Neighbor discovery (ND) protocols are used for establishing a first contact between multiple wireless devices. The energy consumption and discovery latency of this procedure are determined by the parametrization of the protocol. In most existing protocols, reception and transmission are temporally coupled. Such schemes are referred to asslotted, for which the problem of finding optimized parametrizations has been studied thoroughly in the literature. However, slotted approaches are not efficient in applications in which new devices join the network gradually and only the joining devices and a master node need to run the ND protocol simultaneously. For example, this is typically the case in IoT scenarios or bluetooth low energy (BLE) piconets. Here,slotlessprotocols that decouple reception and transmission can achieve significantly lower worst-case latencies than slotted ones. In this paper, we study slotless, BLE-like protocols, which schedule receptions and transmissions independently using periodic intervals (PI). For this class of protocols, optimal parameter values remain unknown. To address this, we propose an optimization framework for PI-based protocols, which translates any specified duty-cycle (and therefore energy budget) into a set of optimized parameter values. We show that the parametrizations resulting from one variant of our proposed scheme are optimal when one receiver discovers one transmitter, and no other parametrization or ND protocol – neither slotted nor slotless – can guarantee lower discovery latencies for a given duty-cycle in this scenario. Since the resulting protocol utilizes the channel more aggressively than other ND protocols, beacons will collide more frequently. Hence, due to collisions, the rate of successful discoveries gracefully decreases for larger numbers of devices discovering each other simultaneously. We also propose a scheme for configuring the BLE protocol (and not just BLE-likeprotocols). Though it is not clear whether the resulting parametrizations minimize the latencies of BLE, reasonably low worst-case latencies can be guaranteed.

Compared to flat PV arrays, PV cells on curved surfaces such as vehicles, wearable devices and building rooftops have varying inclination angles and therefore, non-uniform operating conditions. Dynamic reconfiguration techniques used for tackling partial shading effects can also be deployed for non-uniform operating conditions on curved surfaces. However, designing a reconfigurable PV system for irregular surfaces is significantly different because the placement of reconfiguration switches should account for the curvature, not just random partial shading patterns. In this paper, we propose a designtime framework for identifying the optimal placement of reconfiguration switch sets as well as a dynamic reconfiguration algorithm for a PV array on a given irregular surface. Case studies performed for different irregular surfaces show that our proposed technique reduces the number of reconfiguration switches by 83% while still generating 81% power compared to having reconfiguration switches on all PV atomic units. While the cost reduction due to the reduced number of switches is marginal in most applications because the PV panel cost dominates the total cost, the technique helps in significantly reducing the wiring harness required for dynamic reconfiguration switches, which is a burden for manufacturing.

Modern public transport solutions based on autonomous electric vehicles are on the rise. Public transportation as a service on demand is becoming a reality. Therefore, vehicles suitable for these kinds of applications need to be developed. One critical factor for such vehicles is a short turnaround time at the charging spot. Maximizing the utilization of a given battery pack capacity and minimizing the time spent charging are therefore of central importance. In this paper, we propose a novel preconditioning algorithm to minimize the time an EV is connected to the charging station. Our proposed approach uses existing Active Cell Balancing (ACB) hardware of the battery pack to precondition the State of Charge (SoC) of cells such that all cells reach the top SoC threshold at the same time without requiring an additional balancing phase during charging. This is done by considering the individual cells' charging rate to precondition them for achieving an equal time to full charge. Applying the same approach for discharging, we also extend the driving range of an EV, which otherwise is limited by the cell with the lowest SoC in the pack. Case studies show that our proposed preconditioning algorithm increases the usable energy of the battery pack by up to 3% compared to conventional balancing algorithms all while effectively halving the time connected to a charging station, all without requiring any additional hardware components.

Active cell balancing is the process of equalizing the charge levels of individual cells in a series-connected high power Lithium-Ion battery packs to improve its usable capacity. Several hardware circuit architectures for exchanging charge between the cells and multiple heuristics for controlling the balancing architectures have been proposed in the literature. However, formulating an optimal balancing algorithm that guarantees minimum energy dissipation has not been studied so far. In this paper, for the first time, we propose an optimal cell balancing strategy for minimizing the energy dissipation in a charge equalization process. Our proposed optimization approach consists of two stages. In the first stage, we formulate the charge equalization as a Mixed Integer Linear Programming problem for identifying the set of charge transfer pairs of cells that will guarantee minimum energy dissipation. For these obtained pairs, we compute the lower bound for the balancing time considering the constraints of the balancing architecture. In the second stage, we propose an iterative scheduling strategy to achieve this lower bound by solving an Integer Linear Programming problem at each iteration. Multiple case studies show that our proposed strategy results up to 41% less energy dissipation than the state-of-the-art approaches and always achieves the computed lower bound for the balancing time.

