Sophisticated Battery Management Research Articles

Electrochemical and aging model of li-ion batteries and estimation of total number of cycles during the lifecycle of the battery

Fast charging is a critical factor for the widespread adoption of electric vehicles (EVs), yet it poses challenges to the durability of lithium-ion batteries (LiBs). This study develops an aging model for a 10 Ah LiB cell using COMSOL Multiphysics, integrating a one-dimensional electrochemical model with a zero-dimensional thermal model. The model is constructed using cell design and electrochemical parameters derived from existing literature, tailored to the specific electrode and electrolyte materials utilized in this study. Key aging phenomena, such as the Solid Electrolyte Interface (SEI) growth and lithium plating, are embedded within the model to simulate the aging patterns and the interplay between these factors across various C-rates and temperatures. Simulation results suggest that while higher charging rates can reduce charging time, they also lead to increased degradation, with lithium plating becoming more severe at these elevated rates. The model also indicates that temperature management during cell operation can influence cycle life, with strategic temperature increases potentially mitigating some negative effects of fast charging. The study also explores the concept of total charging time as an alternative metric for battery wear, examining its relationship with the charging current profile and temperature. Results indicate that while fast charging offers convenience, it significantly impacts cycle life, underscoring the need for optimized charging strategies. The insights from this work enhance the understanding of Li-ion cell aging mechanisms, shedding light on the complex trade-offs between charging speed, temperature management, and lifespan. This contributes to the development of more sophisticated battery management systems and charging protocols that aim to extend the operational life of LiBs while accommodating the demands of fast charging.

Effective Strategies for EV Battery Monitoring and Maintenance

Abstract: The growing popularity of Electric Vehicles (EVs) has led to an increased demand for sophisticated battery management systems that guarantee safety and efficiency. In order to track and control the operation of electric vehicle batteries in real time, this project presents a novel pairing of an Internet of Things gadget with an Android application. This integrated solution gives customers greater safety and extends battery life by delivering critical insights and notifications regarding battery health, percentage, temperature, and essential events including full charge, low battery, overheating, and fire detection. The technology prioritizes user safety and battery longevity while optimizing the EV experience through user-friendly interfaces and timely notifications.

Possibilities for a Quick Onsite Safety-State Assessment of Stand-Alone Lithium-Ion Batteries

Electric vehicles’ high-voltage lithium-ion batteries are complex systems and can be sources of several hazards for interacting people. Sophisticated battery management systems (BMS) therefore constantly monitor their characteristics and varying states, to keep the battery within desired operational conditions and to mitigate safety risks as well as excessive degradation. However, there can be several situations where the battery is not in normal operation (e.g., a stand-alone battery) and a fully functional BMS monitoring function is not available. When necessary to interact with the system, its safety state must be deduced to ensure the safety of interactors. This can be a challenging task depending on a situation’s characteristics (time pressure, technical knowledge of involved people). Thus, this article discusses how the safety state of electric vehicle batteries can be evaluated quickly even by untrained people. To develop a solution, different scenarios, which require a battery’s state assessment, and the options for collecting relevant information are motivated and discussed, respectively. Finally, a mobile interface that can evaluate and display the safety state by using BMS-internal data is described and demonstrated.

Performance of GPS/GPRS tracking devices improves with increased fix interval and is not affected by animal deployment.

