On-board Battery Research Articles

Effects of State-of-Charge and Penetration Location on Variations in Temperature and Terminal Voltage of a Lithium-Ion Battery Cell during Penetration Tests

The nail penetration test has been widely adopted as a battery safety test for reproducing internal short-circuits. In this paper, the effects of cell initial State-of-Charge (SOC) and penetration location on variations in cell temperature and terminal voltage during penetration tests are investigated. Three different initial SOCs (10%, 50%, and 90%) and three different penetration locations (one is at the center of the cell, the other two are close to the edge of the cell) are used in the tests. Once the steel cone starts to penetrate the cell, the cell terminal voltage starts to drop due to the internal short-circuit. The penetration tests with higher initial cell SOCs have larger cell surface temperature increases during the tests. Also, the penetration location always has the highest temperature increment during all penetration tests, which means the heat source is always at the penetration location. The absolute temperature increment at the penetration location is always higher when the penetration is close to the edge of the cell, compared to when the penetration is at the center of the cell. The heat generated at the edges of the cell is more difficult to dissipate. Additionally, a battery cell internal short-circuit model with different penetration locations is built in ANSYS Fluent, based on the specifications and experimental data of the tested battery cells. The model is validated with an acceptable discrepancy range by using the experimental data. Simulated data shows that the temperature gradually reduces from penetration locations to their surroundings. The gradients of the temperature distributions are much larger closer to the penetration locations. Overall, this paper provides detailed information on the temperature and terminal voltage variations of a lithium-ion polymer battery cell with large capacity and high power under penetration tests. The presented information can be used for assessing the safety of the onboard battery pack of electric vehicles.

Design and Multi-Objective Optimization of a 12-Slot/10-Pole Integrated OBC Using Magnetic Equivalent Circuit Approach

Permanent magnet machines (PMs) equipped with fractional slot concentrated windings (FSCWs) have been preferably proposed for electric vehicle (EV) applications. Moreover, integrated on-board battery chargers (OBCs), which employ the powertrain elements in the charging process, promote the zero-emission future envisaged for transportation through the transition to EVs. Based on the available literature, the employed machine, as well as the adopted winding configuration, highly affects the performance of the integrated OBC. However, the optimal design of the FSCW-based PM machine in the charging mode of operation has not been conceived thus far. In this paper, the design and multi-objective optimization of an asymmetrical 12-slot/10-pole integrated OBC based on the efficient magnetic equivalent circuit (MEC) approach are presented, shedding light on machine performance during charging mode. An ‘initial’ surface-mounted PM (SPM) machine is first designed based on the magnetic equivalent circuit (MEC) model. Afterwards, a multi-objective genetic algorithm is utilized to define the optimal machine parameters. Finally, the optimal machine is compared to the ‘initial’ design using finite element (FE) simulations in order to validate the proposed optimization approach and to highlight the performance superiority of the optimal machine over its initial counterpart.

Optimisation of a Pump-Controlled Hydraulic System using Digital Displacement Pumps

When electrifying working machines, energy-efficient operation is key to maximise the use of the limited capacity of on-board batteries. Previous research indicate high energy savings by means of component and system design. In contrast, this paper focuses on how to maximise energy efficiency by means of both design and control optimisation. Simulation-based optimisation and dynamic programming are used to find the optimal electric motor speed trajectory and component sizes for a scooptram machine equipped with pump control, enabled by digital displacement pumps with dynamic flow sharing. The results show that a hardware configuration and control strategy that enable low pump speed minimise drag losses from parasitic components, partly facilitated by the relatively high and operation point-independent efficiencies of the pumps and electric motor. 5–10% cycle energy reductions are indicated, where the higher figure was obtained for simultaneous design and control optimisation. For other, more hydraulic-intense applications, such as excavators, greater reductions could be expected.

