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SOC estimation for Li-Ion batteries based on equivalent circuit diagrams and the application of a Kalman filter
Abstract
Batteries play an essential role in electric vehicles (EV), and obtain more and more importance also in smart grids due to the non-constant power generation of renewable energy sources. In order to achieve an optimum operation of systems with batteries it is necessary to develop accurate mathematical models for the calculation of the state of charge (SOC), taking into account the individual operation by the user. This paper presents the fundamentals of a method how to determine SOC of lithiumion batteries on the basis of two different equivalent circuit diagrams and an Extended Kalman Filter (EKF). The comparison between measurement and computation results shows a good accordance. The accurate determination of SOC of a battery in an EV is of high importance for the prediction of the distance that can be driven. In the first step the dependency of these parameters on the temperature and on the battery age is neglected.
... Although PF and UPF can obtain better results, the computational complexity is large. To overcome this difficulty, Rahmoun et al. [18] used extended kalman filter (EKF) to estimate SOC based on the first-order ECM and the second-order ECM respectively. The experimental results show that the SOC estimated based on the second-order ECM has better results than the first-order ECM. ...
... where x(k∕k − 1) and x(k∕k) denote the priori estimate and posteriori estimate of the state vector, K g is Kalman gain, Q and R are the noise covariance matrix. KF algorithm mainly consists of two stages: First, the first two equations of Equation (18) are used to complete the state prediction, and the estimated state vector x (k − 1/ k − 1) and covariance matrix P (k − 1 / k − 1) at k − 1 time are used to update x (k / k − 1) and P (k / k − 1) at k − 1 time, respectively. Then, the last three equations of Equation (18) are used to update the state, and y (k) is used to update the a posteriori estimation to complete the x (k/k) and P (k/k) estimation. ...
... KF algorithm mainly consists of two stages: First, the first two equations of Equation (18) are used to complete the state prediction, and the estimated state vector x (k − 1/ k − 1) and covariance matrix P (k − 1 / k − 1) at k − 1 time are used to update x (k / k − 1) and P (k / k − 1) at k − 1 time, respectively. Then, the last three equations of Equation (18) are used to update the state, and y (k) is used to update the a posteriori estimation to complete the x (k/k) and P (k/k) estimation. Based on the above analysis, the main steps are as follows: ...
Abstract Lithium‐ion batteries (LIBs) are widely used in electric vehicles because of their high energy density and less pollution. As an important parameter of the battery management system, accurate estimation of the state of charge (SOC) of the battery can ensure the energy distribution and safe use of the battery. This paper obtains better estimation accuracy from four aspects. First, the battery model is established via Thevenin equivalent circuit model, and the parameters are identified by the forgetting factor recursive least squares. Second, the influence of dual extended Kalman filter on SOC estimation is analysed, a novel algorithm‐based improved dual Kalman filter is proposed. Besides, to reduce the influence of the system noise on the estimation results, an adaptive intelligent algorithm is applied to promote the accuracy of SOC estimation. Finally, compared with the estimated SOC results of the traditional algorithm, the experimental results show the effectiveness of the algorithm.
... At present, the ECM has been widely used in the BMS of EVs. Although the main disadvantage is their computational burden, the method based on the Kalman filter (KF) is proved to be an effective algorithm to improve the accuracy of SOC estimation [19,20,25,26]. KF is widely used in state estimation and has achieved better results in aerospace, military, and other fields [21][22][23][24]. ...
... Ye et al. [19] used the particle swarm optimization to optimize the EKF to estimate SOC and the errors less than 3%. Rahmoun et al. [20] used EKF to estimate SOC based on the first-order ECM and the second-order ECM, respectively. The experimental results show that the SOC estimated based on the second-order ECM has better results than the first-order ECM. ...
... The ECM has many advantages, such as low computational complexity, simple parameter identification, and good electrochemical characteristics, which have been widely used in BMS. According to Ref. [20], the Thevenin model has been widely used in SOC estimation due to its simple structure and good dynamic adaptability. Therefore, the Thevenin model is applied in this paper is shown in Fig. 1. ...
