Pack Voltage Research Articles

Machine learning modeling for fuel cell-battery hybrid power system dynamics in a Toyota Mirai 2 vehicle under various drive cycles

Electrification is considered essential for the decarbonization of mobility sector, and understanding and modeling the complex behavior of modern fuel cell-battery electric-electric hybrid power systems is challenging, especially for product development and diagnostics requiring quick turnaround and fast computation. In this study, a novel modeling approach is developed, utilizing supervised machine learning algorithms, to replicate the dynamic characteristics of the fuel cell-battery hybrid power system in a 2021 Toyota Mirai 2nd generation (Mirai 2) vehicle under various drive cycles. The entire data for this study is collected by instrumenting the Mirai vehicle with in-house data acquisition devices and tapping into the Mirai controller area network bus during chassis dynamometer tests. A multi-input - multi-output, feed-forward artificial neural network architecture is designed to predict not only the fuel cell attributes, such as average minimum cell voltage, coolant and cathode air outlet temperatures, but also the battery hybrid system attributes, including lithium-ion battery pack voltage and temperature with the help of 15 system operating parameters. Over 21,0000 data points on various drive cycles having combinations of transient and near steady-state driving conditions are collected, out of which around 15,000 points are used for training the network and 6,000 for the evaluation of the model performance. Various data filtration techniques and neural network calibration processes are explored to condition the data and understand the impact on model performance. The calibrated neural network accurately predicts the hybrid power system dynamics with an R-squared value greater than 0.98, demonstrating the potential of machine learning algorithms for system development and diagnostics.

Verification of Screening Conditions Related to Reuse of Used Lithium-Ion Batteries

（Introduction, Background/Research objectives）Lithium-ion batteries (hereinafter LIB) are becoming explosively popular around the world, and demand for electric vehicles (hereinafter EV) is particularly remarkable. However, lithium and rare metals are finite and their prices are soaring worldwide, which is hindering LIB market growth. On the other hand, technology for recycling and reusing used LIBs (EVs) is attracting attention. If safety, performance, and economic efficiency are ensured, a new market will be created and it will also contribute to the realization of a recycling-oriented society.However, there are many challenges to actually reusing LIBs, so we will consider screening methods that enable LIB reuse in an optimal, safe, and low-cost manner.This time, we will perform deterioration diagnosis and capacity estimation of reused LIBs whose usage history is unknown, and construct assembled batteries. The battery is actually installed in an EV, driven, and the battery status monitored. We will devise combinations of assembled batteries and verify the accuracy required for screening in order to achieve both performance and economy.Keywordsreused LIB, BMS, LIB screening, EVProposed approachUp until now, we have been able to obtain satisfactory results with battery packs that have taken time to ensure that battery deterioration and capacity are as consistent as possible. However, this time, we purposely created an assembled battery by adding batteries with different deterioration conditions and different capacities, and monitored what kind of behavior and performance changes occur when installed and operated in an EV. We will verify these results and provide feedback to the screening method.Materials and methodsFirst, we screened 100 LIB modules taken from an EV that had traveled 100,000 km. The screening method involves estimating capacity using a charger/discharger manufactured in-house and diagnosing deterioration by measuring internal resistance. These LIBs will form a 12-module assembled battery in three patterns and will be installed in a converted EV manufactured in-house. The status of the onboard LIB will be remotely monitored and logged using our own BMS with IoT functionality, and statistical verification will be performed.The following three patterns are used for the 12-module assembled battery configuration.condition A: Match all capacitance and internal resistance.condition B: 1 module capacity is low.condition C: High internal resistance of 1 module.The capacity of the entire battery pack was taken as the average capacity of the installed batteries. 100% SOC was defined as an average voltage of 4.2V, and 0% was defined as an average voltage of 3.2V. Further, the lower limit voltage of each cell is set to 3.2V, and if the OCV of even one cell falls below the lower limit voltage, it is assumed that the battery is used up and the running test is terminated.ResultsThe pack voltage, SOC, and standard deviation when running from SOC 100 to 90% to 5 to 0% are shown in Fig. 1 to 3. The standard deviation was used as an index for observing voltage variations among the 24 cells. Only in the case of condition A, the battery capacity could be used up until the SOC reached 0%, but in the cases of B and C, the battery capacity could not be used up because some cells had a voltage lower than the lower limit voltage when the SOC was 4%. Under all conditions, the variation in each cell voltage tends to increase during acceleration/deceleration (charging/discharging) and at low SOC (20% or less), but we observed a large difference in the magnitude of the variation and the focusing value.Discussion and conclusionIn the case of condition A, the fluctuation and dispersion of the pack voltage are small, and the focusing time and focusing value when the pack voltage varies are also short and small. In the case of condition B, although the pack voltage fluctuation was large, the variation was slightly increased compared to condition A, and the convergence time and convergence value were also slightly increased. Condition C had large fluctuations and dispersions in the pack voltage, and the convergence time when the dispersion occurred was also long and large. From these results, it can be said that the important condition to consider during screening is to match the capacity of the batteries. This result not only improves the accuracy of screening, but also has the potential to increase the reuse rate of used batteries. Figure 1

