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Tula Technology has developed a novel, patented motor control strategy called Dynamic Motor Drive (DMD®), which reduces the light-load losses of electric motor drives by intermittently pulsing motor torque. Since passenger vehicles frequently operate at light loads, DMD can significantly reduce the energy consumption of electric vehicles, extending their range. DMD is particularly beneficial for rare-earth-free or reduced-rare-earth permanent magnet (PM)-based electric motor drives, and can help make these less expensive, more environmentally friendly motors range-competitive with traditional PM motors. This paper details the efficiency improvement when DMD is applied to a rare-earth-free synchronous reluctance motor (SynRM). We present recent experimental results to show the loss reduction by DMD in a prototype 8-kW three-phase SynRM, and compare them to simulation results.

Dynamic Skip Fire (DSF) is an advanced cylinder deactivation technology to reduce fuel consumption and emissions of internal combustion engines. The firing sequence may vary dynamically depending on driver demanded torque with all cylinders capable of deactivation. This creates a challenge for managing noise vibration and harshness (NVH) caused by the low frequency excitation in the engine’s torque profile, especially in smaller engines with 3 or 4 cylinders. Due to the varying nature of firing sequences, the excitation is not limited to one or two engine orders and can vary with time, requiring broadband mitigation of the driveline. This work proposes the optimization of flywheel inertia combined with careful control of torque converter slip to overcome this challenge. Four different flywheel configurations and varying levels of torque converter slip were tested on a VW Jetta fitted with a 1.8L 4-cylinder engine with DSF control capability.

Catalytic aftertreatment is commonly used to reduce regulated gas emissions from Internal Combustion (IC) engines. Achieving fast catalyst light-off has always been a challenge for automobile IC engine applications. This paper experimentally studied the thermal management and regulated gas emissions from a Spark Ignition (SI) engine with Dynamic Skip Fire (DSF®) technology during cold start period. The study has found that DSF can increase exhaust gas temperature at the catalyst inlet by up to 100°C, and the exhaust enthalpy by up to 20%. Cold start tailpipe carbon monoxide (CO) and hydrocarbon (HC) emissions can be reduced by 10% to 20% largely due to the increased exhaust gas temperature and enthalpy. Dynamic air pumping can further increase exhaust gas temperature by 30 °C, and can nearly double enthalpy delivered to the catalyst, which reduces cold start HC emissions by more than 50%.

Engines equipped with Dynamic Skip Fire (DSF) technology generate low frequency and high amplitude excitations that could reduce vehicles drive quality if not properly calibrated. The excitation frequency of each firing pattern depends on its length and on the rotational speed of the engine. Excitation amplitude mainly depends on the requested engine torque by the driver. During the calibration process, the torque characteristics that results in production level of noise, vibration, and harshness (NVH), must be identified, for each firing pattern and engine speed. This process is quite time consuming but necessary. To improve our process, a novel machine learning technique is utilized to accelerate the calibration effort. The idea is to automate the vibration rating procedure such that given the relevant power-train parameters, a vibration rating associated with that driving condition can be predicted. This process is divided into two (2) prediction models.

Automotive companies are studying to add extra value in their vehicles by enhancing powertrain sound quality. The objective is to create a brand sound that is unique and preferred by their customers since quietness is not always the most desired characteristic, especially for high-performance products. This paper describes the process of developing a brand powertrain sound for a high-performance vehicle using the DFSS methodology. Initially the customer's preferred sound was identified and analyzed. This was achieved by subjective evaluations through voice-of-customer clinics using vehicles of similar specifications. Objective data were acquired during several driving conditions. In order for the design process to be effective, it is very important to understand the relationship between subjective results and physical quantities of sound. Several sound quality metrics were calculated during the data analysis process.