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Developing a fast and safe charging strategy has been one of the key breakthrough points in lithium battery development owing to its range anxiety and long charging time. The majority of current model-based charging strategies are developed for deterministic systems. Real battery dynamics are, however, affected by model mismatches and process uncertainties, which may lead to constraint violations and even premature aging. This article proposes a fast charging scheme based on distributionally robust model predictive control (DRMPC) against uncertainty. Specifically, a coupled electrothermal-aging model is first introduced to describe the battery behavior, and electrothermal parameters of the adopted model are identified online based on the recursive least-squares algorithm. Subsequently, an online DRMPC-based charging framework is proposed, utilizing the Wasserstein ball centered on the empirical distribution to characterize uncertainty. Finally, the proposed algorithm is compared to model predictive control and constant current-constant voltage algorithms, and its effectiveness is validated on a real battery simulator. Results show that the proposed algorithm can handle the uncertainty effectively while satisfying the constraints, and significantly improve the charging speed.

The state of health (SOH) plays a significant role in the mileage and safety of an electric vehicle (EV). In recent years, many methods based on data-driven analysis and laboratory measurements have been developed for SOH estimation. However, most of these proposed methods cannot be applied to real-world EVs. Here, we present a method for SOH estimation based on real-world EV data. A battery-aging evaluation health index (HI) with a strong correlation to the SOH is retrieved from battery-aging data and then modified with thermal factors to depict the former SOH. Afterward, a local weighted linear-regression algorithm is used to qualitatively characterize the declining trend of the HI, which eliminates the local HI fluctuation caused by data noise. Subsequently, a series of features-of-interest (FOIs) is extracted according to the battery consistency, cell-voltage extrema, and cumulative mileage, and validated using the grey relational analysis. Finally, a battery-degradation model is built using the extreme gradient-boosting algorithm with the selected FOIs. The experimental results from real-world data indicate that the proposed method has high estimation accuracy and generalization, and the maximum error is around 2% for batteries in real-world EVs.

Metal ion batteries with “water-in-salt” electrolyte are well known for their low cost and high safety, which are especially suitable for powering wearable electronics. Among them, the Al ion battery holds a great advantage in metal abundance and energy density, which has also been proved reliable with the “water-in-salt” electrolyte system. To facilitate its efficient manufacturing, screen-printing is adopted to develop a “water-in-salt” Al ion battery for the first time. Both the Al anode ink and the graphite cathode ink are prepared and printed onto opposite sides of a cellulose paper, while the “water-in-salt” AlCl3electrolyte is stored inside the paper by an absorption-evaporation process. Specifically, three anode printing configurations are compared, whose effect on battery performance and durability is studied comprehensively. In general, the battery prototype can sustain a high specific current of 1.0 A g−1and specific capacity of 120 mAh g−1. A satisfactory lifetime of 550 cycles is also achievable at a lower specific current of 0.25 A g−1. Moreover, this battery can tolerate repetitive bending of 1000 times and even the puncture of its electrodes, proving its great flexibility, robustness and safety for wearable applications. The monolithic battery pack printing is also demonstrated successfully in the end.

Accurately end-of-discharge prediction of lithium-ion batteries is of great significance for user convenience and optimal path-planning and energy-dispatching in various applications. However, accurate state-of-charge estimation, dynamic and stochastic operating conditions, and uncertainty quantification have remained major challenges. This paper thus proposes a probabilistic scheme for forecasting dischargeable time via a robust observer design and statistical characterization of future loading profiles. First, a robust observer with stability and convergence analysis using an equivalent-circuit model and Lyapunov theory is proposed to guarantee the robustness of state-of-charge estimations. Then, a power histogram is formulated to characterize future loading profiles. Next, a Gaussian process regression and a bootstrap sampling technique are combined to perform dischargeable time forecasting and uncertainty quantification. Finally, the proposed scheme is validated using experimental data acquired from battery cells at different temperatures and loading profiles. Experimental results show that the robust state-of-charge estimation can converge to the true value within an error of 3.50% against over 10% capacity biases. It also demonstrates that the proposed forecasting method can provide dischargeable time prediction within an error of 0.66h, about 20% of the total dischargeable time.

The continuously increasing energy and power density of lithium-ion batteries will aggravate the safety and reliability concerns of advanced battery management systems (BMSs). To ensure the safety and reliability of lithium-ion batteries, the BMS must implement anomaly detection algorithms that are capable of capturing abnormal behaviors. Thermal anomalies are one of the most critical anomalies that can be potentially catastrophic. Motivated by this, a model-based strategy of anomaly detection of thermal parameters for lithium-ion-batteries is presented in this paper. The algorithm is based on a multiple-model adaptive estimation framework. Firstly, an equivalent-circuit-model-based electrothermal model is proposed to describe battery dynamic behaviors. Then, a combination of the recursive-least-square method and Kalman-filter is employed to generate residual signals for thermal anomaly detection. Furthermore, the probability of the signature anomaly is evaluated through the multiple-model adaptive estimation technique. Distinguished from existing threshold-based methods, the proposed method can determine particular anomalies according to the value of the generated conditional probability, without a manually determined threshold. Simulations are developed to simulate different faults and generate data for algorithm validation. The results show signature thermal anomaly can be detected accurately.