Accurate estimation of the state of charge (SOC) of batteries is very important for real-time monitoring and safety control of electric vehicles. Four aspects of efforts are applied to promote the accuracy of SOC estimation. Firstly, the state-space equation of the battery model based on the Thevenin model is established and the parameters of the model are identified by the forgetting factor recursive least square method. Secondly, aiming at the nonlinear relationship between the open-circuit voltage (OCV) and SOC, the least square support vector machine is proposed to establish the mapping relationship between OCV and SOC. Thirdly, the influence of fitting accuracy of the OCV-SOC curve on SOC estimation is analyzed. Based on this, an error model is proposed, and a joint estimator using an adaptive unscented Kalman filter algorithm combining the error model is proposed. Finally, compared with the estimated SOC results of the traditional SOC estimation method, the experimental results show that the proposed model has better estimation ability and robustness.
... In the bode plot, magnitude and phase as a function of frequency can be observed. Based on the previous discussion and in order to select models, we first have to analyze the general behavior of output voltage during charge and discharge as can be seen from the literature [20][21][22][23]. ...
... The reaction voltage, IR drop during discharging, and diffusion voltage when the discharging turns off are the factors that need to be considered while selecting the appropriate circuit-based model of the battery. Based on the previous discussion and in order to select models, we first have to analyze the general behavior of output voltage during charge and discharge as can be seen from the literature [20][21][22][23]. ...
Over the last few decades, lithium-ion batteries have grown in importance for the use of many portable devices and vehicular applications. It has been seen that their life expectancy is much more effective if the required conditions are met. In one of the required conditions, accurately estimating the battery’s state of charge (SOC) is one of the important factors. The purpose of this research paper is to implement the probabilistic filter algorithms for SOC estimation; however, there are challenges associated with that. Generally, for the battery to be effective the Bayesian estimation algorithms are required, which are recursively updating the probability density function of the system states. To address the challenges associated with SOC estimation, the research paper goes further into the functions of the extended Kalman filter (EKF) and sigma point Kalman filter (SPKF). The function of both of these filters will be able to provide an accurate estimation. Further studies are required for these filters’ performance, robustness, and computational complexity. For example, some filters might be accurate, might not be robust, and/or not implementable on a simple microcontroller in a vehicle’s battery management system (BMS). A comparison is made between the EKF and SPKF by running simulations in MATLAB. It is found that the SPKF has an obvious advantage over the EKF in state estimation. Within the SPKF, the sub-filter, the central difference Kalman filter (CDKF), can be considered as an alternative to the EKF for state estimation in battery management systems for electric vehicles. However, there are implications to this which include the compromise of computational complexity in which a more sophisticated micro-controller is required.
... The state space (SS) description for the SOC corresponds to an empirical model based on the reduction of the battery pack operation into an equivalent circuit (Pola et al., 2015;Rahmoun, Biechl, & Rosin, 2012). It is discrete in time and has only two states, x 1 (k), an unknown model parameter, and x 2 (k), the SOC. ...
Recent developments in lithium-ion technology have enabled a revolution in the automotive industry. Fully electric vehicles (EVs) operate under distinctly variable conditions, requiring high-voltage battery packs to meet their torque/power demands. Our goal is to provide a simulation engine which, for a given battery pack size, determines when recharging or battery pack replacement are needed. To that end, we study both the State-of-Charge (SOC) and the State-of-Health (SOH) indicators, using discrete state space models for both. Predictions are based on a probabilistic characterization of EV usage profiles, which in turn are a function of generic user-input, such as mission maps, vehicle mechanical characteristics,driving schedules, and battery pack configuration. State space models benefit from the incorporation of metamodels for the ohmic internal resistance and the Coulomb efficiency of the pack. Both meta-models i) effectively introduce additional phenomenology –such as dependency onthe magnitude of discharged current and depth of discharge (DoD)–, and ii) provide a link between SOC/SOH and how each discharge cycle affects the health status of the battery pack as a whole. The approach for the simulation engine presented here is stochastic in nature, meaning that prognostics for the SOC and SOH are generated in a particle filter-based scheme. Thus risk and confidence intervals can be obtained for the end-of-discharge and end-of-life respectively
... The state-of-the-art algorithms for SoC estimation have been discussed. The algorithm takes Generic Random probabilistic approach into consideration to estimate the dynamically changing states of Li-ion battery when subjected to various charge discharge cycles [6][7]. The state space equations are formed with the help of mathematical model obtained from implementation of Enhanced Selfcorrecting model which replicates the dynamics of battery to a better extent. ...
