Balancing traffic efficiency and passenger comfort is a fundamental challenge in autonomous intersection management. This paper presents a novel framework that integrates comfort-related metrics, such as minimizing jerk, alongside traffic efficiency optimization. Experimental evaluations demonstrate that the framework effectively balances efficiency and comfort under varying traffic conditions, outperforming methods that prioritize only one objective.

Despite numerous advantages cooperative perception offers, it is also prone to sensor faults and data manipulation, where malicious agents or sensors can intentionally or unintentionally share faulty information. As a result, vehicles might generate faulty perception of the traffic situation and make erroneous decisions, potentially compromising road safety. In this paper, we propose a decentralized algorithm that leverages consensus-based detection using the states and their covariances to identify manipulated data in collective perception messages (CPM). The algorithm focuses on detecting attacks on objects that can be observed by multiple vehicles by quantifying how aligned each observation pairs are. In a scenario involving three vehicles perceiving the environment, the algorithm detected 67.57% of 37 attacks, which is 13.5% higher true positive rate than the baseline model.

Teleoperated driving (ToD) is increasingly considered as a fallback solution for autonomous driving. Up to now, ToD requires a highly reliable mobile network capable of transmitting multiple video streams with low latency. Recently, significant advancements have been made in vehicular sensors and perception algorithms, which have huge implications for the challenges faced in ToD. We envisage that a real-time digital twin that tracks the remote vehicle's environment will play a crucial role in reducing the required communication band-width and providing a more convenient teleoperator interface, ultimately enhancing safety of ToD in crowded environments. Furthermore, it would allow various degrees of cooperation between automated driving functionalities and human teleoperators. In this paper, the concept of digital twin for ToD is outlined and a proof of concept is implemented using a real-world vehicle simulator and a teleoperator hardware setup. A significant reduction in required bandwidth is reported by transmitting less video data and reconstructing the scene from the digital twin.

Decarbonizing heavy-duty vehicles through electrification is a demanding task due to their large energy and power requirements. One promising solution to address this problem are electric road systems (ERS), which provide dynamic charging and traction power for electric vehicles. While their technological feasibility has been proven in numerous pilot projects, its reliability under realistic traffic scenarios is yet to be thoroughly investigated. In this paper, we define the reliability criteria for ERS and investigate how they are influenced by various realistic traffic scenarios. Then, the relationship between the ERS reliability criteria and the key system parameters such as the DC link voltage, distance between substations and feeding arrangement is analyzed. We find that the continuous current limit is the most critical criterion under heavy traffic conditions. Furthermore, we look into a number of run-time techniques to cope with electrical stress on the system and propose a technique based on successive approximation of the total current demand. We compare and evaluate the effectiveness of the run-time techniques and show that our proposed technique is able to improve ERS utilization while ensuring reliable operation.

Efficient waste management is a defining feature of a future smart city and one of the core challenges to reduce the environmental impact of its inhabitants. Given the growing amount of waste that is discarded into public dustbins and the labour required for their maintenance, autonomous robots to empty the bins could help in mitigating those challenges. For this task, the mobile robotics platform MURMEL (Mobiler Urbaner Roboter zur Mülleimer EntLeerung) has been designed. However, the acceptance of this solution by the municipality and the public highly depends on the efficiency of task performance and the environmental impact of the system. To achieve that, we propose a communication framework involving smart dustbins, which inform a central planning entity about the filling level. A novel route planning algorithm is proposed based on the filling level communicated over LoRaWAN. It can avoid unavailing rides, where the robots cannot empty the dustbins due to its capacity limits and assembles a route to exploit the full capacity. We demonstrate the feasibility of our approach using a realistic simulation of 52 dustbins in an urban park, resulting in 13% less energy consumption, 29% less round-trips and a 42% increase in capacity utilization compared to a baseline route planning without communication.

