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Lithium-ion batteries have always been a focus of research on new energy vehicles, however, their internal reactions are complex, and problems such as battery aging and safety have not been fully understood. In view of the research and preliminary application of the digital twin in complex systems such as aerospace, we will have the opportunity to use the digital twin to solve the bottleneck of current battery research. Firstly, this paper arranges the development history, basic concepts and key technologies of the digital twin, and summarizes current research methods and challenges in battery modeling, state estimation, remaining useful life prediction, battery safety and control. Furthermore, based on digital twin we describe the solutions for battery digital modeling, real-time state estimation, dynamic charging control, dynamic thermal management, and dynamic equalization control in the intelligent battery management system. We also give development opportunities for digital twin in the battery field. Finally we summarize the development trends and challenges of smart battery management.

Energy density of current generation battery packs is insufficient for next generation electric vehicles nor the electrification of the aerospace industry. Currently, approximately a third of energy density is lost due to ancillary demands (e.g., cooling and instrumentation) within a pack, relative to cell energy density. Smart cells, instrumented cells with sensors and circuitry, offer a means to monitor cell performance (e.g. temperature, voltage, current data). Uniquely here we demonstrate our 21700 cells instrumented with internal thermistor sensing arrays with custom miniature interface circuitry including data acquisition and communication components. This circuitry including a power line communication (PLC) system, enables sensor data to be collected and transmitted to a master controller without requiring additional wiring, and can achieve an excellent

Effective battery thermal management (BTM) is critical to ensure fast charging/discharging, safe, and efficient operation of batteries by regulating their working temperatures within an optimal range. However, the existing BTM methods not only are limited by a large space, weight, and energy consumption but also hardly overcome the contradiction of battery cooling at high temperatures and battery heating at low temperatures. Here we propose a near-zero-energy smart battery thermal management (SBTM) strategy for both passive heating and cooling based on sorption energy harvesting from air. The sorption-induced reversible thermal effects due to metal–organic framework water vapor desorption/sorption automatically enable battery cooling and heating depending on the local battery temperature. We demonstrate that a self-adaptive SBTM device with MIL-101(Cr)@carbon foam can control the battery temperature below 45 °C, even at high charge/discharge rates in hot environments, and realize self-preheating to ∼15 °C in cold environments, with an increase in the battery capacity of 9.2%. Our approach offers a promising route to achieving compact, liquid-free, high-energy/power-density, low-energy consumption, and self-adaptive smart thermal management for thermo-related devices., A smart battery thermal management strategy based on reversible water sorption of metal−organic framework provides an efficient method of desorption cooling in summer and sorption heating in winter.

The Battery is the most basic part of the Electric Vehicle, which serves as a major source of energy and gives it sustainable mobility. In electric vehicles, the technology that is highly recognized and used for energy storage is based on lithium chemistry. Nevertheless, the room for research is still open. This involves the collection of materials for the development of cells. The development of algorithms and the design of electronic circuits for a better and more efficient use of batteries is also one area of study. It is important to keep an eye on the critical operating parameters of the battery during charging for the optimum output of the batteries. A battery management system (BMS) is one of those mechanisms for monitoring internal and ambient battery temperature, current, voltage, and charging and discharge operations. Within this paper we speak about some of the popular battery control methods and framework. We also speak about the state-of-the-art device criteria for optimum battery efficiency and its general architecture.

The capacity and voltage rating of battery packs for electric vehicles or stationary energy storages are increasing, which challenge battery management and monitoring. Breaking the larger pack into smaller modules and using power electronics to achieve dynamic reconfiguration can be a solution. Reconfigurable batteries come with their own set of problems, including many sensors and complex monitoring systems, high-bandwidth communication interfaces, and additional costs. Online parameter estimation methods can simplify or omit many of these problems and reduce the cost and footprint of the system. However, most methods require many sensors or can only estimate a subset of the elements in the module’s equivalent circuit model (ECM). This paper proposes a simple decoupling technique to derive individual modules’ voltage and current profiles from the output measurements without direct measurement at the modules. The determined profiles can achieve a high sampling rate with minimum communication between the battery management system (BMS) and the modules. With accurate profiles, an estimation technique can easily determine the parameters of the modules. Provided simulations and experiments confirm this claim by estimating the parameters of a first-order ECM with a parallel capacitor. The proposed technique reduces the number of sensors from 2N + 2 to only two at the pack’s output terminals.

