Abstract
The Li-Ion battery state-of-charge estimation is an essential task in a continuous dynamic automotive industry for large-scale and successful marketing of hybrid electric vehicles. Also, the state-of-charge of any rechargeable battery, regardless of its chemistry, is an essential condition parameter for battery management systems of hybrid electric vehicles. In this study, we share from our accumulated experience in the control system applications field some preliminary results, especially in modeling, control and state estimation techniques. We investigate the design and effectiveness of two state-of-charge estimators, namely an extended Kalman filter and a proportional integral observer, implemented in a real-time MATLAB environment for a particular Li-Ion battery. Definitely, the aim of this work is to find the most suitable estimator in terms of estimation accuracy and robustness to changes in initial conditions (i.e., the initial guess value of battery state-of-charge) and changes in process and measurement noise levels. By a rigorous performance analysis of MATLAB simulation results, the potential estimator choice is revealed. The performance comparison can be done visually on similar graphs if the information gathered provides a good insight, otherwise, it can be done statistically based on the calculus of statistic errors, in terms of root mean square error, mean absolute error and mean square error.
Highlights
Nowadays, the most advanced battery technologies existing in electric and hybrid electric vehicles (EVs/HEVs) from the automotive industry are the nickel-metal hydride (NiMH), lithium-ion (Li-Ion) and nickel-cadmium (NiCad) batteries
The state-space equivalent model is combined with the output-states-input equation suggested in [2,3,4,7,10,11,12,13,14,15] to provide the Li-Ion battery terminal voltage, such that the whole model has the ability to capture the entire dynamics of the battery and easy to be implemented in real time, and lastly is useful as a support to build the proposed state estimation algorithms: dx1 dt dx2 dt dx3 dt
The extended Kalman filter (EKF) SOC estimator is easy to be implemented in real time and has only three parameters to be tuned, namely the noise covariance matrices Q(k) and R(k), and the initial value of the state covariance matrix P(0) = P(0|0)
Summary
The most advanced battery technologies existing in electric and hybrid electric vehicles (EVs/HEVs) from the automotive industry are the nickel-metal hydride (NiMH), lithium-ion (Li-Ion) and nickel-cadmium (NiCad) batteries. Batteries 2018, 4, 19 ranging from small battery packs used in cell phones or cameras to large battery systems for EVs or temporary energy storages for photovoltaic systems. Li-Ion batteries are the most suitable existing technology for EVs because they can output high energy and power per unit of battery mass, allowing them to be lighter and smaller than other rechargeable batteries. These features explain why Li-Ion batteries are already integrated into cell phones, laptops, digital cameras/video cameras, and portable audio/game players.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