Electric vehicle adoption is being promoted as a major counteract to mitigate vehicular pollution. This calls for improvement of technologies to enhance the reliability and safety of electric vehicles (EV). The battery pack of an EV being its major component needs to be monitored properly in every charge-discharge cycle as it is subjected to different drive profiles in terms of current flowing through them. Battery Management System (BMS) considers the dynamic parameters of the pack and performs various functionalities which are helpful for the safekeeping of vehicles as well as users. State of Charge (SoC) estimation is an important function that helps BMS to monitor deterioration in charge available in cell. This paper implements the state of art techniques on an accurate Enhanced Self-Correcting model of Li-ion cell which considers dynamics and hysteresis profile observed in cell subjected to drive cycles. This paper describes three approaches i.e. EKF, SPKF and Bar delta algorithms for SoC estimation. Case studies and comparison of all the techniques are presented.
... where x is the state vector, A is the state transfer matrix, u is the state control vector, B is the control variable matrix, y is the measurement vector, H is the transformation matrix from the state vector to the measurement vector, w and v are both noises obeying a Gaussian distribution, and P is the covariance matrix. Extended Kalman Filter (EKF): KF is somehow limited to linear systems [134]. In ref. [135], the researcher linearizes the non-linear OCV-SOC curves into seven segments to meet the requirements of KF for a linear system model. ...
With the widespread use of Lithium-ion (Li-ion) batteries in Electric Vehicles (EVs), Hybrid EVs and Renewable Energy Systems (RESs), much attention has been given to Battery Management System (BMSs). By monitoring the terminal voltage, current and temperature, BMS can evaluate the status of the Li-ion batteries and manage the operation of cells in a battery pack, which is fundamental for the high efficiency operation of EVs and smart grids. Battery capacity estimation is one of the key functions in the BMS, and battery capacity indicates the maximum storage capability of a battery which is essential for the battery State-of-Charge (SOC) estimation and lifespan management. This paper mainly focusses on a review of capacity estimation methods for BMS in EVs and RES and provides practical and feasible advice for capacity estimation with onboard BMSs. In this work, the mechanisms of Li-ion batteries capacity degradation are analyzed first, and then the recent processes for capacity estimation in BMSs are reviewed, including the direct measurement method, analysis-based method, SOC-based method and data-driven method. After a comprehensive review and comparison, the future prospective of onboard capacity estimation is also discussed. This paper aims to help design and choose a suitable capacity estimation method for BMS application, which can benefit the lifespan management of Li-ion batteries in EVs and RESs.
... Its target is to obtain accurate information from inaccurate data. This method can be utilized to calculate the SOC in real-time by using the terminal current and voltage measurements [21][22][23]. It is suitable for the SOC estimation of EVs in which the battery current is unstable [24]. ...
Currently, Lithium-ion batteries (LiB) are widely applied in energy storage devices in smart grids and electric vehicles. The state of charge (SOC) is an indication of the available battery capacity, and is one of the most important factors that should be monitored to optimize LiB's performance and improve its lifetime. However, because the SOC relies on many nonlinear factors, it is difficult to estimate accurately. This paper presented the design of an effective SOC estimation method for a LiB pack Battery Management System (BMS) based on Kalman Filter (KF) and Artificial Neural Network (ANN). First, considering the configuration and specifications of the BMS and LiB pack, an ANN was constructed for the SOC estimation, and then the ANN was trained and tested using the Google TensorFlow open-source library. An SOC estimation model based on the extended KF (EKF) and a Thevenin battery model was developed. Then, we proposed a combined mode EKF-ANN that integrates the estimation of the EKF into the ANN. Both methods were evaluated through experiments conducted on a real LiB pack. As a result, the ANN and KF methods showed maximum errors of 2.6% and 2.8%, but the EKF-ANN method showed better performance with less than 1% error.
