Battery Usage Research Articles

Optimized Power Management Approach for Photovoltaic Systems with Hybrid Battery-Supercapacitor Storage

The paper addresses the ongoing and continuous interest in photovoltaic energy systems (PESs). In this context, the study focuses on an isolated photovoltaic system with hybrid battery-supercapacitor storage (HBSS). The integration of supercapacitors (SCs) in this system is particularly important because of their high specific power density. In photovoltaic (PV) systems, multi-storage systems use two or more energy storage technologies to enhance system performance and flexibility. When batteries and supercapacitors are combined in a PV system, their benefits are maximized and offer a more reliable, efficient, cost-effective energy storage option. In addition, effective multi-storage power management in a PV system needs a solid grasp of the energy storage technologies, load power demand profiles, and the whole system architecture. This work establishes a battery-supercapacitor storage system (HBSS) by combining batteries and supercapacitors. The primary objective is to devise a novel management algorithm that effectively controls the different power sources. The algorithm is designed to manage the charge and discharge cycles of the hybrid battery-supercapacitor energy storage system (HBSS), thereby guaranteeing that the state of charge (SOC) for both batteries and supercapacitors is maintained within the specified range. The proposed management algorithm is designed to be simple, efficient, and light on computational resources. It efficiently handles the energy flow within the HBSS, optimizing the usage of both batteries and supercapacitors based on real-time conditions and energy demands. The proposed method ensures their longevity and maximizes their performance by maintaining the SOC of these energy storage components within the specified limits. Simulation results obtained from applying the management strategy are found to be satisfactory. These results show that the proposed algorithm maintains the SOC of batteries and supercapacitors within the desired range, leading to improved energy management and enhanced system efficiency.

Optimization of speed trajectory for electric wheel loaders: Battery lifetime extension

The electrification of wheel loaders is considered a leading trend due to its advantage of zero‑carbon emissions. However, the inevitable phenomenon of battery degradation has led to increased battery usage and maintenance costs. This study first extends the battery lifetime by optimizing the speed trajectory based on the typical loading cycle of the wheel loader. The optimal control problem is formulated by systematically modeling the wheel loader's powertrain and using a precise semi-empirical battery aging model. To reduce computational costs, the modified optimal control problem includes a weighted penalty on travel time. Unlike the conventional dynamic programming method that takes substantial computation time, a combined algorithm of dynamic programming and Brent's method (DP-BM) is first introduced to provide a numerical solution to the optimization problem with a reduced computational burden. Simulation results demonstrate that the optimized trajectory can decrease the average power consumption of the battery and reduce the number of full equivalent cycles, resulting in a 4.48% improvement in the average battery lifetime compared to the typical trajectory. Modeling errors are analyzed to make sure the results are reliable. Furthermore, the proposed approach significantly reduces computation time compared to the conventional dynamic programming method, with an average reduction of 95%. The total cost for each year is finally summarized, and the results indicate that the optimized trajectory offers a distinct advantage in terms of cost-effectiveness.

Enhanced Whale Optimization Algorithm with Wavelet Decomposition for Lithium Battery Health Estimation in Deep Extreme Learning Machines

Lithium battery health state estimation can help optimize battery usage and management strategies. In response to the challenges faced by traditional battery management systems in accurately estimating the State of Health of lithium-ion batteries and addressing issues such as capacity recovery and noise interference, this paper proposes a method based on wavelet decomposition and an improved whale optimization algorithm optimized deep extreme learning machine for estimating the SOH of lithium-ion batteries. Firstly, the lithium-ion battery capacity degradation sequence is extracted, and the wavelet decomposition method is used to decompose the battery capacity into global and local degradation trends. Next, the non-linear convergence factor and the whale optimization algorithm with adaptive weights are employed to optimize the deep extreme learning machine for predicting each trend component. Finally, the prediction results are effectively integrated to obtain the lithium-ion battery SOH. This experimental method is validated using NASA and CALCE datasets, and the results indicate that the root mean square error and mean absolute percentage error are both below 0.95%, with relative accuracy and absolute correlation coefficients exceeding 98%. This demonstrates the method’s excellent accuracy and robustness.