Smart Battery (SB) is a new concept that combines advanced power electronics, wireless communication, and artificial intelligence to increase the performance and extend the lifetime of the battery. In this paper, the concept of wireless control of SB systems is introduced. The hardware design of the electronic circuit needed to implement the smart battery is presented. Moreover, Bluetooth Low Energy (BLE) with connection-oriented is the most interesting solution to obtain lower power consumption and security of the system. The wireless architecture proposed can establish at least 133 connections per second among central control and smart cells. The wireless communication was validated through an experimental wireless development platform.

Lithium-ion batteries have high energy density, lightweight and long life cycle, thus they are the choice for powering electric vehicles. The needed high voltage battery pack is achieved using series-connected cells, that ideally should be identical. However, parameter variations of cells in EVs, along with different working conditions can cause State of Charge (SoC) and temperature imbalances that shorten battery lifetime. Moreover, a series connection can potentially be exposed to single-cell failure. This paper proposes a redundant smart battery topology based on the series connection of individual cell modules. Each module is formed by a cell with an insertion/bypass circuit and a wireless processor that monitors cell states and communicates with a Master controller. To synthesize the nominal voltage, a battery pack with a small redundant number of cells is considered. In this way, it can be made both fault-tolerant and reach higher safety. The problem is then shifted to the control algorithm that at each sampling time has to select "n" cells out of the total cells, according to some goals. A Machine Learning-based control algorithm was developed and tested in Matlab to insert/bypass the cells and reach simultaneous balancing of both SoC and temperature. The new method based on the K-nearest algorithm has been compared in simulation with a conventional sorting balancing method and showed superior performance, especially in temperature balancing.

In this paper, a new modular, reconfigurable battery energy storage system is presented. The presented structure integrates power electronic converters with a switch-based reconfigurable array to build a smart battery energy storage system (SBESS). The proposed design can dynamically reconfigure the connection between the battery modules to connect a module in series/parallel or bypass a faulty module. The reconfigurability along with the use of the converters will bring a few important advantages, including better safety, robust power supply despite faults, variable voltage or power output, flexible individual control of battery modules, and balanced use of batteries. Further, the modular design allows to scale up to construct large-size SBESS. This work also elaborates the operation principles for the proposed SBESS design and illustrates its effectiveness through simulation study.

The automotive sector digitalization accelerates the technology convergence of perception, computing processing, connectivity, propulsion, and data fusion for electric connected autonomous and shared (ECAS) vehicles. This brings cutting-edge computing paradigms with embedded cognitive capabilities into vehicle domains and data infrastructure to provide holistic intrinsic and extrinsic intelligence for new mobility applications. Digital technologies are a significant enabler in achieving the sustainability goals of the green transformation of the mobility and transportation sectors. Innovation occurs predominantly in ECAS vehicles’ architecture, operations, intelligent functions, and automotive digital infrastructure. The traditional ownership model is moving toward multimodal and shared mobility services. The ECAS vehicle’s technology allows for the development of virtual automotive functions that run on shared hardware platforms with data unlocking value, and for introducing new, shared computing-based automotive features. Facilitating vehicle automation, vehicle electrification, vehicle-to-everything (V2X) communication is accomplished by the convergence of artificial intelligence (AI), cellular/wireless connectivity, edge computing, the Internet of things (IoT), the Internet of intelligent things (IoIT), digital twins (DTs), virtual/augmented reality (VR/AR) and distributed ledger technologies (DLTs). Vehicles become more intelligent, connected, functioning as edge micro servers on wheels, powered by sensors/actuators, hardware (HW), software (SW) and smart virtual functions that are integrated into the digital infrastructure. Electrification, automation, connectivity, digitalization, decarbonization, decentralization, and standardization are the main drivers that unlock intelligent vehicles' potential for sustainable green mobility applications. ECAS vehicles act as autonomous agents using swarm intelligence to communicate and exchange information, either directly or indirectly, with each other and the infrastructure, accessing independent services such as energy, high-definition maps, routes, infrastructure information, traffic lights, tolls, parking (micropayments), and finding emergent/intelligent solutions. The article gives an overview of the advances in AI technologies and applications to realize intelligent functions and optimize vehicle performance, control, and decision-making for future ECAS vehicles to support the acceleration of deployment in various mobility scenarios. ECAS vehicles, systems, sub-systems, and components are subjected to stringent regulatory frameworks, which set rigorous requirements for autonomous vehicles. An in-depth assessment of existing standards, regulations, and laws, including a thorough gap analysis, is required. Global guidelines must be provided on how to fulfill the requirements. ECAS vehicle technology trustworthiness, including AI-based HW/SW and algorithms, is necessary for developing ECAS systems across the entire automotive ecosystem. The safety and transparency of AI-based technology and the explainability of the purpose, use, benefits, and limitations of AI systems are critical for fulfilling trustworthiness requirements. The article presents ECAS vehicles’ evolution toward domain controller, zonal vehicle, and federated vehicle/edge/cloud-centric based on distributed intelligence in the vehicle and infrastructure level architectures and the role of AI techniques and methods to implement the different autonomous driving and optimization functions for sustainable green mobility.

