Series-connected Lithium-ion Battery Research Articles

State-of-health estimation and classification of series-connected batteries by using deep learning based hybrid decision approach

In rechargeable battery control and operation, one of the primary obstacles is safety concerns where the battery degradation poses a significant factor. Therefore, in recent years, state-of-health assessment of lithium-ion batteries has become a noteworthy issue. On the other hand, it is challenging to ensure robustness and generalization because most state-of-health assessment techniques are implemented for a specific characteristic, operating situation, and battery material system. In most studies, health status of single cell batteries is assessed by using analytical or computer-aided deep learning methods. But, the state-of-health characteristics of series-connected battery systems should be also focused with advances of technology and usage, especially electric vehicles. This study presents a data-driven, deep learning-based hybrid decision approach for predicting the state-of-health of series-connected lithium-ion batteries with different characteristics. The paper consists of generating series-connected battery degradation dataset by using of some mostly used datasets. Also, by employing deep learning-based networks along with hybrid-classification aided by performance metrics, it is shown that estimating and predicting the state-of-health can be achieved not only by using sole deep-learning algorithms but also hybrid-classification techniques. The results demonstrate the high accuracy and simplicity of the proposed novel approach on datasets from Oxford University and Calce battery group. The best estimated mean squared error, root mean square error and mean-absolute percentage error values are not more than 0.0500, 0.2236 and 0.7065, respectively which shows the efficiency not only by accuracy but also error indicators. The results show that the proposed approach can be implemented in offline or online systems with best average accuracy of 98.33 % and classification time of 58 ms per sample.

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.

Multi-Fault Diagnosis for Series-Connected Lithium-Ion Battery Packs Based on Improved Sensor Topology and Correlation Coefficient Method

Multi-Fault Diagnosis for Series-Connected Lithium-Ion Battery Packs Based on Improved Sensor Topology and Correlation Coefficient Method

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.

Research on equalization scheme of lithium-ion battery packs based on consistency control strategy

In order to address the inconsistency problem of series-connected lithium-ion battery groups in practice, a two-level balanced topology based on bidirectional Sepic-Zeta circuit is designed in this article. Two-level equalization topology uses bidirectional Sepic-Zeta circuits both within and between groups, which can achieve the equilibrium between any cells in a battery group and between any battery groups. With the state of charge (SOC) of the battery as the equalization variable, and the equalization control strategy is designed based on the consistency controller and PI controller to achieve fast and efficient equalization by dynamic adjustment of equalization current, and to efficiently decrease the inconsistency of the equalized battery packs. The experiments are conducted by Matlab/Simulink platform. According to the experimental results it can be obtained that compared with the topology based on single Cuk equalizer, the topology based on single Sepic-Zeta equalizer and the adjacent topology based on Sepic-Zeta equalizer, the proposed topology shortens the equilibrium time by about 51.57 %, 51.43 % and 42.24 %, respectively. Under the same operating conditions, the equalization control strategy designed in this article shortens the equilibrium time by about 29.4 % and 11.19 % compared to the conventional fixed-threshold method and the Fuzzy Logic Control (FLC) algorithm, respectively, and also achieves better results in the equalization efficiency and consistency problem, which effectively verifies the feasibility of the equalization scheme in this article.

A multi-winding transformer-based active cell equalizer with self-driven switches for series-connected lithium-ion batteries and super-capacitors

This work introduces a new multi-winding transformer-based cell equalizer with self-driven switches series-connected energy storage cells. With the equalizer, all series-connected cells can be equalized in both charging and discharging manners, towards the same balance voltage. In order to reduce the conduction loss in balancing process, all switching devices in the new equalizer are implemented by MOSFETs. By introducing a center tap, each secondary winding can be divided into two sub-windings, one of which is used to control the MOSFET switches while another one is used to supply the balancing voltage of cells. Therefore, the proposed cell equalizer can achieve the equalization automatically without any switch driving circuits, voltage sensors and complex control strategies. It greatly simplifies the complexity of the balance circuit and reduces the system cost. The circuit structure can be extended by adding more balancing units to meet the requirements of high voltage. Circuit configuration, operation principle, modeling and design considerations are analyzed in detail. Finally, simulation and experimental results are provided to verify the feasibility of the proposed cell equalizer.

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.

A multi‐objective parallel layered equalizer for large‐scale lithium ion battery system

The equalisers available are difficult to give consideration to both high balance efficiency and fast balance speed, and their performances are unstable as the number of the series-connected batteries increases, so they are unsuitable for a large-scale battery system. A multi-objective parallel layered equaliser based on battery working states for the large-scale lithium-ion battery system is presented. The equaliser has two types of balance objects and two corresponding layers, and the first layer and the second layer take the single battery and the battery unit as the balance object, respectively. The multi-objective parallel layered balance means that multiple balance objects are selected as the balance objectives simultaneously at each layer. The balance speed is fast due to the multi-objective parallel balance. The balance efficiency is high due to the short energy path and the complementary pulse width modulation (PWM) control in the first layer, and the double voltage value of the balance object in the second layer. Moreover, the performance of the equaliser is stable, even when the number of series-connected batteries is large. In short, the proposed equaliser features high efficiency, fast speed, and strong scalability. Further, the experimental platform for a battery system with twelve series-connected lithium-ion phosphate batteries is built, and then the balance experiments have been completed. Finally, the effectiveness of the equaliser is verified by the corresponding experimental results.

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.