Lithium-ion (Li-ion) batteries are currently the preferred choice among energy storage systems for a wide range of applications due to their high energy density, long life span and cost effectiveness. However, in several high-power applications such as electric vehicles, the heat generation of Li-ion battery cells can cause several problems such as accelerated aging and thermal runaway. Although cooling systems are typically utilized to overcome these issues, they suffer from drawbacks such as uneven cooling of the battery cells as well as cumbersome architectures and high energy consumption. In this paper, we propose an active cell balancing (ACB) strategy to mitigate the impact of the temperature gradient on divergent aging of the cells with the goal of increasing the life span of a battery module. We explore balancing architectures dedicated to this goal and study their effectiveness via a simulation framework based on an electrochemical cell model, a computational fluid dynamics (CFD) cooling model and a battery aging model. The experiments show that the life extension of the module is proportional to the amount of ACB circuit, which points toward cost-effective solutions for battery manufacturers. In addition, the results indicate further cost reduction from relaxed cooling system requirements. Our investigation on a 40-cell battery module with standard thermal gradient shows that the life span can be prolonged by about 2.8%. Finally, our extended analysis shows that a life cycle extension of 19.8% is possible with ACB if a relaxed thermal gradient constraint of 12.4°C is assumed.

Vehicle-to-vehicle (V2V) communication is an emerging technology which enables vehicles to share data, facilitating functions such as traffic information service, collision warning or cooperative control to potentially increase throughput and enhance road safety. However, the benefits of V2V communication during the early stages of market penetration, where communicating and non-communicating vehicles are co-existing, are not yet clear. This paper aims at quantifying the relationship between the penetration ratio of communicating vehicles and the additional safety that can be achieved thereby. We design a rule-based method for early detection of aggressive lane change behaviors using communicated motion data and evaluate its effectiveness according to different penetration ratios and traffic scenarios. Furthermore, we propose a proactive countermeasure which can be applied to mitigate the disruptive impact of erratic lane changes and analyze its performance in case different number of vehicles implement the measure. The results from a microscopic traffic simulator indicate that in partially-connected traffic, anomalies such as abrupt lane changes even of non-connected cars can be detected by observing the response of the following vehicles. In addition, by applying pre-braking as a countermeasure, traffic safety can be improved in terms of surrogate safety measures (SSM),

Thermal efficient resource mapping and scheduling techniques are particularly important for mobile processors because of limited opportunities for external cooling. In mobile processors such as the ones using ARM’s big.LITTLE architectures, the cores of either the big or the LITTLE processor cannot be individually voltage/frequency scaled. However, we show that by forcing all the application threads to a single core, and not having any workload on the other cores of a processor, there is still considerable thermal benefit. This is counter intuitive since all the cores run at the same frequency. We show real measurements and discuss what impact this has on thermal-aware scheduling for such multicore processors.

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.

Advertisements, or ads, are a major source of income for Internet-related service companies. Meanwhile, ads and trackers consume significant computing resources and power, which are crucial for mobile devices, and can drain a phone's battery. Moreover, most users find ads annoying, which led to the development of ad blockers. In this paper, we characterize the energy consumption of different ads. We find that ad blocking may either not affect the power consumption at all, or result in power savings of up to 50% dependent on the web site, reducing the battery life of the mobile device. Our studies are based on the ad blocking engines provided by the Brave browser. We believe that our results might impact how such engines can be designed in the future.

The smart energy grid features real-time monitoring of electricity usage such that it can control the generation and distribution of electricity as well as utilize dynamic pricing in response to the demands. For this purpose, smart metering systems continuously monitor the electricity usage of customers, and report it back to the Utility Provider (UP). This raises privacy concerns regarding the undesired exposure of human activity and time-of-use of home appliances. Photovoltaics (PV) and a residential Electrical Energy Storage (EES) have proven to be effective in mitigating the privacy concerns. However, this comes at several costs: Installation of PV and EES, their subsequent aging and the possibly increased electricity cost. We quantify the trade-off between privacy exposure and financial costs by formulating a stochastic dynamic programming problem. Our analysis shows that i) there is a quantifiable trade-off between the financial cost and privacy leakage, ii) proper control of the system is crucial for both metrics, iii) a strategy solely focusing on privacy results in high financial costs, and iv) that for a typical residential setting, the costs for a trade-off solution lie in the range of 600 US$-1700 US$. As the load flattening has a peak shaving effect desirable for UPs, increasing privacy is mutually beneficial for both, customers and UPs.

