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.

Microprocessor-based state of charge gauge for secondary batteries

Abstract: A state of charge gauge for measuring the state of charge of secondary batteries, such as the type employed in electric vehicles, includes a microprocessor which, when supplied with data varying in accordance with battery discharge current and battery terminal voltage, determines battery resistance. Having determined battery resistance which is a dynamically varying parameter dependent on battery temperature and age, the microprocessor computes the total battery charge capacity. Comparison of the quantity of battery charge already depleted with the previously computed total battery charge capacity yields an accurate indication of remaining battery charge.

Online Estimation of Lithium Polymer Batteries State-of-Charge Using Particle Filter-Based Data Fusion With Multimodels Approach

Abstract: In this paper, a robust model-based battery state-of-charge (SOC) estimating algorithm is proposed with a novel approach based on combination of multimodels data-fusion technique and particle filter (PF). The proposed method is particularly adapted for SOC estimation under real-time conditions and the presence of measurement noise. In this innovative approach, multiple battery models have been used in order to accurately estimate a battery SOC. During the estimation process, the measured battery terminal voltage is compared with the multiple battery models output to generate individual residual, which is then used to calculate the weight of estimated value from each battery model. This weight, which represents the accuracy of observation equation of each battery model, is inversely proportional to the residual. The estimated SOC values from different models are then fused and the weights of estimated values from each battery model are adjusted dynamically using PF and weighted average methodology, in order to calculate the final SOC estimation of the battery. For each proposed battery model, the corresponding parameter-tuning strategies are also presented. In addition, the proposed method has been validated by experimental results. The results demonstrate that the proposed multimodels-based algorithm can be implemented effectively for real-time application, and achieve better accuracy than single model-based methods.

Plug-In Hybrid Electric Vehicles: Replacing Internal Combustion Engine With Clean and Renewable Energy Based Auxiliary Power Sources

Abstract: A plug-in hybrid electric vehicle (PHEV) uses an internal combustion engine to extend its cruising range, and to produce the electric power needed to be supplied to its electric motor when the charge level of the vehicle's battery becomes low and reaches a predetermined state of charge (SOC). This paper provides a better solution by replacing the internal combustion engine of a PHEV with a small-size photovoltaic (PV) module located on the roof of the PHEV, and a micro wind turbine located in front of the PHEV, behind the condenser of the air conditioning system. Thus, this study proposes a novel battery/PV/wind hybrid power source to be utilized in PHEVs. The power source equipped with vehicle-to-grid (V2G) technology is composed of a 19.2-kWh Lithium (Li)-ion battery used as the main energy storage device, and a PV module and a wind energy conversion system. A prototype of the battery/PV/wind hybrid power source has been constructed and utilized in a PHEV. Experimental verifications are presented that demonstrate utilizing the PV module and micro wind turbine adds 19.6 km to the cruising range of a PHEV with the weight of 1880 kg during two sunny days, and provides higher power efficiency (91.2%) and speed (121 km/h). Highly accurate dc-link voltage regulation and producing an appropriate three-phase stator current for the traction motor by using pulse width modulation technique are the other contributions of this paper.

Online Embedded Impedance Measurement Using High-Power Battery Charger

Abstract: This paper presents a new functionality for high-power battery chargers by incorporating an impedance measurement algorithm. The measurement of battery impedance can be performed by the battery charger to provide an accurate equivalent model for battery management purposes. In this paper, an extended control capability of the onboard battery charger for electric vehicles is used to measure the online impedance of the battery. The impedance of the battery is measured by the following: 1) injecting ac current ripple on top of the dc charging current; 2) transforming voltage and current signals using a virtual $\alpha{-}\beta$ stationary coordinate system, a $d{-}q$ rotating coordinate system, and two filtering systems; 3) calculating ripple voltage and current values; and 4) calculating the angle and magnitude of the impedance. The contributions of this paper are the use of the $d{-}q$ transformation to attain the battery impedance, theta, and its ripple power, as well as providing a controller design procedure which has impedance measurement capability. The online impedance information can be utilized for diverse applications such as the following: 1) a theta control for sinusoidal current charging; 2) the quantifying of reactive current and voltage; 3) ascertaining the state of charge; 4) determining the state of health; and 5) finding the optimized charging current. Therefore, the benefit of this method is that it can be deployed in already existing high-power chargers regardless of battery chemistry. Validations of the proposed approach were made by comparing measurement values by using a battery charger and a commercial frequency response analyzer.

Hybrid electric vehicle with reduced auxiliary power to batteries during regenerative braking

Abstract: An electric vehicle is controlled to conform its operation to that of a conventional internal-combustion-engine powered vehicle In some embodiments, the charging of the batteries by the auxiliary source of electricity and from dynamic braking is ramped in magnitude when the batteries lie in a state of charge between partial charge and full charge, with the magnitude of the charging being related to the relative state of charge of the battery The deficiency between traction motor demand and the energy available from the auxiliary electrical source is provided from the batteries in an amount which depends upon the state of the batteries, so that the full amount of the deficiency is provided when the batteries are near full charge, and little or no energy is provided by the batteries when they are near a discharged condition At charge states of the batteries between near-full-charge and near-full-discharge, the batteries supply an amount of energy which depends monotonically upon the charge state Charging of the batteries from the auxiliary source is reduced during dynamic braking when the batteries are near full charge Control of the amount of energy returned during dynamic braking may be performed by control of the transducing efficiency of the traction motor operated as a generator