A High-Efficiency Capacitor-Based Battery Equalizer for Electric Vehicles.

Technology in electric vehicles has increased substantially in the past decade. Moreover, it is projected to grow at record highs in the coming years since these vehicles are needed to reduce the contamination related to the transportation sector. One of the essential elements of an electric car is its battery, due to its cost. Batteries comprise parallel and series-connected cell arrangements to meet the power system requirements. Therefore, they require a cell equalizer circuit to preserve their safety and correct operation. These circuits keep a specific variable of all cells, such as the voltage, within a particular range. Within cell equalizers, capacitor-based ones are very common as they have many desirable characteristics of the ideal equalizer. In this work, an equalizer based on the switched-capacitor is proposed. A switch is added to this technology that allows the disconnection of the capacitor from the circuit. In this way, an equalization process can be achieved without excess transfers. Therefore, a more efficient and faster process can be completed. In addition, it allows another equalization variable to be used, such as the state of charge. This paper studies the operation, power design, and controller design of the converter. Moreover, the proposed equalizer was compared to other capacitor-based architectures. Finally, simulation results were presented to validate the theoretical analysis.

Distributed Intelligent Battery Management System Using a Real-World Cloud Computing System.

In this work, a decentralized but synchronized real-world system for smart battery management was designed by using a general controller with cloud computing capability, four charge regulators, and a set of sensorized battery monitors with networking and Bluetooth capabilities. Currently, for real-world applications, battery management systems (BMSs) can be used in the form of distributed control systems where general controllers, charge regulators, and smart monitors and sensors are integrated, such as those proposed in this work, which allow more precise estimations of a large set of important parameters, such as the state of charge (SOC), state of health (SOH), current, voltage, and temperature, seeking the safety and the extension of the useful life of energy storage systems based on battery banks. The system used is a paradigmatic real-world example of the so-called intelligent battery management systems. One of the contributions made in this work is the realization of a distributed design of a BMS, which adds the benefit of increased system security compared to a fully centralized BMS structure. Another research contribution made in this work is the development of a methodical modeling procedure based on Petri Nets, which establishes, in a visible, organized, and precise way, the set of conditions that will determine the operation of the BMS. If this modeling is not carried out, the threshold values and their conditions remain scattered, not very transparent, and difficult to deal with in an aggregate way.

Determination of Lamb Wave Modes on Lithium-Ion Batteries Using Piezoelectric Transducers.

This work presents a method to determine the type of Lamb mode (antisymmetric or symmetric) that propagates through a lithium-ion pouch cell. To determine the type of mode and the group velocity at a specific frequency, two- and three-transducer setups were created. For these setups, it is important that all transducers have the same polarization direction. Two transducers are affixed to the center of the cell at a distance of several centimeters from each other so that the group velocity can be determined. Using cross-correlation, the group velocity of the emerging mode can be calculated. The measurement setup and the processing method was first validated with experiments on acrylic glass and aluminum plates. The measurements were supported with FEM simulations and a numerically calculated model. The output voltages of the receiving piezo-elements obtained in the FEM simulation are in agreement with the underlying theories. The phase shift, which results from the output voltage of the piezo-elements mounted one above the other on different sides of the plate, shows the type of mode. The results of the experimental determination of the Lamb mode that propagates through a lithium-ion pouch cell were validated with a numerically calculated multi-layer model and therefore validate this novel experimental approach.

Design and Development of a Battery State of Health Estimation Model for Efficient Battery Monitoring Systems.

An uninterruptible power supply (UPS) is a device that can continuously supply power for a certain period when a power outage occurs. UPS devices are used by national institutions, hospitals, and servers, and are located in numerous public places that require continuous power. However, maintaining such devices in good condition requires periodic maintenance at specific time points. Efficient monitoring can currently be achieved using a battery management system (BMS). However, most BMSs are administrator-centered. If the administrator is not careful, it becomes difficult to accurately grasp the data trend of each battery cell, which in turn can lead to a leakage or heat explosion of the cell. In this study, a deep-learning-based intelligent model that can predict battery life, known as the state of health (SoH), is investigated for the efficient operation of a BMS applied to a lithium-based UPS device.

