Urban Dynamometer Driving Schedule Research Articles

Optimized EMS and a Comparative Study of Hybrid Hydrogen Fuel Cell/Battery Vehicles

This paper presents a new Fuel Cell Fuel Consumption Minimization Strategy (FCFCMS) for Hybrid Electric Vehicles (HEVs) powered by a fuel cell and an energy storage system, in order to minimize as much as possible the consumption of hydrogen while maintaining the State Of Charge (SOC) of the battery. Compared to existing Energy Management Strategies (EMSs) (such as the well-known State Machine Strategy (SMC), Fuzzy Logic Control (FLC), Frequency Decoupling and FLC (FDFLC), and the Equivalent Consumption Minimization Strategy (ECMS)), the proposed strategy increases the overall vehicle energy efficiency and, therefore, minimizes the total hydrogen consumption while respecting the constraints of each energy and power element. A model of a hybrid vehicle has been built using the TruckMaker/MATLAB software. Using the Urban Dynamometer Driving Schedule (UDDS), which includes several stops and accelerations, the performance of the proposed strategy has been compared with these different approaches (SMC, FLC, FDFLC, and ECMS) through several simulations.

Development and parameterization of a control-oriented electrochemical model of lithium-ion batteries for battery-management-systems applications

A precise electrochemical battery model is critical for advanced battery management systems to improve the safety and efficiency of electric vehicles. This paper presents a novel methodology to develop and parameterize the electrochemical model through cell teardown and current/voltage data estimation. The partial differential equations of ionic electrolyte and potential dynamics in the solid and liquid phases are solved and reduced to a low-order system with Padé approximation. The systematic identification procedure is proposed by first dividing the parameters into fixed geometric properties, thermodynamics, and kinetics. Then the cells are dismantled. Subsequent chemical and thermodynamic analyses, including half-cell tests, are conducted for parameter extraction. Next, the parameterized model is validated with extensive experimental data, illustrating the superior capability of predicting cell voltage with root-mean-square errors of 8.90 mV at 2C and 13.98 mV for Urban Dynamometer Driving Schedule profile at 0 °C. The accuracy of the cell internal electrochemical states of the reduced model is verified as well. Comparative studies concerning model accuracy and computation efficiency on hardware reveal that the model is 31% more accurate than equivalent circuit models but occupies similar computation resources. Finally, the need and advantages of combining cell teardown and parameter estimation in achieving a precise electrochemical model are addressed.

State of charge estimation for electric vehicle lithium-ion batteries based on model parameter adaptation

Accurate state of charge (SOC) estimation is the basis of the battery management system in electric vehicles. In order to reduce the influence of time-varying model parameters on SOC estimation accuracy for lithium-ion batteries under complex operating conditions, this paper proposes an improved method for online model parameter identification using variable forgetting factor recursive least squares (VFFRLS), which is based on the sliding window time-varying forgetting factor theory. The VFFRLS was used for online parameter identification at the macroscopic timescale, and extended Kalman filter (EKF) was used for estimating the battery SOC at the microscopic timescale. The accuracy of parameter identification was verified using pulsed discharging and urban dynamometer driving schedule (UDDS) tests, and recursive least squares (RLS) was used to identify the parameters of lithium-ion batteries under UDDS test and estimate the SOC of lithium-ion batteries in combination with EKF, and the experimental results verify the accuracy and robustness of VFFRLS-EKF.

Parameter identification and SOC estimation for power battery based on multi-timescale double Kalman filter algorithm

Accurate modelling and state of charge (SOC) estimation are of great significance to improve efficiency of power batteries and extend their life. In order to solve the issue of time-varying model parameters resulting in inaccurate SOC estimation, a combined online identification of model parameters and SOC estimation method for lithium-ion batteries based on extended Kalman filter (EKF) and unscented Kalman filter (UKF) with different timescales is proposed. A second-order RC circuit model is established and model parameters are identified online by UKF on a macroscopic timescale, and the battery SOC is estimated by EKF on a microscopic timescale. Compared with conventional SOC estimation methods in which model parameters are identified offline, the proposed method can obtain more accurate SOC estimation. The SOC mean absolute error (MAE) and root mean square error (RMSE) are both significantly reduced under the urban dynamometer driving schedule (UDDS) test. The SOC estimation results demonstrate the accuracy and robustness of the proposed method.

