Charging Profile Research Articles

Challenges and Opportunities in Electric Vehicle Charging: Harnessing Solar Photovoltaic Surpluses for Demand-Side Management

The recharging of electric vehicles will undoubtedly entail an increase in demand. Traditionally, efforts have been made to shift their recharging to off-peak hours of the consumption curve, where energy demand is lower, typically during nighttime hours. However, the introduction of photovoltaic solar energy presents a new scenario to consider when synchronizing generation and demand curves. High-generation surpluses are expected during the central day hours, due to the significant contribution of this generation; these surpluses could be utilized for electric vehicle recharging. Hence, these demand-side management analyses present important challenges for electricity systems and markets. This research explores this overdemand avenue and presents a method for determining the ideal recharge curve of the electric vehicle. Consequently, with this objective of maximizing photovoltaic generation to cover as much of the foreseeable demand for electric vehicles as possible in future scenarios of the electrification of the economy, the six fundamental electric vehicle charging profiles have been analyzed. A practical scenario for 2040 is projected for the Canary Islands, estimating the potential levels of demand-side management and associated coverage. The coverage ranges from less than 20% to over 40%, considering the absence of demand-side management measures and the maximum displacement achievable through such measures.

Charge Scheduling of Electric Vehicle Fleets: Maximizing Battery Remaining Useful Life Using Machine Learning Models

Reducing greenhouse emissions can be done via the electrification of the transport industry. However, there are challenges related to the electrification such as the lifetime of vehicle batteries as well as limitations on the charging possibilities. To cope with some of these challenges, a charge scheduling method for fleets of electric vehicles is presented. Such a method assigns the charging moments (i.e., schedules) of fleets that have more vehicles than chargers. While doing the assignation, the method also maximizes the total Remaining Useful Life (RUL) of all the vehicle batteries. The method consists of two optimization algorithms. The first optimization algorithm determines charging profiles (i.e., charging current vs time) for individual vehicles. The second algorithm finds the charging schedule (i.e., the order in which vehicles are connected to a charger) that maximizes the RUL in the batteries of the entire fleet. To reduce the computational effort of predicting the battery RUL, the method uses a Machine Learning (ML) model. Such a model predicts the RUL of an individual battery while taking into account common stress factors and fabrication-related differences per battery. Simulation results show that charging a single vehicle as late as possible maximizes the RUL of that single vehicle, due to the lower battery degradation. Simulations also show that the ML model accurately predicts the RUL, while taking into account fabrication-related variability in the battery. Additionally, it was shown that this method schedules the charging moments of a fleet, leading to an increased total RUL of all the batteries in the vehicle fleet.

Multi-objective optimization estimation of state of health for lithium-ion battery based on constant current charging profile

The capacity decline, i.e., the state of health trajectory of lithium-ion battery is strongly nonlinear and volatile. Currently, most studies on the state of health assessment focus more on improving the estimation accuracy, rarely taking result stability into consideration. In this paper, a method adopting the multi-objective optimization extreme learning machine for estimating the state of health of lithium-ion battery based on the constant-current charging curve of the battery is proposed to improve both the estimation accuracy and stability. Firstly, apart from isovoltage rise charging time, a logit polynomial fitting model is taken to fit the constant-current charging curve to extract the features which highly associated with battery capacity level and proven by the Spearman coefficient. Then, the MOWOA-ELM prediction model is built for state of health estimation where the multi-objective whale search algorithm improved by non-dominated sorting and congestion calculation is used to optimize the hyperparameters of the extreme learning machine. Finally, the experimental and comparative results show that the root mean square error and the standard deviation of the state of health assessment results are only 0.43 % and 0.28 % respectively, demonstrating the feasibility and validity of the framework built in this paper.

