Effectively adapting the hybrid electric vehicle (HEV) powertrain operation as a function of the battery state-of-health (SOH) can lead to significant energy savings over the entire vehicle lifetime. This paper demonstrates the potential of a heuristic HEV energy management strategy (EMS) that is calibrated to adaptively minimize fuel consumption as the battery loses capacity due to ageing. A power-split HEV powertrain is used for this case study, and a heuristic EMS approach which does not adapt to battery ageing is used as the baseline. Particle swarm optimization (PSO) is implemented to tune the parameters of the baseline EMS to minimize fuel consumption. Then, the battery SOH adaptive HEV EMS is developed with parameters which are a function of battery SOH. The parameters are tuned with PSO as well. A battery SOH dependent model is created from experimental data from an ageing test which continued until SOH reached 16%. Simulation results demonstrate that the proposed battery SOH adaptive EMS achieves as much as 20% better fuel economy than the non-adaptive controller as the battery ages. An EMS for HEVs which controls the vehicle powertrain as a function of battery SOH is therefore critical to maintaining minimal fuel consumption throughout the life of the vehicle.

Battery State-of-health Adaptive Energy Management of Hybrid Electric Vehicles / Anselma, Pier Giuseppe; Kollmeyer, Phillip; Emadi, Ali. - (2022), pp. 1035-1040. ((Intervento presentato al convegno 2022 IEEE Transportation Electrification Conference & Expo (ITEC) tenutosi a Anaheim, CA, USA nel 15-17 June 2022 [10.1109/ITEC53557.2022.9813796].

Battery State-of-health Adaptive Energy Management of Hybrid Electric Vehicles

Anselma, Pier Giuseppe;Kollmeyer, Phillip;
2022

Abstract

Effectively adapting the hybrid electric vehicle (HEV) powertrain operation as a function of the battery state-of-health (SOH) can lead to significant energy savings over the entire vehicle lifetime. This paper demonstrates the potential of a heuristic HEV energy management strategy (EMS) that is calibrated to adaptively minimize fuel consumption as the battery loses capacity due to ageing. A power-split HEV powertrain is used for this case study, and a heuristic EMS approach which does not adapt to battery ageing is used as the baseline. Particle swarm optimization (PSO) is implemented to tune the parameters of the baseline EMS to minimize fuel consumption. Then, the battery SOH adaptive HEV EMS is developed with parameters which are a function of battery SOH. The parameters are tuned with PSO as well. A battery SOH dependent model is created from experimental data from an ageing test which continued until SOH reached 16%. Simulation results demonstrate that the proposed battery SOH adaptive EMS achieves as much as 20% better fuel economy than the non-adaptive controller as the battery ages. An EMS for HEVs which controls the vehicle powertrain as a function of battery SOH is therefore critical to maintaining minimal fuel consumption throughout the life of the vehicle.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2969960