Active cell balancing is performed to minimize the variation in the charge levels of the individual cells in a high-power battery pack, to improve its usable capacity. The process of charge equalization is carried out by scheduling pairs of cells to transfer charge over a hardware circuit. Improving the time for charge equalization has been studied in the power electronics and the electronic design automation domains. However, these approaches have focused on the electronics issues and used heuristics to determine the charge transfer schedule. Hence, no optimality results on charge equalization times are known. We, for the first time, take a real-time systems approach and propose an optimal scheduling framework for active cell balancing. The proposed framework employs a hybrid optimization technique consisting of two sequential stages. In the first stage, we solve a mixed-integer linear programming problem to identify the time-optimal set of charge transfers required to achieve charge equalization. In the second stage, we construct a conflict graph based on the obtained charge transfers, to which we apply the minimum vertex coloring algorithm to synthesize the minimum length schedule. Results show that our proposed framework can reduce the charge equalization time by more than 50% (e.g., from 11 h to 5h). Hence, this has real benefits, e.g., in the context of charging electric vehicles. While task and message scheduling problems have been extensively studied in the real-time systems literature, the scheduling problem we study here, has not been addressed before.

Recently, reconfigurable battery packs that can dynamically modify the electrical connection topology of their individual cells are gaining importance. While several circuit architectures and management algorithms are proposed in the literature, the electrical characteristics of the reconfiguration circuit architectures are not sufficiently studied so far. In this paper, we derive a detailed analytical model for a state-of-the-art reconfiguration architecture capturing the losses introduced by the parasitic resistances of the circuit components. Based on this model we evaluate a novel fast charging strategy, which for the first time utilizes the reconfigurable battery pack architecture in order to reduce the losses during fast charging by switching between parallel and series connection of the cells. In order to do this, we consider the electrical characteristics of the reconfigurable circuit components. Since the component resistances are the main driver for the power losses during charging, we are able to give a realistic perspective on the overall pack performance and the efficiency increase due to the utilized fast charging strategy. Furthermore, using the analytical model, we highlight the challenges faced by existing reconfiguration architectures using state-of-the-art components and we derive specifications for the switches for further improving the energy efficiency. Experimental results show that our strategy improves the charging efficiency by up to 10 %.

Short drive range due to limited battery capacity and high battery depreciation costs persist to be the main deterrents to the wide adoption of Electric Vehicles (EVs). High power battery packs consisting of a large number of battery cells require extensive management, such as State of Charge (SOC) balancing and thermal management, in order to keep the operating conditions within a safe and efficient range. In this paper, we propose a novel State of Health (SOH)-aware active cell balancing technique, which is capable of extending the cycle life of the whole battery pack. In contrast to the state-of-the-art active cell balancing techniques, the proposed technique reduces the load current of cells with low SOH using the active cell balancing architecture. Based on the observation that assigning the smallest possible load current to cells with lower SOH extends cycle life, the technique identifies the most beneficial charge transfers. We find that with our proposed scheme, aging could be mitigated by up to 23.5% over passive cell balancing and 17.6% over active SOC cell balancing.

High power Lithium-Ion (Li-Ion) battery packs used in stationary Electrical Energy Storage (EES) systems and Electric Vehicle (EV) applications require a sophisticated Battery Management System (BMS) in order to maintain safe operation and improve their performance. With the increasing complexity of these battery packs and their demand for shorter time-to-market, decentralized approaches for battery management, providing a high degree of modularity, scalability and improved control performance are typically preferred. However, manual design approaches for these complex distributed systems are time consuming and are error-prone resulting in a reduced energy efficiency of the overall system. Here, special design automation techniques considering all abstraction-levels of the battery system are required to obtain highly optimized battery packs. This paper presents from a design automation perspective the recent advances in the domain of battery systems that are a combination of the electrochemical cells and their associated management modules. Specifically, we classify the battery systems into three abstraction levels, cell-level (battery cells and their interconnection schemes), module-level (sensing and charge balancing circuits) and pack-level (computation and control algorithms). We provide an overview of challenges that exist in each abstraction layer and give an outlook towards future design automation techniques that are required to overcome these limitations.