A decentralized optimization approach to the power management of electric vehicles parking lots

To reduce the transportation sector's environmental impact, a transition to electric mobility is being witnessed worldwide. However, to avoid any issues on the grid, the charging process of electric vehicles (EVs) must be carefully managed. One valid solution to this problem is aggregating EVs and managing their collective charge and discharge. Parking lots equipped with charging stations are likely to become more prevalent in the future. Careful management of such infrastructures is essential to avoid local overloads and to provide flexibility to the electrical grid. In this context, this paper formalizes an optimization model for the optimal power management of a smart PL, with the objective of minimizing the power withdrawn from the grid while considering customers’ needs. The main novelties regard how the PL is modeled and how the solution to the optimization problem is achieved. In fact, as far as the modeling is concerned, both single- and three-phase EVs are considered, and batteries are modeled via a non-linear charging profile. As regards the solution method, an augmented Lagrangian-based decentralized model predictive control technique is proposed. The adopted algorithm has been tested on a smart PL in a Genova Municipality (Italy) and its performance has been evaluated by implementing two different scenarios: in the first one only 5 EVs are considered, while the second one regards a scalability analysis where the number of served EVs is increased to 40. The conducted tests confirm that the proposed algorithm can provide a computationally efficient solution, while preserving an accurate model.

Accuracy comparison and improvement for state of health estimation of lithium-ion battery based on random partial recharges and feature engineering

State of health (SOH) estimation of e-mobilities operated in real and dynamic conditions is essential and challenging. Most of existing estimations are based on a fixed constant current charging and discharging aging profiles, which overlooked the fact that the charging and discharging profiles are random and not complete in real application. This work investigates the influence of feature engineering on the accuracy of different machine learning (ML)-based SOH estimations acting on different recharging sub-profiles where a realistic battery mission profile is considered. Fifteen features were extracted from the battery partial recharging profiles, considering different factors such as starting voltage values, charge amount, and charging sliding windows. Then, features were selected based on a feature selection pipeline consisting of filtering and supervised ML-based subset selection. Multiple linear regression (MLR), Gaussian process regression (GPR), and support vector regression (SVR) were applied to estimate SOH, and root mean square error (RMSE) was used to evaluate and compare the estimation performance. The results showed that the feature selection pipeline can improve SOH estimation accuracy by 55.05%, 2.57%, and 2.82% for MLR, GPR and SVR respectively. It was demonstrated that the estimation based on partial charging profiles with lower starting voltage, large charge, and large sliding window size is more likely to achieve higher accuracy. This work hopes to give some insights into the supervised ML-based feature engineering acting on random partial recharges on SOH estimation performance and tries to fill the gap of effective SOH estimation between theoretical study and real dynamic application.

A Constant-Power and Optimal-Transfer-Efficiency Wireless Inductive Power Transfer Converter for Battery Charger

To improve the battery-charging rate while alleviating its aging problem, it's important to vitalize constant power (CP) charging with respect to the traditional constant current (CC) charging. Herein a single-stage inductive-power-transfer converter is proposed for both wireless CP charging and optimal transfer efficiency. Specifically, this system adopts an inductor-capacitor-capacitor-series (LCC-S) compensation topology, and a semi-active rectifier (SAR) at the receiver side, without involving any DC-DC converter(s) or switch-controlled capacitor circuits. The load-independent transfer characteristic and system parameter design freedom are introduced by the LCC-S compensation topology, while the conditions of soft-switching are analyzed. On this basis, a novel control strategy using communication-free pulse-density-modulation based SAR regulation is identified and implemented, such that it directly regulates the output to comply with the CP charging profile and modulates the optimal equivalent load throughout the charging process. However, a 230-W experimental platform is built to verify the performance of the proposed system, and then, experimental results demonstrate a CP output with a maximum efficiency of 89.8%, and also, the efficiency can be maintained at the optimal level throughout the charging profile. Furthermore, the efficiency remains at 88.6% even at 16% dislocation.

Optimization of Electric Bus Charging: Integrating Discrete Event Modeling in Public Transportation Systems

The transition to electric transportation is gaining more and more importance in the last years leading to new challenges to be addressed. A key role in this framework is represented by public transportation systems. This paper introduces a Discrete Event (DE) optimization model to optimize the charging scheduling of electric buses (EBs). By accounting for time tabling constraints, energy demand satisfaction, and the capability for simultaneous multi-socket charging, our model addresses the complexities inherent in managing EB fleets. Utilizing a periodic model to capture cyclical system behavior, we incorporate a detailed battery model reflecting nonlinear charging profiles to enhance accuracy in power requirement prediction and charging duration estimation. Validation of our model is performed using real-world data, demonstrating its practical applicability.

