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The paper provides a detailed description of the 62-kWh battery pack of the Nissan LEAF e-plus. Experimental measurements are collected and analyzed to identify electrical and thermal properties. The parameters identified are open circuit voltage, polarization resistance, diffusion resistances and capacitances. Measurements are carried out for different Stateof-Charge and temperatures values. The current interruption method is used to identify the properties of the battery, since it is an effective way to analyze the whole battery pack without disassembling the vehicle. Afterwards, a cooldown analysis is described to identify thermal properties such as thermal capacitance and resistance. The paper is concluded with an early-stage assessment of the degradation of the battery pack after 1.5 years by using on-board readings of State-of-Health, temperature, and State-of-Charge. Stepwise changes in the State-of-Health are observed every 90 days superimposed to the expected degradation trend.

Lithium-Ion battery packs are an essential component for electric vehicles (EVs). These packs are configured from hundreds of series and parallel connected cells to provide the necessary power and energy for the vehicle. An accurate, adaptable battery management system (BMS) is essential to monitor and control such a large number of cells. Series and parallel connected cells also experience different production and operational conditions, which makes it challenging for the BMS to ensure the safe operation of each individual cell. The main functions of the BMS include battery state estimation, cell balancing, thermal management, and fault diagnosis. Robust estimation of the state of charge (SOC) is crucial for providing the driver with an accurate indication of the remaining range. This paper presents the state of art of battery pack SOC estimation methods along with the impact of cell inconsistency on pack performance and SOC estimation. Cell balancing methods, which are necessary due to cell inconsistencies, are discussed as well. Four categories of pack SOC estimation methods are presented, including individual cell, lumped cell, reference cell, and mean cell and difference estimation methods. The SOC estimation methods are compared in terms of algorithm type, computational load, and engineering effort to help practitioners decide which method best fits their application.

This paper deals with the real-time simulation of a power tool battery pack on a mechatronic powertrain test bench. The ability of an easy-to-use model for quick and iterative test runs mainly depends on the effort of parameterization. For this purpose, an easily parameterizable battery model is required. The battery model used is based on the current state of research and simulates the battery’s behavior with an adequate precision. The suggested parameterization allows building the model without the necessity of experimental investigation. Three different procedures for model parameterization were used and compared with the real battery behavior. In conclusion, this paper shows a good tradeoff between precision and an easy way to handle a battery model for testing mechatronic powertrains.

Due to the increased interest in electric vehicle (EV) technologies and the cost reduction of photovoltaic systems, industrial development of battery charging stations for electric vehicles based on solar energy has started. Although it is convenient to charge EVs at night, when there is less electricity consumption, there will always be consumers who prefer or need to charge their EV during daytime and even during peak hours. In this paper, an EV charging station integrating renewable energy in the form of solar energy is proposed and analyzed. Using a local battery pack, the charging station allows semi-fast and fast charging and can be installed in individual homes and apartment buildings, where the maximum power delivered by the grid is limited to 3.6kW. The charging station uses converters widely available on the market. A simulation model for this charging station is developed and various distinct operation modes are presented, validating the correct operation of the entire system.

In order to solve the problem of individual voltage unevenness of energy storage battery for electric power in series operation, a multi-level uniform conditioning circuit structure based on flyback transformation is proposed, which can effectively realize the individual voltage regulation of energy storage batteries for power grid. Individual battery current through the conditioning circuit, in the multi-level multi-directional flow process, at the same time without mutual influence, this scheme through the special high-precision multi-channel acquisition circuit module, in the charging process of equalization and floating charge stage, real-time acquisition of each cell accurate voltage value, according to the voltage value monitored, the single battery outside the preset voltage range is taken as the conditioning object. Select a group of three-year running battery pack, do the comparative experiment of adding conditioning circuit and no conditioning circuit, and carry out three-stage charging experiment of constant current and voltage changing, constant voltage changing current and floating charge. The experimental results show that the scheme can realize the rapid voltage correction of battery pack, improve the overall efficiency of battery pack, slow down the performance degradation speed, and extend the service life of the whole battery.

