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Matching between low-voltage high-current capacity battery/fuel cell with high-voltage low-current motors in electric vehicles (EVs) requires paralleling-connection multi-stage power electronic converter with accurate modeling and control system. For high-voltage low-current drives of EVs with high-boosting/bucking gain, single-stage single-arm DC/DC converters suffer from unstable output voltage and the impractical choice of charging/discharging elements. Therefore, a multi-stage multi-arm DC/DC converter is researched to solve the problems of high boosting/bucking gain and minimized the voltage and current ripples. This paper presents parametric sizing, dynamic modeling, and analysis of all logical operational modes for a new high-gain DC/DC converter suitable for deployment in EV applications. The proposed topology composes of three-stage interleaved-input composed of cascaded two stages and double float-output as a third stage with bidirectional current capability. In this study, parametric design, and sizing for the three stages are performed according to the prerequisite ratings. Then, an analysis is conducted on rules of operating the DC/DC converter along with a discussion on considerations taken into account for such a critical design. An investigation of the dynamic behavior of the suggested DC/DC converter is carried out through a state-space model with switches’ states. Furthermore, a simulation and measurement-based validation of the derived mathematical models are conducted. The theoretical based analysis, design, and boosting gains are confirmed through a constructed and tested prototype given data on power supply and load utilization. A discussion on the choice of semiconductor equipment and reactive elements is provided for the proposed DC/DC converter. The experimental results show that the three stages can be used in a separate mode or in a cascaded booster.

Efficient heat dissipation in lithium-ion battery packs is crucial for safety, necessitating a thorough assessment of thermal performance during the design phase. This study utilizes Newman’s Pseudo two-dimensional (P2D) model and three-dimensional computational fluid dynamics to depict heat generation and dissipation. The main objectives include evaluating heat dynamics, establishing optimal temperature limits, and assessing State of Charge (SOC) and State of Health (SOH) estimations. The battery pack, configured in 6s2p, features plastic-wrapped cylindrical modules with air-filled domains, connected in parallel pairs with aluminum wires. Active materials comprise positive electrodes like Lithium Cobalt Oxide (LCO), Lithium Nickel Manganese Cobalt Oxide (NMC), Lithium Iron Phosphate (LFP), and Lithium Manganese Oxide (LMO), with graphite as the negative electrode and a separator (Lithium Hexa Fluoro Phosphate). Results indicate that air-cooled NMC electrode chemistry achieves a peak heat dissipation of 102.48W, maintaining an optimal temperature range of 21.3deg C to 32.8deg C. After 1000 cycles, the optimal temperature limit of 32.8deg C with an 80% capacity loss minimizes negative impacts during charge/discharge cycles. SOC and SOH remain at 100% and 80%, utilizing an 8Ah battery with a nominal voltage of 4.2V, and a 3.9-year life expectancy, addressing charge/discharge cycle issues.

Nowadays, the series-parallel (SP) and the parallel-series (PS) configurations represent the main architectures considered for designing a battery pack. In both architectures, cell-to-cell parameters’ variations due to manufacturing tolerances, thermal gradients, and cell degradation can strongly impact the overall performance of battery packs. However, the effect of the parameter variation on the pack performance changes depending on the architecture and the number of cells. This manuscript aims at providing a method for the assessment of the impact of cell-to-cell parameters’ variations due to potential different aging and thermal conditions for the cells in a generic SP and PS pack architecture. For this purpose, a generalized reduced order equivalent circuit model for the battery pack is defined, leading to a set of steady-state equations for easily and systematically calculating the current and voltage imbalances due to capacity and internal resistance variations independently from the pack architecture and the number of cells. A parametric analysis is reported for quantitatively evaluating the severity of the imbalances due to the cell-to-cell parameters’ variations for both battery pack architectures. The results of the comparative analysis demonstrate that PS architecture is more impacted by parameters’ variations with respect to the SP one, since a higher current imbalance is always obtained. The difference in the severity of the current imbalance in the pack architectures strongly increases as the number of series-connected cells rises. Moreover, among the cell parameters’ variations, a major impact of the capacity imbalance on the reduction of the overall performance of the battery pack is observed.