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.

A feature extraction approach for state-of-health estimation of lithium-ion battery

Accurate state-of-health (SOH) estimation is essential to ensure the reliable and safe usage of lithium-ion batteries (LIBs). A novel health feature extraction approach is proposed in this manuscript for battery SOH estimation. Firstly, the degradation data are collected from LIBs with two different life stages, and then the discrete incremental capacity (IC) curve is obtained under different constant voltage intervals dv. The corresponding charging voltage range with obvious variation trend of IC is selected and divided into several subintervals with Δv. The average IC of each subinterval is obtained. Furthermore, the consistency between the average IC of each voltage subinterval and battery capacity is analyzed and evaluated based on raw discrete IC curve. The average IC with the most consistent in relation to battery capacity degeneration is selected as the health feature. The impact of varying dv and Δv values on the feature is conducted based on Spearman correlation analysis, and the health feature with maximum Spearman correlation coefficient is used to build battery SOH estimation model. Finally, two SOH estimation models and comparative analysis of the performance between proposed health feature and other accepted features are utilized to verify the proposed health feature extraction approach. The results demonstrate that our extracted health feature effectively reveals the battery performance degeneration.

Flyback converter employed non-dissipative cell equalization in electric vehicle lithium-ion batteries

The effective and dependable usage of rechargeable batteries has emerged as a central topic for automobile manufacturers in the wake of the rise of electric vehicle technology. When it comes to rechargeable batteries with high specific energy and specific power, lithium-ion battery technology is the most well-known. The low terminal voltage battery cells in the lithium-ion battery pack are linked in series to provide the necessary voltage for the electric vehicle system. The low charge cell in the string limits the usable capacity of the battery pack, though. Disturbances in the battery pack's charge are due to variations in manufacturing quality and to the unique operating circumstances of each individual cell. These inconsistencies cause a decline in usable capacity, a quickening of cell deterioration, and, most significantly, substantial safety issues including overcharging. The cell balancing controller is a critical component of the battery management system in all electric vehicle and hence performs a crucial function in extending battery life and ensuring the battery's safety. This paper presents a hardware-in-the-loop simulation of a RCD buffer included fly-back converter-based active cell equaliser for lithium-ion batteries in electric vehicles. With the equaliser, all the series-connected cells may be brought to a more even State of Charge. All of the MOSFETs employed in the proposed approach are selected for their low conduction loss. The suggested equaliser is able to produce equalization without the need for a switch driving circuit or sophisticated control method, allowing it to function automatically. The system's overall price is reduced, and the balanced circuit's complexity is considerably reduced. In-depth discussions on circuit configuration, operating principle, modeling, and design consideration are presented. Finally, both Matlab simulation results and Hardware-in-loop based experimental findings are offered to back up the claims that the suggested cell equaliser is both practical and effective.

A novel methodology to study and compare active energy-balance architectures with dynamic equalization for second-life battery applications

The continuous growth of the electric vehicles market and the increasing environmental awareness impose to search for innovative solutions to reuse the exhausted vehicle batteries in different applications. One of the main problems of second-life batteries is the very high mismatch among their cell capacities which reduces the overall battery performance. The mismatch effect can be overcome by using dynamic equalization. This technique aims to keep balanced as much as possible the State of Charge of the cells during the battery operation, i.e. in both the charge and discharge phases. In order to do this, the dynamic equalization approach requires a high-current active energy balancing system able to move a quantity of charge among the battery cells much higher than the active balancing circuits sometimes used in first-life batteries. The use of a high equalization current increases the design complexity of the balance system.A methodology to study and compare the main balance system topologies suitable for second-life batteries with dynamic equalization approach is presented in this work. The Adjacent Cell-to-Cell, Direct Cell-to-Cell, Cell-to-Pack, and Pack-to-Cell active balance topologies are analyzed considering the case study of a second-life battery composed of 10 series-connected cells with capacity values uniformly distributed around±15% of the nominal value.The investigation proves that the Direct Cell-to-Cell topology has the best performance. This balancing topology improves the usable battery capacity of around 16% with respect to the case in which no dynamic equalization is applied if a DC/DC converter with a power efficiency of only 70% is used. Finally, the results show that the Adjacent Cell-to-Cell performance is strongly affected by the position of the cell mismatches in the battery.