Aiming at the problems that the application of conventional energy storage batteries in DC distribution networks, such as high cost, complicated control, and post-maintenance, this paper proposes an adaptive control strategy for charging and discharging DC distribution network energy storage systems on the basis of retired batteries, and its port output voltage can change with the grid voltage fluctuations of the DC distribution network adaptively by building a set of direct-mounted bus-type energy storage system based on retired batteries, so as to realize the adaptive charging and discharging of energy storage batteries. Finally, when the network voltage of the DC distribution network system changes suddenly, the energy storage system can realize the adaptive charging and discharging function of the energy storage system by switching on and off the retired battery pack module, which is verified by the SIMULINK simulation and experiment platform.

Lithium-ion batteries have high energy density, lightweight and long life cycle, thus they are the choice for powering electric vehicles. The needed high voltage battery pack is achieved using series-connected cells, that ideally should be identical. However, parameter variations of cells in EVs, along with different working conditions can cause State of Charge (SoC) and temperature imbalances that shorten battery lifetime. Moreover, a series connection can potentially be exposed to single-cell failure. This paper proposes a redundant smart battery topology based on the series connection of individual cell modules. Each module is formed by a cell with an insertion/bypass circuit and a wireless processor that monitors cell states and communicates with a Master controller. To synthesize the nominal voltage, a battery pack with a small redundant number of cells is considered. In this way, it can be made both fault-tolerant and reach higher safety. The problem is then shifted to the control algorithm that at each sampling time has to select "n" cells out of the total cells, according to some goals. A Machine Learning-based control algorithm was developed and tested in Matlab to insert/bypass the cells and reach simultaneous balancing of both SoC and temperature. The new method based on the K-nearest algorithm has been compared in simulation with a conventional sorting balancing method and showed superior performance, especially in temperature balancing.

In order to reliably operate Lithium-ion battery systems at their maximum power capability, it is advantageous to track the battery electrical impedance. Direct-Current Fast Charging (DCFC) stations provide a low-cost opportunity to introduce in-situ impedance measurements through the shared use of new hardware across many users. The feasibility of DCFC-based EV battery pack impedance measurement is explored in this work. To the authors’ knowledge, this is the first published measurement and analysis of impedance data from a commercial EV dc charging plug. A custom 50 kW CHAdeMO-interface DCFC is implemented, featuring electrochemical impedance spectroscopy with 5 A perturbation current at 400V, up to 3.1 kHz. The high-voltage system impedance is measured for a prototype EV featuring a custom 27kWh battery pack, and a 2015 Nissan Leaf EV through the CHAdeMO port. It is found that while the vehicle-level impedance is significantly affected by the DCFC charging cable and internal vehicle components, battery impedance variation due to temperature can still be detected. This work motivates the future development of charger-side battery diagnostics, including 1) large-scale vehicle fleet battery health monitoring, 2) data collection for improved battery degradation modeling, and 3) direct information exchange with EVs for battery performance optimisation., IECON 2021: 47th Annual Conference of the IEEE Industrial Electronics Society, October 13-16, 2021, Toronto, ON, Canada

Electric city buses usually possess a large battery pack and demand overnight charging at the depot. As a result, it offers a high capital cost, longer charging times, and limits the passenger capacity. Opportunity charging of electric vehicles (EVs) during brief stops is considered to be one of possible solutions to conquer these problems. This paper presents a prospective battery sizing methodology and charger evaluation of electric city buses enabled by wireless charging at frequent stops to achieve driving range extension while minimizing battery size. The quasi-instantaneous power connection can be possible using wireless power transfer (WPT) charging technology. Battery size reduction and range extension of electric city buses employing opportunity wireless charging is illustrated for various WPT charger power ratings under Federal test procedure (FTP) drive cycle and New European driving cycles (NEDC).

This article explores the behavior of lithium-ion (Li-ion) batteries in underwater temperature environments and develops a thermal management system to control the battery's temperature. When the battery is operating at low temperature conditions, the life span of the battery is shortened due to increasing internal resistance and results in reduced capacity. A Li-ion battery pack was tested inside a thermal chamber to simulate underwater conditions, which maintained room temperature during high discharge rates, but decreased to the environment temperature at low discharge rates. To maximize battery performance, a heat pad and thermal control mechanism is proposed. The thermal management system was able to reduce the internal resistance of the battery, thereby reducing the power loss. From this result, we expect the battery lifespan to increase with this method. The proposed system was tested with a 18Ah, 14.8V, 4 series 6 parallel 18650 Li-ion battery pack in a water-tight enclosure inside the thermal chamber simulating underwater temperature conditions.