Flexible photovoltaic (PV) arrays often have to be mounted on surfaces that have a significant amount of curvature. These include solar-powered vehicles, planes, and also some wearable devices. However, this inevitably leads to non-uniform solar irradiance among connected PV cells. If one cell among series-connected PV cells receives significantly lower solar irradiance, the overall power generation of the string is reduced. While previous works dealt with this by employing sophisticated run-time techniques, we show that design-time approaches that determine the electrical series-parallel connection of a PV array could also significantly enhance the power output. In this paper, we propose a k-means clustering-based algorithm to group PV cells/modules with similar solar irradiance to form a PV string, even allowing irregular arrays, to maximize the power generation of the array for a given irradiance profile. Our experimental results show that the power generation of a PV array could be increased by 84% compared to usual PV array organizations that do not take the curvature of the mounted surface into account.

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.

Over the last years, web browsing has been steadily shifting from desktop computers to mobile devices like smartphones and tablets. However, mobile browsers available today have mainly focused on performance rather than power consumption, although the battery life of a mobile device is one of the most important usability metrics. This is because many of these browsers have originated in the desktop domain and have been ported to the mobile domain. Such browsers have multiple power hungry components such as the rendering engine, and the JavaScript engine, and generate high workload without considering the capabilities and the power consumption characteristics of the underlying hardware platform. Also, the lack of coordination between a browser application and the power manager in the operating system (such as Android) results in poor power savings. In this paper, we propose a power manager that takes into account the internal state of a browser -- that we refer to as a phase -- and show with Google's Chrome running on Android that up to 57.4% more energy can be saved over Android's default power managers. We implemented and evaluated our technique on a heterogeneous multiprocessing (HMP) ARM big.LITTLE platform such as the ones found in most modern smartphones.

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.

Games are one of the most popular and at the same time most computation intensive and energy consuming class of applications on mobile devices like smartphones and tablets. Dynamic voltage and frequency scaling (DVFS) is a common technique for reducing the processing power. However, highly variable and non-deterministic workload characteristics of mobile games mandate sophisticated workload prediction models to predict low-utilization phases of games during which the processor's frequency can be decreased to save energy. While prior works exhibit significant improvements, one main question is left open: How large is the gap between the developed techniques and the theoretically optimal power manager, i.e., a power manager which exactly knows the future workload and, hence, can select the optimal sequence of frequencies that minimizes the power consumption under given timing constraints. In this paper, we discuss that estimating the savings from such an optimal power manager is non-trivial due to the non-deterministic nature of games and the underlying system. In order to address this, we suggest a statistical model of the optimal power manager using which we estimate the potential savings of popular closed-source games. The results of our work have several implications: We reveal a significant gap between savings obtained from recently proposed game power managers and the theoretically optimum savings (up to 54.4% energy savings are possible). Our work strongly motivates future research endeavors to minimize the gap between the optimum and the existing power managers.

Recent advances in battery and drone technologies have opened up possibilities of commercial use of drones. Private companies are looking into the possibilities of using drones for commercial deliveries from the legal, technical, and economical perspective. Nevertheless, the battery management perspective of such businesses has not yet been thoroughly investigated. In this paper, we identify that battery management of such application has a major impact of the costs, and formulate an optimization problem to reduce the aging of batteries. We identify two sub-problems, battery assignment, and battery scheduling to derive a solution that minimizes the aging of the batteries. We show that the formulation enables leveraging the trade-off relationships between the packet waiting time and battery purchasing cost. The experimental results show the proposed method reduce the electricity and battery purchasing cost by 25%, and average packet waiting time by more than 50%.

Drones, also referred to as unmanned aerial vehicles (UAVs), are recently expanding their field of usage beyond military surveillance and tactical applications. Commercial drone delivery service is one of the promising applications in the near future, and a number of companies are already pushing forward the legal and technical barriers to realize the concept. Unlike conventional applications of drones, the success of a commercial application depends critically on the profitability. The major sources of expense are the electricity cost and battery purchasing cost due to aging. Hence, it is crucial to maximize the energy efficiency and mitigate battery aging of the drone delivery business. However, no prior work has extensively assessed the problem for the business as a whole. This paper, for the first time, proposes a holistic and detailed analysis on the profitability and time to delivery of the drone delivery business. This paper identifies the major design parameters and runtime management potentials that affect the profitability and time to delivery of the business. We have implemented a discrete event simulator based on detailed models of the comprising components. We perform a design space exploration to understand the effects of the various battery configurations, battery attachment technique, drone flight speed, etc., on time to delivery, electricity cost, and battery purchasing cost. Our results show that such control knobs have a significant impact on the time to delivery and the operating cost of the business.