Optimized Low-Powered Wide Area Network within Internet of Things

The Internet of Things (IoT) is rapidly becoming an integral part of everyday life. LPWANs have been introduced to support the billions of internet-connected devices and the data they produce. LPWANs are capable of providing reliable connectivity even in low-density areas and devices consuming a low amount of energy. The exponential increase in the use of IoT applications across the globe will continue to generate more and more data traffic within the IoT network. Hence, it will increase device battery usage that may reduce the battery life expectancy limits. Thus, End Devices (EDs) within the IoT network in the near future will rise up to billions of devices operating in public, industry, and personal networks, generating a necessity for more correct and reliable energy conservation technology. This prompted the research work on an optimized low-powered wide area network within IoT. This paper focuses on three different strategies: LoRa power consumption model design, simulation of IoT wireless sensor networks, and implementation of SF allocation across the wireless sensor network and results analysis. The experiment has been carried out in various stages: firstly running a simulation over a wireless sensor network without optimization using MATLAB Simulink and obtaining the following result of 6.3997e-17 joules power consumption. Secondly, the authors test the network with power optimization using particle swarm optimization algorithms and obtained a better result of 2.5230e-17 joules. The LoRa energy consumption is reduced by 60%. Lastly, different simulation tests of LoRaWAN protocols with respect to throughput, packet loss, delay, data transmission, buffer size, and network density. The results presented on the graph showed that the proposed model outperforms the existing models. Hence, appropriate spreading factor allocation has increased the power efficiency of LoRa end device battery.

Low Temperature Research and Improvement on Lithium Ion Batteries

Lithium ion batteries have been the dominant power sources in portable electronics and electric vehicles due to their high energy density, long lifetime and environmental benignity. However, it is a big challenge for lithium ion batteries to be used at low temperature. Compared with the performance at room temperature, lithium ion batteries deliver much less capacity and energy at low temperature. The poor low temperature performance restricts the usage of lithium ion batteries for power electronics and electric vehicles in the winter. The results of previous research, suggest that the poor low temperature performance is attributed to the freezing of the liquid electrolyte which leads to low ionic conductivity and high interfacial resistance, especially on the anode side. Other researchers tried using a solvent with low melting point to partially replace ethylene carbonate or adding some additives to the electrolyte to enhance the ionic conductivity or modify the interface to reduce the resistance and then deliver more capacity and energy.In this research, we investigated the discharge capacity, energy, pulse power and electrochemical impedance spectroscopy at different depths of discharge of NCM622/Graphite lithium ion batteries at varied temperatures. We found that the discharge energy and pulse power are sensitive to the temperature decreasing. For the NCN622/Graphite cell (2.5 mAh cm-2) with commonly used LiPF6-EC/EMC electrolyte, at -20 oC the cell is able to deliver 65% capacity of that at 30 oC with C/3 discharge current rate, while the delivered energy is only 60%. The 30 seconds pulse power became much worse at -20 oC compared with the power at room temperature. In our research the poor low temperature performance is not from the low ionic conductivity and interfacial resistance. The dominant factor is the charge transfer impedance from the cathode side of the cell.To improve the low temperature performance, we explored the addition inexpensive nano-particles as the additive into the electrolyte. The cell composed of this electrolyte is able to release more than 70% capacity and energy at -20 oC of that measured at 30 oC. The charge transfer resistance in the fully charge state decreased almost 80% compared to a cell with baseline electrolyte. Meanwhile, the additive has no effect on the long-term cycling (1000 cycles) and stability at high temperature (55 oC) Fig.1. Normalized capacity and energy. Fig.2. The EIS of the full cell at 30 oC and -20 oC. Figure 1

