Abstract
With the increasing environmental concerns, plug-in electric vehicles will eventually become the main transportation tools in future smart cities. As a key component and the main power source, lithium-ion batteries have been an important object of research studies. In order to efficiently control electric vehicle powertrains, the state of charge (SOC) of lithium-ion batteries must be accurately estimated by the battery management system. This paper aims to provide a more accurate dynamic SOC estimation method for lithium-ion batteries. A dynamic Thevenin model with variable parameters affected by the temperature and SOC is established to model the battery. An unscented Kalman particle filter (UPF) algorithm is proposed based on the unscented Kalman filter (UKF) algorithm and the particle filter (PF) algorithm to generate nonlinear particle filter according to the advantages and disadvantages of various commonly used filtering algorithms. The simulation results show that the unscented Kalman particle filter algorithm based on the dynamic Thevenin model can predict the SOC in real time and it also has strong robustness against noises.
Highlights
With the development of renewable energy and the wide concern on global warming, plug-in electric vehicles become the main transportation tool in reducing transport emissions for future smart cities
In order to properly control the electric vehicles to meet acceleration, braking, and normal driving needs, the battery management system must properly control the charging/discharging status of the lithium-ion battery pack, and such control is usually based on accurate monitoring and estimation of key battery parameters such as the battery state of charge (SOC)
This paper aims to propose a new SOC estimation method combining the advantages of the above algorithms, that is, the particle filter algorithm will be applied to generate the proposed distribution of nonlinear particle filter, and it is further combined with the unscented Kalman particle filter (UPF) algorithm to estimate SOC
Summary
With the development of renewable energy and the wide concern on global warming, plug-in electric vehicles become the main transportation tool in reducing transport emissions for future smart cities. Among all these estimation methods, the extended Kalman filter (EKF) algorithm is very popular [12] This algorithm needs to linearize the nonlinear system in the actual estimation process, and it has the disadvantage of performance degradation or divergence when solving nonlinear problems. To deal with this problem, the unscented Kalman filter (UKF) algorithm [13] is proposed. As an effective algorithm to solve nonlinear problems, the particle filter algorithm can be applied to the highly nonlinear characteristics of a power battery system [14] It uses sampling approximation with different probability distributions to overcome the disadvantage of the Kalman filtering algorithm, which can only be used for the Gaussian noise density distribution of a linear system.
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