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Online State-of-Health Assessment for Battery Management Systems
... 24 Mihai et al. proposed a least square algorithm based on Cramer's algorithm (CLS) to identify the parameters of the polynomial model. 25 The algorithms porposed above have the assumption that all the collected data are measurable. However, in application, some data are usually missing due to network congestion. ...
... where C pk is the normalized battery capacities of the polynomial models, b 1 , b 2 , and b 3 are the parameters to be estimated, and k is the number of cycles. 25 The Polynomial Model ...
... Mihai et al. proposed a least square algorithm based on Cramer's algorithm (CLS) to identify the parameters of the polynomial model. 25 In this section, we consider a more complex case: some data of the system are missing. ...
With the popularity of lithium-ion batteries, battery state of health (SOH) estimation has become one of the current research hotspots. Due to network congestion, collected data usually encounter time-delay or packet loss. Here, an expectation maximization (EM) algorithm is proposed for the SOH model which is approximated by a polynomial model. Based on the EM method, the missing data are computed in the E step, and the parameters are updated in the M step. Compared with the least square method, the proposed algorithm has more accurate estimation accuracy. The simulation example shows the effectiveness of the proposed algorithm.
... There are a variety of different regression techniques used from the least squares fit to a curve [11,12] through to Gaussian process regression [5] and particle swarm optimization regression [3]. The training data can be a measured capacity with time or a health indicator (HI), e.g., time to go from maximum voltage to minimum voltage under a constant current discharge or the shape of the discharge or charge curve [13]. ...
... A number of different curve types have been used in previous literature including exponential formula [7,14,15], Polynomial formula [11], the power law [12], and some formulae that are based on empirical examination on test results [1,4]. ...
... Equation (11) has been adapted as shown in Figure 5 to deal with partial SOC cycling as follows: ...
There is increased talk about using second-life batteries in applications. In first-life applications, the batteries start from new, and a range of life cycle estimation techniques are applied. However, it is not clear how second-life batteries should be monitored compared to first life batteries. This paper investigated different algorithms from first-life applications for estimating and forecasting battery cell state of health in conjunction with capacity calculations using second life cells under long term durability testing. The paper looks at how close these models predict capacity fade based on a set of second-life batteries that have been undertaking sweat testing over six different applications. The paper concludes that there are two methods that could be suitable candidates for predicting lifespan. One of these needed to be modified from the original.
... There are a variety of different regression techniques used from the least squares fit to a curve [1], [2] through to Gaussian process regression [3] and particle swarm optimisation regression [4] The training data can be a measured capacity with time or a health indicator (HI) e.g. time to go from maximum voltage to minimum voltage under a constant current discharge or the shape of the discharge or charge curve [5]. ...
... A number of different curve types have been used in previous literature including exponential formula [6]- [8], Polynomial formula [1] the power law [2] and some formula that are just based on empirical examination on test results [9], [10]. ...
... 100%-50% 2 types of small cells with 4 of each, full charge and discharge cycle for around 200-800 cycles depending on type [1] NiMH 50-0% possibly of life 2 small cells full charge and discharge cycle for around 85 cycles [27] Li Ion Oxford university data set [28] Up to 3600 charging cycles, looking for changes in the incremental capacity data as a function of probability [2] LiFePO4 4 small cells, full charge and discharge cycle for between 363 to 1549 cycles, undertaken during charging above 70% SOC [29] LiCoO2 100%-70% At least 7 small cells, discharge rates 10% to 90% DOD up to 4000+ cycles at 50% DOD [30] Lithium Ion 100%-70% small cells, 0.5C discharge cycle to 1000 cycles [12] Li(NiCoMn)1/ 3O2 100%-80% 6 small cells, 840 cycles undertaken at high temperature to speed aging [31] Li(NiCoAl)O2 Panasonic cylindrical 100%-about 60-70% 18 small cells, full charge and discharge cycle at cycle rates 0.5C, 1C and 2C and different temperatures to full DOD to up to 800 cycles [18] Lithium Ion 100%-80% 2 small groups of cells, between 2500 cycles one set with vibration [21] Li(NiCoAl)O2 cylindrical batteries 100%-80% (approx.) 0-100% DOD cycles at different charge rates (1C, 2C and 3.5C) and temperature (25oC and 40oC) to around 600 cycles. ...
p>There is increased talk about using second life batteries in applications. In first life applications, the batteries start from new and a range of life cycle estimation techniques are applied. However, it is not clear how second life batteries should be monitored compared to first life batteries. This paper investigated different algorithms from first life applications, for estimating and forecasting battery cell state of health in conjunction with capacity calculations using second life cells under long term durability testing. The paper looks at how close these models predict capacity fade based on a set of second life batteries that have been undertaking sweat testing over six different applications. The paper concludes that there are two methods that could be suitable candidates for predicting lifespan. One of these needed to be modified from the original.</p
... There are a variety of different regression techniques used from the least squares fit to a curve [1], [2] through to Gaussian process regression [3] and particle swarm optimisation regression [4] The training data can be a measured capacity with time or a health indicator (HI) e.g. time to go from maximum voltage to minimum voltage under a constant current discharge or the shape of the discharge or charge curve [5]. ...
... A number of different curve types have been used in previous literature including exponential formula [6]- [8], Polynomial formula [1] the power law [2] and some formula that are just based on empirical examination on test results [9], [10]. ...
... 100%-50% 2 types of small cells with 4 of each, full charge and discharge cycle for around 200-800 cycles depending on type [1] NiMH 50-0% possibly of life 2 small cells full charge and discharge cycle for around 85 cycles [27] Li Ion Oxford university data set [28] Up to 3600 charging cycles, looking for changes in the incremental capacity data as a function of probability [2] LiFePO4 4 small cells, full charge and discharge cycle for between 363 to 1549 cycles, undertaken during charging above 70% SOC [29] LiCoO2 100%-70% At least 7 small cells, discharge rates 10% to 90% DOD up to 4000+ cycles at 50% DOD [30] Lithium Ion 100%-70% small cells, 0.5C discharge cycle to 1000 cycles [12] Li(NiCoMn)1/ 3O2 100%-80% 6 small cells, 840 cycles undertaken at high temperature to speed aging [31] Li(NiCoAl)O2 Panasonic cylindrical 100%-about 60-70% 18 small cells, full charge and discharge cycle at cycle rates 0.5C, 1C and 2C and different temperatures to full DOD to up to 800 cycles [18] Lithium Ion 100%-80% 2 small groups of cells, between 2500 cycles one set with vibration [21] Li(NiCoAl)O2 cylindrical batteries 100%-80% (approx.) 0-100% DOD cycles at different charge rates (1C, 2C and 3.5C) and temperature (25oC and 40oC) to around 600 cycles. ...
p>There is increased talk about using second life batteries in applications. In first life applications, the batteries start from new and a range of life cycle estimation techniques are applied. However, it is not clear how second life batteries should be monitored compared to first life batteries. This paper investigated different algorithms from first life applications, for estimating and forecasting battery cell state of health in conjunction with capacity calculations using second life cells under long term durability testing. The paper looks at how close these models predict capacity fade based on a set of second life batteries that have been undertaking sweat testing over six different applications. The paper concludes that there are two methods that could be suitable candidates for predicting lifespan. One of these needed to be modified from the original.</p
... He et al. [18] put forward a two-term exponential model to model battery capacity degradation and predicted the RULs via Bayesian Monte Carlo and Dempster-Shafer theory. Micea et al. [19] proposed a quadratic polynomial model to fit the degradation data and predict battery RULs. Xing et al. [20] fused a polynomial model and a two-term exponential model to fit the capacity degradation and predict the RULs based on PF framework. ...
