With the rapid development of new energy electric vehicles and smart grids, the demand for batteries is increasing. The battery management system (BMS) plays a crucial role in the battery-powered energy storage system. This paper presents a systematic review of the most commonly used battery modeling and state estimation approaches for BMSs. The models include the physics-based electrochemical models, the integral and fractional order equivalent cir. With the rapid development of new energy electric vehicles and smart grids, the demand for batteries is increasing. The battery management system (BMS) plays a crucial role in the battery-powered energy storage system. This paper presents a systematic review of the most commonly used battery modeling and state estimation approaches for BMSs. The models include the physics-based electrochemical models, the integral and fractional order equivalent circuit models, and data-driven models. The state estimation approaches are analyzed from the perspectives of remaining capacity and energy estimation, power capability prediction, lifespan and health prognoses, and other crucial indexes in BMS. This present paper, through the analysis of literature, includes almost all states in the BMS. The estimation approaches of state-of-charge (SOC), state-of-energy (SOE), state-of-power (SOP), state-of-function (SOF), state-of-health (SOH), remaining useful life (RUL), remaining discharge time (RDT), state-of-balance (SOB), and state-of-temperature (SOT) are reviewed and discussed in a systematical way. Moreover, the challenges and outlooks of the research on future battery management are disclosed, in the hope of providing some inspirations to the development and design of the next-generation BMSs.••••Battery modeling methods are systematically overviewed.••Battery state estimation methods are reviewed and discussed.••Future research challenges and outlooks are disclosed.••Battery management scheme based on big data and cloud computing is proposed.Battery modelingState estimationEnergy storageBattery managementEnergy storage technology is one of the most critical technology to the development of new energy electric vehicles and smart grids. Benefit from the rapid expansion of new energy electric vehicle, the lithium-ion battery is the fastest developing one among all existed chemical and physical energy storage solutions. In recent years, the frequent fire accidents of electric vehicles have pushed electric vehicles to the subject of public opinion, and also put forward high requirements and challenges for battery management technology. As one of the key components of electric vehicles, the lithium-ion battery management system (BMS) is crucial to the industrialization and marketization of electric vehicles. Therefore, developing advanced and intelligent BMSs for the lithium-ion battery packs has become a hot research topic.The main technical difficulties restricting the development of battery management technology can be concluded in the following three aspects: (1) the lithium battery system is highly nonlinear, with multi-spatial scale (such as nanometer active materials, millimeter cell, and meter battery pack, etc.) and multi-time scale aging, making it difficult to accurately modeling; (2) the internal states of the battery cannot be obtained by direct measurement approach and is easily affected by environmental temperature, noise, etc. The upsizing of power batteries reduces the representativeness of measured values, and reduces th. The battery models presented in literature mainly fall into the following three main categories: the physics-based electrochemical models, the electrical equivalent circuit models (include the integral-order and fractional-order models) [8,9], and the data-driven models establish by artificial intelligence algorithms such as the neural network.
