An adaptive finite element method for computing emergency manoeuvres of ground vehicles in complex driving scenarios

In emergency cases a vehicle has to avoid colliding with one or more obstacles, stay within road boundaries, satisfy acceleration and jerk limits, fulfil stability requirements and respect vehicle system dynamics limitations. The real time solution of such a problem is difficult and as a result various approaches, which usually relax the problem, have been proposed until now. In this study, a new method for computing emergency paths for complex driving scenarios is presented. The method which is based on the finite element concept formulates the dynamic optimisation problem as a linear algebraic one. An empirical formula adapts the size of the finite elements to optimise the dynamics of the emergency path. The proposed approach is evaluated in Matlab and Carsim simulation environments for different driving scenarios. The results show that with the proposed approach complex emergency manoeuvres are planned with improved performance compared to other known methods

Comparison between RLS-GA and RLS-PSO For Li-ion battery SOC and SOH estimation: a simulation study

This paper proposes a new method of concurrent SOC and SOH estimation using a combination of recursive least square (RLS) algorithm and particle swarm optimization (PSO). The RLS algorithm is equipped with multiple fixed forgetting factors (MFFF) which are optimized by PSO. The performance of the hybrid RLS-PSO is compared with the similar RLS which is optimized by single objective genetic algorithms (SOGA) as well as multi-objectives genetic algorithm (MOGA). Open circuit voltage (OCV) is treated as a parameter to be estimated at the same timewith internal resistance. Urban Dynamometer Driving Schedule (UDDS) is used as the input data. Simulation results show that the hybrid RLS-PSO algorithm provides little better performance than the hybrid RLS-SOGA algorithm in terms of mean square error (MSE) and a number of iteration. On the other hand, MOGA provides Pareto front containing optimum solutions where a specific solution can be selected to have OCV MSE performance as good as PSO