... In short, the square-root-of-time model [27] and CE model [12] are both typical semi-empirical models. To validate the modelling performance of power model sufficiently, four empirical models including two-term logarithmic model [22], one-term exponential model [21], two-term exponential model [18], and quadratic polynomial model [19], are selected. The comparative results on model fitting and RUL prediction will be presented on Section 4. In this section, we give a brief review on selected five other models. ...
... Using battery capacity as the characteristic index to measure battery degradation, the empirical degradation model method combines battery use conditions, degradation mechanism, and failure mechanism to achieve RUL prediction, taking into account the battery charging and discharging electrochemical mechanism and the influence of battery aging on the external characteristics of the battery. Several common empirical degradation models include the polynomial model [45], single exponential model [46], double exponential model [47], Fourier model [48], ensemble model [49], etc. The models are as follows: The RUL prediction method based on the equivalent circuit model is simpler compared with the electrochemical model, but the existing equivalent circuit models still have some defects, such as poor practicality, difficult identification of model parameters, long test cycles, and complicated calculations. ...
... Using battery capacity as the characteristic index to measure battery degradation, the empirical degradation model method combines battery use conditions, degradation mechanism, and failure mechanism to achieve RUL prediction, taking into account the battery charging and discharging electrochemical mechanism and the influence of battery aging on the external characteristics of the battery. Several common empirical degradation models include the polynomial model [45], single exponential model [46], double exponential model [47], Fourier model [48], ensemble model [49], etc. The models are as follows: ...
Lithium-ion batteries are a green and environmental energy storage component, which have become the first choice for energy storage due to their high energy density and good cycling performance. Lithium-ion batteries will experience an irreversible process during the charge and discharge cycles, which can cause continuous decay of battery capacity and eventually lead to battery failure. Accurate remaining useful life (RUL) prediction technology is important for the safe use and maintenance of energy storage components. This paper reviews the progress of domestic and international research on RUL prediction methods for energy storage components. Firstly, the failure mechanism of energy storage components is clarified, and then, RUL prediction method of the energy storage components represented by lithium-ion batteries are summarized. Next, the application of the data–model fusion-based method based on kalman filter and particle filter to RUL prediction of lithium-ion batteries are analyzed. The problems faced by RUL prediction of the energy storage components and the future research outlook are discussed.
... Accurate battery State of health (SOH) information is an efficient tool to give indication of expected battery performance and useful life, and the remaining time before the next replacement [1]. Therefore, integrating SOH estimation feature in the new Battery Management Systems (BMS) and Software Tools has become a hot research topic [2][3][4][5][6][7][8]. ...
... The existing commercial solutions and planning tools usually implement simple methods (such as Coulomb counting) and offer a wide range of configurability. Indeed, a simpler model with acceptable accuracy is relatively convenient to implement, compared to more theoretical approaches [3,26,[28][29][30] The ampere-hour (Ah) integral or Coulomb counting method [31,32] is a tracking method considered in the most used technology of the moment and implemented in several commercial solutions for SOH and SOC estimation [5,28,[33][34][35]. This is due to its simplicity and its low computational cost. ...
Accurate estimation of the battery state of health (SOH) is necessary for effective monitoring and prediction of battery performances and useful life in PV systems. This paper proposes an improvement of a previous SOH model to reflect the battery aging state by monitoring the working zone and temperature. It presents an important contribution for simple, easy-to-implement, dynamic and non-destructive estimation of battery SOH. Unlike the former model, the proposed model overcomes the problems associated with the inaccurate SOH estimations found in the aged state of the battery. This is achieved by completely modifying the SOH equation and including several important battery characteristics not considered in the traditional model – mainly by introducing the battery design life parameter in the calculation of the safe working zone factor. These enhancements allow the user to adjust the new model to different solar battery types simply from the datasheet information. Using real measurements, model parameters were computed to reduce the estimation error of the battery SOH. To verify the accuracy of the proposed model, an experimental rig that comprises a solar battery tested for a long time is set up. The promising part of the method is the improvement shown in the SOH estimation results. The mean error, in the aged state of the battery, is reduced from 90% to 5%. Furthermore, the proposed model is integrated into a PV designer software for the prediction of battery lifetime and SOH degradation. Using meteorological data, the software tool shows the ability to predict the endurance of the solar batteries in a designed PV system for any location. It is expected that this work will benefit a large number of BMS designers and simulator developers who require a Battery SOH estimation method with a good compromise between simplicity and accuracy.
... The main contributions are as follows: (1) The state equation of battery based on the Thevenin model is established and the parameters of the model are identified by the forgetting factor recursive least squares (FFRLS). (2) Different from the polynomial fitting method in Ref. [20], this paper proposed to use neural network to establish the nonlinear relationship between the OCV and SOC, which can effectively improve the fitting accuracy. (3) An improved AUKF based on residual innovation sequence is proposed in this paper. ...
... Refs. [20,23,24] show that the mapping relationship between OCV and SOC is obtained by polynomial fitting. In this paper, the 5-order polynomial is used for fitting: ...
Accurately estimating the state of charge (SOC) of batteries is of particularly important for real time monitoring and safety control in electric vehicles. Four aspects of efforts are used to promote the accuracy of SOC estimation. Firstly, the equivalent circuit model based on Thevenin model is established, and the parameters of the model are identified by the forgetting factor recursive least square method (FFRLS). Secondly, aiming at the nonlinear relationship between the open circuit voltage (OCV) and SOC, the neural network is proposed to fit OCV and SOC. Besides, an improved adaptive unscented Kalman filter is created in this paper. By using the residual innovation sequence (RIS) to adjust the fixed window in the adaptive algorithm, which can promote the accuracy of SOC estimation. Finally, the effectiveness of the proposed model is verified under dynamic cycles. The experimental results indicate that the proposed method can effectively improve the estimation accuracy of SOC.
... Because of the mathematical simplicity, wide validity and high flexibility, RUL can be predicted [12]. Most studies in the literature have utilised a model that is generally linear, exponential and polynomial [13][14][15]. Model-based approaches are also associated with advanced Bayesian, Kalman and Particle filters (KF and PF, respectively) [16]. These can update the parameters of the model as part of the diagnostic process, to ensure accurate RUL prediction. ...
... For example, in [21,22], a method was proposed to predict failure using the exponential model and classical PF. In addition, although a second-order polynomial was presented in [14], which contains fewer parameters than the exponential model, this model is less accurate than the exponential model. ...
Accurate prediction of the remaining useful life (RUL) in Lithium-ion Batteries (LiBs) is a key aspect of managing their health, in order to promote reliable and secure systems, and to reduce the need for unscheduled maintenance and costs. Recent work on RUL prediction has largely focused on refining the accuracy and reliability of the RUL prediction. This work introduces a new online RUL prediction for LiB using Smooth Particle Filter- (SPF) based likelihood approximations method. The proposed algorithm can accurately estimate the unknown degradation model parameters and predict the degradation state by solving the optimization problem at each iteration, rather than only taking a gradient step, that tends to lead to rapid convergence, avoids instability issues and improves predictive accuracy. From the experimental datasets published by Prognostics Centre of Excellence (PCoE) NASA, a second order degradation model was created to explore the degradation of LiB, utilizing non-linear characteristics and non-Gaussian capacity degradation. RUL prediction was tested with various predicted starting points to assess whether the amount of data and parameters uncertainty influenced the accuracy of the prediction. Results show that the proposed prediction approach gives an improved prediction accuracy and improves the convergence rate in comparison with Particle Filter (PF) and other methods such as Unscented Particle Filter (UPF). Since the maximum error of the SPF predicting approach is relatively small, RUL prediction in the best case at prediction starting point 80 cycles is 127 cycles. The prediction relative error was approximately 0.024, and the absolute error of the proposed algorithm is around 2 cycles, lower than the PF (around 16 cycles). RUL prediction is nearby 108 cycles and relative error is around 0.136, while the absolute error prediction is approximately 16
... a polynomial model (cf. [19,21]) ...
