Estimation of State of Health (SoH) of Lithium-ion (Li-ion) battery is essential to predict the lifespan of batteries of an electric vehicle (EV). The efficient prediction of battery health indicates to the effective and safe operation of EV. However, delivering an effective and accurate method for the estimation of SoH in the real condition is truly a challenging task. The present study proposed a holistic procedure of combining both experimental and numerical investigations to conduct the fundamental study on coupled mechanical-electrochemical behavior of Li-ion battery. The proposed investigation highlighted the effect of stress on the capacity of the battery, considering capacity fade as an equivalent parameter to its health for real-time estimation of SoH. Finally, a simple model of Artificial Neural Network (ANN) is provided, which shows the linear dependency of stress with the SoH. The results obtained from the ANN model are validated with a Linear Regression (LR) model for a better understanding of the inspection. The predicted value of mean Square Error (MSE) and R square error in the ANN training model are found to be 0.000309 and 0.849687, respectively. Whereas for the test model, these predicted values are found to be 0.000438 and 0.819347, respectively.

锂离子（Li-ion）电池的健康状态（SoH）估算对于预测电动汽车（EV）的电池寿命至关重要。电池健康状况的有效预测表明电动汽车的有效和安全运行。但是，提供一种有效，准确的方法来估算实际状态下的SoH确实是一项艰巨的任务。本研究提出了一种综合程序，将实验和数值研究相结合，以进行有关锂离子电池机械-电化学行为耦合的基础研究。拟议的调查强调了应力对电池容量的影响，将容量衰减作为其健康状况的等效参数用于SoH的实时估算。最后，提供了一个简单的人工神经网络（ANN）模型，它显示了应力与SoH的线性关系。从ANN模型获得的结果已通过线性回归（LR）模型进行了验证，以更好地了解检查情况。在ANN训练模型中，均方误差（MSE）和R方误差的预测值分别为0.000309和0.849687。而对于测试模型，发现这些预测值分别为0.000438和0.819347。