The use of GPS tracking technologies has revolutionized the study of animal movement providing unprecedentedly detailed information. The characterization of GPS accuracy and precision under different conditions is essential to correctly identify the spatial and temporal resolution at which studies can be conducted. Here, we examined the influence of fix acquisition interval and device deployment on the performance of a new GPS/GSM solar powered device. Horizontal and vertical accuracy and precision of locations were obtained under different GPS fix acquisition intervals (1min, 20 min and 60 min) in a stationary test. The test devices were deployed on pre-fledgling white storks (Ciconia ciconia) and we quantified accuracy and precision after deployment while controlling for bias caused by variation in habitat, topography, and animal movement. We also assessed the performance of GPS-Error, a metric provided by the device, at identifying inaccurate locations (> 10 m). Average horizontal accuracy varied between 3.4 to 6.5 m, and vertical accuracy varied between 4.9 to 9.7 m, in high (1 min) and low frequency (60 min) GPS fix intervals. These values were similar after the deployment on white storks. Over 84% of GPS horizontal positions and 71% of vertical positions had less than 10m error in accuracy. Removing 3% of data with highest GPS-Error eliminated over 99% of inaccurate positions in high GPS frequency intervals, but this metric was not effective in the low frequency intervals. We confirmed the suitability of these devices for studies requiring horizontal and vertical accuracies of 5-10m. For higher accuracy data, intensive GPS fix intervals should be used, but this requires more sophisticated battery management, or larger batteries and devices.

Overcharge Tolerance of LiFePO4 Cathode Pouch Cells

In recent years, LiFePO4 (LFP) has gained significant market acceptance in the area of electrical vehicles and grid energy storage due to the absence of Ni and Co, thermal stability, low toxicity, and long cycle life. However, the long-term performance of LFP may be degraded in mild overcharge scenarios. When placed in a battery pack, overcharge of an individual cell can occur due to charge imbalance between cells. Hence, it is important to understand the effect of overcharge on the cell materials.The results of overcharge on pouch cells with a 10 Ah capacity utilizing LFP as the cathode and graphite as the anode will be presented. Post overcharge electrochemical and material characterization are used to elucidate the effects of overcharge on performance and materials degradation. Electrochemical experiments included in-operando electrochemical impedance spectroscopy (EIS) and differential capacity as a function of voltage scans. EIS revealed changes in internal resistances as a function of state of overcharge. Differential capacity scans determined the point during overcharge at which damage to the cell begins, loss of active material, and loss of Li inventory. Materials characterization included X-ray diffraction (XRD), scanning electron microscopy (SEM), and inductively coupled plasma (ICP) spectroscopy. XRD exposed changes to the crystal structure, composition, and contaminants. SEM highlighted changes in the morphology of the cathode due to damage from overcharging. ICP was used to detect the presence of cathode material in the anode and vice versa, which is detrimental to future cell performance. Results show cell degradation occurring at low overcharge percentages, indicating the sensitivity of LFP to overcharge scenarios. As a result, the inclusion of an LFP cathode in battery packs may require more significant SOC buffers and/or a more sophisticated battery management system that can monitor overcharge at the individual cell level.

Passive Cell Balancing of Li-Ion batteries used for Automotive Applications

Due to the increasing demand of Li-ion batteries in automobiles and home applications due to their high volumetric energy density, high gravimetric energy density, low self-discharge and high efficiency. However, due to their high energy carrying capability, they tend to be more unstable compared to other batteries like lead acid batteries and hence need extensive monitoring to make sure they are operating within their specified safe operating limits; failing to do so may result in fire hazards and explosions. Hence this creates a demand for sophisticated Battery Management System (BMS) which will not only optimize power draw from the batteries but also keep them operating within safe limits, thus not putting the users at risk. This research paper begins with battery modelling using passive components and discusses the major factors which are important while designing an effective BMS. It also provides simulation to help better understand the functioning of a BMS.

