Dynamic Battery Research Articles

Sliding Mode Observer for State-of-Charge Estimation Using Hysteresis-Based Li-Ion Battery Model

Lithium-ion battery devices are essential for energy storage and supply in distributed energy generation systems. Robust battery management systems (BMSs) must guarantee that batteries work within a safe range and avoid the damage caused by overcharge and overdischarge. The state-of-charge (SoC) of Li-ion batteries is difficult to observe after batteries are manufactured. The hysteresis phenomenon influences the existing battery modeling and SoC estimation accuracy. This research applies a terminal sliding mode observer (TSMO) algorithm based on a hysteresis resistor-capacitor (RC) equivalent circuit model to enable accurate SoC estimation. The proposed method is evaluated using two dynamic battery tests: the dynamic street test (DST) and the federal urban driving schedule (FUDS) test. The simulation results show that the proposed method achieved high estimation accuracy and fast response speed. Additionally, real-time battery information, including battery output voltage and SoC, was acquired and displayed by an automatic monitoring system. The designed system is valuable for all battery application cases.

A new design of experiment method for model parametrisation of lithium ion battery

Equivalent circuit models (ECMs) have been widely used to describe the electrical dynamics of lithium-ion batteries. A high model accuracy is important for effective simulation and control of the battery system. The model accuracy depends on the design of experiment (DoE) method for battery test and the optimisation approach for ECM parameter identification. While many optimisation approaches have been proposed in literature to identify the parameters, the effect of DoE on the model accuracy is usually overlooked and undervalued. A novel DoE method is proposed in this paper which uses both partial discharge test (PDT) and deep discharge test (DDT) for battery testing. It is shown through careful test data analysis that, the conventional DoE methods using either PDT or DDT cannot capture the battery's dynamics to sufficient accuracy. Experimental data are collected using a commercial lithium ion battery. Results show the new DoE method can significant improve the ECM accuracy, i.e., reducing the root mean square error by ~70% in comparison with conventional DoE approach. In addition, the improved model accuracy contributes to a significant increase in the SoC estimation accuracy using extended Kalman filter.

A Hierarchical Approach for Finite-Time H-$\infty$ State-of-Charge Observer and Probabilistic Lifetime Prediction of Lithium-Ion Batteries

Accurate state-of-charge (SOC) estimation and lifetime prognosis of lithium-ion batteries are of great significance for reliable operations of energy storage systems. This paper proposes a novel two-layer hierarchical approach for online SOC estimation and remaining-useful-life (RUL) prediction based on a robust observer and Gaussian-process-regression (GPR). At the bottom layer, an equivalent-circuit model is first developed to describe battery dynamics. Second, a combination method of a recursive least square method and a finite time H- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\infty$</tex-math></inline-formula> observer is designed to estimate battery open-circuit-voltage (OCV) and SOC through stability and robustness analysis. Next, the estimated OCV and SOC are fed into the top layer to generate the incremental-capacity-analysis-based aging feature, through which a robust signature associated with battery aging is identified. The feature is further employed for RUL prediction based on GPR. The salient advantages of the proposed approach are that it can provide robust parameter estimation in a given finite-time interval, and the GPR-based RUL prediction can tackle long-term uncertainties in a principled Bayesian manner. Theoretical analysis and experimental results demonstrate the effectiveness of the proposed SOC observer and lifetime prediction methods.

Energy storage battery SOC estimate based on improved BP neural network

The SOC estimation of the battery is the most significant functions of batteries’ management system, and it is a quantitative evaluation of electric vehicle mileage. Due to complex battery dynamics and environmental conditions, the existing data-driven battery status estimation technology is not able to accurately estimate battery status. Aiming at this problem, the multi-implicit BP neural network model and the error elimination due to genetic algorithm are combined to appraise the battery’s state of charge. Firstly, a multi-hidden layer BP neural network is applied to learn about the nonlinear connection between the battery SOC and the measurable variables of lithium-ion batteries, for instance, current, voltage, and temperature. Secondly, the prediction error of the neural network type is denoised by the genetic method to smooth the prediction results. The method proposed in this paper captures long-term dependencies between measurable variables and battery state. Finally, the improvement effect of the method proposed in this paper is verified by comparison with the traditional neural network method.