Active cell balancing is the process of improving the usable capacity of a series-connected Lithium-Ion (Li-Ion) battery pack by redistributing the charge levels of individual cells. Depending upon the State-of-Charge (SoC) distribution of the individual cells in the pack, an appropriate charge transfer pattern (cell-to-cell, cell-to-module, module-to-cell or module-to-module) has to be selected for improving the usable energy of the battery pack. However, existing active cell balancing circuits are only capable of performing limited number of charge transfer patterns and, therefore, have a reduced energy efficiency for different types of SoC distribution. In this paper, we propose a modular, multi-pattern active cell balancing architecture that is capable of performing multiple types of charge transfer patterns (cell-to-cell, cell-to-module, module-to-cell and module-to-module) with a reduced number of hardware components and control signals compared to existing solutions. We derive a closed-form, analytical model of our proposed balancing architecture with which we profile the efficiency of the individual charge transfer patterns enabled by our architecture. Using the profiling analysis, we propose a hybrid charge equalization strategy that automatically selects the most energy-efficient charge transfer pattern depending upon the SoC distribution of the battery pack and the characteristics of our proposed balancing architecture. Case studies show that our proposed balancing architecture and hybrid charge equalization strategy provide up to a maximum of 46.83% improvement in energy efficiency compared to existing solutions.

Active cell balancing improves the performance of a battery pack by transferring charge from one cell to another. Associated design questions require multiple simulations with 100 cells over several hours. Since the most efficient transfer methods switch between phases in the kilohertz range, these simulations require high computational effort or reduced accuracy. To enable detailed analysis on a large scale, this paper includes state-of-the-art electrical battery models in active balancing simulation while keeping the computation effort for one transfer in the low millisecond range. This is achieved in three steps. First, we model the dynamics of each transfer phase using standard equivalent circuit abstraction. Next, we find closed form equations for the so-defined phase dynamics, yielding an iterative approach that saves computation time by replacing the numerical solver. Finally, we employ error control techniques to aggregate phases in that iteration, systematically reducing the millions of phase evaluations that would be necessary otherwise. Our experiments show that the speedup from equivalent circuit dynamics to error-controlled aggregation almost reaches five orders of magnitude while introducing virtually no additional error. This enables simulations of realistic balancing scenarios in less than a second and is hence suitable for design space exploration.

High-voltage battery packs consist of series-connected lithium-ion cells and require sophisticated battery management systems (BMSs) to maintain safe operating conditions. Active cell balancing is an important task of a BMS, performed in order to improve the usable capacity of the battery pack by equalizing the charge levels of individual cells. With the emerging trend of distributed BMS topologies, the associated balancing architectures are required to be modular, consisting of homogeneous units that minimize integration efforts. In this paper, we propose a modular active charge balancing architecture along with its control scheme for implementation toward such distributed BMSs. Compared with existing approaches, our proposed architecture provides increased charge transfer capabilities, with reduced hardware and control complexity. We propose a closed-form analytical model of the balancing architecture, which can be used to perform fast system-level simulation studies and design space exploration for analyzing efficient device combinations. A hardware implementation of the proposed balancing architecture is developed and measurements made with it are used to validate each part of our analytical model. Using the validated analytical model, we performed a case study, which shows that our proposed architecture provides a 14.5 % improvement in charge transfer efficiency compared with existing approaches.

This paper presents an overview of recent trends in Battery System Management Architectures (BSMAs). After introducing the main characteristics of large battery packs, the state of the art in BSMAs is discussed. Two emerging concepts are in the focus of this contribution. On the one hand, there is a development from centralized battery management architectures with a single control entity towards decentralized management where the computational resources are distributed across the battery pack and, hence, move closer to the individual battery cells. This enables a more scalable and modular battery system architecture, while, at the same time, posing challenges regarding hardware and management algorithm design. On the other hand, the static setup of the series- and parallel-connected cells forming the battery pack may be developed towards a reconfigurable architecture such that the electrical topology of the pack can be adaptively changed. Such reconfigurability could increase the reliability of battery packs and reduce management efforts such as cell balancing. At the same time, limited energy efficiency of the additional hardware poses a challenge. We give an outlook how these two trends could be combined into distributed reconfigurable BSMAs. This introduces a set of challenges which have to be solved in order to benefit from the increased scalability, reliability and safety such designs could offer.

