Charging Profile Research Articles

A Single-Stage Charger for LEV Based on Quadratic Buck-Boost AC-DC Converter Topology

A single-stage AC-DC converter, which uses a quadratic buck-boost voltage gain, for LEVs' (Light Electric Vehicles) battery charging is presented in this work. Unlike conventional BCs (Battery Chargers), which employ transformer (low or high frequency) based approaches to charge low voltages LEVs' batteries, the presented charger utilizes transformerless single-stage power architecture. In order to realize desired charging of LEVs and to maintain proper transformerless voltage gain, the presented quadratic buck-boost AC-DC converter ensures high step-down gain characteristics between AC mains and low voltage battery packs. Besides realizing desired battery charging profile, the presented single-stage BC ensures high PQ (Power Quality) indices (low input current distortions and unity power factor operation) at the supply side. Notably, the presented quadratic buck-boost AC-DC converter exhibits an intrinsic power factor correction (PFC) feature at the AC input mains under discontinuous inductor current mode (DICM) operation, thereby, incurring minimum complexities during the control implementation, which further reduces the cost of the charger. The DICM operation facilitates negligible switch turn-on and diode reverse recovery losses, and therefore, ensures an improved conversion efficiency of the BC. Even if, the presented AC-DC converter employs two switching devices, the simultaneous switching of both switches shrinks the cost and complexity of driving circuitry even further. Finally, a comprehensive operational analysis, component selection criteria, and modelling of the presented quadratic buck-boost AC-DC converter-based LEVs BC are carried out and its performance is validated through a test bench set-up in a laboratory environment. Relevant results are presented to validate the efficacy of the presented quadratic buck-boost AC-DC converter for LEVs charging applications.

Single-Phase Wireless Electric Vehicle Charger Using EF2 Inverter

This paper proposes a wireless battery charger topology for electric vehicle (EV) application that can be installed in public charging infrastructure as well as in home premises. The proposed topology can maintain constant current as well as constant voltage at the battery terminal and simultaneously can maintain near unity power factor at the input side using power factor correction (PFC) operation and thus satisfy all the criteria of a standard EV charger. The heart of the wireless power transfer (WPT) scheme is a high-frequency (HF) inverter. In this work, a class EF2 inverter is used to generate HF AC in such a way that it can deliver power with variable loading condition while maintaining constant current (CC) or constant voltage (CV) according to the requirement of CC-CV charging profile. To supply power to this EF2 inverter, an AC-DC front-end converter (stage-1) is integrated with the charger. The stage-1 is operated as a constant voltage source to the EF2 inverter for CC mode and constant current source for CV mode operation. CC-CV at the battery end and PFC at the input end are achieved only by controlling the gate pulse of stage-1. The WPT coils are first simulated using Ansys Maxwell package, and the complete charger is simulated with PSIM simulation software. A scaled-down 200 W laboratory prototype of the proposed charger is developed and tested with a resistive load to validate the idea. The wireless power transfer is achieved for a maximum distance of 12 cm between the transmitting and receiving coil. Finally, the charger is tested to charge both 12 V and 24 V battery packs and the CC-CV charging profile is presented for the 24 V, 30 Ah battery pack.

A PV variability tolerant generic multifunctional control strategy for battery energy storage systems in solar PV plants

This paper proposes a multifunctional control strategy for battery energy storage systems (BESSs) in solar photovoltaic (PV) plants to avoid the unacceptable PV-power ramp-rate caused by PV variability. In addition to the PV variability tolerant ramp-rate control, the proposed multifunctional BESS control strategy is capable of maintaining a user-specified charging profile to store excess PV power while providing backup power support and charge maintenance, by accommodating diverse control functions time to time. Four control functions, namely, Rest (RST), Surplus Energy Storage (STR), Back-up Power Support (SPT) and Charge Maintenance (CMT) functions, are proposed in this article. Besides, extendibility of the proposed multifunctional strategy is presented by adding two action strategies – one with the STR function and another with the SPT function. Validation of the proposed control strategy is conducted by using real historical data from a utility-scale solar PV plant located in Queensland, Australia. Numerical analyses show that the strategy can successfully keep the PV ramp-rate within specified limit along with the user specified charge profile. Further, exploiting the real-field data, the proposed strategy is demonstrated through a real-time co-simulation study conducted in a MATLAB interfaced RSCAD-RTDS platform that can be used as Digital Twin (DT) for the PV plant.

