Series-connected Lithium-ion Battery Pack Research Articles

An active equalization strategy for series-connected lithium-ion battery packs based on a dual threshold trigger mechanism

It is well acknowledged to all that an active equalization strategy can overcome the inconsistency of lithium-ion cell's voltage and state of charge (SOC) in series-connected lithium-ion battery (LIB) pack in the electric vehicle application. In this regard, a novel dual threshold trigger mechanism based active equalization strategy (DTTM-based AES) is proposed to overcome the inherent inconsistency of cells and to improve the equalization efficiency for a series-connected LIB pack. First, a modified dual-layer inductor equalization circuit is constructed to make it possible for the energy transfer path optimization. Next, based on the designed dual threshold trigger mechanism provoked by battery voltage and SOC, an active equalization strategy is proposed, each single cell's SOC in the battery packs is estimated using the extended Kalman particle filter algorithm. Besides, on the basis of the modified equalization circuit, the improved particle swarm optimization is adopted to optimize the energy transfer path with aiming to reduce the equalization time. Lastly, the simulation and experimental results are provided to validate the proposed DTTM-based AES.

Research on Two-Level Equalization Strategy of Lithium-Ion Battery Based on Graph Theory

Abstract To solve the problem of inconsistency in the use of series-connected lithium-ion battery packs, this paper proposed a topological structure of dual-layer equalization based on a flying capacitor circuit and Cuk circuit, as well as a control strategy seeking the shortest equalization path. In this structure, batteries are divided into two forms: intra-group and inter-group; the intra-group equalization is the lower-level equalization while the flying capacitor circuit is used as an equalization circuit to achieve equalization between individual battery cells; and the inter-group equalization is the upper-level equalization while Cuk circuit is used as equalization circuit to achieve equalization between battery packs; each battery pack shares a battery cell, thus to obtain more options on equalization path. The proposed strategy, with state of charge as the balancing variable, represents the topological structure of the circuit in the form of graph by adopting graph theory control, seeks the optimal equalization path via ant colony optimization algorithm with global search, thus to improve the equalization speed and efficiency. At last, the structure and the strategy proposed in this paper were simulated in matlab/simulink to compare with the maximum value equalization method in the condition of static, charging, and discharging. The result of the simulation experiments shows that the equalization method based on graph theory control reduces the equalization duration by approximately 17%, and improves the equalization efficiency by approximately 2%, which verifies the superiority and effectiveness of the structure and strategy proposed in this paper.

A double-layer ring-structured equalizer for series-connected lithium-ion battery pack based on model predictive control

The inconsistency of lithium-ion batteries will seriously affect the performance and safety of the battery pack in series, resulting in a decrease in the available capacity and shortening of the life span of the series battery pack. To alleviate this inconsistency, a double-layer ring-structured equalization topology is proposed, which has the advantages of flexible equalization path and fast equalization speed. In order to further improve the equalization speed and reduce the equalization loss, this paper proposes an equalization strategy based on model predictive control (MPC). By establishing a prediction model and solving the composite optimization objective function, the optimal control strategy is obtained. To verify the feasibility of the proposed equalizer, the simulation study has been carried out in Matlab-Simulink. The simulation results in static operating condition show that, compared with the single-layer Buck-Boost equalizer based on the mean-difference algorithm, the equalization time of the proposed equalizer based on MPC is reduced by 38.15 % and the energy transfer efficiency is increased by 27.25 %. The rationality of the proposed equalizer and equalization strategy is further verified in constant current (CC) charging and New York Bus Cycle (NYBC) discharging operating conditions.

A multi-fault diagnosis method for lithium-ion battery pack using curvilinear Manhattan distance evaluation and voltage difference analysis

In order to ensure the safety and reliability of electric vehicles, and also to reduce the occurrence of accidents, a fast and efficient multi-fault diagnosis methodology for lithium-ion batteries appears to be particularly important. With this motivation, based on curvilinear Manhattan distance and voltage difference analysis technique, a rapid multi-fault diagnosis method for the lithium-ion battery pack is developed. Specifically, the curvilinear Manhattan distance is presented to quantize the charging voltage variation curves, and then detect and locate the faulty cells within the lithium-ion battery pack. The voltage difference analysis approach is proposed to determine the faulty type with different criteria according to three fault characteristics. Diagnosis experiments are implemented to demonstrate the effectiveness of the proposed multi-fault diagnosis technique based on the aging data of the series-connected lithium-ion battery pack. Results of the diagnosis experiments prove that the proposed approach can sensitively and reliably detect and isolate multiple faults with the merits of low computational cost and high accuracy.