Energy storage system (ESS) is an essential component of smart micro grid for compensating intermittent renewable generation and continuous power supply. Batteries are most commonly used in ESS. For optimal energy management of micro grid, the optimization algorithm needs knowledge of battery parameters like state of charge (SOC), voltage, temperature etc. Further for implementing various control and stability strategies, there is need of communication of battery parameters among various components of micro grid. With knowledge of battery parameter, grid operator can make better utilization of available ESS resources and also reduce renewable curtailment. A smart battery management system (BMS) is developed which calculates and communicates battery parameters. Various communication protocols namely Modbus, CAN, Ethernet and Wifi are incorporated in the smart BMS which makes it compatible for many applications. Smart BMS additionally performs active cell balancing using cell to cell balancing topology. The BMS is successfully implemented in a smart micro grid in India and the findings of the implementation are discussed in this paper. They serve as the foundation for further implementation of optimal energy management in the smart micro grid for minimizing operation costs.

A smart battery thermal management (SBTM) strategy is proposed to ensure safer batteries with a long lifetime.

Design of battery charging system on solar tracker based PV system and its application has been presented in this paper. To improve the system performance, a solar tracking system as an innovative device of PV has been developed with an intelligent controller. PV equipped by solar tracker can significantly enhace its performance up to 40% of conventional system. In this research solar tracker designed has active tracking mode with double axis. In order to keep the PV performance optimum, a smart battery charging system has been developed and provided to store the electricity generated by PV system. A novel algorithm was implemented to the system which allows the battery charging process to operate quickly and safely. Besides, the components involved in the system are DC-DC converter, sensor, actuator and battery. DC-DC Converter used is Single Ended Primary Inductance Mode (SEPIM) with MOSFET as its actuator. Battery charging system has used intelligent control based on fuzzy-PSO algorithm. In this case, PSO functions to optimize and modify fuzzy parameters to obtain the best model. Optimized fuzzy controller has then been implemented and programmed in an Arduino microcontroller module to generate control signal which commands actuator element to control the voltage of battery through duty cycle manipulation variable. This algorithm has been able to improve the solar charging controller significantly and more convincingly increase PV performance.

Current generation battery electric vehicles lack sufficient systems to monitor battery degradation and aging; consumers demand longer range, faster charging and longer vehicle lifetime. Smart cells, incorporating sensors (e.g. temperature, voltage, and current) offer manufacturers a means to develop longer lasting packs, enabling faster charging and extending range. In this work, instrumented cells (cylindrical, 21700) have been developed. Our novel data logging solution (using power line communication, PLC) permits a comprehensive range of sensors to be installed on each cell. Utilizing the cell bus bars, this reduces the necessary wiring harness size and complexity to instrument packs, which can enable higher density energy storage per volume and weight within the vehicle. In this initial feasibility study, a module (4S2P cells) was tested using two diverse cycles (stepped current, 200 mins $\times10$ cycles, and transient drive, 50 min) in a laboratory climate chamber. The interface system enables research-prototype or traditional sensors to be connected via the PLC network. Miniature sensors (6 temperature, 1 current, 1 voltage) were installed externally on each cell. Excellent performance was observed from the communication system; maximum 0.003% bit error rate, 50ms message receive time (compared to dedicated wired link). Variation in the measured parameters (originally identical cells, temperature 1.0 °C, voltage 5% state-of-charge, current $\sim 10$ %) support the need for improved cell instrumentation to understand cell manufacturing tolerances and aging. This work shows a proof-of-concept study using PLC with instrumented cells, and leads to future work to further reduce the cost and physical size of smart cells.

