Electric vehicles (EVs) are emerging as a greener and more efficient alternative to internal combustion engine vehicles. However, their adoption is still limited by reduced driving range and extended charging times. Nevertheless, EV charging can theoretically occur almost anywhere, enabling users to recharge while engaging in daily activities, thereby reducing the need for fast charging and improving overall user’s satisfaction. This work investigates this charging approach by introducing a novel classification of EV charging strategies within a “vehicle-centric” versus “user-centric” framework. A new methodology for “user-centric” strategies is proposed, which adapts the charging profiles to user’s requirements and the EV condition. In particular, the proposed method optimizes a default multistep constant current profile to satisfy time constraints imposed by user’s activities, such as the duration of a stay at a shopping mall or public office, while minimizing EV battery degradation. The method leverages a digital twin of the EV battery incorporating both electrothermal and aging models to simulate a given charging profile and assess its compliance with the constraints of the charging event. Then, a golden-search-based optimization algorithm is used to identify the most suitable profile. A realistic case study based on real-world charging data is used to assess the performance of the proposed method. Simulation results demonstrate strong alignment with user’s time preferences, acceptable battery health degradation, and improved charging equipment efficiency, making the approach appealing for both EV users and charging station operators.

Sodium-ion batteries are gaining attention as an alternative to lithium-ion batteries, because of the more abundant and less toxic materials used for their construction. However, this technology is novel and requires extensive experimental research to fully understand its behavior. Electrochemical impedance spectroscopy is commonly used to identify the internal battery state. For this reason, electrochemical impedance spectroscopy is applied to two commercial sodium-ion batteries in the present work. The effects of different values of the state-of-charge and relaxation times are investigated. The results show that the measurements performed on the two cells are almost the same, showing good reproducibility of the EIS values. In addition, the EIS values decrease for state-of-charge levels from 100

Sodium-ion batteries are attracting growing interest as potential alternatives to lithium-ion batteries, with first commercial products already making their way into the market. Their primary advantages include the low cost and abundance of raw materials used in their production. In addition, the similarity in the operating principles between sodium-ion and lithium-ion batteries enables the repurposing of existing knowledge and technologies. However, most of the current literature remains focused on the electrochemical development of the sodium-ion battery components, while relatively little attention has been paid to the functionalities required from battery management systems tailored to these batteries. Therefore, the aim of this review is to examine the main modeling strategies, the state of charge and state of health estimation techniques, and the safety considerations specific to sodium-ion batteries. In addition, key challenges and differences compared to lithium-ion batteries are highlighted. The ultimate goal is to provide a foundational background to support the development of battery management systems specifically designed for sodium-ion batteries.

Battery parallelization enables the use of second-life electric vehicle batteries and low-cost battery technology in energy storage systems for smart grid applications. Partial power DC/DC converters can connect batteries in parallel with higher efficiency compared to traditional AC/DC and DC/DC converters thanks to their reduced power requirements. This work presents the design and the implementation of a scaled prototype of a partial power DC/DC converter based on the buck-boost topology. The prototype was characterized achieving efficiencies from 84.1

Lithium-ion batteries are widely used, but accurate estimation of their state of charge and state of health is still an open issue. Integrating chemical and physical sensor data with electrical measurements can improve the estimation algorithms and foster the early detection of destructive failures. One-dimensional dilatometry systems based on the eddy currents principle represent low-cost and promising solutions to measure the dilation of the cells caused by charging operations and health degradation phenomena. These systems are based on a simple inductance-to-digital converter that measures the inductance of a PCB coil to evaluate the distance between the coil and a target. These devices require an accurate design since the coil sizing strongly influences the measurement accuracy. This work aims to optimize the design of the coil considering different parameters, notably the shape, the trace width, and the internal filling of the spiral.