A novel state-of-energy simplified estimation method for lithium-ion battery pack based on prediction and representative cells

Accurate online state of energy (SOE) estimation of a series-connected lithium-ion battery pack is very important for the driving range estimation of electric vehicles, which is still an urgent problem to be solved due to the high computational complexity caused by cell-to-cell inconsistency under practical application of the existing battery management system. In the manuscript, a novel low-complexity SOE simplified estimation method for lithium-ion battery pack based on prediction and representative cells is proposed. Firstly, two cells in the battery pack, one of which has the smallest remaining discharge capacity and the other of which reaches at or closest to the lower cut-off voltage during the discharge process, are selected as representative cells. Secondly, after the EKF algorithm is applied to achieve SOC estimation, the future states of the representative cells are accurately predicted to obtain the cut-off discharge time and the discharge capacity based on the thermoelectric coupling equivalent circuit model under the condition that the future output current of the battery pack has been predicted. The output energy of the representative cells during prediction is divided into three output energy generation parts contributed by OCV, ohmic resistance and polarization resistance respectively, and the three parts are cumulatively calculated and summed to obtain the remaining discharge energy of the representative cells. Thirdly, the approximately proportional relationships in three output energy generation parts between the representative and the non-representative cells are established based on the approximately proportional expressions of the three parts of all cells, and a simplified calculation method is subsequently applied to obtain the remaining discharge energy of non-representative cells based on the results of representative cells. Finally, the remaining discharge energy and SOE of the battery pack can be obtained. The results verified by complex dynamic condition experiments at different temperatures show that the maximum absolute error of the SOE estimation results is less than 3% and more than 85% of the calculation time is saved, which demonstrates that the developed method can achieve accurate and simple SOE estimation of the battery pack.

Multi-fault diagnosis for series-connected lithium-ion battery pack with reconstruction-based contribution based on parallel PCA-KPCA

Various faults of the lithium-ion battery threaten the safety and performance of the battery system. The early faults are difficult to detect and isolate owing to unobvious abnormality and the nonlinear time-varying characteristics of the battery. Herein, a multi-fault diagnosis strategy is proposed that focuses on detecting and isolating different types of faults, and estimating fault waveforms of the battery, including inconsistency evaluation, virtual connection fault, and external short circuit. First, the principal component analysis (PCA) model of the battery is established and the contribution is employed to detect the abnormity in the battery pack. Once the fault is detected, the parallel kernel principal component analysis (KPCA) technology is adopted to reconstruct the fault waveform of the battery parameters, including ohmic resistance, terminal voltage, and open-circuit voltage. These parameters are jointly taken as fault indexes improving the reliability of fault diagnosis. Finally, the proposed method is verified using amounts of tested data of eight cells in series. The results indicate that the contribution-based PCA method can accurately detect the fault. Furthermore, the reconstruction-based parallel PCA-KPCA can accurately estimate the fault waveform of the faulty battery, which helps investigate the fault degree and causes.

A novel dual time-scale voltage sensor fault detection and isolation method for series-connected lithium-ion battery pack

Considering the limitations in existing correlation coefficient-based, entropy-based and big data analysis-based voltage sensor fault diagnosis methods, we develop a novel dual time-scale voltage sensor fault detection and isolation method for series-connected lithium-ion battery pack in this paper. Firstly, a “ohmic resistance”-based selection method is periodically performed to artificially divide all in-pack cells into “representative cell” and non-representative cells. Secondly, during the “representative cell”-based battery pack state-of-charge (SOC) and cell SOC inconsistence estimation process, the measurement innovation (MI) between measured and estimated voltage of the “representative cell” and non-representative cells is generated in micro time-scale and macro time-scale, respectively. Regarding the “representative cell”, the faulty voltage sensor is immediately detected at the moment of the voltage sensor fault occurrence by catching the abnormal MI. As for the non-representative cells, through analyzing the discreteness degree of generated MI under faultless and faulty voltage sensors, an abnormal variance-based voltage sensor fault diagnosis method and an abnormal variance contribution-based fault location method are developed. The validation results through three sophisticated cases demonstrate that this method can rapidly catch the abnormal features for further voltage sensor fault diagnosis with low complexity and satisfactory robustness even though there exist certain faulty current.

