Partial Charging Research Articles

A SOH estimation method of lithium-ion batteries based on partial charging data

Accurate and reliable assessment of lithium-ion battery SOH is critical to improving the safety performance of electric vehicles. In the existing data-driven methods, the models are often complex, the calculation is large and the interpretation ability is not strong, so it is difficult to realize the commercial application. In this paper, a novel SOH estimation method is proposed. Firstly, the incremental capacity (IC) curve is used to extract the aging features from part of the charging data, and a linear battery aging state space equation is established based on the features. The aging state of the battery can be directly observed under the condition of small calculation amount. Second, the aging state of the battery can be tracked and corrected by double Kalman filter to improve estimation accuracy. The performance of the proposed method is verified on CACLE dataset, Oxford dataset and DUT dataset respectively. The estimated errors are all <2.5 %, and the mean RMSE is 0.0066. The results show that the method can be effectively applied to different lithium-ion batteries. In order to further demonstrate the advantages of this estimator, we also compare it with literature methods using the same dataset. In the CACLE data set and Oxford data, the average time of this method is 0.848 s and the average RMSE is 0.0050, which is in a dominant position. The proposed method provides a simple and feasible way to estimate the SOH of electric vehicles.

Smart home power management algorithm using real-time model predictive control for a stand-alone PV system with battery energy storage

A smart home power management system is critical for stand-alone home-photovoltaic (HPV) with battery energy storage. Existing approaches often focus on maximizing power extraction from PV systems without considering real-time power adjustments or battery state of charge (SoC), which can lead to over-current or over-voltage issues that damage the battery. To address these limitations, this paper proposes a home power management algorithm that dynamically adjusts the power flow between the PV system, home loads, and the battery based on real-time system power measurements and battery SoC. This dynamic adjustment ensures an uninterrupted power supply to the home loads while maintaining battery safety. The proposed home manager algorithm features multiple operating modes: Maximum power point (MPP) charging, partial charging, fast charging, float charging, and partial discharging. Each mode is integrated with its dedicated model predictive controller (MPC) to achieve its specific control objective. Finally, through a semi-experimental simulation using a process-in-the-loop (PIL) test approach with the embedded board eZdsp TMS320F28335, testing under various irradiance levels shows that the home manager achieves significant improvement over conventional approaches. For instance, over the MPP mode it achieve a power efficiency of 99.36% with a 2.05% SoC increase, while fast charging mode resulted in 87.44% efficiency with an 11.2% SoC increase. However, float charging mode maintained an 87.27% efficiency with a 2.6% SoC increase, and partial discharging led to a 1.3% SoC decrease. The overall battery efficiency reached 96.45%, demonstrating the algorithm’s ability to optimize power flow, ensure a reliable energy supply, and maintain battery safety under varying weather conditions.

Enhancing data-driven-based state of health estimation for diverse battery applications through effective feature construction

Data-driven-based State of Health (SOH) estimation methods are popular for their ability to avoid considering the battery's complex aging mechanisms. However, aging features extracted from specific voltage intervals and the SOH estimation models established under specific conditions may no longer be applicable to other battery applications. This work first reveals the necessary conditions for constructing effective aging features in a partial voltage interval, and specifically proposes an applicable method for determining the voltage interval of constructing aging features based on the incremental capacity curve and the voltage curve's shape. The proposed method can be adapted to different battery applications and is suitable for partial charging conditions. Given the different aging patterns present in different battery types and applications, a feature self-scaling -based online learning strategy is developed to reduce the model's reliance on aging datasets and enable it to adapt to real-time battery aging patterns. The proposed methods are validated through both an actual aging experiment and three publicly available aging datasets, showcasing their effectiveness in extending the applicability of the proposed SOH estimation methods for diverse battery applications.

A Novel Tourist Trip Design Problem with Stochastic Travel Times and Partial Charging for Battery Electric Vehicles

This study proposes a novel mathematical model for the Multi-Day Tourist Trip Design Problem with Stochastic Travel Time and Partial Charging for Battery Electric Vehicle (MD-TTDP-STT-PCBEV). To the best of our knowledge, no prior study has fully incorporated the use of BEVs into TTDP models. Given the limited driving range of BEVs, the model requires decisions regarding the locations and policy for recharging the vehicle’s battery. The problem also incorporates real-world uncertainty by considering travel time as a random variable subjected to normal distribution. The model is formulated using chance-constraint programming, aiming to find optimal tourist routes for BEVs that maximize tourist satisfaction. Numerical experiments were conducted to compare solutions between stochastic and deterministic environments. Computational experiments using the LINGO optimization solver demonstrated that the total rating scores obtained from the stochastic model with chance-constraint programming were generally lower than those from the deterministic model due to travel time uncertainties. These results highlight the importance of incorporating real-world uncertainty and variability to achieve more accurate and reliable planning.