... The results are used to estimate the remaining useful cycles as well, and the estimations show similar performance in comparison to the double exponential model. Referring to [22], Micea et al. [21] propose the polynomial model (2) for the capacity degradation of Nickel-Metal Hydride (Ni-MH) batteries. The model parameters are determined by a least squares approach to calculate the failure time for a single battery. ...
Capacity degradation of lithium-ion batteries under long-term cyclic aging is modeled via a flexible sigmoidal-type regression setup , where the regression parameters can be interpreted. Different approaches known from the literature are discussed and compared with the new proposal. Statistical procedures, such as parameter estimation, confidence and prediction intervals are presented and applied to real data. The long-term capacity degradation model may be applied in second-life scenarios of batteries. Using some prior information or training data on the complete degradation path, the model can be fitted satisfactorily even if only short-term degradation data is available. The training data may arise from a single battery.
... 9 de signal required for EIS measurements. To address these issues, many researches advocate prediction based on empirical regression models [8], [14]. ...
Accurately estimating of the age and condition of lithium ion batteries (LIBs) is paramount for their safe and economically viable utilization. However, assessing the degradation of these power units proves to be challenging due to their dependence on various environmental and usage factors. In this study, we propose an efficient Particle Swarm Optimization (PSO)-based Grey Theory prediction model to determine the Remaining Useful Life (RUL) of lithium ion batteries. The proposed model utilizes PSO to optimize the coefficients of a grey prediction model, enabling accurate forecasting of the remaining useful life of LIBs. Our results demonstrate that the presented model outperforms conventional grey prediction models in terms of both accuracy and stability. Furthermore, the proposed model offers simpler predictions compared to existing models in the literature. By introducing this promising technique, our study contributes to the precise forecasting of the RUL of lithium-ion batteries and holds potential for applications in similar domains. This research serves as a significant step towards ensuring effective management and utilization of LIBs, promoting their reliability and safety.
... The first aspect was selecting different nonlinear models and fitting the degradation process in order to improve the fitting accuracy. Currently, there are various types of nonlinear models for modeling lithium-ion batteries, such as the double exponential function [30][31][32], the combination of the exponential function and the linear function [33,34], the combination of the exponential function and the quadratic function [35], the combination of the power function and the exponential function [21], the combination of the logarithmic function and the polynomial [36], and polynomial functions (quadratic polynomial [37,38], cubic polynomial [39], quintic polynomial [40]). However, current research focuses mostly on analyzing the effects of different nonlinear functions from the perspective of model fitting of data, and there are few comparisons of typical performance degradation characteristics of lithium-ion batteries. ...
Remaining useful life (RUL) prediction has become one of the key technologies for reducing costs and improving safety of lithium-ion batteries. To our knowledge, it is difficult for existing nonlinear degradation models of the Wiener process to describe the complex degradation process of lithium-ion batteries, and there is a problem with low precision in parameter estimation. Therefore, this paper proposes a method for predicting the RUL of lithium-ion batteries based on a cubic polynomial degradation model and envelope extraction. Firstly, based on the degradation characteristics of lithium-ion batteries, a cubic polynomial function is used to fit the degradation trajectory and compared with other nonlinear degradation models for verification. Secondly, a subjective parameter estimation method based on envelope extraction is proposed that estimates the actual degradation trajectory by using the average of the upper and lower envelope curves of the degradation data of lithium-ion batteries and uses the maximum likelihood estimation (MLE) method to estimate the unknown model parameters in two steps. Finally, for comparison with several typical nonlinear models, experiments are carried out based on the practical degradation data of lithium-ion batteries. The effectiveness of the proposed method to improve the accuracy of RUL prediction for lithium-ion batteries was demonstrated in terms of the mean square error (MSE) of the model and MSE of RUL prediction.
... Data-driven method [24][25][26][27][28][29][30][31][32][33][34][35] (1) No need to analyze the aging mechanism inside the battery; ...
Lithium-ion batteries (LIBs) have been widely used in various fields. In order to ensure the safety of LIBs, it is necessary to accurately estimate of the state of health (SOH) of the LIBs. This paper proposes a SOH hybrid estimation method based on incremental capacity (IC) curve and back-propagation neural network (BPNN). The voltage and current data of the LIB during the constant current (CC) charging process are used to convert into IC curves. Taking into account the incompleteness of the actual charging process, this paper divides the IC curve into multiple voltage segments for SOH prediction. Corresponding BP neural network is established in multiple voltage segments. The experiment divides the LIBs into five groups to carry out the aging experiment under different discharge conditions. Aging experiment data are used to establish the non-linear relationship between the decline of SOH and the change of IC curve by BP neural network. Experimental results show that in all voltage segments, the maximum mean absolute error does not exceed 2%. The SOH estimation method proposed in this research makes it possible to embed the SOH estimation function in battery management system (BMS), and can realize high-precision SOH online estimation.
... Reference [17] used second-order polynomials to model and estimate battery capacity. ...
Electrified vehicles (EV) and marine vessels represent promising clean transportation solutions to reduce or eliminate petroleum fuel use, greenhouse gas emissions and air pollutants. The presently commonly used electric energy storage system (ESS) is based on lithium-ion batteries. These batteries are the electrified or hybridized powertrain’s most expensive component and show noticeable performance degradations under different use patterns. Therefore, battery life prediction models play a key role in realizing globally optimized EV design and energy control strategies. This research studies the data-driven modelling and prediction methods for Li-ion batteries’ performance degradation behaviour and the state of health (SOH) estimation. The research takes advantage of the increasingly available battery test and data to reduce prediction errors of the widely used semi-empirical modelling methods. Several data-driven modelling techniques have been applied, improved, and compared to identify their advantages and limitations. The data-driven approach and Kalman Filter (KF) algorithm are used to estimate and predict the degradation of the battery during operation. The combined algorithm of Gaussian Process Regression (GPR) and Extended Kalman Filter (EKF) showed higher accuracy than other algorithms.
... The existing literature classifies the remaining life prediction of lithium batteries into three main categories of approaches, namely model-based approaches, data-driven, and hybrid approaches. Model-based approaches aim to establish a mathematical model describing the degradation behavior of the battery, which can be subdivided into three approaches: electrochemical models [1], equivalent circuit models [2], and empirical models [3]. Electrochemical models require an in-depth understanding of the reaction mechanism within the cell and have high model accuracy, but the models are overly complex. ...
With the widespread use of lithium-ion batteries in various fields, battery Prognostics and Health Management (PHM) technologies are gaining more and more attention. Repeated use of batteries can lead to degradation of battery performance and thus affect battery life. Accurate prediction of the remaining useful life (RUL) of batteries is crucial and is the most central issue in battery PHM. In this paper, a method based on a combination of fuzzy information granulation (FIG) and support vector regression with artificial bee colony optimization (ABC-SVR) is proposed to estimate the RUL of Lithium-ion batteries. First, the capacity degradation data are divided into several windows using the FIG method. Second, the maximum and minimum values of each window are predicted separately using the ABC-SVR algorithm to obtain the information of the prediction window. Finally, the missing values of the prediction windows are complemented by the linear interpolation method to obtain the complete capacity prediction values, and the remaining useful life of the battery can be calculated according to the failure threshold. The results show that the proposed method obtains the RUL value with high accuracy.
... The same limitations are also present for the least square circuit parameter identification [10], or extended Kalman filter [11] as they include iterative algorithms (Levenberg-Marquardt) for covariance matrices calculations. Alternative approaches such as the time-window algorithm and the history-based algorithm are more accurate as more inputs are used, requiring a microcontroller with powerful memory resources such as the ARM7 family [9]. The use of a grey-box identification approach [21], requires iterative covariance estimate and numerical minimization which is not suitable for real-time in-situ parameter estimation. ...
