• Energy Research
• 7. Clean energy close
• GB close
• IT close
• PL close
• AT close
• Transport Research close

This paper presents a tradeoff analysis in terms of accuracy and computational cost between different architectures of artificial neural networks for the State of Charge (SOC) estimation of lithium batteries in hybrid and electric vehicles. The considered layouts are partly selected from the literature on SOC estimation, and partly are novel proposals that have been demonstrated to be effective in executing estimation tasks in other engineering fields. One of the architectures, the Nonlinear Autoregressive Neural Network with Exogenous Input (NARX), is presented with an unconventional layout that exploits a preliminary routine, which allows setting of the feedback initial value to avoid estimation divergence. The presented solutions are compared in terms of estimation accuracy, duration of the training process, robustness to the noise in the current measurement, and to the inaccuracy on the initial estimation. Moreover, the algorithms are implemented on an electronic control unit in serial communication with a computer, which emulates a real vehicle, so as to compare their computational costs. The proposed unconventional NARX architecture outperforms the other solutions. The battery pack that is used to design and test the networks is a 20 kW pack for a mild hybrid electric vehicle, whilst the adopted training, validation and test datasets are obtained from the driving cycles of a real car and from standard profiles.

Abstract Cost-effectively decarbonising the power sector and household energy use using variable renewable energy will require that electricity consumption becomes much more flexible and responsive to constraints in supply and the distribution network. In recent years residential demand response (DR) has received increasing attention that has sought to answer, based on current evidence, questions about how much consumers will engage with DR. This paper critically reviews the evidence base for residential consumer engagement with DR and draws out several important limitations in it. We argue for a more action-oriented focus on developing practical strategies to enable and unlock greater loadshifting and consumer engagement with DR within a changing technology and regulatory context. A number of recommendations are put forward for accelerating UK consumer engagement with DR, presented under three broad strategies: (a) promote awareness of smart tariffs, smart meters and storage and automation behind-the-meter devices as mutually-supportive components within a common ‘DR technology cluster’; (b) deliver targeted support for adoption of electric vehicles and other storage and automation technologies; (c) enable and support informed adoption of DR-enabling products and services through ‘smarter’ digital comparison tools (DCTs), data portability, and faster, simpler switching. The interdependency between components within this DR technology cluster delivers efficiency but also poses a risk that one delayed component (e.g., smart metering) will hold-up policy and industry support for other components. The urgency of decarbonisation goals makes it necessary to push forward as many of these elements as possible rather than the pace being set by the slowest.

In recent years, a ban on short-haul flights for which alternative, more environmentally friendly transportation is available has been on European regulatory agendas, particularly in countries like Germany, with its well-developed high-speed rail (HSR) and a dense network of low-cost, point-to-point airline traffic. This study aims to quantify the potential impact on CO2 emissions of substituting short-haul flights with rail frequencies in 87 German air travel corridors. Using OAG data on passenger bookings and airline schedules for 2019, as well as current rail travel times, we determine the target flights by looking at the actual proportion of connecting passengers per frequency. We estimate a potential reduction in CO2 emissions of between 2.7% and 22%, depending on how strict the flight substitution is. However, the social benefits of those carbon emissions might fall short of the travel time losses experienced by the passengers. Increased investment to improve rail speeds and intermodal accessibility appears necessary before the substitution policies can be implemented.

Renewable energy sources have become necessary for long-term energy sustainability due to the increased demand for electric cars and worrisome rises in carbon dioxide emissions from traditional energy sources. Furthermore, transportation is one of the sectors that uses the most energy on the planet, accounting for 24% of overall consumption. Fossil fuels are still the dominant energy source for balancing global demand/supply dynamics. Supporting laws and regulations have enhanced the first phase of environmentally friendly energy-resource consumption. This has spurred the development of new solutions that cut greenhouse-gas emissions and reduce the air pollution produced by internal combustion engines that are fuelled by fossil fuels. Wind energy is one of the clean energy sources that may be utilised for this purpose. Wind energy has been used to power electric-car-charging infrastructure, generally in a hybrid mode with another renewable source. This research examines the possibility of using wind energy as a standalone energy source to support electric-vehicle-charging infrastructure. Using data from Malacca, Malaysia, and HOMER software, the project will build and optimise a standalone wind-powered charging station. An RC-5K-A wind turbine coupled to a battery and converter is the appropriate choice for the system. The findings demonstrate that the turbine can produce 214,272 kWh per year at the cost of USD 0.081/kWh, confirming wind’s future feasibility as an energy-infrastructure support source.

Instrumented cells, equipped with miniature sensors, are proposed to aid the next stage of electrification in the automotive and aerospace industries. To optimize the energy density available within a lithium ion (li-ion) pack we demonstrate how a power line communication (PLC) network can be formed at an individual cell level. This reduces the need for complex communication cables within a vehicle wiring loom.\ud \ud Here we show a unique prototype smart cell (instrumented cell equipped with interface circuitry and processing capability) can be connected via a PLC network, to enable monitoring of vital parameters (temperature, voltage, current), regardless of cell state of charge (2.5 V to 4.2 V DC operating voltage). In this proof-of-concept study, we show the reliable system (0 errors detected over ∼24 hr experiment, acquired data (logged at 10 Hz) from cells (in a parallel configuration), and comparative data for cell internal and external temperature was recorded. During a prolonged discharge (1C, 5A discharge) a peak core temperature >3 °C hotter than surface temperature was observed, highlighting the need to understand cell operation in cooling system design.

As a critical indictor in the Battery Management System (BMS), State of Charge (SOC) is closely related to the reliable and safe operation of lithium-ion (Li-ion) batteries. Model-based methods are an effective solution for accurate and robust SOC estimation, the performance of which heavily relies on the battery model. This paper mainly focuses on battery modeling methods, which have the potential to be used in a model-based SOC estimation structure. Battery modeling methods are classified into four categories on the basis of their theoretical foundations, and their expressions and features are detailed. Furthermore, the four battery modeling methods are compared in terms of their pros and cons. Future research directions are also presented. In addition, after optimizing the parameters of the battery models by a Genetic Algorithm (GA), four typical battery models including a combined model, two RC Equivalent Circuit Model (ECM), a Single Particle Model (SPM), and a Support Vector Machine (SVM) battery model are compared in terms of their accuracy and execution time.