A systematic and low-complexity multi-state estimation framework for series-connected lithium-ion battery pack under passive balance control

To reduce computational burden and achieve accurate states estimation, this paper presents a systematic and low-complexity multi-state estimation framework for series-connected lithium-ion battery pack under passive balance control, including pack state-of-charge (SOC), state-of-health (SOH) and cell SOC inconsistences estimation. Firstly, through SOC and SOH calculation simplification of battery pack under passive balance control, “representative cell” is determined among all in-pack cells to reflect battery pack's behavior by a rapid and reliable selection method. Secondly, a variable multi time-scale based framework is further applied to co-estimate SOC and SOH of the selected “representative cell”, which are also the co-estimation results of battery pack. Subsequently, with the estimated SOC of “representative cell”, a second-order extended Kalman filter is designed to realize cell SOC inconsistences tracking in macro time-scale, and the non-representative cells’ SOC can be further calculated. The validation results through sophisticated driving simulation show that both mean-absolute-error (MAE) and root-mean-square-error (RMSE) between battery pack's real SOC and representative cell's estimated SOC are below 1%, and the relative error band between battery pack's precise capacity and representative cell's calculated capacity is between 0 and 1.5%. Moreover, based on the monitored cell SOC inconsistences, both MAE and RMSE between the non-representative cells’ estimated SOC and reference SOC can be roughly limited below 3%.

A novel low-complexity state-of-energy estimation method for series-connected lithium-ion battery pack based on “representative cell” selection and operating mode division

In this paper, we present a novel low-complexity state-of-energy (SOE) estimation method for series-connected lithium-ion battery pack based on “representative cell” selection and operating mode division. Firstly, an “ohmic resistance”-based “representative cell” selection method is proposed to determine the freshest cell and the oldest cell among all in-pack cells reliably and rapidly. Subsequently, according to the cell inconsistences degree, battery cell operating mode is artificially divided into two classes. In mode 1, only the oldest cell's SOE needs to be online tracked, which can be directly seen as battery pack's estimated SOE because of the low cell inconsistences degree. In mode 2, a second-order extended Kalman filter is designed to estimate the SOE difference between the freshest cell and the oldest cell under macro time-scale to further compute the freshest cell's SOE. With the freshest cell's and the oldest cell's estimated SOE, battery pack's SOE in mode 2 can be finally obtained by the adaptive weighted strategy. The validation results through sophisticated driving simulation show that the developed method can achieve accurate SOE estimation for battery pack, where the SOE error bands after convergence by two different data are limited within −2% and 0, and within −3% and 0, respectively. • A low-complexity SOE estimation method for lithium-ion battery pack is presented. • An “ohmic resistance”-based “representative cell” selection method is proposed. • Cell operating mode is artificially divided into two classes to reduce complexity. • A dual estimation framework is proposed to track the oldest cell's states. • The chosen two cells' SOE difference is estimated under macro time-scale in mode 2.

Fault diagnosis of external soft-short circuit for series connected lithium-ion battery pack based on modified dual extended Kalman filter

Battery fault diagnosis is intractable to guarantee the safety and reliability in an advanced battery management system, especially the diagnosis of external soft-short circuit is still an open and challenging issue. Herein, a novel dual-Kalman filter diagnostic method is developed through deeply analyze the characteristics of the external soft-short circuit fault in the series-connected lithium-ion battery pack. First, a modified extended Kalman filter is applied to obtain the open-circuit voltage online, where the relationship between the curve and state of charge is constructed previously that time-saving not to update the relationship in real-time. Second, the SOC difference between the “median cell” and “minimum cell” is obtained by interpolation. Then, the external soft-short circuit current and resistance are calculated by linearly fitting the difference curve before the inflection point. Where after the point, the SOC difference does not always increase with time even if the short-circuit resistance is fixed owing to the nonlinear characteristics of the battery. The experiments are performed that on one hand is to clarify the fault characteristics and on the other is to validate the effectiveness of the diagnostic algorithm. The proposed diagnostic method gives an excellent performance for detecting the fault of the external soft-short circuit.