Enhancement of dynamic wireless power transfer system by model predictive control

Wireless power transfer (WPT) system based on a dynamic wireless charging (DWC) scheme, eliminates waiting time for charging electric vehicles (EVs), increases the range of motion, reduces the size of Li-ion battery, and automates the charging process. In the DWC method, an EV frequently passes the charger transmitter pads at maximum speed to charge the onboard battery. The charger must have a quick and smooth transient response that employs the proper charging strategy for the battery. Here, a model predictive controller (MPC) is proposed to deploy a suitable DWC based on constant current/voltage (CC/CV) charging protocol. The designed MPC functionality is demonstrated by simulation and experimental results for both CC/CV strategies while battery state of charge (SOC) is estimated by a simple and stable technique in the primary side. The applied CC/CV MPC scheme performs properly in all conditions with a fast critically damped start-up, which makes it a potential choice to charge EV in dynamic and static modes. The simulation results of the proposed controller are verified by implementing a 90 W WPT testbed at 85.5 kHz switching frequency and 100 mm coils’ air gap.

Comprehensive study and improvement of experimental methods for obtaining referenced battery state-of-power

As a soft sensor, the state-of-power (SoP) estimator reveals critical information on battery-based energy storage systems. A set of reliable ‘referenced values’ is the key to evaluate the precision of such soft sensors at their designing stage and could influence the overall reliability of the battery systems. However, experimentally obtaining the ‘referenced SoP’ is non-trivial since high-current pulse tests (>10C) are required to charge/discharge the batteries to their cut-off conditions. The associated high-power experimental platforms could be expensive, while frequently applying large current at boundary conditions may leave potential safety issues. Aiming at these problems, this paper focuses on obtaining referenced SoP, rather than onboard SoP estimations. A novel equivalent discharging test is designed to accurately recover the voltage response of high-current pulses from a set of low-current tests, resulting in a 33% reduction of the peak discharging current. In addition, a flexible softmax neural network is further proposed to generate SoP values for the intervals between pulse tests. With these tools, reliable SoP values with errors lower than 0.5% can be readily obtained. The SoP obtained from our approach can be further utilised as a highly accurate benchmark to evaluate the accuracy of other onboard battery SoP estimators.

Control Methods for Performance Improvement of an Integrated On-Board Battery Charger in Hybrid Electric Vehicles

This paper presents control methods for performance improvement of an integrated on-board battery charger (OBC) in hybrid electric vehicles (HEVs). HEVs generally consist of an OBC and a starter generator system (SGS). Since these each have a power conversion device for independent operation, such as battery charging and starter generator driving for engine starting, it necessarily increases the number of components, weight, and volume of the HEV. In order to overcome these disadvantages, recent research concerning the integrated OBC has progressed. Although it demands installation of power relays and an additional circuit, the integrated OBC is effectively operated for battery charging and starter generator driving. This paper proposes not only a harmonic reduction method of grid current, but also a feed-forward control method for performance improvement of the integrated OBC in HEVs. The effectiveness of the proposed control methods is verified by simulation and experimental results.

Particle swarm optimization based sliding mode controllers for electric vehicle onboard charger

High-performance onboard chargers are needed to proliferate the popularity of electric vehicles considering the charging convenience and flexibility. In this study, a sliding mode controller (SMC) for power converters is presented to enhance the performance of the onboard battery charger. The SMC parameters are selected using the particle swarm optimization (PSO) algorithm. A two-stage charging topology is adopted with independent controllers. The grid side AC–DC converter mitigates the harmonic content in the grid current and smoothens the voltage and current in the dc bus. The bidirectional DC–DC converter at the battery side regulates the charging characteristics of the Li-ion battery with a constant current–constant voltage (CC–CV) profile. The steady-state and dynamic behavior of the charging system is presented. Hardware in the loop real-time emulator is utilized to confirm the validity of this study. Experiments show that optimization-based SMC contributes to the overall dynamic performance of the onboard battery charger.