Accurate modeling of electrochemical sources is very important to predict how a source will perform in specific applications related to the load or environmental parameters. A Randles circuit is considered as a reliable equivalent electrical circuit in studying and modeling various electrochemical systems and processes. The classical parameter estimation approach based on use of software packages (ZSim, MEISP, LEVMW, and so on) requires high computational performance processing units, decreasing the reliability as proper maintenance actions can be delayed because of offline analysis. Advancing the state of the art, we propose a low-complexity approach for embedded hardware-based parameter estimation of the Randles circuit. Our noniterative method uses only the measured real and imaginary parts of impedance, with numerical approximation of the first derivative of real/imaginary part quotient, to create closed-form expressions with a unique solution. The initial estimated values are available from partial dataset (after measurement at only three frequencies). Moreover, it is not software platform-specific, which enables a high level of portability. The presented method is verified with theoretical, numerical, and experimental analysis, with more than 1000 datasets. We also demonstrated the applicability in parameter estimation of the Randles circuit of a Li-ion battery. Finally, we verified suitability for embedded hardware platforms, with deployment on a microcontroller-based platform with a clock speed of 16 MHz and 8 kB of SRAM. Reliable parameter estimation processing of a 100-point dataset was performed in just 106 ms with 1% relative error, requiring less than 53 mJ of energy.
... e model-based methods define the battery degradation behavior by using differential, algebraic, or empirical equations. Different researchers presented empirical models [13][14][15], mechanistic models (also known as chemical models) [16][17][18], equivalent circuit models [19,20], and fused models [21] to capture the battery degradation behavior. Hu et al. [22] presented a model-based method for coestimation of SoC and SoH of Li-ion batteries. ...
In this paper, a novel multistep ahead predictor based upon a fusion of kernel recursive least square (KRLS) and Gaussian process regression (GPR) is proposed for the accurate prediction of the state of health (SoH) and remaining useful life (RUL) of lithium-ion batteries. The empirical mode decomposition is utilized to divide the battery capacity into local regeneration (intrinsic mode functions) and global degradation (residual). The KRLS and GPR submodels are employed to track the residual and intrinsic mode functions. For RUL, the KRLS predicted residual signal is utilized. The online available experimental battery aging data are used for the evaluation of the proposed model. The comparison analysis with other methodologies (i.e., GPR, KRLS, empirical mode decomposition with GPR, and empirical mode decomposition with KRLS) reveals the distinctiveness and superiority of the proposed approach. For 1-step ahead prediction, the proposed method tracks the trajectory with the root mean square error (RMSE) of 0.2299, and the increase of only 0.2243 RMSE is noted for 30-step ahead prediction. The RUL prediction using residual signal shows an increase of 3 to 5% in accuracy. This proposed methodology is a prospective approach for an efficient battery health prognostic.
... The BMS is an embedded system device based on electronic components that continuously monitors various parameters. The primary function of a BMS, besides determining the SOC and SOH, is to protect the battery(ies) from over-voltage and high current draw, and to prevent the battery(ies) from reaching a high temperature [8,9]. Figure 1 shows a graphic representation for a series configuration battery management system. ...
In mobile robotics, since no requirements have been defined regarding accuracy for Battery Management Systems (BMS), standard approaches such as Open Circuit Voltage (OCV) and Coulomb Counting (CC) are usually applied, mostly due to the fact that employing more complicated estimation algorithms requires higher computing power; thus, the most advanced BMS algorithms reported in the literature are developed and verified by laboratory experiments using PC-based software. The objective of this paper is to describe the design of an autonomous and versatile embedded system based on an 8-bit microcontroller, where a Dual Coulomb Counting Extended Kalman Filter (DCC-EKF) algorithm for State of Charge (SOC) estimation is implemented; the developed prototype meets most of the constraints for BMSs reported in the literature, with an energy efficiency of 94% and an error of SOC accuracy that varies between 2% and 8% based on low-cost components.
... Yang, Song, Dong, & Tsui, 2019), power-law (Schmalstieg, Käbitz, Ecker, & Sauer, 2014;Han, Ouyang, Lu, & Li, 2014), exponential (X. Zhang, Miao, & Liu, 2017;Tang et al., 2019;Perez et al., 2018), polynomial (Micea, Ungurean, Cârstoiu, & Groza, 2011), sigmoid (Johnen et al., 2020) or a combination of these (Xing, Ma, Tsui, & Pecht, 2013). More complicated models also account for differences in C-rates and temperatures (Ji et al., 2020;Singh, Chen, Tan, & Huang, 2019 (Kandasamy, Badrinarayanan, KAnamarlapudi, Tseng, & Soong, 2017;Schimpe et al., 2018;Naumann, Spingler, & Jossen, 2020;Bian et al., 2020). ...
Battery systems are becoming an increasingly attractive alternative for powering ocean going ships, and the number of fully electric or hybrid ships relying on battery power for propulsion and maneuvering is growing. In order to ensure the safety of such electric ships, it is of paramount importance to monitor the available energy that can be stored in the batteries, and classification societies typically require that the state of health of the batteries can be verified by independent tests – annual capacity tests. However, this paper discusses data-driven diagnostics for state of health modelling for maritime battery systems based on operational sensor data collected from the batteries as an alternative approach. Thus, this paper presents a comprehensive review of different data-driven approaches to state of health modelling, and aims at giving an overview of current state of the art. Furthermore, the various methods for data-driven diagnostics are categorized in a few overall approaches with quite different properties and requirements with respect to data for training and from the operational phase. More than 300 papers have been reviewed, most of which are referred to in this paper. Moreover, some reflections and discussions on what types of approaches can be suitable for modelling and independent verification of state of health for maritime battery systems are presented.
... where Q N represents the rated capacity of the battery. The relationship between the open-circuit voltage and the SOC is described by combining the Shepherd model and the Nerst model [29] as: ...
An accurate state of charge (SOC) estimation is of great importance for the battery management systems of electric vehicles. To improve the accuracy and robustness of SOC estimation, lithium-ion battery SOC is estimated using an adaptive square root unscented Kalman filter (ASRUKF) method. The square roots of the variance matrices of the SOC and noise can be calculated directly by the ASRUKF algorithm, which ensures the symmetry and nonnegative definiteness of the matrices. The process values and measurement noise covariance can be adaptively adjusted, which greatly improves the accuracy, stability, and self-adaptability of the filter. The effectiveness of the proposed method has been verified through experiments under different operating conditions. The obtained results were compared with those of extended Kalman filter (EKF) and unscented Kalman filter (UKF) , which indicates that the ASRUKF method provides better accuracy, robustness and convergence in the estimation of battery SOC for electric vehicles. The proposed method has a mean SOC estimation error of 0.5% and a maximum SOC estimation error of 0.8%. These errors are lower than those of other methods.
div class="section abstract"> EVs are extensively utilised with lithium-ion batteries. Predicting the SOH of batteries is desired to achieve optimal operation and health management. The most significant obstacle to accurately predicting battery health is choosing battery features. This study introduces numerous data analysis strategies to manage feature irrelevancy and help determine which features can be selected and used in real-time and edge computing. The first step in manually crafting features is to analyse the evolution pattern of numerous essential battery characteristics. Second, the correlation between selected features and degraded capacity was analysed. Then, selected features are fed into a representative machine learning regression model to effectively predict the remaining capacity of the battery to find the SOH status. Finally, the remaining battery capacity is selected as a feature to predict the RUL in terms of remaining charge-discharge cycles.
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The evolution of CE in each cycle follows a time-variable logarithmic function closely related to the cycle number, which can be used for predicting the lifespan of LIBs.
