Urban Dynamometer Driving Schedule Research Articles

Thermal uniformity analysis of a hybrid battery pack using integrated phase change material, metal foam, and counterflow minichannels

This study investigates the thermal performance and temperature uniformity of a hybrid battery thermal management system (BTMS) that integrates phase change material (PCM), metal foam, and minichannels. Computational fluid dynamics is used to model the PCM melting process and heat transfer between all components. The primary goal of the work is to investigate BTMS architectures which can enhance thermal uniformity and prevent critical temperature rise in a high-voltage battery pack under fast discharging and real-world driving cycle. Four BTMS designs are compared. The design that integrates PCM, metal foam, and counterflow minichannels is shown to have the best performance. At low pumping power (coolant Reynolds number Re = 10), this design reduces the peak battery temperature by 11.5 K compared to a design employing pure PCM only. This configuration also ensures a temperature difference of less than 5 K among individual battery cells, addressing thermal safety considerations and extending battery lifespan. Further analysis revealed that the inclusion of metal foam delays PCM melting, enhances both system and battery thermal uniformity, and offers a higher performance-to-weight ratio compared to designs without metal foam. Although wavy-shaped minichannels offer minimal temperature improvement (0.3 K) over straight minichannels, their higher cost and increased pumping power requirements do not justify their practicality. Under both fast discharging and real driving conditions, the first design with pure PCM provides uniform heat distribution within batteries but fails to maintain the maximum battery temperature within the optimal range. Overall, this study highlights the effectiveness of the proposed hybrid BTMS design in providing uniform temperature distribution and maintaining the maximum battery temperature within the optimal range under harsh environmental conditions, fast discharging, and the Urban Dynamometer Driving Schedule (UDDS) drive cycle.

Estimation of Lithium-Ion Battery SOC Based on IFFRLS-IMMUKF

The state of charge (SOC) is a characteristic parameter that indicates the remaining capacity of electric vehicle batteries. It plays a significant role in determining driving range, ensuring operational safety, and extending the service life of battery energy storage systems. Accurate SOC estimation can ensure the safety and reliability of vehicles. To tackle the challenge of precise SOC estimation in complex environments, this study introduces an improved forgetting factor recursive least squares (IFFRLS) method, which integrates the Golden Jackal optimization (GJO) algorithm with the traditional FFRLS method. This integration is grounded in the formulation of a lithium battery state equation derived from a second-order RC equivalent circuit model. Additionally, the research utilizes the interactive multiple model unscented Kalman filter (IMMUKF) algorithm for SOC estimation, with experimental validation conducted under various conditions, including hybrid pulse power characterization (HPPC), urban dynamometer driving schedule (UDDS), and real underwater scenarios. The experimental results demonstrate that the SOC estimation method of lithium batteries based on IFFRLS-IMMUKF exhibits high accuracy and a favorable temperature applicability range.

Multi-innovation adaptive Kalman filter algorithm for estimating the SOC of lithium-ion batteries based on singular value decomposition and Schmidt orthogonal transformation

The state of charge (SOC) of lithium battery is a key parameter for effective management of battery management systems (BMS). To address the problems of low precision, complex computation and poor robustness of traditional charging state estimation methods, an enhanced algorithm based on Unscented Kalman filter (UKF) is proposed. The singular value decomposition (SVD) method is adopted to ensure the normal operation of the Unscented Kalman Filter (UKF) algorithm even when the matrix P lacks positive semi-definiteness from a mathematical perspective. This enhancement significantly improves the theoretical robustness of UKF. Schmidt orthogonal transformation is concurrently used to reduce the computational complexity in the sampling point selection process, and the multi-innovation theory is combined with adaptive noise control to further improve the accuracy of SOC estimation. The algorithm is validated using the Urban Dynamometer Driving Schedule (UDDS) condition. The simulation results are excited using Singular value decomposition-multi-innovation adaptive Schmidt orthogonal unscented Kalman filter method (SVD-MIASOUKF). 0.95 % and 1.29 % of maximum errors are obtained at 25 °C and −10 °C, while the maximum errors are 2.46 %–2.99 % using SVD-UKF and UKF. The proposed approach shows faster convergence speed and higher estimation accuracy in comparison to traditional algorithms.

