Abstract
This paper presents the design and hardware-in-the-loop (HIL) experimental validation of a data-driven estimation method for the state of charge (SOC) in the lithium-ion batteries used in hybrid electric vehicles (HEVs). The considered system features a 1.25 kWh 48 V lithium-ion battery that is numerically modeled via an RC equivalent circuit model that can also consider the environmental temperature influence. The proposed estimation technique relies on nonlinear autoregressive with exogenous input (NARX) artificial neural networks (ANNs) that are properly trained with multiple datasets. Those datasets include modeled current and voltage data, both for charge-sustaining and charge-depleting working conditions. The investigated method is then experimentally validated using a Raspberry Pi 4B card-sized board, on which the estimation algorithm is actually deployed, and real-time hardware, on which the battery model is developed, namely a Speedgoat baseline platform. These hardware platforms are used in a hardware-in-the-loop architecture via the UPD communication protocol, allowing the system to be validated in a proper testing environment. The resulting estimation algorithm can estimate the battery SOC in real-time, with 2% accuracy during real-time hardware testing.
Highlights
In response to current growing worries about global warming and the increasing scarcity of natural resources, the need for more efficient cars is, strictly linked to even more stringent regulations about fuel economy, emissions, and energy consumption [1]
This paper presents the design and HIL assessment of a data-driven method for state of charge (SOC) estimation in lithium-ion batteries
The study was conducted to evaluate the feasibility of the investigated algorithm in an industrial-grade framework, using state-ofthe-art, real-time hardware, such as Raspberry Pi 4B and Speedgoat baseline, which have been connected via UDP protocol
Summary
In response to current growing worries about global warming and the increasing scarcity of natural resources, the need for more efficient cars is, strictly linked to even more stringent regulations about fuel economy, emissions, and energy consumption [1]. Out of all the potential estimating methods, this methodology is the most effective at capturing the non-linear connection between various battery parameters, while minimizing the anxiety associated with uncertain EV operating conditions and the complicated electrochemical process of batteries [33,34] It does not require detailed physical knowledge of the battery, and the proposed layout ensures the deployment on a battery management system (BMS), due to the limited number of neurons and layers. To the best of the authors’ knowledge, the real-time assessment of HIL implementation of SOC estimation ANN-based algorithms is limited to simple charge/discharge cycles that are not representative of real driving situations in HEVs [35]. Electronics 2021, 10, 2828 of the authors’ knowledge, the real-time assessment of HIL implementation of SOC e3stoif- 15 mation ANN-based algorithms is limited to simple charge/discharge cycles that are not representative of real driving situations in HEVs [35]. The training algorithm and datasets are discussed in this subsection
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