Studying the Mechanics of NMC Cathode Particles for Direct Battery Recycling

As the global usage of lithium-ion batteries skyrockets, there is increasing interest in recycling spent batteries. While the economic and environmental impacts of reclaiming high-value elements such as cobalt, nickel, and lithium are obvious, directly-recycling cathode materials takes this one step further by both recovering these elements as well as preserving particle structure, which takes significant processing cost and materials to manufacture. Previous literature has found a correlation between mechanical strength and cyclability of cathode materials, indicating that restoring the structural integrity of active particles is crucial to direct recycling efforts [1, 2]. Cathode materials experience significant fracture and cracking during the lifetime of a battery, caused by both the calendering process during formation and the volume expansion of the particles upon lithium insertion during cycling [1]. Many recycling processes rely on high-temperature annealing to repair cracks in end-of-life cathode particles. As fracture due to calendering exists in pristine electrodes, it is possible that the annealing step does not need to heal all cracks in end-of-life material to match pristine electrode performance. To lower recycling costs, it is vital that the requirements for the annealing step are well understood.To address this, we characterize the mechanical strength of nickel manganese cobalt (NMC) cathode particles at pristine, end-of-life, and recycled-annealed states. We leverage multiple experimental techniques, including bulk compressive testing, nanoindentation, and nano-CT measurements, to characterize the particles’ bulk modulus, Weibull distribution, and fracture toughness, among others, and correlate these mechanical properties to cycling performance. From this, we provide insight into an optimal annealing step that minimizes cost without sacrificing recycled material mechanical and electrochemical quality.

Life cycle assessment of lithium ion battery from water-based manufacturing for electric vehicles

Lithium ion batteries produced using the water-based manufacturing processes, as a greener technology, have great potential to be used in future electric vehicles (EVs). A cradle-to-grave life cycle assessment model configured for actual EV applications has been developed for the water-based manufactured lithium nickel manganese cobalt oxide (NMC)-graphite battery pack. Experimental and modeling results cover raw material extraction and processing, water-based battery manufacturing processes, battery usage during EV driving, and direct recycling at End-of-Life. The ReCiPe method is employed to investigate the environmental impacts of the water-based battery pack and benchmark it against the impacts of a conventional N-methyl-2-pyrrolidone (NMP)-based battery pack with the same mass. The cradle-to-grave energy consumption of the studied water-based battery pack is 0.976 MJ/km EV driving, equivalent to a 4.5% reduction over the NMP-based battery pack. Aside from energy usage, we find reductions in all environmental impact categories (3.0%∼85%) compared to the conventional battery pack.

Study of Effective Powertrain Sizing for Electric Vehicles

Rapid growth of electric vehicles technology and its efficiency management techniques has led to dramatic rise in the necessity for effective sizing of powertrain components. The recent market need for this technology is the optimized powertrain design to encourage further evolvement. In present study, thorough calculations involved in the powertrain sizing through various assessments are emphasized. Forces, Power, Energy, Motor and Battery Pack calculations are derived along with discharge rate and battery usage time. Effective powertrain components capacitive size is calculated for an estimated vehicle performance which can eventually assist in the optimal vehicle design which helped in range extension.
 Keywords: Electric vehicles, Powertrain design, Battery Pack calculations, vehicle design, hybridization, Hybrid vehicles, fuel powered vehicles, Electrical Drive system, PHEVs, FEVs, fuel cells

Coupled electrochemical–mechanical model for fracture analysis in active materials of lithium ion batteries

Mechanical degradation is a significant cause of battery aging: the stress arising in the electrode microstructure during operation causes fractures, leading to capacity and power fade. This work aims to quantify the fracture behavior of LCO-graphite battery by computing the stress intensity factor. At first, the full electrochemistry of the cell is modeled to obtain realistic boundary conditions for the fracture model linked to user-defined battery usage. The fracture model of a spherical active material particle is implemented in Ansys to compute stress intensity factor with modified J-integral for mechanical-diffusive phenomena. Three aspects are deepened: (a) The effects of the mechanical-diffusive coupling at the crack tip, and its influence on the stress intensity factor; (b) Assessing fracture propagation due to static loading and its stability; (c) Creating a fracture diagram which quantifies the level of fracture due to the combination of different operating conditions and geometry of the electrode microstructure. Results show that crack propagation in a single cycle is limited to high current, but it is likely to be unstable. Furthermore, it is quantified how greater current and particle radius increase the stress intensity factor, aiming to provide electrode design advice in the perspective of increasing battery life.