Active Equalization Strategy for Lithium-Ion Battery Packs Based on Multilayer Dual Interleaved Inductor Circuits in Electric Vehicles

Lithium-ion batteries (LIBs) are widely used in electric vehicles (EVs) due to their superior power performance over other batteries. However, when connected in series, overcharged cells of LIBs face the risk of explosion, and undercharged cells decrease the life cycle of the battery. Eventually, the inconsistency phenomenon between cells resulting from manufacturing tolerance and usage process reduces the overall charging capacity of the battery and increases the risk of explosion after long-time use. Research has focused on synthesizing active material to achieve higher energy density and extended life cycle for LIBs while neglecting a comparative analysis of equalization technology on the performance of battery packs. In this paper, a nondissipative equalization structure is proposed to reconcile the inconsistency of series-connected LIB cells. In this structure, a circuit uses high-level equalization units to enable direct energy transfer between any two individual cells, and dual interleaved inductors in each equalization unit increase the equalization speed of a single cell in one equalization cycle by a factor of two. The circuit is compared with the classical inductor equalization circuit (CIEC), dual interleaved equalization circuit (DIEC), and parallel architecture equalization circuit (PAEC) in the states of standing, charging, and discharging, respectively, to validate the advantages of the proposed scheme. Considering the diversity of imbalance states, the state of charge (SOC) and terminal voltage are both chosen as the equalization criterion. The second-order RC model of the LIB and the adaptive unscented Kalman filter (AUKF) algorithm are employed for SOC estimation. For effective equalization, the adaptive fuzzy neural network (AFNN) is utilized to further reduce energy consumption and equalization time. The experiment results show that the AFNN algorithm reduces the total equalization time by approximately 37.4% and improves equalization efficiency by about 4.89% in contrast with the conventional mean-difference algorithm. Particularly, the experiment results of the equalization circuit verification certify that the proposed equalization structure can greatly accelerate the equalization progress and reduce the equalization loss compared to the other three equalization circuits.

Degradation Behavior of Graphite–Nickel Cobalt Aluminum Oxide Lithium Ion Cells with Series Connections Including an Overcharged Cell

Cycle life of series-connected lithium-ion battery (LIB) modules in the presence or absence of an overcharged cell is reported, and degradation behavior is characterized by electrochemical impedance spectroscopy (EIS) and differential voltage analysis (DVA). Three-stage capacity decay is solely observed in modules in the presence of an overcharged cell, which is exposed to a state of charge (SoC) of ≥105% during cycling. The module without overcharged cells or with an overcharged cell at an SoC of <103% exhibits capacity decay as a function of the square root of cycle numbers. EIS and DVA analyses confirmed that the overcharged cell with an SoC of ≥105% in the module deteriorates more harshly than the other cells and that deterioration occurs especially at the cathode. Cycle dependence of the charge/discharge cutoff voltage of LIBs revealed that the cell capacity of the overcharged cell shrinks with the end point on charge as a fulcrum, thereby decreasing the lower cutoff voltage as cycling progresses. Such a voltage change of an overcharged cell directly affects module capacity, leading to three-stage capacity decay. This study reveals a potential risk in series-connected cells using a wide SoC range and provides critical insights for safe operation without balance circuits.

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.

Automatic and fast balancing circuit for Series-Connected lithium-ion batteries based on forward-flyback hybrid converter

Automatic and fast balancing circuit for Series-Connected lithium-ion batteries based on forward-flyback hybrid converter

Detection of Unbalanced Voltage Cells in Series-connected Lithium-ion Batteries Using Single-frequency Electrochemical Impedance Spectroscopy

For a battery module where single cells are connected in series, the single cells should each have a similar state of charge (SOC) to prevent them from being exposed to an overcharge or over-discharge during charge–discharge cycling. To detect the existence of unbalanced SOC cells in a battery module, we propose a simple measurement method using a single-frequency response of electrochemical impedance spectroscopy (EIS). For a commercially available graphite/nickel-cobalt-aluminum-oxide lithium-ion cell, the cell impedance increases significantly below SOC20%, while the impedance in the medium SOC region (SOC20%–SOC80%) remains low with only minor changes. This impedance behavior is mostly due to the elementary processes of cathode reactions in the cell. Among the impedance values (Z, Z′, Z″), the imaginary component of Z″ regarding cathode reactions changes heavily as a function of SOC, in particular, when the EIS measurement is performed around 0.1 Hz. Thanks to the significant difference in the time constant of cathode reactions between ≤SOC10% and ≥SOC20%, a single-frequency EIS measurement enlarges the difference in impedance between balanced and unbalanced cells in the module and facilitates an ~80% improvement in the detection signal compared to results with conventional EIS measurements.

Optimal SoC Balancing Control for Lithium-Ion Battery Cells Connected in Series

The optimal state of charge (SoC) balancing control for series-connected lithium-ion battery cells is presented in this paper. A modified SoC balancing circuit for two adjacent cells, based on the principle of a bidirectional Cuk converter, is proposed. The optimal SoC balancing problem is established to minimize the SoC differences of cells and the energy loss subject to constraints of the normal SoC operating range, the balancing current, and current of cells. This optimization problem is solved using the sequential quadratic programming algorithm to determine the optimal duties of PWM signals applied to the SoC balancing circuits. An algorithm for the selection of the initial points for the optimal problem-solving process is proposed. It is applied in cases where the cost function has no decreasing part. Experimental tests are conducted for seven series-connected Samsung cells. The optimal SoC balancing control and SoC estimation algorithms are coded in MATLAB and embedded in LabVIEW to control the SoC balancing in real time. The test results show that the differences between the SoCs of cells converges to the desired range using the proposed optimal SoC balancing control strategy.