Laboratory ageing campaigns elucidate the complex degradation behaviour of most technologies. In lithium-ion batteries, such studies aim to capture realistic ageing mechanisms to optimize cell chemistries and designs as well as to engineer reliable battery management systems. In this study, we systematically compared dynamic discharge profiles representative of electric vehicle driving to the well-accepted constant current profiles. Surprisingly, we found that dynamic discharge enhances lifetime substantially compared with constant current discharge. Specifically, for the same average current and voltage window, varying the dynamic discharge profile led to an increase of up to 38% in equivalent full cycles at end of life. Explainable machine learning revealed the importance of both low-frequency current pulses and time-induced ageing under these realistic discharge conditions. This work quantifies the importance of evaluating new battery chemistries and designs with realistic load profiles, highlighting the opportunities to revisit our understanding of ageing mechanisms at the chemistry, material and cell levels.

Lithium-ion batteries have been widely used in electric vehicles. To ensure safety and reliability, accurate prediction of the battery’s future degradation trajectory is critical. However, early prediction capability and adaptive prediction capability under various battery aging conditions remain two main challenges. Either physics-based or data-driven methods have their advantages and limitations. In this study, a novel hybrid method that combines the physics-based and data-driven approaches is proposed to achieve early prediction of the battery capacity degradation trajectory. This framework consists of three steps. First, to improve the generality of the method, a hybrid feature is extracted using an electrochemical model and measured voltage data. Second, the clustering algorithm is adopted to divide battery degradation data into different clusters, and the data augmentation technique is used to enrich the training dataset. Finally, the training dataset in each cluster is used to train the sequence-to-sequence deep neural network, and the future degradation trajectory can be predicted. The proposed method provides accurate predictions using only 20% training data, and it has strong robustness under noisy input. Validation results under different aging conditions show that the mean absolute percentage errors of capacity degradation trajectory and remaining useable cycle life are below 2.5% and 6.5%, respectively.

In this paper, a compact all-solid-state pulsed power modulator was designed based on Marx generator and pulse transformer. It is consisted of a six-stage Marx generator using power MOSFETs as switches to generate primary voltage pulses and a pulse transformer through a closed magnetic core to boost and convert the polarity of the output high-voltage (HV) pulse. A MOSFET driver circuit based on optical-electrical isolation was designed for controlling the Marx generator. It shows that the output pulse shapes, especially the rising and falling phases, are determined by the design of the pulse transformer such as its leakage inductance, distributed capacitance, magnetization inductance, and step-up ratio. The designed modulator is able to generate HV pulses with amplitude ranging from -35 to 35 kV, repetition frequency up to 10 kHz, minimum pulsewidth 250 ns. Various types of plasma discharges, including air microdischarge for local treatments, plate-to-plate air dielectric-barrier discharge, underwater discharge, are able to be driven with good reproducibility by this compact pulse modulator.

Battery health prediction is significant while challenging for intelligent battery management. This article proposes a general framework for both short-term and long-term predictions of battery health under unseen dynamic loading and temperature conditions using domain-adaptive multitask learning (MTL) with long-term regularization. First, features extracted from partial charging curves are utilized for short-term state of health predictions. Then, the long-term degradation trajectory is directly predicted by recursively using the predicted features within the multitask framework, enhancing the model integrity and lowering the complexity. Then, domain adaptation (DA) is adopted to reduce the discrepancies between different working conditions. Additionally, a long-term regularization is introduced to address the shortcoming that arises when the model is extrapolated recursively for future health predictions. Thus, the short-term prediction ability is maintained while the long-term prediction performance is enhanced. Finally, predictions are validated through aging experiments under various dynamic loading profiles. By using partial charging capacity–voltage data, the results show that the early-stage long-term predictions are accurate and stable under various working profiles, with root mean square errors below 2% and fitting coefficients surpassing 0.86.

Electrochemical models provide insights into the battery internal states and have become powerful tools for battery design and management. These models consist of partial differential equations (PDEs) that are solved numerically. In this paper, we compare two spatial discretization methods commonly used to numerically solve the governing PDEs in the context of Lithium ion batteries, namely finite difference method (FDM) and finite volume method (FVM) in terms of model accuracy and mass conservation guarantee. First, we provide the mathematical details to carry out the spatial discretization for both FDM and FVM to solve the battery single particle model (SPM). SPM parameters are identified from experimental data, and sensitivity analysis is conducted to study parameter identifiability under different current input profiles, followed by model accuracy and mass conservation analysis of the two numerical schemes. Leveraging the third order Hermite extrapolation approach, an enhanced FVM scheme is proposed in this paper to improve the model accuracy of standard FVM which relies on linear extrapolation. This paper shows that the FVM scheme with Hermite extrapolation leads to accurate and robust control-oriented battery model while guaranteeing mass conservation and high accuracy.