Advanced parameter estimation for lithium-ion battery model using the information sharing group teaching optimization algorithm

Accurate battery modeling is crucial for optimizing the performance and safety of Lithium-ion batteries (LiBs), particularly in applications such as electric vehicles and smart grids. This paper introduces the Information Sharing Group Teaching Optimization Algorithm (ISGTOA), a novel human-based metaheuristic algorithm designed to estimate the 21 parameters of third-order Equivalent Circuit Model (ECM) for LiBs. Unlike existing methods, ISGTOA effectively manages the increased complexity of a model through advanced group teaching strategies and sophisticated information-sharing mechanisms inspired by classroom dynamics. We validated the effectiveness of ISGTOA using two distinct datasets—High Dynamic Profile (HDP) and Urban Dynamometer Driving Schedule (UDDS)—across various LiB chemistries (NCA and NMC) and temperature conditions (−5 °C, 25 °C and 45 °C). The results demonstrate that ISGTOA achieves superior parameter estimation accuracy and convergence speed compared to established and recent optimization methods such as TLBO, TLSBO, AISA, MTBO, and DTBO. Specifically, ISGTOA attained a minimum RMSE of 8.357 mV with rapid convergence and high stability in the HDP dataset and minimized RMSE to 10 mV within ten iterations at both 25 °C and 45 °C in the UDDS dataset. Additionally, ISGTOA exhibited high efficiency (up to 97.75 %) under varying thermal environments. This work not only introduces a novel algorithm for complex battery modeling but also sets the stage for future advancements in real-time battery management systems, emphasizing the importance of robust, versatile, and efficient parameter estimation techniques.

Effect of module configurations on the performance of parallel-connected lithium-ion battery modules

To meet the power and energy of battery storage systems, lithium-ion batteries have to be connected in parallel to form various battery modules. However, different single module collector configurations (SCCs) and unavoidable interconnect resistances lead to inhomogeneous currents and state-of-charge (SoC) within the module, thereby significantly reducing the module lifetime and safety. Therefore, the correlation of SCCs to module inhomogeneous currents and SoC are worth investigating. Based on mathematical permutations and combinations calculations, all SCCs are firstly defined and obtained in this paper. Then, two parallel modules are developed in Matlab/Simscape based on fresh cell and degradation cell separately, which are used to quantify the influence of all SCCs. Furthermore, multiple module collector configurations (MCCs) and Urban Dynamometer Driving Schedule (UDDS) working condition are considered in the analysis. Results indicate that both module collectors connected to same edge cell of module cause the most inhomogeneity. The maximum SoC difference under the UDDS is only 46 % of the 0.5C. Moreover, MCCs can reduce inhomogeneous currents and SoC within the parallel module, while not all MCCs perform better homogeneous performance than SCCs. Finally, the effect of changes in SCCs on degradation module inhomogeneous currents and SoC is verified as same as for fresh cells parallel module. Based on the above results, the optimal single module collector configuration of the N cells parallel module is obtained through mathematical analysis, which can greatly improve the currents and SoC homogeneity of the module.

