Maximum State Of Charge Research Articles

Primary Frequency Response From Electric Vehicles in the Great Britain Power System

With the increasing use of renewable energy in the Great Britain (GB) power system, the role of electric vehicles (EVs) contributes to primary frequency response was investigated. A tool was developed to estimate the EV charging load based on statistical analysis of EV type, battery capacity, maximum travel range and battery state of charge. A simplified GB power system model was used to investigate the contribution of EVs to primary frequency response. Two control modes were considered: disconnection of charging load (case I) and discharge of stored battery energy (case II). For case II, the characteristic of the EV charger was also considered. A case study shows results for the year 2020. Three EV charging strategies: “dumb” charging, “off-peak” charging, and “smart” charging, were compared. Simulation results show that utilizing EVs to stabilize the grid frequency in the GB system can significantly reduce frequency deviations. However the requirement to schedule frequency response from conventional generators is dynamic throughout the day.

Research on the characteristics of the vanadium redox-flow battery in power systems applications

Vanadium redox flow batteries (VRFBs) have power rating and energy durations that are independent of one another, which make them attractive for power systems applications. This paper focuses on the energy and power response capability of the VRFBs, which has been experimentally researched based on 5 kW/10 kWh and 0.5 MW/1 MWh systems. With three experimental operating modes, three threshold values and one baseline have been obtained based on the experimental results. The maximum state of charge (“SOC”), charged by 1.4 times rated power at 1 time ratio (“TR”), is 47% SOC. The minimum TR, charged by times rated power at SOC = 100%, is 1.5. The maximum charge/discharge power rating is 1.35 times rated power. The reserve SOC curves are a baseline on which the VRFB can respond to equally charge/discharge energy demand.

Evaluation on State of Charge Estimation of Batteries With Adaptive Extended Kalman Filter by Experiment Approach

An accurate State-of-Charge (SoC) estimation plays a significant role in battery systems used in electric vehicles due to the arduous operation environments and the requirement of ensuring safe and reliable operations of batteries. Among the conventional methods to estimate SoC, the Coulomb counting method is widely used, but its accuracy is limited due to the accumulated error. Another commonly used method is model-based online iterative estimation with the Kalman filters, which improves the estimation accuracy in some extent. To improve the performance of Kalman filters in SoC estimation, the adaptive extended Kalman filter (AEKF), which employs the covariance matching approach, is applied in this paper. First, we built an implementation flowchart of the AEKF for a general system. Second, we built an online open-circuit voltage (OCV) estimation approach with the AEKF algorithm so that we can then get the SoC estimate by looking up the OCV-SoC table. Third, we proposed a robust online model-based SoC estimation approach with the AEKF algorithm. Finally, an evaluation on the SoC estimation approaches is performed by the experiment approach from the aspects of SoC estimation accuracy and robustness. The results indicate that the proposed online SoC estimation with the AEKF algorithm performs optimally, and for different error initial values, the maximum SoC estimation error is less than 2% with close-loop state estimation characteristics.

Investigation of a method to hinder charge imbalance in the vanadium redox flow battery

In common with most aqueous batteries, the vanadium redox flow battery generates a small amount of hydrogen during operation. Over the lifetime of the battery this leads to a gradual imbalance in the state-of-charge (SoC) of the positive and negative electrolytes, with a consequent loss in discharge energy. To slow the rate of capacity fade to an acceptable level commercial vanadium redox flow batteries operate with a rather restricted maximum SoC. Increasing this SoC limit would improve the electrolyte utilisation, but also increase the rate of hydrogen evolution. Therefore a novel approach to alleviate this imbalance is examined, namely by reacting the evolved H2, from the parasitic reaction at the negative electrodes, with the charged positive electrolyte. Due to the very slow native rate of reaction between VO2+ and H2 at room temperature a series of potential catalysts are examined. Finely dispersed Pt, Ir and Pt–Ru on carbon paper are found to accelerate the reaction, with Pt–Ru being the most active.

Design an Unscented Kalman Filter-Based SoC Estimator for HEV Application

An unscented Kalman filter (UKF) is adopted to estimate the state of charge (SoC) of a lithium ion battery for application in hybrid electric vehicles (HEVs). Generally, the extended Kalman filter (EKF) can be selected to estimate a non-linear system state. However, it may leads to large errors since the strong non-linear and stochastic performance. In this paper, the performance of the lithium-ion battery is tested by a design of experiment, such as hysteresis, polarization, coulomb efficiency, etc. And a combined battery model is selected for SoC estimation, while the model parameter was identified by using UKF algorithm. Finally, the federal urban driving schedule (FUDS) is used to evaluate the proposed method accuracy. And the results show that the maximum SoC estimation error is less than 3%.

Effect of low ambient temperature on fuel consumption and pollutant and CO2 emissions of hybrid electric vehicles in real-world conditions

Hybrid electric vehicles (HEVs) can potentially reduce vehicle fuel consumption and CO2 emissions by using recuperated kinetic vehicle energy stored as electric energy in a hybrid system battery (HSB). Low ambient temperatures can affect the overall HEV powertrain operation under warm-up and hot driving conditions and, consequently, affect fuel consumption and emission performance. The present study investigates the influence of low ambient temperatures on HEV fuel consumption and pollutant and CO2 emissions for five in-use HEV models. Chassis dynamometer measurements have been conducted at different set ambient temperatures using a real-world driving cycle suitable for investigating vehicle cold-start emissions. The main observation is that the amount of HEV cold-start extra emissions (CSEEs) of regulated pollutants are reduced by 30% to 85% on average in comparison to sample CSEEs of conventional gasoline vehicles. The results for HEV CSEEs of CO2 and fuel consumption are mainly similar than those of conventional gasoline vehicles except for CSEEs of some HEVs at the ambient temperature of 23°C. There, increased CSEEs are observed that exceed maximum sample CSEEs of conventional gasoline vehicles, reaching values for CO2 between 155 [g/start] and 300 [g/start] even though the test runs were initiated with maximum initial state of charge (SOC) of the HSB. Because SOC of the HSB considerably influences the fuel consumption of HEVs, this aspect should be further investigated in regard to the effect of low ambient temperatures on HEV fuel consumption and CO2 emissions. Moreover, no particular influence of low ambient temperatures on HSB performance was observed during hot-phase operation.