From ROM of Electrochemistry to AI-Based Battery Digital and Hybrid Twin

Lithium-ion batteries are widely used in the automobile industry (electric vehicles and hybrid electric vehicles) due to their high energy and power density. However, this raises new safety and reliability challenges which require development of novel sophisticated Battery Management Systems (BMS). A BMS ensures the safe and reliable operation of a battery pack and to realize it a model must be solved. However, current BMSs are not adapted to the specifications of the automotive industry, as they are unable to give accurate results at real-time rates and during a wide operation range. For this reason, the main focus of this work is to develop a Hybrid Twin, as introduced in Chinesta et al. (Arch Comput Methods Eng (in press), 2018. https://doi.org/10.1007/s11831-018-9301-4), so as to meet the requirements of the new generation of BMS. To achieve this, three reduced order model techniques are applied to the most commonly used physics-based models, each one for a different range of application. First, a POD model is used to greatly reduce the simulation time and the computational effort for the pseudo-2D model, while maintaining its accuracy. In this way, cell design, optimization of parameters, and simulation of battery packs can be done while saving time and computational resources. In addition, its real-time performance has been studied. Next, a regression model is constructed from data by using the sparse-Proper Generalized Decomposition (s-PGD). It is shown that it achieves real-time performance for the whole electric vehicle (EV) system with a battery pack. In addition, this regression model can be used in a BMS without issues because of the simple algebraic expression obtained. A simulation of the EV with the proposed approach is demonstrated using the system simulation tool SimulationX (ESI ITI GmbH. Dresden, Germany). Furthermore, the Digital Twin created using the s-PGD does not only allow for real-time simulations, but it can also adapt its predictions taking into consideration the real driving conditions and the real driving cycle to change the planning in real-time. Finally, a data-driven model based on the employment of Dynamic Mode Decomposition techniques is developed to extract an on-line model that corrects the gap between prediction and measurement, thus constructing the first (to our knowledge) hybrid twin of a Li-ion battery able to self-correct from data. In addition, thanks to this model, the above gap is corrected during the driving process, taking into consideration real-time restrictions.

Modeling and Estimation for Advanced Battery Management

The commercialization of lithium-ion batteries enabled the widespread use of portable consumer electronics and serious efforts to electrify trans-portation. Managing the potent brew of lithium-ion batteries in the large quantities necessary for vehicle propulsion is still challenging. From space applications a billion miles from Earth to the daily commute of a hybrid electric automobile, these batteries require sophisticated battery management systems based on accurate estimation of battery internal states. This system is the brain of the battery and is responsible for estimating the state of charge, state of health, state of power, and temperature. The state estimation relies on accurate prediction of complex electrochemical, thermal, and mechanical phenomena, which increases the importance of model and parameter accuracy. Moreover, as the batteries age, how should the parameters of the model change to accurately represent the performance, and how can we leverage the limited sensor information from the measured terminal voltage and sparse surface temperatures available in a battery system? With a frugal sensor set, what is the optimal sensor placement? This article reviews estimation techniques and error bounds regarding sensor noise and modeling errors, and concludes with an outlook on the research that will be necessary to enable fast charging, repurposing of batteries for grid energy storage, degradation prediction, and fault detection.

Operando Spectroscopic Microscopy of LiCoO2 Cathodes Outside Standard Operating Potentials

LiCoO2 can experience over-lithiation (over-discharge) in an electrochemical cell due to poor battery management, failure such as a short circuit, or when LiCoO2 is utilized as a negative electrode conversion material. In order to understand the chemical and morphological changes which occur during over-lithiation, LiCoO2 electrodes were studied during deep discharge to 0.8V with operando X-ray absorption spectroscopy and spectroscopic X-ray microscopy. During over-lithiation, micron-sized LiCoO2 particles suffer significant cracking, pulverization and an incomplete conversion to Co metal. These irreversible morphological and structural changes inflict permanent damage on the cathode even during a single over-lithiation event and highlight the need for more sophisticated battery management systems.

Modular Active Charge Balancing for Scalable Battery Packs

High-voltage battery packs consist of series-connected lithium-ion cells and require sophisticated battery management systems (BMSs) to maintain safe operating conditions. Active cell balancing is an important task of a BMS, performed in order to improve the usable capacity of the battery pack by equalizing the charge levels of individual cells. With the emerging trend of distributed BMS topologies, the associated balancing architectures are required to be modular, consisting of homogeneous units that minimize integration efforts. In this paper, we propose a modular active charge balancing architecture along with its control scheme for implementation toward such distributed BMSs. Compared with existing approaches, our proposed architecture provides increased charge transfer capabilities, with reduced hardware and control complexity. We propose a closed-form analytical model of the balancing architecture, which can be used to perform fast system-level simulation studies and design space exploration for analyzing efficient device combinations. A hardware implementation of the proposed balancing architecture is developed and measurements made with it are used to validate each part of our analytical model. Using the validated analytical model, we performed a case study, which shows that our proposed architecture provides a 14.5 % improvement in charge transfer efficiency compared with existing approaches.

