Federal Urban Driving Schedule Cycle Research Articles

Joint Estimation of State-of-Health and State-of-Charge for Lithium-Ion Battery Based on Electrochemical Model Optimized by Neural Network

Accurate estimation of battery state including state-of-charge (SOC) and state-of-health (SOH) plays an important role in improving the performance and lifespan of battery packs in electric vehicles. From the perspective of electrochemical mechanisms, the battery SOC and SOH strongly depends on the status of battery electrochemical reaction processes and associated variables, especially the number of lithium-ion in solid phases. In this article, a method based on a simplified battery electrochemical model, which is optimized by back propagation neural network, was proposed to estimate the battery SOC and SOH. The simplified battery electrochemical model called single particle model is employed to obtain the number of recyclable lithium-ions at different aging degrees and the lithium-ion changes of solid particles with different loaded currents. The parameters of the electrochemical model are identified by an algorithm hybridizing Coyote Optimization Algorithm and Grey Wolf Optimizer, abbreviated to Hybrid Coyote Optimization Algorithm and Grey Wolf Optimizer for its great balance in operational speed and optimization accuracy. To improving the speed and accuracy of model parameter identification, the rang of the number of recyclable lithium-ions is narrowed by battery capacity, which is estimated by the back propagation neural network. The accuracy and robustness of the proposed method are systematically evaluated, and with the mean absolute percentage errors of promising battery capacity and SOC prediction results are less than 2% and 1.8%, respectively. The operation time of the battery SOC prediction during federal urban driving schedule cycles are less than 2 s.

Self-powered heating strategy for lithium-ion battery pack applied in extremely cold climates

Serious performance loss of lithium-ion batteries at subzero temperatures is the major obstacle to promoting battery system in cold regions. This paper proposes a novel heating strategy to heat battery from extremely cold temperatures based on a battery-powered external heating structure. The strategy contains two stages: preheating process for battery cold-start, and temperature holding process for battery temperature control after preheating. The strategy switches from the preheating to the temperature holding according to the power capability of battery pack. Both the electrical and thermal behaviors of battery pack are described based on equivalent circuit models, and the state-of-charge and state-of-power of battery pack are co-estimated online to support the implementation of the strategy. Systematic battery-in-the-loop tests are conducted for a series-connected battery pack to verify the effectiveness of the strategy at – 40 °C. Experimental results show that the battery pack can not only be heated from −41.4 °C to −14.0 °C in 371 s by consuming 6.1% of nominal capacity without being over-discharged, but also output 64.81% and 66.59% of nominal energy for loading the modified dynamic stress test cycles and the modified federal urban driving schedule cycles respectively after preheating.

Robust state-of-charge estimation for lithium-ion batteries based on an improved gas-liquid dynamics model

The battery analytical model often plays an important role in accurately estimating the online state-of-charge of the battery. The improved gas-liquid dynamics battery model is proposed to simulate the physicochemical behaviors of a lithium-ion battery. The estimation equations of both iterative open-circuit voltage and terminal voltage are deduced according to this model. A strong robust state-of-charge estimation method is designed without coupling optimization algorithm based on the iterative open-circuit voltage estimation equation. Experimental results of the Li(NiMnCo)O2 batteries under the Dynamic Stress Test cycle, the New European Driving Cycle, the Federal Urban Driving Schedule cycle, the Urban Dynamometer Driving Schedule cycle and the constant current test confirm the efficacy of the proposed approach. Moreover, this method has excellent robustness against the initial error of 50% state-of-charge which is eliminated within 6 s under five working conditions and it provides a reliable state-of-charge estimation for the sampling data of different sampling periods between a second and 60 s.

Online State-of-Charge Estimation Based on the Gas–Liquid Dynamics Model for Li(NiMnCo)O2 Battery

Accurately estimating the online state-of-charge (SOC) of the battery is one of the crucial issues of the battery management system. In this paper, the gas–liquid dynamics (GLD) battery model with direct temperature input is selected to model Li(NiMnCo)O2 battery. The extended Kalman Filter (EKF) algorithm is elaborated to couple the offline model and online model to achieve the goal of quickly eliminating initial errors in the online SOC estimation. An implementation of the hybrid pulse power characterization test is performed to identify the offline parameters and determine the open-circuit voltage vs. SOC curve. Apart from the standard cycles including Constant Current cycle, Federal Urban Driving Schedule cycle, Urban Dynamometer Driving Schedule cycle and Dynamic Stress Test cycle, a combined cycle is constructed for experimental validation. Furthermore, the study of the effect of sampling time on estimation accuracy and the robustness analysis of the initial value are carried out. The results demonstrate that the proposed method realizes the accurate estimation of SOC with a maximum mean absolute error at 0.50% in five working conditions and shows strong robustness against the sparse sampling and input error.