Heat transfer and economic characteristics of direct oil cooling for electric traction motor under driving cycles

Currently, the energy density of electric traction motor is continuously increasing to match the performance and reliability of electric vehicles with IC engine vehicles. The commercially employed water jacket cooling fails to achieve the desired efficiency of high energy density electric traction motor. Direct oil cooling is one of potential cooling strategies to replace the conventional cooling method for achieving the desired thermal management of next generation electric traction motors. However, the database to commercialize the direct oil cooling based on thermal management and economic aspects is not enriched sufficiently and thus extensive research studies need to be conducted in this field. Therefore, the heat transfer and economic characteristics of direct oil cooling for electric traction motor are investigated considering different cooling oils and driving duty cycles in the present work. The integrated electromagnetic-thermal numerical model is developed using ANSYS Motor-Cad commercial software to simulate the heat transfer and economic characteristics. The reliability of coupled numerical model is assured by validating the simulated results with experimental and reference data in error range of ± 5 %. The winding temperature, heat transfer coefficient, expected insulation lifetime, levelized cost of energy (LCOE), and net cumulative savings are evaluated as heat transfer and economic characteristics. Three driving duty cycles namely, UDDS (Urban Dynamometer Driving Schedule), NEDC (New European Driving Cycle) and US06 (Highway Driving Cycle) and transmission oil, shell thermal oil and Novec dielectric oil are investigated. The performance of electric traction motor with direct oil cooling is superior compared to that with water jacket cooling. The direct cooling with Novec dielectric oil shows improved heat transfer and economic characteristics compared to transmission oil and shell thermal oil. The direct cooling with Novec dielectric oil restricts the maximum winding temperature within 80.21 °C, 67.65 °C and 116.82 °C under driving cycles of UDDS, NEDC and US06, respectively. In addition, the enhanced insulation lifetime, heat transfer coefficient, LCOE and net cumulative savings of 7.91 × 105h, 23036.73 W/m2-K, 0.170 $/kWh and 47151.13 $, respectively are reflected for direct cooling with Novec dielectric oil.

Performance Evaluation of Model-Based Online Condition Monitoring Algorithms for Li-Ion Battery State Estimation

This discovery examines and tests four model-based charge-state (SOC) estimation methods for lithium-ion (Li-ion) batteries. This work evaluates some parts of the SOC estimation, such as error distribution evaluation, rise time estimation, consumption time estimation, etc., instead of the former probing. The battery comparison model is introduced and the state function of the model is inferred. The first step is to study four model-based SOC estimation strategies. The four systems are then tested using simulation and analysis. To mimic the driving conditions of an electric vehicle, the Urban Dynamometer Driving Schedule (UDDS) flow profiles are used. A genetic calculation is then used to find the limits of the model to determine the optimal limits of the Li-ion battery model. The simulations are run continuously and without interruption, and the results are analyzed. To test the device in circle discovery, a battery test bench is developed and uses a Li-ion battery.

Dual‐inertia flywheel energy storage system for electric vehicles

AbstractManaging the high‐rate‐power transients of Electric Vehicles (EVs) in a drive cycle is of great importance from the battery health and drive range aspects. This can be achieved by high power‐density storage, such as a high‐speed Flywheel Energy Storage System (FESS). It is shown that a variable‐mass flywheel can effectively utilise the FESS useable capacity in most transients close to optimal. Novel variable capacities FESS is proposed by introducing Dual‐Inertia FESS (DIFESS) for EVs. The feasibility of the proposed concept is evaluated by deriving the size of a Single‐Inertia FESS (SIFESS) for a battery EV, which runs the well‐known Urban Dynamometer Driving Schedule. The sizing framework consists of an Energy Management System using the constrained Pontryagin's minimum principle and a proposed sizing algorithm. Then, by splitting the derived SIFESS inertia into two separate inertias, the appropriate engaging control of inertias is determined for some driving cycles including, the Artemis Urban, Braunschweig City, and Worldwide Harmonised Light‐duty Vehicles Test Cycle. The dual inertias suitable sizes are derived using a proposed algorithm, which targets maximising the FESS useable capacity. The results show that compared to the SIFESS, the DIFESS can employ the FESS's useable capacity more effectively.

