Battery Lifetime Estimation Research Articles

Optimal charging of Li-ion batteries using sparse identification of nonlinear dynamics

Optimal charging of Li-ion batteries requires careful management of charge rates, as high rates can lead to accelerated degradation, while low rates significantly extend charging times. Traditional methods for determining charge rates often rely on rule-based approaches, which typically fail to effectively balance battery performance with charging duration. To address this, we introduce a novel optimization approach that directly integrates the dual objectives of minimizing charge time and maximizing battery lifetime into the optimization process. Unlike most existing charge optimization methods that do not directly track battery lifetime and charge time simultaneously, our method employs a data-driven model that facilitates direct and dynamic estimation of both battery lifetime and charge time at each step of the optimization process. Specifically, we utilize the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm to predict battery capacity and voltage dynamics, which informs the calculations of lifetime and charge time required to solve the optimization problem. This approach provides a balanced optimization strategy that enhances the effectiveness of battery’s performance while maintaining the efficiency of the charging process. We applied this method to a novel next-generation NMC811 battery, featuring a cathode comprised of 80 % nickel, 10 % manganese, and 10 % cobalt, and a lithium metal foil anode - a combination not extensively studied previously. Experimental validation demonstrated that when optimized charge rates are applied every 10 cycles in a 100-cycle operation, the method leads to more stable cycling and improved capacity retention of approximately 7.4 % over the nominal charge rate, demonstrating the potential of the developed approach.

Energy consumption analytical modeling of NB-IoT devices for diverse IoT applications

Internet of things (IoT) has been employed in recent years across multiple disciplines in the Fifth Generation (5G) era. The low power consumption of the devices allows long battery lifetimes with increasing wireless coverage ranges. This paper presents a comprehensive power consumption model for battery lifetime estimation in narrowband IoT (NB-IoT), which is based on the user equipment (UE) state diagram and considers the extended discontinuous reception (eDRX) and power saving mode (PSM) mechanisms. This model has been used to simulate the effect of varying NB-IoT link parameters, covering a range of values defined in the specification, corresponding to a wide range of applications. Indeed, this study focuses on mathematical analysis of UE power consumption, mainly for NB-IoT communication applications. Repetitions, as defined in the standard, have also been considered in the analysis. Extensive simulations have been carried out to show the power of the proposed model, which provides accurate results for a wide variety of network parameters and let the network designer to select the most appropriated setup. They also show that the value of link parameters must be carefully selected to achieve a battery lifetime of more than a decade with a standard battery of 5 Wh capacity.

Learning model for battery lifetime prediction of LoRa sensors in additive manufacturing

Today, an innovative leap for wireless sensor networks, leading to the realization of novel and intelligent industrial measurement systems, is represented by the requirements arising from the Industry 4.0 and Industrial Internet of Things (IIoT) paradigms. In fact, unprecedented challenges to measurement capabilities are being faced, with the ever-increasing need to collect reliable yet accurate data from mobile, battery-powered nodes over potentially large areas. Therefore, optimizing energy consumption and predicting battery life are key issues that need to be accurately addressed in such IoT-based measurement systems. This is the case for the additive manufacturing application considered in this work, where smart battery-powered sensors embedded in manufactured artifacts need to reliably transmit their measured data to better control production and final use, despite being physically inaccessible. A Low Power Wide Area Network (LPWAN), and in particular LoRaWAN (Long Range WAN), represents a promising solution to ensure sensor connectivity in the aforementioned scenario, being optimized to minimize energy consumption while guaranteeing long-range operation and low- cost deployment. In the presented application, LoRa equipped sensors are embedded in artifacts to monitor a set of meaningful parameters throughout their lifetime. In this context, once the sensors are embedded, they are inaccessible, and their only power source is the originally installed battery. Therefore, in this paper, the battery lifetime prediction and estimation problems are thoroughly investigated. For this purpose, an innovative model based on an Artificial Neural Network (ANN) is proposed, developed starting from the discharge curve of lithium-thionyl chloride batteries used in the additive manufacturing application. The results of experimental campaigns carried out on real sensors were compared with those of the model and used to tune it appropriately. The results obtained are encouraging and pave the way for interesting future developments.