Lithium battery state-of-health estimation and remaining useful lifetime prediction based on non-parametric aging model and particle filter algorithm

State of health estimation (SOH) and remaining useful lifetime (RUL) prediction are significant health indicators for improving the safety and reliability of battery systems. Herein, a data-fusion method is developed to establish a non-parametric degradation model and a particle filter algorithm for forecasting battery health conditions. Firstly, a dynamic battery aging state-space system is developed, in which Gaussian process regression is applied to establish state equation using historical capacity series and current capacity as input and output variables, respectively. Meanwhile, multi-output Gaussian process regression maps the relationship between capacity degradation and battery health indicators to construct an observation equation. Second, two filter methods are unitized to obtain the smooth differential thermal voltammetry curves and the significant health indicators are extracted from partial differential thermal voltammetry curves. Third, the short-term SOH estimation and long-term RUL prediction are carried out using a particle filter algorithm. Moreover, two types of five batteries with various designed cases are conducted to verify and analyze the proposed method. The results show that the estimation errors of short-term SOH are within 4% and prediction errors of long-term RUL are around 7% (relative error/EOL, 12/159), which indicate the proposed method has an excellent capability for accurate and robust forecasting battery health conditions. • A non-parametric state-space representation for battery degradation is developed. • Battery health conditions are estimated and predicted in multi-timescale perspective. • The health indicators are extracted from partial DTV curves in different dimensions. • Two types of five batteries with various designed cases are discussed and analyzed.

Effects of sizing on battery life and generation cost in PV–wind battery hybrid systems

Battery energy storage system (BESS) is a crucial part of standalone renewable hybrid power systems. Dynamic battery degradation analysis and life prediction are essential for better techno-economic estimation of standalone PV–wind battery hybrid power systems. With this viewpoint, this paper aims to study battery degradation using a physics-based pseudo-two-dimensional (P2D) thermal battery model integrated with renewable PV–wind hybrid power systems and investigates the impact of BESS size variation on its degradation and its effect on the energy generation costs A power management and control strategy is developed to ensure continuous power flow with two regulation modes; (a) maximum power point tracking and (b) controlled power generation. Yearly real-world load data, operating, and ambient conditions are used to study five different percentage mixes of PV and wind power generation scenarios. For example, a mix of 70% PV-30% wind, 350 kWh BESS is needed (base case) based on the demand. The yearly degradation rate for this case is calculated to be 3.80%. The degradation rates vary from 3.80 to 2.33% per year for every 10% increment in the BESS size from the base case. Performing a techno-economic analysis reveals that the least energy generation cost is achieved when increasing the BESS size by 20%. This increases BESS life from 5.3 years to 7.3 years and reduces the generation cost from 35.19 ₹ kWh−1 (0.482 $ kWh−1) to 34.34 ₹ kWh−1 (0.470 $ kWh−1). These results provide essential insights to analyse the impact of BESS sizing on degradation and energy generation cost in a standalone PV–wind battery hybrid power system framework. The oversized BESS provides extended life and reduces the energy generation cost for a standalone PV–wind–battery hybrid power system.

