Range Efficiency and Assistance for Zero-Carbon Haulage (REACH)

Research Project, 2026 – 2029

Long-distance road transport of goods is essential for society, but diesel engines drive up carbon emissions and fossil fuel reliance. Switching to electric propulsion is harder for heavy vehicles than for cars, mainly due to costly, bulky batteries and very limited fast charging infrastructure. Driver assistance functions relying on data about route ahead can help, especially during the transition, to aid the driver to plan and to make informed decisions that increase transport efficiency.

Functions, already on market and developed for diesel propulsion, such as Driver coach and Residual range predictor use information from the previous and present driving, as opposed to from the route ahead. One of few on-market functions which uses information about the route ahead is Predictive cruise control. It optimizes the speed trajectory over a prediction horizon of some kilometres ahead. It reduces diesel consumption by utilizing the road grades ahead, i.e., uphills and downhills, with typically 5%.

Previous projects (OCEAN, COVER, and U-FEEL) have developed an operating condition (OC) format. The OC format describes the conditions of where to operate with the vehicle. The OC format is divided in road, weather, traffic, and mission. The most comprehensive use of the OC is for simulations, both forward or backward simulation, of a single vehicle together with the environment and driver. However, the same OC format is useful also for above mentioned functions.

Functions (algorithms, both in clouds and in vehicle) which support a migration are not fully developed for novel propulsion systems, and they are far from exploiting all available information of the route ahead: road, traffic, weather, and mission.

Better understanding (models) of some of the most important motion resistances is still needed. Also, estimation and management of uncertainties are crucial to gain trust in the functions.

Goals

Use information of a selected route ahead, together with information of the vehicle design and present state, in real-time functions for each individual vehicle. Foreseen developments of the OC format:

 - Better models of traffic influence on the air drag and weather influence on the rolling resistance. Also, analysis of how well same data can be used by different vehicle types (e.g., cars and heavy trucks) is important.

 - More attributes of the charging stations, e.g., que time, charging power, and possibly cost per energy charged.

Propagate and use stochastics in the OC to estimate the uncertainties in the real-time functions for each individual vehicle.

 - promising novel propulsion systems. Generic propulsion system models should be used, but a minimum of system specific parameters is foreseen to be necessary. Two novel types are pointed out as examples/case studies: FCEV (fuel cell electric vehicles) and electrically propelled trailing units are identified as novel propulsion systems. Both examples include Power split.

 - Express Predictive cruise control as minimization of operational costs rather than energy consumption, because price per energy unit is often different for different energy (diesel, electricity, hydrogen) and the prices will change over the vehicle’s lifetime. Minimizing operational costs should also include component wear (battery, fuel cells, tyres, brake pads, etc.). However, traffic rules and safety are constraints in a cost optimization, so they must be fulfilled.

 - Arrange the architecture for the functions. This is about parameters and signal interfaces between the functions. The purpose is to facilitate efficient development through reusability of algorithms and subsystems in the vehicle. Synergies are likely in sharing information about the route ahead and include uncertainties.