Games belong to the most popular but power-hungry applications on smartphones. Gaming workloads exhibit highly variable and user-interactive behavior, which makes it hard to predict the workload. Modern MPSoC (multiprocessor system-on-chip) platforms are equipped with heterogeneous multi-processing (HMP) processors comprising performance-oriented and energy-efficiency cores in order to better exploit power-performance trade-offs among different types of applications. To minimize the energy consumption of games on HMP platforms, it is essential to precisely predict the gaming workload and perform joint thread-to-core allocation as well as dynamic voltage and frequency scaling (DVFS). In this paper, we propose a frame- and thread-based MPSoC power management strategy for games. We focus on the fact that gaming workload has high temporal correlation among frames and evaluate selected workload predictors on a per-frame basis. Moreover, we find that there are two categories of thread workloads, periodic and aperiodic, and hence, propose to use a hybrid workload predictor. Based on the per-thread predictions, the power manager allocates the threads among the heterogeneous cores in an evenly distributed fashion in order to minimize the operating frequency while keeping the frames-per-second (FPS) constraint. We implement the game power manager as an Android governor on a state-of-the-art platform based on the Exynos5422 SoC, which is also incorporated in the Samsung Galaxy S5 smartphone. Our measurement results show that we save on average 41.9% of energy compared to the Android default governor. Further, we have performed a user study to evaluate the user perception of our governor. The gaming experience was rated between good and very good for all games.

This paper pioneers the maximum power point tracking (MPPT) of photovoltaic (PV) cells that directly supply power to a microprocessor without an energy storage element (a battery or a large-size capacitor) nor power converters. The maximum power point tracking is conventionally performed by an MPPT charger that stores in the energy storage element, and a voltage regulator (typically a DC-DC converter) produces a proper voltage level for the microprocessor. The energy storage element is an energy buffer and makes it possible to perform MPPT of the PV cells and power management of the microprocessor independently. However, the energy storage element, MPPT charger and DC-DC converter cause seriously limited lifetime (when a typical battery is adopted), significant energy loss (typically over 20%), increased weight/volume and high cost, etc. The proposed method enables extremely fine-grain dynamic power management (DPM) in every a few hundred microseconds and performs the MPPT without using an MPPT charger and a DC-DC converter as well as an energy storage element. We achieve 84.5% of energy harvesting efficiency using the proposed setup with huge reduction in cost, weight and volume, and extended lifetime, which is not even numerically comparable with conventional MPPT methods.

As the building stock consumes 40% of the U.S. primary energy consumption, it is critically important to improve building energy efficiency. This involves reducing the total energy consumption of buildings, reducing the peak energy demand, and leveraging renewable energy sources, etc. To achieve such goals, hybrid energy supply has becoming popular, where multiple energy sources such as grid electricity, on-site fuel cell generators, solar, wind, and battery storage are scheduled together to improve energy efficiency.

Electric vehicles (EV) are considered as a strong alternative of internal combustion engine vehicles expecting lower carbon emission. However, their actual benefits are not yet clearly verified while the energy efficiency can be improved in many ways. The carbon emission benefits from EV is largely diminished if we charge EV with electricity from petroleum power plants due to power loss during generation, transmission, conversion and charging. On the other hand, regenerative braking is direct power conversion from the wheel to battery and one of the most important processes that can enhance energy efficiency of EV. Power loss during regenerative braking can be reduced by hybrid energy storage system (HESS) such that supercapacitors accept high power as batteries have small rate capability.

Integrating residential photovoltaic (PV) power generation and electrical energy storage (EES) systems into the Smart Grid is an effective way of utilizing renewable power and reducing the consumption of fossil fuels. This has become a particularly interesting problem with the introduction of dynamic electricity energy pricing models since electricity consumers can use their PV-based energy generation and EES systems for peak shaving on their power demand profile from the grid, and thereby, minimize their electricity bill. Due to the characteristics of a realistic electricity price function and the energy storage capacity limitation, the control algorithm for a residential EES system should accurately account for various energy loss components during operation. Hybrid electrical energy storage (HEES) systems are proposed to exploit the strengths of each type of EES element and hide its weaknesses so as to achieve a combination of performance metrics that is superior to those of any of its individual EES components. This paper introduces the problem of how best to utilize a HEES system for a residential Smart Grid user equipped with PV power generation facilities. The optimal control algorithm for the HEES system is developed, which aims at minimization of the total electricity cost over a billing period under a general electricity energy price function. The proposed algorithm is based on dynamic programming and has polynomial time complexity. Experimental results demonstrate that the proposed HEES system and optimal control algorithm achieves 73.9% average profit enhancement over baseline homogeneous EES systems.

