Telemetry Data Compression for Battery Electric Vehicle Energy Consumption

Telemetry data is used to model and assess the energy consumption of battery electric buses. This paper presents telemetry data compression methods, which require less communication bandwidth to capture the energy consumption data without a loss of accuracy. These methods include calculating the longitudinal acceleration more reliably and reordering the energy consumption calculation process to include all acceleration events, even at low sample rates. Two of the developed methods can reduce the data transfer by 39%, while the energy consumption error does not increase.Telemetry data is used to model and assess the energy consumption of battery electric buses. This paper presents telemetry data compression methods, which require less communication bandwidth to capture the energy consumption data without a loss of accuracy. These methods include calculating the longitudinal acceleration more reliably and reordering the energy consumption calculation process to include all acceleration events, even at low sample rates. Two of the developed methods can reduce the data transfer by 39%, while the energy consumption error does not increase

The State-of-the-Art of Battery Electric City Buses

Over the last decade, many manufacturers brought battery electric city buses to the market. By organizing and categorizing the reported specifications of more than 100 of these vehicles, this paper aims to create an overview of the state-of-the-art. The used information is obtained from manufacturer websites and news sources. The results indicate that Lithium-ion Iron Phosphate is the most often used battery cell chemistry. Based on the reported range and battery capacity, the energy consumption is 1.3 kWh/km on average. Therefore, at an occupancy rate of 38% (seated), battery electric buses offer the same energy consumption per person as an average electric passenger car. The current lack of standardization in the reported range makes direct a comparison of individual vehicles difficult

The State-of-the-Art of Battery Electric City Buses

Over the last decade, many manufacturers brought battery electric city buses to the market. By organizing and categorizing the reported specifications of more than 100 of these vehicles, this paper aims to create an overview of the state-of-the-art. The used information is obtained from manufacturer websites and news sources. The results indicate that Lithium-ion Iron Phosphate is the most often used battery cell chemistry. Based on the reported range and battery capacity, the energy consumption is 1.3 kWh/km on average. Therefore, at an occupancy rate of 38% (seated), battery electric buses offer the same energy consumption per person as an average electric passenger car. The current lack of standardization in the reported range makes direct a comparison of individual vehicles difficult

On-line Test of a Real-Time Velocity Prediction for E-bus Energy Consumption Estimation

To facilitate dynamic vehicle scheduling for battery electric city buses, a real-time on-line energy consumption prediction model is proposed. The model utilizes the current vehicle velocity and position, combined with knowledge of the remaining route, to predict the total trip energy. The model consists of a remaining velocity profile predictor and a longitudinal dynamics model. The algorithm is demonstrated in a Hardware-in-the-Loop experiment with a battery electric bus. The model has an average error of 3.1% with respect to the total trip energy and adapts in real-time to unexpected acceleration and deceleration events

Assessing the impact of cornering losses on the energy consumption of electric city buses

In view of the increasing electrification of public city transport, an accurate energy consumption prediction for Battery Electric Buses (BEBs) is essential. Conventional prediction algorithms do not consider energy losses that occur during turning of the vehicle. This is especially relevant for electric city buses, which have a limited battery capacity and often drive curvy routes. In this paper, the additional energy consumption during steering of a BEB is modeled, measured, and assessed. A nonlinear steady-state cornering model is developed to establish the additional energy losses during cornering. The model includes large steer angles, load transfer, and a Magic Formula tire model. Model results show that both cornering resistance and tire scrub of the rear tires cause additional energy losses during cornering, depending on the corner radius and vehicle velocity. The energy consumption model is validated with full scale vehicle tests and shows an average deviation of 0.8 kW compared to the measurements. Analysis of recorded real-world bus routes reveals that on average these effects constitute 3.1% of the total powertrain energy. The effect is even more significant for routes crossing city centers, reaching values up to 5.8%. In these cases, cornering losses can be significant and should not be neglected in an accurate energy consumption prediction

Combined Rolling Resistance and Road Grade Estimation Based on EV Powertrain Data

Energy consumption prediction is increasingly important for eco-driving, energy management, and charging scheduling of electric vehicles. Detailed knowledge of the rolling resistance and road grade, combined here in a road-resistance profile, improves the accuracy of these predictions. This paper presents a recursive method to identify the position-dependent road-resistance coefficient using GPS position, powertrain power, and vehicle speed. The calculations make explicit assumptions regarding the spatial continuity of both road gradient and rolling resistance by defining road segments. A recursive least-squares method with Gaussian basis functions allows the estimates to be updated whenever a route segment is traversed anew. The method is tested on data gathered by a 12 m battery electric bus. The resulting road-resistance profile shows a strong resemblance to the road slope and captures changes in rolling resistance well, including a dependency on ambient temperature, which is in accordance with literature on tire rolling resistance. Including the resistance profile in a vehicle model reduces the error of the predicted powertrain power by 1.7 percent point compared to a conventional method, without the limitation of requiring a high-resolution digital elevation model

A Microscopic Energy Consumption Prediction Tool for Fully Electric Delivery Vans

For cost-optimal utilization of battery electric delivery vans, energy consumption prediction is important. This paper presents a microscopic energy consumption tool, which requires the intended route as input. Both the velocity profile prediction algorithm and the subsequent energy consumption model are based on data obtained from dedicated vehicle tests. Secondly, up-to-date environmental data on the weather, the road slope profile, and local speed legislation are obtained through API’s via the internet. The results show good correspondence with the measured energy consumption. Validation with several measured trips shows that the energy consumption is predicted with an error that rarely exceeds 10 %