Highly Efficient Three-Phase Bi-Directional SiC DC–AC Inverter for Electric Vehicle Flywheel Emulator

Flywheels are nowadays a solution for the dynamic charging of electric vehicles since they act as transient energy storage. The need for a top efficient reversible power converter for the flywheel system is crucial to assure high dynamic performance. The paper presents the design of a 50 kW highly efficient reversible three-phase DC–AC inverter involving the most recent silicon carbide metal oxide semiconductor field effect transistors, and its experimental validation on a home-made emulator. Highest efficiency in reversible mode, compactness, and thermal enhancement are the targeted objectives that have been achieved. The power converter prototype evaluated on an original pulse width modulation testing-bench is able to emulate the working of the flywheel system. High frequency pulse width modulation switching, speed cycle operating, and thermal losses are evaluated. In addition, an efficiency above 99% for the converter has been attained, enabling robust functioning of the flywheel system emulator to perform specific charging profiles for electric vehicles.

Boosting battery state of health estimation based on self-supervised learning

State of health (SoH) estimation plays a key role in smart battery health prognostic and management. However, poor generalization, lack of labeled data, and unused measurements during aging are still major challenges to accurate SoH estimation. Toward this end, this paper proposes a self-supervised learning framework to boost the performance of battery SoH estimation. Different from traditional data-driven methods which rely on a considerable training dataset obtained from numerous battery cells, the proposed method achieves accurate and robust estimations using limited labeled data. A filter-based data preprocessing technique, which enables the extraction of partial capacity-voltage curves under dynamic charging profiles, is applied at first. Unsupervised learning is then used to learn the aging characteristics from the unlabeled data through an auto-encoder-decoder. The learned network parameters are transferred to the downstream SoH estimation task and are fine-tuned with very few sparsely labeled data, which boosts the performance of the estimation framework. The proposed method has been validated under different battery chemistries, formats, operating conditions, and ambient. The estimation accuracy can be guaranteed by using only three labeled data from the initial 20% life cycles, with overall errors less than 1.14% and error distribution of all testing scenarios maintaining less than 4%, and robustness increases with aging. Comparisons with other pure supervised machine learning methods demonstrate the superiority of the proposed method. This simple and data-efficient estimation framework is promising in real-world applications under a variety of scenarios.

Microbial fuel Cell-based Co-generation system for bioethanol production and power management solution

A Stacked Microbial Fuel Cell set-up using wheat straw hydrolysate as substrate is suitable for simultaneous ethanol production and power management solution. The system's performance is demonstrated by charging a standard 1.2 V and 3.7 V battery with three modules. The running time for ethanol production and the battery charging profiles of the microbial fuel cell is 300 min. It produces a maximum ethanol of 0.14 % (w/v) along with battery charging from 1.09 to 1.17 V, similarly, 0.12 % (w/v) of ethanol production along with battery charging from 3.50 to 3.57 V in 300 min. Additionally, a mathematical model estimates the possible production of ethanol and Open Circuit Voltage. Thus, the system may be further explored for possible industrial applications towards future energy sustainability by producing clean electricity and bioethanol on a large scale.

