Driver driving style plays an important role in vehicle energy management as well as driving safety. Furthermore, it is key for advance driver assistance systems development, toward increasing levels of vehicle automation. This fact has motivated numerous research and development efforts on driving style identification and classification. This paper provides a survey on driving style characterization and recognition revising a variety of algorithms, with particular emphasis on machine learning approaches based on current and future trends. Applications of driving style recognition to intelligent vehicle controls are also briefly discussed, including experts’ predictions of the future development.

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Driving Style Recognition for Intelligent Vehicle Control and Advanced Driver Assistance: A Survey

The potential for reduced fuel consumption of hybrid electric vehicles by the use of predictive powertrain control was assessed on measured-drive data from an urban route with varying topography. The assessment was done by evaluating the fuel consumption using three optimal controllers, each with a different level of information access to the driven route. The lowest information case represents that the vehicle knows that it is being driven in a certain environment, e.g., city driving, and that the controller has been optimized for that type of environment. The second highest information level represents a vehicle equipped with a GPS combined with a traffic-flow information system. In the highest information level, the future power demand is completely known to the control system, hence, the corresponding optimal controller results in the minimal attainable fuel consumption. This paper showed that good performance (1%-3% from the minimal attainable fuel consumption) can be achieved with the lowest information case, with a time-invariant controller that is optimized to the environment. The second highest information level results in less than 0.2% higher consumption than the minimal attainable on the studied route. This means that it is possible to design a predictive controller based on information supplied by the vehicle-navigation system and traffic-flow-information systems that can come very close to the minimal attainable fuel consumption. A novel algorithm that uses information supplied by the vehicle-navigation system was presented. The proposed algorithm results in a consumption only 0.3% from the minimal attainable consumption on the studied route.

Assessing the potential of predictive control for hybrid vehicle powertrains using stochastic dynamic programming

Experimental and numerical investigation of the effects of low-pressure, high-pressure and internal EGR configurations on the performance, combustion and emission characteristics in a hydrogen-enriched heavy-duty lean-burn natural gas SI engine

Deep reinforcement learning based energy management for a hybrid electric vehicle

Comparative investigation of NOx emission characteristics from a Euro 6-compliant diesel passenger car over the NEDC and WLTC at various ambient temperatures

Perfect knowledge of future driving conditions can be rarely assumed on real applications when optimally splitting power demands among different energy sources in a hybrid electric vehicle. Since performance of a control strategy in terms of fuel economy and pollutant emissions is strongly affected by vehicle power requirements, accurate predictions of future driving conditions are needed. This paper proposes different methods to model driving patterns with a stochastic approach. All the addressed methods are based on the statistical analysis of previous driving patterns to predict future driving conditions, some of them employing standard vehicle sensors, while others require non-conventional sensors (for instance, global positioning system or inertial reference system). The different modelling techniques to estimate future driving conditions are evaluated with real driving data and optimal control methods, trading off model complexity with performance.

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Modelling driving behaviour and its impact on the energy management problem in hybrid electric vehicles

An optimal solution to the energy management problem in hybrid electric vehicles has been extensively addressed in the literature during the last decade, especially with the application of dynamic programming, the Pontryagin minimum principle, or equivalent consumption minimization strategy. However, most of the works consist in finding cycle-specific optimal trajectories, which are far from being general control strategies. The aim of this work is to derive an analytical expression, general and not cycle specific, for the energy management problem that summarizes the optimal controls for a series hybrid electric vehicle. Starting from a simple definition of the powertrain, an explicit formulation is deducted to minimize fuel consumption based on an analytic analysis of the Pontryagin minimum principle. Explicit expressions for control variables and costates are provided. The result is a general control strategy that specifies the optimal generator set usage for a given probability distribution of expected traction demands. This methodology is benchmarked with common methods in the literature (dynamic programming and numerical Pontryagin minimum principle) showing near identical results but with strongly reduced computational time. The general form of this control strategy can also be used to analyze the optimal operation range of the engine, which could be useful for designing purposes.

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Analytical Optimal Solution to the Energy Management Problem in Series Hybrid Electric Vehicles

Switching strategy between HP (high pressure)- and LPEGR (low pressure exhaust gas recirculation) systems for reduced fuel consumption and emissions

During the last decade, hybrid electric vehicles have gained a presence in the automotive market. On the streets, in motorsports and in society, hybrid electric vehicles are increasingly common. Ma...

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Alberto Reig

Papers

Modelling driving behaviour and its impact on the energy management problem in hybrid electric vehicles

Analytical Optimal Solution to the Energy Management Problem in Series Hybrid Electric Vehicles

Switching strategy between HP (high pressure)- and LPEGR (low pressure exhaust gas recirculation) systems for reduced fuel consumption and emissions

Cost of ownership-efficient hybrid electric vehicle powertrain sizing for multi-scenario driving cycles:

Optimal control as a method for diesel engine efficiency assessment including pressure and NOx constraints