Evaluate Prototype Performance of Battery Pack Monitoring for PT XYZ E-Bus Maintenance

One of the primary issues with electric vehicles is battery longevity. This study uses Kodular as an application developer for mobile app monitoring. Furthermore, using the monitoring data, PT XYZ may increase the operational efficiency of electric buses, lengthen battery life, and lower the risk of damage and maintenance costs. The following parameters are used: power voltage, SoC, SoH, system insulation resistance, positive insulation, negative insulation, pack voltage, and current. Inspectors can perform maintenance, diagnose faults, and improve overall system performance by keeping track of these parameters. It also contributes to the prevention of equipment failure, the avoidance of safety hazards, and the reliable operation of electrical and electronic systems. The goal of this research is to examine the performance of prototype monitoring with battery pack measurement parameters on a mobile app using prototype monitoring of battery packs on electric bus maintenance at PT XYZ. In this trial, interval changes happened every 30 minutes from 20:10 to 21:40. This research was carried out four times. The output parameters are pack voltage (545.9 V), current (-67.7 A), power voltage (26676), SoC (81.4%), SoH (100%), system insulation resistance (1233 KΩ), positive insulation (2952 KΩ), and negative insulation (1233 KΩ).

IoT-enabled advanced monitoring system for tubular batteries: Enhancing efficiency and reliability

Monitoring battery health is crucial for predicting the lifespan of batteries in electric vehicles. Real time voltage, current, and operating temperature are the key factors influencing longevity. An efficient data acquisition (DAQ) system becomes essential for accurately monitoring real-time battery operating parameters. For commercially viable three-wheeled electric vehicles, a cost-effective yet highly accurate DAQ is imperative. Current commercially available lead-acid three-wheeled electric vehicles lack a dedicated DAQ for monitoring battery health. In this investigation, we address this gap by developing a low-cost, multi-channel DAQ tailored for such electric vehicles, integrated with a cloud server for subsequent data analysis and Remaining Useful Life (RUL) prediction. The implemented prototype excels in measuring individual battery terminal voltage and battery pack voltage under operating conditions, employing the node voltage subtraction method. It also features real-time detection of high current discharges up to 30A using a Hall effect-based current sensor. A custom temperature sensor probe was designed to detect battery electrolyte temperatures up to 60 °C, with multiple probes operating in multiplexed mode to reduce circuit complexity. The system is proficient in storing recorded data locally and in the cloud. A mobile device platform has been developed and interfaced with the system for real-time monitoring of battery parameters. After testing multiple prototypes in running vehicles for over 1000h without failure, the system demonstrated a high accuracy level of 98.6 %. Multiple prototypes were designed to validate the measured accuracy.

