The accurate estimation of the state of charge, the state of health and the prediction of remaining useful life of lithium–ion batteries is an important component of battery management. It is of great significance to prolong battery life and ensure the reliability of the battery system. Many researchers have completed a large amount of work on battery state evaluation and RUL prediction methods and proposed a variety of methods. This paper first introduces the definition of the SOC, the SOH and the existing estimation methods. Then, the definition of RUL is introduced, and the main methods are classified and compared. Finally, the challenges of lithium–ion battery state estimation and RUL prediction are summarized, and the direction for future development is proposed.

The state of health and remaining useful life of lithium-ion batteries are important indicators to ensure the reliable operation of these batteries. However, because they cannot be directly measured and are affected by many factors, they are difficult to predict. This paper presents method of jointly predicting state of health and RUL based on the long short-term memory neural network and Gaussian process regression. This method extracts the batteries’ health factors from the charging curve, selects health factors with more relevance than the setting standard as the characteristic of capacity by the maximum information coefficient method, and establishes the battery aging and remaining useful life prediction models with Gaussian process regression. On this basis, the long short-term memory neural network is used to predict the trend of the change in health factors with the increase in cycles, and the results are input into a Gaussian process regression aging model to predict the state of health. Taking the health factors and state of health as the characteristics of remaining useful battery life, a battery remaining useful life model based on Gaussian process regression is established, and the change trend in the remaining useful life can be obtained by inputting the predicted health factors and state of health. In this study, four battery data sets with different depths of charge were used to verify the accuracy and adaptability of the algorithm. The results show that the proposed algorithm has high accuracy and reliability.

Solar‐aided coal‐fired power generation (SAPG) has been attracting more and more attentions in recent years. However, the multi‐objective optimization of SAPG system considering off‐design work conditions has not been fully studied. In this paper, a general system integration optimization method (GIOM) has been developed for integration schemes optimization, and Nondominated Sorting Genetic Algorithm‐II (NSGA‐II) algorithm is used for multi‐objective optimization of SAPG system. As a case study, a SAPG system that refers to a real 600 MWe supercritical coal‐fired power plant is optimized based on a typical day. By simultaneous optimization of both initial investment (P) and solar net electric generation (E_solar), the solar subsystem design schemes are optimized as Pareto optimal set. At last, to maximum E_solar/P, the final design scheme is selected from the Pareto optimal set, whose E_solar and P are 237.6 MWh and 113.5 M$, respectively. The results show that, the final design scheme is a heliostat field with instantaneous optical efficiency of 63.24% at the design point, the receiver power rating is 133 MWt, the tower height is 136.6 m, and distances of successive rows of the three zones are 13.6, 17.7, and 24.5 m, respectively. A larger or smaller size of solar subsystem is considered to be uneconomical.