An Optimized Fuzzy Controlled Charging System for Lithium-Ion Batteries Using a Genetic Algorithm

Fast charging is an attractive way of charging batteries; however, it may result in an undesired degradation of battery performance and lifetime because of the increase in battery temperature during fast charge. In this paper we propose a simple optimized fuzzy controller that is responsible for the regulation of the charging current of a battery charging system. The basis of the method is a simple dynamic equivalent circuit type model of the Li-ion battery that takes into account the temperature dependency of the model parameters, too. Since there is a tradeoff between the charging speed determined by the value of the charging current and the increase in temperature of the battery, the proposed fuzzy controller is applied for controlling the charging current as a function of the temperature. The controller is optimized using a genetic algorithm to ensure a jointly minimal charging time and battery temperature increase during the charging. The control method is adaptive in the sense that we use parameter estimation of an underlying dynamic battery model to adapt to the actual status of the battery after each charging. The performance and properties of the proposed optimized charging control system are evaluated using a simulation case study. The evaluation was performed in terms of the charge profiles, using the fitness values of the individuals, and in terms of the charge performance on the actual battery. The proposed method has been evaluated compared to the conventional contant current-constant voltage methods. We have found that the proposed GA-fuzzy controller gives a slightly better performance in charging time while significantly decreasing the temperature increase.

Energy management for an All-Electric Aircraft via Optimal Control

In this paper, we develop an integrated approach to energy optimization for an All-Electric Aircraft (AEA). Our approach involves the formulation of the aircraft energy optimization as a set of optimal control problems (OCP)&#x2013;integrating battery and flight dynamics&#x2013;for the cruise and climb phases, as well as the complete flight profile. The corresponding OCPs are then examined in the context of Pontryagin&#x0027;s Minimum Principle (PMP), providing necessary optimality conditions for the proposed integrated approach. Our analysis is then followed by utilizing the numerical solver Tomlab to devise computational solutions of the energy optimization OCPs. We then proceed to characterize the performance of the energy optimal solutions for distinct models of the battery. In particular, we show that the choice of the battery model does not alter the form of optimal control for the flight system; however, the current profiles for the battery pack and the total operating costs are affected by the adopted models used for optimization. Finally, we develop a Simulink model built around physics-based battery dynamics to further characterize the interplay between aircraft energy operating costs and the underlying battery dynamics

Dynamic Equivalent Circuit Model to Estimate State-of-Health of Lithium-Ion Batteries

Lithium-ion (Li-ion) batteries have increasingly been used in diverse applications. Accurate estimation of the state of health (SOH) of the Li-ion batteries is vital for all stakeholders and critical in various applications such as electric vehicles (EVs). The electrical equivalent circuit (EEC) 2-RC model is often used to model the battery operation but has not been used to capture the degradation of battery cells over time. This paper uses the 2-RC model to capture the degradation of the Li-ion battery. The proposed model is not only time-dependent but also captures the effect of temperature on battery degradation. The proposed approach estimates the SOH accurately and is also considerably flexible for diverse cells of different chemistry. We further generalize an N-RC model approach to evaluate the SOH of the battery. We compare the proposed model (2-RC) with the 1-RC model, and through numerical results, we show that the 2-RC model outperforms 1-RC and reduces the computational cost significantly. Similarly, the 2-RC model outperforms 3-RC and higher-order circuits. We also show that the proposed approach can capture the battery dynamics better for specific smaller orders of the polynomial (associated with Arrhenius equation) when compared with the 1-RC approach with considerably reduced (up to 60%) root mean square error (RMSE). Lastly, the average testing RMSE for 2-RC is 52.4%.

Reduction of Lithium-Ion Battery Models with Degradation Dynamics: A Comparison of Techniques

