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Batteries are a fundamental storage component due to its various applications in mobility, renewable energies and consumer electronics among others. Regardless of the battery typology, one key variable from a user's perspective is the remaining energy in the battery. It is usually presented as the percentage of remaining energy compared to the total energy that can be stored and is labeled State Of Charge (SOC). This work addresses the development of a hybrid model based on a Lithium Iron Phosphate (LiFePO4) power cell, due to its broad implementation. The proposed model calculates the SOC, by means of voltage and electric current as inputs and the latter as the output. Therefore, four models based on k-Means, Agglomerative Clustering, Gaussian Mixture and Spectral Clustering techniques have been tested in order to obtain an optimal solution. [ABSTRACT FROM AUTHOR]

In this study, an energy management model for electric vehicles including the entire vehicle such as the cabin, electric motors, battery, and the heating–cooling system was prepared. The heating and cooling processes for electric vehicles were run according to the internationally recognized driving cycles as well as at constant speeds to investigate them under different ambient conditions. The heating–cooling processes were managed in line with the cabin temperature target determined by considering the comfort conditions. The energy consumption of each of the system elements and the system in the heating–cooling process in electric vehicles was analyzed. Under different operating conditions, the variation of cabin temperature with time, instantaneous power, and cumulative energy consumption was calculated. The effect of heating and cooling processes on energy consumption, charging rate, and range were analyzed and interpreted. The results showed that the heating–cooling system for the heating process consumed more energy when the ambient temperature decreased, and the charge consumption ratio as well as the range deformation rate increased to about 30% when the ambient temperature was –10 °C. Similarly, the heating–cooling system for the cooling process consumed more energy when the ambient temperature increased, and the charge consumption ratio as well as the range deformation rate reached up to 40% when the ambient temperature was 40 °C. When the outdoor conditions were close to the thermal comfort temperature of 23 °C inside the cabin, the total energy consumption and the range deformation rates were reduced to less than 10%. [ABSTRACT FROM AUTHOR]

Battery Management System (BMS) including measurements errors that causes decrease in ‎the quality of ‎calculated State of the Charge (SOC). It will limit the accurate estimation of ‎the SOC that is a critical challenge in ‎some of the engineering fields such as medical science, ‎robotics, navigation and industrial applications. These ‎facts implies on the significance of ‎SOC estimation from battery measurements that is the matter of the literature ‎through the ‎recent years. Due to the dependency of the EKF to the system model, the change in the ‎battery ‎parameters and noise information cause losing performance in the SOC estimation ‎over the time. In this paper, we ‎assume that the battery parameters including internal ‎resistance and capacitor and also the noise information are ‎varying over the time. To solve ‎that, two separate on-line identification algorithms for parameters and noise ‎information are ‎introduced. In more details, a Recursive Least Square (RLS) algorithm is used to identify ‎the ‎resistance and capacitor values. Moreover, the process and measurement noise covariance are ‎estimated based ‎on iterative noise information identification algorithm. Then all of the ‎updated values are used in the EKF ‎algorithm. This paper aims to address the issue of uncertainty in SOC estimation by proposing two algorithms. ‎The first algorithm focuses on identifying deterministic uncertainty, which refers to uncertainty in model ‎parameters. To address the challenge of uncertain model parameters, RLS is introduced.

This paper presents an advanced methodology for SOC estimation by integrating Recursive Least Squares (RLS) techniques with Adaptive Extended Kalman Filter (AEKF). The proposed methodology aims to mitigate the challenges associated with fluctuating battery parameters and varying noise characteristics over time, which can significantly impact the accuracy of SOC estimation. By dynamically adjusting to evolving system dynamics and noise statistics, the proposed approach exhibits enhanced robustness and accuracy compared to traditional techniques. The proposed methodology assumes that battery parameters, including internal resistance, capacitance, and noise information, undergo variations over time. To address this assumption, two distinct online identification algorithms for parameters and noise information are introduced. Specifically, the RLS algorithm is utilized to ascertain resistance and capacitance values. Process and measurement noise covariance is also estimated based on an iterative noise information identification algorithm. Subsequently, all updated values are incorporated into the EKF. The results demonstrate that the RLS-AEKF approach achieves higher accuracy than the EKF. The results based on Fast Urban Driving Schedule (FUDS) and Urban Dynamometer Driving Schedule (UDDS) working current profiles validate the effectiveness of the proposed approach in enhancing SOC estimation accuracy under realistic operating conditions. [ABSTRACT FROM AUTHOR]

The rapid development in the field of electric vehicles requires a careful evaluation of the design process. The presence of a simulation model of the electric vehicle can effectively detect many faulty areas during the development process without risks. The MATLAB and Simulink environment is considered one of the most important tools used in the simulation process. In this paper, we will present the model of electric car used for transporting postal parcels (postal cars). The model includes simulating the operation of a permanent magnet synchronous electric motor. We will assume that the car is moving according to a driving cycle. The results will show the torque forces required to achieve the required speed. We will further calculate the traction and the resistance forces during the driving cycle and the engine efficiency in addition. Perhaps the most important problem facing electric car designers is calculating the amount of energy consumed from the battery or hydrogen fuel, and this is what was achieved as the result of the simulation process in this research. In the end, use one of the artificial intelligence tools (fuzzy controller) to improve battery life by providing the electric car driver with an alert system that will increase the ability to monitor the battery condition and thus increase battery life. The benefit of this paper emerges in realizing the importance of modeling and using simulation using artificial intelligence in developing the design of the electric car, specially the electric motor and battery size, and thus achieving one of the most important goals of the United Nations of preserving the environment and reducing carbon emissions.

