Analysis of potential lifetime extension through dynamic battery reconfiguration

Growing demands for electrification result in increasingly larger battery packs. Due to factors such as cell position in the pack and variations in the manufacturing process, the packs exhibit variations in the performance of their constituent cells. Moreover, due to the fixed cell configuration, the weakest cell renders the pack highly susceptible to these variations. Reconfigurable battery pack systems, which have increased control flexibility due to additional power electronics, present a promising solution for these issues. Nevertheless, to what extent they can prolong the battery lifetime has not been investigated. This simulation study analyzes the potential of dynamic reconfiguration for extending battery lifetime w.r.t. several parameters. Results indicate that the lifetime extension is larger for series than for parallel configurations. For the latter, the dominant factor is equivalent full cycles spread at the end of life, but resistance increase with age and the number of cells in parallel are also influential. Finally, for the former, the number of series-connected elements amplifies these effects.Comment: Accepted to the 25th European Conference on Power Electronics and Applications (EPE 2023 ECCE Europe

Combining offline and online machine learning to estimate state of health of lithium-ion batteries

This article reports a new state of health (SOH) estimation method for lithium-ion batteries using machine learning. Practical problems with cell inconsistency and online implementability are addressed using a proposed individualized estimation scheme that blends a model migration method with ensemble learning. A set of candidate models, based on slope-bias correction (SBC) and radial basis function neural networks (RBFNNs), are first trained offline by choosing a single-point feature on the incremental capacity curve as the model input. For online operation, the prediction errors due to cell inconsistency in the target new cell are next mitigated by a proposed modified random forest regression (mRFR) for high adaptability. The results show that compared to prevailing methods, the proposed SBC-RBFNN-mRFR-based scheme can achieve considerably high SOH estimation accuracy with only a small amount of early data and online measurements are needed for practical operation

Fast charging control of Lithium-ion batteries: Effects of input, model, and parameter uncertainties

The foundation of advanced battery management is computationally efficient control-oriented models that can capture the key battery characteristics. The selection of an appropriate battery model is usually focused on model order, whereas the effects of input and parameter uncertainties are often overlooked. This work aims to pinpoint the minimum model complexity for health-conscious fast charging control of lithiumion batteries in relation to sensor biases and parameter errors. Starting from a high-fidelity physics-based model that describes both the normal intercalation reaction and the dominant side reactions, Pad\ue9 approximation and the finite volume method are employed for model simplification, with the number of control volumes as a tuning parameter. For given requirements on modeling accuracy, extensive model-based simulations are conducted to find the simplest models, based on which the effects of current sensor biases and parameter errors are systematically studied. The results show that relatively loworder models can be well qualified for the control of voltage, state of charge, and temperature. On the other hand, high-order models are necessary for health management, particularly during fast charging, and the choice of the safety margin should also take the current sensor biases into consideration. Furthermore, when the parameters have a certain extent of uncertainties, increasing the model order will not provide improvement in model accuracy

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

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 non-monotonic 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

Nonlinear Model Inversion-Based Output Tracking Control for Battery Fast Charging

We propose a novel nonlinear control approach for fast charging of lithium-ion batteries, where health- and safety-related variables, or their time derivatives, are expressed in an input-polynomial form. By converting a constrained optimal control problem into an output tracking problem with multiple tracking references, the required control input, i.e., the charging current, is obtained by computing a series of candidate currents associated with different tracking references. Consequently, an optimization-free nonlinear model inversion-based control algorithm is derived for charging the batteries. We demonstrate the efficacy of our method using a spatially discretized high-fidelity pseudo-two-dimensional (P2D) model with thermal dynamics. Conventional methods require computationally demanding optimization to solve the corresponding fast charging problem for such a high-order system, leading to practical difficulties in achieving low-cost implementation. Results from comparative studies show that the proposed controller can achieve performance very close to nonlinear and linearized model predictive control but with much lower computational costs and minimal parameter tuning efforts

State of health estimation for lithium-ion batteries under arbitrary usage using data-driven multi-model fusion

Accurately estimating the state of health (SoH) of batteries is indispensable for the safety, reliability, and optimal energy and power management of electric vehicles. However, from a data-driven perspective, complications, such as dynamic vehicle operating conditions, stochastic user behaviors, and cell-to-cell variations, make the estimation task challenging. This work develops a data-driven multi-model fusion method for SoH estimation under arbitrary usage profiles. All possible operating conditions are categorized into six scenarios. For each scenario, an appropriate feature set is extracted to indicate the SoH. Based on the obtained features, four machine learning algorithms are applied individually to train SoH estimation models using time-series data. In addition to the estimates at the current time step, a histogram data-based and online adaptive model is taken from previous work for predicting the next-step SoH. Then, a Kalman filter is applied to systematically fuse the results of all the estimation and prediction models. Experimental data collected from different types of batteries operated under diverse profiles verify the effectiveness and practicability of the developed method, as well as its superiority over individual models

Model-based state of charge estimation algorithms under various current patterns

Numerous model-based techniques have been proposed to estimate the state of charge (SOC) of lithium-ion batteries. In automotive applications, the algorithms are subjected to changing load profiles, requiring investigations into their general performance under various working conditions. In this study, three different load patterns derived from a customized dynamic driving profile, a standard driving cycle, and a constant discharge are used for the experimental verification. Four selected algorithms including the Ampere-hour counting, the extended Kalman filter, the particle filter, and the recursive least square filter are implemented. Their performance in terms of accuracy and robustness are compared. In addition, the load profile is analyzed in the frequency domain. The results show that the filter performance is dependent on the current patterns and can be correlated to the frequency spectrum of the load profile

Next-Generation Battery Management Systems: Dynamic Reconfiguration

Batteries are widely applied to the energy storage and power supply in portable electronics, transportation, power systems, communication networks, etc. They are particularly demanded in the emerging technologies of vehicle electrification and renewable energy integration for a green and sustainable society. To meet various voltage, power, and energy requirements in large-scale applications, multiple battery cells have to be connected in series and/or parallel. While battery technology has advanced significantly in the past decade, existing battery management systems (BMSs) mainly focus on state monitoring and control of battery systems packed in fixed configurations. In fixed configurations, though, the battery system performance is in principle limited by the weakest cells, which can leave large parts severely underutilized. Allowing dynamic reconfiguration of battery cells, on the other hand, allows individual and flexible manipulation of the battery system at cell, module, and pack levels, which may open up a new paradigm for battery management. Following this trend, this paper provides an overview of next-generation BMSs featuring dynamic reconfiguration. Motivated by numerous potential benefits of reconfigurable battery systems (RBSs), the hardware designs, management principles, and optimization algorithms for RBSs are sequentially and systematically discussed. Theoretical and practical challenges during the design and implementation of RBSs are highlighted in the end to stimulate future research and development