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Batteries are widely used as storage devices and they have recently gained popularity due to their increasing smaller sizes, lighter weights and greater energy densities. These characteristics also render them suitable for powering electric vehicles. However, a key gap exists in that batteries are solely used as storage devices with a lack of information flow. Next-generation battery technologies will constitute the enabling tools that would lead to information-rich batteries, thus allowing the transparent assessment of a battery's health as well as the prediction of a battery's remaining-useful-life (RUL) and its subsequent impact on vehicle mobility. Various methods and techniques have been employed to predict battery RUL in order to improve the accuracy of the State of Charge (SoC) estimation.

Li-ion Batteries are one of the most critical components of the next generation Hybrid Electric Vehicles (HEV) as degradation or failure of the Li-ion battery could lead to reduced performance, operational impairment and even catastrophic safety issues. An effective diagnostics and prognostics system for Li-ion battery health monitoring would greatly improve the reliability of such systems and thus secure general public acceptance. This paper presents a similarity-based health assessment method for Li-ion battery. Instead of physically diagnosing the health of the Li-ion battery, the proposed method defines the healthy operations (charging and discharging) as the baseline and the deviation from this baseline is treated as the degradation. Specifically, novel features are extracted from the voltage, current and temperature measurements firstly. Then Principal Component Analysis (PCA) is applied to minimize the dimensionality of the multivariate feature space.

Vehicle electronics and sensor systems have become indispensable parts in providing safety, comfort, personal communication mobility and many other advanced functions in today's vehicles. As a result, reliability requirements for these critical parts have become extremely important. To meet these requirements, more advanced technologies and tools for degradation monitoring and failure prevention are needed. Currently, the development of diagnostics and prognostics techniques, which employ accurate degradation quantification by appropriate sensor selection, location decision, and feature selection and feature fusion, still remains a vital and unsolved issue. This paper addresses several realistic concerns of failure prevention in vehicle electronics and sensor systems. A unified monitoring and prognostics approach that prevents failures by analyzing degradation features, driven by physics-of-failure, is suggested as a general framework to overcome the unsolved challenge.