A Predictive Energy Management Strategy for Multi-Energy Source Vehicles Based on Full-Factor Trip Information.

To achieve the real-time application of a dynamic programming (DP) control strategy, we propose a predictive energy management strategy (PEMS) based on full-factor trip information, including vehicle speed, slip ratio and slope. Firstly, the prediction model of the full-factor trip information is proposed, which provides an information basis for global optimization energy management. To improve the prediction’s accuracy, the vehicle speed is predicted based on the state transition probability matrix generated in the same driving scene. The characteristic parameters are extracted by a feature selection method taken as the basis for the driving condition’s identification. Similar to speed prediction, regarding the uncertain route at an intersection, the slope prediction is modelled as a Markov model. On the basis of the predicted speed and the identified maximum adhesion coefficient, the slip ratio is predicted based on a neural network. Then, a predictive energy management strategy is developed based on the predictive full-factor trip information. According to the statistical rules of DP results under multiple standard driving cycles, the reference SOC trajectory is generated to ensure global sub-optimality, which determines the feasible state domain at each prediction horizon. Simulations are performed under different types of driving conditions (Urban Dynamometer Driving Schedule, UDDS and World Light Vehicle Test Cycle, WLTC) to verify the effectiveness of the proposed strategy.

Regulation of Electric Vehicle Speed Oscillations Due to Uneven Drive Surfaces Using ISMDTC

An unbalance in the electromagnetic torque and the load torque occurs when Electric Vehicles (EVs) are driven through uneven terrains. This leads to non-periodic and time-varying speed oscillations. As a result, torsional vibrations increase and leads to passenger discomfort and mechanical stress on the EV body. The EV drive control should be designed to mitigate these speed oscillations. In this paper, an Integral Sliding Mode Control Based Direct Torque Control (ISM-DTC) of Induction Motor (IM) is proposed to inherit the intrinsic properties of disturbance rejection, robustness against parametric variations, operational uncertainties, and improve the conventional Proportional-Integral (PI)-based DTC (PIDTC) performance in uncertain EV drive conditions. The proposed control is integrated with PIDTC, which maintains the speed and acts as a nominal control. The non-linear ISM loop cancels out the speed fluctuations by balancing the electromagnetic torque with dynamic load torque demand. The proposed control cancels out the matched uncertainties without amplifying the unmatched uncertainties in system dynamics. An analysis of the uncertainty decomposition and control gain derivation is presented. The proposed ISMDTC is verified through simulations and experiments considering different scenarios of torque, reference speed variations, uncertainties, and the standard New European Driving Cycle (NEDC) and Urban Dynamometer Driving Schedule (US) drive test cycles.

리튬이온 배터리 노화상태 추정을 위한 건전성 지표 추출 및 CNN 적용

Due to the output characteristics of lithium-ion batteries used in electric vehicles (EVs) vary according to battery aging, accurate prediction of the state-of-health (SOH) reflecting the aging state is important. However, it is difficult to consider factors affecting battery characteristics, such as frequent charging and discharging, operating temperature, and state of charge, so there is a limitation in predicting battery SOH. Even in a model that considers the aging state of the battery under these various conditions, there is also a problem in that the complexity of the calculation process and parameters becomes serious. Therefore, in this paper, in order to research on the estimation of the aging state during operation of the INR18650-25R battery, a health indicator (HI) that can reflect the internal state of the battery according to aging is extracted. This research produce a learning image through the extracted HI and build a model study that enables algorithm learning through the image. The experimental profile used for model training and validation was an Urban Dynamometer Driving Schedule (UDDS), and a Convolutional Neural Network (CNN) with strength in image learning was applied for the estimation algorithm.

