Battery Model Parameterization Using Manufacturer Datasheet and Field Measurement for Real-Time HIL Applications

Xie, Fuhong; Yu, Hui; Long, Qian; Zeng, Wente; Liu, Ning

doi:10.1109/tsg.2019.2953718

Cited by 5 publications

(2 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because the battery model parameters are usually limited within a range, the trust region reflective method can be used to bound the model parameters by applying a single reflection transformation. By nature, trust region reflective is a local exploitation method, making its performance heavily affected by initial points [30]. In [30] they use the trust-region method to parameterize battery models.…”

Section: Appendix a Initial Battery Parameters Calculationmentioning

confidence: 99%

See 1 more Smart Citation

Battery Model Identification Approach for Electric Forklift Application

et al. 2021

View full text Add to dashboard Cite

Electric forklifts are extremely important for the world’s logistics and industry. Lead acid batteries are the most common energy storage system for electric forklifts; however, to ensure more energy efficiency and less environmental pollution, they are starting to use lithium batteries. All lithium batteries need a battery management system (BMS) for safety, long life cycle and better efficiency. This system is capable to estimate the battery state of charge, state of health and state of function, but those cannot be measured directly and must be estimated indirectly using battery models. Consequently, accurate battery models are essential for implementation of advance BMS and enhance its accuracy. This work presents a comparison between four different models, four different types of optimizers algorithms and seven different experiment designs. The purpose is defining the best model, with the best optimizer, and the best experiment design for battery parameter estimation. This best model is intended for a state of charge estimation on a battery applied on an electric forklift. The nonlinear grey box model with the nonlinear least square method presented a better result for this purpose. This model was estimated with the best experiment design which was defined considering the fit to validation data, the parameter standard deviation and the output variance. With this approach, it was possible to reach more than 80% of fit in different validation data, a non-biased and little prediction error and a good one-step ahead result.

show abstract

Section: Appendix a Initial Battery Parameters Calculationmentioning

confidence: 99%

“…By nature, trust region reflective is a local exploitation method, making its performance heavily affected by initial points [30]. In [30] they use the trust-region method to parameterize battery models.…”

Section: Appendix a Initial Battery Parameters Calculationmentioning

confidence: 99%