Abstract
Lithium-Ion Batteries play an increasingly important role as high-rate transient power sources
for hybrid electric vehicles, cycling around a relatively narrow state-of-charge (SOC) band. A
crucial step in enhancing the performance of Lithium-Ion Cells, batteries and the battery package
is the estimation of the SOC as a function of load. Rather than applying empirical models
relying on electric circuits or pole-placement using an arbitrary set of parameters, we propose
an electrochemical-based approach. An electrochemical cell model is able to take into account
physical limitations such as kinetic, transport, diffusion and thermodynamic constraints. A set
of appropriate partial differential equations forms the solid basis for estimating SOC, ageing
mechanism and cell/battery/package failures given uncertain measurements. Due to the better
insight into the cell performance, cell design can be significantly improved.
On the downside the computational time required to solve rigorous models eliminates the utility
of such models for onboard diagnostics. However, by introducing recently developed model
reduction techniques15 , a trade-off between model accuracy, estimation performance and computational
cost can be found. In that case, parameters can be estimated that hold physical significance.
Such intermediate models are comparable to equivalent circuit models in terms of efficiency
which is a requirement for implementation of on-board embedded controllers. Based
on the electrochemical nature of the model, cell and battery degradation can be estimated using
nonlinear parameter identification methods, e.g. adaptive filter approaches.
for hybrid electric vehicles, cycling around a relatively narrow state-of-charge (SOC) band. A
crucial step in enhancing the performance of Lithium-Ion Cells, batteries and the battery package
is the estimation of the SOC as a function of load. Rather than applying empirical models
relying on electric circuits or pole-placement using an arbitrary set of parameters, we propose
an electrochemical-based approach. An electrochemical cell model is able to take into account
physical limitations such as kinetic, transport, diffusion and thermodynamic constraints. A set
of appropriate partial differential equations forms the solid basis for estimating SOC, ageing
mechanism and cell/battery/package failures given uncertain measurements. Due to the better
insight into the cell performance, cell design can be significantly improved.
On the downside the computational time required to solve rigorous models eliminates the utility
of such models for onboard diagnostics. However, by introducing recently developed model
reduction techniques15 , a trade-off between model accuracy, estimation performance and computational
cost can be found. In that case, parameters can be estimated that hold physical significance.
Such intermediate models are comparable to equivalent circuit models in terms of efficiency
which is a requirement for implementation of on-board embedded controllers. Based
on the electrochemical nature of the model, cell and battery degradation can be estimated using
nonlinear parameter identification methods, e.g. adaptive filter approaches.
| Original language | English |
|---|---|
| Title of host publication | Proceedings 9th Stuttgart Int. Symposium "Automotive Engine Technology" |
| Pages | 1-10 |
| Publication status | Published - 2009 |
| Event | Internationales Stuttgarter Symposium Kraftfahrwesen und Verbrennungsmotoren - Stuttgart, Germany Duration: 24 Mar 2009 → … |
Conference
| Conference | Internationales Stuttgarter Symposium Kraftfahrwesen und Verbrennungsmotoren |
|---|---|
| Country/Territory | Germany |
| City | Stuttgart |
| Period | 24/03/09 → … |