Model prediction-based battery-powered heating method for series-connected lithium-ion battery pack working at extremely cold temperatures

The degraded performance of lithium-ion batteries at low temperatures is a key obstacle to the development of battery energy storage system applied in extremely cold environment. Therefore, this paper proposes a heating method based on model prediction to support the low-temperature operation of battery pack without additional power sources. Battery pack model is developed based on Thevenin equivalent circuit model. A co-estimator is established to update model parameters and state-of-charge online using adaptive recursive least squares and extended Kalman filter. The permissible discharging current of pack is predicted based on multiple constraints to prevent over-discharge. Then, the battery-powered heating structure, control circuit, and heating strategy are designed. The strategy contains a preheating process for cold-start and a holding process for stabilizing cell temperature. The method is verified experimentally through systematic battery-in-the-loop tests at the environmental temperature of – 40 °C. Results show that the method can uniformly preheat all in-pack cells from − 40 °C to − 20 °C in 330 s consuming 4.7% of nominal capacity. In holding process, it is energy-efficient to raise cell temperature continuously and then maintain at 5 °C, which makes 68.3% of nominal capacity available when loading a modified federal urban driving schedule.

State of energy estimation for a series-connected lithium-ion battery pack based on an adaptive weighted strategy

Due to the inconsistency among battery cells, it is very difficult to estimate the state of energy (SOE) of a battery pack online. In this paper, an adaptive SOE estimation method for a series-connected lithium-ion battery pack based on representative cells is proposed. The dynamic characteristics of a battery are modeled by a first-order resistor-capacitor model. The key parameters and the SOEs of the representative cells are estimated by the recursive least squares algorithm and an adaptive cubature Kalman filter, respectively. The SOE of the series-connected battery pack is obtained by weighting the SOEs of the representative cells based on an adaptive strategy. Experimental results indicate that the SOE estimation result of the series-connected battery pack is close to the SOE of the “strongest” representative cell at the fully charged state, while it is close to the SOE of the “weakest” representative cell at the ending point of discharging. Even with a large initial error, the estimated SOE can quickly track the reference value. The root-mean square errors of the SOE estimation results at 25 °C, 50 °C and 0 °C are 1.3%, 2.2% and 1.7%, respectively.

A Novel Active Equalization Topology for Series-Connected Lithium-ion Battery Packs

A novel nondissipative two-stage equalization circuit topology based on the traditional buck–boost circuit is proposed to achieve balancing of series-connected lithium-ion battery packs with higher efficiency and less cost, considering the background on international energy issues and the development trend of battery balancing. The proposed topology achieves high efficient balancing of lithium-ion battery packs without adding additional devices. Detailed illustration of the presented topology, the operation principles and control approaches are described with visualized figures in this article. Then, under the condition of accurate modeling of the lithium-ion battery, relying on the experimental data, the simulation block is built based on MATLAB. Furthermore, the prototype of the proposed topology was designed and manufactured. The effectiveness of the proposed topology is verified by both the simulation and experiment.

Online detection of early stage internal short circuits in series-connected lithium-ion battery packs based on state-of-charge correlation

• Online detection of early stage internal short circuits (ISCs) is proposed. • Moving-window correlation coefficient across adjacent cells improves robustness. • The proposed method is applicable under any operation condition, with a low computation burden. • Four methods of ISC detection in battery packs are compared and evaluated. • Results verify accurate early stage ISC detection using the proposed method. Internal short circuits (ISCs) may occur in lithium-ion battery packs during their use and lead to the depletion of battery power at an early stage or to thermal runaways and safety risks at a later stage. In this study, a state-of-charge (SOC) correlation-based early stage ISC detection method for the online detection of ISCs under dynamic conditions is proposed to improve battery safety. Herein, we elucidate the principle and algorithm of the method. The SOC of each cell is estimated via the extended Kalman filter, and the correlation coefficient is calculated for adjacent cells using a moving window to ensure estimation accuracy and stability. To prevent false positives, the two correlation coefficients of cells adjacent to a target one should be below a predefined threshold to indicate ISC. Experiments are conducted using an external resistor to simulate ISCs under various dynamic conditions, and four ISC detection methods are compared to detect ISCs at three severity levels. The results show that the proposed method is fast, highly accurate, and that it enables the online detection of an early stage ISC of 100 Ω under dynamic conditions within 20.4 h, which is suitable for improving battery safety.