This article provides a diagnosing cell-to-cell variance with state-of-charge (SOC) estimate in series connected lithium-ion batteries. SOC estimation in voltage-based balancing increases the computational complexity of implementation. To avoid such problems, a simple current sensorless SOC estimation algorithm is proposed using a generalized extended state observer, which can realize the robustness of the system to disturbance. A constant current discharge/charging profile is applied to a series linked battery pack to check the parameters and establish the practicality of the proposed method. The experimental results show that the proposed controller have higher estimation accuracy and better SoC balancing mechanism.

In electric vehicles and battery energy storage systems, hundreds of battery cells are grouped into battery modules and they are connected in series to make a battery pack for effective energy management. In addition to the conventional cell balancing within a module, module equalization within a pack is also required, but another external circuit makes cost and volume issues. This paper proposes a novel battery equalizer-charger symbiosis structure to mitigate the problem. A bi-directional three-port converter is presented and six possible operation modes are suggested, where the secondary port serves as a pack-charger and the tertiary port is docked to a switch-matrix to balance the battery modules. The test results from the real-time simulation for 5 battery modules are implemented to verify the performance of the proposed structure. It found that the SOC levels of modules are equalized within 0.5% in our tests.

In this paper, two different non-linear Kalman Filters for lithium-ion battery state of charge estimation are presented and compared. Nowadays, lithium-ion batteries are extensively used for hybrid and electric vehicles; in such applications, cells are assembled in module and pack to achieve high performance. At this scope, a Battery Management Systems BMS is required to control each cell and improve the battery pack performance, safety, reliability, and lifecycle. One of the major tasks a BMS must fulfill is an accurate online estimation of the State Of Charge (SOC) of the battery pack. In this paper, the Extended Kalman Filter and Sigma Points Kalman filter are developed and compared. A battery equivalent circuit model has been chosen to have a good compromise between complexity and accuracy and model parameters have been identified from Hybrid Pulse Power Characterization (HPPC) tests carried out at different temperatures and current rates to obtain a model valid for a wide range of operating conditions. The SOC estimation strategies are developed starting from the experimental results and it is validated through different driving cycling simulations. The results show that the Sigma Points Kalman filter produces a better estimate of SOC with respect to the Extended Kalman Filter, due to its better capability to deal with system non-linearities, with comparable computational complexity.

Battery Management System (BMS) is a critical part of Electric Vehicles (EVs). The introduction of a wireless communication and networking inside the BMS in order to replace the traditional wired bus brings multiple benefits. As it is a critical application, the network has stringent requirements such as high reliability, low energy consumption, and bounded latency. In this paper, we propose a network architecture based on an enhanced version of the IEEE Std 802.15.4 Time Slotted Channel Hopping (TSCH) Medium Access Control (MAC) mode running over the physical layer of Bluetooth Low Energy (BLE). To orchestrate the data transmissions, we present a reliable and predictable schedule based on the Low Latency Deterministic Network (LLDN) Group Acknowledgement (GACK) method which dynamically manages the retransmissions. We implement a WBMS for the Renault Zoe battery pack to demonstrate that our proposed architecture achieves 100% of network reliability with bounded latency, and low energy consumption.

Battery cell modeling is becoming increasingly important for EMC simulation, because batteries are a fundamental part of many modern electric networks, such as electric vehicle powertrains or powerline communication systems. The battery's impedance and reaction on transient events can have a dominant impact on electromagnetic distortions and susceptibility of the overall network to electromagnetic interference. Simulation models which depict the interior of a battery pack are usually composed of models of the individual cells, whereas the cell impedance data is obtained by measurement on single cells. However, when individual cells are not accessible, the single cell impedance needs to be extracted from measurement of the complete pack. This work describes a solution to this problem by combining numerical multiport de-embedding with measured pack impedance and 3D simulation of the pack assembly. For validation of the approach, cell impedances obtained by pack measurement and subsequent de-embedding are compared to single cell measurement results for frequencies from 9 kHz to 1 GHz. The demonstration is conducted with a commercial PP3 9V battery made of six closely packed LR61 alkaline cells.

