second, the CNN has multiple one‐dimensional convolutional layers to improve the efficiency of feature extraction from new datasets, and the resulting features are used as inputs to the LSTM to predict SOH. Finally, the CNN‐LSTM model integrates a fully connected layer that outputs the estimation of SOH for different operating conditions. The results show that the mean absolute error of the SOH estimation results is within 2.33% and 3.01% for the same and different working conditions, respectively.

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“…Due to the electrochemical and thermal models of the batteries being highly nonlinear, EKF is used for estimating its parameters, followed by SoC [45,80,90]. The EKF method is also used in combination with CC and/or OCV, as proposed in [11,45,50,103]. The works presented in [47,68,104] use a multiple model approach that uses a bank of EKFs to estimate the SoC of the battery.…”

Section: Real Batterymentioning

confidence: 99%

SoC Estimation for Lithium-ion Batteries: Review and Future Challenges

2017

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Abstract:Energy storage emerged as a top concern for the modern cities, and the choice of the lithium-ion chemistry battery technology as an effective solution for storage applications proved to be a highly efficient option. State of charge (SoC) represents the available battery capacity and is one of the most important states that need to be monitored to optimize the performance and extend the lifetime of batteries. This review summarizes the methods for SoC estimation for lithium-ion batteries (LiBs). The SoC estimation methods are presented focusing on the description of the techniques and the elaboration of their weaknesses for the use in on-line battery management systems (BMS) applications. SoC estimation is a challenging task hindered by considerable changes in battery characteristics over its lifetime due to aging and to the distinct nonlinear behavior. This has led scholars to propose different methods that clearly raised the challenge of establishing a relationship between the accuracy and robustness of the methods, and their low complexity to be implemented. This paper publishes an exhaustive review of the works presented during the last five years, where the tendency of the estimation techniques has been oriented toward a mixture of probabilistic techniques and some artificial intelligence.

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Verification oriented development of a scalable battery management system for lithium-ion batteries

Cited by 8 publications

References 3 publications

Review on Health Management System for Lithium-Ion Batteries of Electric Vehicles

Review on Health Management System for Lithium-Ion Batteries of Electric Vehicles

Convolutional Neural Network‐Long Short‐Term Memory‐Based State of Health Estimation for Li‐Ion Batteries under Multiple Working Conditions

SoC Estimation for Lithium-ion Batteries: Review and Future Challenges

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