Strategic electromagnetic interferences suppression in boost converters: zero-switch techniques

This article delves into the growing demand for efficient power conversion technologies accompanying the rise in electric vehicle (EV) adoption. Boost converters, essential for increasing the battery pack voltage to propel EV motors, pose a challenge due to the electromagnetic interference (EMI) generated by the high switching frequency of power devices. To address this issue, practitioners employ zero-voltage switching (ZVS) and zero-current switching (ZCS) techniques. In this comparative study, we systematically evaluate the effectiveness of these soft switching techniques in reducing conducted EMI in boost converters designed for EV applications. The results illuminate the potential of both ZVS and ZCS in significantly mitigating EMI emissions when compared to conventional hard-switching methods. Notably, ZVS soft switching emerges as more efficient and effective, particularly under higher loads, while ZCS soft switching excels in reducing EMI at lighter loads. In conclusion, the study asserts that ZVS soft switching presents a more promising solution for curtailing conducted EMI in boost converters for EV applications, particularly in high-load scenarios. However, it underscores the importance of considering specific operational conditions when deciding between the two techniques.

Three-Leg DC–DC Converter for Efficient Inductive Power Transfer of Electric Vehicles for Wide-Range Battery Applications

The design of an inductive power transfer system for different electric vehicle (EV) models with widely varied battery pack voltages has been a challenging task. The majority of modern EV models are equipped with 400 V or 800 V battery packs. To charge both batteries efficiently, an additional DC-DC converter on the receiver side is employed, which reduces the overall system efficiency and also increases the cost. This article proposes a reduced switch count novel converter to charge distinct EV models without degrading the efficiency of the system. The proposed converter has two operating modes, a voltage doubler mode to charge an 800 V battery and a current doubler mode to charge a 400 V battery at the same power level. MATLAB/Simulink simulations have been carried out to verify the performance of the 3-leg converter for both 400 V and 800 V batteries at 7.2 kW. Furthermore, a laboratory prototype of the proposed converter for 500 W has been built using the SiC devices, and the results obtained are provided.

전기자동차 직접 충전기술 동향 분석 및 고전압 배터리팩 설계기술 검토

The existing electric vehicle was released with a 400 V battery pack. Assuming that a battery pack of 80 kWh is made, a capacity of 200 Ah should be used for a 400 V battery pack. However, when using 200 Ah, the thickness and power loss of the wiring due to the current increase, and the heat generation loss also increases. To overcome this limitation, vehicles equipped with 800 V battery packs are recently being released. When the 800 V battery pack is mounted, the capacity is reduced by half, that is, 100 Ah, thereby reducing the thickness of the wiring, power, and heat generation loss and improving mileage and performance. Furthermore, the charging time caused by the rise in charging voltage is shortened, and the heat generation is reduced, simplifying the cooling system. Many studies have recently been conducted to increase the battery pack voltage to a high voltage of 800 V or higher. However, various considerations arise when designing a high-voltage battery pack, and this paper examines the voltage specifications of EV chargers and battery packs, analyzes the structure of battery packs that change to high voltages, and discusses high-voltage battery pack design technologies and considerations.

A quasi‐two‐switch power factor correction converter for on‐board battery chargers

SummaryThis paper describes a quasi‐two‐switch buck‐boost power factor correction (PFC) converter for use in on‐board battery chargers to create a variable output voltage that is less than or greater than the peak input voltage. A two‐stage converter links the input grid power to the battery pack both in battery‐operated electric cars (BEVs) and plug‐in hybrid electric vehicles (PHEVs), with battery pack voltages ranging from 100 to 500 V depending on vehicle size and capacity. A universal charger that can manage such a wide range of battery pack voltages is appropriate for all vehicle designs. This requirement is met by supplying a changeable DC link voltage at the input of the DC/DC converter, which is a major obstacle in battery chargers when it comes to achieving universal output voltages. The major contribution of this research is the analysis and design of a dual‐control technique for a cascaded buck‐boost converter suited for a power factor correction (PFC) rectifier. The control loop is designed to allow a seamless transition from buck‐boost operation while putting less stress on the devices. The converter's power loss and small‐signal model are also investigated. From an economic standpoint, this concept allows the automobile industry to manufacture a single power converter, which is flexible and capable of charging numerous vehicle variants. Results have been verified both with a PSIM (11.0) simulation model and an experimental setup for a 1‐kW PFC converter suitable for universal input voltages of 85–265 Vrms and broad output voltages.

