Modelling and Evaluation of Battery Packs with Different Numbers of Paralleled Cells

Chang, Fengqi; Roemer, Felix; Baumann, Michael A.; Lienkamp, Markus

doi:10.3390/wevj9010008

Cited by 14 publications

(10 citation statements)

References 17 publications

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“…A circuit diagram of the used four switched synchronous DC/DC Buck-boost converter is shown in Figure 7 in which the battery cells are charged by the PV supply voltage V PV [28,29]. Four peculiar MOSFET switches (S 1~S4 ) are tangled into the DC/DC converter to standardize the energy transference of battery arrangements (V nBat ).…”

Section: Dc/dc Buck-boost Convertermentioning

confidence: 99%

Active Charge Balancing Strategy Using the State of Charge Estimation Technique for a PV-Battery Hybrid System

et al. 2020

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Charging a group of series-connected batteries of a PV-battery hybrid system exhibits an imbalance issue. Such imbalance has severe consequences on the battery activation function and the maintenance cost of the entire system. Therefore, this paper proposes an active battery balancing technique for a PV-battery integrated system to improve its performance and lifespan. Battery state of charge (SOC) estimation based on the backpropagation neural network (BPNN) technique is utilized to check the charge condition of the storage system. The developed battery management system (BMS) receives the SOC estimation of the individual batteries and issues control signal to the DC/DC Buck-boost converter to balance the charge status of the connected group of batteries. Simulation and experimental results using MATLAB-ATMega2560 interfacing system reveal the effectiveness of the proposed approach.

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Section: Dc/dc Buck-boost Convertermentioning

confidence: 99%

Active Charge Balancing Strategy Using the State of Charge Estimation Technique for a PV-Battery Hybrid System

et al. 2020

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“…However, the work, which has been done in this field so far, does not include a comprehensive analysis of the costs and losses associated with different configurations of the CHB inverter, e.g., the number of levels, selection of switches, etc. There are setups described with only three levels [21] and up to eleven [14,22], twenty-one [23,24] or even twenty-three levels [25,26] with a big majority focusing on seven level CHB inverters [8,[27][28][29][30][31][32]. Only one publication was found comparing different configurations, but only with regards to efficiency and without any cost consideration [33].…”

Section: Of 19mentioning

confidence: 99%

“…Baumhoefer et al [7] have shown that the cells with different initial capacities in a battery pack age differently over time, and the variation of capacities increases. An explanation for this is that putting the same load on weaker cells would degrade them faster as compared to the healthier cells [8]. These inconsistencies may limit the performance of a battery pack and reduce its lifetime.…”

Section: Introductionmentioning

confidence: 99%

Optimization of a Cascaded H-Bridge Inverter for Electric Vehicle Applications Including Cost Consideration

et al. 2019

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This paper presents a method to find the optimal configuration for an electric vehicle energy storage system using a cascaded H-bridge (CHB) inverter. CHB multilevel inverters enable a better utilization of the battery pack, because cells/modules with manufacturing tolerances in terms of capacity can be selectively discharged instead of being passively balanced by discharging them over resistors. The balancing algorithms have been investigated in many studies for the CHB topology. However, it has not yet been investigated to which extend a conventional pack can be modularized in a CHB configuration. Therefore, this paper explores different configurations by simulating different switch models, switch configurations, and number of levels for a CHB inverter along with a reference load model to find the optimal design of the system. The configuration is also considered from an economically point of view, as the most efficient solution might not be cost-effective to be installed in a common production vehicle. It is found that four modules per phase give the best compromise between efficiency and costs. Paralleling smaller switches should be preferred over the usage of fewer, larger switches. Moreover, selecting specific existing components results in higher savings compared to theoretical optimal components.

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“…In order to accurately predict the SoP of a battery system including parallel-connected units, comprehensive modeling and analysis of the entire battery system's operation, especially the dynamic parallel current distribution, are required. Parallel operation of battery cells has been modeled for performance evaluation [16]- [19] as well as sensitivity analysis to heterogeneous cell resistance [16], [17], [19]- [21], charge capacity [16], [17], [19], [21]- [24], number of parallel cells [18], [23], and system configuration [16]. However, to the best of our knowledge, the complete battery system modeling considering time-varying parallel current distribution has not been applied to the battery system SoP prediction due to significantly increased complexity in system modeling and algorithm design as compared to those commonly used cell-SoP-based methods.…”

Section: Introductionmentioning

confidence: 99%

State of Power Prediction for Battery Systems With Parallel-Connected Units

Han

Altaf

Zou

et al. 2022

IEEE Trans. Transp. Electrific.

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To meet the ever-increasing demand for energy storage and power supply, battery systems are being vastly applied to, e.g., grid-level energy storage and automotive traction electrification. In pursuit of safe, efficient, and cost-effective operation, it is critical to predict the maximum acceptable battery power on the fly, commonly referred to as the battery system's state of power (SoP). As compared to the SoP prediction at the battery cell level, predicting the SoP of a multi-battery system, especially including parallel-connected cells/modules/packs, is much more complicated and far less investigated. To solve this problem, a system-model-based SoP prediction method is first proposed in this paper. Specifically, based on the formulated system model and generic state-space representation, the challenge of nonmonotonic system state evolution, arising from the dynamic parallel current distribution, is identified and systematically addressed by the proposed method. As demonstrated by tests on a battery system set up with experimentally verified parameter values, the proposed method outperforms the commonly applied cell-SoP based methods for providing a more accurate and reliable prediction of the battery system SoP. Moreover, the proposed prediction framework presented in generic forms can be readily applied to other system structures.

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Modelling and Evaluation of Battery Packs with Different Numbers of Paralleled Cells

Cited by 14 publications

References 17 publications

Active Charge Balancing Strategy Using the State of Charge Estimation Technique for a PV-Battery Hybrid System

Active Charge Balancing Strategy Using the State of Charge Estimation Technique for a PV-Battery Hybrid System

Optimization of a Cascaded H-Bridge Inverter for Electric Vehicle Applications Including Cost Consideration

State of Power Prediction for Battery Systems With Parallel-Connected Units

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