... The online identification of model parameters is based on recursive filtering methods. The literature provides several examples for this parameters identification, such as Kalman Filter (KF) [24], [25] Extended Kalman (EKF) [26]- [28] Unscented Kalman filter (UKF) [28]- [31] Recursive Least Square algorithm [32] and so on. In [3], Ettihir shows that the RLS filter is well suited for online identification a FC model. ...
... where f [ x ( t )] represents the relationship between OCV and SOC. The definitions of A, B, C and D are given in Eq.(22) : ...
Accurate estimation of the battery state of charge (SOC) is of great significance for enhancing its service life and safety. In this study, based on the fractional-order equivalent circuit model of lithium-ion battery, the SOC estimation methods using dual Kalman filter (DKF) and dual extended Kalman filter (DEKF) are simulated and compared, in terms of model accuracy and SOC estimation accuracy. Then, combining the advantages of the DKF and DEKF algorithms, an SOC estimation algorithm based on adaptive double Kalman filter is proposed. This algorithm uses the recursive least squares (RLS) method to update the battery model parameters online in real time, and employs the DKF algorithm to filter the SOC twice to reduce the interferences from the battery model error and the current measurement error. In the experimental studies, the measured SOC values are compared with the estimated SOC values produced by the proposed algorithm. The comparison results show that SOC estimation error of the proposed algorithm is within the range of ±0.01 under most test conditions, and it can automatically correct SOC to true value in the presence of system errors. Thus, the validity, accuracy, robustness and adaptability of the proposed algorithm under different operation conditions are verified.
... The state space (SS) description for the SOC corresponds to an empirical model based on the reduction of the battery pack operation into an equivalent circuit (Pola et al., 2015;Rahmoun, Biechl, & Rosin, 2012). It is discrete in time and has only two states, x 1 (k), an unknown model parameter, and x 2 (k), the SOC. ...
Recent developments in lithium-ion technology have enabled a revolution in the automotive industry. Fully electric vehicles (EVs) operate under distinctly variable conditions, requiring high-voltage battery packs to meet their torque/power demands. Our goal is to provide a simulation engine which, for a given battery pack size, determines when recharging or battery pack replacement are needed. To that end, we study both the State-of-Charge (SOC) and the State-of-Health (SOH) indicators, using discrete state space models for both. Predictions are based on a probabilistic characterization of EV usage profiles, which in turn are a function of generic user-input, such as mission maps, vehicle mechanical characteristics , driving schedules, and battery pack configuration. State space models benefit from the incorporation of meta-models for the ohmic internal resistance and the Coulomb efficiency of the pack. Both meta-models i) effectively introduce additional phenomenology –such as dependency on the magnitude of discharged current and depth of discharge (DoD)–, and ii) provide a link between SOC/SOH and how each discharge cycle affects the health status of the battery pack as a whole. The approach for the simulation engine presented here is stochastic in nature, meaning that prognostics for the SOC and SOH are generated in a particle filter-based scheme. Thus risk and confidence intervals can be obtained for the end-of-discharge and end-of-life respectively
... In [91,92], NN; in [93], adaptive wavelet neural network (AWNN); and in [94], Elman neural network (ENN) methods are proposed to estimate the SOC of lithium ion batteries. Many researchers pay attention to KF [95,96]; and its derivatives broadly such as series Kalman filter (SKF) [97]; EKF [10,[98][99][100][101][102][103][104][105][106][107][108]; improved extended Kalman filter (IEKF) [109]; AEKF [110][111][112][113]; model adaptive extended Kalman filter (MAEKF) [114]; robust extended Kalman filter (REKF) [115]; multiscale extended Kalman filter (MEKF) [116]; UKF [117][118][119]; adaptive unscented Kalman filter (AUKF) [120,121]; sigma point Kalman filter (SPKF) [122,123] and iterated extended Kalman filter (ITEKF) [124]. In [119], a modified battery equivalent circuit model is designed that contains the impact of different temperatures and current rates on the SOC. ...