DeepTimeNet: A novel architecture for precise surface temperature estimation of lithium-ion batteries across diverse ambient conditions

With the growing demand for battery-powered devices and electric vehicles, the need for improved battery performance and safety is paramount. A key determinant of battery health is the accurate monitoring of surface temperature (ST). Conventional ST estimation often depends on direct sensor measurements, which may not be cost-effective and can impact system reliability. This paper presents DeepTimeNet, a novel approach leveraging deep learning (DL) architectures for sensorless ST prediction in lithium-ion batteries. DeepTimeNet combines Convolutional Neural Networks (CNN), ResNet blocks, Inception modules, Bidirectional LSTM, and GRU layers to precisely model the time-dependent behaviour of batteries. A comprehensive evaluation against traditional models, across temperatures ranging from -20 °C to 25 °C and under various driving profiles, including US06 and Urban Dynamometer Driving Schedule (UDDS), is conducted. DeepTimeNet’s performance is quantified by metrics such as mean absolute error (MAE), surpassing that of models like Gated Recurrent Unit-Recurrent Neural Network (GRU-RNN), Convolutional Neural Network-Long Short Term Memory Network (CNN-LSTM), and Long Short Term Memory Network (LSTM). The results demonstrate DeepTimeNet’s superior performance, with an RMSE of 0.0971, MSE of 0.0099, MAE of 0.0912, and MAXE of 0.3963, validating it as an advanced tool for enhancing the efficacy of battery management systems and underscoring its potential as a benchmark for future innovations.

Experimental study and performance evaluation of R744 thermal management systems with enhanced parallel cooling for electric vehicles

As worldwide environmental concerns grow, R744 (CO2) has become one of the preferred refrigerants in electric vehicle (EV) thermal management system due to its environmentally friendly characteristics. This study presents the development and evaluation of an Enhanced Parallel Cooling (EPC) system, a novel advancement designed to optimize the performance of R744 thermal management systems in EVs. A series of experiments are carried out to investigate the effects of key operating variables such as the coolant flow rate, and air velocity on the system efficiency. Meanwhile, Response Surface Methodology (RSM) is employed to determine the optimal control contour map under various environmental conditions. The maximum cooling capacity of the EPC system in supercharging mode is experimentally evaluated. Furthermore, a comparative analysis of driving range improvement under the Urban Dynamometer Driving Schedule (UDDS) and the Highway Fuel Economy Test (HWFET) cycles is carried out. With the help of the Series Cooling Unit (SCU) in the EPC system, the maximum cooling capacity increases by 27.8 % to 9.89 kW, and the driving range increases by up to 14.6 km. The findings underscore the potential of the innovative R744 thermal management system architecture to improve operational efficiency and extend the driving range of EVs.

Simulation analysis of BOP thermal management system for hydrogen fuel cell bus

Hydrogen fuel cell bus has been rapidly developed. As an important part of the hydrogen fuel cell system, its BOP (Balance of plant) mainly includes air compressor, controller, and intercooler. In practical applications, the BOP thermal management system generally includes separated cooling circuits. This study proposes an integrated BOP thermal management system, in which the air compressor, the controller and the intercooler share the same cooling circuit. A 1-D numerical model is built in this paper. On the basis of the model, the 13 possible layout schemes of cooling circuit are investigated. Additionally, the BOP temperature variation characteristics under the UDDS (Urban Dynamometer Driving Schedule) condition are also analyzed. The results indicate that not all layouts of integrated scheme can meet the cooling requirements. To meet cooling requirements of the whole BOP, the coolant should pass through the intercooler first in the serial schemes. As for the parallel schemes, the air compressor should be in a single branch, and the controller and the intercooler should be in series successively at another branch. Under the same total flow rate, the average temperature of BOP in the series scheme can be lower than that in parallel scheme by 8 %. In the three components, the intercooler is most affected by the stack power. This study provides reference for the design of the BOP thermal management system.