In today's world, traditional auto-rickshaws are one of the most commonly used modes of public transportation, and they also contribute significantly to air pollution. The fact that they use an internal combustion (i/c) engine is one of the key reasons for this. For generating power to run the vehicle, all internal combustion engines use petrol/diesel fuel. Fuel efficiency can be enhanced via engine right-sizing, load leveling, and regenerative braking if conventional vehicles are converted to hybrid electric vehicles (HEV s) by adding an electric power train to the conventional power train. This study aims is to design a cost-effective electric regenerative hybrid system for traditional auto rickshaw that does not require major structural changes. The electric regenerative hybrid system would be capable of operating the auto rickshaw in two modes: electric mode and i/c engine mode. The brushless DC electric motor (BLDC motor) in the auto rickshaw will be powered by a Lithium-ion battery in electric mode. In engine mode, the Auto Rickshaw will operate on an internal combustion engine while also generating electricity to charge the battery. The simulation was run on the Matlab/Simulink platform to look at battery and vehicle output while using a battery pack.

Electrochemical Impedance Spectroscopy (EIS) is one possible technique to determine the State of Health (SOH) in Li-Ion batteries. This article describes some challenges involved in the implementation of EIS analysis in a battery charger. As an application example, some design issues on the control of power converters that can be used to perform the EIS analysis are addressed. The description focuses on several aspects, from the development of the measurement channels for current and voltage to the design of the perturbation signal control system. The device performances are tested in several conditions such as to determine the impedance of a singular Li-ion cell having 3.63 V and 2.6 Ah, but also in a small-scale battery pack having 25.2 V and 11.2 Ah, using different perturbation signals.

The proposed research study focuses on the leakage location system of the electric vehicle battery pack based on the Wavelet transform. Under the same equalization time, the equalization efficiency of the method that has been tested from the battery pack to the cell to the battery pack is 91.4%, and the overall equalization efficiency of this method is 93.8%. When the battery pack is in a discharging state, the equalization circuit module can complete the migration of the battery pack's power to the battery with the lowest terminal voltage or SOC. With the considerations of the mentioned features, this paper applies the wavelet model to construct the efficient location system. The proposed model is tested on the different scenarios with different data sets. The performance guides us that the accuracy of proposed model is much higher.

The battery is an important component in an electric vehicle. Batteries are connected in series into a battery pack to meet system voltage requirements. Problems will arise if the health conditions of the battery cells used are not uniform. For this reason, it is necessary to analyze the health of the battery cell on the battery pack. Analysis of the health of each battery cell will increase the cost of A Battery Management System because it uses many sensors. This work performs a comparison of the Kalman Filter and H infinity Filter algorithms to analyze the health of battery cells without using a current sensor. Reducing the current sensor will provide financial benefits because the current sensor is one of the expensive sensors in the BMS. The comparison results show both methods can estimate the battery cell voltage. The estimation results are then used to determine the residual voltage in each battery cell. A battery that has a good condition when compared to other battery cells in the battery pack will provide the smallest residual value. The Kalman Filter method provides a better sensitivity picture than the H Infinity Filter, although it needs to be validated in the next research.

A significant parameter of the spacecraft battery pack simulator is the state of charge (SOC) parameter, which is one of the parameters that users are most concerned about during the use of the spacecraft battery pack simulator. Other parameters of the spacecraft battery pack simulator, such as terminal voltage, current, pressure, temperature, etc., are always given together with the value of the state of charge (SOC) in which they are located. The battery SOC parameter is a process quantity, and only one SOC estimation value can be obtained. In the battery simulation, the simulator gives the time-varying SOC value during the charging and discharging process according to the initial SOC state, charging and discharging conditions. Therefore, the time-varying SOC value given by the battery pack simulator must be accurately calibrated to ensure the accuracy of the battery pack simulator SOC change.

The Advancement in battery technology and need of pollution free machines rises the demand of electrical vehicles exponentially. The performance and cost of electrical vehicles are majorly relied on the battery pack used. Battery packs are available with numerous configurations of cells as per the energy demand of electrical system. The electrical cell has been undergoing in protentional research in term of material used and methods of arrangement in battery pack. Now a days, most of the electrical vehicles are preferred the Li-ion battery due to the high energy density and low self-discharge rate. The performance of Li-Ion battery depends on the load arrangement and temperature mainly, so proper discharge analysis and temperature management is needed. Present work includes the discharge analysis of LiFePO 4 battery pack and develop a control unit, which managed the cell temperature under critical ranges and ensure safe operation. The configured battery pack 1P3S has been modelled on MATLAB-SIMULINK, and battery's performances were studied with the implementation of design control mechanism. The designed control unit is used to rheostat a power switch and cooling mechanism. A MOSFET is used as a power switch to regulate the discharging current through load, and ambient temperature is regulated by optimal use of cooling mechanism.