A Family of IPT Battery Chargers With Small Clamp Coil for Configurable and Self-Sustained Battery Charging Profile

To self-sustain the constant current (CC) output to constant voltage (CV) output transition for inductive power transfer (IPT) chargers, the clamp coil assisted IPT battery chargers are better than the existing solutions due to the merits of no additional switching components, sensors, and control circuits. However, the clamp coil of these IPT chargers is bulky and hard to design, as the mutual inductances between the clamp coil and the main coupler coils are significant to the output performances. To facilitate the small clamp coil in the transmitter, this paper proposes a family of IPT battery chargers with a proper secondorder compensation network on the secondary side. The output thresholds of the IPT charger with the proper compensation can be readily configured to cope with the desired charging profile, and the clamp coil can be designed very small to decrease the corresponding cost and size. In addition, the proposed threecoil IPT charger still features automatic and smooth CC-to-CV transition to eliminate wireless communication, near unity power factor to minimize voltage-ampere (VA) ratings, soft switching to improve transfer efficiency, and open-circuit protection during CC charging. An experimental prototype of a 48 V/2.5 A battery charger is built to verify the analysis.

A Weather-Independent and Renewable Power Supply With Wireless Power Transfer Feature for Powering Online Monitoring Systems in Smart Grid

The merge of the power and information infrastructures in smart grid requires increasing number of sensors and online monitoring systems on the high-voltage (HV) transmission towers. These systems are traditionally powered by small solar panels or wind turbines that are weather-dependent. This new contribution involves a full analysis of a complete weather-independent power supply to replace existing approaches. For the first time, the complete system comprising the energy harvesting, wireless power transfer (WPT) and output power stages is practically evaluated using printed circuit board (PCB) resonators embedded in a 35-kV composite insulator. We leverage the nearly constant current, transresistance, and voltage gains characteristics of the PCB domino-resonator structure under different self-oscillating frequencies and propose a coordinated control scheme of transmitter-side and receiver-side converters to regulate the constant current (CC) or constant voltage (CV) through entire charging process. Any current surge caused by the burst operation of the active rectifier on the receiver side triggers a transition between different self-oscillating operations on the transmitter side, leading to operating region extension of the active rectifier. Thus, the proposed system can easily adjust the output to follow different battery charging profiles without wireless communication between the transmitter and receiver.

Photovoltaic system for maximum power point tracking using hybrid firefly and perturbation and observation algorithm

This work presents the novel maximum power point tracking (MPPT) approach for a small 50 W photovoltaic (PV) system using the dc-dc converter. The method of modeling of PV module is discussed. The firefly (FFY) algorithm and the perturbation and observation (P&O) algorithm are combined to implement MPPT of the PV system connected to battery load. The operating principle is discussed in detail and steady-state analysis of the proposed system is implemented through simulation. The charging profile of the 7.5 Ah VRL battery is also studied using simulation. Furthermore, a low-cost microcontroller-based experimental setup rated at 50 W system connected to battery load was built to implement a hybrid FFY-P&O algorithm. The experimental results are in same as the simulation result. In contrast to the traditional P&O approach, it demonstrated the quick and efficient maximum power point operation triggered by a sudden transition in the environment.

One-way station-based electric carsharing service design considering route selection based on road congestion

ABSTRACT In the field of one-way station-based electric carsharing systems (OSECSs), determining the optimal service design is a major challenge faced by operators. To address this issue, This study proposes a simulation-optimisation framework that takes into account several factors, such as fleet size, station capacity, rental pricing, vehicle relocation, and staff rebalancing to determine the optimal service design. Specially, the simulation module of this framework is used to evaluates the performance of different carsharing service designs by taking into account the selection of optimal routes based on road congestion, nonlinear charging profiles, and elastic demand influenced by price and travel time. In the optimisation module of this framework, the study introduces a novel greedy genetic search algorithm to solve the optimisation problem. The proposed simulation-optimisation framework is tested on a carsharing network with 15 stations in Chengdu, China, and the numerical experiment shows its efficiency. Additionally, the study analyzes the impact of different degrees of demand and travel time variation, demand scenarios, and road congestion scenarios on the OSECS. By doing so, it provides a more comprehensive understanding of the system and the factors that affect its performance.