In recent years, a significant interest in hybrid and battery electric vehicles has arisen globally due to reducing fuel consumption, mitigating dependence on imported oil and decreasing greenhouse gas emissions. The overall success of these vehicles mostly depends on the performance of sub-systems that they are created. In order to enhance the performances of these sub-systems, estimation of their parameters with high accuracy is required. Furthermore, estimation strategies play an important role in battery management, vehicle energy management and vehicle control by undertaking different tasks. There have been a limited number of review studies related with estimation strategies that are only focused on battery state of charge (SOC) and state of health (SOH) estimation. This paper presents a comprehensive review on various estimation strategies used in hybrid and battery electric vehicles for the first time in the literature. The existing estimation strategies are classified and different methodologies used in each estimation strategy are elaborated. Recent research advances on existing estimation strategies are clearly emphasized by reviewing numerous studies over 200 papers.
... The first advanced type is the extended KF (EKF). It is used in different variations and with various battery models, e.g., in [39], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76], [77], [78], [79], [80], [81], [82], [83], [84], [85], [86], [87], [88], [89], [90], [91], [92], [93], [94], [95], [96], [97], [98], [99], [100], and [101]. ...
Lithium-ion battery packs in hybrid and pure electric vehicles are always equipped with a battery management system (BMS). The BMS consists of hardware and software for battery management including, among others, algorithms determining battery states. The continuous determination of battery states during operation is called battery monitoring. In this paper, the methods for monitoring of the battery state of charge, capacity, impedance parameters, available power, state of health, and remaining useful life are reviewed with the focus on elaboration of their strengths and weaknesses for the use in on-line BMS applications. To this end, more than 350 sources including scientific and technical literature are studied and the respective approaches are classified in various groups.
Battery applications are increasing day by day from a single cell to MWh range in electric vehicles, electric traction, PV systems, grid support, etc. Considering energy density and price, correctly apply, size and analyze batteries for these systems remains a major challenge and requires sufficient battery characteristics. Most battery manufacturers provide limited sets of battery charging & discharging characteristic data, which are not adequate for battery simulation in a system that different types of loads are connected, or different charging scenarios are applied. Therefore, accurate & comprehensive battery modeling is needed. A non-linear open circuit voltage (Voc) model is derived with respect to state of charge (SOC) from battery charge-discharge characteristic curves. For hysteresis, two sets of non-linear Voc data are maintained for charging and discharging. This model is general for major types of batteries and results are acceptable as simulation error is minimal.
The future of mass transportation is clearly moving towards the increased efficiency of hybrid and electric vehicles. Electrical energy storage is a key component in most of these advanced vehicles, with the system complexity and vehicle cost shifting from combustion engines to battery and electric drive systems. To assist engineers and technicians in this transfer, the Battery Application Technology Testing and Energy Research Laboratory (BATTERY) of the Thomas D. Larson Pennsylvania Transportation Institute in the College of Engineering at The Pennsylvania State University partnered with an advanced bus manufacturer to study lithium titanate oxide battery chemistry for use in transit buses. The research team found, other than proprietary data/models, scant technical information or research on electrical and thermal modeling of this advanced chemistry. The research team developed lithium titanate oxide modules to study their characteristic behaviors and produce state-of-charge estimators capable of running on the limited embedded processing power and memory of a typical battery management system. The team also investigated the thermal performance of this chemistry in the large format, producing a physics-based empirical thermal model for use in system-level simulations. This model predicts pack-level thermal behavior by reporting the minimum, maximum, and average temperatures within a system typically used for large automotive applications, as testing was concentrated on transit bus usage profiles. This work supports battery system integration and management. The tools produced are intended to assist automotive engineers to achieve optimal system performance and ultimately a more efficient vehicle.