In this study, an energy management model for electric vehicles including the entire vehicle such as the cabin, electric motors, battery, and the heating–cooling system was prepared. The heating and cooling processes for electric vehicles were run according to the internationally recognized driving cycles as well as at constant speeds to investigate them under different ambient conditions. The heating–cooling processes were managed in line with the cabin temperature target determined by considering the comfort conditions. The energy consumption of each of the system elements and the system in the heating–cooling process in electric vehicles was analyzed. Under different operating conditions, the variation of cabin temperature with time, instantaneous power, and cumulative energy consumption was calculated. The effect of heating and cooling processes on energy consumption, charging rate, and range were analyzed and interpreted. The results showed that the heating–cooling system for the heating process consumed more energy when the ambient temperature decreased, and the charge consumption ratio as well as the range deformation rate increased to about 30% when the ambient temperature was –10 °C. Similarly, the heating–cooling system for the cooling process consumed more energy when the ambient temperature increased, and the charge consumption ratio as well as the range deformation rate reached up to 40% when the ambient temperature was 40 °C. When the outdoor conditions were close to the thermal comfort temperature of 23 °C inside the cabin, the total energy consumption and the range deformation rates were reduced to less than 10%.

This paper proposes a integrated starter and generator(ISG) braking control method considering battery condition. Recently, hybrid brake which combines mechanical brake and electric brake causes from ISG regenerative operation has been researched in automotive application for efficient and fast braking operation. However, previous works for hybrid brake are focus on braking performance improvement, this can deteriorate the current flowing through battery, it can cause reducing the battery life time. In order to improve this, another previous works use DC/DC converter and ultracapacitor to regulate the battery current. However, it increases cost and size. This paper firstly analyze the DC-link current ripple of inverter drive. After that, analyze the affection of this current ripple during in constant current (CC) and constant voltage control (CV) operation. Finally, a novel regenerative torque control method of ISG considering battery condition will be illustrated. Proposed control method is verified by experimental results.

In this study, an electrical vehicle model, which has 75kW AC asynchronous engine and 25kW NiCad batteries, has been composed by means of (ADVISOR-Advanced Vehicle Simulator) programme. During the driving cycle formed for the designed electrical vehicle, charging state of batteries, braking losses, battery temperatures and fuel consumption have been analysed at different road slopes. The study has shown that power to batteries is provided by regenerative braking at all slopes and if the slope is downside, more energy is stored into batteries due to regenerative braking. In simulation of the modelled device, maximum brake power loss of 4.43 kW has decreased at road slope of ∝=-%1.5 by means of regenerative recovery. At road slope of α=-4.5, the highest charging level has been obtained as 99.1%.

The rapid development in the field of electric vehicles requires a careful evaluation of the design process. The presence of a simulation model of the electric vehicle can effectively detect many faulty areas during the development process without risks. The MATLAB and Simulink environment is considered one of the most important tools used in the simulation process. In this paper, we will present the model of electric car used for transporting postal parcels (postal cars). The model includes simulating the operation of a permanent magnet synchronous electric motor. We will assume that the car is moving according to a driving cycle. The results will show the torque forces required to achieve the required speed. We will further calculate the traction and the resistance forces during the driving cycle and the engine efficiency in addition. Perhaps the most important problem facing electric car designers is calculating the amount of energy consumed from the battery or hydrogen fuel, and this is what was achieved as the result of the simulation process in this research. In the end, use one of the artificial intelligence tools (fuzzy controller) to improve battery life by providing the electric car driver with an alert system that will increase the ability to monitor the battery condition and thus increase battery life. The benefit of this paper emerges in realizing the importance of modeling and using simulation using artificial intelligence in developing the design of the electric car, specially the electric motor and battery size, and thus achieving one of the most important goals of the United Nations of preserving the environment and reducing carbon emissions., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Ghaeth fandi reports financial support, administrative support, and article publishing charges were provided by 10.13039/100007655Czech Technical University in Prague. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

For electric vehicle (EV) battery chargers, inductive power transfer (IPT) has become popular day by day due to its features such as being safe, comfortable and weather proof. The constant current (CC) and the constant voltage (CV) charge control modes are important for high-efficiency charging and long-life use of Lithium-ion (Li-ion) batteries commonly used in EVs. However, IPT method requires a wide range of operating frequency in order to provide CC/CV charge control modes. In IPT applications, CC and CV charge control modes are mainly achieved with dc-dc circuits using compensation networks at the transmitter and receiver sides. In this study, performances of inductor-capacitor/series compensation and double-sided inductor-capacitor-capacitor compensation topologies are evaluated based on CC/CV charge control modes. The analytical evaluation is presented in terms of voltage and current regulations during the entire charge control period. Finally, presented analytical evaluation is confirmed with ANSYS software providing field-electric common simulation to predict real response of compensation topologies. In the simulation work, both compensation topologies are operated for the maximum 2.5 kW output power and at the 250 V-450 V output voltage range. [ABSTRACT FROM AUTHOR]

