Estimation of the energy consumption of battery driven electric buses by integrating digital elevation and longitudinal dynamic models: Malaysia as a case study

Abstract This work proposes a generic framework to estimate the energy consumption and investigate the penetration impact of a distributed network of battery electric buses (BEBs). The core of this work builds on a novel framework to determine the energy demand of BEBs and their potential as a replacement for diesel-powered buses in transportation networks. This paper uses data mapping technology from the Geographical Information System to cover the potential analysis of BEB penetration for large-scale bus networks. State-of-the-art methods have previously estimated the energy consumption of a BEB by using simulator models for each driving cycle, but these studies have not considered the actual elevation data of a local bus route. In fact, the elevation of the bus route is considered the main factor that varies the energy consumption of BEBs. This study developed a longitudinal dynamic model with a spatial version of a digital elevation model to determine the energy demand of a large-scale BEB network. Additionally, this work assessed two charging protocols—opportunity charging and overnight charging—according to the operating environments of electric buses. The application of the framework is validated in a case study to electrify the entire Rapid Kuala Lumpur bus (Bus-Rapid KL) network in Malaysia. The proposed model used real-world data, which are typically available only to bus transit administration operators. In this paper, the data for bus route lines, bus station locations, and the number of passengers riding the Bus-Rapid KL were considered to formulate the forecasting longitudinal and temporal model by using passenger information system data. The results showed a penetration impact of the BEB charging demand during daytime and nighttime in an urban area in Kuala Lumpur. The proposed forecasting paradigm may permit power network operators to predict the optimal electric bus charging demand based on actual BEB consumption through the bus paths.