This works studies the reduction of electrochemical lithium-ion battery cell models. Battery cell modeling is crucial to predicting battery behavior under different conditions and enables battery management system (BMS) control algorithms to ensure efficient and reliable operation. The finite difference method (FDM), commonly used for describing nonlinear battery dynamics, requires a high number of states to accurately capture the battery behavior. When computational resources are limited, model reduction is necessary to increase computational tractability and decrease runtime. The literature presents a variety of reduced-order modeling techniques, though these techniques do not commonly incorporate degradation dynamics. To address this gap, this study explores model reduction with the inclusion of degradation effects. Two methods are used to accomplish this. The first extends the application of the Padé approximation technique to incorporate degradation terms found in the boundary conditions of the single-particle model (SPM). This novel solution of the Padé approximation is compared against the Padé approximation solution that excludes degradation. The second uses optimization-based techniques to identify reduced-order models. The models produced are compared for different operating conditions of a lithium-ion battery cell. For the simulated case studies explored, optimization-based reduced-order models outperform Padé-based models for input conditions used for parameter optimization. However, the Padé-based models are more consistent with regards to negative electrode concentration accuracy for a separate validation input profile determined from a drive cycle's demanded current.

A Novel Modal Representation of Battery Dynamics

Electrochemical cells are complicated energy storage systems with nonlinear voltage dynamics. There is a need for accurate dynamic modeling of the battery system to predict its behavior over time when discharging. The study conducted in this paper developed an intuitive model for electrochemical cells based on a simple mechanical analogy. A three-degree-of-freedom, spring-mass-damper system was decomposed into modal coordinates that represent the overall discharge, mass transport, and double-layer effect of the electrochemical cell. The developed model was experimentally demonstrated through pulsed discharge tests of commercially available lithium-ion and nickel metal hydride cells. The modal parameters of the natural frequency and damping ratio for each mode were determined by numerically minimizing the error in the time responses. Additionally, the mechanical analog was applied to two datasets developed by the Center for Advanced Life Cycle Engineering (CALCE). The first dataset was used to optimize the modal parameters whereas the second dataset was utilized to validate the tuned parameters. It was found that the modal representation of the mechanical analog could accurately predict the time-response dynamics of all the cells considered. Additionally, by considering the discharge modal coordinate, the open-circuit voltage was determined and validated to that measured experimentally from the voltage relaxation peaks.

Beyond Traditional Doe: Deep Reinforcement Learning for Optimizing Experiments in Model Identification of Battery Dynamics

Beyond Traditional Doe: Deep Reinforcement Learning for Optimizing Experiments in Model Identification of Battery Dynamics

State of Charge Estimation by Joint Approach With Model-Based and Data-Driven Algorithm for Lithium-Ion Battery

In order to ensure the safety and service life of lithium-ion batteries for automotive applications, accurate state of charge is required for system management in the process of driving. It is particularly challenging to estimate the state of charge by using online approach, for which battery dynamics model is nonlinear. Many of researchers have focused on model-based or data-driven algorithms alone, but comparatively little of them use a joint approach with the two type of algorithms. The data-driven algorithm is self-learning and has better adaptability, while the model-based algorithm is more stable and has stronger robustness. If these advantages can be combined, a better state of charge estimation approach will be developed. In this paper, based on battery charge dynamics, complex fraction order model of battery is simplified into discrete fraction model for engineering application of control algorithm. A Bayesian belief network is used to estimate the battery model parameters, and the adaptive extended Kalman particle filter is used to estimate the state of charge. In order to obtain accurate parameters of battery model for training, linear programming is used to identify the parameters online. Collectively, this paper design a joint approach of how the adaptive extended Kalman particle filter with Bayesian belief network estimate the state of charge precisely. Developed approach has been downloaded into a battery control unit, and tested in real-world conditions using battery test bench to realize practical application of the joint approach.

Dynamic Battery Swapping Strategies for E-Bike Sharing Systems with Electric Fences

Dynamic Battery Swapping Strategies for E-Bike Sharing Systems with Electric Fences

Co-estimating the state of charge and health of lithium batteries through combining a minimalist electrochemical model and an equivalent circuit model

Accurate estimation of the state of charge (SOC) and state of health (SOH) is a fundamental requirement for the management system of a lithium-ion battery, but also important to the safety and operational effectiveness of electric vehicles and energy storage systems. Here a model-based method is implemented to assess the SOC and SOH simultaneously. An equivalent circuit model is employed to describe the battery dynamics with recursive least squares online identifying model parameters and unscented Kalman filter estimating battery state. A minimalist electrochemical model is proposed to describe the distribution of the lithium content inside the battery relating the SOH to the capacity fading due to irreversible loss of Li. Based on the real-time capacity value, the state of charge could further be estimated. Comparing the experimental results shows that the battery capacity, i.e., SOH could be predicted timely with a mean error around 2%, which confirms the validity of the proposed co-estimation method for SOC and SOH.