This paper presents 60 MW grid connected three phase hybrid systems. The proposed model contains two stages of conditioning energy conversion power unit, while a Nickel–Cadmium battery is used for DC power saving generated from the (PV) array. The 9 bus IEEE grid is used in this article as an example. The basic contribution of this article is the best utilization of batteries as an additional source to increase the generated power based on demand. Two (ANFIS) controllers are desired for this purpose. one is used for organizing the battery charging process and the other is used for on/off battery switching to grid in case of incensement of load. The system is designed and studied using MATLAB/Simulink.

Across sub-Saharan Africa many communities lack reliable access to mains electricity, and therefore depend upon unconventional power sources to recharge their mobile phones. Many of these informal recharging centres are powered by a diesel generator or solar panel. Furthermore, many of these same communities are frequently served by broken water pumps. Previous reports indicate that many individuals are prepared to pay a small regular fee to recharge their mobile phone, while their local water point committee lacks sufficient funds to keep their water infrastructure maintained. This paper presents a novel funding strategy aimed at helping communities cover the maintenance costs of their local water supply. This premise was demonstrated using a pilot system in The Gambia which combined a solar-powered community water point with an off-grid smart battery rental hub for everyday electricity needs such as mobile phone charging. The paper presents preliminary field data from this site and explores the wider context surrounding the pilot system.

Air-conditioner (AC) accounts for a significant share of residential energy consumption. Considering the widespread rise in electric vehicle (EV) usage, its charging would also contribute a considerable percentage of consumer's total energy consumption. Consequently, the concurrent operation of AC and EV charging would result in peaky load curves. Hence, this study proposes a system of agents for AC and EV charging applications, which incorporates load-management strategies to flatten the load curve. Thereby, the presented system includes two agents, namely: a smart load node for a thermostatically controlled load (SLN-TCL) and a smart battery charge controller. Subsequently, a subagent, namely micro-node, has been introduced to support SLN-TCL and to implement the concept of distributed temperature sensing (DTS). The implementation of DTS subdues the conventional temperature sensing mechanism of AC and ensures a more flexible operation. This study includes the design, development, and features of agents and subagents for AC and EV applications. Furthermore, this study also demonstrates the agent-based control actions for peak-shaving under real conditions to showcase the performance of this system.

The present research work attempts to develop a Smart Residential Electricity Distribution System (SREDS) for enabling Smart Residences (SR) to participate in Demand Response (DR) schemes of Power Distribution Company (PDC) with either Flat Rate tariff, Time of Use tariff or Real Time Pricing. SREDS includes a Smart Energy Meter (SEM) enabled with Internet of Things, an Intelligent Residential Load Management System (IRLMS) with Wi-Fi connectivity, distributed Wi-Fi enabled Smart Load Nodes (SLN) and Smart Battery Charing/Discharging Controller (SBCDC). The SEM receives the key parameters such as electricity pricing, maximum demand limit, etc. as per the DR schemes from the PDC through Meter Data Management System (MDMS) using Internet. IRLMS receives the required operational timings of the loads as per the desire and comfort of the user either through the user interface or from the SLN connected to the appliances participating in DR. If the SR is equipped with battery storage system, then the battery units are interfaced with the IRLMS through SBCDC. The present status of the battery is communicated to IRLMS by SBCDC. The communication between SEM, IRLMS, SLNs and SBCDC is established through Home Area Network using Wi-Fi. The IRLMS processes the PDC parameters and load requirements, and executes an optimization algorithm to find the best possible scheduling of appliances in order to get minimized electricity bill. The SEM communicates back the energy consumption data to the PDC through MDMS.

In a reconfigurable battery pack, the connections among cells can be changed during operation to form different configurations. This can lead a battery, a passive two-terminal device, to a smart battery that can reconfigure itself according to the requirement to enhance operational performance. Several hardware architectures with different levels of complexities have been proposed. Some researchers have used existing hardware and demonstrated improved performance on the basis of novel optimization and scheduling algorithms. The possibility of software techniques to benefit the energy storage systems is exciting, and it is the perfect time for such methods as the need for high-performance and long-lasting batteries is on the rise. This novel field requires new understanding, principles, and evaluation metrics of proposed schemes. In this article, we systematically discuss and critically review the state of the art. This is the first effort to compare the existing hardware topologies in terms of flexibility and functionality. We provide a comprehensive review that encompasses all existing research works, starting from the details of the individual battery including modeling and properties as well as fixed-topology traditional battery packs. To stimulate further research in this area, we highlight key challenges and open problems in this domain.