As an energy storage unit Lithium-ion batteries play an important role in More Electrical Aircraft and accurate knowledge of its behaviour and state of charge (SOC) are required to ensure the safety of the Aircraft Electrical System (AES) and its energy management. This paper details the conception of a 2nd order equivalent electrical circuit (EEC) battery model with SOC, current (magnitude and direction) and temperature dependant parameters. A 50Ah LiFePO4 battery is subjected to Hybrid Pulse Power Characterisation (HPPC) for parameter identification and constant current discharge tests for model validation. The model shows good performance between 100% & 10% SOC with a maximum error of less than ±1% Subsequently, for SOC estimation, the Discrete Simple Kalman Filter (KF) is applied with the Open-Circuit Voltage (OCV) depending linearly on SOC, I and T. Secondly the Discrete Extended KF (EKF) is applied with the model parameters and OCV varying non-linearly with SOC. Their performance is then compared to that of the Coulomb-counting method with the EKF showing good tracking performance: converging to the true SOC given false initial values and the mean SOC estimation error reducing from ±7% with KF to less than ±1% with EKF.
Lithium-ion battery systems employed in high power demanding systems such as electric vehicles require a sophisticated monitoring system to ensure safe and reliable operation. Three major states of the battery are of special interest and need to be constantly monitored, these include: battery state of charge (SoC), battery state of health (capcity fade determination, SoH), and state of function (power fade determination, SoF).
In a series of two papers, we propose a system of algorithms based on a weighted recursive least quadratic squares parameter estimator, that is able to determine the battery impedance and diffusion parameters for accurate state estimation. The functionality was proven on different battery chemistries with different aging conditions.
The first paper investigates the general requirements on BMS for HEV/EV applications. In parallel, the commonly used methods for battery monitoring are reviewed to elaborate their strength and weaknesses in terms of the identified requirements for on-line applications. Special emphasis will be placed on real-time capability and memory optimized code for cost-sensitive industrial or automotive applications in which low-cost microcontrollers must be used. Therefore, a battery model is presented which includes the influence of the Butler–Volmer kinetics on the charge-transfer process. Lastly, the mass transport process inside the battery is modeled in a novel state-space representation.
To improve the use of lithium-ion batteries in electric vehicle (EV) applications, evaluations and comparisons of different equivalent circuit models are presented in this paper. Based on an analysis of the traditional lithium-ion battery equivalent circuit models such as the Rint, RC, Thevenin and PNGV models, an improved Thevenin model, named dual polarization (DP) model, is put forward by adding an extra RC to simulate the electrochemical polarization and concentration polarization separately. The model parameters are identified with a genetic algorithm, which is used to find the optimal time constant of the model, and the experimental data from a Hybrid Pulse Power Characterization (HPPC) test on a LiMn2O4 battery module. Evaluations on the five models are carried out from the point of view of the dynamic performance and the state of charge (SoC) estimation. The dynamic performances of the five models are obtained by conducting the Dynamic Stress Test (DST) and the accuracy of SoC estimation with the Robust Extended Kalman Filter (REKF) approach is determined by performing a Federal Urban Driving Schedules (FUDS) experiment. By comparison, the DP model has the best dynamic performance and provides the most accurate SoC estimation. Finally, sensitivity of the different SoC initial values is investigated based on the accuracy of SoC estimation with the REKF approach based on the DP model. It is clear that the errors resulting from the SoC initial value are significantly reduced and the true SoC is convergent within an acceptable error.
Batteries play an essential role in electric vehicles (EV), and obtain more and more importance also in smart grids due to the non-constant power generation of renewable energy sources. In order to achieve an optimum operation of systems with batteries it is necessary to develop accurate mathematical models for the calculation of the state of charge (SOC), the temperature distribution within the battery, the residual capacity, the internal resistance and the lifetime, taking into account the individual operation by the user. This paper presents the fundamentals of a method how to determine SOC of lithium-ion batteries on the basis of two different equivalent circuit diagrams and an Extended Kalman Filter (EKF). It is described how to identify the parameters of these circuits by characteristic measurements. The comparison between measurement and computation results shows a good accordance. The accurate determination of SOC of a battery in an EV is of high importance for the prediction of the distance that can be driven. In the first step the dependency of these parameters on the temperature and on the battery age is neglected.
I.