Evaluation of the entropy of reaction using modified frequency-domain method and a physics-based thermoelectrochemical model of a lithium-ion battery

The entropy of reaction (ΔS) is one of the key parameters required for predicting temperature and performance in lithium-ion batteries. A frequency-domain method that reduces the ΔS measurement time by factor of 100 compared to traditional potentiometric methods was introduced by Schmidt et al. [Electrochimica Acta. 311(137), 2014]. The method applies a sinusoidal current of an angular frequency ω, and observes temperature signals with frequencies of ω and 2ω for the reversible entropic heat and irreversible Joule heat, respectively. In this work, the previous frequency-domain method was modified to implement a physics-based thermoelectrochemical model that physically couples the internal state changes of batteries to their open-circuit potentials. The modified frequency-domain method was applied to a commercial pouch battery (graphite/LiCoO2) to show its validity. The physics-based model was used to fit the entropy of reaction while operating the battery, and the obtained ΔS values correlate well with previously reported values that were measured by traditional potentiometric methods. This work shows that the modified frequency domain method can be an alternative to the time-consuming potentiometric method, with potential application in on-board vehicle battery diagnostics.

Disaster Relief Wireless Networks: Challenges and Solutions

Reliable and flexible emergency networks are of paramount essence for disaster relief during or after the event of disasters due to the destruction or lack of terrestrial communication infrastructures. Thanks to fast deployment and flexible mobilities, unmanned aerial vehicles (UAVs) emerge as a promising paradigm to efficiently establish emergency networks and perform immediate disaster relief tasks in affected areas. However, in such UAV-assisted disaster relief networks (UDRNs), the limited onboard batteries and computational capacities of UAVs hinder them from performing computation-intensive missions. Moreover, critical security vulnerabilities arise in data transmission among UAVs owing to the untrusted environment, open communication channels, and unreliable misbehavior tracing. To this end, this article investigates UDRNs based on blockchain and machine learning to achieve secure and efficient data transmission. Specifically, we first present a lightweight blockchain-enabled collaborative aerial-ground networking framework to safeguard data delivery under disasters, where a credit-based delegated proof-of-stake consensus protocol is further devised to enhance consensus efficiency while promoting UAVs' honest behaviors. In addition, by harnessing the idle computing power of ground vehicles (referred to as vehicular fog computing), a novel reinforcement learning-based algorithm is developed to intelligently offload UAVs' computation missions to the moving vehicles in the dynamic environment. Experimental results demonstrate that the proposed framework outperforms the existing approaches, in terms of consensus security, user utility, task latency, and energy consumption. Finally, future research directions in this emerging area are discussed.

In Situ MIMO-WPT Recharging of UAVs Using Intelligent Flying Energy Sources

Unmanned Aerial Vehicles (UAVs), used in civilian applications such as emergency medical deliveries, precision agriculture, wireless communication provisioning, etc., face the challenge of limited flight time due to their reliance on the on-board battery. Therefore, developing efficient mechanisms for in situ power transfer to recharge UAV batteries holds potential to extend their mission time. In this paper, we study the use of the far-field wireless power transfer (WPT) technique from specialized, transmitter UAVs (tUAVs) carrying Multiple Input Multiple Output (MIMO) antennas for transferring wireless power to receiver UAVs (rUAVs) in a mission. The tUAVs can fly and adjust their distance to the rUAVs to maximize energy transfer gain. The use of MIMO antennas further boosts the energy reception by narrowing the energy beam toward the rUAVs. The complexity of their dynamic operating environment increases with the growing number of tUAVs and rUAVs with varying levels of energy consumption and residual power. We propose an intelligent trajectory selection algorithm for the tUAVs based on a deep reinforcement learning model called Proximal Policy Optimization (PPO) to optimize the energy transfer gain. The simulation results demonstrate that the PPO-based system achieves about a tenfold increase in flight time for a set of realistic transmit power, distance, sub-band number and antenna numbers. Further, PPO outperforms the benchmark movement strategies of “Traveling Salesman Problem” and “Low Battery First” when used by the tUAVs.

