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To achieve carbon neutrality by reducing carbon dioxide (CO2) emissions, vehicles with an internal combustion engine have started to be replaced by electrification vehicles such as hybrid electric vehicles (HEVs), plug-in HEVs (PHEVs), and battery EVs (BEVs) worldwide, which have motors in their transaxles (T/As). Reducing transmission torque loss in the transaxles is effective to reduce CO2 emissions, and lowering the viscosity of lubrication fluids in T/As is a promising method for reducing churning and drag loss. However, lowering viscosity generally leads to thin oil films and makes the lubrication condition severe, resulting in worse anti-fatigue and anti-seizure performance. To deal with these issues, we made improvements on the additive formulation of fluid, such as the addition of an oil-film-forming polymer, chemical structure change of calcium detergents, and an increase of anti-wear additives including phosphorus and sulfur.

Routing quality always dominates the top 20% of in vehicle- navigation customer complaints. In vehicle navigation routing engines do not customize results based on customer behavior. For example, some users prefer the quickest route while some prefer direct routes. This is because in vehicle navigation systems are traditionally embedded systems. Toyota announced that new model vehicles in JP, CN, US will be connected with routing function switching from the embedded device to the cloud in which there are plenty of probe data uploaded from the vehicles. Probe data makes it possible to analyze user preferences and customize routing profile for users. This paper describes a method to analyze the user preferences from the probe data uploaded to the cloud. The method includes data collection, the analysis model of route scoring and user profiling. Furthermore, the evaluation of the model will be introduced at the end of the paper.