With the development of lithium-ion battery (LIB) technology and the increasing popularity of electric vehicles, the issue of battery safety has become increasingly urgent. The state of health (SOH), known as a critical parameter in the prognosis and health management of LIBs, has considerable attention from industry and academia. This paper proposes a novel method for estimating the SOH of LIBs based on sliding window sampling of charging curves and independently recurrent neural network (IndRNN). Considering the number of battery cycles and practical applications, the sliding window sampling based on cycle number is utilized to determine the different partial voltages as the inputs to the SOH estimation model. To address the gradient disappearance and gradient explosion problems, in the proposed SOH estimation model, we suggest the IndRNN which introduces independent weights between inputs and outputs, trains the IndRNN with rectified linear units, and learns the long-term dependencies by stacking multiple layers of IndRNN to achieve long-term accurate aging tracking of batteries. Finally, experiments are validated on the most widely used Oxford University battery dataset, and the effectiveness of our method is also verified by comparing it against three methods on our laboratory data with different operating conditions.
This article addresses the design of a fully automated photovoltaic (PV) power plant inspection process by a fleet of unmanned aerial and ground vehicles (UAVs/UGVs). More specifically, we consider the problem of assigning a set of target points to be inspected to a fleet of UAVs/UGVs so as to minimize the overall energy consumption while accounting for the battery degradation and the vehicle deterioration. In order to cope with the combinatorial complexity of the problem, we propose a suboptimal resolution strategy that integrates a graph-theoretic approach for local path planning at the robot level into a market-based approach for task assignment at the fleet level. The resulting coordination scheme is scalable and plug and play, and requires a minimal amount of information exchange between the robots and a central auctioneer, thus allowing the interoperability of different robotic platforms. Extensive numerical analysis via a multiagent simulation software shows the effectiveness of the proposed approach.
Renewable energy penetration and distributed generation are key for the transition towards more sustainable societies, but they impose a substantial challenge in terms of matching generation with demand due to the intermittent and unpredictable nature of some of these renewable energy sources. Thus, the role of energy storage in today’s and future electricity markets is undisputed. Batteries stand out among the different alternatives for energy storage. The R&D effort into different battery chemistries contributes to reducing the investment associated with battery systems. However, optimizing their operation according to the users’ and the electricity markets’ needs is the turning point to finally make these systems attractive. This review delves into the topic of battery management systems from a battery-technology-independent perspective, and it also explores more fundamental but related aspects, such as battery modeling or state estimation. The techno-economic part of battery energy storage systems is also covered in this document to understand their real potential and viability.
The success of deep learning in the field of state‐of‐health (SOH) estimation relies on a large amount of battery data and the fact that all data possess the same probability distribution. While in real situations, a model based on one working condition data set may not be valid for another working condition data set due to distribution differences. Therefore, this article proposes a transfer learning method using soft‐dynamic time warping (soft‐DTW) as the statistical feature in the feature transfer method, called soft‐DTW domain adaptation network (SDDAN). By combining the prediction error with the time‐series gap in the model training process, the feature transformation can make the obtained prediction results more similar to the source domain results, which can help us to obtain better prediction results in the target domain. Experimental results show that SDDAN can effectively predict the SOH of Li‐ion batteries and significantly improve the performance of feature learning and knowledge transfer.
The lithium-ion batteries used in electric vehicles have a shorter lifespan than other vehicle components, and the degradation mechanism inside these batteries reduces their life even more. Battery degradation is considered a significant issue in battery research and can increase the vehicle’s reliability and economic concerns. This study highlights the degradation mechanisms in lithium-ion batteries. The aging mechanism inside a battery cannot be eliminated but can be minimized depending on the vehicle’s operating conditions. Different operating conditions affect the aging mechanism differently. Knowing the factors and how they impact battery capacity is crucial for minimizing degradation. This paper explains the detailed degradation mechanism inside the battery first. Then, the major factors responsible for the degradation and their effects on the battery during the operation of electric vehicles are discussed. Also, the different techniques used to model the degradation of a battery and predict its remaining life are explained in-depth, along with the techniques to abate the aging process. Finally, this study focuses on the research gaps, difficulties in predicting the lifetime, and reducing the degradation mechanism of a battery used in electric vehicles.
The analytical model of starter batteries state of function, which considers the
change of output voltage parameters and electrolyte (ambient air) temperature, is worked out. The correlation between the success of starting the automobile engine and the state of function of batteries has been established.
One of the most common causes of vehicle failures in our country and abroad is battery failure. The main reason for this is a lack of information about changes in the technical condition of the battery, which makes battery faults (including low charge) sudden, causing social and economic damage. Informing the driver of the battery’s state of charge only partially solves the problem, as this parameter does not fully reflect the battery’s state of health. A solution to this problem could be a battery condition monitoring system based on real-time assessment of battery voltage variations. The purpose of this work is to establish the relationship between the battery voltage and the degree of battery performance in the simulation of characteristic faults. Analysis of publications has established that among the most common faults are decrease of charge, sulphation of plates, oxidation of pole terminals and short circuits. To obtain information about changes in battery parameters in the process of occurrence and development of faults simulation methods have been developed. Changes in battery performance due to ageing or faults can be estimated based on the voltage at its terminals under the influence of the load, which is the starter motor in full braking mode. The change of voltage under load for 5 seconds is determined for new batteries, batteries with running hours variation as well as for simulated discharging, oxidation of the terminals and sulphation of the plates. The serviceability of batteries is determined in two ways: by a tester using the battery current for calculations and computationally using the minimum voltage during the loading process. It was found that the state of heath of serviceable working and faulty batteries at the same voltage differs significantly, which can be used to identify the technical condition of starter batteries. The established relationships between the battery voltage and the state of heath in the physical simulation of characteristic faults have scientific novelty. Further research is aimed at developing an algorithm for on-line monitoring of batteries and manufacturing a prototype on-board device for its implementation.
Abstract. One of the most common causes of vehicle failures in our country and abroad is battery failure. The main reason for this is a lack of information about changes in the technical condition of the battery, which makes battery faults (including low charge) sudden, causing social and economic damage. Informing the driver of the battery's state of charge only partially solves the problem, as this parameter does not fully reflect the bat-tery's state of health. A solution to this problem could be a battery condition monitoring system based on real-time assessment of battery voltage variations. The purpose of this work is to establish the relationship between the battery voltage and the degree of battery performance in the simulation of characteristic faults. Analysis of publications has established that among the most common faults are decrease of charge, sulphation of plates, oxidation of pole terminals and short circuits. To obtain information about changes in battery parameters in the process of occurrence and development of faults simulation methods have been developed. Changes in battery performance due to ageing or faults can be estimated based on the voltage at its terminals under the influence
of the load, which is the starter motor in full braking mode. The change of voltage under load for 5 seconds is determined for new batteries, batteries with running hours variation as well as for simulated discharging, oxidation
of the terminals and sulphation of the plates. The serviceability of batteries is determined in two ways: by a tester using the battery current for calculations and computationally using the minimum voltage during the loading process. It was found that the state of heath of serviceable working and faulty batteries at the same voltage differs significantly, which can be used to identify the technical condition of starter batteries. The established relationships between the battery voltage and the state of heath in the physical simulation of characteristic faults have scientific novelty. Further research is aimed at developing an algorithm for on-line monitoring of batteries and manufacturing a prototype on-board device for its implementation.
The commercialization of electric vehicles (EVs) demands higher performances of rechargeable batteries. Accurate assessments of state of health (SOH) and remaining useful life (RUL) of batteries are important to indicate battery status and ensure EVs safety. However, the accuracies of existing battery capacity degradation models are not sufficient to describe battery states under the complicated impacts of usage environments. Various operating conditions will make degradation modeling more challenging and difficult, for instance, different discharge rates and discontinuous charge and discharge can influence the capacity degradation tendencies of batteries. To address the above issues, two statistical degradation models are respectively proposed to implement battery prognostics in different usage conditions based on the knowledge of big data and data science. Results show that the proposed methods outperform many existing works.