A Simulated Annealing Approach to the Scheduling of Battery-Electric Bus Charging

With an increasing adoption of battery-electric bus (BEB) fleets, developing a reliable charging schedule is vital to a successful migration from their fossil fuel counterparts. In this paper, a simulated annealing (SA) implementation is developed for a charge scheduling framework for a fixed-schedule fleet of BEBs that utilizes a proportional battery dynamics model, accounts for multiple charger types, allows partial charging, and further considers the total energy consumed by the schedule as well as peak power use. Two generation mechanisms are implemented for the SA algorithm, denoted as the “quick” and “heuristic” implementations, respectively. The model validity is demonstrated by utilizing a set of routes sampled from the Utah Transit Authority (UTA) and comparing the results against two other models: the BPAP and the Qin-Modified. The results presented show that both SA techniques offer a means of generating operationally feasible schedules quickly while minimizing the cost of operation and considering battery health.

Need of voltage and capacity profile reconstruction in incremental capacity analysis for second life batteries from repurposer perspective

Incremental Capacity Analysis (ICA) is a widely used non-destructive technique to predict State of Health (SoH) of Lithium-ion Battery (LiB). Till today, the SoH is judged on ICA magnitude of peaks and valleys for defining the health indicators. This research work provides the state-of-the art in voltage and capacity profile reconstruction available for ICA through Constant Capacity (CC), Constant Voltage (CV), and hybrid sampling. Here, hybrid sampling offers flexibility for the repurposer to define voltage bands for partial charge and discharge limits according to ICA peaks. This research work aims to address the scope of reducing time and efforts on partial charging and discharging tests conducted by the repurposers for SoH and Remaining useful Life (RuL) estimation of Second Life Battery (SLB). Nissan Leaf battery 2011 model taken from EV application is tested for health indicators catering insights on SoH and RuL by reconstructing the voltage and capacity profile. The necessity of a filter can be neglected as a part of ICA post-processing. The peaks and valleys from reconstructed profile is aiding the repurposer to define voltage limits for partial charging and discharging based on SoH. This led to reduction in testing time and efforts in the range of 16% to 83% during SLB characterization. The accuracy of this technique is validated by varying the sampling frequencies ranging from 0.1 Ah, 0.01 Ah and 0.001 Ah before proceeding with reconstruction. Sandia National Laboratories open source data is considered for verifying the proof of concept. Pearson correlation coefficient is deployed to differentiate strong and weak SLB based on sampled data. The actual data obtained from battery cycler fails to do the same and hence, the sampled data aids repurposer in reducing the charging and discharging time of SLB through narrowed voltage bands.

Co-optimizing electric bus dispatching and charging considering limited resources and battery degradation

This paper aims to formulate a mathematical model for a multi-type electric bus scheduling problem to determine the optimal fleet composition, bus-to-trip assignment, and partial charging schedule, where the battery degradation, nonlinear charging, and the constraint of charging station capacity are considered. A time-expanded network is proposed to represent the bus-to-trip assignment and partial charging. An adaptive large neighborhood search algorithm is designed to solve the problem. Using a multi-line bus network in Nanjing as the case, empirical operational data is used to generate monthly timetable samples to simulate the uncertainty of trip travel time and energy consumption. The result shows that the charging station capacity can be reduced from 20 (real-world case) to 12, considering the cost-effectiveness and robustness of the bus system. The result of this study also provides suggestions on the charging duration choices and the starting state-of-charge for different periods of the day. In peak and off-peak hours, 20-30-minute charging is recommended for electric buses with state-of-charge lower than 30 %, and 10-minute charging is more recommended when the state-of-charge of the electric bus is between 30 % and 70 %.

Optimal electric bus scheduling considering battery degradation effect and charging facility capacity

ABSTRACT Electric buses are environmentally friendly with the features of low noise levels and zero-emissions. However, higher upfront costs due to the battery degradation effect, charging facility capacity and operational issues are the main obstacles to large-scale application of electric buses. This study proposes a mixed-integer nonlinear model for optimizing electric bus scheduling with a partial charging strategy, battery degradation and constraints of charging facility capacity. A genetic algorithm is then proposed to solve the model. Last, a case study based on a real transit network in Nanjing, China is conducted. The experimental results show that compared with the existing scheduling scheme, the optimal scheduling model can reduce the total system costs by 8.20%, which validates the effectiveness of the proposed model. The findings in this paper can provide a reference for operators to formulate electric bus scheduling schemes and promote the sustainable development of public transport.