Experimental and simulation investigation on suppressing thermal runaway in battery pack.

In order to address the issue of suppressing thermal runaway (TR) in power battery, a thermal generation model for power batteries was established and then modified based on experimental data. On the basis of simulation calculations, a scheme was designed to suppress thermal runaway of the battery module and battery pack, and samples were produced for testing. The results of the test and simulation calculations were very consistent, confirming the accuracy of the simulation calculation model. The results of thermal runaway test also demonstrate that the measures designed to suppress thermal runaway are effective and meet the design requirements.

Development of dual polarization battery model with high accuracy for a lithium-ion battery cell under dynamic driving cycle conditions.

Lithium-ion batteries are a key technology for electric vehicles. They are suitable for use in electric vehicles as they provide long range and long life. However, Lithium-ion batteries need to be controlled by a Battery Management System (BMS) to operate safely and efficiently. The BMS continuously controls parameters, such as current, voltage, temperature, state of charge (SoC), and state of health (SoH), and protects the battery against overcharging and discharging, imbalances between cells, and thermal runaways. The battery models and several prediction algorithms that the BMS uses to carry out these checks are essential to the system's performance. This research assesses the Dual Polarization (DP) model's ability to mimic actual battery performance in different dynamic driving conditions. In the study, a battery model for a Lithium-Nickel-Manganese-Cobalt-Oxide (Li-NMC) cell with a nominal capacity of 2 Ah is developed. A DP model was used in the study. Modeling and parameter estimation were performed in MATLAB Simulink/Simscape. Firstly, the model parameters are estimated depending on the SoC using the current and voltage data obtained from the Hybrid Pulse Power Characterization (HPPC) test. A further validation study of the model for low dynamic and high dynamic driving cycles is then presented. Dynamic Stress Test (DST), the US06 Supplemental Federal Test Procedure (SFTP) and Worldwide harmonized Light vehicles Test Procedure (WLTP) cycles were used for model validation. As a result of the study, the model's Root Mean Square (RMS) error values were obtained as 0.0053 V for DST, 0.0059 V for US06, and 0.008 V for WLTP. The obtained model is particularly successful for simulating a battery under dynamic current conditions and for use in control and prediction algorithms.

Abstraction and simulation of EV battery systems-resilience engineering by biological transformation.

While the demand for electric vehicles (EVs) is continuously growing, safety issues still remain, specifically related to fire hazards. This research aims to improve the resilience of battery systems in EVs by transferring concepts found in biology to a bioinspired battery system. Due to the complexity of modern battery systems, the biological concepts cannot be applied directly. A simplified simulation battery system for EVs is modelled, which contains the essential battery components necessary to understand both, software and battery dynamics. This is used as a baseline model to study the effects of typical heat-related disturbances. Subsequently, this simulation model is modified to demonstrate the transfer of biological concepts underlying specifically the hypersensitization and vasospasm mechanisms related to wound healing, and to test the effects of disturbances and alterations comparable to damages caused by vehicle accidents. As a battery system's mass and volume should not be increased by additional hardware, the biological concepts target the interaction within, and the composition of, the system, while leaving single components relatively unchanged. It is found that small bioinspired alterations to the battery system can have significant impacts on their vulnerability to common hazards.

The Role of Lithium-Ion Batteries in the Growing Trend of Electric Vehicles.

Within the automotive field, there has been an increasing amount of global attention toward the usability of combustion-independent electric vehicles (EVs). Once considered an overly ambitious and costly venture, the popularity and practicality of EVs have been gradually increasing due to the usage of Li-ion batteries (LIBs). Although the topic of LIBs has been extensively covered, there has not yet been a review that covers the current advancements of LIBs from economic, industrial, and technical perspectives. Specific overviews on aspects such as international policy changes, the implementation of cloud-based systems with deep learning capabilities, and advanced EV-based LIB electrode materials are discussed. Recommendations to address the current challenges in the EV-based LIB market are discussed. Furthermore, suggestions for short-term, medium-term, and long-term goals that the LIB-EV industry should follow are provided to ensure its success in the near future. Based on this literature review, it can be suggested that EV-based LIBs will continue to be a hot topic in the years to come and that there is still a large amount of room for their overall advancement.