In this paper, we propose a neural network model to converge the marine electric propulsion system and deep learning algorithm to predict the DC/DC converter output current in the electric propulsion regeneration system and to predict the battery charge during regeneration. In order to experiment with the proposed neural network, the input voltage and current of the PCM were measured and the data set was secured on the prototype PCM board. In addition, in order to improve the learning results in the insufficient data set, the scale of the data set was increased through data fitting and its learning was executed further. After learning, the difference between the data prediction result of the neural network model and the actual measurement data was compared. The proposed neural network model effectively showed the prediction of battery charge according to changes in input voltage and current. In addition, by predicting the characteristic change of the analog circuit constituting the DC/DC converter through a neural network, it is determined that the characteristics of the analog circuit should be considered when designing the regeneration system. [ABSTRACT FROM AUTHOR]

The digital level (SOKKIA SDL-30) and total station (SOKKIA CX-105) are highlighted as examples of digital surveying instruments. In this study, it is clarified how some surrounding factors can affect their efficiency such as vibration, sunlight, and its direction, temperature, and battery capacity. Through vibration testing as one of these factors,both the digital level and the total station using the reflector-less were exposed to vibration up to 25 KHZ/Sec, the average and the standard deviations elevation error for the digital level increased by 2 times at 80 m distance, and the average and the standard deviations positioning error increased 3 times for the total station using the reflector-less total at 80 m distance between the instrument and bar code. 64.3% and 51.25% are the increasing ratios in average error for digital level and total station with reflector-less prism by testing the effect of sunlight and its position. The increase in temperature leads to an increase in the average error with 150 % and 66.6% for digital level and reflectorless total station. Finally, the lack of battery efficiency led to a shortage of accuracy of the Surveying Instruments. [ABSTRACT FROM AUTHOR]

In this study, a thermoelectric recovery system was designed to convert the exhaust waste heat of an internal combustion diesel engine directly to electric power and the performance was measured at different engine speeds in the unloaded state. The performances of two different thermoelectric generators were compared in a system designed using four modules. Maximum 0.92 W power was obtained for four modules at 3500 r/min, at an area of 0.0016 m2. Internal resistance of modules has increased according to the engine speed. The highest internal resistance obtained during the experiments is 11.69 Ω at engine speed of 3500 r/min. The characteristics of the overall thermoelectric generator performance is coherent with the analysis model. In the current graph according to engine speed, the maximum absolute error is calculated for modules TEG 12-8 and TEG1-199 as 0.010 and 0.044, respectively (at experimented 3500 r/min). To charge the battery under maximum power point conditions, 133 thermoelectric modules were required (TEG1-199). Maximum power transfer is obtained when the load resistor is connected in parallel at 10 Ω. It is seen that modular structure thermoelectric generators are more important alternative than Rankine cycle system in terms of waste heat recovery, despite thermoelectric system has low efficiency. [ABSTRACT FROM AUTHOR]

In this paper, the case of a battery charger for electric vehicles based on a wireless power transmission is addressed. The specificity of every stage of the overall system is presented. Based on calculated and measured results, relevant capacitive compensations of the transformer and models are suggested and discussed in order to best match the operating mode and aiming at simplifying as much as possible the control and the electronics of the charger.

Significant growth in residential solar photovoltaic (PV) installations and the ongoing decline in battery costs have increased interest in household solar battery energy storage projects in Finland in recent years. Among various potential applications, considerable attention is drawn to the use of the battery energy storage system (BESS) for the purpose of the households’ self-consumption enhancement. This study presents the results of a techno-economic study of the LiFePO(4)-based battery storage added to residential roof-top PV installations in Finland to maximise self-utilisation of on-site solar energy generation. Using a comprehensive DC model of BESS, the battery charge and discharge levels under the typical household load and PV production conditions are simulated. The estimates of the average retail electricity prices are used to assess the value that the customer-sited solar battery storage can provide to the household end-users in Finland. The economic attractiveness of the battery storage projects is evaluated considering the present and forecasted BESS costs and the electricity tariff levels in Finland and the conditions for profitable operation of the solar energy storage systems are determined.

Network survivability is an attribute that network is continually available even if a communication failure occurs, and is regarded as one of the most important concepts to design dependable computer networks. In the existing work, a power-aware mobile ad hoc network (MANET) is described by a Markov regenerative process, and takes account of the variability in power level, which is caused by the possible low-battery state in each communication node. However, it implicitly ignores effects by the so-called border effects, and lacks the reality in modeling. In this paper, we revisit a power-aware MANET model taking account of border effects and quantify the network survivability more accurately.