The invention is a method for managing electric vehicle (EV) driving, which identifies the most energy efficient route to a destination based on vehicle’s battery characteristics. This is the first ever method to optimize navigation for both energy efficiency and the vehicle’s battery lifetime simultaneously.
Increasing environmental concerns over greenhouse gas emissions, together with recent advances in electric vehicle (EV) technologies, have pushed the EVs to the forefront of appealing transportation alternatives. However, a number of energy challenges unique to EVs, such as increased demand on the power grid they impose and limited capacity and lifetime of batteries they rely on for propulsion, still need to be addresses before ubiquitous adoption. In addition, EVs’ limited driving range, constrained by battery capacities in combination with currently low availability and convenience of charging options, incites “range anxiety” in drivers and lowers the overall appeal of this “green” technology. As a result, state of the art EV driving management systems place main focus on finding routes that reduce user’s transient “range anxiety” (i.e. by reducing energy consumption in traffic), while completely ignoring concerns over overall battery lifetime. The driving range of an EV is dependent on characteristics of its battery, which, in turn, change with different usage patterns. Moreover, EV batteries remain quite costly to replace despite technological advancements. It is, therefore, important for EV users to have navigation options for routes that both optimize energy usage and prolong the battery lifetime. Researchers at UCI have now developed an innovative driving management method, which utilizes the detailed EV model, battery model, and power grid model in order to estimate vehicle’s power consumption, battery lifetime, and energy consumption. The obtained estimates are then used to select an optimized driving route that not only minimizes the energy consumption and costs, but also extends the battery lifetime for both short-term and long-term uses. The implemented system has shown up to a 17% improvement in battery lifetime and a 12% reduction in energy consumption – all without imposing any additional constrains on vehicle’s design or cost. The proposed method could be instrumental in significant lessening of the overall demand of EVs on the power grid – a factor of growing importance, as the number of EVs in service continues to increase.
Implementation of the method decreases energy consumption by up to 12% Extends battery lifetime by up to 17%
Algorithm is fully developed and tested simulated environments