A Sensor Fault Diagnosis Method for a Lithium-Ion Battery Pack in Electric Vehicles

In electric vehicles, a battery management system highly relies on the measured current, voltage, and temperature to accurately estimate state of charge (SOC) and state of health. Thus, the normal operation of current, voltage, and temperature sensors is of great importance to protect batteries from running outside their safe operating area. In this paper, a simple and effective model-based sensor fault diagnosis scheme is developed to detect and isolate the fault of a current or voltage sensor for a series-connected lithium-ion battery pack. The difference between the true SOC and estimated SOC of each cell in the pack is defined as a residual to determine the occurrence of the fault. The true SOC is calculated by the coulomb counting method and the estimated SOC is obtained by the recursive least squares and unscented Kalman filter joint estimation method. In addition, the difference between the capacity used in SOC estimation and the estimated capacity based on the ratio of the accumulated charge to the SOC difference at two nonadjacent sampling times can also be defined as a residual for fault diagnosis. The temperature sensor which is assumed to be fault-free is used to distinguish the fault of a current or voltage sensor from the fault of a battery cell. Then, the faulty current or voltage sensor can be isolated by comparing the residual and the predefined threshold of each cell in the pack. The experimental and simulation results validate the effectiveness of the proposed sensor fault diagnosis scheme.

A low-complexity state of charge estimation method for series-connected lithium-ion battery pack used in electric vehicles

Practical and accurate state of charge estimation for battery pack is a challenging task due to the inconsistency among in-pack cells. In this paper, we propose a low-complexity state of charge estimation method for series-connected battery pack. Firstly, to reduce computation cost, the capacity and state of charge calculations of battery pack are effectively simplified based on the probability theory. Secondly, we propose a selection method for the representative cells, which is validated by the simulation under Dynamic Stress Test. Subsequently, the state of charge of each representative cell is estimated by the recursive least squares-adaptive extend Kalman filter algorithm successively. Finally, the aging experiments for LiNCM and LiFePO4 battery packs are carried out to validate the feasibility of the simplified method, and the complexity of the three estimation methods is compared. Experimental results indicate that the capacity of battery pack depends only on the representative cells at different cycles, and the proposed “representative cell” method can estimate the state of charge of battery pack with high accuracy and low complexity. The mean absolute errors and root mean square errors for state of charge estimation are less than 3% under Urban Dynamometer Driving Schedule test at different cycles.

Voltage-SOC balancing control scheme for series-connected lithium-ion battery packs

Abstract The basis for determining whether the cell needs to be balanced is generally the voltage or SOC (state of charge), the voltage balancing control scheme is simple but performs poorly, the SOC balancing control scheme performs well but needs to estimate the SOC of all cells. Based on the analysis why existing naive approaches are not effective for voltage balancing control scheme, the voltage-SOC balancing control scheme is proposed, which has the advantages of simple of voltage balancing control scheme and well performance of SOC balancing control scheme. By utilizing the difference of voltage of cell and SOC of battery pack, the difference of SOC between two cells can be obtained indirectly, and the transition from voltage balancing control scheme to SOC balancing control scheme can be realized, while the advantages of voltage balancing control scheme and SOC balancing control scheme are retained. The 4 experiments designed show that the estimated errors of the difference between the SOCs of the two cells are 0.24%, 5.3%, -5%, and 0.8%, respectively, which proves the excellent performance of voltage-SOC balancing control scheme.

A model-based state-of-charge estimation method for series-connected lithium-ion battery pack considering fast-varying cell temperature

Accurately estimating the state-of-charge (SOC) of lithium-ion batteries under complicated temperature conditions is crucial in all-climate battery management systems. This paper proposes a model-based SOC estimation method for series-connected battery pack with time-varying cell temperature. Systematic battery experiments are conducted to investigate the influences of changing temperature on both cell characteristics and cell-to-cell inconsistencies. A normalized open-circuit voltage (OCV) model is developed and applied in cell Thevenin model to describe the temperature-dependent OCV-SOC characteristic. The battery pack SOC is analyzed considering the effect of passive balance control. Then, a lumped parameter battery pack model is established by connecting cell models in series. To reduce computational complexity, a dual time-scale parameter identification framework is proposed which is supported by an online filtering process of selecting variable reference cell (VRC). An adaptive co-estimator is presented to update pack parameters in dual time-scale using an optimized recursive least squares algorithm, and to estimate the battery pack SOC using an extended Kalman filter. Experimental verifications are conducted under time-varying environmental temperature ranging from −40 °C to 40 °C. Results indicate the established model can well describe the dynamic behavior of battery pack, and the proposed method can estimate the battery pack SOC with considerably high precision.

Back Cover Image

The back cover image is based on the Research Article A graphical model for evaluating the status of series-connected lithium-ion battery pack, by Xuning Feng et al., https://doi.org/10.1002/er.4305.

A graphical model for evaluating the status of series-connected lithium-ion battery pack

A graphical model for evaluating the status of series-connected lithium-ion battery pack