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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 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.

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.

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.

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.

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.

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.

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).

Households accounted for 35% of total UK electricity consumption in 2019 and have considerable potential to support the target of net-zero CO2 emissions by 2050. However, there is little understanding of the potential to reduce emissions from household energy systems using emissions-responsive battery charging, and existing investigations use average emissions factors rather than marginal. To understand the overall carbon reduction potential of household energy systems, a life cycle assessment has been conducted for a typical house in the UK, with annual electricity consumption of 3960 kWh. Household energy systems comprising solar photovoltaics arrays and battery energy storage systems are assessed using time-series consumption and generation data, determined by combining a validated demand model, marginal emissions factor calculations, storage system models, and assumptions regarding the future grid. Marginal emissions factors are used to calculate the life cycle carbon emissions of electricity consumption. The carbon emissions and financial costs of household electricity consumption have been estimated for different combinations of battery storage, photovoltaics arrays, and smart battery charging systems. Results show that the deployment of a rooftop photovoltaics array and lithium nickel-manganese-cobalt battery operating in response to grid emissions factors could achieve 14 tons of CO2 savings through the system’s life span, though total electricity costs would be increased considerably. The household with just a photovoltaics array and no battery storage could increase total electricity costs by £2170 and achieve 12 tons of CO2 savings through the system’s life span, providing much improved marginal abatement costs over systems with battery storage. The battery operation mode and the characteristics of batteries and photovoltaics systems in carbon emissions reduction are discussed. High cost is the main factor limiting the deployment of household battery systems.

Power integrated circuits (ICs) have developed solutions for charging systems and power management system for battery powered devices. The main purpose of a battery management system comprises handling the charging cycle to limit the charging time without focusing on the battery. It also monitors the present status of the battery and reports the fault conditions if any malfunction or error occurs. This system is based on a smart LiFePO4 battery for offline UPS, which is used to power up a home AC application with 230VAC/50Hz. The main reason to select the LiFePO4 battery life cycle, it is made up of non-toxic material and this battery is not overheated or catches fire in case of overcharging or overload. In this system, the Arduino UNO microcontroller is used and it takes care of the parameters like battery charging voltage, charging current, and high voltage cutoff at backup mode. It also monitors the battery voltage, battery current, low voltage cutoff, and overload protection at inverter mode.

State of Charge (SOC) is essential for a smart Battery Management System (BMS). Traditional SOC estimation methods of lithium-ion batteries are usually conducted using battery equivalent circuit models (ECMs) and the impact of current sensor bias on SOC estimation is rarely considered. For this reason, this paper proposes an online SOC estimation based on a simplified electrochemical model (EM) for lithium-ion batteries considering sensor bias. In EM-based SOC estimation structure, the errors from the current sensor bias are addressed by proportional–integral observer. Then, the accuracy of the proposed EM-based SOC estimation is validated under different operating conditions. The results indicate that the proposed method has good performance and high accuracy in SOC estimation for lithium-ion batteries, which facilitates the on-board application in advanced BMS.

Unmanned underwater vehicles (UUV) are widely used for survey and mining of natural resources in the region of underwater and seabed in the last two decades. In underwater, vision and depth perception will be poor, thus navigating and maneuvering a remotely operated vehicles (ROVs) will not be an easy task. The umbilical cable might get entangled. In the event that the ROV loses power from its umbilical cord or an autonomous underwater vehicle (AUV) power failure, an emergency power system will be needed to power the critical equipment on the ROV or AUV to facilitate its recovery and to limit the loss of data. In this paper, a large format 2 KWh lithium iron phosphate (LiFePO4) battery stack power system is proposed for the emergency power system of the UUV. The LiFePO4 stacks are chosen due to their high energy density, modularity and ready availability. The proposed LiFePO4 battery system includes the design and development of a smart battery management system (BMS) with high efficiency active cell balancing technology and intelligent self-learning battery state of charge (SOC) estimation for the LiFePO4 battery. The proposed BMS will lead to better utilization of battery’s potential capacity and maximize the cycle life of the battery. The battery system has a pressure-resistance enclosure to eliminate extra battery pressure chamber and associated risks, therefore increase the reliability of the power system amidst high pressures down to 3km of deep-water.