Model-Based Optimization Approach for PID Control of Pitch–Roll UAV Orientation

This study focuses on the modeling and control optimization of the pitch–roll angles orientation in a quad-rotor UAV (unmanned aerial vehicle) drone system. To address the control challenge, PID (proportional–integral–derivative) controllers are used, which have been tuned through a model-based optimization approach. A non-linear model of the system is obtained and further validated experimentally. The model is implemented in Simulink to conduct model-based optimization. The controller design uses an IMC (internal model control) model-based approach to establish the initial parameters for a PID controller. In the subsequent design phase, a multiobjective approach is taken, incorporating weighted cost functions that account for IAE (integral absolute error) and ITAE (integral of time-weighted absolute error) forms, along with battery usage. The outcomes of this design are showcased through simulations and real-world platform testing.

A Formal Verification of a Reputation Multi-Factor Authentication Mechanism for Constrained Devices and Low-Power Wide-Area Network Using Temporal Logic.

There are many security challenges in IoT, especially related to the authentication of restricted devices in long-distance and low-throughput networks. Problems such as impersonation, privacy issues, and excessive battery usage are some of the existing problems evaluated through the threat modeling of this work. A formal assessment of security solutions for their compliance in addressing such threats is desirable. Although several works address the verification of security protocols, verifying the security of components and their non-locking has been little explored. This work proposes to analyze the design-time security of the components of a multi-factor authentication mechanism with a reputation regarding security requirements that go beyond encryption or secrecy in data transmission. As a result, it was observed through temporal logic that the mechanism is deadlock-free and meets the requirements established in this work. Although it is not a work aimed at modeling the security mechanism, this document provides the necessary details for a better understanding of the mechanism and, consequently, the process of formal verification of its security properties.

Power Saving Approach of a Smart Watch for Monitoring the Heart Rate of a Runner

Heart Rate (HR) monitoring in a smart watch is a battery consuming process. Therefore, some smart watches prevent continuous HR sampling during low-level physical activities for improving the power usage of batteries. However, high acceleration events like running require continuous HR sampling for recording the varying HR readings, which consumes a lot of battery power. Therefore, the challenge is to reduce the sampling during running while recording an individual’s HR variation (HRV). Our approach prevents continuous HR sampling for long-running events without missing important HR information. In this context, we analyze the existing real-life HRs and acceleration datasets recorded on humans during long runs. We design an adaptive HR sampling algorithm, A-HeaRing, from the extracted HR variation details. A-HeaRing reduces the battery power consumption of a smart watch by more than 50% while recording a running event. Additionally, we design a data regeneration system for missing HR readings. The regeneration system provides a minimum of 90% accuracy in the HR zone estimation.

From Grayscale Image to Battery Aging Awareness—A New Battery Capacity Estimation Model With Computer Vision Approach

Accurate detection of capacity degradation is critical to the safe and efficient utilization of battery systems. Many datadriven capacity estimators were proposed based on emerging intelligent algorithms, but their accuracy depends on the data of complete charged/discharged process and complex algorithm structures. This paper developed a computer vision (CV)-based method, constructing battery multidimensional aging features as the key image to estimate capacity using specific charging data segment. Specifically, the designed image-aging recognition method is used to extract multidimensional aging features from the partial charging current sequence and then establish map inputs for a computer vision model that recognizes the constructed feature maps. Consequently, the mapping relationship between the charging information and capacity degradation can be obtained as the two-dimensional grayscale images which contain massive extracted features in their small size hence greatly simplify the network structure in CV model so as to improve estimation accuracy and efficiency significantly. More importantly, since the model input is a specific charging current segment rather than the data of complete charging process, the model applicability to the random and incomplete charging process of electric vehicles can be greatly improved. Battery cycling data from different types of Li-ion cells was utilized for performance verification. Compared with the conventional estimation methods proposed previously, the proposed method demonstrates the great superiority in terms of the model applicability, estimation accuracy and computational efficiency for online capacity estimation in actual battery usage.