The increasing adoption of electric vehicles is a key element in fighting climate change. Unfortunately, charging operations significantly stress the existing energy infrastructure. For this reason, low-power charging approaches that leverage user working hours to charge the electric vehicles are gaining attention. Realistic usage data are essential for the effective sizing of low-power charging facilities and designing their power flow management algorithms. Unfortunately, available datasets only provide aggregate quantities but do not offer detailed information on the charging facility. This paper aims to fill this research gap by presenting a dataset of real usage data collected in two pilot charging facilities developed at the University of Pisa, Italy. One simple facility consisting of three EV chargers directly connected to the electric grid, while the second one is based on the microgrid concept. The detailed information on charging events, anonymous user data, PV production, and battery usage is collected in the proposed dataset. The database is provided freely to the scientific community as a benchmark for the sizing, development, and validation of workplace charging facilities. It currently contains the data collected during the first 16 months of activity, but it will continue to be updated.

The constant growth of the electric vehicles market is exposing the open issues related to battery management. One of them is the need for long recharging time compared with the refueling of thermal vehicles. Several fastcharging protocols have been developed and studied in the literature aiming to mitigate this issue. However, fast-charging involves high charging current and determines consistent heat generation, causing detrimental effects on battery health and premature aging of electric vehicle batteries. This paper proposes a simulation platform developed in Matlab-Simulink environment that embeds a vehicle model and a battery model including thermal, electrical, and aging phenomena. The aim is to evaluate the performance of the charging protocols and estimate their effects on battery health in a realistic scenario. A case study was considered to assess the functionalities of the platform. A year of daily usage of an electric vehicle with typical driving and different fast charging profiles was simulated. The results highlight the battery health degradation due to the specific usage profile, charging protocols, and thermal-related issues. Moreover, the results suggest that the proposed platform could serve as a preliminary benchmark in designing optimized fast-charging protocols.

The growing demand for energy storage in residential photovoltaic (PV) systems highlights the potential of second-life electric vehicle batteries. Their residual capacity offers economic and environmental benefits. However, their cells suffer from a significant capacity mismatch, which reduces the overall usable capacity of the battery. Dynamic equalization appears to be a promising solution to fully exploit the battery capacity. This work aims to present a simulation platform designed to evaluate the benefits of dynamic equalization in second-life batteries (SLBs) used for residential PV energy storage. Through the simulation platform, various scenarios were simulated based on real production and consumption data collected over a one-year operating period to account for seasonal variations. Simple control algorithms are investigated and compared with an improved one based on a digital twin of the battery. In addition, the effects of the equalization system efficiency, battery size, and cell capacity mismatch on the dynamic equalization behavior are discussed. This study demonstrates that the use of dynamic equalization increases energy utilization by up to $8\%$, even when the efficiency of the equalization dc-dc converter is as low as $70\%$. Finally, an economic analysis is performed to compare brand-new lithium-ion batteries and SLBs with and without dynamic equalization.

Second-life lithium-ion batteries retrieved from electric vehicles are a very appealing resource for stationary applications such as smart grids. However, their performance is limited by the strong mismatch among their cells. Dynamic equalization is a very promising approach to maximize the second-life battery capacity, transferring charge from the best-performing cells to the least-performing ones. Even if this functionality is very similar to the active balancing approach used in first-life batteries, the dynamic equalization approach requires high-current DC/DC converters. Therefore, converter design is crucial for obtaining high efficiency in all possible battery states. A novel approach to determine in closed form the efficiency of a super-capacitor-based active balancing architecture was developed. It can estimate the efficiency of the system with comparable accuracy, but in a significantly shorter time than other methods presented in the literature. Such considerable advantages make the proposed approach a valuable tool for implementing optimization procedures, thus helping the designer in the selection of the components for the design of the active balancing circuit considered.

The reuse of exhausted electric vehicle batteries in less demanding second-life applications is a promising solution to reduce waste and address concerns about environmental sustainability. Unfortunately, the cells used in second-life batteries show large capacity variability as a consequence of aging. This cell capacity mismatch strongly reduces the total battery capacity that is limited by the capacity of the least-performing cell. The dynamic equalization approach allows the battery system to maximize its usable capacity by transferring charges among the battery cells during the entire battery operation time. At the same time, the design of a dynamic equalization system requires particular care to obtain the best trade-off between performance and cost. A simulation platform is developed and presented in this paper to emulate a second-life battery storage system equipped with a dynamic equalization system. The platform aims to help the designer to compare the main active balancing architectures available and to identify their main design constraints. A second-life battery application case study is used to evaluate the simulation platform functionality. The obtained results highlight the capability of the platform to quantify the effects of the dynamic equalization system and its correct sizing.