International audience; Smart battery management systems require reliable information on the internal state of the battery, which are unavailable through direct measurements. Electrochemical models are relevant in this context as these describe the internal phenomena governing the battery dynamics. These models can thus be used to design observers, which provide estimates of the state variables online. In this paper, we consider a single particle electrochemical model of a lithium-ion battery given by a set of ordinary differential equations built from the spatial discretization of partial differential equations (PDEs) that locally describe the mass and charge transport of lithium. We then design a nonlinear observer, whose global exponential stability and robustness are guaranteed provided that a linear matrix inequality holds. The latter is shown to be verified for standard model parameters values. Simulation results based on plug-in hybrid electric vehicle tests on the original PDE model are presented, which illustrate the good performances of the observer in terms of speed of convergence and robustness.

A major issue in the ad hoc networks with energy constraints is to find ways that increase their lifetime. The use of multihop radio relaying requires a sufficient number of relaying nodes to maintainnetwork connectivity. Hence, battery power is a precious resource that must be used efficiently in order to avoid early termination of any node. In this paper, a new battery power scheduling policy based on dynamic programming is proposed for mobile devices.This policy makes use of the state information of each cell provided by the smart battery package and uses the strategy of dynamic programming to optimally satisfy a request for power. Using extensive simulation it is proved that dynamic programming based schedulingpolicyimproves the lifetime of the mobile nodes.Also a hardware support is proposed to succeeds in distinguishing between real-time and non-real-time traffic and provides the appropriate grade of service, to meet the time constraints associated with real time traffic.

Highly nonlinear characteristics of lithium-ion batteries (LIBs) are significantly influenced by the external and internal temperature of the LIB cell. Moreover, a cell temperature beyond the manufacturer’s specified safe operating limit could lead to thermal runaway and even fire hazards and safety concerns to operating personnel. Therefore, accurate information of cell internal and surface temperature of LIB is highly crucial for effective thermal management and proper operation of a battery management system (BMS). Accurate temperature information is also essential to BMS for the accurate estimation of various important states of LIB, such as state of charge, state of health and so on. High-capacity LIB packs, used in electric vehicles and grid-tied stationary energy storage system essentially consist of thousands of individual LIB cells. Therefore, installing a physical sensor at each cell, especially at the cell core, is not practically feasible from the solution cost, space and weight point of view. A solution is to develop a suitable estimation strategy which led scholars to propose different temperature estimation schemes aiming to establish a balance among accuracy, adaptability, modelling complexity and computational cost. This article presented an exhaustive review of these estimation strategies covering recent developments, current issues, major challenges, and future research recommendations. The prime intention is to provide a detailed guideline to researchers and industries towards developing a highly accurate, intelligent, adaptive, easy-to-implement and computationally efficient online temperature estimation strategy applicable to health-conscious fast charging and smart onboard BMS.

An increasing number of residential customers install the Hybrid Rooftop Solar Battery System (HRSBS). Most household HRSBS are installed behind the meter (BTM), where only netload is measured by the utility meter, which brings challenges to the system operation. Disaggregating BTM PV generation and battery charging/discharging profile of customers can enhance grid-edge observability. To this end, this paper proposes a joint energy disaggregation method to separate the PV generation and battery charging/discharging power from the netload. First, a Home Smart Battery Management model is built to generate the battery charging/discharging profile. Second, an optimal disaggregation model is established based on Contextually Supervised Source Separation method. Third, the feature vectors of PV, load, and battery are extracted as the input of the disaggregation model. Case study results show that the proposed method has promising disaggregation performance for PV and original load, and the estimation error of battery charging/discharging profile is less than 30% in most cases. Finally, the impact of PV system size and battery capacity on the performance of the proposed method is analyzed.

Innovation of the battery technology is of paramount importance for the development and widespread of electrification in transport modes. In addition to the ongoing evolution and research on novel and better chemistries, the application of sensors and measurement techniques at the battery cell level could foster the electrification process by introducing novel concepts like the Smart Battery. This work presents the Smart Battery paradigm and reviews the state-of-the-art sensor technologies that can be of interest for its development in automotive.