Accurate prediction and monitoring of the state of charge (SOC) of each cell in a battery pack are of great significance for safe driving of electric vehicles. Subject to the factors of differences between cells, temperature, and aging, the accuracy of existing SOC prediction methods may be affected in applications. This article employs a data-driven method with a proposed novel transfer learning framework considering conditional probability distribution adaptation to solve the impact of the aforementioned influencing factors on SOC prediction and obtains a favorable SOC prediction result for each cell. As experiments of actual battery pack provided by China First Automobile Work based demonstrated, the proposed method can accurately predict SOC of each cell under different influencing factors by using only one cell modeling data. Moreover, the proposed method is robust against model parameter uncertainties, sensor noise, etc. [ABSTRACT FROM AUTHOR]

This article presents an efficient and fast echelon battery equalization method based on wide voltage range bidirectional converter combined with DBSCAN (density-based spatial clustering of applications with noise) clustering control strategy. Thanks to the wide voltage range and bidirectional buck-boost characteristics of the four-switch bidirectional converter, the battery energy can be reasonably redistributed between the battery and the super capacitor. The converter operating in synchronous rectification mode can not only achieve bidirectional energy flow in a wide voltage range, but also has a high energy conversion efficiency. The DBSCAN clustering algorithm is used to divide all battery cells into groups, and each group may contain one or more adjacent and nonadjacent battery cells. First, the nonadjacent single cells are balanced and integrated into adjacent group with the closest voltage. Then, the groups with different voltages are balanced by group-to-group, thereby realizing rapid balance of the entire battery pack. In order to verify the effectiveness of the proposed method, simulations and experimental verification were carried out. The experimental results show that, comparing with traditional battery balancing methods, the proposed method achieves shorter balancing time and higher balancing efficiency. [ABSTRACT FROM AUTHOR]

Large battery packs are used in electric vehicles. Heat is generated when the battery pack is being used. Therefore, it is necessary to predict battery heat generation. An enhanced electro-thermal model is developed to describe the temperature distribution inside a battery pack. It combines the dynamic resistance model and the current distribution model. The resistance model is affected by the thermal and electrical parameters, while the current distribution model considers the interaction between cell status and current variation in the parallel branch. The proposed model can accurately predict the temperature change of cells in the pack under static and dynamic current conditions. Experiments are conducted to validate the prediction accuracy. Most of the average absolute errors (AEave) between the predicted value and test value displayed on the experimental device do not exceed 0.4 °C under static current conditions, and all of them are below 0.1 °C under dynamic current conditions. The two existing models, namely the state-of-charge (SOC)-dependent resistance model [R(SOC)] and SOC-T-dependent resistance model [R(SOC, T)], have AEave values of 1.6 and 0.54 °C when the pack is discharged at 0.5 C. In contrast, the AEave value achieved by our proposed model is 0.4 °C. Under dynamic current conditions, the maximum AEaves are 0.42 °C for the R(SOC) model, 0.26 °C for the R(SOC, T) model, and 0.16 °C for the proposed model. These results demonstrate that the proposed model provides more accurate predictions of the temperature rise inside the pack than the popular existing models. [ABSTRACT FROM AUTHOR]