A novel joint estimation for core temperature and state of charge of lithium-ion battery based on classification approach and convolutional neural network

The core temperature (CT) of lithium-ion batteries (LIBs) is crucial for effective thermal management, and is significantly influenced by the state of charge (SOC) among other battery parameters. However, directly measuring CT and SOC poses challenges. This study introduces a novel joint estimation method for CT and SOC, in which classification approach is pioneeringly employed through a two-dimensional convolutional neural network (2D-CNN). SOC is initially estimated and subsequently utilized as an input to refine CT estimation. This process is facilitated by two 2D-CNNs that incorporate measured indices such as current, terminal voltage, surface temperature, and ambient temperature into their inputs. Techniques including data augmentation, sequential optimization, and Gaussian filtering are employed to enhance estimation effect. The methodology is validated using LIB charge/discharge data derived from experiments based on the urban dynamometer driving schedule (UDDS). Results demonstrate that the root-mean-square errors (RMSEs) for CT and SOC estimation are below 0.267 °C and 0.7 %, respectively, across a broad ambient temperature range of −15 °C–55 °C. Consequently, this study confirms the effectiveness of using classification for CT and SOC estimation, illustrating that the proposed method can precisely estimate CT and SOC of LIBs across a wide temperature spectrum. The effectiveness of the deep learning method based on classification in estimating CT and SOC has been validated. Furthermore, a comparative analysis reveals that incorporating SOC as an input for CT estimation notably reduces the training time required by the method.

Humidification-Thermal Cycling-Based Mechanical Degradation Analysis of Low-Temperature PEMFC Using a Full-Scale Transient Physics-Based Model

Low-Temperature Polymer electrolyte membrane fuel cells (LT-PEMFCs) have garnered huge attention as green energy technology due to low environmental impact and high efficiency. However, the long-term durability of these systems remains a critical concern, particularly regarding mechanical degradation of the proton exchange membrane. It is one of the most critical PEMFC components, facilitating ionic conduction and restricting the reactant gas cross-over. An examination of aged membranes through ex-situ analysis has uncovered various degradation features, including the emergence of crazes, cracks, pinholes, membrane thinning, and elongation1. Partly these defects appear due to cyclic stress arising from the repetitive expansion/compression of the membrane under water absorption/desorption2,3. Consequently, these defects reduce the open circuit voltage and increase the ohmic loss in the cell4. The crossover of reactants beyond a certain limit can instigate the formation of localized hotspots, further accelerating membrane degradation and eventually catastrophic failure of the cell. Multiple investigations have highlighted the influence of clamping force and humidity-temperature cycling on LT-PEMFC performance5,6. Nevertheless, most of these studies have primarily focused on structural mechanics, overlooking other critical aspects such as electrochemical reactions, fluid flow, heat transfer, and species transport. This type of decoupled approach does not provide real-time stress evolution inside the membrane with the cell operation. Moreover, existing models are often either two-dimensional or simplified single-channel 3D representations, with simplifications like isothermal operation, simplified reaction kinetics and more5,7,8. These models lack spatiotemporal resolution of the membrane deformation or stress distribution under diverse operating conditions.In this study, we present a comprehensive analysis of mechanical degradation in the LT-PEMFC membranes through the implementation of a transient physics-based model. The research focuses on understanding the intricate interplay between mechanical stress, environmental factors, and the resulting degradation mechanisms within the membrane. A transient model is developed using COMSOL Multiphysics to simulate the mechanical behaviour of the membrane under various operational conditions and stress scenarios encountered during the fuel cell's lifespan. It is a full-scale Multiphysics model that comprises charge transfer, species transport, porous media flow, electrochemical reactions, heat generation, ionomer water transport, liquid water transport, and solid mechanics. The model accuracy is confirmed with the lab experiment on 32 cm2 cell with multi-serpentine channels at 80% RH & 75 °C. This model incorporates factors such as humidity, temperature variations, and clamping pressure. By analyzing stress accumulation and its effects on the Nafion membrane, we aim to identify critical locations that lead to mechanical degradation and permanent deformation. Furthermore, the transient response of the cell parameters like liquid water dynamics, stress accumulation and membrane water content dynamics are studied under the urban dynamometer driving schedule (UDDS), which has not been reported earlier. Preliminary results of the liquid water dynamics in Figure 1 (a) suggest that the liquid water accumulates during the higher current density operations between 1500 and 1800 seconds. Also, the liquid water saturation is unable to reach its initial state of 0.12 during a sudden change in the power demand that leads to the cumulative accumulation of liquid water inside diffusion media. Though It improves the water content and ionic conductivity of the membrane, it increases the chance of cell flooding and stress accumulation due to membrane swelling. Figure 1 (b) shows the cell deformation under the clamping pressure of 1 and 2 MPa, reducing the species transport inside the diffusion media. The outcomes of this study aim to provide deeper insights into the stress accumulation and permanent deformation mechanisms affecting membrane degradation. Understanding these degradation pathways is crucial for the development of improved materials and design strategies that enhance the durability and long-term performance of PEMFCs, ultimately contributing to the advancement and widespread adoption of clean energy technologies.

