Synthetic state of charge estimation for lithium-ion batteries based on long short-term memory network modeling and adaptive H-Infinity filter

Accurate state of charge estimation is essential to improve operation safety and service life of lithium-ion batteries. This paper proposes a synthetic state of charge estimation method for lithium-ion batteries based on long short-term memory network modeling and adaptive H-infinity filter. Firstly, the long short-term memory network is exploited to roughly estimate state of charge with the input of voltage, current, operating temperature and state of health. Then, to mitigate the output fluctuation and improve the estimation robustness of long short-term memory network, the adaptive H-infinity filter is employed to flatten the estimation results and further improve the estimation accuracy. A main advantage of the proposed synthetic method lies in that precise battery modeling and burdensome model parameter identification tasks that are imperative in traditional observers or filters can be omitted, thus improving the application efficiency of the proposed algorithm. The proposed method is verified effective on two types of lithium-ion batteries under dynamic working scenarios including the varying temperature and aged conditions. The experimental results highlight that the estimation error of state of charge can be restricted within 2.1% in wide temperature range and different aging states, manifesting its high-precision estimation capacity and strong robustness