Synergizing physics and machine learning for advanced battery management

Communications Engineering, vol. 3, pp. 134

Abstract

Improving battery health and safety motivates the synergy of a powerful duo: physics and machine learning. Through seamless integration of these disciplines, the efficacy of mathematical battery models can be significantly enhanced. This paper delves into the challenges and potentials of managing battery health and safety, highlighting the transformative impact of integrating physics and machine learning to address those challenges. Based on our systematic review in this context, we outline several future directions and perspectives, offering a comprehensive exploration of efficient and reliable approaches. Our analysis emphasizes that the integration of physics and machine learning stands as a disruptive innovation in the development of emerging battery health and safety management technologies. Lithium-ion batteries are integral to modern technologies but the sustainability of long-term battery health is a significant and persistent challenge. In this perspective Borah and colleagues discuss the integration of physics and machine learning to support developments in battery performance and safety.

Authors 5

1. Manashita Borah corresponding

   Tezpur University · University of California, Berkeley

   Affiliation as printed

   Department of Electrical Engineering, Tezpur University, Tezpur, Assam, 784028, India. manashitaborah@berkeley.edu

   Energy, Controls and Application Laboratory, Department of Civil and Environmental Engineering, University of California, Berkeley, CA, 94720, USA. manashitaborah@berkeley.edu

2. Qiao Wang Aachen Center for Ageing

   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, 52074, Aachen, Germany

3. Scott Jason Moura

   University of California, Berkeley

   Affiliation as printed

   Energy, Controls and Application Laboratory, Department of Civil and Environmental Engineering, University of California, Berkeley, CA, 94720, USA

4. Dirk Uwe Sauer Aachen Center for Ageing

   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, 52074, Aachen, Germany

5. Weihan Li Aachen Center for Ageing

   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, 52074, Aachen, Germany