Dynamic K-Decay Learning Rate Optimization for Deep Convolutional Neural Network to Estimate the State of Charge for Electric Vehicle Batteries

This paper introduces a novel convolutional neural network (CNN) architecture tailored for state of charge (SoC) estimation in battery management systems (BMS), accompanied by an advanced optimization technique to enhance training efficiency. The proposed CNN architecture comprises multiple one-dimensional convolutional (Conv1D) layers followed by batch normalization and one-dimensional max-pooling (MaxPooling1D) layers, culminating in dense layers for regression-based SoC prediction. To improve training effectiveness, we introduce an advanced dynamic k-decay learning rate scheduling method. This technique dynamically adjusts the learning rate during training, responding to changes in validation loss to fine-tune the training process. Experimental validation was conducted on various drive cycles, including the dynamic stress test (DST), Federal Urban Driving Schedule (FUDS), Urban Dynamometer Driving Schedule (UDDS), United States 2006 Supplemental Federal Test Procedure (US06), and Worldwide Harmonized Light Vehicles Test Cycle (WLTC), spanning four temperature conditions (−5 °C, 5 °C, 25 °C, 45 °C). Notably, the test error of DST and US06 drive cycles, the CNN with optimization achieved a mean absolute error (MAE) of 0.0091 and 0.0080, respectively at 25 °C, and a root mean square error (RMSE) of 0.013 and 0.0095, respectively. In contrast, the baseline CNN without optimization yielded higher MAE and RMSE values of 0.011 and 0.014, respectively, on the same drive cycles. Additionally, training time with the optimization technique was significantly reduced, with a recorded time of 324.14 s compared to 648.59 s for the CNN without optimization at room temperature. These results demonstrate the effectiveness of the proposed CNN architecture combined with advanced dynamic learning rate scheduling in accurately predicting SoC across various battery types and drive cycles. The optimization technique not only improves prediction accuracy but also substantially reduces training time, highlighting its potential for enhancing battery management systems in electric vehicle applications.

Research on precise lithium battery state of charge estimation method based on CALSE-LSTM model and pelican algorithm

This paper presents an innovative fusion model called “CALSE-LSTM,” which integrates Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), self-attention mechanisms, and squeeze-and-excitation attention mechanisms to optimize the estimation accuracy of the State of Charge (SoC). The model incorporates battery historical data as input and employs a dual-attention mechanism based on CNN-LSTM to extract diverse features from the input data, thereby enhancing the model's ability to learn hidden information. To further improve model performance, we fine-tune the model parameters using the Pelican algorithm. Experiments conducted under Urban Dynamometer Driving Schedule (UDDS) conditions show that the CALSE-LSTM model achieves a Root Mean Squared Error (RMSE) of only 1.73 % in lithium battery SoC estimation, significantly better than GRU, LSTM, and CNN-LSTM models, reducing errors by 31.9 %, 31.3 %, and 15 %, respectively. Ablation experiments further confirm the effectiveness of the dual-attention mechanism and its potential to improve SoC estimation performance. Additionally, we validate the learning efficiency of CALSE-LSTM by comparing model training time with the number of iterations. Finally, in the comparative experiment with the Kalman filtering method, the model in this paper significantly improved its performance by incorporating power consumption as an additional feature input. This further verifies the accuracy of CALSE-LSTM in estimating the State of Charge (SoC) of lithium batteries.