While large scale deployment of plug-in electric vehicles (PEVs) offers promising advantages, such as environmental benefits, energy security, and economic stability, it also poses certain cybersecurity related challenges. Unlike power grid security, PEV cybersecurity is significantly underexplored. However, cyberattacks on PEVs may lead to disastrous situations such as out-of-service EVs via denial-of-charging (DoC) or battery pack damage via overcharging. In this article, we attempt to address this issue by exploring control-oriented approaches for PEV cybersecurity. Specifically, we focus on designing algorithms for detecting cyberattacks that can potentially affect PEV battery packs during charging. We discuss two algorithms: first, static detector that utilizes only measured variables, and, second, dynamic detector that utilizes the knowledge of system dynamics along with the measurements. Furthermore, we propose a filter-based design approach for the dynamic detector that considers a multiobjective criterion including stability, robustness, and attack sensitivity. We perform theoretical analysis and simulation studies to evaluate the effectiveness of the algorithms under DoC and overcharging attacks that indicate the superiority of dynamic detector in terms of attack detectability. [ABSTRACT FROM AUTHOR]

Hybrid energy storage systems using battery packs and super capacitor (SC) banks are gaining considerable attraction in electric vehicle (EV) applications. In this article, a new modular reconfigurable multisource inverter (MSI) is proposed for active control of energy storage systems in EV applications. Unlike the conventional approaches, which use massive high-power dc–dc converters with bulk magnetic elements for combining SC banks and battery packs, the new approach utilizing the MSI offers magnetic-less structures. This reduces the weight and volume of the power electronics interface and offers simple control. Along with the proposed MSI, a space vector modulation technique and a deterministic state of charge (SOC) controller are also introduced for the control of the switching actions and operation of the SC bank. Simulations using MATLAB/Simulink and experimental results on a scaled down lab prototype are studied to assess the concepts. [ABSTRACT FROM AUTHOR]

This article newly suggests two-step Hamming neural network (HNN)-based pattern recognition which combines the discrete wavelet transform (DWT)-based multi-resolution analysis (MRA) for providing an innovative and unique cell distinction. In contrast to the conventional methods, there are some remarkable features. First, the discharging/charging current signals (DCCSs) for reflecting load conditions with various dissimilar scenarios are used as representative patterns in the first-step HNN. Specifically, the usage of decomposed low- or high-frequency components (A5/D5) by the DWT-based MRA enables added characteristic parameters for providing the closest matches. Second, according to state-of-charge (SOC) range, especially in high SOC ranges, the allowable maximum charging currents in constant current/voltage (CC/CV) schemes are adjusted for avoiding over-charging. Third, the second-step HNN finally completes the recognition after closely matching several representative discharging/charging voltage signals applying the selected DCCS in the first step. Finally, simultaneous internal parameter extraction in the equivalent electrical circuit model during the second-step HNN provides useful information for verification of this article. Then, unnecessary experimental procedure and the time required for getting parameters of an arbitrary lithium-ion cell are abbreviated or reduced. [ABSTRACT FROM AUTHOR]

In order to estimate the state-of-charge (SoC) for all cells in the battery pack, this paper proposed an average cell model to represent every cell in the pack. The average cell model consisted of a basic model and a bias function. First, the parameter identification of the basic model was conducted, and the inconsistencies between cells were calibrated by the uncertainties of the basic model parameters. Second, artificial neural networks were used to construct the response surface approximate model of the bias function. In order to make the average cell model more adaptable to different working conditions, a novel bias function considering the polarization voltage and the temperature was proposed to correct the basic model, and it was compared with other bias functions. Then, the extended Kalman filtering algorithm was used for SoC estimation based on the corrected model. Finally, a case study with six lithium-ion battery cells was performed for the verification and evaluation of the proposed method. The results indicated that the average model corrected by the proposed bias function showed good adaptability to different working conditions, and the maximum absolute SoC estimate errors of all cells in the battery pack were less than 2% at 25°C, and 3.5%at 10 °C or 40 °C.