Due to the encouraging characteristics in decreasing CO2 gas emissions, electric vehicles (EV) have acknowledged vast support in recent years. One of the clear ideas is that electric vehicle function as a distributed energy source and provide additional services to the electricity grid. In the vehicle-to-grid (V2G) model, collection of EV is used for the delivery of energy services, including supplementary services to the grid. Contribution in the V2G outline may involve supplementary charge and discharge of the EV batteries. Vehicle to grid can provide the distributed stored power in terms of rotatory reserve power to the utility grid. V2G power has the potential to revive the frequency deviance observed at the charging terminal and the required battery SOC (State-of-Charge). In case of grouping an enormous number of electric vehicles at a distribution utility grid bus, operation in V2G mode may be used for grid support during faulty situations. In this paper, the proposed vehicle control procedure scheme is executed to an electric vehicle and battery charging system. The results describe the proposed smart battery energy management prediction service considerably reduces the actual number of communications of electric vehicles with charging units and grid as well as forecasting the load demand. Simulation results show that the suggested method significantly performed the rule-based method.

This paper deals with managing the load across distribution transformer through demand response and smart battery electric vehicle (BEV) charging techniques. A neighborhood of 40 households connected across a 200-kVA distribution transformer is investigated in this paper. Each house is provided with a single BEV. Residential and BEV loads across each house are managed through time of use and non-time of use demand response-based techniques, managed under shifting the load based on unit pricing. To implement demand response technique, appliances in each household are classified as shiftable and non-shiftable. Operating time of shiftable appliances is controlled to attain the required peak shaving. The load levelling is effective in minimizing the transformer loading obtained under time-variant unit pricing used to control both residential and BEV charging load. This paper investigates electrical loading of the distribution transformer under residential and BEV charging load.

Mobility in urban cities is increasing day by day, which further increases traffic, fuel consumption, and air pollution. Many cities introduced cycling and electric bikes to being cheap and eco-friendly as an equitable way for transportation. However, the utilization of solar energy in electric bikes can overcome these major issues. In solar electric bikes, batteries are considered to be the major energy storage device with a renewable source of energy. This research aims to design and development for a smart battery charging strategy with solar power ignition for electric bikes. The experimental setup consists of electronic components data-path and the controller design. The controller design is used to implement on microcontroller ATmega328P. The voltage of the fully charged battery and the charging current is considered as 60 Volts and 1.2 Amperes respectively. In the proposed design, the charging current is maintained with Maximum Power Point Tracking. The controller design makes sure to charge the battery up to 95% and discharge up to 25% in order to increase the lifespan of the battery. The development of a controlled charging strategy using a small-sized microcontroller keeps the current of the battery to remain constant and in turn, increase the battery lifetime.

This paper utilizes a Wireless Smart Battery Management System (WSBMS) to manage battery cells in Electric Vehicles (EVs). WSBMS is the cell-level Battery Management System (BMS) based on wireless communication. Compared with the conventional modularized BMS, the proposed system has the advantages of high fault tolerance and sufficient scalability. In addition, the proposed balancing algorithm based on the state-of- health (SOH) and the state-of-charge (SOC) can balance battery cells with any number, different aging states, and reasonable capacity deviation. A prototype is built, and the balancing algorithm is verified by simulation and experimental results considering four cases with different states of battery cells.

In this paper we present and compare four different State-of-Charge (SoC) balancing strategies including continuous-time, discretized, event-triggered and self-triggered balancing strategies for Lithium smart battery systems. Under the assumption that the communication topology among smart battery units is connected and undirected, average SoC balancing can be achieved under all four balancing strategies. Compared to continuous-time strategies, discretized and event-triggered balancing strategies reduce the communication among battery units as well as the computation of controllers. Self-triggered strategies further relax the requirement of continuously monitoring the SoC of each unit and its neighbors. Simulation results are provided to verify the effectiveness and compare the differences of the four strategies.