Thermal runaway of batteries is a cause of concern in most applications. Effective measures to prevent thermal runaway are important to ensure safe operation of the battery pack. This paper furnishes cell-to-cell heat transfer simulations which helps to forecast onset of thermal runaway. The simulation is conducted by choosing a trigger cell placed at two different locations within a 25 cell Lithium-ion battery pack. It can be seen that when the cells are placed close to each other without any spacing between them, both radiation and convective heat transfer play a crucial role in battery packs. The location of the trigger cell has a significant impact on thermal propagation within the pack. The paper also mentions practical strategies to mitigate onset of thermal runaway.

Electric vehicles are becoming the front runners for both urban and rural mobility challenges on account of less pollution, rising fuel cost, better performance and environmental impacts. However, the aging and replacement cost of battery packs, resulting from dynamic and nonlinear behavior of the battery degradation is still an unresolved problem in electric automobile applications. The goal of this work is to introduce a novel machine learning based battery degradation control strategy to avert the rapid capacity loss of battery packs bearing in mind of vehicle performance. Battery currents and depth of discharge are chosen as the battery aging control parameters and performance validation is achieved by doing simulation on degrading battery pack in the electric vehicle model using various charging and discharging profiles. The proposed closed-loop control strategy is developed by evaluating different regression models using generated dataset, based on work related to data-driven power management strategy and controlling battery current limit values. From the comparison, it is observed that Gaussian process regression shows better precision over other regression models. The simulation outputs prove the ability of the proposed strategy to extend the electric vehicle battery life by 2.03% over 200,000km. This work can be extended by using deep learning based models and more charge and discharge profiles.

A large number of lithium-ion batteries retired from electric vehicles (EVs) need to be treated because nearly 80% of the capacity remains. However, the retired batteries have inconsistencies in capacity, state of charge (SOC), and internal resistance. If batteries are regrouped directly, the power and energy performance will be restricted. In order to improve the energy performance of the second-use battery pack, this paper studies the regrouping method of retired batteries. A semi-empirical model of LiFePO4 battery is used, and then a capacity fading model for the second-use battery pack considering inconsistencies is established. The maximum available Ah-throughput of the pack after regrouping is set as the optimization goal, and the particle swarm optimization algorithm (PSO) is used to search the optimal regrouping method. Simulation is employed to verify the method and the results show that the value of the maximum available Ah-throughput is about 8.2% larger than that of the traditional method. The proposed method is of great significance for guiding effect on the regrouping of retired batteries.

Lithium battery (LiB) is widely used in electric vehicles (EVs), energy storage power stations and other fields. because of its high voltage, light weight, high energy density, good cycle performance, low self discharge rate and no memory effect, less environmental pollution and other significant advantages. However, due to the limited voltage and capacity of battery cells, battery is usually used as a battery pack composed of hundreds of single cells in parallel or series, The difference between batteries not only affect the charging and discharging capacity of batteries, but also lead to battery aging and potential safety problems. It is of great significance to study the influence of the differences between cells on the battery pack for battery grouping and battery balancing. This paper takes the coupling relationship among the initial state of charge (SOC), initial capacity and initial internal resistance into consideration, and establishes the relationship between the initial parameters. A model consists six cells connected in series is established based on the Rint equivalent circuit model, and simulations under different working conditions are conducted. The simulation results show that the consistency of the capacity of battery pack is mainly affected by the initial SOC and initial capacity. The influence of the two factors on capacity of battery pack consistency under different charging current rates is different. The initial internal resistance has little effect on capacity consistency under different charging current rates.

This article develops optimal power distribution strategies for the fuel cell electric truck. Based on the power system models of the non-plugin fuel cell electric trucks, a convex programming problem is formulated to optimize the power management between a fuel cell system and a battery pack. At the same time, the power following and thermostat control strategy for the fuel cell electric truck, is implemented. The hydrogen consumptions are systematically investigated under the China heavy-duty commercial vehicle test cycle and the China-world transient vehicle cycle. Finally, based on the developed control strategy, we examine the power distribution of the fuel cell electric truck under different drive cycles.