The electrical energy generation is one of the main pollution causes as it is still largely based on oil and coal generators. Renewable energy sources are the most mature alternative but their production is intermittent and then require energy storage systems able to store a high quantity of energy to increase their usability. The second-life batteries of electric vehicles and low-cost battery chemistries are the best candidates to compose low-cost energy storage systems for these applications. These batteries must be parallel-connected to obtain the required capacity. A novel parallelization approach based on Input Parallel/Output Serial DC/DC Partial Power converter series-connected to each battery is presented in this paper. This approach is applied to different case studies showing that controlling only the 15

Lithium-ion Batteries are widely used in several applications but require complex management systems to ensure their safe and effective usage. Algorithm development and functional testing are among the most complex and time-consuming phases of lithium-ion battery management system design. Furthermore, the use of real batteries in those phases introduces safety hazards. The hardware-in-the-loop approach allows the designers to overcome these limitations by replacing real batteries with hardware emulators. This article presents a low-cost battery emulator platform to reproduce the main electrical and thermal behaviors of elementary battery cells. The platform allows the user to manually control the emulator outputs or automatically reproduce specific voltage, temperature, and current profiles. In addition, the platform can independently reproduce the battery cell behaviors by using a customizable 2-RC equivalent electrical model. The functional testing and the balancing algorithm assessment of a custom battery management system are used as a case study to evaluate the developed battery emulator platform. Results demonstrate the effective advantages of the developed platform to assess the battery management system safety functionalities and algorithms. Finally, the platform is made freely available to accelerate research and innovation in the field of energy storage technology.

The dynamic equalization approach can significantly increase the use of electric vehicle second-life batteries in stationary applications. Dynamic equalization aims to face the very high variability of the capacity values of the second-life battery cells that strongly reduces the battery usable capacity. This work proposes an alternative dynamic equalization architecture based on an auxiliary cell to support the less-performing cells, maximizing the battery usable capacity. A theoretical methodology has been presented to analyze the proposed architecture. The obtained preliminary theoretical results are then validated using a simulation platform. The final results show a potential increase in usable battery energy of up to 16 % using an ideal DC/DC converter, and 13 % with a DC/DC converter efficiency of 0.8.

Sodium-ion batteries offer a promising alternative to lithium-ion ones thanks to their lower cost, smaller ecological footprint, and less stringent safety requirements. The development of an accurate model to reproduce their behaviors is still an open issue, despite being essential for enabling their efficient utilization. This work investigates the repurposing of lithiumion equivalent circuit models for sodium-ion cells. Four models with 1, 2, 3, and 4 RC groups are considered to investigate their trade-off between accuracy and computational efficiency. The model parameters are identified with a pulse current test carried out on two nominally identical commercial cells. The performance of the models is evaluated using pulse current tests and a realistic electric vehicle current profile. In both cases, all the models achieve errors lower than 1% of the nominal cell voltage. These values are comparable with those obtained in lithium-ion cell models, suggesting the portability of model-based algorithms from lithium-ion batteries to sodium-ion ones. Moreover, the results highlight that the model with 2 RC groups is the best choice in low computational complexity systems. On the other hand, the model with 3 RC groups has the best trade-off between accuracy and computational complexity.

Characterization data are essential to improve state estimation algorithms for lithium-ion batteries. Unfortunately, only bigger companies can afford extensive test campaigns, as they require expensive and specific equipment. A Low-Cost, Open-Design, and highly configurable electronic load for battery testing is proposed in this paper. The developed equipment is able to sink a desired current profile configured by using a host computer Python interface. A preliminary experimental validation of the designed electronic load is performed obtaining promising results. This equipment could help smaller companies and laboratories to perform battery characterization tests and increase the amount of available data for state estimation algorithms improvement. For this reason, it is released as Open Hardware/Software system and is freely available to the research community.