With the perennial demand for longer runtime of battery-powered Wireless Sensor Nodes (WSNs), several techniques have been proposed to increase the battery runtime. One such class of techniques exploiting the battery recovery effect phenomenon claims that performing an intermittent discharge instead of a continuous discharge will increase the usable battery capacity. Several works in the areas of embedded systems and wireless sensor networks have assumed the existence of this recovery effect and proposed different power management techniques in the form of power supply architectures (multiple battery setup) and communication protocols (burst mode transmission) in order to exploit it. However, until now, a systematic experimental evaluation of the recovery effect has not been performed with real battery cells, using high-accuracy battery testers to confirm the existence of this recovery phenomenon. In this article, a systematic evaluation procedure is developed to verify the existence of this battery recovery effect. Using our evaluation procedure, we investigated Alkaline, Nickel-Metal Hydride (NiMH), and Lithium-Ion (Li-Ion) battery chemistries, which are commonly used as power supplies for Wireless Sensor Node (WSN) applications. Our experimental results do not show any evidence of the aforementioned recovery effect in these battery chemistries. In particular, our results show a significant deviation from the stochastic battery models, which were used by many power management techniques. Therefore, the existing power management approaches that rely on this recovery effect do not hold in practice. Instead of a battery recovery effect, our experimental results show the existence of the rate capacity effect , which is the reduction of usable battery capacity with higher discharge power, to be the dominant electrochemical phenomenon that should be considered for maximizing the runtime of WSN applications. We outline power management techniques that minimize the rate capacity effect in order to obtain a higher energy output from the battery.

This article introduces a Cyber-physical Co-Simulation Framework (CPCSF) for design and analysis of smart cells that enable scalable battery pack and Battery Management System (BMS) architectures. In contrast to conventional cells in battery packs, where all cells are monitored and controlled centrally, each smart cell is equipped with its own electronics in the form of a Cell Management Unit (CMU). The CMU maintains the cell in a safe and healthy operating state, while system-level battery management functions are performed by cooperation of the smart cells via communication. Here, the smart cells collaborate in a self-organizing fashion without a central controller instance. This enables maximum scalability and modularity, significantly simplifying integration of battery packs. However, for this emerging architecture, system-level design methodologies and tools have not been investigated yet. By contrast, components are developed individually and then manually tested in a hardware development platform. Consequently, the systematic design of the hardware/software architecture of smart cells requires a cyber-physical multi-level co-simulation of the network of smart cells that has to include all the components from the software, electronic, electric, and electrochemical domains. This comprises distributed BMS algorithms running on the CMUs, the communication network, control circuitry, cell balancing hardware, and battery cell behavior. For this purpose, we introduce a CPCSF that enables rapid design and analysis of smart cell hardware/software architectures. Our framework is then applied to investigate request-driven active cell balancing strategies that make use of the decentralized system architecture. In an exhaustive analysis on a realistic 21.6kW h Electric Vehicle (EV) battery pack containing 96 smart cells in series, the CPCSF is able to simulate hundreds of balancing runs together with all system characteristics, using the proposed request-driven balancing strategies at highest accuracy within an overall time frame of several hours. Consequently, the presented CPCSF for the first time allows us to quantitatively and qualitatively analyze the behavior of smart cell architectures for real-world applications.

In the context of active cell balancing of electric vehicle battery cells, we deal with circuit architectures for inductor-based charge transfer and the corresponding high-level modeling and strategy development. In this work, we introduce a circuit architecture to transfer charge between arbitrarily many source and destination cells (many-to-many) for the first time and analyze the advantages over one-to-one transfer. Balancing simulation with numerical solvers remains challenging because of non-differentiable PWM signals, while the search space for high-level strategy design - crucial for time and energy efficiency - becomes even larger. Consequently, we develop a closed-form charge transfer model that extends state-of-the-art approaches and is three orders of magnitude faster than step-size controlled simulation. With an initial algorithm design based on experimentally derived rules, we demonstrate that many-to-many transfer dominates neighbor-only approaches in speed and efficiency even though it requires only one additional switch per circuit module.

This paper presents an approach to optimal dimensioning of active cell balancing architectures, which are of increasing relevance in Electrical Energy Storages (EESs) for Electric Vehicles (EVs) or stationary applications such as smart grids. Active cell balancing equalizes the state of charge of cells within a battery pack via charge transfers, increasing the effective capacity and lifetime. While optimization approaches have been introduced into the design process of several aspects of EESs, active cell balancing architectures have, until now, not been systematically optimized in terms of their components. Therefore, this paper analyzes existing architectures to develop design metrics for energy dissipation, installation volume, and balancing current. Based on these design metrics, a methodology to efficiently obtain Pareto-optimal configurations for a wide range of inductors and transistors at different balancing currents is developed. Our methodology is then applied to a case study, optimizing two state-of-the-art architectures using realistic balancing algorithms. The results give evidence of the applicability of systematic optimization in the domain of cell balancing, leading to higher energy efficiencies with minimized installation space.