An early-fault diagnostic method based on phase plane for lithium-ion batteries under complex operation conditions

Timely and accurate diagnosis of lithium-ion battery faults is crucial for ensuring the safety of electric vehicles. While incremental capacity (IC)-based diagnostic methods are effective, they can only be employed under charging conditions. To address this issue, a fault diagnostic method based on phase plane analysis is proposed to extend the application to various operating conditions. In a phase plane, the battery voltage is plotted on the x-axis, while the differential in voltage is plotted on the y-axis. During charging, diagnostic vectors indicating the occurrence and type of faults are extracted from the trajectories in the phase plane. Under complex operating conditions, early faults can be detected based on the morphological and scale deviation of the trajectories. Experimental results have shown that the proposed method can effectively detect minor faults. The fault detection rate and detection accuracy rate under charging conditions are 96.25 % and 97.73 %, respectively, which are 17.5 % and 8.54 % higher than those of the IC-based diagnostic method. Furthermore, the fault detection rate and accuracy rate under UDDS (Urban Dynamometer Driving Schedule) are 91.76 % and 97.23 %, respectively. Additionally, the diagnostic speed of the proposed method is twice as fast as that of the IC-based method.

Online battery model parameters identification approach based on bias-compensated forgetting factor recursive least squares

Accuracy of a lithium-ion battery model is pivotal in faithfully representing actual state of battery, thereby influencing safety of entire electric vehicles. Precise estimation of battery model parameters using key measured signals is essential. However, measured signals inevitably carry random noise due to complex real-world operating environments and sensor errors, potentially diminishing model estimation accuracy. Addressing the challenge of accuracy reduction caused by noise, this paper introduces a Bias-Compensated Forgetting Factor Recursive Least Squares (BCFFRLS) method. Initially, a variational error model is crafted to estimate the average weighted variance of random noise. Subsequently, an augmentation matrix is devised to calculate the bias term using augmented and extended parameter vectors, compensating for bias in the parameter estimates. To assess the proposed method's effectiveness in improving parameter identification accuracy, lithium-ion battery experiments were conducted in three test conditions—Urban Dynamometer Driving Schedule (UDDS), Dynamic Stress Test (DST), and Hybrid Pulse Power Characterization (HPPC). The proposed method, alongside two contrasting methods—the offline identification method and Forgetting Factor Recursive Least Squares (FFRLS)—was employed for battery model parameter identification. Comparative analysis reveals substantial improvements, with the mean absolute error reduced by 25%, 28%, and 15%, and the root mean square error reduced by 25.1%, 42.7%, and 15.9% in UDDS, HPPC, and DST operating conditions, respectively, when compared to the FFRLS method.

Emissions from heavy-duty diesel, natural gas, and diesel-hybrid electric vehicles – Part 1. NOx, N2O and NH3 emissions