Improved power flow management with proposed fuzzy integrated hybrid optimized fractional order cascaded proportional derivative filter (1+ proportional integral) controller in hybrid microgrid systems.

The primary goal of power management in a hybrid microgrid is to maintain the active power balance among renewable energy sources, energy storage system, and loads. In a HMG system, an ESS is required to reduce power fluctuations caused by the intermittent behavior of RESs. A bi-directional dc-dc converter has been used to connect a battery to the dc bus. This paper proposes an improved power management scheme where a novel controller named as hybrid fuzzy integrated fractional order cascaded proportional derivative filter (1+proportional integral) is designed and implemented in a voltage-controlled loop of the BDC. This controller is introduced in this work to achieve optimum power flow management improves voltage stabilization, and enhances the system dynamic response. A hybrid modified sine cosine algorithm-pattern search algorithm is implemented here to tune the parameters of the proposed controller. The extensive time-domain analysis and robustness studies of the proposed controller have been performed and compared with the other conventional control methods Variable solar irradiance, solar temperature, wind speed, and load are used to test the effectiveness of proposed power management strategy. The proposed power management scheme with several case studies has been performed in MATLAB/Simulink and also validated through a Real-time digital platform (Opal-RT).

Ageing characterization data of lithium-ion battery with highly deteriorated state and wide range of state-of-health.

This paper presents ageing characterization data of two lithium-ion battery cells that have been put through a deep ageing process. At the end of ageing process, the values of state-of-health (SOH) of the battery cells drop down to around 15%. The battery cells are aged using a developed autonomous ageing platform which performs functions such as constant current (CC) discharging, CC charging, and constant voltage (CV) charging. Each time the battery cell completes 30 ageing cycles, battery performance tests including dc impedance measurement, minimum impedance measurement, and capacity calibration are conducted to characterize the ageing or health status of the battery cell. The collected battery dc impedance data, minimum impedance data, capacity data, and CC-CV charging time for the deeply aged battery cells are presented in this paper. The presented data has the potential to help in identifying battery ageing behavior patterns. It can also be utilized to investigate the correlation or relationship between different battery ageing characterization data and to develop SOH estimation methods for lithium-ion batteries with high degradation conditions, for examples, for second-use battery and when battery health exhibits unexpected faster deterioration.

Concept Review of a Cloud-Based Smart Battery Management System for Lithium-Ion Batteries: Feasibility, Logistics, and Functionality.

Energy storage plays an important role in the adoption of renewable energy to help solve climate change problems. Lithium-ion batteries (LIBs) are an excellent solution for energy storage due to their properties. In order to ensure the safety and efficient operation of LIB systems, battery management systems (BMSs) are required. The current design and functionality of BMSs suffer from a few critical drawbacks including low computational capability and limited data storage. Recently, there has been some effort in researching and developing smart BMSs utilizing the cloud platform. A cloud-based BMS would be able to solve the problems of computational capability and data storage in the current BMSs. It would also lead to more accurate and reliable battery algorithms and allow the development of other complex BMS functions. This study reviews the concept and design of cloud-based smart BMSs and provides some perspectives on their functionality and usability as well as their benefits for future battery applications. The potential division between the local and cloud functions of smart BMSs is also discussed. Cloud-based smart BMSs are expected to improve the reliability and overall performance of LIB systems, contributing to the mass adoption of renewable energy.

Experimental data on open circuit voltage characterization for Li-ion batteries.

In this article, we present the datasets collected from nine different Li-ion batteries. These datasets contain voltage, current and time measurements during a full charge-discharge cycle of a battery at very low current (that is nearly at C / 30 rate). Such low current rate data is suitable for open circuit voltage characterization. The collection of this data was done through the use of an Arbin battery cycler and a thermal chamber was used to control the test temperature. Data were collected over a wide range of temperatures from - 25 ∘ C to 50 ∘ C.