Models for the simulation of battery pack impedance are usually composed of models for the individual cells which the pack is made of, linked with a description of cell-to-cell and cell-to-housing coupling. Thus, conventional battery pack modeling requires knowledge of the cell first, which is usually obtained by measurement on single cells. In this work, a solution to the inverse problem is described, i.e. measurement of the pack is available and impedance of the cells within shall be derived. Therefore, the pack’s impedance needs to be partitioned into the cells’ ’internal’ impedances and exterior coupling effects, like mutual inductance. Proposed method employs 3D simulation of the battery pack with surrogate cell models. Measurement data and simulation model are then combined to find individual cell impedances by fitting the simulated pack impedance to the measured. For validation of the approach, single cell impedances obtained by virtual de-embedding from different measurement setups are compared and related to reference results from literature. Considered frequencies range from 9 kHz to 1 GHz. This paper proves usability of the concept by using two 18650 Lithium-ion cells connected in series.

Continuous addition of electrically powered peripherals in vehicles has created a need for a more efficient and reliable power source that can replace conventional starter lead-acid (Pb-Acid) batteries. Due to their high energy-to-weight ratio, Lithium-Ion (Li-Ion) batteries can act as a potential alternative to the Pb-Acid batteries used in cars for engine ignition. However, it is not possible to use Li-Ion batteries as a drop-in replacement of Pb-Acid batteries due to differences in internal resistance and voltage thresholds for charging and discharging. In addition, Li-Ion batteries require balancing of cell strings connected in series whereas Pb-Acid batteries manage cell balancing during charging near full charge. To use Li-Ion cells as a battery pack, it is necessary to install a battery management system (BMS) that keeps the voltages of each cell balanced. This paper proposes a new design methodology for a scalable BMS that employs active balancing using analog discrete components. The analog control system of a BMS integrated with a DC-DC bi-directional Cuk converter allows increased reliability and efficiency. For a comparative analysis, balancing of the Li-Ion battery pack is also performed by implementing a digitally controlled BMS, and the results are compared with the designed analog BMS. Finally, an architecture comprising a DC-DC boost converter is also introduced to manage control over battery charging through alternator of the internal combustion engine (ICE) vehicle.

This paper analyzes the feasibility of a wireless charging system for electric vehicles controlled with variable frequency. The design of the battery charger is based on the load requirements, that have been defined with a complete characterization of the battery pack. The coupling system and compensation networks have been sized to guarantee the voltage gain requested by the load and to allow soft-switching operations of the inverter MOSFETs. In particular, with a parallel secondary compensation, the vehicle assembly inductance is reduced when compared with a series compensation, allowing space and weight on the vehicle to be saved. The control system is designed to generate a variable inverter output voltage in the range of variation demanded by the direct supply of a Li-Ion battery pack connected to the WPT output. The paper demonstrates that the output voltage range is obtained with a switching frequency that falls into the range recommended by the SAE J2954:2020 standard.

Renewable energy sources, such as photovoltaic (PV) panels, are spreading day by day. Nevertheless, their intermittent and uncertain generation can be an issue for the connection in the electrical grid or for self-consumption. In order to face these problems, battery energy storage systems (BESSs) are used to store the energy in excess and deliver it when required. With and without the integration of BESSs, many solutions to interface PV panels to the grid are analyzed and proposed in the literature. Focusing on the PV power extraction, several maximum power point trackers have been analyzed, also facing the issue related to the partial shading conditions. In fact, the latter can cause inefficiency in maximizing the power extraction when many PV panels are subjected to different irradiations. Modular multilevel converters (MMCs) are recently used even in low voltage systems for their intrinsic advantages. In particular, PV panels and BESSs can be integrated into the MMC at either the submodule level or the dc-Iink of the MMC itself. Taking the advantages of both kinds of connection, in the present paper, a three-phase MMC with PV panels integrated into its submodules and a battery pack connected to its de-link is proposed and analyzed.

This paper presents the development of a Virtual Instruments Cluster (VIC) to display functional parameters of a stationary platform used for testing of Electric Vehicles (EV). The EV platform contains a 3-phase asynchronous 55 kW electric motor, a DC-AC motor controller, a 96 Volts LiFePO4 battery pack, a 3.3 kW battery charger and a Battery Management System (BMS). The electronic units exchange data between them using the automotive Controller Area Network (CAN) bus, to which a host computer is also connected. In order to get a real-time, comprehensive and intuitive way of monitoring the multitude of functional parameters of the EV testing platform, the proposed VIC is developed in LabVIEW and uses many graphical gauges. It receives all CAN frames from EV electronic units and decodes the information that user needs for. Also, different operating states and fault alarms of the EV testing platform are visually displayed in real-time. Finally, the virtual instrument can also store the acquired information from the CAN bus, which makes it a reliable tool for long-term monitoring and diagnosis of the EV testing platform.