A New Method to Compensate High-Frequency Impedance Artefacts for Three-Electrode Li-Ion Batteries

Lithium-based batteries are today’s most favourable systems to provide energy for battery-powered applications such as (Hybrid) Electric Vehicles ((H)EVs), laptops and smartphones. Generally, (commercial) Li-ion batteries are two-electrode systems. Therefore, only the battery potential or impedance can be measured across the negative and positive electrode. However, for research purposes and to design more sophisticated Battery Management Systems (BMS) it is of interest to distinguish between both electrodes by using reference electrodes (RE), making it possible to measure the electrochemical characteristics of the individual electrodes. RE have already been introduced in many studies1,2. However, Electrochemical Impedance Spectroscopic (EIS) measurements on three-electrode Li-ion battery systems are prone to measurement artefacts. The majority of the research on EIS measurement artefacts focuses on the cell geometry and/or the position of the RE3,4. In the present contribution, new results are presented which show that EIS artefacts in the high frequency range can be compensated by averaging two distinctive EIS measurements. This strikingly results in artefact-free EIS measurements, especially in the high frequency range of the impedance spectra. This new method has been applied to pouch-type Li-ion batteries with integrated metallic lithium-based micro-reference electrodes1,2. Fig. 1 shows EIS measurements, revealing the high-frequency artefacts as well as the compensated EIS measurements. The EIS measurements of the battery (Bat), and those of the positive (P) and negative (N) electrode vs a lithium micro-reference electrode are shown in Fig. 1a. In addition, the impedance spectrum of the summation of P and N is shown (P+N), which obviously should end up with the same result as found for Bat in the entire frequency range. However, in the high frequency range a large deviation between Bat and P+N can be observed, indicating that the impedance measurements of the individual electrodes are not correct. This is because the generated excitation current induces a net voltage in the RE due to an unbalance in the measurement setup. Additional EIS measurements have been carried out with the measurement cables in a reversed connection. These results are shown in Fig. 1b and indicated with the subscript r. It can be observed that also these EIS measurements are deviating in the high frequency range but now in the opposite direction. However, averaging the measurements shown in Fig. 1a and b, results in correct impedance spectra for both the individual electrodes and added spectra, as indicated in Fig. 1c and, at a larger magnification, in Fig. 1d. It can indeed be seen that the impedance spectra of the individual electrodes are now artefact-free in the high frequency range and that the summation of P and N are now in perfect agreement with the total battery impedance in the entire frequency range. It can be concluded that high frequency impedance measurement artefacts observed with micro-reference electrodes integrated in Li-ion batteries can perfectly be compensated by averaging two EIS measurements. This results in artefact-free impedance spectra of (commercial) three-electrode batteries without making complicated measurement setups, which can easily be facilitated by conventional electronic circuitry as will be shown in the near future.

Parameter Identification of Lithium-Ion Batteries Model to Predict Discharge Behaviors Using Heuristic Algorithm

A complete set of parameters that describes lithium-ion batteries is required in order to better control the sophisticated battery management system used during practical application of lithium-ion batteries. Previous work in the literature requires long times and a computer cluster to identify the full set of parameters while using a pseudo-two-dimensional (P2D) model and a genetic algorithm (GA). This work proposes a more efficient parameter identification method based on the heuristic algorithm. The heuristic algorithm includes features such as the requirement of fewer identified parameters and the effective interval spacing of parameters to save searching time during the GA. This algorithm also uses a divide-and-conquer strategy that divides the full set of P2D parameters into two groups and identifies each group, respectively. With this method, the complete identification of P2D model parameters can be performed within 10 h using a single core computer. Simulated data using the above identified parameters are consistent with the reference results from the literature. And the identified paramters could be used for estimation of the state of charge and the state of available power capability of lithium-ion batteries.