A novel state-of-health estimation method for fast charging lithium-ion batteries based on an adversarial encoder network

Deep learning based on big data has been widely implemented with cloud and edge computing for lithium-ion battery online health diagnostics, where enhancing the diagnostics' accuracy, robustness, and real-time application are current research issues. Most research has concentrated on state-of-health estimation methods for diagnostics, but the difference in the degradation feature trajectories of batteries between the training and testing domains has been neglected, which further affects the estimation precision of the trained model. To overcome this issue, a strategy based on adversarial learning and feature selection is proposed. First, the adversarial encoder approach creates a lower-dimensional feature vector to the aggregated domain between the source and target batteries. Then, the feature vector is sent into the estimation module, which consists of a one-dimensional convolutional neural network (1DCNN) to accurately estimate the battery lifespan. The results on the 1C and 6C experimental datasets demonstrate that the adversarial feature selection strategy significantly enhances the estimation performance of the deep learning-based estimation method. Combining an adversarial autoencoder with the prediction module yields statistically superior battery lifetime estimation results.

Modeling and Experimental Validation for Battery Lifetime Estimation in NB-IoT and LTE-M

Internet of Things (IoT) is one of the main features in 5G. Low-power wide-area networking (LPWAN) has attracted enormous research interests to enable large scale deployment of IoT, with the design objectives of low cost, wide coverage area, as well as low power consumption. In particular, long battery lifetime is essential since many of the IoT devices will be deployed in hard-to-access locations. Prediction of the battery lifetime depends on the accurate modelling of energy consumption. This paper presents a comprehensive power consumption model for battery lifetime estimation, which is based on User Equipment(UE) states and procedures, for two cellular IoT technologies: Narrowband Internet of Things (NB-IoT) and Long Term Evolution for Machines (LTE-M). A measurement testbed has been setup and the proposed model has been tested and validated via extensive measurements under various traffic patterns and network scenarios, achieving the modelling inaccuracy within5%. The measurement results show that the battery lifetime of an IoT device can reach up to 10 years as required by 3GPP, with proper configuration of the traffic profile, the coverage scenario, as well as the network configuration parameters.

A Comprehensive Review of Lithium-Ion Batteries Modeling, and State of Health and Remaining Useful Lifetime Prediction

According to the United States environmental protection agency (EPA), every burned gallon of gasoline generates 8.87 Kg of CO2. The pollution created by vehicles’ fuel consumption has been one of the primary sources of environmental contamination that can lead to more climate changes and global warming. Thus, science and technology have converged on the idea that reducing fuel consumption is benefits the environment and human health. One of the ideas for reducing fuel usage is deploying hybrid electric vehicles (HEVs) and electric vehicles (EVs) using renewable energy as alternatives to gasoline. One of the main issues with EV batteries is that over operational time the battery health degrades and ultimately becomes unsafe to use. It is crucial that safety issues be addressed by researchers and battery manufacturers. Assessing and predicting battery health has been a high-priority research topic to attempt to mitigate the danger introduced by EV batteries. Although various techniques have been developed to estimate and predict the battery’s state of health, they do not cover all degradation scenarios that may affect the battery’s lifetime. In addition, the models used in estimating and predicting the battery’s lifetime need to be improved to provide a more accurate battery health state and guarantee battery safety while in use by an EV. Even though all types of EV batteries face similar issues, this paper focuses on Li-ion EV batteries. The main objectives of this paper are 1) to present various Li-ion battery models that are used to mimic battery dynamic behaviors, 2) to discuss the degradation factors that cause the battery lifespan to be degraded and to become unsafe, 3) to provide a review of the estimation and prediction techniques used for Li-ion battery state-of-health (SOH) and remaining useful life (RUL) estimation along with a discussion of their advantages and limitations, and 4) to provide recommendations for improving battery lifetime estimation. This paper represents a concise source of information for battery community researchers to help expedite beneficial and practical outcomes to improve EV battery safety.