Smart battery management systems require reliable state information, which is unavailable through direct measurements. Electrochemical models are relevant in this context as these describe the internal phenomena, which govern the battery These models can thus be used to design observers and hence to estimate the state variables on-line. We propose an electrochemical model of a lithium ion battery given by a set of ordinary differential equations built from the spatial discretisation of partial differential equations that locally describe mass and charge transport of the lithium. We then design an observer and analyse its stability via a polytopic approach, which relies on the satisfaction of linear matrix inequalities. The latter are shown to be verified for standard model parameters values. Simulation results on the original infinite-dimensional model are presented, which show the good performance of the observer.

In this paper, the concept of smart battery pack is introduced. The smart battery pack is based on wireless feedback from individual battery cells and is capable to be applied to electric vehicle applications. The proposed solution increases the usable capacity and prolongs the life cycle of the batteries by directly integrating the battery management system in the battery pack. The battery cells are connected through half-bridge chopper circuits, which allow either the insertion or the bypass of a single cell depending on the current states of charge. This consequently leads to the balancing of the whole pack during both the typical charging and discharging time of an electric vehicle and enables the fault-tolerant operation of the pack. A wireless feedback for implementing the balancing method is proposed. This solution reduces the need for cabling and simplifies the assembling of the battery pack, making also possible a direct off-board diagnosis. The paper validates the proposed smart battery pack and the wireless feedback through simulations and experimental results by adopting a battery cell emulator.

Battery management systems (BMSs) play a critical role to ensure the safety and extend the lifetime of the Lithium-ion batteries. In the conventional BMSs, there are some disadvantages such as low scalability and insufficient flexibility. In order to enhance these performances and further increase their reliability, the smart batteries are proposed. The smart batteries are the cell-level BMSs, which are built by the integrated cell modules connected in series. Each integrated cell module can monitor its own states, control the bypass circuit, and can communicate with the master controller, thus they are also called smart cells. This paper presents the state-of-the-art of management architectures, communication implementations, bypass circuits, and bypass decision strategies in the smart batteries. Four different existing smart batteries are compared. According to the comparison, future trends of the smart batteries are provided.

State of Charge (SoC) is essential in a smart Battery Management System (BMS). Traditional SoC estimation methods consider the lithium battery cell as an isothermal body simplistically, which is not accurate. Due to the heat conduction and convection, the temperature distribution along the radius is nonuniform, which means the battery model parameters subject to temperature are not identical radially. This paper proposed a modified electrochemical-distributed thermal coupled model (MEDTM) for improving the accuracy of SoC estimation. The distributed thermal model is employed to design a robust H-infinity temperature observer for the estimation of the radial temperature distribution of battery cell, where a radial discretized method is used for the realization of the observer design. Based on the observed temperature, a SoC observer is developed with the backstepping method. Finally, experiments and simulation are implemented. The two constant discharging experiments indicate that MEDTM is more accurate than the single particle model with surface temperature (SPMST), and it can generate reliable temperature prediction. A comparative simulation and a constant discharging experiment verify the H-infinity temperature observer can obtain a more accurate estimation of the battery cell’s temperature than the luenberger observer, and the H-infinity temperature observer has an estimation error of 0.3 K below. Then, through a constant discharging experiment and a simulation with UDDS current, the proposed SoC observer is verified to accurately estimate the SoC by comparing with the true SoC, where the estimated error of SoC under the two cases is below 1.5% and below 0.4%, respectively. Therefore, the proposed SoC observer also has good performance.

Lithium-ion batteries (LIBs) has seen widespread applications in a variety of fields like the renewable penetration, electrified transportation, and portable electronics. A reliable battery management system (BMS) is critical to fulfill the expectations on the reliability, efficiency and longevity of LIB systems. Recent research progresses have witnessed the emerging technique of smart battery and the associated management system, which can potentially overcome the deficiencies met by traditional BMSs. Motivated by this, this paper reviews the research progresses on the smart cell and smart battery system from multiple aspects, including the system design, sensing techniques, and the potential innovation of system integration. The transition from conventional LIB system towards higher smartness and the incurred advantages/challenges are overviewed. Special focuses are given to the existing and emerging cell-level, multi-dimensional (electro-mechanical-thermal) sensing techniques that may play critical roles in the management of smart cells. The mitigation from external to internal embedded sensing and its potential benefits/impacts to the performance of battery system, as well as the general requirement for sensor design and integration, are discussed systematically. Considering the design innovation and data transmission burden, the potential change of system-level smart battery integration is further discussed as an open outlook.