Electric vehicles fire protection during charge operation through Vanadium-air flow battery technology

During the last decade, electric vehicles had a remarkable diffusion caused by Li-ion batteries lowered prices and improved performances. Meanwhile, incidents involving fire were reported for electric vehicles during charge operation or simple parking. The potential drawbacks of this technology could play a role on safety in the next years, especially for household or underground charge. This paper presents the development of a novel system concept based on a Vanadium-air flow battery, applied to provide charge and fire safety of electric vehicles through oxygen reduction in a sealed box. When the vehicle is parked inside the box and the passengers are outside of it, a nitrogen injection is operated to reduce the fire risk quickly, during the subsequent vehicle charge operation the oxygen is consumed from the box atmosphere by the cathode of the Vanadium-air battery that supplies energy, then the nitrogen reserve is restored consuming oxygen from the external ambient and the energy output can be supplied to smart grids. The system is mainly composed by the Vanadium-air flow battery, the protection box and the nitrogen reserve, it is sized relatively to the most diffused road and commercial electric vehicles for different values of on-board battery capacity and charge power Moreover, it can be integrated into vehicle-to-grid energy systems improving intersectoral flexibility.

On the Influence of Charging Stations Spatial Distribution on Aerial Wireless Networks

Using drones for cellular coverage enhancement is a recent technology that has shown a great potential in various practical scenarios. However, one of the main challenges that limits the performance of drone-enabled wireless networks is the limited flight time. In particular, due to the limited on-board battery size, the drone needs to frequently interrupt its operation and fly back to a charging station to recharge/replace its battery. In addition, the charging station might be responsible to recharge multiple drones. Given that the charging station has limited capacity, it can only serve a finite number of drones simultaneously. Hence, in order to accurately capture the influence of the battery limitation on the performance, it is required to analyze the dynamics of the time spent by the drones at the charging stations. In this paper, we use tools from queuing theory and stochastic geometry to study the influence of each of the charging stations limited capacity and spatial density on the performance of a drone-enabled wireless network.

Cost- and Energy-Efficient Aerial Communication Networks With Interleaved Hovering and Flying

This work proposes a methodology for the energy-and cost-efficient 3-D deployment of an unmanned aerial vehicle (UAV)-based aerial access point (AAP), that exchanges a given amount of independent data with a set of ground user equipment (UE). Considering a fly-hover-communicate transmission scheme, the most energy-efficient 3-D hovering points (HPs) of the AAP are determined by decoupling the problem in the horizontal and vertical dimensions. First, we derive analytically the optimal hovering altitude that jointly maximizes the downlink and uplink global energy efficiency (GEE) of the system. Next, we propose the multilevel circle packing (MCP) algorithm to determine the minimal number of HPs and their associated horizontal coordinates, such that the AAP covers all the UEs in the given geographical area. A cost analysis is carried out to observe the variation of both fixed and variable costs; these are then minimized by suitably selecting the AAP's battery parameters, like the depth of discharge (DOD), defined as the portion of battery capacity that is consumed during a discharge cycle, and the velocity of the UAV. Simulation results show that: the UAV energy consumption has a significant impact on the 3-D HPs of the AAP; the time spent during the substitution swap of an out of power AAP has a major influence on the operational cost; the cost of the system can be optimized by suitably selecting the onboard battery and the UAV flight parameters.