Remote Microgrids for Energy Access in Indonesia—Part II: PV Microgrids and a Technology Outlook

This paper is the companion paper of Remote Microgrids for Energy Access in Indonesia “Part I: scaling and sustainability challenges and a technology outlook”. This part II investigates the issues of photovoltaic (PV) systems with respect to the planning, design, and operation, and maintenance phases in microgrids in Indonesia. The technology outlooks are also included as PV has an important role in providing electricity in the underdeveloped, isolated, and border areas. The data in this paper are from PV microgrids located in Maluku and North Maluku, which are two provinces where there is barely any grid connection available and thus very dependent on remote microgrids. The data are obtained from interviews with Perusahaan Listrik Negara (PLN) and NZMATES, which are an Indonesian utility company and a program for supporting role for the PV systems in Maluku funded by New Zealand respectively. Common issues with respect to reliability and sustainability are identified based on the provided data. Advanced technologies to increase reliability and sustainability are also presented in this paper as a technology outlook. Among these solutions are online monitoring systems, PV and battery lifetime estimation, load forecasting strategies, and PV inverters technology.

Battery lifetime estimation for energy efficient telecommunication networks in smart cities

There has been a surge in telecommunication network deployments across the globe to facilitate advanced communication infrastructure which is necessary for smart cities. This has in turn increased the power consumption of telecommunication networks, thus motivating the need to adopt green energy solutions like solar energy to power them. Base stations (BSs) are the primary entities contributing to the power consumption in the telecommunication network. To efficiently deploy solar powered base stations, it is imperative to optimally provision them with appropriate Photo Voltaic (PV) panel and battery resources. The ultimate goal of such dimensioning is to provide best possible quality of service (QoS) to the consumers while maintaining an optimal cost of deployment and operation. Both PV panels and the batteries are major contributors while calculating the overall cost of deployment and operation for a solar powered BSs. Therefore an accurate calculation of battery lifetime with respect to different PV panel dimension and battery sizes is an important step in cost optimal resource provisioning for the solar powered BSs. This issue is addressed in this paper by presenting an analytical scheme to estimate the battery lifetime for a particular resource provisioning of PV panels and batteries. This is then used for evaluating the cost-optimal photo-voltaic panel dimensions and battery size for the base station with acceptable limit of outage probability. The proposed methodology would find great relevance in developing energy efficient sustainable telecommunication networks for upcoming smart cities.

Evaluation of LoRa Technology in Flooding Prevention Scenarios.

Global climate change originates frequent floods that may cause severe damage, justifying the need for real-time remote monitoring and alerting systems. Several works deal with LoRa (Long Range) communications over land and in the presence of obstacles, but little is known about LoRa communication reliability over water, as it may happen in real flooding scenarios. One aspect that is known to influence the communication quality is the height at which nodes are placed. However, its impact in water environments is unknown. This is an important aspect that may influence the location of sensor nodes and the network topology. To fill this gap, we conducted several experiments using a real LoRa deployment to evaluate several features related to data communication. We considered two deployment scenarios corresponding to countryside and estuary environments. The nodes were placed at low heights, communicating, respectively, over the ground and over the water. Measurements for packet loss, received signal strength indicator (RSSI), signal-to-noise ratio (SNR) and round-trip time (RTT) were collected during a period of several weeks. Results for both scenarios are presented and compared in this paper. One important conclusion is that the communication distance and reliability are significantly affected by tides when the communication is done over the water and nodes are placed at low heights. Based on the RTT measurements and on the characteristics of the hardware, we also derive a battery lifetime estimation model that may be helpful for the definition of an adequate maintenance plan.

