Examination of Requirements for Testing xEVs on a Chassis Dynamometer with the Road Load Equivalent to Actual Driving Conditions
-1st report: Problems in Chassis Dynamometer Testing Caused by Vehicle Losses in xEVs-

Hisakazu Suzuki (NALTEC)･Isamu Inoue (Ono Sokki)･Tomonobu Furuta (MEIDENSHA)･Yoku Hirose (HORIBA)･Noriaki Nakate (JATA)

In electric vehicles, the absence of a clutch and the motor rotating during coasting can lead to discontinuous changes in vehicle losses. However, this is currently addressed by using an approximate formula when setting the road load on the chassis dynamometer. This point requires improvement to achieve the goal of reproducing actual road driving on the chassis dynamometer, as outlined in the Chassis Dynamometer Testing Method Subcommittee. This report presents the current status and concerns regarding this issue.

An e-Axle Loss Evaluation Method Using Continuous Measurement, Assuming Machine-Learning-Based Data Analysis

Ryoya Kodama･Haruhisa Tsuchikawa･Hiroki Matsui･Takaya Inukai･Kazunao Takashima (Nissan Motor)

Measurement of e-Axle losses, a major loss contributor in electrified vehicles, depends under in-vehicle operating conditions not only on rotational speed and torque but also on oil temperature and voltage. Therefore, measurements across multidimensional conditions are required, resulting in long test durations. In this study, we designed a continuous measurement approach that enables wide variations in oil temperature and voltage over the operating region, and rapidly constructed an e-Axle loss map using machine learning based on the acquired data. The effectiveness of the proposed approach for loss evaluation was confirmed.

Energy Management Based on Driving Pattern Classification Using Clustering Methods

Yuto Ishibashi･Kazuki Hayashi･Makoto Kozuka･Yousuke Hirowatari (Nissan Motor)

Current energy management strategies for series HEVs rely on a comprehensive set of control parameters that take multiple driving patterns into account, leaving room for fuel-economy improvement through pattern-specific optimization. In this study, driving patterns are classified by clustering historical driving data, and pattern-optimized control is applied. The effectiveness of the proposed control is validated.

Development of On-board PV System Model Using Modelica Language and Its Application to xEV Model

Norifumi Mizushima･Hidenori Mizuno (AIST)

In preparation for the implementation of the off-cycle credit framework, it is essential to establish a quantitative evaluation methodology for assessing the fuel economy improvement achieved by energy-saving technologies whose effects are not adequately captured by conventional chassis dynamometer testing. In this study, an on-board photovoltaic power generation system was modeled using the Modelica language, and the developed model was subsequently integrated into a vehicle simulation model developed at AIST. By performing vehicle-level simulations, the impact of the photovoltaic system on fuel consumption was quantified, thereby allowing for a systematic evaluation of its contribution to real-world fuel economy improvement.

Real-World Driving Behavior Analysis and Speed Variation Pattern Optimization for Improving the Energy Efficiency of an Electric Garbage Truck
-A Study Focused on Door-to-Door Waste Collection Operations-

Yiyuan Fang･Jiakai Gao･Yida Bao･Wei-Hsiang Yang･Yushi Kamiya (Waseda University)

This study investigates the real-world driving behavior and energy consumption characteristics of an electric garbage truck during door-to-door garbage collection operations. Analysis of measured operational data revealed characteristics such as frequent stop-and-go driving, long idling periods, and limited opportunities for regenerative braking. Furthermore, speed change pattern optimization was conducted for representative driving trips under identical travel distance and average speed conditions. The results demonstrated that reductions in powertrain losses and driving resistance could improve electricity consumption by approximately 20–28% compared with actual driving operations. These findings confirm the effectiveness of eco-driving speed pattern optimization for electric garbage trucks.

Analysis of fuel efficiency improvement technologies for hybrid vehicles using real road driving

Akira Kato･Kosuke Numata･Riku Anjo (Teikyo University)

The electrification of automobiles is progressing toward achieving carbon neutrality (CN) by 2050. However, there are many challenges to the widespread adoption of battery electric vehicles (BEVs), and in Japan, the sales ratio of hybrid electric vehicles (HEVs) is high. In this study, we conducted road tests using a low-fuel-consumption compact HEV and analyzed the results in comparison with the previous year's HEV model to analyze technologies for improving HEV fuel efficiency using real-road driving.