Estimation of lithium‐ion battery state of charge for electric vehicles using a nonlinear state observer

AbstractThe state of charge (SOC) estimation of lithium‐ion batteries is complex due to the various nonlinear uncertainties present in the battery. However, in this paper, a new nonlinear state observer (NSO) is proposed to be designed for the estimation of accurate and robust SOC. This proposed observer is suitable for both continuous and discrete‐time nonlinear systems. To design the nonlinear observer, two‐RC equivalent circuit model state equations are simulated for the dynamic behavior of the lithium‐ion battery. The seventh‐order polynomial fitting approach is assumed for the nonlinear relationship between open‐circuit voltage (OCV) and SOC, and the exponential fitting method is used to estimate the battery's offline parameters. Lyapunov's stability criterion achieves the stability and convergence capability of the proposed method. An urban dynamometer driving schedule (UDDS) cycle was adopted to estimate the performance of the proposed observer by comparing it with the well‐established methods like unscented Kalman filter (UKF) and sliding mode observer (SMO) algorithms, and it was found that the proposed observer achieved better performance like accurate SOC, high convergence capability, and less SOC error.

Transient Hybrid Electric Vehicle Powertrain Control Based on Iterative Dynamic Programing

Abstract This research proposes an iterative dynamic programing (IDP) algorithm that generates an optimal supervisory control policy for hybrid electric vehicles (HEVs) considering transient powertrain dynamics. The proposed algorithm tries to solve the “curse of dimensionality” and the “curse of modeling” of conventional dynamic programing (DP). The proposed IDP algorithm iteratively updates the DP formulation using a machine learning-based powertrain model. The machine learning model is recursively trained using the outputs from the driving cycle simulation with a high-fidelity model. Once the reduced model converges to the high-fidelity model accuracy, the resulting control policy yields a 9.1% fuel economy (FE) improvement compared to the baseline nonpredictive rule-based control for the urban dynamometer driving schedule (UDDS) driving cycle. A conventional DP control strategy based on a quasi-static powertrain model and a perfect preview of future power demand yields 14.2% FE improvement. However, the FE improvement reduces to 5.7% when the policy is validated with the high-fidelity model. It is concluded that capturing the transient powertrain dynamics is critical to generating a realistic fuel economy prediction and relevant powertrain control policy. The proposed IDP strategy employs targeted state-space exploration to leverage the improving state trajectory from previous iterations. Compared to conventional fixed state-space sampling methods, this method improves the accuracy of the DP policy against discretization error. It also significantly reduces the computational load of the relatively high number of states of the transient powertrain model.

A comparative dynamic analysis between model predictive torque control and field‐oriented torque control of IM drives for electric vehicles

This article compares two torque control methodologies, model predictive control (MPC), and field-oriented control (FOC), for induction motor (IM) control over a wide speed range applied to an electric vehicle driving system (EVDS). The control technologies are designed and developed to control torque to achieve targeted traction according to vehicle specifications and the demands of the driver. For both control techniques, the specified electric vehicle (EV) is driven under and above base speed with stator flux control and field weakening control of the IM, respectively. In EVDS, the dynamic modeling of the power plants, that is, vehicle dynamics, Li-ion battery, IM, and road forces, is considered. The dynamic behavior of the EVDS of a four-passenger city EV is analyzed and evaluated with urban dynamometer driving schedule (UDDS) driving cycle. From simulations and performance appraisal, the comparison between FOC and MPC is investigated.

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.

Enhancing Thermal Performance and Lifetime Cycles of Li-ion Battery in Electric Vehicles

Hybrid energy storage system has essential priority in Electric Vehicle applications. Therefore, the design of an appropriate power sharing algorithm among energy storage components is necessary to improve battery thermal performance and provide extra extension of battery lifetime cycles. This paper presents an analytical study on the effect of using wavelet decomposition-based power sharing algorithm to force the high frequency component to be fed by the supercapacitor and accordingly reduces the thermal stress on the battery. The proposed approach was investigated by applying it on electric vehicle model in ADVISOR Tool/MATLAB using different driving profiles such as Urban Dynamometer Driving Schedule profile, Highway Fuel Economy Test, New York City Cycle, Los Angeles 1992 and new European driving cycle. The results declare that by using proposed power sharing algorithm, the working temperature of lithium battery decreases significantly while battery lifetime cycles increase, apparently. For urban dynamometer driving schedule, the operating temperature of lithium battery is improved much at maximum decomposition levels reaching to only 25.6 °C compared to 35 °C. In addition, the battery lifetime cycles increased from 2213 to 2585 cycles. Neural Networks pattern recognition tool is also applied to classify the driving cycle to the nearest reference cycles chosen to represent the different driving conditions which help to detect the appropriate wavelet decomposition level, achieving better battery thermal performance and battery lifetime cycles.