Design Optimization of a Residential PV-Battery Microgrid With a Detailed Battery Lifetime Estimation Model

The interest in installing photovoltaic (PV)-based microgrids has increased significantly in the last few years due to the urgent need for reducing greenhouse gas emissions and improving the reliability as well as the quality of power supply, particularly in developing countries. The design of a microgrid is challenging since multiple components and complicated operating conditions have to be taken into account. In this article, a comprehensive method for optimal design of a class of residential PV-battery microgrids is proposed to determine the optimal number of lead-acid batteries and PV panels, the optimal battery bank depth of discharge (DOD) value, and the optimal tilt angle of the PV panels. The aim of the optimization is to minimize the levelized cost of energy (LCOE) where the limitation of the annual total loss of the power supply and the operational constraints are considered. In addition, a detailed model for battery lifetime estimation is introduced based on the physico-chemical mechanism of the lead-acid battery. Moreover, the effect of design parameters, e.g., the size and DOD of the battery bank on the battery capacity degradation, the PV-panel number and battery lifetime on LCOE, is investigated. Computation results demonstrate that the proposed method is capable of optimizing the size of the microgrid components and the other design parameters while satisfying all technical and operational constraints.

Analytical Modeling and Experimental Validation of NB-IoT Device Energy Consumption

The recent standardization of 3GPP Narrowband Internet of Things (NB-IoT) paves the way to support low-power wide-area (LPWA) use cases in cellular networks. NB-IoT design goals are extended coverage, low power and low cost devices, and massive connections. As a new radio access technology, it is necessary to analyze the possibilities NB-IoT provides to support different traffic and coverage needs. In this paper, we propose and validate an NB-IoT energy consumption model. The analytical model is based on a Markov chain. For the validation, an experimental setup is used to measure the energy consumption of two commercial NB-IoT user equipments (UEs) connected to a base station emulator. The evaluation is done considering three test cases. The comparison of the model and measurements is done in terms of the estimated battery lifetime and the latency needed to finish the control plane procedure. The conducted evaluation shows the analytical model performs well, obtaining a maximum relative error of the battery lifetime estimation between the model and the measurements of 21% for an assumed interarrival time (IAT) of 6 min.

Charging Automation for Electric Vehicles: Is a Smaller Battery Good for the Wireless Charging Electric Vehicles?

Dynamic wireless charging (DWC) is an emerging technology that enables the batteries of electric vehicles (EVs) to charge automatically while the vehicles are in motion. The DWC-EV system addresses the challenges inherent in battery technology, such as the short driving range, long recharging time, and high price. Compared with conventional plug-in EVs, the DWC-EV can charge a battery more frequently because it can be done while the EV is in motion from the charging infrastructure installed on the road. In this paper, we analyze how this frequent-charging characteristic of DWC-EV can affect the battery lifetime in the DWC-EV. We first introduce a mathematical model to evaluate the economic cost of the DWC-EV for a given battery size. A battery degradation model is incorporated to account for the quantitative relationship between the installation of the charging infrastructure and battery life extension. We then use the model to analyze how the economic cost varies with the size of the battery. Our preliminary findings provide insight into the relationship between DWC from the charging infrastructure and the battery’s lifetime. Note to Practitioners —There is a tradeoff between power track allocation and battery size in DWC buses. It has been known that DWC buses need not equip a large battery because the battery can be charged frequently from the power track along the route. Also, it has been agreed that frequent shallow charging with DWC can improve the battery’s lifetime compared to infrequent deep charging. We verify these common beliefs and agreement with a mathematical approach with a widely used battery lifetime estimation model. Our quantitative model indicates that the DWC can positively improve battery lifetime, but this positive effect is always true only when the battery is large enough, even if the battery is charged frequently with DWC. The presented optimization model suggests that the lifelong economic benefit of DWC-based bus transit can be realized with optimal allocation of the power track with a sufficiently large battery.