Performance Evaluation of Single-Phase On-Board Charger with Advanced Controller

Abstract: The increasing mobility of electric vehicles has inspired vehicle growth to power grid technology. Such as vehicle to grid technology allows to transfer the power from the electric vehicle battery to the power grid. This enable speak load shaving, load leveling, voltage regulation, and improved stability of the power system. To develop the vehicle to grid technology requires a specialized EV battery charger, which permits the bi-directional energy transfer between the power grid and the electric vehicle battery. There is a specific control strategy used for a bi-directional battery charger. The proposed control strategy is used for charge and discharge battery of EV. The charger strategy has two parts: 1) Bidirectional AC-DC Converter in two-way Communication System. 2) Bidirectional DC-DC Buck-Boost Converter. There are two modes of operation for a bidirectional ac-dc converter: for G2V, rectifying mode is used, and for V2G, inverter mode is used. The suggested charge strategy not only allows for two-directional power flow but also provides power quality management of the power grid. Fuzzy logic controller (FLC) transforms linguistic control topology evaluations knowledge into an automated control topology using FLC. The FLC is more stable, has less overshoot, and responds quickly. The operation of a standard PI controller and a FLC was compared in this study using MATLAB and Simulink, and different time domain characteristics were compared as toshow that the FLC had a smaller overshoot and a faster response than the PI controller. Keywords: Bi-directional AC-DC converter, bi-directional DC-DC Buck-Boost converter, electric vehicles (EVs), on-board battery charger (OBC), grid to vehicle (G2V), vehicle to grid (V2G).

Characterization and Analysis of a Flexural Shape Memory Alloy Actuator

Shape memory alloys (SMAs) are popular as actuators for use in soft robots due to their high work density and compatibility with miniaturized on-board batteries and power electronics. However, because SMA actuators are activated through electrical Joule heating, they exhibit poor energy efficiency and low actuator frequencies that arise from long cool-down times. Moreover, in the case of SMA wires that are subject to flexural loading, their load capacity and mechanical work output decrease exponentially with decreasing cross-sectional area. In this study, we perform analytic and numerical analyses to examine the thermal and structural design space around a particular class of flexural SMA wire actuators with the intention of increasing actuator operating frequency and actuation forces. Measurements obtained through experimental testing are consistent with theoretical studies of actuator force output and provide additional insight into the efficiency of electrical-to-mechanical energy conversion. Together, the theoretical and experimental studies provide insights that have the potential to inform SMA wire design and usage in soft robotic applications.

An efficient soft-switched vienna rectifier topology for EV battery chargers

The requirement of high power rated, efficient and high power density grid connected converters has increased due to their extended use in multiple applications such as battery chargers. The proliferation of electric vehicles (EVs) in the transportation market is essentially associated with the performance and reliability of battery chargers. An efficient, compact and fast battery charger is indispensable in order to provide a way to charge an EV in a short time. In view of this fact, this article proposes a soft-switching three-level T-type vienna rectifier, which can be used as a front-end power stage converter in an on-board EV battery charger. The proposed topology achieves high efficiency and high power density by employing soft-switching strategy, which is achieved by incorporating a simple auxiliary network in the proposed circuit. The reduced component stresses and simple control strategy make it an appealing candidate for EV industries to develop this front-end PFC rectifier as a fast battery charger. The high switching operation with reduced power losses, regulated DC at the output, low-harmonic grid current and power factor correction operation are achieved with the proposed topology. The construction, operating principle and simulation analysis of the proposed converter are discussed in detail. In order to show the stand-point of the proposed scheme, a fair comparison of the proposed soft-switched converter with an existing soft-switched 3-level neutral-point diode-clamped converter is carried out in terms of the number of components and complexity in the PWM signals generation. Additionally, an efficiency comparison of the suggested converter is carried out with some existing well-known rectifier topologies at different power loads. The practical implementation of the proposed power converter scheme is checked by building a laboratory-prototype to perform an experimental analysis.

