State of charge

About: State of charge is a research topic. Over the lifetime, 12013 publications have been published within this topic receiving 201419 citations. The topic is also known as: SoC & SOC.

Cost-Benefit Analysis of V2G Implementation in Distribution Networks Considering PEVs Battery Degradation

Abstract: Charging of plug-in electric vehicles (PEVs), especially as market penetration increases, is an important challenge for today's power systems. This paper presents a stochastic methodology for smart charging of PEVs. All of the associated uncertainties are taken into account in the proposed methodology. Moreover, a comprehensive model for impact of charging/discharging strategies on the battery pack degradation in the vehicle has been included. The proposed approach is applied to a typical distribution network that contains wind-based distributed generation (DG) units. A cost-benefit analysis is carried out to extract the benefits of Vehicle to Grid (V2G) implementation in distribution network. The simulation results show that the V2G implementation without considering battery degradation is economical. However, when considering a battery degradation cost, V2G implementation without wind generation is not beneficial, while it is economical when considering wind DG. Moreover, the smart charging is economical in all conditions and also it reduces the battery degradation cost in comparison with uncoordinated charging.

State-of-Charge Estimation of Lithium-Ion Batteries via Long Short-Term Memory Network

Abstract: Accurate state-of-charge (SOC) estimation is critical for driving range prediction of electric vehicles and optimal charge control of batteries. In this paper, a stacked long short-term memory network is proposed to model the complex dynamics of lithium iron phosphate batteries and infer battery SOC from current, voltage, and temperature measurements. The proposed network is trained and tested using data collected from the dynamic stress test, US06 test, and federal urban driving schedule. The performance on SOC estimation is evaluated regarding tracking accuracy, computation time, robustness against unknown initial states, and compared with results from the model-based filtering approach (unscented Kalman filter). Moreover, different training and testing data sets are constructed to test its robustness against varying loading profiles. The experimental results show that the proposed network well captures the nonlinear correlation between SOC and measurable signals and provides better tracking performance than the unscented Kalman filter. In case of inaccurate initial SOCs, the proposed network presents quick convergence to the true SOC, with root mean square errors within 2% and mean average errors within 1%. Moreover, the estimation time at each time step is sub-millisecond, making it appropriate for real-time applications.