Understanding elevated real-world NOx emissions: Heavy-duty diesel engine certification testing versus in-use vehicle testing

For the first time, nitrogen oxide (NOx) emissions were systematically investigated during different operating conditions from a single test article on a chassis dynamometer, under real-world (on-road) conditions, on an engine dynamometer after the engine was pulled out of the vehicle, and again on a chassis dynamometer when the engine was installed back in the vehicle. The main goal of this study was to understand the discrepancies between certification and in-use NOx emission rates from a heavy-duty diesel vehicle (HDDV) with an engine with diesel particulate filter (DPF) and selective catalytic reduction (SCR) aftertreatment. The results showed that in-use NOx emissions over urban driving cycles were above the 0.2 g/bhp-hr NOx standard for most test conditions, ranging from 0.16 to 0.7 g/bhp-hr, with the highest NOx emissions for the chassis dynamometer testing. For the freeway/steady-state testing, the results were generally lower compared with the results of urban driving cycles, with values of 0.3 g/bhp-hr or less. For the Urban Dynamometer Driving Schedule (UDDS) cycle, NOx emissions were lowest for the engine dynamometer testing, followed by the on-road testing, and the chassis dynamometer testing. The differences between the tailpipe NOx emissions for the different types of tests could be attributed to differences in SCR inlet temperatures and engine-out NOx emissions.

Performance assessment of a range-extended electric vehicle under real driving conditions using novel PCM-based HVAC system

In this research paper, a novel Heating, Ventilation, and Air Conditioning (HVAC) system is proposed to mitigate the unfavorable effects of conventional HVAC systems on the Electric Vehicle (EV) range. The HVAC system is composed of a heat pump (HP) system with an additional phase change material (PCM) heat exchanger, which is a shell and tube heat exchanger and is parallel to the external heat exchanger of the heat pump cycle. This heat exchanger is designed to function as both condenser and evaporator in the cycle temporarily. The n-hexadecane is selected as the PCM in the shell, and R134a is the refrigerant in the tubes. The specific melting point of PCM in the comfort zone range leads to diminishing the compressor's power consumption and consequently the whole system. A copper foam is used to enhance the low thermal conductivity of the PCM. The enthalpy-porosity technique with the Volume Averaged Method (VAM) for modeling of the PCM-copper foam is applied for Computational Fluid Dynamics (CFD) analysis of the PCM's melting and solidification cycles in ANSYS Fluent software, and the results are obtained for modeling the heat exchanger in the heat pump cycle. A comprehensive thermal model is developed for the vehicle's cabin to improve the accuracy of the results. Nissan Leaf is modeled as the selected EV at different weather temperatures in the range of −5 °C to 50 °C with the Urban Dynamometer Driving Schedule (UDDS) driving cycle in Simcenter Amesim software. The results show that the proposed system increases the vehicle range by 19% and 11% compared with the conventional heat pump systems at the weather temperatures of 10 °C and 0 °C, respectively. Moreover, the energy consumption of various components in the cold start and hot start are compared and the results are interpreted.

