Load Current and State-of-Charge Coestimation for Current Sensor-Free Lithium-Ion Battery

Wei, Zhongbao; Hu, Jian; He, Haibo; Li, Yang; Xiong, Binyu

doi:10.1109/tpel.2021.3068725

Cited by 74 publications

(31 citation statements)

References 21 publications

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“…Some works [18,19,23] used a regression curve to estimate the SoCs of cells to reduce the computation burden. A load current and SoC co-estimation approach was proposed to mitigate the need for installing a current sensor in [24].…”

Section: Converter Basedmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Converter Basedmentioning

confidence: 99%

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

et al. 2021

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“…In addition to the use in SOC estimation, ECM parameter estimation has other important applications in a battery management system. The literature review suggests that there are several model-based SOC estimation methods that exist, which are already explored by different researchers [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

A Critical Look at Coulomb Counting Approach for State of Charge Estimation in Batteries

et al. 2021

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In this paper, we consider the problem of state-of-charge estimation for rechargeable batteries. Coulomb counting is a well-known method for estimating the state of charge, and it is regarded as accurate as long as the battery capacity and the beginning state of charge are known. The Coulomb counting approach, on the other hand, is prone to inaccuracies from a variety of sources, and the magnitude of these errors has not been explored in the literature. We formally construct and quantify the state-of-charge estimate error during Coulomb counting due to four types of error sources: (1) current measurement error; (2) current integration approximation error; (3) battery capacity uncertainty; and (4) timing oscillator error/drift. It is demonstrated that the state-of-charge error produced can be either time-cumulative or state-of-charge-proportional. Time-cumulative errors accumulate over time and have the potential to render the state-of-charge estimation utterly invalid in the long term.The proportional errors of the state of charge rise with the accumulated state of charge and reach their worst value within one charge/discharge cycle. The study presents methods for reducing time-cumulative and state-of-charge-proportional mistakes through simulation analysis.

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“…VS can also be used to replace or provide redundancy for cases where the installation of a physical sensor is challenging or sensors are unreliable or expensive. For example, pressure measurement in a shock absorber or force measurement in wheel carrier/bearing [12,13]. VS technology requires additional development costs but reduces repetitive maintenance costs [10].…”

Section: Introductionmentioning

confidence: 99%

Feasibility of a Neural Network-Based Virtual Sensor for Vehicle Unsprung Mass Relative Velocity Estimation

Šabanovič

Kojis

Šukevičius

et al. 2021

Sensors

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With the automotive industry moving towards automated driving, sensing is increasingly important in enabling technology. The virtual sensors allow data fusion from various vehicle sensors and provide a prediction for measurement that is hard or too expensive to measure in another way or in the case of demand on continuous detection. In this paper, virtual sensing is discussed for the case of vehicle suspension control, where information about the relative velocity of the unsprung mass for each vehicle corner is required. The corresponding goal can be identified as a regression task with multi-input sequence input. The hypothesis is that the state-of-art method of Bidirectional Long–Short Term Memory (BiLSTM) can solve it. In this paper, a virtual sensor has been proposed and developed by training a neural network model. The simulations have been performed using an experimentally validated full vehicle model in IPG Carmaker. Simulations provided the reference data which were used for Neural Network (NN) training. The extensive dataset covering 26 scenarios has been used to obtain training, validation and testing data. The Bayesian Search was used to select the best neural network structure using root mean square error as a metric. The best network is made of 167 BiLSTM, 256 fully connected hidden units and 4 output units. Error histograms and spectral analysis of the predicted signal compared to the reference signal are presented. The results demonstrate the good applicability of neural network-based virtual sensors to estimate vehicle unsprung mass relative velocity.

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Load Current and State-of-Charge Coestimation for Current Sensor-Free Lithium-Ion Battery

Cited by 74 publications

References 21 publications

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

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

A Critical Look at Coulomb Counting Approach for State of Charge Estimation in Batteries

Feasibility of a Neural Network-Based Virtual Sensor for Vehicle Unsprung Mass Relative Velocity Estimation

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