Optimal Unit Commitment and Generation Scheduling of Integrated Power System with Plug-In Electric Vehicles and Renewable Energy Sources

The integration of wind energy sources and plug-in electric vehicles is essential for the efficient planning, reliability, and operation of modern electric power systems. Minimizing the overall operational cost of integrated power systems while dealing with wind energy sources and plug-in electric vehicles in integrated power systems using a chaotic zebra optimization algorithm (CZOA) is described. The proposed system deals with a probabilistic forecasting system for wind power generation and a realistic plug-in electric vehicle charging profile based on travel patterns and infrastructure characteristics. The objective is to identify the optimal scheduling and committed status of the generating unit for thermal and wind power generation while considering the system power demand, charging, and discharging of electric vehicles, as well as power available from wind energy sources. The proposed CZOA adeptly tackles the intricacies of the unit commitment problem by seamlessly integrating scheduling and the unit’s committed status, thereby enabling highly effective optimization. The proposed algorithm is tested for 10-, 20-, and 40-generating unit systems. The empirical findings pertaining to the 10-unit system indicate that the amalgamation of a thermal generating unit system with plug-in electric vehicles yields a 0.84% reduction in total generation cost. Furthermore, integrating the same system with a wind energy source results in a substantial 12.71% cost saving. Notably, the integration of the thermal generating system with both plug-in electric vehicles and a wind energy source leads to an even more pronounced overall cost reduction of 13.05%. The outcome of this study reveals competitive test results for 20- and 40-generating unit systems and contributes to the advancement of sustainable and reliable power systems, fostering the transition towards a greener energy future.

Accelerated Aging of Lithium-Ion Batteries: Insights from Nonlinear Frequency Response Analysis

Lithium-ion batteries (LiBs) are considered the leading contender in facilitating the global shift from fossil-fuel based to electric mobility. Accurately evaluating and monitoring the state-of-health (SoH) of these batteries is essential to ensure safe operation, including the early identification of accelerated aging. In this work, accelerated aging is defined as the step drop in the capacity fade, which usually occurs at the late ageing stage. This ageing behaviour is detrimental and is influenced by various factors such as charging and discharging rate, depth-of-discharge (DoD), cycling range, and operating temperature.Electrochemical impedance spectroscopy (EIS) is a common method of aging characterization in LiBs. Complementary to EIS, nonlinear frequency response analysis (NFRA) has proven useful in aging studies, such as detecting lithium plating [1] and predicting SoH [2,3]. Via NFRA, a cell is subjected to sinusoidal signals of high current amplitudes, and the response voltage signal is analyzed [4]. By analyzing the nonlinear response of the cell, we can obtain information about processes that are not easily observable using conventional EIS. Here, we propose the combination of both frequency response analyses to study the aging behaviour in LiBs.This study investigates the impact of DoD and cycling range on commercial 18650 cells with NMC as cathode and Si-C as anode. The aging test was performed at 45 °C with 1.33C constant current – constant voltage (cut-off current at C/10) charging and discharge profile. At defined cycling intervals, we captured the instantaneous cell behavior (discharge curve, EIS, NFRA) at 23 °C. We observed larger capacity fade for cells cycled with larger DoD. For cells with the same DoD, SoH fade was more significant for cells cycled at a higher state-of-charge (SoC) range. Via frequency response analysis, we identified a mutual correlation between the kinetic losses of the charge transfer process and SoH fade, which is similar to our prior study [2]. In addition, we found out that the onset of accelerated aging can be pinpointed by the increase in ohmic resistance in EIS. Notably, analyzing the nonlinear response (second Y2 and third Y3 harmonic) of the cells showed that Y3/Y2 can capture the different aging stages (see Figure 1), indicating that not only the nonlinear behaviour but also the nature of the charge transfer process itself has constantly changed during ageing. This compelling finding highlights the potential of NFRA for low-cost offline SoH diagnosis of LiBs.

