Summary Estimating the dynamic status parameters of a battery, such as its state of health (SoH) and remaining useful life (RUL), is still a very difficult and complex task. In this paper we perfor...
Conclusions State estimation is one of the most basic functions of BMS. Accurate state estimation can prolong the battery life and improve battery safety. This paper comprehensively reviews the research status, technical challenges, and development direction of typical battery state estimation (SOC, SOH, SOE, and SOP).
Finally, the development trends of state estimation are prospected. Advanced technologies such as artificial intelligence and cloud networking have further reshaped battery state estimation, bringing new methods to estimate the state of the battery under complex and extreme operating conditions.
The accurate co-estimation of battery states under complex and extreme working conditions is challenging research, and intelligent batteries and advanced technologies are reshaping the battery state estimation methods.
Specifically, limited measurable signals, mismatch of model parameters, severe nonlinearity, and time variability of battery state are the main challenges of battery state estimation. Accordingly, intelligent sensing, cloud computing, big data, and intelligent algorithms are feasible solutions.
By adopting multi-dimensional, multi-level, and multi-scale signal information mining and state estimation representation, combined with the characteristics of discontinuous and continuous information, the combined optimal joint estimation method can solve the problem of battery state estimation accuracy under complex and extreme working.