New SOC estimation method under multi-temperature conditions based on parametric-estimation OCV

A new state of charge (SOC) estimation method under multi-temperature conditions is presented. The model parameters and SOC value of a lithium-ion battery cell are calculated based on the parametric-estimation open circuit voltage (OCV). The main efforts of this study are as follows. First, the OCV value is obtained based on an equivalent circuit model. Second, the model parameters and states are estimated using an adaptive joint extended Kalman filter. Third, the parametric OCV mapping model (OCVPE–SOC) is put forward to reduce systematic errors. Lastly, the online SOC value is estimated based on the battery model, the model parameters and the OCVPE–SOC mapping model. The noise covariance matrix is updated iteratively using an innovation sequence. The mapping model is established under a dynamic stress test cycle test and the verification results are obtained under the federal urban driving schedule cycle test. The obtained experimental results indicate that the proposed method can effectively reduce the system error and that a more accurate SOC estimation value can be obtained under different temperatures.

Comparison of Battery Degradations Induced By Different Synthetic Driving Cycles and Real-World Traffic Conditions

Advances in lithium ion battery pack technology have led to significant market penetration for electric vehicles (EVs), but there is still a need to develop new battery technologies, better control algorithms, and faster charging strategies to enhance further the ownership experience. Once developed, these advances must be validated. Unfortunately, fleet testing is not often an option because of the associated cost. To address this issue and permit the validation of new technologies in the laboratory quickly, many studies are relying on synthetic driving cycles to mimic the usage cells would experience under actual utilization. Among the more popular ones are the Federal Urban Driving Schedule (FUDS), a US standard; the Dynamic Stress Test (DST) which is a simplified version of the FUDS cycle; and the New European Driving Cycle (NEDC), a European standard. The ability of these cycles to replicate EV usage is usually taken for granted; whereas, to the best of our knowledge, no study has scrutinized these cycles to establish if the induced degradation is indeed comparable with what is observed from real driving data. It is proposed here to investigate the impact of the most-used, standardized driving cycles on state-of-the art lithium-ion cells and evaluate them against the impact of real driving in a laboratory-controlled environment. Different analysis techniques and metrics, such as voltage response, capacity, rate capability, and resistance, are employed to compare the most common synthetic driving cycles (FUDS, DST and NEDC) with scaled-down real driving data selected from the Hawai'i Natural Energy Institute (HNEI) in-house database. Two different sets of real driving data was used, one using a single commute in a loop and one using 25 similar commutes in a loop, to reveal any effect of traffic. This study aimed to elucidate any significant discrepancies between simulated and real driving outcomes, determine the effect of traffic conditions, and propose possible improvements.

An Online State of Charge Estimation Algorithm for Lithium-Ion Batteries Using an Improved Adaptive Cubature Kalman Filter

An accurate state of charge (SOC) estimation of the on-board lithium-ion battery is of paramount importance for the efficient and reliable operation of electric vehicles (EVs). Aiming to improve the accuracy and reliability of battery SOC estimation, an improved adaptive Cubature Kalman filter (ACKF) is proposed in this paper. The battery model parameters are online identified with the forgetting factor recursive least squares (FRLS) algorithm so that the accuracy of SOC estimation can be further improved. The proposed method is evaluated by two driving cycles, i.e., the New European Driving Cycle (NEDC) and the Federal Urban Driving Schedule (FUDS), and compared with the existing unscented Kalman filter (UKF) and standard CKF algorithms to verify its superiority. The experimental results reveal that comparing with the UKF and standard CKF, the improved ACKF algorithm has a faster convergence rate to different initial SOC errors with higher estimation accuracy. The root mean square error of SOC estimation without initial SOC error is less than 0.5% under both the NEDC and FUDS cycles.

Dynamic Simulation of Large Scale PEM Fuel Cell under Driving Cycle

A dynamic response of large scale PEMFC model has been studied using computational fluid dynamics (CFD) technique. The power density required during dynamic cycle was taken from one of Federal Urban Driving Schedule (FUDS) cycles to enhance an understanding of flow-field design. The transient simulation of 200-cm2 active area with two different flow-fields showed different response even though those two designs give similar results under steady state operation. Further, the influence of operating condition and flow direction to dynamic performance and distributions were also revealed. The prediction also showed that both operations given in this study can achieve power required from FUDS cycle but automotive condition (cathode dry) shows more non uniform distributions than stationary condition (well humidified condition). Semi counter-flow direction also gives more non-uniform in distributions than co-flow direction under transient operation.

Modelling of a battery pack for electric vehicles using a stochastic fuzzy neural network

A dynamic simulation model for a battery pack is an important prerequisite for the simulation of electric vehicles. As the battery pack is a highly non-linear system, and their dynamic behaviour depends on different parameters, it is difficult to establish the relationship between the load voltage and the current under different temperatures and states of charge. Furthermore, the input and output data usually contain noise; therefore, the traditional neural-network-based model is affected while training the parameters. To solve the problem, a stochastic fuzzy neural network (SFNN) which has a filtering effect on the noisy input is used to model the battery non-linear dynamics in this paper. In the parameter-learning algorithm for the SFNN, a novel cost function which contains the error variables is studied. Then, the modelling test is performed on an 80A h nickel—metal hydride (Ni—MH) battery pack and the SFNN-based model for the Ni—MH battery pack is set up. The Federal Urban Driving Schedule cycle is performed to test the model. Compared with the traditional neural-network-based model, the SFNN model can simulate the battery dynamic better and has a filtering effect on the noisy input which is more suitable for practical applications.