An adaptive central difference Kalman filter approach for state of charge estimation by fractional order model of lithium-ion battery

The key issue of the model-based state of charge estimation approach is the accuracy of the battery model. In this paper, a fractional order model is built to simulate the electrochemistry dynamics of lithium-ion battery, whose model parameters are identified by adaptive genetic algorithm. Based on the computation simplification of central difference algorithm, an adaptive central difference Kalman filter by fractional order model is designed to estimate the state of charge. The designed approach is modelled by simulink and translated into C code, and then embedded in the battery management system for the validation by two dynamic cycles. Comparing experiments adopt two approaches, i.e. the central difference Kalman filter by fractional order model, the adaptive central difference Kalman filter by Thevenin model. Experimental results indicate that the designed approach has the better accuracy and robustness, and also show that fractional order model is more accurate than Thevenin model. With respect ot the ability to deal with noise, the robustness of the designed approach is verified by adding artificial noise. Experimental results show that the proposed approach has the best robustness to noise. Therefore, the proposed approach is a good candidate for the state of charge estimation in engineering practice.

IoT based smart and intelligent smart city energy optimization

With the effective result of IoT architecture in all research areas, we propose IoT framework based energy efficient smart and intelligent street road lighting system that consist of IoT sensor bases smart electric pole with controller for tuning LED lamps. In our work we replace traditional metal halide lamps with mesophic design LED lamps based on human eye sensitivity and it provides significant saving of energy too. Based on the sensor unit information about traffic flow and presence and absence of occupant smart and intelligent decision making module compute intensity level that generate different width pulse using PWM dimming system which triggers LED power switch via DALI controller mounted inside LED Light Controller. For efficient use of sustainable energy resources we use sustainable power systems that consist of PV solar panel units, Battery storage systems and smart electric power grids. For the charging battery system we use MPPT based dynamic battery charging algorithm. From experiment and simulated result, we observe that proposed energy efficient Smart Street lighting saves considerable amount of energy during peak and off peak hours not only at highway roads but at residential and sub-urban pedestrian areas also. It will finally decrease the consumption of energy and carbon emission. We perform comparative result analysis of proposed systems with existing lighting systems based on energy consumption. Our experimentation result also represents the effectiveness of the proposed dynamic battery charging algorithm based on the battery storage PV solar panel system performance.

Dynamic battery equalization scheme of multi-cell lithium-ion battery pack based on PSO and VUFLC

Aiming at three problems of over equalization, energy loss and time consumption, a dynamic equalization scheme is designed to control the equalization process of multi-cell Lithium-ion battery pack. First, a modified Buck-Boost circuit using inductor to transfer energy is proposed, which improves the equalization speed and is easy to realize in hardware modules. Then, for the problem of over equalization, state of charge is estimated by extreme learning machine based on genetic algorithm and used as the equalization variable. In order to improve energy utilization, particle swarm optimization is used to dynamically optimize the equalization process to obtain the optimal equalization energy path. On the basis of the optimal equalization energy path, variable universe fuzzy logic control based on fuzzy inference is used to achieve dynamic adjustment of the equalization current and reduce the time consumption. Finally, the simulation results show that the proposed equalization scheme can reduce the energy loss by 2.39%, improve the equalization speed by 36%, and effectively reduce the inconsistency of multi-cell Lithium-ion battery pack.

