Lithium-ion batteries are widely used in new energy vehicles due to their high energy density, high power density and low self-discharge characteristics. However, the battery life can hardly meet the needs of users, limiting the further development of electric vehicles. Therefore, the aging mechanism of the battery and the effect of battery decay should be considered when optimizing battery design and management.

From the perspective of battery design: At the battery level, the aging mechanism and decay model of the battery need to be studied, especially the key parameters of the battery and the impact of other key parameters (such as energy density and power density) on the battery also need to be discussed. The key parameters noted here include the thickness, porosity, particle size, cell size, cell shape, etc. of the anode and cathode active materials. These parameters can be optimized based on multi-objective optimization algorithms to design better batteries. At the battery system level, battery aging mechanisms and degradation models are also very important. The impact of electrical, mechanical and/or thermal factors on battery life needs to be analyzed based on aging mechanisms and degradation models. In order to ensure the service life of the battery system, the design of the battery system can be optimized, including battery preloading, battery thermal management system (TMS).

From a battery management perspective, battery aging mechanisms and decay models are important for the assessment of battery health (influenced by past usage and current operating conditions) and the prediction of performance.

(1) Generally speaking, the estimation of used battery health is also called State Health Assessment (SOH). Typically battery performance (such as usable capacity, usable energy, and usable power) degrades with the age of the battery. Therefore, the BMS (battery management system) needs to estimate the SOH of the battery according to the aging mechanism of the battery and the decay model of the battery. This result has important reference value for other estimation algorithms in BMS. According to the SOH results, the battery can be used reasonably without abuse and safety accidents.
(2) In general, optimizing the current operating state means SOP (State of Power) evaluation and thermal management. Of course, different operating conditions have different effects on the future service life of the battery. Therefore, based on the aging mechanism under different working conditions and the corresponding battery degradation model, BMS can predict the damage of the battery under different working conditions. Then based on the life and performance analysis of the battery, using an online optimization method, the BMS can coordinate the charge-discharge state and temperature of the battery.
(3) Usually, prediction of future performance means prediction of RUL (Remaining Useful Life). RUL is very useful for the on-line management of batteries, the evaluation of used cars and the cascade use of batteries, especially the evaluation of the residual value of batteries. Considering the non-linear fading characteristics of batteries, traditional extrapolation methods cannot accurately predict the remaining life of batteries. It is necessary to achieve reliable predictions based on the main aging mechanisms of different decay states under different operating conditions and the corresponding battery life models.

From the perspective of the system, it can be seen that to solve a series of battery design and management problems related to battery aging, it is necessary to review, summarize and analyze the current research status of battery aging, including influencing factors, aging mechanisms, aging models and diagnostic methods. However, existing review papers mainly focus on a typical point.

The battery life cycle includes battery design, production, EV application and secondary use. The degradation of battery performance should be considered in the earliest battery design steps. At different stages, the decay phenomenon and internal aging mechanism of the battery may be very different.

This paper provides a comprehensive review of the key issues of battery degradation from a system perspective, including the following aspects: battery internal aging mechanism and external characteristics, analysis of factors affecting battery life from a design perspective, production and application, battery degradation model, Battery aging mechanisms and models.

Generally, the battery aging analysis should be carried out from several aspects such as influencing factors, internal side reactions, aging modes, and external influences, as shown in Figure 3. The most intuitive external features of battery fade are capacity fade and/or power fade. At present, most of the papers still focus on these two points to do battery aging research and modeling. Typically, power attenuation is more difficult to study and is replaced by the study of internal impedance.

Regarding the battery's decay mode, for battery management and online diagnosis, the aging mechanism of the battery can be summarized as: Lithium-ion storage loss (LLI) and anode/cathode active material loss (LAM). The two-box model can describe the corresponding aging mechanism. Generally speaking, the charge-discharge process of a battery is intrinsically related to the insertion and delamination of lithium ions on the positive and negative active materials. Therefore, the battery capacity is directly determined by the amount of active material and the number of available lithium ions. The active material is like a water tank, and the lithium ions are like the water in the tank, as shown in Figure 4. So the main aging mechanisms for Li-ion batteries are LAM (which is like changes in the tank itself) and LLI (which is like water loss in the tank). In addition, the decay mode of the battery also includes internal resistance increase (RI) and electrolyte loss (LE). The increase in internal resistance will directly lead to the power attenuation of the battery, and the available capacity of the battery will also decrease. Loss of electrolyte is also a very important mode of decay. A small amount of electrolyte loss has little effect on battery performance, while excessive electrolyte loss may directly lead to a sudden capacity drop.

Inside the battery, these decay modes are caused by some internal physical or chemical side effects, and the side effects related to aging are very complex. LAM may be caused by these factors: graphite exfoliation; metal dissolution and electrolyte decomposition; active material loss of contact due to current collector corrosion and binder decomposition. The formation of LLI may be related to SEI (solid electrolyte interface) film formation and continuous thickening, CEI (cathode electrolyte interface) formation, lithium ion deposition and other factors. The formation of LE may be due to electrolyte consumption caused by side reactions such as SEI film thickening and high potential-induced electrolyte decomposition. Whereas, RI may be caused by the formation and continued thickening of SEI and LE, etc.

The results show that there are many factors that affect the side reactions inside the battery, including battery design, production and working conditions. These factors will affect the rate of side reactions inside the battery, thereby affecting the life characteristics of the battery.