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In order to improve the accuracy of internal temperature estimation in batteries, a 10-parameter time-varying multi-surface heat transfer model including internal heat production, heat transfer and external heat transfer is established based on the structure of a lithium iron phosphate pouch battery and its three directional anisotropic heat conduction characteristics. The entropy heat coefficient, internal equivalent heat capacity and internal equivalent thermal resistance related to the SOC and temperature state of the battery were identified using experimental tests and the least square fitting method, and were then used for online calculation of internal heat production and heat transfer in the battery. According to the time-varying and nonlinear characteristics of the heat transfer between the surface and the environment of the battery, an internal temperature estimation algorithm based on the square root cubature Kalman filter was designed and developed. By iteratively calculating the estimated surface temperature and the measured value, dynamic tracking and online correction of the internal temperature of the battery can be achieved. The verification results using FUDS and US06 dynamic working condition data show that the proposed method can quickly eliminate the influence of initial temperature deviations and accumulated process errors and has the characteristics of a high estimation accuracy and good robustness. Compared with the estimation results of the adaptive Kalman filter, the proposed method improves the estimation accuracy of FUDS and US06 working conditions by 67% and 54%, respectively, with a similar computational efficiency.

To enhance path tracking precision in intelligent vehicles, this study proposes a lateral–longitudinal control strategy optimized with a Backpropagation (BP) neural network. The strategy employs the BP neural network to dynamically adjust prediction and control time-domain parameters within an established Model Predictive Control (MPC) framework, effectively computing real-time front-wheel steering angles for lateral control. Simultaneously, it integrates an incremental Proportional–Integral–Derivative (PID) approach with a meticulously designed acceleration–deceleration strategy for accurate and stable longitudinal speed tracking. The strategy’s efficiency and superior performance are validated through a comprehensive CarSim(2020)/Simulink(2020b) simulation, demonstrating that the proposed controller adeptly modulates control parameters to adapt to various road adhesion coefficients and vehicle speeds. This adaptability significantly improves tracking and driving dynamics, thereby enhancing accuracy, safety, stability, and real-time responsiveness in the intelligent vehicle tracking control system.

Wave power is a potential technology for generating sustainable renewable energy. Several types of wave energy converters (WECs) have been proposed for this purpose. WECs operate in a harsh maritime environment that sets strict limitations on how and when the device can be economically and safely reached for maintenance. Thus, to ensure profitable energy generation over the system life cycle, system reliability is a key aspect to be considered in WEC development. In this article, we describe a reliability analysis approach for WEC development, based on the use of reliability block diagram (RBD) modelling. We apply the approach in a case study involving a submerged oscillating wave surge converter device concept that utilizes hydraulics in its power take-off system. In addition to describing the modelling approach, we discuss the data sources that were used for gathering reliability data for the components used in a novel system concept with very limited historical or experimental data available. This includes considerations of the data quality from various sources. As a result, we present examples of applying the RBD modelling approach in the context of WEC development and discuss the applicability of the approach in supporting the development of new technologies.

Wave energy conversion is a promising field of renewable energy, but it still faces several technological and economic challenges. One of these challenges is to improve the energy efficiency and adaptability of Wave Energy Converters to varying wave conditions. A technological approach to solve this efficiency challenge is the negative spring mechanisms illustrated in recent studies. This paper proposes and analyzes a novel negative spring technological concept that dynamically modifies the mass and inertia of a Wave Energy Converter by transferring seawater between its compartments. The added value of the presented technology relies on interoperability, ease of manufacturing and operating, and increased energy efficiency for heterogeneous sea states. The concept is presented in two analyzed alternatives: a passive one, which requires no electrical consumption and is purely based on the relative motion of the bodies, and an active one, which uses a controlled pump system to force the water transfer. The system is evaluated numerically using widely accepted simulation tools, such as WECSIM, and validated by physical testing in a wave flume using decay and regular test scenarios. Key findings include a relevant discussion about system limitations and a demonstrated increase in the extracted energy efficiency up to 12.7% while limiting the maximum power extraction for a singular wave frequency to 3.41%, indicating an increased adaptability to different wave frequencies because of the amplified range of near-resonance operation of the WEC up to 0.21 rad/s.

A reasonable plan for charging stations is critical to the widespread use of electric vehicles. In this paper, we propose an optimal planning method for electric vehicle charging stations. First of all, we put forward a forecasting method for the distribution of electric vehicle fast charging demand in urban areas. Next, a new mathematical model that considers the mutual benefit of electric vehicle users and the power grid is set up, aiming to minimize the social cost of charging stations. Then, the model is solved by the Voronoi diagram combined with improved particle swarm optimization. In the end, the proposed method is applied to an urban area, simulation results demonstrate that the proposed method can yield optimal location and capacity of each charging station. A contrasting case is carried out to verify that improved particle swarm optimization is more effective in finding the global optimal solution than particle swarm optimization.

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.

The combination of car-sharing and electric vehicles can increase the acceptance of electric vehicles and facilitate car-sharing to be a more sustainable means of transport. However, this also poses more challenges for the good planning of electric car-sharing systems. To assist car-sharing companies in improving the planning decisions, this paper developed an evaluation framework from a comprehensive view. In the first step, four evaluation criteria were identified according to the planning process: construction of stations; routine inspection; vehicle usability and relocation management; and the maintenance and replacement of stations. Then, a combinatorial method based on analytic hierarchy process (AHP), cost-benefit analysis (CBA), and Voronoi diagram (VD) is developed to determine the relative weight of the four criteria and evaluate the alternative. Finally, the evaluation framework was applied in a realistic case of EVCARD, which is the most influential electric car-sharing company in China. The performance of two different operational districts of EVCARD—Jingan and Changning—were compared. The results showed that vehicle usability and relocation management is the greatest criterion influencing the planning performance of the electric car-sharing system in China, and that routine inspection is a negligible but important factor. According to the relative scores, Jiagan District performed better than Changning district.