Predicting the battery lifetime at its early stage is a promising technology for accelerating the battery development, production, and design optimization. However, it is a challenging task for most existing prediction methods because information is too limited in early life cycles, and the early-cycle capacity data exhibits a weak correlation with the target battery lifetime. In this paper, to realize an accurate battery lifetime prediction via data obtained from just first few life cycles, we propose a three-stage deep learning framework. First, we develop an emerging two-channel data feature engineering process, which jointly consider a convolutional neural network based latent feature extraction and domain knowledge based handcrafted features. Next, a wrapper feature selection method is adopted to further compress the dimension of developed features via eliminating linearly correlated ones. Finally, processed data features are fed into a data-driven model to realize the early-stage battery lifetime prediction. Results of computational experiments show that our proposed joint consideration of machine-learned features and handcrafted features can improve early-stage battery lifetime predictions via comparing with state-of-the-art benchmarks. We also show that the proposed framework can be generalized to batteries cycled under different operation conditions.
This paper presents a novel real-time hybrid battery state of charge (SoC) and state of health (SoH) estimation technique with less computational effort for optimal operation in renewable energy integrated microgrid applications. The proposed SoC estimation technique utilizes battery terminal voltage and current information along with stress factors like battery charge–discharge rates and temperature effects to accurately estimate the SoC. In addition, it considers the open-circuit voltage (OCV) and SoC relation to dynamically recalibrate the SoC during idle conditions. The proposed SoH estimation technique uses a modified coulomb counting method and variation of battery capacity at different charge–discharge rates to precisely estimate the SoH of the battery. Simulation studies are carried out by considering the aging factor, temperature effect, and charge–discharge rates to analyze the performance of the proposed techniques under various dynamic conditions. A LabVIEW-based application is developed, and experimental verification in terms of estimation accuracy, real-time monitoring is carried out to verify the efficacy of the proposed technique. A comparative analysis with state-of-the-art estimation techniques is presented for validating the effectiveness and usefulness in real-time applications.
Due to the inevitable degradation of Lithium-ion batteries (LIBs) during its lifetime, remaining useful life (RUL) prediction methods are adopted for ensuring the stable and safety operation of electrical equipment. To make up the deficiencies of single model-based or data-driven prediction approach, this paper proposes a new hybrid framework using a weight optimization unscented Kalman filter (WOUKF) and attention based bi-directional long short-term memory (BiLSTM-AM). To be specific, firstly, a Fourier model is proposed to describe the degradation of LIBs instead of the traditional double exponential model. Then, a WOUKF algorithm is designed to identify the model parameters efficiently even in the case of poor initialization. Next, the trends of prediction residual between the WOUKF and the true capacity is established by BiLSTM-AM, which is fed back to the WOUKF for updating model parameters during the prediction period. In addition, an error compensation scheme is developed to further improve prediction performance. Finally, the effectiveness of the proposed prognosis framework is verified on two battery datasets. The simulation results show that the proposed method has better prediction accuracy. The maximum root mean squared error (RMSE) and mean absolute error (MAE) of the proposed hybrid framework are 3.5% and 3%, respectively.
The grid-scale battery energy storage system (BESS) plays an important role in improving power system operation performance and promoting renewable energy integration. However, operation safety and system maintenance have been considered as significant challenges for grid-scale use of BESS. Remaining useful life (RUL) is a useful indicator of the health condition of batteries but it is especially difficult to estimate because it is dependent on many monitoring quantities from BESS. This work presents a data-driven approach that is able to fully utilize BESS monitoring data obtained from the battery management system (BMS) in order to provide an accurate and robust estimation of RUL for each individual battery cells inside a BESS. Based on raw data from historical cycling records, the proposed approach employs elastic net regression to extract characteristic features from both primary and secondary data; a back-propagation neural network based model is then established to build the relationship of refined features and the resultant RUL. The effectiveness of the RUL predictor model is verified using a large-scale data set from real-world lithium-ion battery cells and expected to be applicable to practical grid-scale BESS.
Battery systems are becoming an increasingly attractive alternative for powering ocean going ships, and the number of fully electric or hybrid ships relying on battery power for propulsion and manoeuvring is growing. In order to ensure the safety of such electric ships, it is of paramount importance to monitor the available energy that can be stored in the batteries, and classification societies typically require that the state of health of the batteries can be verified by independent tests — annual capacity tests. However, this paper discusses data-driven state of health modelling for maritime battery systems based on operational sensor data collected from the batteries as an alternative approach. Thus, this paper presents a comprehensive review of different data-driven approaches to state of health modelling, and aims at giving an overview of current state of the art. More than 300 papers have been reviewed, most of which are referred to in this paper. Moreover, some reflections and discussions on what types of approaches can be suitable for modelling and independent verification of state of health for maritime battery systems are presented.
This chapter looks at popular rechargeable battery chemistries as dynamic systems. A more detailed discussion on each chemistry with aspects of electrochemistry is presented without dwelling into mathematical details, but with the idea of presenting more detailed equivalent circuits useful in an electronic engineer's viewpoint. Nyquist plots for battery equivalent circuits are discussed in each case to depict the frequency behavior of the battery and battery test procedures to estimate the simplified equivalent circuits useful to predict the state of charge and state of health are also discussed from an application’s viewpoint. A summary of battery communication standards and battery safety standards is also included.
Accurately predicting the lifetime of lithium-ion batteries in early cycles is crucial for ensuring the safety and reliability, and accelerating the battery development cycle. However, most of existing studies presented poor prediction results for early prediction, due to the nonlinear battery capacity fade with negligible variation in early cycles. In this paper, to achieve an accurate early-cycle prediction of battery lifetime, a comprehensive machine learning (ML) based framework containing three modules, the feature extraction, feature selection, and machine learning based prediction, is proposed. First, by analysing the evolution pattern of various informative parameters, forty-two features are manually crafted based on the first-100-cycle charge-discharge raw data. Second, to manage feature irrelevancy and redundancy, four typical feature selection methods are adopted to generate an optimal lower-dimensional feature subset. Finally, the selected features are fed into six representative ML models to effectively predict the battery lifetime. Numerical experiments and paired t-test are conducted to statistically evaluate the performance of the proposed framework. Results show that the wrapper-based feature selection method outperforms other methods, and significantly improves the prediction performance of subsequent ML models. Both before and after wrapper feature selection, the elastic net, Gaussian process regression, and support vector machine present better performance than other complex ML prediction models. The support vector machine model combined with wrapper feature selection statistically presents the best result for battery lifetime prediction, with a root-of-mean-square-error of 115 cycles, and a R² of 0.90. Finally, when compared with an existing work, the root-of-mean-square-error is substantially decreased from 173 to 115 cycles, by using the proposed framework.
Lithium-ion batteries have become an integral parts of our lives, and it is important to find a reliable and accurate long-term prognostic scheme to supervise the performance degradation and predict the remaining useful life of batteries. In the perspective of information fusion methodology, an interacting multiple model framework with particle filter and support vector regression is developed to realize multi-step-ahead estimation of the capacity and remaining useful life for batteries. During the multi-step-ahead prediction period, the support vector regression model with sliding windows is used to compensate the future measurements online. Thus, the interacting multiple model with particle filter can relocate the particles and update the capacity estimation. The probability distribution of the remaining useful life is also obtained. Finally, the proposed method is compared and validated with particle filter model using the benchmark data. The experimental results prove that the proposed model yields stable forecasting performance and narrows the uncertainty in remaining useful life estimation.
This thesis describes the subject of Battery Management Systems (BMS), in particular the design of BMS with the aid of simulation models.
