State-of-health monitoring of lithium-ion batteries in electric vehicles by on-board internal resistance estimation

By Jürgen Remmlinger, M. Buchholz, M. Meiler, Peter Bernreuter, Klaus C. J. Dietmayer. 2011

377 citations recorded by the source corpus.

Subfields: Automotive Engineering, Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/47c06067795340ce2a63ad393b006ab196e5a62e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Carlos Vidal
2. Pawel Malysz
3. P. Kollmeyer
4. Ali Emadi

In Control and Systems Engineering

1. Ali Emadi
2. Carlos Vidal
3. Pawel Malysz
4. P. Kollmeyer

In Electrical and Electronic Engineering

1. Carlos Vidal
2. Pawel Malysz
3. P. Kollmeyer
4. Ali Emadi

Citations in the corpus

• Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art (2020, methodology): “Alternatively, using a model to estimate the battery’s internal resistance is possible and can be implemented onboard to provide real-time input information [20].”