Impedance is closely related to the internal physical and chemical processes of lithium-ion batteries. And the properties of the processes can be characterized and thus more detailed information be provided based on it. In the past decades, the impedance is frequently reported as a powerful tool used in the field of lithium-ion battery state estimation and diagnosis. This paper reviews more than 170 papers and organizes the related research according to three themes of impedance modeling, acquisition, and application under the premise of electric vehicle implementation. The strength and weaknesses of the research in each theme are discussed. Based on the research, the possibility and the value of impedance in onboard battery management are revealed. However, challenges are still faced due to the cost limitations, the complex vehicular conditions, and the time-varying battery states. The unsolved issues are summarized in the conclusion. To realize a more smart battery management system with the impedance, more significant work is still needed.

In order to recover and fully charge batteries in electric vehicles, smart battery chargers should not only work under different loading conditions and output voltage regulations (close to zero to 1.5 times the nominal output voltage), but also provide a ripple-free charging current for battery packs and a noise-free environment for the battery management system (BMS). In this paper, an advanced $LLC$ design procedure is investigated to provide advantageous extreme regulation and eliminate detrimental burst mode operation. A modified, special $LLC$ tank driven by both variable frequency and phase shift proves to be a successful solution to achieve all the regulation requirements for battery charging (from recovery, bulk, equalization, to finish). The proposed solution can eliminate the negative impact of burst mode noises on the BMS, provide a ripple-free charging current for batteries in different states of charge, reduce the switching frequency variation, and facilitate the EMI filter and magnetic components designs procedure. In order to fully consider the characteristics of the full bridge $LLC$ resonant converter, especially the output voltage regulation range and soft transitions of the MOSFETs in the fixed frequency phase shift mode, a new set of analytical equations is obtained for the $LLC$ resonant converter with consideration of separated primary and secondary leakage inductances of the high frequency transformer. Based on the proposed strategy and analytical equations, multivariate statistical design methodology is employed to design and optimize a 120 VDC, 3-kW battery charger. The experimental results exhibit the excellent performance of the resulting converter, which has a peak efficiency of 96.5% with extreme regulation capability.

Energy storage system (ESS) technology is still the logjam for the electric vehicle (EV) industry. Lithium-ion (Li-ion) batteries have attracted considerable attention in the EV industry owing to their high energy density, lifespan, nominal voltage, power density, and cost. In EVs, a smart battery management system (BMS) is one of the essential components; it not only measures the states of battery accurately, but also ensures safe operation and prolongs the battery life. The accurate estimation of the state of charge (SOC) of a Li-ion battery is a very challenging task because the Li-ion battery is a highly time variant, non-linear, and complex electrochemical system. This paper explains the workings of a Li-ion battery, provides the main features of a smart BMS, and comprehensively reviews its SOC estimation methods. These SOC estimation methods have been classified into four main categories depending on their nature. A critical explanation, including their merits, limitations, and their estimation errors from other studies, is provided. Some recommendations depending on the development of technology are suggested to improve the online estimation.

In next 5-10 years, there will be 5-8 million tons of spent batteries accumulated as waste. There exists methods for recovery of materials from a battery for reuse or remanufacturing purposes. The research question arises on how about the recycling of batteries stacked in the battery modules in series or parallel configurations. This paper shall propose the qualitative framework based on intelligent robots for safer and efficient disassembly of battery modules for recycling. The framework combines the battery pack’s automatic removal procedure with the battery’s intelligent sorting program to recycle the reusable batteries. It has higher degree of automation and higher recycling efficiency than the existing frameworks. This also includes a smart battery information collection system (machine vision based information reading), an intelligent robot based battery disassembly system and a smart battery classification system. In addition, the framework also includes AI model based programming of robot so that it can sort out the batteries in ascending order of their remaining life.

In recent years, the Chinese government and State Grid Corporation of China (SGCC) have paid great attention to the technical development and infrastructure construction for electric vehicles (EVs). This paper focuses on the mechanism of Smart Battery Charging and Swapping Operation Service Network for EVs including its overall architecture and operational mode. The overall architecture based on the internet of things is analyzed and clarified through terminal device, station management and management center layers. Then two different types of demonstration projects are presented which expound on the condition of EV's infrastructure construction. Lastly, performance analysis of the charging behaviors of electric taxis in fast charging station based on the Queuing theory is proposed. The simulation results show that the service time and the number of generators has an influence on the average waiting time and the length of queue.