Prognostics is an emerging concept in condition based maintenance (CBM) of critical systems. Along with developing the fundamentals of being able to confidently predict Remaining Useful Life (RUL), the technology calls for fielded applications as it inches towards maturation. This requires a stringent performance evaluation so that the significance of the concept can be fully exploited. Currently, prognostics concepts lack standard definitions and suffer from ambiguous and inconsistent interpretations. This lack of standards is in part due to the varied end-user requirements for different applications, time scales, available information, domain dynamics, etc. to name a few issues. Instead, the research community has used a variety of metrics based largely on convenience with respect to their respective requirements. Very little attention has been focused on establishing a common ground to compare different efforts. This paper surveys the metrics that are already used for prognostics in a variety of domains including medicine, nuclear, automotive, aerospace, and electronics. It also considers other domains that involve prediction-related tasks, such as weather and finance. Differences and similarities between these domains and health maintenance have been analyzed to help understand what performance evaluation methods may or may not be borrowed. Further, these metrics have been categorized in several ways that may be useful in deciding upon a suitable subset for a specific application. Some important prognostic concepts have been defined using a notational framework that enables interpretation of different metrics coherently. Last, but not the least, a list of metrics has been suggested to assess critical aspects of RUL predictions before they are fielded in real applications.
In a ubiquitous environment, intellignet functions are embedded in objects around the user, thus enabling him to create variuos functionalities by combining those objects. The collaborative robotic environments are a new approach to the coordination of multirobot systems which usually consist of numerous, relatively simple, small sized robots. The CORE-TX system (COllaborative Robotic Environment - the Timisoara eXperiment) is conceived as a complex platform composed by a heterogeneous set of autonomous microsystems with embedded intelligence, a collaborative communication environment and a central entity with supervising functions. This paper describes the general architecture of the CORE-TX system, the system model; this paper also contains a brief comparison bBetween CORE-TX and state-of-the-art collaborative environments.
From the early days of its discovery, humanity has depended on electricity, a phenomenon without which our technological advancements would not have been possible. With the increased need for mobility, people moved to portable power storage—first for wheeled applications, then for portable and finally nowadays wearable use. Several types of rechargeable battery systems, including those of lead–acid, nickel–cadmium, nickel–metal hydride, lithium ion and lithium-ion polymer exist in the market. The most important of them will be discussed in this review. Almost as long as rechargeable batteries have existed, systems able to give an indication about the state-of-charge (SoC) of a battery have been around. Several methods, including those of direct measurements, book-keeping and adaptive systems (Bergveld et al 2002 Battery Management Systems, Design by Modelling (Philips Research Book Series) vol 1 (Boston: Kluwer)) are known in the art for determining the SoC of a cell or battery of cells. An accurate SoC determination method and an understandable and reliable SoC display to the user will improve the performance and reliability, and will ultimately lengthen the lifetime of the battery. However, many examples of poor accuracy and reliability can be found in practice (Bergveld et al 2002, cited above). This review presents an overview on battery technology and the state-of-the-art of SoC methods. The goal of all the presented SoC indication methods is to design an SoC indication system capable of providing an accurate SoC indication under all realistic user conditions, including those of spread—in both battery and user behaviour, a large temperature and current range and ageing of the battery. Figure 1. General architecture of a battery-management system [1].
In most applications, wireless sensor networks (WSNs) will deploy a large number of distributed sensor nodes in remote or inhospitable places, making batteries their main source of energy; thus, the stored energy is a key resource of a WSN. Sensor nodes should balance their limited resources to increase the lifetime of the network. The knowledge of the available amount of energy becomes an important requirement for the maintenance, implementation of self-management techniques, and viability of the WSN. Therefore, the research of the state-of-charge (SoC), or the remaining capacity estimation, is of key importance. This paper presents an energy-efficient battery-remaining capacity-estimation technique. The experiments were conducted using the MICA2 wireless sensor node platform, which shows that the voltage-only-based estimation presented an available 18% of the battery maximum capacity, although the battery had been fully discharged, and a current-based estimation technique is presented with minimal hardware intervention.
This paper explores how the remaining useful life (RUL) can be assessed for complex systems whose internal state variables are either inaccessible to sensors or hard to measure under operational conditions. Consequently, inference and estimation techniques need to be applied on indirect measurements, anticipated operational conditions, and historical data for which a Bayesian statistical approach is suitable. Models of electrochemical processes in the form of equivalent electric circuit parameters were combined with statistical models of state transitions, aging processes, and measurement fidelity in a formal framework. Relevance vector machines (RVMs) and several different particle filters (PFs) are examined for remaining life prediction and for providing uncertainty bounds. Results are shown on battery data.
The problem considered in this paper is the analysis of a battery State-of-Charge estimation algorithm: in particular the mix estimation algorithm. This algorithm provides the estimation mixing two estimation approaches: namely the Coulomb-Counting and the Model-Based. The mix algorithm is qualitatively able to provide a more robust and accurate estimation with respect to the estimation provided by the approaches the algorithm mixes together. The aim of this paper is to analyze the differences between the three algorithms and the advantages produced by the mixing procedure. In particular the paper presents the comparison of the mix algorithm behavior with the Coulomb-Counting and the Model-Based behaviors in case of measure errors.
This paper presents the implementation of a fully digital smart charging system for traction lead-acid batteries for electric vehicles (EV). The charger uses a fast charge strategy, through the combination of constant high current charge periods with pulsant current charge periods. This charge system takes into account the actual battery charge state as well as the battery record, referred to previous charges and discharges. To obtain this smart charging system, it is necessary to develop a fast battery charge control system, a power source and a battery data acquisition system to periodically store the most important battery parameters during the discharge process. The fast charging of electric vehicle batteries promises a technically feasible approach to increase customer acceptability of electric vehicles (EV), and its main objective includes short recharge times, high charge efficiencies and improved battery cycle life. The aim of the present work is the development of a smart charging strategy integrated with a digital data acquisition system implemented with a XC40010E FPGA from Xilinx.
An effective fast-charger for NiCd/NiMH batteries used in portable
applications must be able to estimate previous battery charge state and
to make decisions to minimize charging time without negative effects on
battery-life. In this paper, some new methods for estimating and making
decisions are presented. Due to these methods can be easily implemented,
effective and universal battery fast-chargers can be obtained by using
few and inexpensive components
This paper addresses the problem of the predictability of the critical digital-signal acquisition and processing applications while interacting with signals. The hard real-time compact kernel (HARETICK) is briefly presented along with the model of the hard real-time tasks: the ModX. This paper focuses on the specification, analysis, scheduling, and implementation of the applications able to generate perfectly periodic signals on the HARETICK-based platforms. A specific nonpreemptive technique for scheduling a set of the ModXs with fixed-execution times during their periods-the fixed execution nonpreemptive (FENP) algorithm-was introduced. Some of the most interesting experimental results are also discussed.
Recent advances of sensor technologies have been powered by high-speed and low-cost electronic circuits, novel signal processing methods, and advanced manufacturing technologies. The synergetic interaction of new developments in these fields provides promising technical solutions increasing the quality, reliability, and economic efficiency of technical products. With selected examples, we will give an overview about the significant developments of methods, structures, manufacturing technologies, and signal processing characterizing today's sensors and sensor systems. Predominantly observed development trends in the future are discussed.
This chapter gives general information on Battery Management Systems (BMS) required as a background in later chapters. Section 2.1 starts with the factors that determine the complexity of a BMS and shows a general block diagram. The function of each part in a BMS is discussed in more detail in section 2.2 and examples of adding BMS intelligence are given. The BMS aspects of two types of portable devices are discussed in section 2.3. This serves to illustrate the theory presented in sections 2.1 and 2.2.
Collections of batteries are used to supply energy to a variety of applications. By utilizing the energy in such a collection efficiently, we can improve the lifetime over which energy can be supplied to the application. We say that the discharge of a collection of batteries is coordinated when, at the end of discharge, the difference in the remaining capacity of individual batteries is small. This paper presents a decision-maker based on a goal-seeking formulation that coordinates the discharge of a collection of batteries. This formulation allows us to use a simple battery model and simple decision-making algorithms. We present results from MATLAB simulations that demonstrate the performance of the decision-maker when energy is drawn out of the collection in three different discharge scenarios. The new decision-maker consistently improves the discharge efficiency obtained using scheduling methods. Our results show that when the discharge is coordinated, the lifetime of the collection is extended.