Real-time vehicle relocation and staff rebalancing problem for electric and shared vehicle systems

ABSTRACT This study addresses the challenging real-time vehicle relocation and staff rebalancing (RT-VR&SR) problem in electric carsharing services. The complexity arises from ad-hoc demand, charging requirements of electric vehicles (EVs), and staff scheduling constraints. The problem aims to maximize the profit of carsharing operators by determining strategies for vehicle relocation, vehicle charging, and staff rebalancing in real-time. It considers the uncertainty of demand and the practical nonlinear charging profile of EVs. We formulate this problem as a Markov Decision Process (MDP), and propose an efficient concurrent-scheduler-based policy. Numerical experiments are conducted to demonstrate the effectiveness of the proposed policy and methodology. The results show that the proposed policy significantly improves service level and profitability compared to a benchmark policy. It is also found that ignoring staff rebalancing in decision making can lead to overestimation of service level and profitability. In conclusion, this study presents a real-time solution for vehicle relocation and staff rebalancing in one-way electric carsharing services. The proposed policy and methodology improve performance and highlight the importance of considering staff rebalancing in decision making.

Comparative techno-economic assessment of electric vehicle charging preferences: A quantitative study for sustainable EV policy in Pakistan

Sustainable induction of Electric vehicles (EV) adoption in developing countries requires a comprehensive policy and planning. Among several challenges, one of the biggest challenges is a sustainable charging station infrastructure integration to the existing weak distribution power network. Based on technological advancements, EV owners enjoy better milage in one charge. This has given a liberty to charge EVs as per the desire of user. There are two kinds of charging preferences, one is home charging and the other is fast charging. Both charging choices have distinct economic and technical aspects that need to be quantified in line with the available power infrastructure so as to develop a policy regulation. This study aims to quantitatively assess home charging, level 3 fast charging and their techno-economic aspects using social demographics of car owners. To analyze the demographics of local vehicle mobility practices in Pakistan, a comprehensive survey comprising online questionnaire was conducted. >100 car owners were surveyed about their daily driving practices to understand the driving distances and their preference of charging vehicles. The driving owners from distinct occupations ranging from cab drivers, job holders and businessmen participated in the survey. The survey facilitated to identify the demographics of the car owners which helped in conducting simulations and quantification analysis. Monte Carlo simulation was used for 1000 cars samples to estimate the level 1 and level 3 charging profiles in Python. In second step, HOMER Grid software was used to economically assess the impact of home charging and fast charging. It was revealed that charging choices have significant impact on the daily load profile of EVs. While having home charging, 59.9 % higher units are purchased from the grid. Whereas 65.5 NPC is increased for the project. On the other hand, the daily profile of preferred home charging is 61.4 % higher compared to the fast charging due to much concentrated charging impact during the limited time of the day. Results highlighted that Solar PV plays an important role for fast and home charging. Without solar PV charging stations will consume 60 % more grid units and 58 % higher Net present cost. The study concludes with a set of policy recommendations that must be taken into account to support the weak grid and lessen the EV charging impact on main utility.

Optimizing mechanical compression for cycle life and irreversible swelling of high energy and high power lithium-ion pouch cells

Efficient cell packaging is crucial to increase the battery energy density and the driving range of modern electric vehicles. However, mechanical compression of the cells during pack assembly has a significant impact on cycle life and cell swelling of the cells. Therefore, this study follows the research question how lithium-ion (li-ion) pouch cells should be integrated, and at which pressure level, in order to optimize cycle life and to reduce irreversible swelling.A high precision compression test bench was utilized to cycle 5Ah NMC622/graphite li-ion pouch cells at 0.075MPa, 0.2MPa, 0.5MPa, 1.0MPa, and 1.75MPa flexible compression at constant pressure and rigid compression at constant cell thickness, respectively. To obtain transferable results, a high energy (HE) and a high power (HP) cell configuration were investigated. The cells were stressed with individual multi-step constant current (MSCC) charging profiles for each cell type, representing electric vehicle (EV) usage profiles with frequent fast charging. After cycling, post-mortem analysis was conducted at 80% capacity retention to evaluate individual electrode aging.In comparison between flexible compression and rigid compression, flexible compression achieved on average 19% increase in cycle life until 80% capacity retention. It was observed that compression could reduce deterioration of electrical contact in the cathode. Simultaneously, higher compression increased the overpotential on the anode during charging and thereby increased the risk of lithium plating. Ranging from 0.2MPa to 0.5MPa flexible compression, cycle life of the HE cells increased by up to 25% in comparison to the lowest compression. In addition, cells reached around double equivalent full cycles (EFC) before reaching 150% DCIR. The irreversible swelling, normalized to the uncompressed cell thickness at begin of life (BoL), was reduced from around 3.2% at the lowest compression to 2.1% at moderate compression. As a result, flexible compression between 0.2MPa to 0.5MPa is recommended for both cell types as a trade-off optimizing capacity fade, direct current internal resistance (DCIR) increase, and irreversible swelling during cycle life.