Integrated framework for SOH estimation of lithium-ion batteries using multiphysics features

This study proposes a highly reliable, robust, and accurate integrated framework to estimate the state-of-health (SOH) of lithium-ion batteries (LIBs), focusing on feature extraction and manipulation. This framework comprises three phases: feature extraction, feature manipulation, and SOH estimation. First, multiphysics features are extracted from mechanical and electrochemical evolutionary responses as distinct health indicators (HIs) to account for the multiphysics degradation mechanisms. Second, these features are manipulated to eliminate outliers and noises. This phase is especially effective for impedance HIs, considering the high sensitivity of these HIs to minor environmental perturbations. Third, a multivariate Gaussian distribution theory estimates the SOH combined with a nonlinear quadratic kernel to account for nonlinear characteristics in degradation modes of LIBs. The estimated results under various environments verify that the multiphysics feature primarily increases accuracy, whereas the feature manipulation ensures reliability and robustness. However, both phases are complementary in securing the accuracy, reliability, and robustness of the framework. Although the lifespan of LIBs is estimated using the training set in the 5 % SOH range, the estimation errors of the proposed framework are less than 2.5 % in all test sets. Thus, the proposed method ensures its potential applicability in practical implementations of onboard battery management systems.

Energy Management System in the Vehicles using Three Level Neuro Fuzzy Logic

This paper has proposed a hybrid electric vehicle that uses intelligent energy management strategy to decrease the energy consumption of the vehicle. Here, the total energy consumption of the vehicle is initially modelled and further investigated to reduce the amount of energy used to be identified as a sum of electrical energy provided by consumed fuels and on-board batteries. In particular, an intelligent controller is proposed in this work to execute its ability to decrease the total amount of energy consumed and improve the energy efficiency of the vehicle. A fuzzy system is utilized in an account supervisory controller to decide the appropriate mode of operation for the system. The part of the proposed work involves development of optimal control strategies by using neuro-fuzzy logic. In order to obtain optimal performance, the controllers are used to regulate vehicle subsystems and set points. The biggest advantage of this work is the reduction in energy consumption and their ability to execute the operation online. Simulink/MATLAB is used to simulate and validate the performance of the proposed work under various conditions and under several dataset values.

Design of a Bidirectional Wireless Power Transfer System for Vehicle-to-Home Applications

Energy storage plays a fundamental role in balancing the power fluctuations induced by the distributed generation of renewable energy sources. In this scenario, electric vehicles can strongly contribute to exchange power with the grid through their on-board batteries. When the vehicle is parked, the battery can be discharged, injecting active power into the grid, provided that its state of charge will be restored before vehicle utilization. This paper presents a comprehensive step-by-step design of a wireless charger for a Vehicle-to-Home application. The design procedure begins from the constraints disposed by the Italian reference technical rules for Low Voltage utilities and by the standard SAE J2954 for Wireless Power Transfer for electric vehicles. The selection of the output power of the battery is followed by the power sizing of each stage of the bidirectional wireless charger.

The Role of the Electric Vehicle in Smart Homes: Assessment and Future Perspectives

The wide spreading of electric mobility is imposing the application of new technologies of hardware and software for supporting new operation paradigms, not only in the perspective of improving smart grids, but also each time more in the perspective of boosting the preponderance of smart homes. Concretely, nowadays it is clear that the emergent technologiesfor smart homes, targeting power quality and a more efficient power management, are also altering the conventional electrical power grids toward smart grids. In this panorama, by relating the strategic pillars of electric mobility and smart homes, this paper has as main objective the characterization of the role of the electric vehicle (EV) in smart homes, presenting a broad assessment based on the state of the art, as well as creating a link with upcoming advanced operations. From the smart home point of view, since it should be equipped to deal with the requirements of the EV, the assessment of both on-board and off-board EV battery charging systems are considered along the paper. Furthermore, the presence of technologies such as energy storage systems, renewable energy sources and dc electrical appliances in smart homes towards sustainability is also reflected in this paper, but framed from the point of view of an off-board EV battery charging system, since it is more convenient for enabling the interface of these mentioned technologies. Looking the paper as a whole, a relevant contribution is the discussion of future operations for the EV in smart homes, also envisioning the opportunity for ac, dc, or hybrid power grids. Illustrative and strategic results are shown, covering all these features with specific details, not only based on numerical simulations, but also based on experimental results, which were obtained with a proof-of-concept laboratory prototype.