Online Parameters Identification and State of Charge Estimation for Lithium-Ion Battery Using Adaptive Cubature Kalman Filter

The state of charge (SOC) of a lithium-ion battery plays a key role in ensuring the charge and discharge energy control strategy, and SOC estimation is the core part of the battery management system for safe and efficient driving of electric vehicles. In this paper, a model-based SOC estimation strategy based on the Adaptive Cubature Kalman filter (ACKF) is studied for lithium-ion batteries. In the present study, the dual polarization (DP) model is employed for SOC estimation and the vector forgetting factor recursive least squares (VRLS) method is utilized for model parameter online identification. The ACKF is then designed to estimate the battery’s SOC. Finally, the Urban Dynamometer Driving Schedule and Dynamic Stress Test are utilized to evaluate the performance of the proposed method by comparing with results obtained using the extended Kalman filter (EKF) and the cubature Kalman filter (CKF) algorithms. The simulation and experimental results show that the proposed ACKF algorithm combined with VRLS-based model identification is a promising SOC estimation approach. The proposed algorithm is found to provide more accurate SOC estimation with satisfying stability than the extended EKF and CKF algorithms.

More intelligent and robust estimation of battery state-of-charge with an improved regularized extreme learning machine

State-of-charge (SOC) is the key parameter for battery management, and the accurate estimation of SOC is pretty important for the safe and stable operation of lithium batteries. This paper investigates a regularized extreme learning machine trained with the spectral Fletcher–Reeves algorithm and tuned with the beetle antennae search algorithm (BAS-SFR-RELM) for intelligent and robust SOC estimation. In the experiment section, the urban dynamometer driving schedule (UDDS) profile and the Los Angeles 92 (LA92) profile are performed on a battery test platform for data collection. In the simulation section, the root mean squared error (RMSE) and the mean absolute error (MAE) are adopted to evaluate the performance of the model. Compared with the linear regression (LR), the back propagation (BP) network, the multi-layer perceptron (MLP), and the long short-term memory (LSTM) network, the BAS-SFR-RELM method can efficiently obtain the optimal regularization coefficient to effectively prevent overfitting with faster convergence speed. Increasing the number of hidden neurons in the BAS-SFR-RELM appropriately can improve the SOC estimation precision. Implementing the BAS-SFR-RELM with the noise-added data set gives high robustness for SOC estimation

Impact of cost-optimized dedicated hybrid transmission (DHT) constraints on powertrain optimal control

In the transition from internal combustion engines (ICE) based vehicles to pure battery electric vehicles (BEVs), the powertrain is becoming increasingly electrified (xEV). Based on their capability to recuperate brake energy in the legislative test cycles, intermediate xEV vehicles are classified as mild and strong electrified vehicles. Growing xEV sales currently pave the way for the application of cost-optimized dedicated components. The B segment vehicle analyzed in this work is currently equipped with a conventional coaxial transmission, which can offer multiple power sources (combustion engine, electric machine, or hybrid) in a single shift with an additional clutch. A cost-optimized dedicated hybrid transmission (DHT) is designed by eliminating the launch clutch, synchronizers, and redundant energy travel paths to replace the coaxial transmission. The DHT requires one shift for each energy path to connect the power source to the wheels. The flexible coaxial transmission requires only a low specific number of gear shifts to drive a typical test cycle. However, the DHT has constrained energy paths, leading to a high specific number of gear shifts to drive identical test cycles when applied with the standard Adaptive Equivalent Consumption Minimization Strategy (A-ECMS) optimal control. A one dimensional closed-loop simulation model for the passenger car longitudinal dynamics is used in this analysis. The vehicle velocity and evaluation criteria for the simulation are based on the Worldwide harmonized Light vehicles Test Procedure (WLTP). The loss maps from steady-state test bench measurements for the internal combustion engine (ICE), the electric motor (EM), and the transmission unit are used as inputs in building the simulation model. A drivability penalty approach is introduced in the standard A-ECMS optimal control to reduce the number of gear shifts. The work analyzes the gear shift behavior and the absolute CO2 emission between the cost-optimized DHT and the existing coaxial transmission for the considered vehicle application. Detailed power flow using a Sankey diagram from different onboard vehicle sources for the cost-optimized DHT solution is included in the results section. The developed model is validated with a prototype vehicle environment on the Urban Dynamometer Driving Schedule (UDDS).

