Accurate prediction of batteries’ future degradation is a key solution to relief users’ anxiety on battery lifespan and electric vehicle’s driving range. Technical challenges arise from the highly nonlinear dynamics of battery aging. In this paper, a feed-forward migration neural network is proposed to predict the batteries’ aging trajectories. Specifically, a base model that describes the capacity decay over time is first established from the existed battery aging dataset. This base model is then transformed by an input-output slope-and-bias-correction (SBC) method structure to capture the degradation of target cell. To enhance the model’s nonlinear transfer capability, the SBC-model is further integrated into a four-layer neural network, and easily trained via the gradient correlation algorithm. The proposed migration neural network is experimentally verified with four different commercial batteries. The predicted RMSEs are all lower than 2.5% when using only the first 30% of aging trajectories for neural network training. In addition, illustrative results demonstrate that a small size feed-forward neural network (down to 1-5-5-1) is sufficient for battery aging trajectory prediction.

Lithium-ion (Li-ion) batteries undergo complex electrochemical and mechanical degradation. This complexity is pronounced in applications such as electric vehicles, where highly demanding cycles of operation and varying environmental conditions lead to non-trivial interactions of ageing stress factors. This work presents the framework for an ageing diagnostic tool based on identifying and then tracking the evolution of model parameters of a fundamental electrochemistry-based battery model from non-invasive voltage/current cycling tests. In addition to understanding the underlying mechanisms for degradation, the optimisation algorithm developed in this work allows for rapid parametrisation of the pseudo-two dimensional (P2D), Doyle-Fuller-Newman, battery model. This is achieved through exploiting the embedded symbolic manipulation capabilities and global optimisation methods within MapleSim. Results are presented that highlight the significant reductions in the computational resources required for solving systems of coupled non-linear partial differential equations.

Accurate monitoring of the internal statuses is highly valuable for the management of the lithium-ion battery (LIB). This article proposes a thermal-model-based method for multistate joint observation, enabled by a novel smart battery design with an embedded and distributed temperature sensor. In particular, a novel smart battery is designed by implanting the distributed fiber optical sensor internally and externally. This promises a real-time distributed measurement of LIB internal and surface temperature with a high space resolution. Following this endeavor, a low-order joint observer is proposed to coestimate the thermal parameters, heat generation rate, state of charge, and maximum capacity. Experimental results disclose that the smart battery has space-resolved self-monitoring capability with high reproducibility. With the new sensing data, the heat generation rate, state of charge, and maximum capacity of LIB can be observed precisely in real time. The proposed method validates to outperform the commonly-used electrical-model-based method regarding the accuracy and the robustness to battery aging.

A 1D electrochemical-thermal model of an electrode pair of a lithium ion battery is developed in Comsol Multiphysics. The mathematical model is validated against the literature data for a 10 Ah lithium phosphate (LFP) pouch cell operating under 1 C to 5 C electrical load at 25 °C ambient temperature. The validated model is used to conduct statistical analysis of the most influential parameters that dictate cell performance; i.e., particle radius ( $r_p$ ); electrode thickness ( $L_{pos}$ ); volume fraction of the active material ( ${\epsilon }_{s,pos}$ ) and C-rate; and their interaction on the two main responses; namely; specific energy and specific power. To achieve an optimised window for energy and power within the defined range of design variables; the range of variation of the variables is determined based on literature data and includes: $r_p$ : 30–100 nm; $L_{pos}$ : 20–100 μm; ${\epsilon }_{s,pos}$ : 0.3–0.7; C-rate: 1–5. By investigating the main effect and the interaction effect of the design variables on energy and power; it is observed that the optimum energy can be achieved when (r_p < 40 nm); (75 μm < L_pos < 100 μm); (0.4 < ε_s,pos < 0.6) and while the C-rate is below 4C. Conversely; the optimum power is achieved for a thin electrode ( $L_{pos}$ < 30 μm); with high porosity and high C-rate (5 C).

Within Li-ion batteries, lithium plating is considered as one of the main reasons behind the capacity fade that occurs during low temperature and fast charging conditions. Previous studies indicate that plating is influenced by the levels of loss of lithium inventory (LLI) and the loss of active material (LAM) present in a battery. However, it is not clear from the literature on how lithium plating influences battery degradation in terms of LAM and LLI. Quantifying the undesirable impacts of lithium plating can help in understanding its impact on battery degradation and feedback effects of previous lithium plating on the formation of present plating. This study aims to quantify the degradation modes of lithium plating: LLI, LAM at the electrode level. A commercial Li-ion cell was first, aged using two different cases: with and without lithium plating. Second, a degradation diagnostic method is developed to quantify the degradation modes based on their measurable effects on open-circuit voltage (OCV) and cell capacity. The results highlight that LAM_NE and LLI levels under the fast charge profile are increased by 10% and 12%, respectively, compared to those under the less aggressive charge profile. Further, limitations of the degradation analysis methods are discussed.