The current study characterized nitrogen oxide (NOx), nitrous oxide (N2O), and ammonia (NH3) emissions from 14 heavy-duty vehicles from different vocations (school bus, transit bus, refuse hauler, delivery vehicle, and goods movement vehicle) over different drive cycles using a chassis dynamometer. Test vehicles with engines ranging in model year from 2009 to 2018 and including diesel and compressed natural gas (CNG) vehicles. The diesel vehicles included both vehicles without selective catalytic reduction (no-SCR) and with SCR certified to the 0.2 g per brake horsepower-hour (g/bhp-hr) [0.27 g/kilowatt-hour (kW-hr)] NOx emissions standard that were operated with diesel fuel, with a subset tested on hydrotreated vegetable oil (HVO). The CNG vehicles were all three-way catalyst (TWC) equipped and certified to either the 0.2 or 0.02 g/bhp-hr NOx standard [0.27 or 0.027 g/kW-hr]. This study is part of a larger project that included over 200 in-use heavy-duty vehicles, which is one of the most extensive studies of emissions from modern heavy-duty vehicles to date. NOx, NH3, and N2O emissions varied depending on the vocation and the technology. Average NOx emissions for the Urban Dynamometer Driving Schedule (UDDS) cycle over all vehicles ranged from 0.003 to 6.16 g/bhp-hr [0.004 to 8.25 g/kW-hr] and from 0.02 to 17.2 g/mile [0.012 to 10.7 g/km], with the diesel vehicles showing the highest emissions, the 0.2 g/bhp-hr certified CNG vehicles showing lower emissions, and the 0.02 g/bhp-hr certified CNG vehicles showing very low emissions. NOx emissions over other different cycles were generally in the same range as those for the hot start UDDS, with the highway cycles generally showing the lowest emissions. CNG vehicles had relatively high NH3 emissions ranging from 0.13 to 0.34 g/bhp-hr [0.17 to 0.46 g/kW-hr] and from 0.44 to 0.97 g/mile [0.27 to 0.6 g/km] over the hot start UDDS compared to the SCR-equipped diesel vehicles due to NH3 formation across the TWC. N2O emissions did not show consistent trends and were near/at the detection limits for a number of the vehicles.

Improved deep learning based state of charge estimation of lithium ion battery for electrified transportation

The increasing interests and recent advancements in artificial intelligence and machine learning have significantly accelerated the development of novel techniques for the state estimation of batteries in the battery management systems (BMS) of electrified vehicles. To ensure the safe and reliable operation of an electric vehicle, it is essential to determine the state of charge (SOC) among the battery's numerous states. Deep learning models have been effective for SOC estimation due to the nonlinear and time-varying characteristics of a battery management system. This article seeks to describe an SOC estimator design method using deep neural network (DNN) models, particularly non-recurrent and recurrent neural networks, owing to their excellent computational intelligence and capacity for self-learning. A thorough investigation has been conducted at various temperatures, and the model is trained on data received from the cell during discharging processes like the urban dynamometer driving schedule (UDDS). Finally, the accuracy of the models is compared based on various performance indices. From the results, it has been concluded that the DNN models acquire a Mean Absolute Error (MAE) of less than 1 % and a Root Mean Square Error (RMSE) of close to 1 % when validated over various datasets at temperatures of 10 °C, 25 °C, and 40 °C.

Particle Swarm-Optimized Fuzzy Logic Energy Management of Hybrid Energy Storage in Electric Vehicles

A lithium-ion battery–ultracapacitor hybrid energy storage system (HESS) has been recognized as a viable solution to address the limitations of single battery energy sources in electric vehicles (EVs), especially in urban driving conditions, owing to its complementary energy features. However, an energy management strategy (EMS) is required for the optimal performance of the HESS. In this paper, an EMS based on the particle swarm optimization (PSO) of the fuzzy logic controller (FLC) is proposed. It aims to minimize battery current and power peak fluctuations, thereby enhancing its capacity and lifespan, by optimizing the weights of formulated FLC rules using the PSO algorithm. This paper utilizes the battery temperature as the cost function in the optimization problem of the PSO due to the sensitivity of lithium-ion batteries (LIBs) to operating temperature variations compared to ultracapacitors (UCs). An evaluation of optimized FLC using PSO and a developed EV model is conducted under the Urban Dynamometer Driving Schedule (UDDS) and compared with the unoptimized FLC. The result shows that 5.4% of the battery’s capacity was conserved at 25.5 °C, which is the highest operating temperature attained under the proposed strategy.