Electrical energy systems (EESs) include energy generation, distribution, storage, and consumption, and involve many diverse components and sub-systems to implement these tasks. This paper represents a first step towards the computer-aided design for EESs, encompassing modeling, simulation, design and optimization of these systems. CAD for EESs is a challenging task that mandates a multidisciplinary and heterogeneous approach. We identify similarities and differences between electrical energy systems and electronics systems in order to inherit as much as possible the profound legacy resources of electronic design automation (EDA). We introduce fundamental concepts, from the general problem formulation to the development and deployment of efficient, scalable, and versatile CAD and EDA methods and framework for the optimal or near-optimal EESs.

Battery life of high-end smartphones and tablet PCs is becoming more and more important due to the gap between the rapid increase in power requirements of the electronic components and the slow increase in energy storage capacity of Li-ion batteries. Energy harvesting, on the other hand, is a promising technique that can prolong the battery life without compromising the users' experience with the devices and potentially without the necessity to have access to a wall AC outlet. Such energy harvesting products are available on the market today, but most of them are equipped with only a large battery pack, which exhibits poor capacity utilization during solar energy harvesting. In this paper, we propose and demonstrate that using a supercapacitor instead of a large capacity battery can be beneficial in terms of improving the charging efficiency, and thereby, significantly reducing the charging time. However, this is not a trivial task and gives rise to many problems associated with charging the supercapacitor via the USB charging port. We analyze the USB charging standard and commercial USB charger designs in smartphones to formulate an energy efficiency optimization problem and propose a dynamic programming-based online algorithm to solve the aforesaid problem. Experimental results show up to 34.5% of charging efficiency improvement compared with commercial solar charger designs.

Dynamic voltage and frequency scaling (DVFS) has been studied for well over a decade. Nevertheless, existing DVFS transition overhead models suffer from significant inaccuracies; for example, by incorrectly accounting for the effect of DC-DC converters, frequency synthesizers, voltage, and frequency change policies on energy losses incurred during mode transitions. Incorrect and/or inaccurate DVFS transition overhead models prevent one from determining the precise break-even time and thus forfeit some of the energy saving that is ideally achievable. This paper introduces accurate DVFS transition overhead models for both energy consumption and delay. In particular, we redefine the DVFS transition overhead including the underclocking-related losses in a DVFS-enabled microprocessor, additional inductor IR losses, and power losses due to discontinuous-mode DC-DC conversion. We report the transition overheads for a desktop, a mobile and a low-power representative processor. We also present DVFS transition overhead macromodel for use by high-level DVFS schedulers.

A hybrid electrical energy storage (HEES) system that consists of multiple, heterogeneous electrical energy storage (EES) elements is a promising solution to achieve a cost-effective EES system because no storage element has ideal characteristics. The state-of-the-art HEES systems are based on a shared-bus charge transfer interconnect (CTI) architecture. Consequently, they are quite limited in scalability which is a function of the number of EES banks. This paper is the first introduction of a HEES system based on a networked CTI architecture, which is highly scalable and is capable of accommodating multiple, concurrent charge transfers. The paper starts by presenting a router architecture for the networked CTI and an effective on-line routing algorithm for multiple charge transfers. In the proposed algorithm, negotiated congestion (NC) routing for multiple charge transfers is performed and any lack of routing resources is addressed by merging two or more charge transfers while maximizing the overall energy efficiency by setting the optimal voltage level for the shared CTI. Examples of the proposed networked CTI are presented and the efficacy of the routing algorithm is demonstrated on a mesh-grid networked CTI.

The design of electric vehicles require a complete paradigm shift in terms of embedded systems architectures and software design techniques that are followed within the conventional automotive systems domain. It is increasingly being realized that the evolutionary approach of replacing the engine of a car by an electric engine will not be able to address issues like acceptable vehicle range, battery lifetime performance, battery management techniques, costs and weight, which are the core issues for the success of electric vehicles. While battery technology has crucial importance in the domain of electric vehicles, how these batteries are used and managed pose new problems in the area of embedded systems architecture and software for electric vehicles. At the same time, the communication and computation design challenges in electric vehicles also have to be addressed appropriately. This paper discusses some of these research challenges.