Coordinated pricing mechanism for parking clusters considering interval-guided uncertainty-aware strategies

With the increasing penetration of electric vehicles into the existing power networks, the development of proper energy management tools become essential to mitigate the damaging effects of emerging high-power charging modes. To facilitate the management of vehicles, they can be gathered into clusters in order to aggregate their charging profiles. This paradigm is called cluster of vehicles and can be forward developed by gathering a group of parking, thus forming a parking cluster where the characteristics of different parking lots are aggregated. In this paper, a coordinated charging price mechanism for clusters of parking lots is proposed. The new proposal is casted as a game-based framework which seeks for the equilibrium between the cluster coordinator and parking lots, instead of conventional tools where the pricing strategy is decided by a central entity disregarding the interests of users. The developed model is established as a bi-level optimization problem, which is further linearized to be tractable and solvable by off-the-shelf solvers. The implications of vehicle-to-grid are also studied by fully modelling bi-directional power flows in chargers. In addition, two uncertainty-aware strategies are proposed to cope with uncertain energy margins in parking lots. A case study is conducted and various results are analysed and discussed. It is shown that enabling vehicle-to-grid characteristics discloses significant economic benefits for users and the cluster coordinator. In addition, vehicle-to-grid impacts notably on the risk-averse character of the uncertainty-aware strategies proposed. The developed pricing mechanism is also compared with the conventional pricing policies based on key of repartitions, showing that the new proposal is able to reduce the cost for users, avoiding to directly translate the energy cost to charging points.

Design and Analysis of a Peak Time Estimation Framework for Vehicle Occurrences at Solar Photovoltaic and Grid-Based Battery-Swappable Charging Stations

Due to global environmental impacts, the electric vehicle (EV) adoption rate is increasing. However, unlike conventional petrol vehicles, EVs take a considerable time to charge. EVs on the road with different battery charging statuses and driving demographics may cause uncertain peak time arrivals at charging stations. Battery-swappable charging stations are a quick and easier way to replace uncharged batteries with charged ones. However, charging due to uncertain EV arrival causes higher charging profiles posing load to the grid, management of charged and discharged batteries, and peak time charging tariffs. These challenges hinder the wide operation of battery-swappable charging stations. Nevertheless, a pre-assessment of peak hours using EV demographics can reduce congestion. In recent literature surveys for battery-swappable charging stations, spot congestion has not been given much attention, which has a direct influence on the sizing and operation of battery-swappable charging stations. This research study is focused on estimating peak time events using a novel integrated techno-economic assessment framework. A fuzzy-based parametric assessment tool is developed that identifies the factors that influence higher congestion events. Based on the peak event assessment, grid, and solar PV-based generation is optimized using mixed integer linear programming. In the final step, an environment analysis of a swappable charging station is performed. Furthermore, the results achieved using the proposed framework for battery-swappable charging stations (BSCSs) were compared with fast-charging (FC) stations. FC can economically perform well if integrated with solar PV systems; however, the capital cost is 80% greater than the BSCSs designed under the proposed framework. The operational cost of BSCSs is 39% higher than FC stations as they use 29% higher grid units than FC stations due to night operations under congestion.