A Resonant Inductor Integrated-Transformer-Based Receiver for Wireless Power Transfer Systems

Wireless power transfer charging of modern electric vehicles is complicated by widely varying charging battery pack voltages between different models. This article proposes a new resonant inductor integrated-transformer (RIIT)-based receiver. It is compact, low cost, and can adapt to different battery pack voltages without any significant change of resonant parameters. The design approach, power losses, and power transfer capacity of the proposed system are presented. In addition, methods of natural current sharing and voltage sharing among different RIITs are proposed. An LCC-LCC WPT prototype compatible with both 400 and 800-V battery pack voltages is built, and the system efficiency reaches 94% at 9.5 kW, which verifies the feasibility and performance of the proposed receiver.

An Early Minor-Fault Diagnosis Method for Lithium-Ion Battery Packs Based on Unsupervised Learning

Dear Editor, Any fault of a battery system that is not handled timely can cause catastrophic consequences. Therefore, it is significant to diagnose battery faults early and accurately. Due to the complex nonlinear features and inconsistency of lithium batteries, traditional fault diagnosis methods usually fail to detect battery minor faults in the early stages. Therefore, this letter proposes a real-time unsupervised learning diagnosis approach for early battery faults based on improved principal component analysis. The technique rotates the battery pack voltage sequence into a new coordinate space through linear combination, while the detection metrics of square prediction errors and modified contribution plots are employed to achieve minor fault traceability. In addition, the training sample relies on the voltage sequence of the battery health state instead of the fault data, which is difficult to collect. Moreover, this approach can not only locate the battery cell where the fault occurs but also diagnose battery open-circuit and short-circuit faults as well as the occurrence and duration of the fault in real-time. Furthermore, the feasibility and stability of the proposed method are verified by applying different experimental data. In summary, the presented approach provides an easy-to-implement option that does not require accurate mathematical modeling, expert understanding, and complex computational processes.

Universal Bridgeless Nonisolated Battery Charger With Wide-Output Voltage Range

The existing onboard battery chargers (OBCs) are incompatible to adopt for different electric vehicle (EV) powertrain configurations owing to different battery pack voltages and input power supplies. This letter proposes a new universal battery charger to charge EV batteries whose output voltage is ranging from 48 to 450 V. A dual-loop distinct current controller with a seamless transition for boost and buck operations is designed, which regulates the output voltage and maintains the unity power factor at the grid side. Furthermore, an active harmonic minimization method is incorporated with the designed controller to attenuate the prominent third-harmonic in the reference current and eradicate subharmonic oscillations. The performance of the proposed OBC is validated through the MATLAB simulations and experiments on a 1.5-kW laboratory prototype with an universal ac power supply and compared with existing OBCs.

Electrification of a Class 8 Heavy-Duty Truck Considering Battery Pack Sizing and Cargo Capacity

The design and performance optimization of fully electric trucks constitute an integral goal of the transport sector to meet climate emergency measures and local air quality requirements. Most studies in the literature have determined the optimum pack size based on economic factors, without accounting for the details of pack behavior when varying the size. In this paper, the effect of battery pack sizing and cargo capacity of a class 8, 41-ton truck on its overall energy performance and technical parameters of its powertrain is investigated. For this purpose, the proposed electric truck is designed and mathematically modelled using AVL CRUISE M software. The second-order equivalent circuit model is developed to predict the battery packs’ parameters. The proposed battery pack model is extracted from experimental analysis on SONY VTC6 lithium-ion batteries performed in the lab. The weight changes due to adding the battery packs to the truck are also estimated and have been taken into account. The mathematical model of the powertrain is simulated in the long-haul driving cycle considering different cargo capacities and battery pack sizes. The results of this study revealed that the battery pack voltage reached its minimum value when the maximum cargo capacity was applied for the 399 kWh battery pack. In addition, increasing the occupied cargo capacity from 10% to 100% resulted in an increase in the regenerative brake energy of up to 9.87 kWh, while changing the battery size imposed minimal impacts on regenerative brake energy recovery as well as energy consumption.