In recent years there has been a significant increase in the production of electric vehicles (EVs), in the global strive to reduce polluting gases produced by conventional fossil-fuel driven vehicles. Therefore, many optimization algorithms have been proposed for EV mobility and the charging of battery packs in the stations connected to power grids. However, there are situations in which experimental results are not sufficient, and simulations are needed. In this work, we address the effects of the charge demands of an EV fleet on the grid by considering the attitude of EV drivers, and especially their range anxiety. This influences their decision of when to recharge the battery pack. To this end, an agent-based model has been developed for the simulation of a power grid considering different scenarios based mainly on the state of charge (SOC) of battery packs at the time of the charging requests of EVs at service stations. The results indicate that in general a high battery SOC at the beginning of charging increases the probability of reaching higher power peaks on the grid.

An active heating system has been developed for application in smart clothing for mountain rescuers. It uses a set of sensors and is aimed at gathering necessary data for the elaboration and testing of the automatic control algorithm. The system is powered from a Li-ion battery pack, which can be additionally charged from flexible PV modules. The paper presents the estimation of system power requirements and its energy budget. Since the system maximum operation time strongly depends on the efficiency of its main voltage converter, the design of the latter was based on a model enabling power loss estimation in the particular components. Characteristics ultimately measured on a prototype showed a high agreement with simulations.

State of charge (SOC) estimation is one of the most critical functions in battery management systems. Identifying and quantifying the contribution made by each error source plays an important role in improving the accuracy of SOC estimation. This paper proposes a novel framework to analyze each error source and quantify their contributions. To demonstrate the framework, a case study was conducted to assess the error contributions from the current sensor, the Ah integration software and hardware, and the estimation of the reference Ah capacity in the Ah counting method. Three standard tests such as the capacity test, pulse test, and drive cycle test were performed on a commercial battery pack. The results indicate that the software and hardware that perform Ah integration contributed most of the inaccuracy. Also, the inaccuracy from the estimation of the reference Ah capacity contributed much more than from the current sensor.

As a promising trend, hybrid electric technology improves the vehicle fuel economy and lowers the vehicle emission significantly, without making drivers to worry about vehicle charging. The onboard battery pack stores the electric energy generated from the generator driven by regenerative braking and engine and provide the stored electric energy to the motor to drive the vehicle. The capacity of the onboard battery pack in a hybrid electric vehicle has large effects on the vehicle's fuel economy, weight, and cost. If the capacity is too small, only limited electric energy from the regenerative braking can be stored, which results in a worse fuel economy. If the capacity is too high, the engine needs to work harder to drive the generator to charge the battery pack, which also results in a worse fuel economy. Also, a battery pack with high capacity makes the vehicle heavier and more expensive. In this paper, the effects of battery pack capacity on the fuel economy of the hybrid electric vehicle are studied. A hybrid electric vehicle model based on the 2016 Ford Fusion Hybrid is built in GT-SUITE using experimental battery cell data including open-circuit voltage (OCV), 1C-rate charging and discharging voltage, and internal resistance. Five different battery pack capacities (1Ah, 3Ah, 5Ah, 7Ah, and 9Ah) are used in the vehicle model to study their effects on the engine fuel consumption rate and overall gas mileage in different standard driving cycles. Based on this research, battery pack capacity between 5Ah to 7Ah is suitable for a mid-sized hybrid electric sedan during most driving conditions.

To ensure a suitable temperature range and temperature difference performance for on-board power battery, the heat transfer characteristics analysis and structural parameter optimization design of cold plate were completed. The research of battery heat generation and cold plate heat transfer mechanism were carried out, and three-dimensional transient heat transfer fluid-solid coupling model for battery and cold plate was also established with the application of Fluent software. Based on the results of multiple numerical simulation analysis and experimental verification of cold plate's heat transfer performance, the optimized design scheme of structure and parameters for cold plate was proposed. The simulation results under typical working conditions show that the battery cold plate could realize the heat dissipation of battery pack rapidly and equably. The average temperature of battery pack is 30.6°C, with the maximum temperature difference of 2.9°C, which could meet the requirements of temperature control for on-board power battery.