In order to fully obtain the working performance of the power battery, each test needs to be tested separately at multiple different temperatures and different aging conditions to realize the full-life efficient management of the full-climate power battery.

Data completion time: individual: 2014-1-31, module: 2013-11-30

1.Basic battery parameters

2.Test process

Battery individual: Arbin BT2000-01 is used as a charging and discharging device for experiments. The battery individual test flow chart is shown in the figure below.

Figure 1 Battery individual test process

Battery pack: Arbin is used as a charging and discharging device for experiments. The battery pack test process is shown in the figure below.

Figure 2 Battery pack test process

3.Introduction to the data set

Battery individual: The maximum usable capacity test/rate characteristic test/composite pulse test/OCV test/AC impedance test/DST test/FUDS test/UDDS test. Except the electrochemical impedance spectroscopy test, other experiments are performed at 25°C.

Battery pack: The capacity/working condition experiment was carried out.

4.Battery characteristic curve

Figure 3 Constant current discharge curve of LiPB under different currents

Figure 4 Lithium-ion battery individual composite pulse test

Figure 5 LiPB OCV curve of power battery and its related characteristics: (a) the corresponding relationship between OCV and SOC; (b) the difference of OCV in different charging and discharging states; (c) the change of SOC corresponding to each millivolt of OCV

Figure 6 DST test data

Figure 6 Test data of dynamic working conditions: (a) FUDS working condition; (b) UDDS working condition

5、Related papers

[1] F. Sun; R. Xiong* and H. He, “Estimation of state-of-charge and state-of-power capability of lithium-ion battery considering varying health conditions”, J. Power Sources, vol.259, pp.166–176, Aug 2014. (Download)

[2] R. Xiong*, F. Sun, X. Gong and C. Gao, “A data-driven based adaptive state of charge estimator of lithium-ion polymer battery used in electric vehicles”, Applied Energy, vol. 113, pp. 1421–1433, Jan 2014. (Download)

[3] R. Xiong*, F. Sun, Z. Chen and H. He, “A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion polymer battery in electric vehicles”, Applied Energy, vol. 113, pp. 463-476, Jan 2014. (Download)

[4] R. Xiong, X. Gong and C. Mi*, “A robust state-of-charge estimator for multiple types of lithium-ion batteries using adaptive extended Kalman filter”, Journal of Power Sources, vol. 243, pp. 805–816, Dec 2013. (Download)

[5] R. Xiong*, F. Sun, X. Gong and H. He, “Adaptive state of charge estimator for lithium-ion cells series battery pack in electric vehicles”, Journal of Power Sources, vol. 242, pp. 699–713, Nov 2013. (Download)