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In this paper, a novel cost-effective air hybrid powertrain concept for buses and commercial vehicles, Brunel Regenerative Engine Braking Device (RegenEBD) technology, is presented and its performance during the braking process is analysed using the Ricardo WAVE engine simulation programme. RegenEBD is designed to convert kinetic energy into pneumatic energy in the compressed air saved in an air tank. Its operation is achieved by using a production engine braking device and a proprietary intake system design. During the braking operation, the engine switches from the firing mode to the compressor mode by keeping the intake valves from fully closed throughout the four-strokes by installing the Variable Valve Exhaust Brake (VVEB) device on the intake valves. As a result, the induced air could be compressed through the opening gap of intake valves into the air tank through the modified intake system.

The automobile industry is under intense pressure to reduce carbon dioxide (CO2) emissions of vehicles. There is also increasing pressure to reduce the other tail-pipe emissions from vehicles to combat air pollution. Electric powertrains offer great potential for eliminating tailpipe CO2 and all other tailpipe emissions. However, current battery technology and recharging infrastructure still present limitations for some applications, where a continuous high-power demand is required. Furthermore, not all markets have the infrastructure to support a sizeable electric fleet and until the grid energy generation mix is of a sufficiently low carbon intensity, then significant vehicle life-cycle CO2 savings could not be realized by the Battery Electric Vehicles. This investigation examines the effects of combustion, efficiencies, and emissions of two alcohol fuels that could help to significantly reduce CO2 in both tailpipe and the whole life cycle.

An appropriate energy management strategy can further reduce the fuel consumption of P2 hybrid electric vehicles (HEV) with simple hybrid configuration and low cost. The rule-based real-time energy management strategy dominates the energy management strategies utilized in commercial HEVs, due to its robustness and low computational loads. However, its performance is sensitive to the setting of parameters and control actions. To further improve the fuel economy of a P2 HEV, the energy management strategy of the HEV has been re-designed based on the globally optimal control theory. An optimization strategy model based on the longitudinal dynamics of the vehicle and Bellman’s dynamic programming algorithm was established in this research and an optimal power split in the dual power sources including an internal combustion engine (ICE) and an electric machine at a given driving cycle was used as a benchmark for the development of the rule-based energy management strategy.

Climate change mitigation is the main challenge for the automotive industry, as the government issues legislation to combat CO2 emissions. In addition to electrification and battery electric vehicles, using low-carbon and zero-carbon fuels in Internal Combustion (IC) engines can also be an effective way to reach net zero-carbon transport. This study investigated and compared the combustion characteristics, performance and emissions of a highly boosted spark ignition (SI) engine fuelled with EU VI 95 RON E10 gasoline and blends of second-generation bio-gasoline with different ethanol contents of 5% (E5), 10% (E10), and 20% (E20). The single-cylinder SI engine was equipped with a centrally mounted high-pressure injector and supplied externally boosted air. Engine experiments were conducted at 2000 RPM and 3000 RPM with low and high load operations.

The absence of combustion information continues to be one of the key obstacles to the intelligent development of engines. Currently, the cost of integrating cylinder pressure sensors remains too high, prompting attention to methods for extracting combustion information from existing sensing data. Mean-value combustion models for engines are unable to capture changes of combustion parameters. Furthermore, the methods of reconstructing combustion information using sensor signals mainly depend on the working state of the sensors, and the reliability of reconstructed values is directly influenced by sensor malfunctions. Due to the concentration of operating conditions of hybrid vehicles, the reliability of priori calibration map has increased. Therefore, a combustion information reconstruction method based on priori calibration information and the fused feature deviations of existing sensing signals is proposed and named the "Deviation-based Centroid Displacement Method" (DCDM).