Battery management systems in hybrid electric vehicle battery packs must estimate values descriptive of the pack’s present operating condition. These include: battery state of charge, power fade, capacity fade, and instantaneous available power. The estimation mechanism must adapt to changing cell characteristics as cells age and therefore provide accurate estimates over the lifetime of the pack.In a series of three papers, we propose a method, based on extended Kalman filtering (EKF), that is able to accomplish these goals on a lithium ion polymer battery pack. We expect that it will also work well on other battery chemistries. These papers cover the required mathematical background, cell modeling and system identification requirements, and the final solution, together with results.In order to use EKF to estimate the desired quantities, we first require a mathematical model that can accurately capture the dynamics of a cell. In this paper we “evolve” a suitable model from one that is very primitive to one that is more advanced and works well in practice. The final model includes terms that describe the dynamic contributions due to open-circuit voltage, ohmic loss, polarization time constants, electro-chemical hysteresis, and the effects of temperature. We also give a means, based on EKF, whereby the constant model parameters may be determined from cell test data. Results are presented that demonstrate it is possible to achieve root-mean-squared modeling error smaller than the level of quantization error expected in an implementation.
Battery management systems (BMS) in hybrid-electric-vehicle (HEV) battery packs must estimate values descriptive of the pack’s present operating condition. These include: battery state of charge, power fade, capacity fade, and instantaneous available power. The estimation mechanism must adapt to changing cell characteristics as cells age and therefore provide accurate estimates over the lifetime of the pack.In a series of three papers, we propose a method, based on extended Kalman filtering (EKF), that is able to accomplish these goals on a lithium-ion polymer battery pack. We expect that it will also work well on other battery chemistries. These papers cover the required mathematical background, cell modeling and system identification requirements, and the final solution, together with results.This first paper investigates the estimation requirements for HEV BMS in some detail, in parallel to the requirements for other battery-powered applications. The comparison leads us to understand that the HEV environment is very challenging on batteries and the BMS, and that precise estimation of some parameters will improve performance and robustness, and will ultimately lengthen the useful lifetime of the pack. This conclusion motivates the use of more complex algorithms than might be used in other applications. Our premise is that EKF then becomes a very attractive approach. This paper introduces the basic method, gives some intuitive feel to the necessary computational steps, and concludes by presenting an illustrative example as to the type of results that may be obtained using EKF.
In 1960, R.E. Kalman published his famous paper describing a recursive
solution to the discrete-data linear filtering problem. Since that
time, due in large part to advances in digital computing, the Kalman
filter has been the subject of extensive research and application,
particularly in the area of autonomous or assisted navigation.
The Kalman filter is a set of mathematical equations that provides
an efficient computational (recursive) means to estimate the state
of a process, in a way that minimizes the mean of the squared error.
The filter is very powerful in several aspects: it supports estimations
of past, present, and even future states, and it can do so even when
the precise nature of the modeled system is unknown.
The purpose of this paper is to provide a practical introduction to
the discrete Kalman filter. This introduction includes a description
and some discussion of the basic discrete Kalman filter, a derivation,
description and some discussion of the extended Kalman filter, and
a relatively simple (tangible) example with real numbers & results.
Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental ApproachL'2012) was born in Damascus in Syria He graduated from Higher Institute for Applied Sciences and Technology (HIAST), Damascus, and finished his Master study of
- H He
- R Xiong
- J Fan
H. He, R. Xiong and J. Fan, "Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach," Energies, pp. 582-598, June 2011. IX. BIOGRAPHIES Ahmad Rahmoun M.Eng.(L'2012) was born in Damascus in Syria, on January 20, 1983. He graduated from Higher Institute for Applied Sciences and Technology (HIAST), Damascus, and finished his Master study of Electrical Engi-neering at the University of Applied Sciences Kempten, Germany. His employment experience included Digital Image and Signal Processing Lab (HIAST), and he is currently a research engineer and a PhD student at the Institute for Applied Battery Research (IABF) at the University of Applied Sciences Kempten. Helmuth Biechl, PhD, is a professor and the director of the Institute for Applied Battery Research (IABF), University of Applied Sciences Kempten, Kempten, Germany. Argo Rosin, PhD, is a Senior Research Scientist at the Department of Electrical Drives and Power Electronics, Tallinn University of Technology, Estonia.