In this paper, an active equalization method based on redundant battery is proposed. A redundant battery is added to a battery pack consisting of several series batteries. According to the capacity and SOC (state-of-charge) of series batteries in the battery pack, one of these batteries is in parallel with the redundant battery in a certain time period. On the one hand, the energy is transferred between the redundant battery and the equalized batteries. On the other hand, during the charging or discharging process, the redundant battery acts as a power source, which reduces the current of the equalized battery. By adjusting the length of time that redundant battery and series batteries are connected in parallel, the purpose of battery equalization will be achieved. The balance current is limited only by the maximum charge–discharge ratio of the batteries. As long as the sum of the difference of series batteries capacity is less than the redundant battery capacity, the balance of all batteries can be achieved. Compared with other active equalization methods, the energy storage element (redundant battery) is used to be the main component of the equalization circuit in this method. It not only has the advantages of simple equalization circuit and large equalization current, but also has higher energy density of battery pack. In this paper, the equalization circuit is given, and the equalization strategy with accurate battery parameters and uncertain battery parameters is proposed. The experimental results also verify the correctness of the theoretical analysis and modeling. [ABSTRACT FROM AUTHOR]

The state-of-charge (SOC) estimation approaches based on the pack model can hardly provide precise estimation due to cell difference, while the approaches based on each cell cost high computation resource, which are not suitable for real-time application. The estimation error can be further enlarged without considering balancing current. The SOC estimation approach of the battery pack considering balancing current is proposed, which dynamically searches for the cell with maximum or minimum voltage, and it only needs to calculate the selected cell in every estimation cycle. Compared to the approaches based on the pack model or each single cell, this approach can achieve precise pack SOC and cost less calculation time and resource. It has been verified with ten series-connected 200 Ah Li(NiCoMn)O2 batteries. The SOC estimation error is limited to 0.3% during the charging process, and a reduction of 2.5% is achieved compared to an error of 2.8% based on the pack model. A reduction of 1% is achieved compared to an error of 1.5% based on the pack model during the discharging process. Compared to an error of 1.7% without considering balancing current, a reduction of 1.4% is achieved during the charging process. [ABSTRACT FROM PUBLISHER]

This paper presents a method for an online real-time electrochemical impedance spectroscopy (EIS) measurement of batteries using closed-loop control of power converter. Unlike the previously proposed method which allows the measurement of the ac impedance for a single frequency, the presented method in this paper allows for obtaining the EIS for a spectrum of frequencies by using the information included in a single perturbation cycle, or a few cycles of perturbation to obtain a more accurate EIS with a very wide frequency range. This will result in faster EIS measurement for a spectrum of frequencies and under the same battery operating conditions. The presented method utilizes closed-loop control operation for the EIS measurement functionality, which allows for better control of the output voltage and for upgrading the concept to be able to achieve no added perturbation ripple at the output of the system. The presented online real-time EIS measurement method utilizes a power converter with closed-loop control in order to create an output voltage step-function perturbation at a given frequency to generate battery voltage and current responses. By applying Fourier analysis to these responses, an EIS can be obtained for a range of frequencies equal or higher than the perturbation frequency of the step function. In addition, this paper presents a method to eliminate the added perturbation ripple when two or more power converters are used. The theoretical basis and experimental prototype results are provided to illustrate and validate the presented method. [ABSTRACT FROM PUBLISHER]

Lithium-ion (Li-ion) batteries are nowadays the best tradeoff between performance, cost, and lifetime to store electric power and energy effectively. The reliable determination of the batteries’ instantaneous power capability and energy content is mandatory for many mobile high-energy and power-consuming applications. Herein, the battery impedance and state of charge (\SOC) are the relevant values. In energy storage systems, these values are to be considered for each battery cell individually, due to arising limitations caused by cell-specific variations. Simple algorithms to determine the battery system’s impedance and \SOC are presented including parameter and state-estimation techniques. Furthermore, methods are derived making the impedance and \SOC determination possible for a large number of particular cells in a battery system. The applicability of the demonstrated algorithms for battery control unit implementation is proved incorporating data achieved from load scenarios applied to a cell stack Li-ion battery module. Cell impedance and \SOC variations could be detected precisely. Moreover, a combination of impedance and \SOC spread was identified during typical battery operation. [ABSTRACT FROM PUBLISHER]