Visualization experiment on the ignition performance and flame-spreading in different charging structures

In order to investigate the influence of different charging structures on the ignition performance and its influence on the particle movement before and after the film rupture of the chamber, a visualization experiment platform of the chamber was designed and built, and the stage tests of the ignition of the full particle charging, partial particle charging, mixed before and after the rod-particle charging and the full rod charging were carried out. The experiment shows that the free space will cause the flame propagation speed to increase significantly, from 77 m/s in the charge area to 575 m/s, and there will be pressure fluctuations in several areas of the chamber. The ignition performance of the rod is obviously better than that of the granular, and the pressure fluctuation in the chamber is smaller, the peak ignition gas pressure in the rod charge structure is only 50 % of that in the granular charge structure (1.6 MPa:3.2 MPa), and the maximum pressure difference is only 20 % of that in the granular propellant structure (0.5 MPa:2.5 MPa). The rod-particle mixed charge bed has good ignition consistency in the rod charge area, and when it reaches the granular charge area, the flame will be significantly hindered. The rod basically stays still during the ignition process in the chamber, while the granular will move with the gas flow when there is free space, and after the film is broken, the particle movement is faster than that before the film is broken.

A DRL-based Partial Charging Algorithm for Wireless Rechargeable Sensor Networks

Breakthroughs in Wireless Energy Transfer technologies have revitalized Wireless Rechargeable Sensor Networks. However, how to schedule mobile chargers rationally has been quite a tricky problem. Most of the current work does not consider the variability of scenarios and how many mobile chargers should be scheduled as the most appropriate for each dispatch. At the same time, the focus of most work on the mobile charger scheduling problem has always been on reducing the number of dead nodes, and the most critical metric of network performance, packet arrival rate, is relatively neglected. In this article, we develop a DRL-based Partial Charging algorithm. Based on the number and urgency of charging requests, we classify charging requests into four scenarios. And for each scenario, we design a corresponding request allocation algorithm. Then, a Deep Reinforcement Learning algorithm is employed to train a decision model using environmental information to select which request allocation algorithm is optimal for the current scenario. After the allocation of charging requests is confirmed, to improve the Quality of Service, i.e., the packet arrival rate of the entire network, a partial charging scheduling algorithm is designed to maximize the total charging duration of nodes in the ideal state while ensuring that all charging requests are completed. In addition, we analyze the traffic information of the nodes and use the Analytic Hierarchy Process to determine the importance of the nodes to compensate for the inaccurate estimation of the node’s remaining lifetime in realistic scenarios. Simulation results show that our proposed algorithm outperforms the existing algorithms regarding the number of alive nodes and packet arrival rate.

Capacity estimation for series-connected battery pack based on partial charging voltage curve segments

Accurate estimation of battery pack capacity is crucial in determining electric vehicle driving range and providing valuable suggestions for battery health management. This article proposes an improved capacity co-estimation framework for cells and battery pack using partial charging process. The transformation characteristics of cell capacity difference within the battery pack on the external voltage curve are discussed. The charging voltage curve is segmented according to the feature points extracted from battery incremental capacity curve, and the linear correlation between the voltage curve segments and capacity decline is analyzed to obtain the optimal transformation part, which is verified through a series of cycle aging data. Subsequently, by optimizing the transformation coefficients, we achieve capacity estimation for each cell within the battery pack utilizing only a partial charging voltage profile at approximately 30 % SOC. We further establish a connection between the battery pack and its series cells to enable pack capacity estimation. The proposed method is verified based on two sets of battery pack tests comprising 60 cells in series and with severe capacity inconsistency. The results show that the estimation errors of cells are <2 % and the errors of battery packs are <1 %.