A novel acid-free leaching route of recovering Li2CO3 and FePO4 from spent LiFePO4 black powder

With the rapid increase in the usage of lithium ion batteries, recovering Li2CO3 from LiFePO4 black powder has become an issue of public concern. Nowadays, the widely used acid leaching technology has some obvious disadvantages, such as complex recovery process, serious equipment corrosion and unavoidable secondary pollution. In this work, a novel acid-free leaching route of recovering Li2CO3 and FePO4 from LiFePO4 black powder is proposed. This novel route does not include inorganic or organic acid leaching, complex pH adjustment and wastewater treatment processes. After optimizing the technological conditions, Li2CO3 and FePO4 with high purity are recovered with high recovery rate of over 96%. Then, the recovered Li2CO3 and FePO4 are used as iron and lithium sources to regenerate LiFePO4 and the electrochemical performances are compared in detail. When the molar ratio of Li2CO3:FePO4:C6H12O6 (glucose) is 1.03:2:0.35, the regenerated LiFePO4 (R-LFP-0.35) cathode displays the optimal electrochemical performance with the discharge capacity of 163.9 mAh g−1 at 0.1 C, 120 mAh g−1 at 4 C and excellent capacity retention ratio of 98.0% after 100 cycles at 0.2 C. This work provides a new green and environmentally friendly strategy for the recycling of LiFePO4 black powder.

Lithium-ion battery degradation trajectory early prediction with synthetic dataset and deep learning

Knowing the long-term degradation trajectory of Lithium-ion (Li-ion) battery in its early usage stage is critical for the maintenance of the battery energy storage system (BESS) in reality. Previous battery health diagnosis methods focus on capacity and state of health (SOH) estimation which can receive only the short-term health status of the cell. This paper proposes a novel degradation trajectory prediction method with synthetic dataset and deep learning, which enables to grasp the characterization of the cell’s health at a very early stage of Li-ion battery usage. A transferred convolutional neural network (CNN) is chosen to finalize the early prediction target, and the polynomial function based synthetic dataset generation strategy is designed to reduce the costly data collection procedure in real application. In this thread, the proposed method needs one full lifespan data to predict the overall degradation trajectories of other cells. With only the full lifespan cycling data from 4 cells and 100 cycling data from each cell in experimental validation, the proposed method shows a good prediction accuracy on a dataset with more than 100 commercial Li-ion batteries.

Environmental Impact Assessment of the Dismantled Battery: Case Study of a Power Lead–Acid Battery Factory in China

With the increase in battery usage and the decommissioning of waste power batteries (WPBs), WPB treatment has become increasingly important. However, there is little knowledge of systems and norms regarding the performance of WPB dismantling treatments, although such facilities and factories are being built across the globe. In this paper, environmental performance is investigated quantitively using life cycle assessment (LCA) methodology for a dismantled WPB manufacturing process in Tongliao city of Inner Mongolia Province, China. The functional unit was selected to be one metric ton of processed WPB, and the average data of 2021 were used. The results indicated that WPB dismantling treatments are generally sustainable in their environmental impacts, because the life cycle environmental effects can be neutralized by the substitution of virgin products with recycled counterparts. Of all the processes of dismantlement, Crude Lead Making, Refining, and Preliminary Desulfurization, were the top three contributors to the total environmental burden. The results of the sensitivity analysis showed that increasing photovoltaic power, wind power, and natural gas usage may significantly reduce the burden on the environment. On the basis of our findings, some suggestions are put forward for a policy to promote environmental green growth of WPB treatment. Although this paper is aimed at the power lead–acid battery, the research method is also of significance for the power lithium-ion battery, and we will conduct relevant research on the disassembly process of the power lithium-ion battery in the future.