Development of a new method to assess fuel saving using gear shift indicators

European regulations set the emissions requirements for new vehicles at 130 g CO2/km, with an additional 10 g CO2/km to be achieved by additional complementary measures, including gear shift indicators. However, there is presently little knowledge of how much fuel or CO2 could actually be saved by the introduction of gear shift indicators, and there is no consensus on how these savings should be quantified. This study presents a procedure which allows these savings to be quantified over a New European Driving Cycle, and explores the trade-off between fuel savings and drivability. A vehicle model was established and calibrated using data obtained from pedal ramp tests conducted at steady speed using a chassis dynamometer, significantly reducing the time required to generate a calibration data set when compared with a steady-state mapping approach. This model was used for the optimisation of gear shift points on the New European Driving Cycle for reduced fuel consumption subject to drivability constraints. During model validation the greatest fuel saving achieved experimentally for a warm engine was 3.6% over the New European Driving Cycle, within the constraints imposed using subjective driver appraisal of vehicle drivability. The same shift strategy for a cold start driving cycle showed a fuel saving of 4.3% over the baseline, with corresponding savings in CO2 of 4.5% or 6.4 g CO2/km. For both hot and cold tests the savings were made entirely in the urban phase of the New European Driving Cycle; there were no significant differences in fuel consumption in the extra-urban phase. These results suggest that the introduction of gear shift indicators could have a substantial impact, contributing significantly towards the 10 g CO2/km to be achieved by additional complementary measures when assessed in this way. It is not clear whether these savings would translate into real world driving conditions, but for legislative purposes an assessment procedure based on the New European Driving Cycle remains a logical choice for simplicity and continuity. </jats:p

A New Method of Vehicle Positioning Using Bumps and Road Surface Defects

This paper presents the first usage of road surface defects as a means of vehicle position detection. Whilst several applications are possible, this work focuses on use in Formula E racing, where several driver information systems depend heavily upon accurate vehicle position estimation, including energy management advice and split time information, and where use of common positioning systems such as GPS is forbidden. Teams currently estimate the position on track by integrating vehicle speed, but this is susceptible to error accumulation throughout a lap, diminishing the precision and value of driver information systems. This work presents a method to improve the vehicle's position estimate by detecting bumps in the road surface using suspension damper displacement data, an approach which is novel because it is independent of common positioning techniques such as GPS. These bumps are used as positionally-fixed checkpoints around the track, allowing the positional estimate to be regularly corrected, mitigating drift in the original estimated position. Results of the bump detection algorithm developed show for the first time that this technique can pinpoint the vehicle position with a precision of 1.41m standard deviation, with scope for further improvement. This result is significant for positioning surface vehicles where other more standard techniques are precluded. In the Formula E application the approach is found to improve the precision (consistency) of the vehicle positional estimate for large parts of the circuit, which will allow higher quality and more reliable information to be given to the driver, thereby giving a competitive advantage

Assessing the Feasibility of a Cold Start Procedure for Solid State Batteries in Automotive Applications

This paper addresses the thermal management of a solid polymer electrolyte battery system, which is currently the only commercialized solid-state battery chemistry. These batteries aim to increase the range of electric vehicles by facilitating a lithium metal anode but are limited by operational temperatures above 60 °C. The feasibility of a cold start procedure is examined, which would enable a solid polymer battery to be used, without preconditioning, in a wide variety of ambient temperatures. The proposed solution involves dividing the solid-state battery into smaller sub-packs, which can be heated and brought online more quickly. Thermal modelling shows a cold start procedure is theoretically feasible when using a small liquid electrolyte lithium battery at the start. The key bottlenecks are the rate at which the solid-state batteries can be heated, and the discharge rates they can provide. After resistive heating is used for the first solid-state module, all subsequent heating can be provided by waste heat from the motor and operating battery modules. Due to the insulation required, the proposed system has lower volumetric, but higher gravimetric energy density than liquid electrolyte systems. This work suggests that with suitable system-level design, solid-state batteries could be widely adopted despite temperature constraints

Effect of Internal AC Heating on the Temperature Homogeneity of Different Size Battery Cells

Rapidly warming up batteries is an important challenge both for conventional lithium-ion batteries, which operate best over 15 °C, and for most solid-state batteries, which currently require operating temperatures over 60 °C. Internal heating using an alternating current (AC) has been proposed as a possible solution in automotive applications, with faster heating rates possible than conventional external heating methods. This paper investigates the performance of internal AC heating on cells of different sizes, for both cylindrical and pouch formats. A novel experimental arrangement is used in which two cells are tested in series while connected with opposing polarity to create a zero-voltage string, allowing the use of less expensive testing equipment. The results show that larger cells exhibit a considerably greater distribution of surface temperature than smaller format cells during internal heating. This is likely due to the more extreme spatial variation in current density in the current collectors, causing an uneven distribution of internal heat generation. This highlights a significant difference compared to external heating methods, which are not affected by this, and has important implications for temperature measurement and battery management if this type of internal heating is to be used, since temperature sensors must be placed in hot spots or supplemented by validated models to ensure all parts of the battery stay within safe temperature limits

A physics-informed Bayesian optimization method for rapid development of electrical machines

Abstract Advanced slot and winding designs are imperative to create future high performance electrical machines (EM). As a result, the development of methods to design and improve slot filling factor (SFF) has attracted considerable research. Recent developments in manufacturing processes, such as additive manufacturing and alternative materials, has also highlighted a need for novel high-fidelity design techniques to develop high performance complex geometries and topologies. This study therefore introduces a novel physics-informed machine learning (PIML) design optimization process for improving SFF in traction electrical machines used in electric vehicles. A maximum entropy sampling algorithm (MESA) is used to seed a physics-informed Bayesian optimization (PIBO) algorithm, where the target function and its approximations are produced by Gaussian processes (GP)s. The proposed PIBO-MESA is coupled with a 2D finite element model (FEM) to perform a GP-based surrogate and provide the first demonstration of the optimal combination of complex design variables for an electrical machine. Significant computational gains were achieved using the new PIBO-MESA approach, which is 45% faster than existing stochastic methods, such as the non-dominated sorting genetic algorithm II (NSGA-II). The FEM results confirm that the new design optimization process and keystone shaped wires lead to a higher SFF (i.e. by 20%) and electromagnetic improvements (e.g. maximum torque by 12%) with similar resistivity. The newly developed PIBO-MESA design optimization process therefore presents significant benefits in the design of high-performance electric machines, with reduced development time and costs