Online Lifetime Prediction for Lithium-Ion Batteries with Cycle-by-Cycle Updates, Variance Reduction, and Model Ensembling

Lithium-ion batteries have found applications in many parts of our daily lives. Predicting their remaining useful life (RUL) is thus essential for management and prognostics. Most approaches look at early life prediction of RUL in the context of designing charging profiles or optimising cell design. While critical, said approaches are not directly applicable to the regular testing of cells used in applications. This article focuses on a class of models called ‘one-cycle’ models which are suitable for this task and characterized by versatility (in terms of online prediction frameworks and model combinations), prediction from limited input, and cells’ history independence. Our contribution is fourfold. First, we show the wider deployability of the so-called one-cycle model for a different type of battery data, thus confirming its wider scope of use. Second, reflecting on how prediction models can be leveraged within battery management cloud solutions, we propose a universal Exponential-smoothing (e-forgetting) mechanism that leverages cycle-to-cycle prediction updates to reduce prediction variance. Third, we use this new model as a second-life assessment tool by proposing a knee region classifier. Last, using model ensembling, we build a “model of models”. We show that it outperforms each underpinning model (from in-cycle variability, cycle-to-cycle variability, and empirical models). This ‘ensembling’ strategy allows coupling explainable and black-box methods, thus giving the user extra control over the final model.

Software-Reconfigurable Multistage Constant Current Wireless Battery Charging Based on Multiharmonic Power Transmission

Wireless power transfer (WPT) technology has been widely applied in electric vehicle battery chargers because of its convenience, reliability, and safety. A typical battery charging profile consists of a constant current (CC) charging process followed by a constant voltage charging process. Recently, a multistage constant current (MSCC) charging profile is proposed to increase the charging speed, flexibility, and other performances. MSCC charging profile consists of multiple CC charging processes, which are applied to the battery in turn. This article proposes a low-cost software-reconfigurable MSCC charging technique for a typical single-stage dc–dc series–series compensated WPT converter using multiple high-order harmonics power transmission approach. Such a technique does not require any additional component and it can achieve inherent load-independent CC output and zero phase angle input characteristics. Moreover, it provides a precharge process for deeply discharged batteries to increase their lifetime. A scaled-down design example and the corresponding laboratory prototype are implemented to verify the proposed method.

Three-Mode Variable-Frequency ZVS Modulation for Four-Switch Buck+Boost Converters With Ultra-High Efficiency

This article introduces a three-mode variable-frequency zero-voltage switching (ZVS) modulation method for the four-switch buck+boost converter. This method makes this circuit concept well suited for applications, such as wireless power charging of electric vehicles, where this circuit operates as a power buffer between the resonant converter and the battery with the function to implement the required charging profile. Herein, the buck+boost converter operation is subdivided into three operating regions according to the converter static voltage gain, i.e., buck-, buck-boost- and boost-type modes. A ZVS turn- <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small> triangular current mode (TCM) control is adopted for buck-type and boost-type modes. In the buck-boost-type mode when the input-to-output voltage gain is close to unit, all the possible modulation cases are studied thoroughly based on the phase shift of the two half bridges in a full switching period. The selection of the most suitable modulation scheme is performed to minimize the rms value of the inductor current while taking into account the simplification of the practical implementation. Closed-form equations are derived, which makes it easy to implement in practice. The proposed strategy is described, analyzed, and finally verified through a 3 kW surface mounted device (SMD) silicon carbide (SiC) <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mosfet</small> -based laboratory prototype with designed input voltage of 300–600 V and the typical output voltage of 400 V class battery. The efficiency from the measured results is remarkably high, i.e., between 99.2% and 99.6% in a power range from 1 to 3 kW. Finally, tests for the operating mode transitions demonstrated the feasibility of the proposed modulation method. The power density of this converter is 4.86 kW/L.