This paper presents the analysis and design of a smart battery management system for Field Programmable Gate Array (FPGA) based portable electronic devices. It is a novel concept of incorporating the functionality of a smart battery management system into the FPGA used by portable electronic devices, which provides the following advantages. (1) It lowers cost since the conventional commercial independent battery management circuit can be eliminated; (2) It offers more flexibility because FPGA based battery management algorithms can be specifically designed for different battery chemistries of different devices and can provide the flexibility of algorithms and functionalities updating as well. Smart battery management system concepts include four aspects: (1) smart charging; (2) battery balancing; (3) smart discharging; and (4) safety operating. One novel charging algorithm, which combines the merits of multistage charging and pulse charging, is proposed to charge a Li-ion battery pack smartly. A Proportional Integral (PI) control method is introduced to achieve the current control of charging circuit with considerable close loop stability. Simulation results from the PSIM 9.0.4 software package and experimental results from the prototype built in the lab are demonstrated to verify the effectiveness of smart charging. The realizations of battery balancing, smart discharging, and safety operating are also briefly described by taking advantage of the proposed FPGA based smart battery management system topology, which verify the feasibility of the proposed FPGA based smart battery management system for portable electronic devices.

This paper discusses new developments in lead–acid battery chemistry and the importance of the system approach for implementation of battery energy storage for renewable energy and grid applications. The described solution includes thermal management of an UltraBattery bank, an inverter/charger, and smart grid management, which can monitor the state of charge (SoC) and the state of health (SoH) of the battery during system operation. With such features, it can allow the battery to operate within an optimum SoC window and thus can further maximize the longevity of the UltraBattery. Importantly, the smart battery management can trend the SoH of the battery and allow cell replacement at a convenient time without affecting the system operation. Furthermore, the advanced system package allows remote monitoring and control of operation, thus reducing the running cost of the system. It is clear that the widespread use of renewable-energy systems, in turn, would lead to a reduction in global consumption of the limited supplies of the fossil fuels and in the associated production of greenhouse-gas emissions.

Online accurate estimation of remaining useful life (RUL) of lithium-ion batteries is a necessary feature of any smart battery management system (BMS). In this paper, a novel partial discharge data (PDD)-based support vector machine (SVM) model is proposed for RUL prediction. The proposed algorithm extracts the critical features from the voltage and temperature of PDD to train the SVM models. The classification and regression attributes of SVM are utilized to classify and predict accurate RUL. The different ranges of PDD were analyzed to find the optimal range for training the SVM model. The SVM model trained with optimal PDD features classifies the RUL into six different classes for gross estimation, and the support vector regression is used to estimate the accurate value of the last class. The classification and predictive performance of SVM model trained using the full discharge data and PDD are compared for publicly available data. Results show that the SVM classification and regression model trained with PDD features can accurately predict the RUL with low storage pressure on BMS. The PDD-based SVM model can be utilized for online RUL estimation in electric vehicles.

We propose a highly efficient active DMFC system for tracking application. With this technology of active DMFC system tracking of animals in forest area has been achieved to a great extent. The system is composed of charging module, a smart battery and tracking unit. The charging module consists of fuel cell stack, mechanical part and circuit parts. Mechanical part for injection speed of pure methanol, temperature control because high fuel efficiency is essential for portable application. Circuit part consists of two synchronous buck-boost converters, first one charges the battery and supplies the power to the second stage converter, second one regulates the output voltage. The tracking unit consists of the GPS, Sensor and controlling circuit. Our existing systems just sense and provide the result to the base station. In proposed system it sense the parameter via sensor nodes and transmits to base station. The control modes of DMFC system and interfacing are done by using microcontroller. The power supply for tracking system is provide by an fuel cell stack module. It has simple structure with high efficiency.

We propose a highly efficient active direct-methanol fuel cell system for portable application in this paper. The system is composed of a fuel cell stack, mechanical parts, a smart battery, and circuit parts. The mechanical parts consist of several pumps, a mixer, a liquid separator, and heat exchange modules. The battery enables the operation of the mechanical parts and powers the output load during the start-up state. It can also improve the system dynamic characteristics by providing or absorbing the power when the load varies. Furthermore, the battery can also provide peak power at overload. The circuit parts consist of two buck-boost converters and a control circuit. The first-stage buck-boost converter maintains the constant stack current and charges the battery. The second-stage buck-boost converter regulates the output voltage. Buck-boost converters use synchronous rectifiers to increase the power conversion efficiency. In order to ensure a highly efficient and a highly reliable operation, four operating modes (battery discharging mode, start-up mode, hybrid mode, and shut-down mode) are defined and operating algorithms are proposed. These control modes facilitate the cold start at start-up and increase fuel efficiency. All control functions are implemented in software with a single-chip microcontroller MSP430F5438. A 25-W prototype is implemented to support the theoretical analysis.