Identification of Typical Sub-Health State of Traction Battery Based on a Data-Driven Approach

As the core component of an electric vehicle, the health of the traction battery closely affects the safety performance of the electric vehicle. If the sub-health state cannot be identified and dealt with in time, it may cause traction battery failure, pose a safety hazard, and cause property damage to the driver and passengers. This study used data-driven methods to identify the two typical types of sub-health state. For the first type of sub-health state, the interclass correlation coefficient (ICC) method was used to determine whether there was an inconsistency between the voltage of a single battery and the overall voltage of the battery pack. In order to determine the threshold, the ICC value of each vehicle under different working conditions was analyzed using box plots, and a statistical ICC threshold of 0.805 was used as the standard to determine the first sub-health type. For the second type of sub-health state, the Z-score and the differential area method were combined to determine whether the single cell voltage deviated from the overall battery pack voltage. A battery whose voltage differential area exceeds the range of u ± 3σ is regarded as having a sub-health state. The results show that both methods can accurately judge the sub-health state type of a single battery. Furthermore, combined with the one-month operation data of the vehicle, we could calculate the sub-health state frequency of each single battery and take single batteries with a high frequency as the key object of attention in future vehicle operations.

Analysis of the THD and Common-Mode Voltage of the Three-Phase Boost-Buck EV Traction Inverter

A three-phase boost-buck inverter (BBI) comprised of three identical boost-buck DC/DC converter modules is presented for an EV traction inverter application. It allows the step-up and/or step-down of the battery pack voltage according to the operating condition of the traction motor so that the overall performance can be optimized, which is essential for EVs with relatively low and varying battery voltages such as a hybrid EV or fuel-cell EV. It also features low switching losses, low harmonic distortion in the output current, and reduced common-mode voltage and/or current. A detailed analysis and performance evaluation of the BBI compared to the conventional technology demonstrate its feasibility in EV traction applications. The functionality and performance of the boost-buck inverter are verified with simulation and experimental results.

Accurate state of charge prediction for real-world battery systems using a novel dual-dropout-based neural network

Accurate prediction of the state of charge is critical to the safety and durability of battery systems in electric vehicles. This paper proposes a novel multi-step SOC prediction method for real-world battery systems using the gated recurrent unit recurrent neural networks, which fully considers the influences of the environment and driving behaviors on the prediction performance. A novel dual-dropout method is proposed to prevent overfitting and optimize training efficiency. The first dropout is based on Pearson correlation analysis approach. It extracts five actual vehicle parameters that are strong and implicitly correlated with predictive SOC as model inputs, including recorded SOC, pack voltage, vehicle speed, temperature of probe, and brake pedal stroke value. A random dropout function is constructed as the second dropout to decrease the network density and improve efficiency, which is applied to the state information passing process of the model. Furthermore, the training samples are constructed by deriving the yearlong operation data of an electric taxi. The optimal model framework and hyperparameters are discussed and determined. Verified by six sets of randomly selected vehicular operation data, the results show that the proposed method can perform real-time 5-min SOC prediction with maximum error of 0.86%.

Comparing power processing system approaches in second-use battery energy buffering for electric vehicle charging

The heterogeneity in pack voltages and capacity of aged packs limits the performance and economic viability of second-use battery energy storage systems (2-BESS) due to issues of reliability and available energy. Overcoming these limitations could enable extended use of batteries and improve environmental impacts of electric vehicles by reducing the number of batteries produced. This paper compares Lite-Sparse Hierarchical Partial Power Processing (LS-HiPPP), a new method for power processing in 2-BESS, to conventional power processing architectures using a stochastic EV charging plaza model. This method for performance evaluation allows a fair comparison among power processing architectures for 2-BESS. Results show that LS-HiPPP increases the battery energy utilization to 94% as compared to 78% for conventional partial power processing (C-PPP) and 23% for full power processing (FPP). These results were obtained with 25% heterogeneity in individual battery capacities and 20% power processing within the 2-BESS. Derating and captured value are two derived performance metrics for comparing LS-HiPPP and C-PPP in this work. The derating for LS-HiPPP is 84.3% in comparison to 63.1% for C-PPP. The captured value for LS-HiPPP is 79.8% versus 51% for C-PPP.