A model-based EVs charging scheduling for a multi-slot Ultra-Fast Charging Station

The scientific literature is paying particular attention to Ultra-Fast Charging Stations (UFCSs) that can make charging times of Electric Vehicles (EVs) comparable with the refuelling time of internal combustion engine vehicles. In this context, scheduling charging algorithms to manage the available resources deserve significant attention. This paper proposes an online scheduling algorithm for UFCSs equipped with Battery Energy Storage Systems. The charging profile is obtained by considering the power and energetic constraints related to both infrastructure and EVs. The constraints are assessed according to the efficiency of the infrastructure, for which a power losses estimation approach was proposed and then detailed for the UFCS realized at our department. Even, the dependence of the maximum EV charging rate on the State of Charge (SoC) is considered. These aspects are usually neglected. Thus, starting from the measurements of the maximum charging rate for two commercial EVs, numerical simulations were performed using the proposed algorithm according to two different scheduling policies, i.e., the not-preemptive ‘First Come Best Served’ and the preemptive ‘Round Robin SoC’. The numerical results highlight the difference between the two policies regarding the allocation of resources among vehicles and how the SoC of the BESS affects the overall charging profiles.

Modeling of an Optimum Fast Charging Multi-Step Constant Current Profile for Lead-Acid Batteries

In this article, the modeling of an optimum fast charging profile for lead-acid batteries (LABs) is proposed. The proposed profile is a multi-step constant current (MSCC) where various current magnitudes in a descending manner are applied to the battery; therefore, it prevents the over-voltage and gassing phenomenon at the end of charging process, and shortens the charging time at the same time. Using an electro-thermal model of a LAB, an optimization problem containing charging time, battery temperature during the process, and battery's terminal voltage as objective functions is defined. The magnitude and duration of all charging steps are taken into account as control variables to be optimized for minimizing the objective functions using a particle swarm optimization (PSO) algorithm. A comparison between different conventional charging profiles for LABs is also provided in terms of charging time and battery's temperature and voltage. The simulation results carried out by MATLAB/Simulink show that the proposed profile reduces the charging time, while keeps the battery's temperature and voltage during the charging process within allowable range. The impacts of model's uncertainties on the proposed approach is evaluated. A comprehensive comparison is also provided to confirm the benefits of the proposed method over MSCC profiles reported in the literature. The effectiveness of the proposed approach is finally validated using the experimental implementation for a 12 V 200Ah lead-acid battery.

Inductive Power Transfer Based on Variable Compensation Capacitance to Achieve an EV Charging Profile With Constant Optimum Load

Wireless charging must be highly efficient throughout the entire battery charging profile to compete in the electric vehicle (EV) industry. Thus, optimum load matching is commonly used: it operates at the equivalent load that maximizes the efficiency, which depends on the coil’s alignment. In this article, the optimum load is made independent of the coils’ position by changing the system’s resonant frequency through switch-controlled capacitors (SCCs). This eliminates the need for load-side voltage control. The output current follows the battery voltage rise during the battery charging cycle to always match the optimum load, which can be achieved by regulating the input voltage via the power factor correction (PFC) converter. This method is called here constant optimum load (COL). Two SCC topologies have been implemented in a 3.7-kW hardware demonstrator. The one implementing the half-wave modulation achieves higher efficiency than the one employing full-wave modulation, with 96.30% at 3.2 kW and aligned coils. When misalignment occurs, the half-wave modulation technique results in higher efficiency than the conventional-fixed compensation, where the efficiency is lower by up to 0.68% at partial load. Based on these results, the proposed COL method is proven suitable for 3.7-kW EV-static wireless charging achieving one of the highest peak efficiencies listed in today’s literature for the same power class.

Frequency-Modulation-Based IPT With Magnetic Communication for EV Wireless Charging

