TY - GEN
T1 - A hybrid DNN-KF model for real-time SOC estimation of lithium-ion batteries under different ambient temperatures
AU - Chen, Guanxu
AU - Jiang, Shancheng
AU - Xie, Min
AU - Yang, Fangfang
PY - 2022
Y1 - 2022
N2 - Accurate state-of-charge (SOC) estimation of lithium iron phosphate (LFP) battery under different ambient temperatures is a long-standing problem in industry. In this paper, a hybrid model combining deep neural network and Kalman filter is proposed for SOC estimation of LFP battery under different ambient temperatures. After estimation via deep neural network, the estimated SOCs are further corrected using Kalman filter of high denoising capability. Data collected from dynamic stress test, US06 test and federal urban driving schedule under 25°C, 30°C, 40°C, and 50°C are used to verify the performance of proposed model, with the first two data as training set and the third data as testing set. Experimental results show that the proposed model can well meet the requirement of real-time estimation with mean absolute error within 2% and root mean square error within 2.4%. In addition, we also test the robustness of proposed model against different initial SOC values, and proves that the proposed model can well generalize the estimation to different initial values.
AB - Accurate state-of-charge (SOC) estimation of lithium iron phosphate (LFP) battery under different ambient temperatures is a long-standing problem in industry. In this paper, a hybrid model combining deep neural network and Kalman filter is proposed for SOC estimation of LFP battery under different ambient temperatures. After estimation via deep neural network, the estimated SOCs are further corrected using Kalman filter of high denoising capability. Data collected from dynamic stress test, US06 test and federal urban driving schedule under 25°C, 30°C, 40°C, and 50°C are used to verify the performance of proposed model, with the first two data as training set and the third data as testing set. Experimental results show that the proposed model can well meet the requirement of real-time estimation with mean absolute error within 2% and root mean square error within 2.4%. In addition, we also test the robustness of proposed model against different initial SOC values, and proves that the proposed model can well generalize the estimation to different initial values.
KW - deep neural network
KW - Kalman filter
KW - lithium iron phosphate battery
KW - SOC estimation
UR - http://www.scopus.com/inward/record.url?scp=85143169018&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85143169018&origin=recordpage
U2 - 10.1109/PHM-Yantai55411.2022.9942155
DO - 10.1109/PHM-Yantai55411.2022.9942155
M3 - RGC 32 - Refereed conference paper (with host publication)
T3 - Global Reliability and Prognostics and Health Management Conference, PHM-Yantai
BT - 2022 Global Reliability and Prognostics and Health Management Conference (PHM-Yantai)
PB - IEEE
T2 - 2022 Global Reliability and Prognostics and Health Management Conference, PHM-Yantai 2022
Y2 - 13 October 2022 through 16 October 2022
ER -