Wireless Sensor Networks offer significant advancement in real-time data collection and analysis in various application scenarios. Sometimes their nodes are placed in difficult-to-reach areas making the replacement of their battery impossible. Therefore, the node is usually equipped with a harvesting system based on PV cells and a lithium-ion battery. A switched-capacitor DC/DC converter is proposed in this paper to optimize the power produced by a single PV cell and maximize the battery charge power. The proposed converter is based on a classic Fibonacci architecture whose control system is improved to make its input/output conversion ratio dynamically adjustable. The simulation results show that the proposed converter allows the usage of more than 90 % of the maximum available PV power to charge the battery of the node. It is important to note that this result is achieved by leveraging the wireless sensor network characteristics to keep the converter as simple as possible.

Data-driven algorithms, such as the neural network ones, seem very appealing and accurate solutions to estimate the lithium-ion battery’s State of Charge. Their accuracy is strongly related to the amount of data used in their training phase. Therefore, huge experimental campaigns are needed to effectively train the neural network used to State of Charge estimation. The main idea behind this paper is to mitigate this drawback by training the algorithm with synthetic datasets generated from simulations of a model of the battery, instead of experimentally collected data. Two instances of the same Long-Short-Term-Memory neural network architecture designed for battery State of Charge estimation are trained, one with an experimental dataset, and the other with a synthetic one. The two neural network instances are then evaluated with the same test dataset derived from experimental data and their estimation accuracies are compared. Results show that the performances of the two networks are comparable. The experimental trained neural network scored a RMSE of only 0.3 % lower than the RMSE of the synthetic trained one. These results suggest the possibility of fruitfully using a synthetic training dataset to speed up and reduce the complexity and cost of the training phase of neural network algorithm for battery state of charge estimation.

Battery use is continuously on the rise for several applications as proven by the rapid growth of electric vehicles market. The development of more accurate battery control and estimation algorithms is an essential requirement to enhance battery performance. Sensor fusion techniques promise to improve control and estimation algorithms by combining the currently used electric and thermal measured data with physical and chemical ones. In particular, the cell volume variation appears to be connected to both State of Charge and State of Health. In this work, an inductive sensor, based on eddy currents, is used to develop a low-cost system to measure the volume variation of prismatic lithium-ion cells. The developed system consists of a cell holder structure, similar to the one used in commercial batteries, equipped with eddy current sensors. The system is applied to both fresh and aged cells. The obtained results prove that the proposed measurement system is able to track the cell expansions due to both the thermal and State of Charge variations. Furthermore, the volume variations found are comparable to literature experiments, in which expensive laboratory equipment is used.

Modern lithium-ion (Li-ion) batteries integrate microcontrollers and dedicated resources for monitoring and control distributed in different physical layers. The overall system is called Battery Management System (BMS) and aims to estimate, for instance, the state of charge and the state of health maximizing the life and reliability of batteries. Anyway, BMSs can be threatened by security issues and attacks. Counterfeiting of components and data manipulation can alter the functionalities of the BMS, causing several consequences, such as the explosion of batteries due to the overcharge. In recent work, we proposed a novel and robust security approach for the integrity and authentication of BMS data and anti-counterfeiting mechanisms. In addition, it can be used to improve the performance (by hardware acceleration) and the security level (with advanced features such as secure storage and isolation of security-critical assets) of other solutions proposed in the literature, which otherwise might be compromised. For this purpose, the integration of a co-processor for the digital signature on elliptic curves (ECDSA) becomes fundamental. In this work, we demonstrate the necessity of integrating such a cryptographic co-processor, and we define its requirements in terms of both security and performance. Moreover, we confirm and extend the analysis related to the scaling of the characteristics (resources, frequency, latency) of ECDSA modules according to the internal data path. To the best of our knowledge, this is the first work in the literature addressing this topic for Li-ion BMS.

Connectivity and cloud computing are key elements in the future of electric mobility. They allow manufacturers to provide advanced fleet management and predictive diagnostic services. In particular, cloud computing dramatically enhances data availability and enables the use of more complex and accurate state estimation algorithms for electric vehicle lithium-ion batteries. A tuning procedure for a moving window least squares algorithm to estimate the parameters of a 2-RC equivalent circuit battery model is presented in this paper. The tuning procedure uses real data collected from a test vehicle and uploaded to the Stellantis-CRF cloud. The tuned algorithm was applied to eight months of road tests and showed very small estimation errors. The errors are comparable to other literature data, even when the literature results were obtained in laboratory tests. The estimated model parameters are tracked through time and seem accurate enough to show the first signs of battery aging.