In this article, a switching frequency-modulation-based inductive power transfer system that provides wireless charging of the scooters by magnetic in-band communication is newly proposed. Although the transmitter (Tx) and receiver (Rx) coils are misaligned, the scooters can be freely charged in the charging station by the proposed frequency modulation method without additional power converters in the Tx and Rx sides. In order to implement frequency modulation control, the Rx side can transmit the required information to the Tx side through the magnetic coupling between Tx and Rx coils, i.e., magnetic in-band communication, in the suggested approach. By virtue of the proposed magnetic in-band communication method, the switching frequency can be modulated so that the load power for charging the battery in the Rx side is appropriately controlled, considering the battery charging profile. Contrary to the Qi standard technology using both amplitude shift keying (ASK) and frequency shift keying, the proposed magnetic communication protocol is optimally and simply designed for targeting scooter wireless charging applications. Because the proposed communication method is based on only ASK technique for unidirectional information delivery, the coil misalignment sensitivity and the complexity of the communication process can be reduced, which guarantees the robust frequency modulation control for load power regulation. A 100 W power level prototype for the charging station and the scooters was fabricated and verified by simulations and experiments. The 97.3% of the coil-to-coil efficiency and 90.6% of the dc-to-dc efficiency were obtained at a nominal operating point of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> = <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nom</sub> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P_L</i> = 100.8 W. As a result, the battery having 44.0–53.0 V and 13 Ah in the Rx side was charged under a wide range of battery charging conditions and coil misalignments.

Maximizing Decarbonization Benefits of Transportation Electrification in the U.S.

Transportation electrification can significantly reduce carbon footprint and accelerate the modernization of aging electric infrastructure. In the U.S., the growing adoption of electric vehicles (EVs) will significantly impact the electrical grid and associated greenhouse gas emissions, but with significant differences between the balancing regions due to the diverse characteristics of their electrical grids. This work assesses the impacts associated with the increasing penetration of EVs in the U.S., considering the characteristics of the grid in the different regions, in order to discuss the needed strategies to maximize the future decarbonization benefits. The assessment considers the variation in generation mix profiles during the day in each region, as well as different charging profiles associated with home, work, and public charging. The results show that more ambitious policies for the increasing share of carbon-free generation in the regions with the highest emissions are needed, emphasizing incentives for the use of work and public charging, and ensuring effective management of the charging flexibility.

Rotating Phase Shedding for Interleaved DC–DC Converter-Based EVs Fast DC Chargers

Fast dc chargers are the key enablers for the massive rollout of electric vehicles due to the reduced charging time. On the other hand, the rapid growth in battery technology with different voltages and charging requirements has imposed additional hurdles on the charger design to meet the efficiency requirements. Multiphase interleaved converters with conventional phase-shedding control improve the efficiency for a wide range of operations. However, they tend to operate certain phases, resulting in uneven thermal stress among the converter phases. This article proposes a rotating phase-shedding control to distribute the switching activities among all phases, enhancing the system's reliability while retaining the efficiency improvement. The proposed technique selects the proper number of active phases based on the required charging profile and periodically swaps them with other phases to even out the stress. The thermal profile is extracted to assess the thermal damage of the power switches. The performance of the proposed approach is evaluated and compared with the conventional phase shedding. The simulation and experimentally validated results confirm that the proposed technique achieves a better even distribution of the thermal damage between the phases compared with the conventional one. This will ultimately extend the lifetime of the system.

State of Health Estimation for Smart Batteries using Transfer Learning with Data Cleaning

Smart battery with optimized pulsed current produced by the bypass is one prospective technology to prolong the service life of the batteries in electric vehicles. The accurate and reliable state of health (SOH) estimation is one significant process before the decision of the control. This paper proposes a proper solution for battery SOH estimation that can be applied to both constant and pulsed current charging scenarios. Specifically, a data cleaning process is proposed for preprocessing the fluctuated measurement, while retaining the main aging information. From the pre-processed data under different charging profiles, four SOH features are extracted, and the correlation coefficients prove their effectiveness with both constant current and pulsed currents. Later, a transfer learning-based model is developed which shows improved accuracy of the SOH estimations under pulsed current scenarios. Finally, experiments have been conducted to verify the proposed method. In the case of model retraining using only the first 10% of unseen data, satisfactory results can be obtained (the error is less than 2.626%). By increasing the data for model retraining to 20%, a fitted coefficient of larger than 0.994 between the estimations and real values is obtained, resulting in a low estimation error of less than 0.8%.