Modelling of photovoltaic production and electrochemical storage in an autonomous solar drone

A simple, efficient simulator has been developed to predict the generation of photovoltaic energy and its storage in Li-ion batteries, for an autonomous drone with four wings covered by solar panels based on thin-film gallium arsenide photovoltaic cells (III–V). This simulator allows prediction of the effective photovoltaic power produced by the solar panels as well as the battery pack voltage when the drone is flying. Flight parameters such as irradiance, sun inclination angles, and drone Euler angles are considered as input parameters. The measured photovoltaic power and battery pack voltage are in good agreement with the simulated values, making practical use by the XSun company possible. This parametric study shows the effects of climatic and geographic conditions on drone autonomy. In optimal weather conditions on a sunny day, drone flight can last 12 ​h.

Research on balance control strategy of lithium-ion battery energy storage system

Abstract Lithium-ion batteries are widely used in grid energy storage, electric vehicles and other occasions because of their excellent performance. Passive equalization is widely used because of its high performance, but traditional passive equalization control strategies do not perform well. This paper proposes a new control strategy, which divides the battery pack into three state of charge (SOC) ranges according to the different SOC of the battery pack and different performance requirements. When the SOC of the battery pack is high, the maximum rechargeable capacity needs to be considered and the battery balancing should be realized in advance; and when the SOC of the battery is low, the maximum discharge capacity needs to be considered, the battery balancing should be delayed and, in other areas, it is the same as the traditional balancing control strategy. Moreover, the balance control strategy proposed in this paper keeps the high-performance battery at a lower voltage and the low-performance battery at a higher voltage; the low-performance battery works with more favorable conditions, so that the battery pack is in a more balanced state. The experimental results show that the battery pack consisting of four cells in series can be almost fully charged and the battery pack voltage is ~16.788 V; during the discharge process, the equalization system hardly operates to ensure its maximum discharge capacity. The strategy proposed in this paper is simple and easy to implement and has better performance.

RANCANG BANGUN BATTERY PACK LITHIUM 48 V 50 AH

<p>The electric vehicle is a vehicle with a driving source of an electric motor, requiring a battery for the stored energy. Battery packs are widely used as energy storage for electric vehicles. The battery pack is a series of several small batteries arranged in series and parallel so that they have an output voltage and capacity as required. 18650 Lithium battery is the most widely used to make battery packs and used on electric motorcycles. a 18650 Lithium battery has an output voltage of 3.7 V with various capacities such as 1500 mAh, 2000 mAh, 3000 mAh, etc. To design a 48 V 50 Ah battery pack requires a series of 18650 3.7 V 3000 mAh batteries which are arranged 13 pieces in series and 19 pieces in parallel. Before assembling, each 18650 battery must be checked for voltage, the voltage in each 18650 battery must be in the range of 3 – 4.2 V. If it is less than 3 V then it needs to be recharged, if it is less than 2 V then the battery condition is not good. The battery pack discharging test is carried out by loading a BLDC 2000W 48V electric motor until the battery pack voltage drops to 39 V. The discharging process lasts for 13 hours with an average current of 3.5 A. From the test results, the battery pack capacity can be calculated, namely 45.5 Ah.</p>

A comparative study of equivalent circuit models for a Li-ion battery pack of an electric Tuk-Tuk

This paper presents developments and validations of Lithium Nickel Manganese Cobalt Oxide (NMC) battery cell and battery pack models. A 7.4 kWh NMC battery pack is installed on an electric Tuk-Tuk equipped with a 3 kW motor. The battery pack is configured of four cells connected in parallel and 20 sets of the four cells connected in series. Based on the first-order and the second-order equivalent circuit models, battery cell and battery pack models are developed in Matlab/Simulink environment. For parameter identifications of the cell model, pulse discharge tests are performed at room temperature with 10% State of Charge (SOC) interval. The lumped heat capacitance method is applied to the battery models. The battery cell model is validated with SOC, cell center temperatures and terminal voltage using the experimental data of constant current discharge tests. With the battery pack configuration, the battery cell model is scaled up to match the battery pack voltage and its capacity. The dynamics of the battery pack models are validated with the vehicle testing data using the no-load, wide-open throttle and real road tests. The simulation results show good agreement with the test data. Using the developed battery models, vehicle performance and its energy consumption can be improved by optimizing battery cell and pack configurations.