A state-of-charge estimation method of the power lithium-ion battery in complex conditions based on adaptive square root extended Kalman filter

A review on online state of charge and state of health estimation for lithium-ion batteries in electric vehicles

As a new type of energy storage device, supercapacitors are widely applied in various fields owing to their irreplaceable extraordinary characteristics. The remaining useful life represents the safe service range of a supercapacitor. Precise monitoring of its value can ensure timely replacement before reaching the service limit. An accurate state-of-charge estimation can ensure that the supercapacitor works in a safe area. Superior precision ensures that the safe area is more explicit. Thus, the supercapacitor can exercise maximum effectiveness without damaging the device. Hence, this paper reviews the above sections. The remaining useful life prediction and state-of-charge estimation of supercapacitors are reviewed based on the model and data. The methods of different innovation points are enumerated, the disparate evaluation frameworks are compared, and their merits and demerits are generalized and reviewed. In the research field, while studying the remaining useful life of supercapacitors based on data, the application of artificial neural networks is emphasized. Hence, this study focuses on reviewing the relevant content for this approach. Finally, the challenges and prospects for the prediction of the above studies are briefly described.

State-of-charge estimation and remaining useful life prediction of supercapacitors

Aging state prediction for supercapacitors based on heuristic kalman filter optimization extreme learning machine

For a lithium battery, a second-order equivalent circuit model is adopted by studying the battery characteristic, and a state space equation with state of charge (SOC) being one state is constructed. To promote the SOC estimation precision of the extended Kalman filter (EKF) method for a lithium battery, this paper explores a multi-innovation extended Kalman filter (MI-EKF) algorithm to estimate the battery SOC by expanding a single innovation at current instant to multi-innovations containing information from current and previous instants. The aim is to increase the amount of information, and to get the more accurate estimated SOC. In addition, based on the battery difference equation, a stochastic gradient algorithm with a forgetting factor (FFSG) is used to identify the battery parameters. Finally, a lithium battery test bench is set up to sample charge-discharge data and to implement MATLAB simulation experiment; the experiment results confirm that the MI-EKF algorithm can accurately and effectively estimate the battery SOC.

Meng Jiao

Papers

A GRU-RNN based momentum optimized algorithm for SOC estimation