A two-level charging scheduling method for public electric vehicle charging stations considering heterogeneous demand and nonlinear charging profile

This paper investigates the electric vehicle (EV) charging scheduling problem for public EV charging stations (EVCSs) that can accommodate heterogeneous charging demands, aiming to flatten the aggregate load on the power grid and reduce the peak demand. In contrast to existing works that mainly focus on a single scheduling strategy, a two-level hierarchical charging scheduling method is proposed, which includes an online booking system (OBS) and a pricing-based charging control system (PCCS). Specifically, the implementation of OBS can reduce the service capacity of bookable chargers, whereas PCCS can facilitate the redistribution of charging demand from peak to off-peak time periods. Moreover, the nonlinear charging profile of the battery is incorporated into the model to better reflect the real charging process. A deep reinforcement learning (DRL)-based approach with a discrete Markov decision process (MDP) formulation is designed to find the optimal scheduling solution, where deep Q-network (DQN) and deep deterministic policy gradient (DDPG) are combined to handle the hybrid action space with both discrete and continuous actions. A comprehensive set of experiments is carried out to examine the effectiveness of the developed two-level scheduling scheme. The results indicate that the proposed method improves the scheduling effectiveness compared to single-strategy methods by 6.7% to 49.44%.

MSCC-DRL: Multi-Stage constant current based on deep reinforcement learning for fast charging of lithium ion battery

The growing electric vehicle market has made extreme fast charging (XFC) a critical challenge for the lithium-ion battery community. With a goal of achieving a 15-minute recharge time or less, the research community has developed several protocols for fast charging, relying on accurate electrochemical models and mathematical optimization algorithms. However, the effectiveness of such techniques is severely limited by their high computational costs as well as the uncertainty and complexity of the mathematical algorithms used. To address these limitations, this paper proposes a model-free multistage constant current technique based on deep reinforcement learning (DRL) while ensuring battery safety and reducing the battery degradation rate with the multi-stage charging profile. The proposed technique uses a Proximal Policy Optimization (PPO) algorithm to train the DRL agent to learn a reliable control policy through interaction with the battery, aided by a comprehensive reward function. The proposed model is validated through a comparison with a 6CC-CV profile for fast charging. Furthermore, the robustness of the proposed DRL model against different electrode thicknesses and porosity is also evaluated. Simulation results demonstrate that the proposed DRL framework can charge the battery in 14 min for the thickest cathode and 6 min for the thinnest cathode while maintaining battery safety.

Hosting capacity of distribution networks for controlled and uncontrolled residential EV charging with static and dynamic thermal ratings of network components

AbstractThe ongoing electrification of road transportation sector, which is expected to continue to strongly increase over the next years, will result in the connection of a significant number of electric vehicle (EV) chargers in LV and MV distribution networks, particularly in residential applications with on‐board (“slow”) EV chargers. In order to evaluate loading limits of existing distribution networks for the maximum number of EV chargers that can be safely connected (commonly denoted as a network EV “hosting capacity”, HC), this paper introduces a general approach to determine one commonly used network design parameter (after‐diversity maximum demand, ADMD) and one new parameter (maximum daily energy demand, MDED), which are both obtained from the load profiles of maximum per‐hour demands for uncontrolled residential EV charging. The presented approach uses actual EV charging data from the UK as the inputs in Monte Carlo simulations to generate daily EV charging profiles for arbitrary numbers of EVs, enabling to identify related ADMD, MDED and per‐hour maximum demand values, as well as their seasonal variations. The assessed ADMD, MDED and hourly maximum EV charging demands for uncontrolled EV charging are then combined with available UK residential daily load profiles before the EVs are connected (“pre‐EV demands”), where their combined coincidental and noncoincidental maximum demands are evaluated against the static thermal rating (STR) and dynamic thermal rating (DTR) loading limits of network components (transformers and overhead lines), taking into account relevant weather/ambient conditions. This is denoted as a network HC for uncontrolled EV charging. Finally, evaluating the resulting per‐hour maximum demand values against the STR and DTR loading limits and MDED values allows to select one particular scheduling method for controlled EV charging, which gives the absolute maximum number of EVs that can be safely connected in the considered network, that is, maximum network HC for fully controlled EV charging. The presented approach is illustrated on the example of the IEEE 33‐bus test network (modelled using typical UK network components), for the pre‐EV residential demands taken from the recordings at a UK MV substation, and for ambient data taken from a UK Met Office weather station. Obtained results allow to evaluate the range of network EV HC values for uncontrolled and controlled EV charging, that is, lower and upper HC limits, which can be correlated with the commonly used allocations of the firm and non‐firm network HC, respectively.

