Range anxiety with electric utility vehicles is real, as a battery failure can have serious consequences. Researchers at Chalmers University of Technology in Sweden have developed tools to help electric delivery vehicles strategically navigate to use as little energy as possible. The secret is to look beyond the simple distance traveled and instead focus on the overall energy use, which has saved up to 20% energy.
âWe have developed systematic tools to learn the optimal use of energy. Additionally, we can ensure that electric vehicles don’t run out of battery or charge unnecessarily in complex traffic networks, âsays BalÃ¡zs KulcsÃ¡r, professor in the electrical engineering department at Chalmers University of Technology.
The research is the latest result of a joint project between Chalmers and the Volvo Group that is studying how electric vehicles can be used for distribution tasks, and the new algorithm for learning and planning the optimal path of electric vehicles is so effective that ‘it is already in use by the Volvo Group.
The shortest distance not always the least energy
In the study, the researchers investigated how a fleet of electric trucks can deliver goods through a complex and congested traffic network. The challenge is how delivery vehicles carrying household items, such as groceries or furniture to several different addresses, should best plan their routes. By working the optimal order to deliver to customers, vehicles can run as long as possible without having to interrupt work to recharge unnecessarily. Route planning for electric vehicles has generally tended to assume that the lowest mileage is also the most efficient, and therefore has focused on finding the shortest route as a priority. Rather, BalÃ¡zs KulcsÃ¡r and his colleagues focused on overall battery usage as the primary goal and looked for routes with the lowest possible power consumption.
âIn real traffic situations, a longer trip may require less energy than a shorter trip, once all other parameters that affect energy consumption have been taken into account,â explains BalÃ¡zs KulcsÃ¡r.
A significant reduction in energy consumption
The researchers modeled the energy consumption of distribution trucks moving through a city by examining many factors; speed, load, traffic information, various hilly routes and charging points of opportunity.
The energy consumption model was then entered into a mathematical formula, resulting in a route calculation algorithm that allows vehicles to make deliveries using as little energy as possible. And, if recharging is required while on the road, the vehicle can save time by taking the most energy-efficient route to a fast-charging point. Taking into account additional factors such as these, the researchers’ new method allowed vehicles to reduce their energy consumption by 5-20%.
Since electric delivery vehicles operate in complex real-world situations, there can often be unforeseen complications that are difficult to explain even if the algorithm is accurate from the start. Energy consumption forecasts will therefore be further optimized through machine learning, with data collected from vehicles being fed back to the tool for further entry and analysis.
âOverall, this will allow us to adapt route planning to uncertain and changing conditions, minimizing energy consumption and ensuring successful urban distribution,â said BalÃ¡zs KulcsÃ¡r.
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