Unlocking electrochemical model-based online power prediction for lithium-ion batteries via Gaussian process regression

The knowledge of the dynamic available charging and discharging power of the battery is a piece of essential information for the safety and longevity of the battery energy storage systems. An accurate real-time prediction of these quantities is very challenging due to the high nonlinearities of battery dynamics. In this paper, an electrochemical model-based online state-of-power prediction algorithm under different time horizons is developed for a safer and more reliable operation of lithium-ion batteries. The safety constraints, which define the safety operation area for the power prediction, are designed based on not only the terminal voltage but also battery internal electrochemical states, i.e., the electrode surface concentration, the electrolyte concentration, and the side reaction overpotential. The algorithm is validated by simulations and experiments under a dynamic load profile, and the dominating constraints in charging and discharging as well as the influence of predictive time horizons on the available battery power are analyzed, providing important information for further researches. Furthermore, the computational speed of the proposed iterative algorithm is improved with the integration of Gaussian process regression by up to 50%. A comparative study with a state-of-the-art equivalent circuit model-based approach highlights the significant benefits of the proposed electrochemical model-based algorithm in operation safety enhancement and battery performance improvement.

Tracking Battery Dynamics by Operando Synchrotron X-ray Imaging: Operation from Liquid Electrolytes to Solid-State Electrolytes

ConspectusLithium-ion batteries have been widely applied in portable electronics due to their high energy density (300 Wh kg–1). However, their potential applications in electric vehicles and grid energy storage call for higher energy density toward 500 Wh kg–1. Solid-state batteries, employing highly safe electrolytes to replace flammable liquid electrolytes, probably achieve this aim by reviving the metallic lithium anode. However, the sluggish lithium transport across the solid–solid interfaces seriously influences the actual battery electrochemistry in applications. Unlike the relatively complete basic theories of solid–liquid electrochemistry, the electrochemical fundamentals and models in the solid-state batteries are still ambiguous, which cannot give a guideline for optimizing strategies for high battery performance. Therefore, building better batteries for next-generation electrochemical energy storage remains a great challenge.Synchrotron X-ray imaging techniques are currently catching increasing attention due to their natural advantages, which are nondestructiveness, chemically responsiveness, elementally sensitivity, and high penetrability to enable operando investigation of a real battery. Based on the derived nanotomography techniques, it can provide 3D morphological information including thousands of slice morphologies from the bulk to the surface. Combined with X-ray absorption spectroscopy, X-ray imaging can even present chemical and phase mapping information, including the oxidation state, local environment, etc., with sub-30 nm spatial resolution, which addresses the issues that we only obtain as averaged information in traditional X-ray absorption spectroscopy. Through an operando charging/discharging setup, X-ray imaging enables the study of the correlation between the morphology change and the chemical evolution (mapping) under different states of charge and cycling. In addition, X-ray imaging breaks up the size limit of nanoscale samples for the in-situ transmission electron microscope imaging, which enables a large, thick sample with a broad field of view, truly uncovering the behavior inside a real battery system.In this Account, we focus on the topic of operando synchrotron X-ray imaging methodology and the emerging applications in the battery operation from liquid electrolytes to solid-state electrolytes, aiming to probe battery dynamics from the classic solid–liquid electrochemistry to emerging solid–solid electrochemistry. First, operando synchrotron X-ray imaging methodology and challenges of characterization in the real-time battery operation are discussed, including operando imaging principles, setup design, and impact factors in the actual experiment. Second, challenges facing operando synchrotron X-ray imaging in the battery development from liquid electrolytes to solid-state electrolytes have been summarized. Third, we highlight the fundamental issues in the battery dynamics obtained by the operando transmission X-ray imaging. Especially, we describe our recent progresses and new findings in probing real-time battery dynamics operation from liquid electrolytes to solid-state electrolytes. Overall, we provide a deep understanding from fundamentals to applications of operando imaging techniques in both battery systems, which will guide the investigation in developing next-generation battery devices.