In last years, NiCd and NiMH technologies have settled in the market of medium/high capacity batteries. Fast-charge is very interesting in different applications (communications systems, electric vehicles, low-earth-orbit spacecrafts) in order to minimizing charge time. The problem is that high currents involved in fast-charge process affect battery behavior modifying parameters as battery charge acceptance. Moreover, battery temperature increase and gasses production related to overcharging make necessary to detect accurately the end of fast-charge to avoid battery damage. Limited information about results of charging process at different charging rates (i.e. time reduction versus charge efficiency) makes difficult to select the optimum rate in each specific application. The lack of information is more problematic in NiMH batteries due to they can not support as well as NiCd batteries extreme working conditions. For these reasons, an intensive study about NiCd and NiMH battery behavior under different charging rates was developed. In this paper, the effects of fast-charging on medium/high capacity NiCd and NiMH batteries are shown. In this way, conclusions about the application range of fast-charging in both technologies are drawn
This paper studies the problem of data communication protocols for multiprocessor smart sensors and embedded applications with hard real-time (HRT) or critical requirements. We propose a time-triggered communication interface and set of protocols, called Predictable ARchitecture for Sensor Communication Systems (PARSECS), specifically designed to sustain, at low costs and complexity, the predictable operation of such HRT systems. The general interface architecture, data format, and communication protocols are discussed, along with a case study-the implementation of PARSECS on the full-duplex serial peripheral interface for the COllaborative Robotic Environment-the Timisoara eXperiment (CORE-TX) smart sensors platform. Its predictability, timeliness, and overall performance evaluation and validation are presented in detail based on experimental results and measurements. A comparative study with some of the most prominent systems in the field is also provided.
Performing accurate average current drain measurements of digital programmable components (e.g., microcontrollers, digital signal processors, System-on-Chip, or wireless modules) is a critical and error-prone measurement problem for embedded system manufacturers due to the impulsive time-varying behavior of the current waveforms drawn from a battery in real operating conditions. In this paper, the uncertainty contributions affecting the average current measurements when using a simple and inexpensive digital multimeter are analyzed in depth. Also, a criterion to keep the standard measurement uncertainty below a given threshold is provided. The theoretical analysis is validated by means of meaningful experimental results
A new SoC estimation algorithm is presented. The estimation is performed mixing the Coulomb-Counting and the Model-Based estimation approaches. The algorithm was tested on a brand new lithium-ion cell which use a nanoscale technology able to grant a more powerful and safer lithium-ion battery than the batteries currently on the market. The cell, called A123-M1, was first characterized and then identified. The cell was identified with a equivalent electrical circuits model which is usually called 2<sup>th</sup> Randle model: the used test-bench and the identification process are described. Then the SoC estimation problem is described. A briefly review of the state of art of SoC estimation is given and the drawbacks of the methods which are usually used are presented. Finally the new mixed estimation algorithm is described: the algorithm scheme and the results are shown.
In this paper, a new fast charger is presented for Ni-Cd and Ni-MH batteries, which are the most frequently used in portable applications. In this charger, the control and supervision of the process has been entrusted to a microcontroller, which provides a powerful and intelligent tool to undertake complex tasks, and reduces the requested circuitry to the microcontroller itself and a few additional components. The resulting charger is able to work out the initial battery state (detecting deteriorated devices), decide the suitable way to charge it (ensuring a long cyclic life), and determine when the charge process must be finished. This way, the state of the battery is always controlled, preventing any damage to it and providing a fully protected operation mode. This paper summarizes the design and construction of the presented charger, as well as shows the experimental results obtained in the prototype tests.
This paper presents the design and implementation of a micro power battery state of charge monitor. The novelty of this design lies in the extreme low power consumption and accurate prediction of the battery reserve time. The average current drawn is successfully limited to 60 muA, which is significantly smaller than those in currently available devices. The proposed design predicts the reserved battery charge with an accuracy of 5% under different discharge conditions. The automated learning scheme using software is utilized. Protection of the battery against excessive current drain and usage outside the specified temperature range is incorporated. This paper details the proposed technique adopted for power reduction. The battery temperature and current sensing circuits are normally in power down mode, they go into active mode for the microcontroller to take measurements. An ON/OFF ratio of 1:153 is achieved which results in power reduction by a factor of 59.30. The average current requirement of the proposed design is reduced from 3302.79 muA to 55.69 muA with the adoption of power reduction approach. The proposed design has been tested on a NiMH, NiCd and Li-Ion battery packs and the experimental results confirm the utility of the proposed design .
This paper presents the design and development of a novel battery charger for simultaneous charging of two NiMH/NiCd batteries. The design supports fast charging of batteries with different configuration and capacity. The charger periodically monitors the battery parameters; voltage, current and temperature; calculates the voltage and temperature gradient; for precise measurement and control of battery charging. The proposed design provides multiple charging current options with automated selection of optimum charging rate for the battery being charged. The novelty of this design lies in accurate procedure adopted to end the fast charging cycle; this ensures complete, fast and safe charging of the battery pack. The design is implemented using an 8-bit microcontroller which controls and monitors the charging operation. The battery status and parameters are displayed on an LCD. Reentrant procedures are adopted in software writing that enable common software to control multiple batteries simultaneously. The proposed design is implemented and tested on NiMH and NiCd battery packs, which confirm the utility of the proposed design.
Performing accurate average current drain measurements of digital programmable components (e.g., microcontrollers, digital signal processors, System-on-Chip, or wireless modules) is a critical and error-prone measurement problem for embedded system manufacturers due to the impulsive time-varying behavior of the current waveforms drawn from a battery in real operating conditions. In this paper, the uncertainty contributions affecting the average current measurements when using a simple and inexpensive digital multimeter are analyzed in depth. Also, a criterion to keep the standard measurement uncertainty below a given threshold is provided. The theoretical analysis is validated by means of meaningful experimental results
In this paper, we propose an efficient implementation of support vector machines (SVMs) on a low-power and low-cost 8-bit microcontroller. The proposed solution can be advantageously used to implement smart sensors and sensor networks for intelligent data analysis and pervasive computing. A new model selection algorithm that allows fitting the resource constraints imposed by the hardware architecture is proposed. Moreover, the performance of an optimized implementation which exploits the CORDIC algorithm is detailed and discussed
An aging model of Ni-MH batteries for use in hybridelectric vehicles
- R H Somogye
R. H. Somogye, "An aging model of Ni-MH batteries for use in hybridelectric vehicles", MSc. Thesis, Department of Electrical Engineering,
Ohio State University, USA, 2004.
Coordinated discharge of a collection of batteries
- S Sastry
- O Gimdogmus
- T Hartley
- R Viellette
CORE-TX: Collective Robotic Environment -the Timisoara Experiment
- R D Cioarga
- M V Micea
- B Ciubotaru
- D Chiuciudean
- D Stanescu
R. D. Cioarga, M. V. Micea, B. Ciubotaru, D. Chiuciudean, D.
Stanescu, "CORE-TX: Collective Robotic Environment -the Timisoara
Experiment", in Proc. 3rd Romanian-Hungarian Joint Symp. Applied
Computational Intellig., SACI 2006, Timisoara, Romania, pp.
(495−506), May 2006.
Battery Referece Book
- R T Crompton
R. T. Crompton, "Battery Referece Book", 3rd Edition, Reed
Educational and Professional Publishing, Ltd., 2000.
LPC2131/2/4/6/8 User manual
- Philips Semiconductors
Philips Semiconductors, "LPC2131/2/4/6/8 User manual", Rev. 02,
Phillips Semiconductors, Jul. 2006.