Optimization of battery swapping stations with heterogeneity, charging degradation and PV-option

Greenhouse gas emissions-related issues have been extensively discussed in the past years, with over 70 countries already committed to a carbon-neutral economy by 2050. The electrification of transportation modals has increased following these goals, where Electric Vehicles (EVs) are starting to take Internal Combustion Engine Vehicles (ICEV) market share all over the globe. Besides the particular complexity in comparing EVs and ICEVs, challenges involving the nature of EVs and their integration with cities, such as the lack of public locals for charging, are also critical and interfere with their development. In this context, this work aims at studying the problem of a Battery Swapping Station (BSS), a structure where the EVs users swap their depleted batteries for fully or partially charged ones. In order to simulate the BSS daily operations and battery charging schedule, a novel Mixed Integer Linear Programming (MILP) model is proposed, taking into account battery heterogeneity, the use of local photovoltaic (PV) production, and battery degradation based on charging profile. A collection of BSS operation metrics are designed to evaluate the solution quality of the proposed scheduling model. A numerical experiment comprising four case studies based on real data from the US power and transportation systems is presented, with insights and analyses on the PV and grid power use, as well as a BSS financial comparison against close-related benchmark scheduling approaches, together with sensitivity analyses on BSS sizing plan and costumers service. Results highlight the importance of battery degradation in the optimization model, since its consideration as an operational cost brought a reduction of 16%.

Investigation of high-frequency pulse charging profiles with different frequencies, duty cycles and end-of-charge voltages

The topic of fast charging lithium-ion batteries is of great importance, especially with regard to e-mobility. Thereby, fast charging while retaining a long life-time in particular is of special significance. The common charging methods are usually based on a continuous charging current. In this work, a different type of charging process is considered – the so-called pulse charging. The focus is set on high-frequency pulse charging profiles. A variation of the parameters frequency, duty cycle, amplitude current as well as the end-of-charge voltage is investigated and their influence on a commercial high-energy cylindrical cell in 21,700 format is determined. Profiles with a frequency of 100 Hz, 500 Hz and 1 kHz and a duty cycle of 50 %, 70 % and 90 % are analyzed. In addition, end-of-charge voltages from 4.2 V up to 4.4 V are investigated. The continuous current (CC) charging process is used as a reference. A cycle test is also performed with a promising pulse charging profile. The results indicate that the investigated pulse charging profiles cannot be recommended as an innovative charging method. It is true that an acceleration of the charging process is achieved. However, this is due to the fact that less capacity is charged into the cell. Significantly more capacity can be charged into the cell by increasing the end-of-charge voltage. However, lithium plating may occur and there is a strong decrease in capacity during the cycle test.

A CNN-GRU Approach to the Accurate Prediction of Batteries’ Remaining Useful Life from Charging Profiles

Predicting the remaining useful life (RUL) is a pivotal step in ensuring the reliability of lithium-ion batteries (LIBs). In order to enhance the precision and stability of battery RUL prediction, this study introduces an innovative hybrid deep learning model that seamlessly integrates convolutional neural network (CNN) and gated recurrent unit (GRU) architectures. Our primary goal is to significantly improve the accuracy of RUL predictions for LIBs. Our model excels in its predictive capabilities by skillfully extracting intricate features from a diverse array of data sources, including voltage (V), current (I), temperature (T), and capacity. Within this novel architectural design, parallel CNN layers are meticulously crafted to process each input feature individually. This approach enables the extraction of highly pertinent information from multi-channel charging profiles. We subjected our model to rigorous evaluations across three distinct scenarios to validate its effectiveness. When compared to LSTM, GRU, and CNN-LSTM models, our CNN-GRU model showcases a remarkable reduction in root mean square error, mean square error, mean absolute error, and mean absolute percentage error. These results affirm the superior predictive capabilities of our CNN-GRU model, which effectively harnesses the strengths of both CNNs and GRU networks to achieve superior prediction accuracy. This study draws upon NASA data to underscore the outstanding predictive performance of the CNN-GRU model in estimating the RUL of LIBs.