Vehicle lead-acid battery state-of-charge meter

We describe a state-of-charge, or “residual-capacity” meter for lead-acid batteries that intelligently synthesizes coulometric and terminal-voltage methods in a new algorithm to provide reliable, continuous readout of remaining capacity. Novel electronic circuit design eliminates the need to install a shunt in the vehicle. The meter learns the characteristics of a battery to which it is attached, removing the need for setup, customisation, programming or calibration at time of installation or battery replacement. The meter can thus be installed by unqualified personnel. Initial measurements suggest the design to be robust and accurate

Generalized Costs of Travel by Solo and Pooled Ridesourcing vs. Privately Owned Vehicles, and Policy Implications

The emergence of “3 Revolutions” in transportation (automation, electrification and shared mobility) presents a range of questions regarding how consumers will travel in the future, and under what conditions there may be rapid adoption of various services. These include individual on-demand taxi-style services, shared mobility in pooled services, and use of public transit, all with or without drivers. There is now enough data and estimates on the costs of these service combinations, and in some cases ridership data, to consider how consumers are making choices and could do so in the future as things evolve. This project involved: (a) reviewing existing literature and data on consumer mode and vehicle choice; (b) developing new “generalized cost” estimates that combine monetary and non-monetary (e.g., hedonic) components of travel choice, notably incorporating value of time; and (c) conducting a comparison of monetary and generalized trip cost for a range of trip types across travel options in the near term (2020) and longer term (2030-35). Three main travel options were considered: privately owned vehicles, ridesourced solo trips, and ridesourced pooled trips. Consideration of internal combustion vs. battery electric and, in the longer term, automated technology was also core to the analysis. The trips considered include urban and suburban types in the San Francisco metro area, using actual trip characteristics. The results suggest that in the near-term, solo ridesourcing is likely to be perceived as significantly more expensive (in terms of monetary and time costs) than pooled ridesourcing or solo private vehicle trips except for those with a very high value of time. Solo ridesourcing does better in dense, slow, urban trips than in faster suburban trips. In the longer term, with automated driverless vehicles, solo ridesourcing could become the cheapest mode for many travelers in a range of situations. This report includes an initial consideration of the implications of these policies for affecting travel choices, presumably to push choices toward pooled ridesourcing as a sustainable option. VMT-based pricing, pricing that could be adjusted with vehicle occupancy, and parking-related approaches are described. A large price signal might be needed to shift travel, given some of the differences in generalized cost found in this analysis

Data Improving in Time Series Using ARX and ANN Models

Anomalous data can negatively impact energy forecasting by causing model parameters to be incorrectly estimated. This paper presents two approaches for the detection and imputation of anomalies in time series data. Autoregressive with exogenous inputs (ARX) and artificial neural network (ANN) models are used to extract the characteristics of time series. Anomalies are detected by performing hypothesis testing on the extrema of the residuals, and the anomalous data points are imputed using the ARX and ANN models. Because the anomalies affect the model coefficients, the data cleaning process is performed iteratively. The models are re-learned on “cleaner” data after an anomaly is imputed. The anomalous data are reimputed to each iteration using the updated ARX and ANN models. The ARX and ANN data cleaning models are evaluated on natural gas time series data. This paper demonstrates that the proposed approaches are able to identify and impute anomalous data points. Forecasting models learned on the unclean data and the cleaned data are tested on an uncleaned out-of-sample dataset. The forecasting model learned on the cleaned data outperforms the model learned on the unclean data with 1.67% improvement in the mean absolute percentage errors and a 32.8% improvement in the root mean squared error. Existing challenges include correctly identifying specific types of anomalies such as negative flows

Power quality and electromagnetic compatibility: special report, session 2

The scope of Session 2 (S2) has been defined as follows by the Session Advisory Group and the Technical Committee: Power Quality (PQ), with the more general concept of electromagnetic compatibility (EMC) and with some related safety problems in electricity distribution systems. Special focus is put on voltage continuity (supply reliability, problem of outages) and voltage quality (voltage level, flicker, unbalance, harmonics). This session will also look at electromagnetic compatibility (mains frequency to 150 kHz), electromagnetic interferences and electric and magnetic fields issues. Also addressed in this session are electrical safety and immunity concerns (lightning issues, step, touch and transferred voltages). The aim of this special report is to present a synthesis of the present concerns in PQ&EMC, based on all selected papers of session 2 and related papers from other sessions, (152 papers in total). The report is divided in the following 4 blocks: Block 1: Electric and Magnetic Fields, EMC, Earthing systems Block 2: Harmonics Block 3: Voltage Variation Block 4: Power Quality Monitoring Two Round Tables will be organised: - Power quality and EMC in the Future Grid (CIGRE/CIRED WG C4.24, RT 13) - Reliability Benchmarking - why we should do it? What should be done in future? (RT 15