Transfer learning from synthetic data for open-circuit voltage curve reconstruction and state of health estimation of lithium-ion batteries from partial charging segments

Data-driven models for battery state estimation require extensive experimental training data, which may not be available or suitable for specific tasks like open-circuit voltage (OCV) reconstruction and subsequent state of health (SOH) estimation. This study addresses this issue by developing a transfer-learning-based OCV reconstruction model using a temporal convolutional long short-term memory (TCN-LSTM) network trained on synthetic data from an automotive nickel cobalt aluminium oxide (NCA) cell generated through a mechanistic model approach. The data consists of voltage curves at constant temperature, C-rates between C/30 to 1C, and a SOH-range from 70% to 100%. The model is refined via Bayesian optimization and then applied to four use cases with reduced experimental nickel manganese cobalt oxide (NMC) cell training data for higher use cases. The TL models’ performances are compared with models trained solely on experimental data, focusing on different C-rates and voltage windows. The results demonstrate that the OCV reconstruction mean absolute error (MAE) within the average battery electric vehicle (BEV) home charging window (30% to 85% state of charge (SOC)) is less than 22mV for the first three use cases across all C-rates. The SOH estimated from the reconstructed OCV exhibits an mean absolute percentage error (MAPE) below 2.2% for these cases. The study further investigates the impact of the source domain on TL by incorporating two additional synthetic datasets, a lithium iron phosphate (LFP) cell and an entirely artificial, non-existing, cell, showing that solely the shifting and scaling of gradient changes in the charging curve suffice to transfer knowledge, even between different cell chemistries. A key limitation with respect to extrapolation capability is identified and evidenced in our fourth use case, where the absence of such comprehensive data hindered the TL process.

Investigation of partial charging of enhanced ice storage systems

AbstractPartial storage strategy can save energy and reduce emissions. In this study, analysis of the partial melting process of ice inserted with nanoparticles inside a square enclosure is investigated for thermal energy storage. The lattice Boltzmann method is for melting and heat transfer in the storage unit. The validation demonstrates strong concurrence between the current findings and the experimental data documented in the literature. The analysis is performed for various Rayleigh numbers, nanoparticle volume fractions, and their effect on melting time and energy storage. Two types of nanoparticles are tested that is, copper and alumina. The outcomes indicate that the Rayleigh number and volume fraction of nanoparticles have a significant impact on the phase change process. The nanoparticles addition leads to homogenous and hence expedited melting process including the final stage of the ice melting process which is very slow without nanoparticles. Furthermore, copper nanoparticles are slightly more effective than alumina. Moreover, using 6% copper nanoparticles can reduce the melting time by up to 12.4%.

Flexible Deep Learning-Based State of Health Estimation of Lithium-Ion Batteries with Features Extracted from Partial Charging Curves

The state of health is a crucial state that suggests the capacity of lithium-ion batteries to store and restitute energy at a certain power level, which should be carefully monitored in the battery management system. However, the state of health of batteries is unmeasurable and, currently, it is usually estimated within a specific area of the whole charging data, which is very limited in practical application because of the incomplete and random charging behaviors of users. In this paper, we intend to estimate the state of health of batteries with flexible partial charging curves and normal multi-layer perceptron based on the degradation data of eight 0.74 Ah batteries. To make the estimation more adaptive and flexible, we extract several features from partial charging curves. Analysis of the relationship between extracted features and the state of health shows that the extracted features are useful in estimation. As the length of the partial charging curve increases, the extracted features still function well, and the root mean square error of the test set is lower than 1.5%. Further validation on the other two types of batteries reveals that the proposed method achieves high accuracy even with different sampling and working conditions. The proposed method offers an easy-to-implement way to achieve an accurate estimation of a battery’s state of health.

Role of aloe vera based nanofluids for cool thermal energy storage system: A comparative study

The present experimental investigation incorporates the preparation of aloe vera gel-based phase change nanofluid (NFPCM) for a cool thermal energy storage (CTES) system. Two sets of NFPCMs were produced by adding graphene nanoplatelets (GNP) in deionized water and aloe vera gel (0.20 wt%, 0.40 wt%, and 0.60 wt%). The zeta potential analysis shows that the NFPCM with aloe vera gel is more stable even after 60 days from the preparation (−44 mV). The thermal conductivity of NFPCMs was analyzed through the transient hot wire method and the enhancement of 42 %, and 37.5 % were measured in the ice phase for NFPCMs with aloe vera gel and DI water (0.60 wt%) respectively at the heat transfer fluid temperature of −15 °C. The NFPCMs with aloe vera gel show that the addition of GNP prepended the peak melting temperature from −16 °C to −10.5 °C, which is due to the quick formation of nuclei as compared to NFPCMs with DI water. Additionally, a slight reduction in the latent heat of only 7 % was observed. The charging characteristics of NFPCMs were studied in a single spherical capsule at −8 °C. The maximum reduction in total charging time of 25 % was recorded for the aloe vera gel-based NFPCM with a maximum concentration of GNP (0.60 wt%). The green synthesis of aloe vera gel-based NFPCM exhibited noteworthy enhancements in thermophysical properties. Additionally, it was observed that the long-term stability of the NFPCM was achieved by excluding surfactants during the preparation process. The incorporation of graphene nanoplatelets (GNP) into aloe vera gel as the foundational phase change material (PCM) facilitates the partial charging of the CTES system with a reduced energy input.