Photovoltaic (PV) power generation systems are one of the most promising renewable power sources to reduce carbon footprint. Grid-connected PV power systems do not generally have a battery to store the excess charge. However, due to severe imbalance between the peak PV power generation and peak load demand, battery-less Grid-connected PV systems are much less effective for the purpose of power generation and demand mismatch mitigation. Grid-connected PV systems equipped with a battery indeed require elaborate management. This is the first paper that introduces a systematic battery management optimization that accommodates arbitrary electricity billing policies. We formulate an optimization framework to determine the battery charging current from the Grid and PV array taking into account the limited battery capacity, power converter efficiency, battery's internal resistance and rate capacity effect, and maximum power tracking of the PV array. Experimental results show that the proposed algorithm effectively reduces the electricity bill by as much as 28% when compared with previous state-of-the-art battery management policies.

Compared with the conventional homogeneous electrical energy storage (EES) systems, hybrid electrical energy storage (HEES) systems provide high output power and energy density as well as high power conversion efficiency and low self-discharge at a low capital cost. Cycle efficiency of a HEES system (which is defined as the ratio of energy which is delivered by the HEES system to the load device to energy which is supplied by the power source to the HEES system) is one of the most important factors in determining the overall operational cost of the system. Therefore, EES banks within the HEES system should be prudently designed in order to maximize the overall cycle efficiency. However, the cycle efficiency is not only dependent on the EES element type, but also the dynamic conditions such as charge and discharge rates and energy efficiency of peripheral power circuitries. Also, due to the practical limitations of the power conversion circuitry, the specified capacity of the EES bank cannot be fully utilized, which in turn results in over-provisioning and thus additional capital expenditure for a HEES system with a specified level of service. This is the first paper that presents an EES bank reconfiguration architecture aiming at cycle efficiency and capacity utilization enhancement. We first provide a formal definition of balanced configurations and provide a general reconfigurable architecture for a HEES system, analyze key properties of the balanced reconfiguration, and propose a dynamic reconfiguration algorithm for optimal, online adaptation of the HEES system configuration to the characteristics of the power sources and the load devices as well as internal states of the EES banks. Experimental results demonstrate an overall cycle efficiency improvement of by up to 108% for a DC power demand profile, and pulse duty cycle improvement of by up to 127% for high-current pulsed power profile. We also present analysis results for capacity utilization improvement for a reconfigurable EES bank.

Quality power estimation is a basis of efficient power management of electronic systems. Indirect power measurement, such as power estimation using a CPU performance monitoring unit (PMU), is widely used for its low cost and area overheads. However, the existing CPU PMUs only monitor the core and cache activities, which result in a significant accuracy limitation in the system-wide power estimation including off-chip memory devices. In this paper, we propose an on-chip bus (OCB) PMU that directly captures on-chip and off-chip component activities by snooping the OCB. The OCB PMU stores the activity information in separate counters, and online software converts counter values into actual power values with simple first-order linear power models. We also introduce an optimization algorithm that minimizes the energy model to reduce the number of counters in the OCB PMU. We compare the accuracy of the power estimation using the proposed OCB PMU with real hardware measurement and cycle-accurate system-level power estimation, and demonstrate high estimation accuracy compared with CPU PMU-based estimation method.

Modern vehicle electronics control units (ECUs) are getting rapidly complicated because of active safety and semi-autonomous driving controls, such as electric stability program (ESP) and adaptive cruise control (ACC). Furthermore, the operational environment of ECUs is extremely harsh, especially in terms of an ambient temperature well exceeding 100°C, which causes a very small temperature headroom. Thus, ECUs require a careful temperature management and high performance at the same time.

In this paper we propose an on-chip bus PMU which makes accurate estimates of system power consumption from a first-order linear power model by utilizing system-level activity information exchanged on the on-chip bus. It can easily be customized for different on-chip and off-chip memory devices, and is not dependent on a specific CPU core. We model memory devices using energy state machines, describe them in XML, and use that description automatic synthesis of the PMU.We compare the short-term accuracy of the proposed PMU with a cycle-accurate system-level power estimator, and assess its long-term accuracy with a real hardware prototype. Experimental results show that the the power estimation deviates less than 5% from real measurements.