A New Method for Battery Lifetime Estimation Using Experimental Testbed for Zigbee Wireless Technology

Many Zigbee-based wireless sensor networks have been developed for outdoor applications such as agriculture monitoring. The main attractiveness of Zigbee wireless module is in its potential to set up self-organizing network that requires no network backbone and extremely low cost with low-power wireless networking. Many simulations have been performed for testing the capabilities of wireless communication device and the battery lifetime. However, the results from the simulation do not capture the actual environment effects and the simulators allow users to isolate certain factors by tuning to different parameters. This paper provides experimental results on actual voltage drop using Zigbee protocol devices when communicating temperature, humidity and soil moisture data using a 900mAh battery both in indoor and outdoor environments. It is observed that the wireless nodes are capable of relaying data up to 143 meters on unobstructed line of sights in an outdoor environment with some observation of packet drop. The results differ from previous researches that perform the experiments on shorter range which only covers 50 cm distance between transmitter node and receiver node for 2 bytes of data transfer. The paper proposes a new method for battery lifetime estimation which is derived from number of times data packet can be transmitted. This study also suggests to conduct experiment by including the environmental factors to capture the actual performance of wireless device and the impact to packet drop. The experiment concludes the suitability of Zigbee wireless communication for short-range applications of up to 143 meters which is significantly farther than other reported experiments.

Predictive Operation and Optimal Sizing of Battery Energy Storage With High Wind Energy Penetration

High penetration of wind energy requires fast-acting dispatchable resources to manage energy imbalance in the power grid. Battery energy storage systems (BESS) are considered as an essential tool to decrease the power and energy imbalance between the scheduled generation (day ahead forecast) and the actual wind farm output. Control methodology or battery management greatly impacts performance of the ESS. Better performance of BESS reduces the minimum required size of batteries for wind variability mitigation. This paper proposes a novel control method for BESS to fulfill a production commitment. This method, called “predictive controller,” is based on updated forecast data to improve the performance of the energy storage and consequently reduce the required size of energy storage. The Sodium–Sulfur (NaS) type battery is selected for the simulation purposes. Results show that the predictive controller reduces the error (between scheduled generation and actual wind farm output) more than the simple method (also known as minute-by-minute method) and other proposed methods in the literature. Also, a new formulation for the battery lifetime estimation is introduced, and it is used to analyze the impact of the proposed method on the battery lifetime depreciation.

Estimating battery lifetimes in Solar Home System design using a practical modelling methodology

The rapid increase in the adoption of Solar Home Systems (SHS) in recent times hopes to ameliorate the global problem of energy poverty. The battery is a vital but usually the most expensive part of an SHS; owing to the least lifetime among other SHS components, it is also the first to fail. Estimating battery lifetime is a critical task for SHS design. However, it is also a complex task due to the reliance on experimental data or modelling cell level electrochemical phenomena for specific battery technologies and application use-case. Another challenge is that the existing electrochemical models are not application-specific to Solar Home Systems. This paper presents a practical, non-empirical battery lifetime estimation methodology specific to the application and the available candidate battery choices. An application-specific SHS simulation is carried out, and the battery activity is analyzed. A practical dynamic battery lifetime estimation method is introduced, which captures the fading capacity of the battery dynamically through every micro-cycle. This method was compared with an overall non-empirical battery lifetime estimation method, and the dynamic lifetime estimation method was found to be more conservative but practical. Cyclic ageing of the battery was thus quantified and the relative lifetimes of 4 battery technologies are compared, viz. Lead-acid gel, Flooded lead-acid, Nickel-Cadmium (NiCd), and Lithium Iron Phosphate (LiFePO4) battery. For the same SHS use-case, State-of-Health (SOH) estimations from an empirical model for LiFePO4 is compared with those obtained from the described methodology, and the results are found to be within 2.8%. The relevance of this work in an SHS application is demonstrated through a delicate balance between battery sizing and lifetime. Based on the intended application and battery manufacturer’s data, the practical methodology described in this paper can potentially help SHS designers in estimating battery lifetimes and therefore making optimal SHS design choices.