A novel numerical implementation of electrochemical-thermal battery model for electrified powertrains with conserved spherical diffusion and high efficiency

The performance of batteries in electrified powertrain systems is highly influenced by mass diffusion and electrochemistry which are often ignored in the simulation of these systems due to the lack of a conserved, efficient, and integrable battery model. Therefore, this work numerically implements an electrochemical-thermal battery model with conserved numerical schemes and efficient numerical methods which include Jacobian-based and Jacobian-Free Newton Krylov (JFNK) solvers. The performance of the developed model is evaluated by simulating measurements of a LiFePO4 battery under constant discharge rates and Urban Dynamometer Driving Schedule (UDDS), as well as by a detailed comparison with existing battery models. The comparison highlights two features of our model: (a) negligible mass imbalances in the spherical diffusion modelling, which are five orders of magnitude smaller than those from a recent battery model in the literature; (b) efficient modelling of real-world driving cycles with the computational time two orders of magnitude shorter than that of the literature model. These advanced features indicate that our model can be applied in both fundamental electrochemical-thermal studies of lithium-ion battery and detailed simulations of electrified powertrains as an accurate and efficient sub-model.

A novel streamlined particle‐unscented Kalman filtering method for the available energy prediction of lithium‐ion batteries considering the time‐varying temperature‐current influence

Effective energy prediction is of great importance for the operational status monitoring of high-power lithium-ion battery packs. It should be embedded in the battery system performance evaluation, energy management, and safety protection. A new Streamlined Particle-Unscented Kalman Filtering method is proposed to predict the available energy of lithium-ion batteries, in which an Adaptive-Dual Unscented Transform treatment is conducted to realize the precise mathematical expression of its working conditions. For the accurate mathematical description purpose, an improved Synthetic-Electrical Equivalent Circuit modeling method is introduced into the internal effect equivalent process considering the influence of time-varying temperature and current conditions. As can be known from the experimental results, the proposed prediction method has a maximum estimation error of 2.27% and an average error of 0.80%, for the complex varying-current Beijing Bus Dynamic Stress Test. Under the Urban Dynamometer Driving Schedule working conditions, the available energy prediction has high accuracy with a maximum error of 1.83% and a voltage traction error of 3.28%. It provides vehicle-mounted available energy prediction schemes for effective management and safety protection of high-power lithium-ion batteries. Highlights A new Streamlined Particle-Unscented Kalman Filtering method is proposed to predict the available energy of lithium-ion batteries. Improved Synthetic-Electrical Equivalent Circuit modeling strategies are established to describe the nonlinear battery characteristics. Adopted predictive correction is investigated by considering the time-varying temperature and current influence. For effective convergence, an adaptive windowing function factor is introduced into the correction process with a maximum estimation error of 2.27% and an average error of 0.80% for the complex varying-current Beijing Bus Dynamic Stress Test working conditions. The vehicle battery available energy prediction is realized with a maximum error of 1.83% and a maximum voltage traction error of 3.28% for the Urban Dynamometer Driving Schedule working conditions.

Physical-based training data collection approach for data-driven lithium-ion battery state-of-charge prediction

Data-Driven approaches for State of Charge (SOC) prediction have been developed considerably in recent years. However, determining the appropriate training dataset is still a challenge for model development and validation due to the considerably varieties of lithium-ion batteries in terms of material, types of battery cells, and operation conditions. This work focuses on optimization of the training data set by using simple measurable data sets, which is important for the accuracy of predictions, reduction of training time, and application to online estimation. It is found that a randomly generated data set can be effectively used for the training data set, which is not necessarily the same format as conventional predefined battery testing protocols, such as constant current cycling, Highway Fuel Economy Cycle, and Urban Dynamometer Driving Schedule. The randomly generated data can be successfully applied to various dynamic battery operating conditions. For the ML algorithm, XGBoost is used, along with Random Forest, Artificial Neural Network, and a reduced-order physical battery model for comparison. The XGBoost method with the optimal training data set shows excellent performance for SOC prediction with the fastest learning time within 1 s, a short running time of 0.03 s, and accurate results with a 0.358% Mean Absolute Percentage Error, which is outstanding compared to other Data-Driven approaches and the physics-based model.