Kalman filtering state of charge estimation for battery management system based on a stochastic fuzzy neural network battery model

Extended Kalman filtering is an intelligent and optimal means for estimating the state of a dynamic system. In order to use extended Kalman filtering to estimate the state of charge (SOC), we require a mathematical model that can accurately capture the dynamics of battery pack. In this paper, we propose a stochastic fuzzy neural network (SFNN) instead of the traditional neural network that has filtering effect on noisy input to model the battery nonlinear dynamic. Then, the paper studies the extended Kalman filtering SOC estimation method based on a SFNN model. The modeling test is realized on an 80 Ah Ni/MH battery pack and the Federal Urban Driving Schedule (FUDS) cycle is used to verify the SOC estimation method. The maximum SOC estimation error is 0.6% compared with the real SOC obtained from the discharging test.

State-of-Charge Estimation of the Lithium-Ion Battery Using an Adaptive Extended Kalman Filter Based on an Improved Thevenin Model

An adaptive Kalman filter algorithm is adopted to estimate the state of charge (SOC) of a lithium-ion battery for application in electric vehicles (EVs). Generally, the Kalman filter algorithm is selected to dynamically estimate the SOC. However, it easily causes divergence due to the uncertainty of the battery model and system noise. To obtain a better convergent and robust result, an adaptive Kalman filter algorithm that can greatly improve the dependence of the traditional filter algorithm on the battery model is employed. In this paper, the typical characteristics of the lithium-ion battery are analyzed by experiment, such as hysteresis, polarization, Coulomb efficiency, etc. In addition, an improved Thevenin battery model is achieved by adding an extra <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RC</i> branch to the Thevenin model, and model parameters are identified by using the extended Kalman filter (EKF) algorithm. Further, an adaptive EKF (AEKF) algorithm is adopted to the SOC estimation of the lithium-ion battery. Finally, the proposed method is evaluated by experiments with federal urban driving schedules. The proposed SOC estimation using AEKF is more accurate and reliable than that using EKF. The comparison shows that the maximum SOC estimation error decreases from 14.96% to 2.54% and that the mean SOC estimation error reduces from 3.19% to 1.06%.

Solar photovoltaic charging of lithium-ion batteries

Solar photovoltaic (PV) charging of batteries was tested by using high efficiency crystalline and amorphous silicon PV modules to recharge lithium-ion battery modules. This testing was performed as a proof of concept for solar PV charging of batteries for electrically powered vehicles. The iron phosphate type lithium-ion batteries were safely charged to their maximum capacity and the thermal hazards associated with overcharging were avoided by the self-regulating design of the solar charging system. The solar energy to battery charge conversion efficiency reached 14.5%, including a PV system efficiency of nearly 15%, and a battery charging efficiency of approximately 100%. This high system efficiency was achieved by directly charging the battery from the PV system with no intervening electronics, and matching the PV maximum power point voltage to the battery charging voltage at the desired maximum state of charge for the battery. It is envisioned that individual homeowners could charge electric and extended-range electric vehicles from residential, roof-mounted solar arrays, and thus power their daily commuting with clean, renewable solar energy.

Application of electrically peaking hybrid (ELPH) propulsion system to a full-size passenger car with simulated design verification

An electrically peaking hybrid electric (ELPH) propulsion system is being developed that has a parallel configuration. A small engine is used to supply power approximately equal to the average load power. The operation of the engine is managed by a vehicle controller and an engine controller such that the engine always operates with nearly full load-the optimal fuel economy operation. An induction motor is used to supply the peaking power required by the electrically peaking load. The motor can also absorb the excess power of the engine while the load power is less than the peak. This power, along with the regenerative braking power, can be used to charge the batteries on board to maintain the battery state-of-charge (SOC) at a reasonable level. With the electrically peaking principle, two control strategies for the drive train have been developed. One is called maximum battery SOC control strategy, by which the engine and electric motor are controlled so that the battery SOC is maintained at its top level as much as possible. This control strategy may be used in urban driving in which accelerating and decelerating driving is common and high-battery SOC is absolutely important for normal driving. The other control strategy is called engine turn-on and turn-off control by which the engine is controlled to operate in a turn-on and turn-off manner. This control strategy may be used in highway driving. Based on the ELPH principle and the drive train control strategies, a drive train for a full-size five-seat passenger car has been designed and verified using the V-ELPH computer simulation package.

98/02156 Kinetic analysis of capacity fade in lithium/coke half-cells

In this work, we systematically investigated the influence of the memory effect of LiFePO4 cathodes in large-format full batteries. The electrochemical performance of the electrodes used in these batteries was also investigated separately in half-cells to reveal their intrinsic properties. We noticed that the memory effect of LiFePO4/graphite cells depends not only on the maximum state of charge reached during the memory writing process, but is also affected by the depth of discharge reached during the memory writing process. In addition, the voltage deviation in a LiFePO4/graphite full battery is more complex than in a LiFePO4/Li half-cell, especially for a large-format battery, which exhibits a significant current variation in the region near its terminals. Therefore, the memory effect should be taken into account in advanced battery management systems to further extend the long-term cycling stabilities of Li-ion batteries using LiFePO4 cathodes.