V tem diplomskem delu je opisan postopek predelave baterijskega paketa, ki služi napajanju naprav za zajemanje podatkov v zahtevnejših okoljih, kot je na primer povečana temperatura. Potreba po reviziji se je pojavila po tem, ko se je s trga umaknil modul, ki so ga v podjetju uporabljali do sedaj. Naša naloga je bila narediti krmilnik za polnjenje dveh baterijskih sklopov z že vgrajenim uravnovešanjem, krmilno logiko za celoten izdelek in zaščito proti prevelikemu praznilnemu toku posamezne baterije. Za dokončanje izdelka smo načrtovali celotno shemo vezij in pripravili tiskana vezja za izdelavo. Naredili smo tudi funkcionalne, temperaturne, izolativne in električno-varnostne teste v laboratoriju in tudi pravi test na motornem kolesu, s katerim smo opravili približno 60 km. Prav tako smo tudi spisali programsko kodo za mikrokrmilnik, ki krmili in nadzoruje celoten sistem. Zaradi spremembe same mehanike smo morali spremeniti in dodelati tudi aluminijasto ohišje. In this bachelor's degree thesis we have described a complete process of re-designing battery pack for powering DAQ devices that can operate in harsh invorement, for example increased temperature. The need for re-design came from market, because previously used batery charging controller went obsolete. Our job was, to make a controller, that will be able to charge two baterries with allready embeded balancing circuit, control logic for whole system, and overcurrent protection for each battery. One of requriment was also, that batteries are hot-swappable. For final product we have designed all the schematics, PCBs, ordered them, tested and assembled first two prototypes. We have made functional, temperature, isolation and electrical-safety (burst, surge) tests in laboratory, and we have also driven battery pack for about 60 km on the back of motorcycle. We have also written firmware, which controls whole system. Due to change in shape of PCBs we had to adapt aluminium case for the product.

Effective management of lithium-ion batteries is a key enabler for a low carbon future, with applications including electric vehicles and grid scale energy storage. The lifetime of these devices depends greatly on the materials used, the system design and the operating conditions. This complexity has therefore made real-world control of battery systems challenging. However, with the recent advances in understanding battery degradation, modelling tools and diagnostics, there is an opportunity to fuse this knowledge with emerging machine learning techniques towards creating a battery digital twin. In this cyber-physical system, there is a close interaction between a physical and digital embodiment of a battery, which enables smarter control and longer lifetime. This perspectives paper thus presents the state-of-the-art in battery modelling, in-vehicle diagnostic tools, data driven modelling approaches, and how these elements can be combined in a framework for creating a battery digital twin. The challenges, emerging techniques and perspective comments provided here, will enable scientists and engineers from industry and academia with a framework towards more intelligent and interconnected battery management in the future.

Smart cells, instrumented with miniature sensors, can help meet both the consumer and regulatory demands for the battery packs of next-generation of electric vehicles (EVs). Currently, adding sensors to battery packs entails increasing vehicle weight and complexity with the associated wiring loom expansion. In this work, we demonstrate sensors for voltage, current and temperature can be installed on cylindrical cells (21700 format) and connected to a data logger via power line communication (PLC). We achieved zero error rate with our laboratory setup, and transfer times of < 40 ms (relative to dedicated wired connection). This reliable PLC method (max. data transfer rate 115 kbps) was sufficient for logging cell data and can operate over a wide DC voltage range (e.g. target 10 to 60 V typical in an EV). Our initial experiments highlight the lack of understanding of the performance of a cell during its lifetime in an EV, where temperature gradients were observed between the terminals (0.2 °C with a peak increase of +0.6 °C, discharge rate of 0.5 C).

Compared to conventional empirical or equivalent circuit-based Battery Management Systems (BMS), Advanced Battery Management Systems (ABMS) incorporate sophisticated electrochemical battery models that estimate health-related states with improved accuracy to achieve the desired outcomes. Equivalent Circuit Models based on electrochemical principles are used to reduce the complexity of the sophisticated electrochemical battery models while ensuring the required accuracy. Side reaction induced battery capacity loss is one of the crucial aspects to be addressed in ABMS. This paper proposes a control strategy to minimize the side reaction induced capacity loss by changing the cell series-parallel configuration dynamically inside the battery pack. Reduced-order equivalent circuit model based on electrochemical theory is adopted to model the internal kinetics of Li-ion battery. The cells which have a better State of Health (SOH) condition given the higher priority to contribute to the output voltage and connect in series than the cells with lower SOH. Simulation results show a considerable amount of capacity loss reduction after introducing the proposed method.

This paper presents the software design for a smart integrative system developed to monitor the balance of batteries, system designed and realized in the work [1]. The following software applications have been developed: the software of the voltage measurement modules, the software for the currents’ measurement and the software of the central unit. There are also presented the user interaction with the central unit, some experimental results and the Battery Management System (BMS) testing on an electric scooter.