Search Results

This paper proposes a battery state estimation on a battery management system (BMS) for hybrid electric vehicles (HEVs) and electric vehicles (EVs). It is important to estimate a state of charge (SOC) and parameters of the battery such as a state of health (SOH), internal resistances and dynamics of electrochemical reactions. The BMS can provide information on the driving range of the EVs to the drivers by accurately estimating SOC and SOH. It can also calculate a state of power (SOP) to use the battery safely by accurately estimated SOC, internal resistances and others. For that purpose, this paper proposes the BMS adopted a simultaneous state of charge (SOC) and parameter estimation method using log-normalized unscented Kalman filter (LnUKF). The key idea is a lognormalization of the parameters to improve numerical stability and robustness of the algorithm. The proposed system is verified by a series of simulations using experimental data with EVs.

Recently, emission regulations have been strict in many countries, and it is very difficult technical issue to reduce emissions of diesel cars. In order to reduce the emissions, various combustion technologies such as Massive EGR, PCCI, Rich combustion, etc. have been researched. The combustion technologies require precise control of the states of in-cylinder gas (air mass flow, EGR rate etc.). However, a conventional controller such as PID controller could not provide sufficient control accuracy of the states of in-cylinder gas because the air-pass system controlled by an EGR valve, a throttle valve, a variable nozzle turbo, etc. is a multi-input, multi-output (MIMO) coupled system. Model predictive control (MPC) is well known as the advanced MIMO control method for industrial process. Generally, the sampling period of industrial process is rather long so there is enough time to carry out the optimization calculation for MPC.