State-of-Charge Estimation for Lithium-Ion Batteries Using a Kalman Filter Based on Local Linearization

By Zhihao Yu, Ruituo Huai, Lin-jing Xiao. 2015

117 citations recorded by the source corpus.

State of charge (SOC) estimation is of great significance for the safe operation of lithium-ion battery (LIB) packs. Improving the accuracy of SOC estimation results and reducing the algorithm complexity are important for the state estimation. In this paper, a zeroaxial straight line, whose slope changes along with SOC, is used to map the predictive SOC to the predictive open circuit voltage (OCV), and thus only one parameter is used to linearize the SOC-OCV curve near the present working point. An equivalent circuit model is used to simulate the dynamic behavior of a LIB, updating the linearization parameter in the measurement equation according to the present value of the state variables, and then a standard Kalman filter is used to estimate the SOC based on the local linearization. This estimation method makes the output equation of the nonlinear battery model contain only one parameter related to its dynamic variables. This is beneficial to simplify the algorithm structure and to reduce the computation cost. The linearization method do not essentially lose the main information of the dynamic model, and its effectiveness is verified experimentally. Fully and a partially charged battery experiments indicate that the estimation error of SOC is better than 0.5%.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/08408f23806a34d0d14d42947960cd9f2b964430

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In Automotive Engineering

1. Najoua Essoukri Ben Amara
2. Bilal Manai
3. Sabeur Jemmali
4. Ines Baccouche
5. Noshin Omar

In Electrical and Electronic Engineering

1. Najoua Essoukri Ben Amara
2. Bilal Manai
3. Sabeur Jemmali
4. Ines Baccouche
5. Noshin Omar

Citations in the corpus

• Improved OCV Model of a Li-Ion NMC Battery for Online SOC Estimation Using the Extended Kalman Filter (2017, methodology): “Unlike KF where the system is linearly formulated, the EKF requires a local linearization that consists of using the Jacobian function to bring the system to the conventional form, with interdependent variables rather than a constant matrix [52].”