The novel application of optimization and charge blended energy management control for component downsizing within a plug-in hybrid electric vehicle

The adoption of Plug-in Hybrid Electric Vehicles (PHEVs) is widely seen as an interim solution for the decarbonization of the transport sector. Within a PHEV, determining the required energy storage capacity of the battery remains one of the primary concerns for vehicle manufacturers and system integrators. This fact is particularly pertinent since the battery constitutes the largest contributor to vehicle mass. Furthermore, the financial cost associated with the procurement, design and integration of battery systems is often cited as one of the main barriers to vehicle commercialization. The ability to integrate the optimization of the energy management control system with the sizing of key PHEV powertrain components presents a significant area of research. Contained within this paper is an optimization study in which a charge blended strategy is used to facilitate the downsizing of the electrical machine, the internal combustion engine and the high voltage battery. An improved Equivalent Consumption Method has been used to manage the optimal power split within the powertrain as the PHEV traverses a range of different drivecycles. For a target CO2 value and drivecycle, results show that this approach can yield significant downsizing opportunities, with cost reductions on the order of 2%–9% being realizable

Prospects for Plug-in Hybrid Electric Vehicles in the United States and Japan: A General Equilibrium Analysis

Abstract and PDF report are also available on the MIT Joint Program on the Science and Policy of Global Change website (http://globalchange.mit.edu/)The plug-in hybrid electric vehicle (PHEV) may offer a potential near term, low carbon alternative to today's gasoline- and diesel-powered vehicles. A representative vehicle technology that runs on electricity in addition to conventional fuels was introduced into the MIT Emissions Prediction and Policy Analysis (EPPA) model as a perfect substitute for internal combustion engine (ICE-only) vehicles in two likely early-adopting markets, the United States and Japan. We investigate the effect of relative vehicle cost and all-electric range on the timing of PHEV market entry in the presence and absence of an advanced cellulosic biofuels technology and a strong (450ppm) economy-wide carbon constraint. Vehicle cost could be a significant barrier to PHEV entry unless fairly aggressive goals for reducing battery costs are met. If a low cost vehicle is available we find that the PHEV has the potential to reduce CO2 emissions, refined oil demand, and under a carbon policy the required CO2 price in both the United States and Japan. The emissions reduction potential of PHEV adoption depends on the carbon intensity of electric power generation and the size of the vehicle fleet. Thus, the technology is much more effective in reducing CO2 emissions if adoption occurs under an economy-wide cap and trade system that also encourages low-carbon electricity generation.BP Conversion Research Project and the MIT Joint Program on the Science and Policy of Global Change through a consortium of industrial sponsors and Federal grants

Tracking battery state-of-charge in a continuous use off-grid electricity system

The growing importance of batteries in the delivery of primary energy, for example in electric vehicles and isolated off-grid electricity systems, has added weight to the demand for simple and reliable measures of a battery’s remaining stored energy at any time. Many approaches to estimating this battery state-of-charge exist, ranging from those based on a full appreciation of the chemistry and physics of the storage and delivery mechanisms used, and requiring extensive data on which to base an estimate, to the naïve and simple, based only, for example, on the terminal voltage of the battery. None, however, is perfect, and able to deliver a simple percentage-full figure, as in a fuel gauge. The shortcomings are due to a range of complicating factors, including the impact of rate of charge, rate of discharge, battery aging, and temperature, to name just some of these. This paper presents a simple yet effective method for tracking state-of-charge in an off-grid electricity system, where batteries are in continuous use, preventing static parameter measurements, and where charge/discharge cycles do not necessarily follow an orderly sequence or pattern. A reliable indication of state-of-charge is, however, highly desirable, but need be only of fuel gauge precision, say to the nearest 12-20%. The algorithm described utilises knowledge of the past, and constantly adapts parameters such as charge efficiency and total charge capacity based on this knowledge, and on the occurrence of specific identifiable events such as zero or full charge