Recent studies have shown promising results for making plug-in hybrid electric vehicles (PHEVs) 30% more efficient by combining connected vehicle technology and evolutionary algorithms, a subset of evolutionary computation in artificial intelligence.
"Maybe in one decade, most of the vehicles on road will be electric and autonomous. The vehicles will be not only 'self-driving' that most people are talking about recently, but also 'self-learning' for its energy management inside the vehicle," Xuewei Qi, the project's lead, told Xinhua on Friday.
Engineers at the University of California, Riverside (UCR) managed to improve the efficiency of PHEVs by more than 30 percent, according to the latest study, published on the journal IEEE Transactions on Intelligent Transportation Systems.
"All these abilities are actually existing in the nature, we just borrow the idea from the nature to make our vehicles smarter and more efficient," Qi said.
PHEVs, which combine a gas or diesel engine with an electric motor and a large rechargeable battery, offer advantages over conventional hybrids because they can be charged using mains electricity, which reduces their need for fuel.
However, the race to improve the efficiency of current PHEVs is limited by shortfalls in their energy management systems (EMS), which control the power split between engine and battery when they switch from all-electric mode to hybrid mode, according to the research.
But in reality, drivers may switch routes, traffic can be unpredictable, and road conditions may change, meaning that the EMS must source that information in real-time.
By combing vehicle connectivity information, such as cellular networks and crowdsourcing platforms, and evolutionary algorithms-a mathematical way to describe natural phenomena such as evolution, insect swarming and bird flocking, Qi and his team developed and simulated the highly efficient EMS.
The algorithm is intended to solve the long-standing issue of apparent unpredictability on the road.
"With the increasingly available information in connected vehicle environment, vehicles will be able to improve its energy efficiency by learning from the historical driving behaviors and evolving itself by adapting to the changing driving behaviors," Qi said.
The current paper builds on previous work by the team showing that individual vehicles can learn how to save fuel from their own historical driving records.
Together with the application of evolutionary algorithms, vehicles will not only learn and optimise their own energy efficiency, but will also share their knowledge with other vehicles in the same traffic network through connected vehicle technology.
According to researchers, the series of research is trying to revolutionize the energy management of PHEVs by taking advantage of smart algorithms that are inspired by the natural evolutionary process and natural learning process.
This is just first step. "I am trying to covert the proposed EMS model for PHEV into a EV version by considering the unique characteristics of EVs, such as the regenerative braking which can collect energy from deceleration," Qi said.