Asynchronous Multi-Agent Reinforcement Learning for Collaborative Partial Charging in Wireless Rechargeable Sensor Networks

Asynchronous Multi-Agent Reinforcement Learning for Collaborative Partial Charging in Wireless Rechargeable Sensor Networks

Implementation of adaptive neuro-fuzzy inference system in design and process optimization of latent heat storage system

This study focuses on implementing an Adaptive Neuro-Fuzzy Inference System (ANFIS) as an artificial neural network model to optimize latent heat thermal energy storage (LHTES) systems. Experimental data comprising seven input parameters (length, shell diameter, tube diameter, mass flow rate, difference in temperature, latent heat, and density) and two output parameters (charging and partial charging time) are collected and used to train the ANFIS model. Initially, the model is developed using a grid partition method, employing Triangular and Gbell membership functions and assigning diverse membership rules. This initial model demonstrated satisfactory accuracy based on quality metrics (R2, mean absolute, and root mean square error). Then, a subtractive clustering approach is applied to further enhance the model's accuracy, which groups different data clusters together. This enhanced model exhibited superior performance compared to the grid partition method, making it the optimal choice. In addition, an experimentally validated numerical model is developed using COMSOL Multiphysics and is utilized to crosscheck the predicted output of the ANFIS model. From a set of 100 combinations of storage capacity between 0.45 and 0.55 MJ, a fixed latent heat storage capacity of 0.456 MJ was identified by considering the combination with the least charging time based on the input parameters. Using this storage capacity, a minimum charging time of 223 min and a partial charging time of 180 min was determined. The numerical model also confirmed these optimal values.

An intelligent partial charging navigation strategy for electric vehicles

ABSTRACT The rise of electric vehicles (EVs) has highlighted the issue of insufficient charging infrastructure, a major barrier to their widespread adoption. This problem is due to the limited capacity of existing charging networks and the performance constraints of EV batteries. Intelligent route planning, which optimizes travel time and energy use, could be a solution. Traditional studies on electric vehicle route planning oversimplify the charging process, assuming a linear relationship between charging level and time, neglecting its actual nonlinear nature. Additionally, these studies underestimate the impact of dynamically changing traffic conditions and charging station queue times on travel time. Integrating these factors into route planning can significantly reduce energy consumption and travel time. Our work proposes an intelligent partial charging strategy, relaxing full recharging restrictions, and fully considering the nonlinear battery characteristics, dynamic traffic conditions, and charging station queue times. Employing an improved genetic algorithm for route planning, our method demonstrates a dual advantage – reducing computational complexity and saving an average of 15% in travel time. Experimental results over a 400 km2 traffic road network validate the efficiency of our approach, outperforming existing methods facing similar challenges.

An efficient partial charging and data gathering strategy using multiple mobile vehicles in wireless rechargeable sensor networks

An efficient partial charging and data gathering strategy using multiple mobile vehicles in wireless rechargeable sensor networks

A hybrid metaheuristic to optimize electric first-mile feeder services with charging synchronization constraints and customer rejections

This paper addresses the on-demand meeting-point-based feeder electric bus routing and charging scheduling problem under charging synchronization constraints. The problem considered exhibits the structure of the location routing problem, which is more difficult to solve than many electric vehicle routing problems with capacitated charging stations. We propose to model the problem using a mixed-integer linear programming approach based on a layered graph structure. An efficient hybrid metaheuristic solution algorithm is proposed. A mixture of random and greedy partial charging scheduling strategies is used to find feasible charging schedules under the synchronization constraints. The algorithm is tested on instances with up to 100 customers and 49 bus stops/meeting points. The results show that the proposed algorithm provides near-optimal solutions within less one minute on average compared with the best solutions found by a mixed-integer linear programming solver set with a 4-hour computation time limit. A case study on a larger sized case with 1000 customers and 111 meeting points shows the proposed method is applicable to real-world situations.