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Battery electric vehicle powertrain strategy 2020-07-14T11:19:24+00:00

Project Description

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Defining the battery electric vehicle powertrain development strategy

Overview

In the context of new product development for Battery Electric Vehicle (BEV) powertrain, the company plans to extensively analyze various viable powertrain options for multiple vehicle applications to come up with a coherent roadmap for new driveline products.

Client Profile

A leading tier-I supplier for automotive driveline systems for light vehicle, commercial vehicle as well as for off-highway applications.

Project - Battery electric vehicle powertrain strategy

Challenge

First, sheer volume of possible combinations to design variables necessitates a wholesome approach to the problem as brute-force or manual analysis of every single combination is not possible. Secondly, credible input data in terms costs, performance and efficiency of each component when the components aren’t in the market yet is not straight forward. Third hurdle is the complete mathematical framework for evaluation which must be modular and should be able to perform optimization on multiple levels, energy optimization over a usage scenario at the same time performing cost and performance optimization. Lastly, the data analysis of multi-dimensional results to extract usable conclusion out of seemingly random cloud of data-points is challenging.

Solution

Team of experts in different areas, motors, power electronics, vehicle simulation and optimization together created a coherent framework of models that’s scalable within the design space of each subsystem. Quasi-static models with detailed efficiency map allowed fast simulation time. Particle swarm optimization (PSO) algorithm were used to optimize designs with upto 10 degrees of freedom. Each probable solution was extensively evaluated on KPIs like vehicle range on WLTC cycle, vehicle performance and system cost. Banking on the past experiences, engineering sense and using these advanced optimization algorithm, the challenges were overcome to support the management with making informed strategy decisions.

Results

  • In-house computation design synthesis framework

  • Strategic decision backed by substantial data

  • New product development roadmap

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