Electric Vehicle Battery Cycle Aging Evaluation in Real-World Daily Driving and Vehicle-to-Grid Services

In this paper, battery lifetime estimation of an electric vehicle (EV) using different driving styles on arterial roads integrating recharging scenarios in the neighborhood of the vehicle-to-grid integration is studied. The real-world driving cycles from a fleet of connected vehicles are evaluated in an EV model with different charging options. Daily utility services are added to the simulations to explore the whole day performance of the battery and its daily degradation. Fifty driving cycles from different drivers on arterial roads are classified into aggressive, mild, and gentle drivers based on their driving acceleration behavior. The standard levels 1 and 2 chargers are considered for recharging and the frequency regulation, and peak shaving and solar energy storage are assumed for the daily ancillary services. The results indicate that the aggressive driving and recharging behavior have significant effect on the battery life reduction. In addition, the daily utility services impose extra degradation of the battery. Also, the effect of temperature change on the battery degradation is explored. Simulation of active versus passive thermal management systems in three different climates shows the significant impact of the battery temperature on its capacity fade.

A holistic aging model for Li(NiMnCo)O2 based 18650 lithium-ion batteries

Knowledge on lithium-ion battery aging and lifetime estimation is a fundamental aspect for successful market introduction in high-priced goods like electric mobility. This paper illustrates the parameterization of a holistic aging model from accelerated aging tests. More than 60 cells of the same type are tested to analyze different impact factors. In calendar aging tests three temperatures and various SOC are applied to the batteries. For cycle aging tests especially different cycle depths and mean SOC are taken into account. Capacity loss and resistance increase are monitored as functions of time and charge throughput during the tests. From these data physical based functions are obtained, giving a mathematical description of aging. To calculate the stress factors like temperature or voltage, an impedance based electric-thermal model is coupled to the aging model. The model accepts power and current profiles as input, furthermore an ambient air temperature profile can be applied. Various drive cycles and battery management strategies can be tested and optimized using the lifetime prognosis of this tool. With the validation based on different realistic driving profiles and temperatures, a robust foundation is provided.

Comparison of different lead–acid battery lifetime prediction models for use in simulation of stand-alone photovoltaic systems

Lifetime estimation of lead–acid batteries in stand-alone photovoltaic (PV) systems is a complex task because it depends on the operating conditions of the batteries. In many research simulations and optimisations, the estimation of battery lifetime is error-prone, thus producing values that differ substantially from the real ones. This error can indicate that the “optimal” system selected by the optimisation tool will not be optimal. In this paper, all of the components of a PV system have been considered simultaneously to simulate the behaviour of the system. One of these important components is the battery charge controller, which significantly affects the lifetime of batteries. The results of the simulations have allowed a comparison of the most common methods of battery lifetime prediction used by simulation and/or optimisation tools with a weighted Ah-throughput method developed a few years ago. The results show that this recent method provides more accurate lifetime values. In a simulation of a real off-grid household PV system where the real battery lifetime was 6.2years, the weighted Ah-throughput model predicted a lifetime of 5.8years; however, the other methods obtained lifetimes of more than 15years. In a simulation of another PV system designed to supply the load of an alarm where the real batteries lifetime was 5.1years, the weighted Ah-throughput model predicted a lifetime of 4.4years; however, the other methods obtained lifetimes of more than nine years.

Energy management for battery-powered embedded systems

Portable embedded computing systems require energy autonomy. This is achieved by batteries serving as a dedicated energy source. The requirement of portability places severe restrictions on size and weight, which in turn limits the amount of energy that is continuously available to maintain system operability. For these reasons, efficient energy utilization has become one of the key challenges to the designer of battery-powered embedded computing systems.In this paper, we first present a novel analytical battery model, which can be used for the battery lifetime estimation. The high quality of the proposed model is demonstrated with measurements and simulations. Using this battery model, we introduce a new "battery-aware" cost function, which will be used for optimizing the lifetime of the battery. This cost function generalizes the traditional minimization metric, namely the energy consumption of the system. We formulate the problem of battery-aware task scheduling on a single processor with multiple voltages. Then, we prove several important mathematical properties of the cost function. Based on these properties, we propose several algorithms for task ordering and voltage assignment, including optimal idle period insertion to exercise charge recovery.This paper presents the first effort toward a formal treatment of battery-aware task scheduling and voltage scaling, based on an accurate analytical model of the battery behavior.