Smart Battery Management System with Active Cell Balancing

Background/Objectives: Batteries are extensively being used in transportation, military, aerospace and portable applications like mobile phones and laptops. Also, battery driven Electric Vehicles (EVs) are replacing conventional vehicles to reduce pollution and dependence on conventional energy resources like petroleum and diesel etc. Methods/Statistical analysis: Generally batteries operate in the form of battery packs consisting of string of cells in series and parallel manner providing high voltage, current and power as required for different applications. Findings: Batteries faces many problems like charge imbalance, thermal runaway, over voltage and under voltage due to high charge/discharge profile. Charge imbalance affects overall pack performance as well. An efficient and sophisticated Battery Management System (BMS) that monitors and controls the critical parameters of the battery for smooth running and better performance of a battery. An embedded system was designed using a battery monitoring fuel gauge IC (BQ76PL536) and a buck-boost converter for cell balancing. Conclusion: This paper demonstrates a novel battery management system which actively monitors the critical parameters like voltage, capacity and performs as an active balancing of cells in a battery pack whenever required. The system is integrated with MSP430 microcontroller for monitoring and controlling purpose. Proposed battery management system continuously monitors each cell voltage in a battery pack and performs cell balancing if voltage difference between cells exceeds the threshold value.

Low-temperature charging of lithium-ion cells Part II: Model reduction and application

Lithium-ion cells, especially when used in electric vehicles at varying operation conditions, require a sophisticated battery management to ensure an optimal operation regarding operation limits, performance, and maximum lifetime. In some cases, the best trade-off between these conflictive goals can only be reached by considering internal, non-measurable cell characteristics. This article presents a data-driven model-reduction method for a strict electrochemical model. The model describes the charging process of a lithium-ion cell and possibly occurring degradation effects in a large temperature range and is presented in Part I of this contribution. The model-reduction process is explained in detail, and the gained model is compared to the original electrochemical model showing a very high approximation quality. This reduced model offers a very low computation complexity and is therefore suitable for the implementation in a battery management system (BMS). Based on this model, an advanced charging strategy is presented and evaluated for possible reductions in charging times especially at low temperatures.

A Solar Cell System for Extension of Battery Run Time in a Moving Actuator Total Artificial Heart

An implantable total artificial heart (TAH) system has strong dependence upon the external battery performance for operation. Even under sophisticated battery management control, the usable external battery performance continues to decrease, which limits TAH performance. One of the ways to overcome this energy problem is to use a solar system (SS). An SS can provide electrical power for the partial TAH drive or battery recharge. This article presents a new concept for use of the solar cell for obtaining double external battery performance. To achieve it, numeric simulations were carried out to obtain the proper magnitude of solar parameters. In the TAH used, the battery power for a cardiac output of 4-6 L/min is approximately 17 W/20 min. From simulated results, the optimal power and voltage of the SS were found to be 7 W, Voc = 27 V in the case of the 24 V motor. Each solar cell includes Voc = 0.5 V, Isc = 37 mA/cm2, FF (fill factor) = 0.77, and efficiency = 10%. Based on the simulation, the effect of solar power capacity on battery run time was studied. With use of 6.5 W SS (W 304 x H 245 x D 16 mm, 1.1 kg), battery performance decreased in vitro from 100% (fully charged) to > 55% vs 0% in the conventional battery system after 20 min operation. However, it dropped to below 20% when using W SS (W 192 x H 192 x D 16 cm, 0.6 kg). The results showed doubled battery run time could be obtained compared with a system without the SS. It was concluded that the proposed SS can be put to practical use as a future energy source for a TAH.