Data sufficiency for transferable lithium-ion battery periodical SOH estimation under resource constraints

Cell Reports Physical Science, vol. 6, pp. 102901

Abstract

Battery state-of-health (SOH) estimation is vital for the safety of energy storage systems, yet full-life-cycle data curation remains resource intensive. Here, we present an empirical examination of data sufficiency (DS) to identify the data amount needed for SOH estimation algorithms with anticipated predictability and transferability. DS is defined as a linear function of normalized predictability and transferability of physical features. The effectiveness of DS is validated on 7 datasets (involving 310 batteries over 300,000 cycles), encompassing 6 materials (i.e., LFP, NCM811, NCM333, NCM523, NCA, and LCO) and 7 transfer scenarios (i.e., temperature, charging rate, discharging rate, and cutoff voltage). Results show that, on average, no more than 8% of lifetime data can achieve a median mean absolute percentage error (MAPE) of 1% under the investigated transfer scenarios, with the calculated DS aligning with post hoc DS. This work suggests the careful evaluation of DS for building data-driven battery algorithms before massive, expensive, and time-consuming data curation.

Authors 6

1. Lin Su

   Tsinghua Shenzhen International Graduate School · Tsinghua University

   Affiliation as printed

   Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong 518055, China

2. Shengyu Tao corresponding

   Tsinghua Shenzhen International Graduate School · University of California, Berkeley · Tsinghua University

   Affiliation as printed

   Department of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, CA 94720, USA

   Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong 518055, China

3. Weihan Li Aachen Center for Ageing Institute for Power Electronics and Electrical Drives (ISEA)

   RWTH Aachen University

   Affiliation as printed

   Center for Ageing, Reliability and Lifetime Prediction of Electrochemical and Power Electronic Systems (CARL), RWTH Aachen University, Campus-Boulevard 89, Aachen, Germany

   Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Campus-Boulevard 89, Aachen, Germany

4. Dirk Uwe Sauer Aachen Center for Ageing Institute for Power Electronics and Electrical Drives (ISEA)

   RWTH Aachen University

   Affiliation as printed

   Center for Ageing, Reliability and Lifetime Prediction of Electrochemical and Power Electronic Systems (CARL), RWTH Aachen University, Campus-Boulevard 89, Aachen, Germany

   Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Campus-Boulevard 89, Aachen, Germany

5. Guangmin Zhou corresponding

   Tsinghua Shenzhen International Graduate School · Tsinghua University

   Affiliation as printed

   Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong 518055, China

6. Xuan Zhang corresponding

   Tsinghua Shenzhen International Graduate School · Tsinghua University

   Affiliation as printed

   Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong 518055, China